From 6bebe5e4f9ec9e00b936a49ea26e2f1c3892997a Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Fri, 19 Jun 2026 14:53:01 +0200 Subject: [PATCH 01/64] Harden deployment readiness --- .dockerignore | 29 + .env.example | 32 + .github/workflows/ci.yml | 32 +- .gitignore | 24 + Dockerfile | 30 + Makefile | 17 +- README.md | 159 +- docker-compose.yml | 133 + elasticsearch/.env | 29 - elasticsearch/.env.example | 12 + elasticsearch/apptainer-run-es.sh | 6 + elasticsearch/certs/ca.crt | 20 - elasticsearch/certs/ca.zip | Bin 2517 -> 0 bytes elasticsearch/certs/ca/ca.crt | 20 - elasticsearch/certs/ca/ca.key | 27 - elasticsearch/certs/certs.zip | Bin 7601 -> 0 bytes elasticsearch/certs/es01/ca.crt | 0 elasticsearch/certs/es01/es01.crt | 20 - elasticsearch/certs/es01/es01.key | 27 - elasticsearch/certs/es02/es02.crt | 20 - elasticsearch/certs/es02/es02.key | 27 - elasticsearch/certs/es03/es03.crt | 20 - elasticsearch/certs/es03/es03.key | 27 - elasticsearch/certs/instances.yml | 19 - 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+elasticsearch/data1/ +elasticsearch/logs/ +elasticsearch/sif/ +elasticsearch/tmp-config/ +source/Parser/input/ +source/Parser/output/ +source/Parser/logs/ +source/Parser/models/ +source/Parser/resources/ +utils/Indexer/*.log diff --git a/.env.example b/.env.example new file mode 100644 index 00000000..e884de1d --- /dev/null +++ b/.env.example @@ -0,0 +1,32 @@ +# Copy this file to .env for local TrialMatchAI runtime configuration. +# Do not commit .env. + +TRIALMATCHAI_ES_HOST=https://localhost:9200 +TRIALMATCHAI_ES_USERNAME=elastic +TRIALMATCHAI_ES_PASSWORD=change-me-strong-password +TRIALMATCHAI_ES_CA_CERTS=elasticsearch/certs/ca/ca.crt +TRIALMATCHAI_ES_AUTO_START=false +KIBANA_PASSWORD=change-me-strong-password +STACK_VERSION=8.13.4 +CLUSTER_NAME=trialmatchai-local +ES_PORT=9200 +KIBANA_PORT=5601 +LICENSE=basic +MEM_LIMIT=2147483648 + +TRIALMATCHAI_PATIENTS_DIR=example +TRIALMATCHAI_OUTPUT_DIR=results +TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons +TRIALMATCHAI_INDEX_TRIALS=clinical_trials +TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY=trials_eligibility + +TRIALMATCHAI_MODEL_BASE_MODEL=microsoft/phi-4 +TRIALMATCHAI_MODEL_COT_ADAPTER_PATH=models/finetuned_phi_reasoning +TRIALMATCHAI_MODEL_RERANKER_MODEL_PATH=google/gemma-2-2b-it +TRIALMATCHAI_MODEL_RERANKER_ADAPTER_PATH=models/finetuned_gemma2 +TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false +TRIALMATCHAI_COT_BACKEND=vllm + +TRIALMATCHAI_BIOMEDNER_AUTO_START=false +TRIALMATCHAI_LOG_LEVEL=INFO +TRIALMATCHAI_LOG_JSON=1 diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e59eeb31..87d32d4f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,18 +2,13 @@ name: CI on: push: - branches: [main, dev, mikael-clean] + branches: [main, dev, deployment-readiness-audit] pull_request: branches: [main, dev] workflow_dispatch: jobs: - test: - strategy: - matrix: - python-version: ["3.11", "3.12"] - fail-fast: false - + deployment-readiness: runs-on: ubuntu-latest steps: @@ -26,10 +21,29 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: ${{ matrix.python-version }} + python-version: "3.11" - name: Install dependencies run: uv sync --frozen + - name: Validate lockfile + run: uv lock --check + + - name: Lint + run: uv run ruff check . + - name: Run tests - run: uv run pytest -v \ No newline at end of file + run: uv run pytest -v + + - name: Secret scan + run: uv run python scripts/scan_secrets.py + + - name: Dependency audit + # vLLM 0.23 pins torch 2.11.0; CVE-2025-3000 has no fixed version listed. + run: uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 + + - name: Validate Compose + run: docker compose config + + - name: Build Docker image + run: docker build . diff --git a/.gitignore b/.gitignore index e21d4329..c6afa6c8 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,10 @@ src/ ablation/ logs/ *.log +.env +.env.* +!.env.example +!elasticsearch/.env.example # ============================ # Finetuning ignore rules @@ -29,11 +33,16 @@ utils/finetuning/finetune_ner # ============================ # ElasticSearch artifacts # ============================ +elasticsearch/.env +elasticsearch/certs/ +elasticsearch/config/ +elasticsearch/tmp-config/ elasticsearch/sif/ elasticsearch/data1/ elasticsearch/data/ elasticsearch/logs/ elasticsearch/sif/*.sif +*.keystore # ============================ # Parser artifacts @@ -43,13 +52,28 @@ source/Parser/resources/ source/Parser/models/ source/Parser/input/ source/Parser/output/ +source/Parser/logs/ Parser/logs/ +# ============================ +# Generated indexing state +# ============================ +utils/DataLoader/nct_ids.txt +utils/Indexer/nct_ids.txt +utils/Indexer/processed_ids.txt +utils/Indexer/*.log + # ============================ # Python cache # ============================ __pycache__/ *.py[cod] +.pytest_cache/ +.ruff_cache/ +.venv/ +build/ +dist/ +*.egg-info/ # ============================ # Temporary / cache files diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..62290d7a --- /dev/null +++ b/Dockerfile @@ -0,0 +1,30 @@ +FROM --platform=linux/amd64 python:3.11-slim AS runtime + +ENV PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + UV_SYSTEM_PYTHON=1 \ + PATH="/app/.venv/bin:${PATH}" + +WORKDIR /app + +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + bash \ + build-essential \ + ca-certificates \ + curl \ + git \ + unzip \ + && rm -rf /var/lib/apt/lists/* \ + && pip install --no-cache-dir uv + +COPY pyproject.toml uv.lock README.md LICENSE ./ +COPY source ./source +COPY scripts ./scripts +COPY utils ./utils + +RUN uv sync --frozen --no-dev --extra gpu + +VOLUME ["/app/data", "/app/models", "/app/results"] + +CMD ["trialmatchai-healthcheck"] diff --git a/Makefile b/Makefile index 91321012..2f3f1b4d 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: venv sync test lock lint healthcheck bootstrap start-es index setup +.PHONY: venv sync sync-gpu test lock lint audit healthcheck bootstrap start-es index run setup venv: uv venv @@ -6,6 +6,9 @@ venv: sync: uv sync +sync-gpu: + uv sync --extra gpu + test: uv run pytest @@ -15,17 +18,23 @@ lock: lint: uv run python -m ruff check . +audit: + uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 + healthcheck: - uv run trialmatchai-healthcheck --config Matcher/config/config.json --start-es + uv run trialmatchai-healthcheck bootstrap: - bash scripts/bootstrap_data.sh + uv run trialmatchai-bootstrap-data start-es: bash scripts/start_es.sh index: - bash scripts/index_data.sh + uv run trialmatchai-index + +run: + uv run trialmatchai-run setup: bash setup.sh diff --git a/README.md b/README.md index 86cc9cd4..ea70958d 100644 --- a/README.md +++ b/README.md @@ -2,106 +2,151 @@ Logo -TrialMatchAI is an AI-driven system for matching patients to relevant clinical trials. It combines retrieval (BM25/vector search), NLP, and LLM-based reasoning to produce transparent, ranked trial recommendations. +TrialMatchAI is a batch-oriented clinical trial matching pipeline. It combines Elasticsearch retrieval, biomedical NLP, embeddings, LLM reranking, and eligibility reasoning to produce ranked trial recommendations with criterion-level explanations. ## Disclaimer -This software is provided for research and informational purposes only. It is not medical advice and must not replace consultation with qualified healthcare professionals. -## Key Features -- AI-powered matching of patient profiles to clinical trial eligibility criteria. -- Two-stage retrieval (BM25 + vector) with optional reranking. -- Explainable recommendations and criterion-level insights. -- Scalable Elasticsearch-backed indexing. -- Modular pipelines for indexing, retrieval, and reasoning. +This software is for research and informational use only. It is not medical advice, is not a medical device, and must not replace review by qualified healthcare professionals. + +## Deployment Target + +The supported v1 deployment path is a single Linux GPU server or VM with Docker Compose for Elasticsearch and a containerized TrialMatchAI worker. HPC/Apptainer support remains available through the scripts under `elasticsearch/`, but production runtime does not auto-start local services by default. ## Requirements -- OS: Linux or macOS -- Python: 3.8+ -- Java: required for NER/normalization components -- Elasticsearch: Docker Compose or Apptainer -- GPU: NVIDIA gpu with at least 40 GB of VRAM is recommended for large-scale processing -- Disk space: 100 GB+ for datasets and indices - -## Quickstart (uv recommended) -1) Install dependencies + +- Python 3.11 +- `uv` recommended, or `pip` with editable install +- Docker Compose for the default Elasticsearch deployment +- NVIDIA GPU with enough VRAM for the selected LLM backend +- 100 GB+ disk space for datasets, models, indices, and results +- Java for BioMedNER/normalization components when those services are enabled + +## Security First + +No real credentials, generated TLS keys, Elasticsearch keystores, Parser outputs, or local indexing state should be committed. Copy templates and rotate any previously exposed credentials before deployment: + +```bash +cp .env.example .env +cp elasticsearch/.env.example elasticsearch/.env +``` + +Set strong local values for `TRIALMATCHAI_ES_PASSWORD`, `ELASTIC_PASSWORD`, and `KIBANA_PASSWORD`. + +Dependency auditing currently ignores `CVE-2025-3000` because vLLM 0.23 pins Torch 2.11.0 and the advisory has no fixed Torch version listed. Revisit that exception whenever upgrading vLLM or Torch. + +## Quickstart + +Install deployment dependencies: + +```bash +uv sync --extra gpu +``` + +For local development, tests, healthchecks, or `TRIALMATCHAI_COT_BACKEND=default`, the default dependency set is enough: + ```bash uv sync ``` -2) Ensure Elasticsearch credentials are configured -- If using Apptainer, set `ELASTIC_PASSWORD` in `elasticsearch/.env`. -- For the runtime pipeline, set `TRIALMATCHAI_ES_PASSWORD` in your environment. +Optional tooling is split out of the default runtime: -3) Start Elasticsearch (auto-start supported) ```bash -trialmatchai-healthcheck --config Matcher/config/config.json --start-es +uv sync --extra llm # OpenAI/LangChain data-generation utilities +uv sync --extra training # fine-tuning and evaluation utilities ``` -4) Run the pipeline +Start Elasticsearch with the root Compose stack: + ```bash -python -m Matcher.main +docker compose up -d elasticsearch ``` -Results are written under `results/`. +Run a healthcheck: -## One-time Provisioning (data + models + indexing) -You can run the all-in-one bootstrap script: ```bash -./setup.sh +uv run trialmatchai-healthcheck ``` -Or run steps individually: +Provision data, models, and indices: + ```bash -bash scripts/bootstrap_data.sh -bash scripts/start_es.sh -bash scripts/index_data.sh +uv run trialmatchai-bootstrap-data +uv run trialmatchai-index ``` -## Package Installation (pip fallback) +Run the batch matcher: + ```bash -pip install -e . +uv run trialmatchai-run ``` -## Configuration Overrides -Use environment variables or a `.env` file to override defaults: +Results are written under `results/`. + +## Docker Worker + +Build and run the worker healthcheck through Compose: + ```bash -# Elasticsearch -TRIALMATCHAI_ES_HOST=https://localhost:9200 -TRIALMATCHAI_ES_USERNAME=elastic -TRIALMATCHAI_ES_PASSWORD=YourNewPassword -TRIALMATCHAI_ES_AUTO_START=true -TRIALMATCHAI_ES_START_SCRIPT=elasticsearch/apptainer-run-es.sh -TRIALMATCHAI_ES_START_TIMEOUT=600 +docker compose build trialmatchai-worker +docker compose up trialmatchai-worker +``` -# Embedder -TRIALMATCHAI_EMBEDDER_MODEL_NAME=BAAI/bge-m3 +To run the full pipeline in the container after provisioning data/models/indices: -# Logging -TRIALMATCHAI_LOG_LEVEL=INFO -TRIALMATCHAI_LOG_JSON=0 +```bash +docker compose run --rm trialmatchai-worker trialmatchai-run ``` -Note: `Matcher/config/config.json` ships with a placeholder password (`CHANGE_ME`). Use env vars for production. +## Configuration + +Configuration defaults live in `source/Matcher/config/config.json`. Runtime overrides use `.env` or environment variables: -## Healthcheck ```bash -trialmatchai-healthcheck --config Matcher/config/config.json --start-es +TRIALMATCHAI_ES_HOST=https://localhost:9200 +TRIALMATCHAI_ES_USERNAME=elastic +TRIALMATCHAI_ES_PASSWORD=change-me +TRIALMATCHAI_ES_CA_CERTS=elasticsearch/certs/ca/ca.crt +TRIALMATCHAI_ES_AUTO_START=false + +TRIALMATCHAI_PATIENTS_DIR=example +TRIALMATCHAI_OUTPUT_DIR=results +TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons +TRIALMATCHAI_INDEX_TRIALS=clinical_trials +TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY=trials_eligibility + +TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false +TRIALMATCHAI_BIOMEDNER_AUTO_START=false +TRIALMATCHAI_LOG_JSON=1 ``` -## Tests +Use `TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=true` only when a selected model explicitly requires custom remote code. + +## CLI Commands + +- `trialmatchai-healthcheck`: validate config, paths, Elasticsearch reachability, and optionally indices. +- `trialmatchai-bootstrap-data`: download and extract external data/model artifacts. +- `trialmatchai-index`: index prepared data into Elasticsearch. +- `trialmatchai-run`: run the batch matching pipeline. + +## Tests and Checks + ```bash +uv run ruff check . uv run pytest +uv run python scripts/scan_secrets.py +uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 +docker compose config +docker build . ``` -Integration tests (requires running ES): +Integration tests require a running Elasticsearch instance: + ```bash -TRIALMATCHAI_RUN_INTEGRATION=1 pytest -m integration +TRIALMATCHAI_RUN_INTEGRATION=1 uv run pytest -m integration ``` -## Contributing -We welcome contributions. Please open an issue or submit a PR with tests. - ## Support + - Email: abdallahmajd7@gmail.com - DOI: https://doi.org/10.5281/zenodo.18329084 - arXiv: https://arxiv.org/abs/2505.08508 diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 00000000..95f63cb2 --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,133 @@ +services: + setup: + image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION:-8.13.4} + user: "0" + volumes: + - ./elasticsearch/certs:/usr/share/elasticsearch/config/certs + command: > + bash -c ' + if [ -z "$${TRIALMATCHAI_ES_PASSWORD}" ]; then + echo "Set TRIALMATCHAI_ES_PASSWORD in .env"; + exit 1; + fi; + if [ ! -f config/certs/ca.zip ]; then + bin/elasticsearch-certutil ca --silent --pem -out config/certs/ca.zip; + unzip config/certs/ca.zip -d config/certs; + fi; + if [ ! -f config/certs/certs.zip ]; then + printf "%s\n" \ + "instances:" \ + " - name: es01" \ + " dns:" \ + " - es01" \ + " - elasticsearch" \ + " - localhost" \ + " ip:" \ + " - 127.0.0.1" \ + > config/certs/instances.yml; + bin/elasticsearch-certutil cert --silent --pem + -out config/certs/certs.zip + --in config/certs/instances.yml + --ca-cert config/certs/ca/ca.crt + --ca-key config/certs/ca/ca.key; + unzip config/certs/certs.zip -d config/certs; + fi; + chown -R root:root config/certs; + find config/certs -type d -exec chmod 750 {} \;; + find config/certs -type f -exec chmod 640 {} \;; + ' + environment: + TRIALMATCHAI_ES_PASSWORD: ${TRIALMATCHAI_ES_PASSWORD:-} + healthcheck: + test: ["CMD-SHELL", "[ -f config/certs/es01/es01.crt ]"] + interval: 1s + timeout: 5s + retries: 120 + + elasticsearch: + image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION:-8.13.4} + depends_on: + setup: + condition: service_healthy + volumes: + - ./elasticsearch/certs:/usr/share/elasticsearch/config/certs:ro + - esdata01:/usr/share/elasticsearch/data + ports: + - "${ES_PORT:-9200}:9200" + environment: + node.name: es01 + cluster.name: ${CLUSTER_NAME:-trialmatchai-local} + discovery.type: single-node + ELASTIC_PASSWORD: ${TRIALMATCHAI_ES_PASSWORD:-} + bootstrap.memory_lock: "true" + xpack.security.enabled: "true" + xpack.security.http.ssl.enabled: "true" + xpack.security.http.ssl.key: certs/es01/es01.key + xpack.security.http.ssl.certificate: certs/es01/es01.crt + xpack.security.http.ssl.certificate_authorities: certs/ca/ca.crt + xpack.security.transport.ssl.enabled: "true" + xpack.security.transport.ssl.key: certs/es01/es01.key + xpack.security.transport.ssl.certificate: certs/es01/es01.crt + xpack.security.transport.ssl.certificate_authorities: certs/ca/ca.crt + xpack.security.transport.ssl.verification_mode: certificate + xpack.license.self_generated.type: ${LICENSE:-basic} + mem_limit: ${MEM_LIMIT:-2147483648} + ulimits: + memlock: + soft: -1 + hard: -1 + healthcheck: + test: + [ + "CMD-SHELL", + "curl -s --cacert config/certs/ca/ca.crt https://localhost:9200 | grep -q 'missing authentication credentials'", + ] + interval: 10s + timeout: 10s + retries: 120 + + trialmatchai-worker: + build: . + depends_on: + elasticsearch: + condition: service_healthy + volumes: + - ./data:/app/data + - ./models:/app/models + - ./results:/app/results + - ./example:/app/example:ro + - ./elasticsearch/certs:/app/elasticsearch/certs:ro + environment: + TRIALMATCHAI_ES_HOST: https://elasticsearch:9200 + TRIALMATCHAI_ES_USERNAME: elastic + TRIALMATCHAI_ES_PASSWORD: ${TRIALMATCHAI_ES_PASSWORD:-} + TRIALMATCHAI_ES_CA_CERTS: /app/elasticsearch/certs/ca/ca.crt + TRIALMATCHAI_ES_AUTO_START: "false" + TRIALMATCHAI_BIOMEDNER_AUTO_START: "false" + TRIALMATCHAI_PATIENTS_DIR: /app/example + TRIALMATCHAI_OUTPUT_DIR: /app/results + TRIALMATCHAI_TRIALS_JSON_FOLDER: /app/data/trials_jsons + TRIALMATCHAI_LOG_JSON: "1" + command: ["trialmatchai-healthcheck"] + + kibana: + profiles: ["kibana"] + image: docker.elastic.co/kibana/kibana:${STACK_VERSION:-8.13.4} + depends_on: + elasticsearch: + condition: service_healthy + volumes: + - ./elasticsearch/certs:/usr/share/kibana/config/certs:ro + - kibanadata:/usr/share/kibana/data + ports: + - "${KIBANA_PORT:-5601}:5601" + environment: + SERVERNAME: kibana + ELASTICSEARCH_HOSTS: https://elasticsearch:9200 + ELASTICSEARCH_USERNAME: kibana_system + ELASTICSEARCH_PASSWORD: ${KIBANA_PASSWORD:-} + ELASTICSEARCH_SSL_CERTIFICATEAUTHORITIES: config/certs/ca/ca.crt + +volumes: + esdata01: + kibanadata: diff --git a/elasticsearch/.env b/elasticsearch/.env deleted file mode 100644 index e9dc0ea8..00000000 --- a/elasticsearch/.env +++ /dev/null @@ -1,29 +0,0 @@ -# Password for the 'elastic' user (at least 6 characters) -ELASTIC_PASSWORD="QQ7wWoB_WnKe*L*X9tAW" - -# Password for the 'kibana_system' user (at least 6 characters) -KIBANA_PASSWORD="QQ7wWoB_WnKe*L*X9tAW" - -# Version of Elastic products -STACK_VERSION=8.13.4 - -# Set the cluster name -CLUSTER_NAME=docker-cluster - -# Set to 'basic' or 'trial' to automatically start the 30-day trial -LICENSE=basic -#LICENSE=trial - -# Port to expose Elasticsearch HTTP API to the host -ES_PORT=9200 -#ES_PORT=127.0.0.1:9200 - -# Port to expose Kibana to the host -KIBANA_PORT=5601 -#KIBANA_PORT=80 - -# Increase or decrease based on the available host memory (in bytes) -MEM_LIMIT=1073741824 - -# Project namespace (defaults to the current folder name if not set) -#COMPOSE_PROJECT_NAME=trialmatchai diff --git a/elasticsearch/.env.example b/elasticsearch/.env.example new file mode 100644 index 00000000..cb41313d --- /dev/null +++ b/elasticsearch/.env.example @@ -0,0 +1,12 @@ +# Copy this file to elasticsearch/.env for local Elasticsearch. +# Do not commit elasticsearch/.env. + +ELASTIC_PASSWORD=change-me-strong-password +KIBANA_PASSWORD=change-me-strong-password +STACK_VERSION=8.13.4 +CLUSTER_NAME=trialmatchai-local +LICENSE=basic +ES_PORT=9200 +KIBANA_PORT=5601 +MEM_LIMIT=1073741824 +COMPOSE_PROJECT_NAME=trialmatchai diff --git a/elasticsearch/apptainer-run-es.sh b/elasticsearch/apptainer-run-es.sh index 3a6d9a55..6f22371f 100644 --- a/elasticsearch/apptainer-run-es.sh +++ b/elasticsearch/apptainer-run-es.sh @@ -29,6 +29,12 @@ ES_PORT3=$((ES_PORT1 + 2)) ELASTIC_PASSWORD="${ELASTIC_PASSWORD:?ELASTIC_PASSWORD not set in .env}" +if [ ! -f "$CERTS_DIR/ca/ca.crt" ] || [ ! -f "$CERTS_DIR/es01/es01.crt" ]; then + echo "[ERROR] Elasticsearch TLS certs are not present in $CERTS_DIR." + echo "[ERROR] Generate local certs with Docker Compose first or provide certs out of band." + exit 1 +fi + #=== PREPARE FOLDERS ===# mkdir -p "$CONFIG_DIR/es01" "$CONFIG_DIR/es02" "$CONFIG_DIR/es03" mkdir -p "$DATA_DIR/es01" "$DATA_DIR/es02" "$DATA_DIR/es03" diff --git a/elasticsearch/certs/ca.crt b/elasticsearch/certs/ca.crt deleted file mode 100755 index d8c6ffd4..00000000 --- a/elasticsearch/certs/ca.crt +++ /dev/null @@ -1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDSjCCAjKgAwIBAgIVAO2BMtuZgADcrNJORc/UB2rj/y6JMA0GCSqGSIb3DQEB -CwUAMDQxMjAwBgNVBAMTKUVsYXN0aWMgQ2VydGlmaWNhdGUgVG9vbCBBdXRvZ2Vu 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b/elasticsearch/certs/ca/ca.key deleted file mode 100755 index 48dca6bf..00000000 --- a/elasticsearch/certs/ca/ca.key +++ /dev/null @@ -1,27 +0,0 @@ ------BEGIN RSA PRIVATE KEY----- -MIIEpAIBAAKCAQEAmreLYETx2FrGa8oAlDQEnHHOMBjjXDc0ZJ+y0Wxfo1lXMdxv -EiytbmOcNI3QvpoqEXvLyFUdWpp4NSrSKAA8WmleN6wjUk3zzug7Az7vQBnaMZR6 -1I5KX+Zj3MtYYUaVXx4tNcrakoqf3XgPto0oatp2jLoOKOmKKnT3Ylbzq8zL7lXR -nhjTa5YoUx5hDbSOr2adVKv6cWWKh4apM5Qg67mb4Xumy6b228hlacenSQOdTLsW -epushLc9Xzc9n4IftrZfdYV6e1mnjA6Rh2ns6nAm4h0jBvb14VPH7IE0wdjTjYho -h/+t+VWXE220vXbQ3lHuGP7xGNLlEVNncgRdywIDAQABAoIBADTHPSv7iPbRzJNT -pwvnjNUje39r1g+Mo3paAiGv0xZBsV2IgXlVNVqNb2l8IUQMCiLJtNQjuO5B+JTG -hUdxASWkgSgDuE7o2a1xCkSKsQoQZ573NEmTOqrpSJK26XDRp735aNnLV/GaiXt+ -6/lNwQZmfP93rsHlHSVrnkJ1QA2QOJXwLiBl6FYDtSX/FMlIvDdEFQJw0ueizUpq -jdjyI0yBDhCBtYbiiPaqqn/JdDLmJh6g5fOQrhZ7ugV28Ko8xiHN8n/X4Dgfil8m -qOizYuwEfTuoTf5b0pB3c9MKiug2URinVIvlSSTmYU5cmMDwUI2OfKABwKxgrJ6g -QcgdJUUCgYEAtxhP7R0CkXTziriw4WH5asqPeQsDaokfo2AGv+OgX2TzR9rYofWe 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a/elasticsearch/certs/es01/es01.key +++ /dev/null @@ -1,27 +0,0 @@ ------BEGIN RSA PRIVATE KEY----- -MIIEogIBAAKCAQEAmpXRQ+THn+VpNZwD+7+9R7IOhisKjCEGKMqSARr5wQPlulxL -DlHoO7pRLw6JcEXmLn4tx+QuLkkm9pFAADYnIcCljzRkFHADKuo+P7BG6AZ+6l1U -VHwya6iUaERda3+VClTHPzRoPn8TRdX78+cY7dAzme0noosw8WCgpL2HgiYciFiw -le80OKaeiVxvRNJ+6n36a/q4lgLx9wQoJvox/2OCP3318UY1SgA2AO9kFk0zDTvJ -ji9ESSz6d+6LCB73Bk2cKWx6q2pVoSTNPhkC5mD9E/zhuXSACfY3H85GZksJ4jzt -dF1UcsOsrjYsMwwxHH4BwVKak97ERJmICSLV/wIDAQABAoIBABDs2Vo1KNQtcoz/ -lYIRVsCMUsHG4aNBFGMP9tdvJCxJaHQ0mbUqK6KqfiwIS+0CgjbR8uJBbfr8YGs7 -sQW06CjuZlIdGt4P+5DNz936R3EtEOVJLawIYx7deM5HufDEqcVVTfFyI/2/vRT2 -3lywj06ubo/qYt4NnmC3Qy92XulVZknvjaolGhqVlYGbHQITSI47KNErFX0KiL77 -dYkO3dY75XIc4NsECk5UyFRyu1Mg6dkG+a8HahPVmX7ctq+bytQ3r9hpwXrY1MZV -RXtEW81kwB0yJV3GmXJRrqKZR+vvpqMRIQhmLZgr0XkKIshBuBneAo4cUX7MVgAX -JcdHsMUCgYEA0P7NgUL4xg9Xtpygf5DWldcCO/cy6yoOkXEJ6d9xOhQny24qi0MN -uOuyCCF4UQnhdEYcR9aY6YOwviQluq6roQT7g1Jex20F2iaaLVNdxaGi5s9nmzgp 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-1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDQDCCAiigAwIBAgIUTk8VxELi8BcE+Q1QJDjl1ijZS0gwDQYJKoZIhvcNAQEL -BQAwNDEyMDAGA1UEAxMpRWxhc3RpYyBDZXJ0aWZpY2F0ZSBUb29sIEF1dG9nZW5l -cmF0ZWQgQ0EwHhcNMjQwNTI3MTk1ODUwWhcNMjcwNTI3MTk1ODUwWjAPMQ0wCwYD -VQQDEwRlczAyMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAyaaCYcE+ -eDk4++1N2j0edhOQeJtRlVAY4okWx/GKZ8bUrFgWeXDmYnty8n2BjOsYy5AikELe -ZhsA6xWqV7/l8Tmhfv27e1qsxB1zNBurFQ4B2/O7YXHRXs2uKJqKb2Sj1+2GM6xQ -xZqZQS39MX2rKmENhud8kyDZUpQfqlUNE+QgRjMKMvB+7U9lrTfP/XUW2QosY+dz -Uub1K6pWf/avccf3WJd6R8aTYv2KDQjFsR6sib/RWHXTvtIDQlynQW4SFxSbhplX -DNN8nkaHa1lygAIHcKu77KulFrbTsJZ+hJIMjKn4HYsMuCH2KYDK+1cZToJ1MDvo -A9Xa2yWjbXWaiwIDAQABo28wbTAdBgNVHQ4EFgQUFSfOsv8EnRbLQI9pMbKNrRh0 -wM8wHwYDVR0jBBgwFoAUUhwT0mQKXn1S1rPBWQf0TizKLPQwIAYDVR0RBBkwF4cE -fwAAAYIEZXMwMoIJbG9jYWxob3N0MAkGA1UdEwQCMAAwDQYJKoZIhvcNAQELBQAD -ggEBAAGEWLu8MtZiav5wxvCb0iu5zUOPDSRibK/wTLtLhOeu4LBqyB9MYuT9wRr3 -XKsKuPV6NHSzUdkAK5wTqXsX3b5FXD6+B7PUMB31g62YnauDdTydszR1NxMp9LB+ 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-+kwYgMPmRCQIALJK4mhBv3Y0vIxzmfFrP7R2PVZAManBJQELsflQtRB9Ssv4eZNX -tGrd5obOmuxwkCV/vPzMoORdnRQas5a0i0ZtlCjv8jirX/t1VOLlsvoaIGs6TbTm -op3s7zi9ETvnCnZ6VKzsSugwUGCRnFqsIgUt8PUpKPXfgvzdRc5/j1NNWocfwLlH -lTSipk4n64KPevvG/deWw6AnnYLSosIQM4MCC9JrgZ1DhacGs23L7WWbz/1JC+oT -AgEP9FkCgYEAz3pj3PdibwhM/VWuOxwWWR4YS8UJZx1lcFPXzgE4t7V+/YSKuUtL -ro2plGUG1Ji5SgsS0exH/mzaF8OhHX1Jt+eGEVJyzdfJN8S1lomVtOoMyjkyBgxM -I4bajIB1Sz9aipKwVAfise9f6Ga3E0/ET9iq6i0/R1wK5EwgdOCxkBMCgYEA+M84 -W4S5P4EyfaeLOw+puyZcgG0ftVVqlAHdkdQlQLXCt3ijI008A0f5GVqf2tTLVrAj -Ub39aDOrM1wA6FWCqsa8LEbtdD4d1xNDi8DegDvjjjmU1z8ULjxSu8iSikhOCw2G -vyXHsr04DYBg4OURD9Pg8hHGtANZSNtc6CwISqkCgYBogbqZi9Z+HQ1Csgy/42by -XrFYQRh6Yxk8Wk8iigT6rCYaJtAFg4LMmrincbfeEEuMm0VQjha5djTosXaPNxOR -2cHzKbeALchCGghpmkXZSedFWUf0Oe+EGaIuEWqDi5bcpATDXvF2NR/3HP3scUpt -+bIloML1+8vUsO/MT33BFwKBgQCiXR+K4Wa94UKgqwf5p7P8VAFDMXLis3XUVg9Q -DZ+txa7maYwUCl+iSIJuoCv28qwqytCRlCjcqfMLlftlof+eEAhV4IcuNybj5kdK -2LaZ+fr6IetWN2yk62qV7kJqiNqc7dvDuxTBOCdu8BrIR9NFf9+oOB9x80l9eOD9 -BVb32QKBgAjHylTABCzS+2zOwUQeMpXQnYeNhMvvvYug/mDh+GQQL7zVeSJXlNGS -PkJqE3xEoVPDOPJ+Kr8nl5jS3YGCWcWerYURd9XiX7lHx7LlOeb6LusNIm/mO563 -y2U3WPIDTdy36vnvz3FZKYIionGFYc5hRGI54GfWKbCdr9ug2Xaw ------END RSA PRIVATE KEY----- diff --git a/elasticsearch/certs/es03/es03.crt b/elasticsearch/certs/es03/es03.crt deleted file mode 100755 index 7d273002..00000000 --- a/elasticsearch/certs/es03/es03.crt +++ /dev/null @@ -1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDQTCCAimgAwIBAgIVAPF8mVPnz7n47MH0IMhGyTp8b8hJMA0GCSqGSIb3DQEB -CwUAMDQxMjAwBgNVBAMTKUVsYXN0aWMgQ2VydGlmaWNhdGUgVG9vbCBBdXRvZ2Vu -ZXJhdGVkIENBMB4XDTI0MDUyNzE5NTg1MFoXDTI3MDUyNzE5NTg1MFowDzENMAsG -A1UEAxMEZXMwMzCCASIwDQYJKoZIhvcNAQEBBQADggEPADCCAQoCggEBAJD8MQOk -+jChQ/ukONKJ5NlmIMvxA30iEF0Dg1raRWu6JktXgl0PcQcqKSbCgQ8V/+jqIsKv -xzURuGH6PNnoeE7B7PSDW5aF8SOh2vIVCcuyqYCTfusv9Pkw0dXb0kkFF7p8wxG7 -xSyQncZxl9qEsEK3sKuR71rHnBF4r7fxuV+QV3ZLzwzJ4xTGFRd7wgrf/N7/iDXC -lrOzslTOj6BxIMQT04Jizm5/Sxcp+9gj89Loxs3Ls3cTNhaWnNl4IDQtaA8LlK2Z -8mC6+UgID3Px0ZfMcp7Qlm8lyX+loFOIFKlBm3RJ/ju566acXM0rrxMCaERxzVh6 -w/RFQ+s9ZEcorrkCAwEAAaNvMG0wHQYDVR0OBBYEFMFV0lcS1xlPM1wnUlztTiKA -2S2WMB8GA1UdIwQYMBaAFFIcE9JkCl59UtazwVkH9E4syiz0MCAGA1UdEQQZMBeH -BH8AAAGCBGVzMDOCCWxvY2FsaG9zdDAJBgNVHRMEAjAAMA0GCSqGSIb3DQEBCwUA -A4IBAQA3jD18KNhrXHJenPB/4FsQhiOgm684fO5mmmEZI14/V0snGIyoXCG4Tvfm -cRmRiOkNLZ79l9O2Feh3defyocED2soPJkjGbGMA6u5CW9B+32yEprfQ/JXOvKKL -zSIXUoa9h22rznuopsq8VQwIV7NFz1ATNllgyyEzKshGpk37Almso3lsX+PFX466 -SUDbNI7f3z8RYUg0uGqjepnJV0v6k5yOWStKmJ9j1uuK0rkKIBUcgOW4Fb2vyqkp -DTCtR8Q6g9+exZV+gezsOXvbf+mrVf48sOOQ0g9YkUTMHoXw/k7pQg/ixWTE6rSm -KhVWEziUuxCqaSOVgEY0qTWgKNP4 ------END CERTIFICATE----- diff --git a/elasticsearch/certs/es03/es03.key b/elasticsearch/certs/es03/es03.key deleted file mode 100755 index ff809b20..00000000 --- a/elasticsearch/certs/es03/es03.key +++ /dev/null @@ -1,27 +0,0 @@ ------BEGIN RSA PRIVATE KEY----- -MIIEowIBAAKCAQEAkPwxA6T6MKFD+6Q40onk2WYgy/EDfSIQXQODWtpFa7omS1eC -XQ9xByopJsKBDxX/6Ooiwq/HNRG4Yfo82eh4TsHs9INbloXxI6Ha8hUJy7KpgJN+ -6y/0+TDR1dvSSQUXunzDEbvFLJCdxnGX2oSwQrewq5HvWsecEXivt/G5X5BXdkvP -DMnjFMYVF3vCCt/83v+INcKWs7OyVM6PoHEgxBPTgmLObn9LFyn72CPz0ujGzcuz -dxM2Fpac2XggNC1oDwuUrZnyYLr5SAgPc/HRl8xyntCWbyXJf6WgU4gUqUGbdEn+ -O7nrppxczSuvEwJoRHHNWHrD9EVD6z1kRyiuuQIDAQABAoIBAAeRot1rLMSCfBIU -WK26aoAO8iyzawM1VrVZtu8fRfssafi08NFQOmOtXR3w5DFCwha0DRcs0LGB41D6 -HK3OPQ701hyjCZJUxmnzIS2ne4Nu0vqAuLd+dsOA5kjL2QaG2t5EH8rlNT3xBCDq -p+AjMoXJISf+eQhm3fbriKuJqwEaFoFs8gFOm7BKSzw4nFdwaBI0hO7QQng9Um8x -g27R+mJAGG+fNFJuDFD6gy2iIN+xkrNP9WGlFmEV4OoCUi1hz5YfxNRs2xCxUA3E -ggzcU0UvmLccHwz6PYK8EzTmsZUJsLRJTyG3WuNuaVTN67oMLYRv7GlGdZ998+HE -7m3KU6ECgYEAurfhPf8ACbMxR49iF62QJUzHM5xu0Qq85z6lWKHiCXWaIcqNv2mI -qnao9a5wBqEsTNNLljDlr99u8stHt+Zx3gAA1F5Pew/3quwOtaFx+zvxqrQKs6pb -cz3q9Ad8sJVthr06SW5W4OvdDGOF52EV+bcITZCetENmk9jvbN6z0ucCgYEAxsgi -enAcripDIT8uxhiEWxEtnsZunOsGWzXKcKJeg7w7cErqHRBb4liPenq3B/6sIp3r -AwPyqRIfVXZNbOpEPwoaF/1bQ9HujHuO0HEMsoeJh12KkBSBuHQXnA/X01Y6Nf0l -ZijizBE/Qc5EkWyxZx+pSTf7RVGf+1DlmKBE3V8CgYBIWUurrA0ltQtZQROvPQ9n -hJKDSxAda926dKm46DEfnTP19/hovMm503SwjcDWsMjrk8vsDFJTjW3+IgpOFbr1 -XGb14v1FH/DFh+ZDNqVlxdpkXJLw/wekZc+OcwA7pArmdJgLL/f1+y6RyFZwS0wq -kGNlOq5kBuHOU/ah5sEi7QKBgQDGiT1mbHM4wJ0rp59f2zzWd+HIowf3UgWXM7Jt -rL4ZdPcowKnzPVOITkt/WPFV2tax/GetK1RB6QfCo9XQ4monTD+jljiBFDvds8qA -BWlZJmYF/TdXkCO/xrON+4TkX0rkgWHJFyzuBIvZfdqeJKFLDiRWLMOaCFxw9eta -9TfSoQKBgEhHjogylvtkkiDIg+ArNhezYCjZ2yVPPsM8Jejh3sFpmjHHLaiw86Np -fRrxp3ealK1DXziRni+USCNM8iM6GFo2I6jBMWTJDTsc6wSyYLu4MlLTXyobZZnc -SwtvyZip2+VL4qEeg9p2c5wxbDBPdxh/8vjW9QxCeAovIr/0lth4 ------END RSA PRIVATE KEY----- diff --git a/elasticsearch/certs/instances.yml b/elasticsearch/certs/instances.yml deleted file mode 100755 index 926d7a15..00000000 --- a/elasticsearch/certs/instances.yml +++ /dev/null @@ -1,19 +0,0 @@ -instances: - - name: es01 - dns: - - es01 - - localhost - ip: - - 127.0.0.1 - - name: es02 - dns: - - es02 - - localhost - ip: - - 127.0.0.1 - - name: es03 - dns: - - es03 - - localhost - ip: - - 127.0.0.1 diff --git a/elasticsearch/config/es01/ca.crt b/elasticsearch/config/es01/ca.crt deleted file mode 100755 index d8c6ffd4..00000000 --- a/elasticsearch/config/es01/ca.crt +++ /dev/null @@ -1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDSjCCAjKgAwIBAgIVAO2BMtuZgADcrNJORc/UB2rj/y6JMA0GCSqGSIb3DQEB -CwUAMDQxMjAwBgNVBAMTKUVsYXN0aWMgQ2VydGlmaWNhdGUgVG9vbCBBdXRvZ2Vu -ZXJhdGVkIENBMB4XDTI0MDUyNzE5NTg0OVoXDTI3MDUyNzE5NTg0OVowNDEyMDAG -A1UEAxMpRWxhc3RpYyBDZXJ0aWZpY2F0ZSBUb29sIEF1dG9nZW5lcmF0ZWQgQ0Ew -ggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQCat4tgRPHYWsZrygCUNASc -cc4wGONcNzRkn7LRbF+jWVcx3G8SLK1uY5w0jdC+mioRe8vIVR1amng1KtIoADxa -aV43rCNSTfPO6DsDPu9AGdoxlHrUjkpf5mPcy1hhRpVfHi01ytqSip/deA+2jShq -2naMug4o6YoqdPdiVvOrzMvuVdGeGNNrlihTHmENtI6vZp1Uq/pxZYqHhqkzlCDr -uZvhe6bLpvbbyGVpx6dJA51MuxZ6m6yEtz1fNz2fgh+2tl91hXp7WaeMDpGHaezq -cCbiHSMG9vXhU8fsgTTB2NONiGiH/635VZcTbbS9dtDeUe4Y/vEY0uURU2dyBF3L -AgMBAAGjUzBRMB0GA1UdDgQWBBRSHBPSZApefVLWs8FZB/ROLMos9DAfBgNVHSME -GDAWgBRSHBPSZApefVLWs8FZB/ROLMos9DAPBgNVHRMBAf8EBTADAQH/MA0GCSqG -SIb3DQEBCwUAA4IBAQB2Hfsz3RT9Y+th7haS4C6yI4ZnwirEehexM9RjjIWlCS/i -c3IZ1dwJlR1pI6jeFjhF6iEnWY24cxH2GvZrynIrwACSJBibzVuMa/4+7pcF2xQS -L9H+18StNK2S51a1xxzfhlQpTizea05rnLpkhJP52PfUaoWD42kPz8t8ITafD5ay -IzKiz+aL1+elT+Tlmcmi2k0s+sJahLrcI62jUsmUB474fsv/00DWEBaSRKVXCy4D -pUv2PKV+EElHiM65UlMxZ7q5KBWsCvotILrIeP7rJZE3uloe6UTeZ0CSTDiQNCOb -jonBnKm6Zw7z2Io9Jl0yJJg0kl8tc8OFsV85p9RO ------END CERTIFICATE----- diff --git a/elasticsearch/config/es01/elasticsearch-plugins.example.yml b/elasticsearch/config/es01/elasticsearch-plugins.example.yml deleted file mode 100644 index b6874e91..00000000 --- a/elasticsearch/config/es01/elasticsearch-plugins.example.yml +++ /dev/null @@ -1,27 +0,0 @@ -# Rename this file to `elasticsearch-plugins.yml` to use it. -# -# All plugins must be listed here. If you add a plugin to this list and run -# `elasticsearch-plugin sync`, that plugin will be installed. If you remove -# a plugin from this list, that plugin will be removed when Elasticsearch -# next starts. - -plugins: - # Each plugin must have an ID. Plugins with only an ID are official plugins and will be downloaded from Elastic. - - id: example-id - - # Plugins can be specified by URL (it doesn't have to be HTTP, you could use e.g. `file:`) - - id: example-with-url - location: https://some.domain/path/example4.zip - - # Or by maven coordinates: - - id: example-with-maven-url - location: org.elasticsearch.plugins:example-plugin:1.2.3 - - # A proxy can also be configured per-plugin, if necessary - - id: example-with-proxy - location: https://some.domain/path/example.zip - proxy: https://some.domain:1234 - -# Configures a proxy for all network access. Remove this if you don't need -# to use a proxy. -proxy: https://some.domain:1234 diff --git a/elasticsearch/config/es01/elasticsearch.keystore b/elasticsearch/config/es01/elasticsearch.keystore deleted file mode 100644 index d4e6e64272bd626f05b93b31052319d000f37e69..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 243 zcmcD&o+B=nnv+;ul9^nbnpl*ap_iRnSzMA|l*+)sz{;>6NIC%V;fh{2-mOv7cfXvk zF1jf5`PbEV=hQfKF10Sz*ZkR&>bAT?vZ9AquOZ-@4X5Qw&3B11@j_Ma^rCk?VGy?a z&=ti4G^5ZYQap6a{Sv9_#B1q5{_(qga*2%s3ZWkN+rvsEO=h2H@7Z`{QS~gY#T;90 z^F?ks*T)&&J{n!rn>X{VVtB~Z1q(O(E}Z-Jcchlrye|uq-K#ck(>6| qJkPSGY-^jfzIdJMrroOD@m0GRCvmedT|b~P`32B6FmRfhya@o&z-7Y# diff --git a/elasticsearch/config/es01/elasticsearch.yml b/elasticsearch/config/es01/elasticsearch.yml deleted file mode 100644 index e0675344..00000000 --- a/elasticsearch/config/es01/elasticsearch.yml +++ /dev/null @@ -1,19 +0,0 @@ -node.name: es01 -path.data: /usr/share/elasticsearch/data -path.logs: /usr/share/elasticsearch/logs -network.host: 127.0.0.1 -cluster.name: docker-cluster -discovery.seed_hosts: ["127.0.0.1:9300", "127.0.0.1:9301", "127.0.0.1:9302"] -cluster.initial_master_nodes: ["es01", "es02", "es03"] -bootstrap.memory_lock: false -xpack.security.enabled: true -xpack.security.http.ssl.enabled: true -xpack.security.http.ssl.key: es01.key -xpack.security.http.ssl.certificate: es01.crt -xpack.security.http.ssl.certificate_authorities: ["ca.crt"] -xpack.security.transport.ssl.enabled: true -xpack.security.transport.ssl.verification_mode: certificate -xpack.security.transport.ssl.key: es01.key -xpack.security.transport.ssl.certificate: es01.crt -xpack.security.transport.ssl.certificate_authorities: ["ca.crt"] -xpack.license.self_generated.type: basic diff --git a/elasticsearch/config/es01/es01.crt b/elasticsearch/config/es01/es01.crt deleted file mode 100755 index 76b8cdf2..00000000 --- a/elasticsearch/config/es01/es01.crt +++ /dev/null @@ -1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDQDCCAiigAwIBAgIUKzkAgntIW7y5IHFE2MoxxHoLvGkwDQYJKoZIhvcNAQEL -BQAwNDEyMDAGA1UEAxMpRWxhc3RpYyBDZXJ0aWZpY2F0ZSBUb29sIEF1dG9nZW5l -cmF0ZWQgQ0EwHhcNMjQwNTI3MTk1ODUwWhcNMjcwNTI3MTk1ODUwWjAPMQ0wCwYD -VQQDEwRlczAxMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAmpXRQ+TH -n+VpNZwD+7+9R7IOhisKjCEGKMqSARr5wQPlulxLDlHoO7pRLw6JcEXmLn4tx+Qu -Lkkm9pFAADYnIcCljzRkFHADKuo+P7BG6AZ+6l1UVHwya6iUaERda3+VClTHPzRo -Pn8TRdX78+cY7dAzme0noosw8WCgpL2HgiYciFiwle80OKaeiVxvRNJ+6n36a/q4 -lgLx9wQoJvox/2OCP3318UY1SgA2AO9kFk0zDTvJji9ESSz6d+6LCB73Bk2cKWx6 -q2pVoSTNPhkC5mD9E/zhuXSACfY3H85GZksJ4jztdF1UcsOsrjYsMwwxHH4BwVKa -k97ERJmICSLV/wIDAQABo28wbTAdBgNVHQ4EFgQUuAQmumKYnqzgVBFsMKNd3Wk5 -bPgwHwYDVR0jBBgwFoAUUhwT0mQKXn1S1rPBWQf0TizKLPQwIAYDVR0RBBkwF4IE -ZXMwMYcEfwAAAYIJbG9jYWxob3N0MAkGA1UdEwQCMAAwDQYJKoZIhvcNAQELBQAD -ggEBAHwCJLqUnUIUV21HxVcEThmOWnxFZzA7VjahpIEb/RaK84Uc/8Cxv7vlVW1T -2mgOFQWpi98lew5K7r9GGuA509TaWuwaWAoqdhJL1T2lNa9eA8XtfRZweedqdw5P -1lCwR4vpJr5vVVEZwJTjKCRg5429owjoqEqPHSYbftVQSC8FlKIjnzNO9b5dHmfJ -y9DV5jYu5zfHZHPHXoksmn7MzLpfE6jA2euI7FSQfzPbkfoaBWBU/ZyvQb/taCyY -LH7JNtTf4Tm+yQe8g1m3hhFOraoQG0W09Ub+wpph50Q12sbHddX0e+ekhcu3+8HM -H4Y2SB9Pv5fsKvXtxix02Xu2m0s= ------END CERTIFICATE----- diff --git a/elasticsearch/config/es01/es01.key b/elasticsearch/config/es01/es01.key deleted file mode 100755 index c2c59819..00000000 --- a/elasticsearch/config/es01/es01.key +++ /dev/null @@ -1,27 +0,0 @@ ------BEGIN RSA PRIVATE KEY----- -MIIEogIBAAKCAQEAmpXRQ+THn+VpNZwD+7+9R7IOhisKjCEGKMqSARr5wQPlulxL -DlHoO7pRLw6JcEXmLn4tx+QuLkkm9pFAADYnIcCljzRkFHADKuo+P7BG6AZ+6l1U -VHwya6iUaERda3+VClTHPzRoPn8TRdX78+cY7dAzme0noosw8WCgpL2HgiYciFiw -le80OKaeiVxvRNJ+6n36a/q4lgLx9wQoJvox/2OCP3318UY1SgA2AO9kFk0zDTvJ -ji9ESSz6d+6LCB73Bk2cKWx6q2pVoSTNPhkC5mD9E/zhuXSACfY3H85GZksJ4jzt -dF1UcsOsrjYsMwwxHH4BwVKak97ERJmICSLV/wIDAQABAoIBABDs2Vo1KNQtcoz/ -lYIRVsCMUsHG4aNBFGMP9tdvJCxJaHQ0mbUqK6KqfiwIS+0CgjbR8uJBbfr8YGs7 -sQW06CjuZlIdGt4P+5DNz936R3EtEOVJLawIYx7deM5HufDEqcVVTfFyI/2/vRT2 -3lywj06ubo/qYt4NnmC3Qy92XulVZknvjaolGhqVlYGbHQITSI47KNErFX0KiL77 -dYkO3dY75XIc4NsECk5UyFRyu1Mg6dkG+a8HahPVmX7ctq+bytQ3r9hpwXrY1MZV -RXtEW81kwB0yJV3GmXJRrqKZR+vvpqMRIQhmLZgr0XkKIshBuBneAo4cUX7MVgAX -JcdHsMUCgYEA0P7NgUL4xg9Xtpygf5DWldcCO/cy6yoOkXEJ6d9xOhQny24qi0MN -uOuyCCF4UQnhdEYcR9aY6YOwviQluq6roQT7g1Jex20F2iaaLVNdxaGi5s9nmzgp -CVfBdLK798GCu/yrRwhy1EW6kaQdIJ9D0K1Fkm8SFOAuQY0gKgbK6BsCgYEAvVpH -AKtcUJiqyenkvyhnxmpZXhS93F2qSJnzBJ/2wAuQ7BCxw/vW7vw1XDDy+IKFXLQ4 -eRnbIs2hk+FBAgAQDJl26dUELaE+BKsmU1XACRBrnpLZXNd0ssLc1dMFewkxURmT -krWCku32OjVVnPuIALE4Bv9hBSvotYFmf7DXL+0CgYBU05bFqFEg0olfbSMXo8n0 -91fIzwSzvlY7Yg4MBs0GLbgZMZXDAGxJaiDQfAVBnykK8In5/ngCD5llE3bc1piC -umr7Witt9iox6Qka7INa+8gKtpPuxFSjniK/Iux4GurdMiiypBM3ZTXcdyf7XalA -wZNDZCGKp5MeuBEd/bPNkQKBgAlVzRBUYm26yRjBRjzCYjNfBN7liOK3X3DK3jdJ -J6IaL9/jhtARt2v61SqhYykrTiXe4LXft3UEzEV9InZVyHTGkB1BGj6hp2wVgAM1 -xAzuWU/tD3hLSv6RKtAD4k5Jirvj1emytyhFQRFnlbvyjqbyFcAKkR7vJj7kjUgY -UNOVAoGACkTgg+e0Cxu/rzPqJTcwB7gXikBK9gXegi9fXv2Q8q860Y37MXkTCStF -elsIix7jrUokjXWG8Gc44GfJUCv+WLZCeqaL3oS+ZznfbESfiLwEecu/6jsb3SEk -Swx3Bb5X8q0/S0kD3ShMVPFnFIO37P37efMFaYJ2Fs1C3FpWfwM= ------END RSA PRIVATE KEY----- diff --git a/elasticsearch/config/es01/jvm.options b/elasticsearch/config/es01/jvm.options deleted file mode 100644 index 0b1af90f..00000000 --- a/elasticsearch/config/es01/jvm.options +++ /dev/null @@ -1,79 +0,0 @@ -################################################################ -## -## JVM configuration -## -################################################################ -## -## WARNING: DO NOT EDIT THIS FILE. If you want to override the -## JVM options in this file, or set any additional options, you -## should create one or more files in the jvm.options.d -## directory containing your adjustments. -## -## See https://www.elastic.co/guide/en/elasticsearch/reference/8.13/jvm-options.html -## for more information. -## -################################################################ - - - -################################################################ -## IMPORTANT: JVM heap size -################################################################ -## -## The heap size is automatically configured by Elasticsearch -## based on the available memory in your system and the roles -## each node is configured to fulfill. If specifying heap is -## required, it should be done through a file in jvm.options.d, -## which should be named with .options suffix, and the min and -## max should be set to the same value. For example, to set the -## heap to 4 GB, create a new file in the jvm.options.d -## directory containing these lines: -## -## -Xms4g -## -Xmx4g -## -## See https://www.elastic.co/guide/en/elasticsearch/reference/8.13/heap-size.html -## for more information -## -################################################################ - - -################################################################ -## Expert settings -################################################################ -## -## All settings below here are considered expert settings. Do -## not adjust them unless you understand what you are doing. Do -## not edit them in this file; instead, create a new file in the -## jvm.options.d directory containing your adjustments. -## -################################################################ - --XX:+UseG1GC - -## JVM temporary directory --Djava.io.tmpdir=${ES_TMPDIR} - -# Leverages accelerated vector hardware instructions; removing this may -# result in less optimal vector performance -20-:--add-modules=jdk.incubator.vector - -## heap dumps - -# generate a heap dump when an allocation from the Java heap fails; heap dumps -# are created in the working directory of the JVM unless an alternative path is -# specified --XX:+HeapDumpOnOutOfMemoryError - -# exit right after heap dump on out of memory error --XX:+ExitOnOutOfMemoryError - -# specify an alternative path for heap dumps; ensure the directory exists and -# has sufficient space --XX:HeapDumpPath=data - -# specify an alternative path for JVM fatal error logs --XX:ErrorFile=logs/hs_err_pid%p.log - -## GC logging --Xlog:gc*,gc+age=trace,safepoint:file=logs/gc.log:utctime,level,pid,tags:filecount=32,filesize=64m diff --git a/elasticsearch/config/es01/log4j2.file.properties b/elasticsearch/config/es01/log4j2.file.properties deleted file mode 100644 index 5bb63ce1..00000000 --- a/elasticsearch/config/es01/log4j2.file.properties +++ /dev/null @@ -1,279 +0,0 @@ -status = error - -appender.console.type = Console -appender.console.name = console -appender.console.layout.type = PatternLayout -appender.console.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%consoleException%n - -######## Server JSON ############################ -appender.rolling.type = RollingFile -appender.rolling.name = rolling -appender.rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_server.json -appender.rolling.layout.type = ECSJsonLayout -appender.rolling.layout.dataset = elasticsearch.server - -appender.rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}-%d{yyyy-MM-dd}-%i.json.gz -appender.rolling.policies.type = Policies -appender.rolling.policies.time.type = TimeBasedTriggeringPolicy -appender.rolling.policies.time.interval = 1 -appender.rolling.policies.time.modulate = true -appender.rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.rolling.policies.size.size = 128MB -appender.rolling.strategy.type = DefaultRolloverStrategy -appender.rolling.strategy.fileIndex = nomax -appender.rolling.strategy.action.type = Delete -appender.rolling.strategy.action.basepath = ${sys:es.logs.base_path} -appender.rolling.strategy.action.condition.type = IfFileName -appender.rolling.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-* -appender.rolling.strategy.action.condition.nested_condition.type = IfAccumulatedFileSize -appender.rolling.strategy.action.condition.nested_condition.exceeds = 2GB -################################################ -######## Server - old style pattern ########### -appender.rolling_old.type = RollingFile -appender.rolling_old.name = rolling_old -appender.rolling_old.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}.log -appender.rolling_old.layout.type = PatternLayout -appender.rolling_old.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%n - -appender.rolling_old.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}-%d{yyyy-MM-dd}-%i.log.gz -appender.rolling_old.policies.type = Policies -appender.rolling_old.policies.time.type = TimeBasedTriggeringPolicy -appender.rolling_old.policies.time.interval = 1 -appender.rolling_old.policies.time.modulate = true -appender.rolling_old.policies.size.type = SizeBasedTriggeringPolicy -appender.rolling_old.policies.size.size = 128MB -appender.rolling_old.strategy.type = DefaultRolloverStrategy -appender.rolling_old.strategy.fileIndex = nomax -appender.rolling_old.strategy.action.type = Delete -appender.rolling_old.strategy.action.basepath = ${sys:es.logs.base_path} -appender.rolling_old.strategy.action.condition.type = IfFileName -appender.rolling_old.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-* -appender.rolling_old.strategy.action.condition.nested_condition.type = IfAccumulatedFileSize -appender.rolling_old.strategy.action.condition.nested_condition.exceeds = 2GB -################################################ - -rootLogger.level = info -rootLogger.appenderRef.console.ref = console -rootLogger.appenderRef.rolling.ref = rolling -rootLogger.appenderRef.rolling_old.ref = rolling_old - -######## Deprecation JSON ####################### -appender.deprecation_rolling.type = RollingFile -appender.deprecation_rolling.name = deprecation_rolling -appender.deprecation_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_deprecation.json -appender.deprecation_rolling.layout.type = ECSJsonLayout -# Intentionally follows a different pattern to above -appender.deprecation_rolling.layout.dataset = deprecation.elasticsearch -appender.deprecation_rolling.filter.rate_limit.type = RateLimitingFilter - -appender.deprecation_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_deprecation-%i.json.gz -appender.deprecation_rolling.policies.type = Policies -appender.deprecation_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.deprecation_rolling.policies.size.size = 1GB -appender.deprecation_rolling.strategy.type = DefaultRolloverStrategy -appender.deprecation_rolling.strategy.max = 4 - -appender.header_warning.type = HeaderWarningAppender -appender.header_warning.name = header_warning -################################################# - -logger.deprecation.name = org.elasticsearch.deprecation -logger.deprecation.level = WARN -logger.deprecation.appenderRef.deprecation_rolling.ref = deprecation_rolling -logger.deprecation.appenderRef.header_warning.ref = header_warning -logger.deprecation.additivity = false - -######## Search slowlog JSON #################### -appender.index_search_slowlog_rolling.type = RollingFile -appender.index_search_slowlog_rolling.name = index_search_slowlog_rolling -appender.index_search_slowlog_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs\ - .cluster_name}_index_search_slowlog.json -appender.index_search_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_search_slowlog_rolling.layout.dataset = elasticsearch.index_search_slowlog - -appender.index_search_slowlog_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs\ - .cluster_name}_index_search_slowlog-%i.json.gz -appender.index_search_slowlog_rolling.policies.type = Policies -appender.index_search_slowlog_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.index_search_slowlog_rolling.policies.size.size = 1GB -appender.index_search_slowlog_rolling.strategy.type = DefaultRolloverStrategy -appender.index_search_slowlog_rolling.strategy.max = 4 -################################################# - -################################################# -logger.index_search_slowlog_rolling.name = index.search.slowlog -logger.index_search_slowlog_rolling.level = trace -logger.index_search_slowlog_rolling.appenderRef.index_search_slowlog_rolling.ref = index_search_slowlog_rolling -logger.index_search_slowlog_rolling.additivity = false - -######## Indexing slowlog JSON ################## -appender.index_indexing_slowlog_rolling.type = RollingFile -appender.index_indexing_slowlog_rolling.name = index_indexing_slowlog_rolling -appender.index_indexing_slowlog_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}\ - _index_indexing_slowlog.json -appender.index_indexing_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_indexing_slowlog_rolling.layout.dataset = elasticsearch.index_indexing_slowlog - - -appender.index_indexing_slowlog_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}\ - _index_indexing_slowlog-%i.json.gz -appender.index_indexing_slowlog_rolling.policies.type = Policies -appender.index_indexing_slowlog_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.index_indexing_slowlog_rolling.policies.size.size = 1GB -appender.index_indexing_slowlog_rolling.strategy.type = DefaultRolloverStrategy -appender.index_indexing_slowlog_rolling.strategy.max = 4 -################################################# - - -logger.index_indexing_slowlog.name = index.indexing.slowlog.index -logger.index_indexing_slowlog.level = trace -logger.index_indexing_slowlog.appenderRef.index_indexing_slowlog_rolling.ref = index_indexing_slowlog_rolling -logger.index_indexing_slowlog.additivity = false - - -logger.org_apache_pdfbox.name = org.apache.pdfbox -logger.org_apache_pdfbox.level = off - -logger.org_apache_poi.name = org.apache.poi -logger.org_apache_poi.level = off - -logger.org_apache_fontbox.name = org.apache.fontbox -logger.org_apache_fontbox.level = off - -logger.org_apache_xmlbeans.name = org.apache.xmlbeans -logger.org_apache_xmlbeans.level = off - - -logger.com_amazonaws.name = com.amazonaws -logger.com_amazonaws.level = warn - -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.name = com.amazonaws.jmx.SdkMBeanRegistrySupport -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.level = error - -logger.com_amazonaws_metrics_AwsSdkMetrics.name = com.amazonaws.metrics.AwsSdkMetrics -logger.com_amazonaws_metrics_AwsSdkMetrics.level = error - -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.name = com.amazonaws.auth.profile.internal.BasicProfileConfigFileLoader -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.level = error - -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.name = com.amazonaws.services.s3.internal.UseArnRegionResolver -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.level = error - - -appender.audit_rolling.type = RollingFile -appender.audit_rolling.name = audit_rolling -appender.audit_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_audit.json -appender.audit_rolling.layout.type = PatternLayout -appender.audit_rolling.layout.pattern = {\ - "type":"audit", \ - "timestamp":"%d{yyyy-MM-dd'T'HH:mm:ss,SSSZ}"\ - %varsNotEmpty{, "cluster.name":"%enc{%map{cluster.name}}{JSON}"}\ - %varsNotEmpty{, "cluster.uuid":"%enc{%map{cluster.uuid}}{JSON}"}\ - %varsNotEmpty{, "node.name":"%enc{%map{node.name}}{JSON}"}\ - %varsNotEmpty{, "node.id":"%enc{%map{node.id}}{JSON}"}\ - %varsNotEmpty{, "host.name":"%enc{%map{host.name}}{JSON}"}\ - %varsNotEmpty{, "host.ip":"%enc{%map{host.ip}}{JSON}"}\ - %varsNotEmpty{, "event.type":"%enc{%map{event.type}}{JSON}"}\ - %varsNotEmpty{, "event.action":"%enc{%map{event.action}}{JSON}"}\ - %varsNotEmpty{, "authentication.type":"%enc{%map{authentication.type}}{JSON}"}\ - %varsNotEmpty{, "user.name":"%enc{%map{user.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.name":"%enc{%map{user.run_by.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.name":"%enc{%map{user.run_as.name}}{JSON}"}\ - %varsNotEmpty{, "user.realm":"%enc{%map{user.realm}}{JSON}"}\ - %varsNotEmpty{, "user.realm_domain":"%enc{%map{user.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm":"%enc{%map{user.run_by.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm_domain":"%enc{%map{user.run_by.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm":"%enc{%map{user.run_as.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm_domain":"%enc{%map{user.run_as.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.roles":%map{user.roles}}\ - %varsNotEmpty{, "apikey.id":"%enc{%map{apikey.id}}{JSON}"}\ - %varsNotEmpty{, "apikey.name":"%enc{%map{apikey.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.name":"%enc{%map{authentication.token.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.type":"%enc{%map{authentication.token.type}}{JSON}"}\ - %varsNotEmpty{, "cross_cluster_access":%map{cross_cluster_access}}\ - %varsNotEmpty{, "origin.type":"%enc{%map{origin.type}}{JSON}"}\ - %varsNotEmpty{, "origin.address":"%enc{%map{origin.address}}{JSON}"}\ - %varsNotEmpty{, "realm":"%enc{%map{realm}}{JSON}"}\ - %varsNotEmpty{, "realm_domain":"%enc{%map{realm_domain}}{JSON}"}\ - %varsNotEmpty{, "url.path":"%enc{%map{url.path}}{JSON}"}\ - %varsNotEmpty{, "url.query":"%enc{%map{url.query}}{JSON}"}\ - %varsNotEmpty{, "request.method":"%enc{%map{request.method}}{JSON}"}\ - %varsNotEmpty{, "request.body":"%enc{%map{request.body}}{JSON}"}\ - %varsNotEmpty{, "request.id":"%enc{%map{request.id}}{JSON}"}\ - %varsNotEmpty{, "action":"%enc{%map{action}}{JSON}"}\ - %varsNotEmpty{, "request.name":"%enc{%map{request.name}}{JSON}"}\ - %varsNotEmpty{, "indices":%map{indices}}\ - %varsNotEmpty{, "opaque_id":"%enc{%map{opaque_id}}{JSON}"}\ - %varsNotEmpty{, "trace.id":"%enc{%map{trace.id}}{JSON}"}\ - %varsNotEmpty{, "x_forwarded_for":"%enc{%map{x_forwarded_for}}{JSON}"}\ - %varsNotEmpty{, "transport.profile":"%enc{%map{transport.profile}}{JSON}"}\ - %varsNotEmpty{, "rule":"%enc{%map{rule}}{JSON}"}\ - %varsNotEmpty{, "put":%map{put}}\ - %varsNotEmpty{, "delete":%map{delete}}\ - %varsNotEmpty{, "change":%map{change}}\ - %varsNotEmpty{, "create":%map{create}}\ - %varsNotEmpty{, "invalidate":%map{invalidate}}\ - }%n -# "node.name" node name from the `elasticsearch.yml` settings -# "node.id" node id which should not change between cluster restarts -# "host.name" unresolved hostname of the local node -# "host.ip" the local bound ip (i.e. the ip listening for connections) -# "origin.type" a received REST request is translated into one or more transport requests. This indicates which processing layer generated the event "rest" or "transport" (internal) -# "event.action" the name of the audited event, eg. "authentication_failed", "access_granted", "run_as_granted", etc. -# "authentication.type" one of "realm", "api_key", "token", "anonymous" or "internal" -# "user.name" the subject name as authenticated by a realm -# "user.run_by.name" the original authenticated subject name that is impersonating another one. -# "user.run_as.name" if this "event.action" is of a run_as type, this is the subject name to be impersonated as. -# "user.realm" the name of the realm that authenticated "user.name" -# "user.realm_domain" if "user.realm" is under a domain, this is the name of the domain -# "user.run_by.realm" the realm name of the impersonating subject ("user.run_by.name") -# "user.run_by.realm_domain" if "user.run_by.realm" is under a domain, this is the name of the domain -# "user.run_as.realm" if this "event.action" is of a run_as type, this is the realm name the impersonated user is looked up from -# "user.run_as.realm_domain" if "user.run_as.realm" is under a domain, this is the name of the domain -# "user.roles" the roles array of the user; these are the roles that are granting privileges -# "apikey.id" this field is present if and only if the "authentication.type" is "api_key" -# "apikey.name" this field is present if and only if the "authentication.type" is "api_key" -# "authentication.token.name" this field is present if and only if the authenticating credential is a service account token -# "authentication.token.type" this field is present if and only if the authenticating credential is a service account token -# "cross_cluster_access" this field is present if and only if the associated authentication occurred cross cluster -# "event.type" informs about what internal system generated the event; possible values are "rest", "transport", "ip_filter" and "security_config_change" -# "origin.address" the remote address and port of the first network hop, i.e. a REST proxy or another cluster node -# "realm" name of a realm that has generated an "authentication_failed" or an "authentication_successful"; the subject is not yet authenticated -# "realm_domain" if "realm" is under a domain, this is the name of the domain -# "url.path" the URI component between the port and the query string; it is percent (URL) encoded -# "url.query" the URI component after the path and before the fragment; it is percent (URL) encoded -# "request.method" the method of the HTTP request, i.e. one of GET, POST, PUT, DELETE, OPTIONS, HEAD, PATCH, TRACE, CONNECT -# "request.body" the content of the request body entity, JSON escaped -# "request.id" a synthetic identifier for the incoming request, this is unique per incoming request, and consistent across all audit events generated by that request -# "action" an action is the most granular operation that is authorized and this identifies it in a namespaced way (internal) -# "request.name" if the event is in connection to a transport message this is the name of the request class, similar to how rest requests are identified by the url path (internal) -# "indices" the array of indices that the "action" is acting upon -# "opaque_id" opaque value conveyed by the "X-Opaque-Id" request header -# "trace_id" an identifier conveyed by the part of "traceparent" request header -# "x_forwarded_for" the addresses from the "X-Forwarded-For" request header, as a verbatim string value (not an array) -# "transport.profile" name of the transport profile in case this is a "connection_granted" or "connection_denied" event -# "rule" name of the applied rule if the "origin.type" is "ip_filter" -# the "put", "delete", "change", "create", "invalidate" fields are only present -# when the "event.type" is "security_config_change" and contain the security config change (as an object) taking effect - -appender.audit_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_audit-%d{yyyy-MM-dd}-%i.json.gz -appender.audit_rolling.policies.type = Policies -appender.audit_rolling.policies.time.type = TimeBasedTriggeringPolicy -appender.audit_rolling.policies.time.interval = 1 -appender.audit_rolling.policies.time.modulate = true -appender.audit_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.audit_rolling.policies.size.size = 1GB -appender.audit_rolling.strategy.type = DefaultRolloverStrategy -appender.audit_rolling.strategy.fileIndex = nomax - -logger.xpack_security_audit_logfile.name = org.elasticsearch.xpack.security.audit.logfile.LoggingAuditTrail -logger.xpack_security_audit_logfile.level = info -logger.xpack_security_audit_logfile.appenderRef.audit_rolling.ref = audit_rolling -logger.xpack_security_audit_logfile.additivity = false - -logger.xmlsig.name = org.apache.xml.security.signature.XMLSignature -logger.xmlsig.level = error -logger.samlxml_decrypt.name = org.opensaml.xmlsec.encryption.support.Decrypter -logger.samlxml_decrypt.level = fatal -logger.saml2_decrypt.name = org.opensaml.saml.saml2.encryption.Decrypter -logger.saml2_decrypt.level = fatal diff --git a/elasticsearch/config/es01/log4j2.properties b/elasticsearch/config/es01/log4j2.properties deleted file mode 100644 index c0d67c8e..00000000 --- a/elasticsearch/config/es01/log4j2.properties +++ /dev/null @@ -1,193 +0,0 @@ -status = error - -######## Server JSON ############################ -appender.rolling.type = Console -appender.rolling.name = rolling -appender.rolling.layout.type = ECSJsonLayout -appender.rolling.layout.dataset = elasticsearch.server - -################################################ - -################################################ - -rootLogger.level = info -rootLogger.appenderRef.rolling.ref = rolling - -######## Deprecation JSON ####################### -appender.deprecation_rolling.type = Console -appender.deprecation_rolling.name = deprecation_rolling -appender.deprecation_rolling.layout.type = ECSJsonLayout -# Intentionally follows a different pattern to above -appender.deprecation_rolling.layout.dataset = deprecation.elasticsearch -appender.deprecation_rolling.filter.rate_limit.type = RateLimitingFilter - -appender.header_warning.type = HeaderWarningAppender -appender.header_warning.name = header_warning -################################################# - -logger.deprecation.name = org.elasticsearch.deprecation -logger.deprecation.level = WARN -logger.deprecation.appenderRef.deprecation_rolling.ref = deprecation_rolling -logger.deprecation.appenderRef.header_warning.ref = header_warning -logger.deprecation.additivity = false - -######## Search slowlog JSON #################### -appender.index_search_slowlog_rolling.type = Console -appender.index_search_slowlog_rolling.name = index_search_slowlog_rolling -appender.index_search_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_search_slowlog_rolling.layout.dataset = elasticsearch.index_search_slowlog - -################################################# - -################################################# -logger.index_search_slowlog_rolling.name = index.search.slowlog -logger.index_search_slowlog_rolling.level = trace -logger.index_search_slowlog_rolling.appenderRef.index_search_slowlog_rolling.ref = index_search_slowlog_rolling -logger.index_search_slowlog_rolling.additivity = false - -######## Indexing slowlog JSON ################## -appender.index_indexing_slowlog_rolling.type = Console -appender.index_indexing_slowlog_rolling.name = index_indexing_slowlog_rolling -appender.index_indexing_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_indexing_slowlog_rolling.layout.dataset = elasticsearch.index_indexing_slowlog - -################################################# - -logger.index_indexing_slowlog.name = index.indexing.slowlog.index -logger.index_indexing_slowlog.level = trace -logger.index_indexing_slowlog.appenderRef.index_indexing_slowlog_rolling.ref = index_indexing_slowlog_rolling -logger.index_indexing_slowlog.additivity = false - -logger.org_apache_pdfbox.name = org.apache.pdfbox -logger.org_apache_pdfbox.level = off - -logger.org_apache_poi.name = org.apache.poi -logger.org_apache_poi.level = off - -logger.org_apache_fontbox.name = org.apache.fontbox -logger.org_apache_fontbox.level = off - -logger.org_apache_xmlbeans.name = org.apache.xmlbeans -logger.org_apache_xmlbeans.level = off - -logger.com_amazonaws.name = com.amazonaws -logger.com_amazonaws.level = warn - -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.name = com.amazonaws.jmx.SdkMBeanRegistrySupport -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.level = error - -logger.com_amazonaws_metrics_AwsSdkMetrics.name = com.amazonaws.metrics.AwsSdkMetrics -logger.com_amazonaws_metrics_AwsSdkMetrics.level = error - -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.name = com.amazonaws.auth.profile.internal.BasicProfileConfigFileLoader -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.level = error - -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.name = com.amazonaws.services.s3.internal.UseArnRegionResolver -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.level = error - -appender.audit_rolling.type = Console -appender.audit_rolling.name = audit_rolling -appender.audit_rolling.layout.type = PatternLayout -appender.audit_rolling.layout.pattern = {\ - "type":"audit", \ - "timestamp":"%d{yyyy-MM-dd'T'HH:mm:ss,SSSZ}"\ - %varsNotEmpty{, "cluster.name":"%enc{%map{cluster.name}}{JSON}"}\ - %varsNotEmpty{, "cluster.uuid":"%enc{%map{cluster.uuid}}{JSON}"}\ - %varsNotEmpty{, "node.name":"%enc{%map{node.name}}{JSON}"}\ - %varsNotEmpty{, "node.id":"%enc{%map{node.id}}{JSON}"}\ - %varsNotEmpty{, "host.name":"%enc{%map{host.name}}{JSON}"}\ - %varsNotEmpty{, "host.ip":"%enc{%map{host.ip}}{JSON}"}\ - %varsNotEmpty{, "event.type":"%enc{%map{event.type}}{JSON}"}\ - %varsNotEmpty{, "event.action":"%enc{%map{event.action}}{JSON}"}\ - %varsNotEmpty{, "authentication.type":"%enc{%map{authentication.type}}{JSON}"}\ - %varsNotEmpty{, "user.name":"%enc{%map{user.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.name":"%enc{%map{user.run_by.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.name":"%enc{%map{user.run_as.name}}{JSON}"}\ - %varsNotEmpty{, "user.realm":"%enc{%map{user.realm}}{JSON}"}\ - %varsNotEmpty{, "user.realm_domain":"%enc{%map{user.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm":"%enc{%map{user.run_by.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm_domain":"%enc{%map{user.run_by.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm":"%enc{%map{user.run_as.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm_domain":"%enc{%map{user.run_as.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.roles":%map{user.roles}}\ - %varsNotEmpty{, "apikey.id":"%enc{%map{apikey.id}}{JSON}"}\ - %varsNotEmpty{, "apikey.name":"%enc{%map{apikey.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.name":"%enc{%map{authentication.token.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.type":"%enc{%map{authentication.token.type}}{JSON}"}\ - %varsNotEmpty{, "cross_cluster_access":%map{cross_cluster_access}}\ - %varsNotEmpty{, "origin.type":"%enc{%map{origin.type}}{JSON}"}\ - %varsNotEmpty{, "origin.address":"%enc{%map{origin.address}}{JSON}"}\ - %varsNotEmpty{, "realm":"%enc{%map{realm}}{JSON}"}\ - %varsNotEmpty{, "realm_domain":"%enc{%map{realm_domain}}{JSON}"}\ - %varsNotEmpty{, "url.path":"%enc{%map{url.path}}{JSON}"}\ - %varsNotEmpty{, "url.query":"%enc{%map{url.query}}{JSON}"}\ - %varsNotEmpty{, "request.method":"%enc{%map{request.method}}{JSON}"}\ - %varsNotEmpty{, "request.body":"%enc{%map{request.body}}{JSON}"}\ - %varsNotEmpty{, "request.id":"%enc{%map{request.id}}{JSON}"}\ - %varsNotEmpty{, "action":"%enc{%map{action}}{JSON}"}\ - %varsNotEmpty{, "request.name":"%enc{%map{request.name}}{JSON}"}\ - %varsNotEmpty{, "indices":%map{indices}}\ - %varsNotEmpty{, "opaque_id":"%enc{%map{opaque_id}}{JSON}"}\ - %varsNotEmpty{, "trace.id":"%enc{%map{trace.id}}{JSON}"}\ - %varsNotEmpty{, "x_forwarded_for":"%enc{%map{x_forwarded_for}}{JSON}"}\ - %varsNotEmpty{, "transport.profile":"%enc{%map{transport.profile}}{JSON}"}\ - %varsNotEmpty{, "rule":"%enc{%map{rule}}{JSON}"}\ - %varsNotEmpty{, "put":%map{put}}\ - %varsNotEmpty{, "delete":%map{delete}}\ - %varsNotEmpty{, "change":%map{change}}\ - %varsNotEmpty{, "create":%map{create}}\ - %varsNotEmpty{, "invalidate":%map{invalidate}}\ - }%n -# "node.name" node name from the `elasticsearch.yml` settings -# "node.id" node id which should not change between cluster restarts -# "host.name" unresolved hostname of the local node -# "host.ip" the local bound ip (i.e. the ip listening for connections) -# "origin.type" a received REST request is translated into one or more transport requests. This indicates which processing layer generated the event "rest" or "transport" (internal) -# "event.action" the name of the audited event, eg. "authentication_failed", "access_granted", "run_as_granted", etc. -# "authentication.type" one of "realm", "api_key", "token", "anonymous" or "internal" -# "user.name" the subject name as authenticated by a realm -# "user.run_by.name" the original authenticated subject name that is impersonating another one. -# "user.run_as.name" if this "event.action" is of a run_as type, this is the subject name to be impersonated as. -# "user.realm" the name of the realm that authenticated "user.name" -# "user.realm_domain" if "user.realm" is under a domain, this is the name of the domain -# "user.run_by.realm" the realm name of the impersonating subject ("user.run_by.name") -# "user.run_by.realm_domain" if "user.run_by.realm" is under a domain, this is the name of the domain -# "user.run_as.realm" if this "event.action" is of a run_as type, this is the realm name the impersonated user is looked up from -# "user.run_as.realm_domain" if "user.run_as.realm" is under a domain, this is the name of the domain -# "user.roles" the roles array of the user; these are the roles that are granting privileges -# "apikey.id" this field is present if and only if the "authentication.type" is "api_key" -# "apikey.name" this field is present if and only if the "authentication.type" is "api_key" -# "authentication.token.name" this field is present if and only if the authenticating credential is a service account token -# "authentication.token.type" this field is present if and only if the authenticating credential is a service account token -# "cross_cluster_access" this field is present if and only if the associated authentication occurred cross cluster -# "event.type" informs about what internal system generated the event; possible values are "rest", "transport", "ip_filter" and "security_config_change" -# "origin.address" the remote address and port of the first network hop, i.e. a REST proxy or another cluster node -# "realm" name of a realm that has generated an "authentication_failed" or an "authentication_successful"; the subject is not yet authenticated -# "realm_domain" if "realm" is under a domain, this is the name of the domain -# "url.path" the URI component between the port and the query string; it is percent (URL) encoded -# "url.query" the URI component after the path and before the fragment; it is percent (URL) encoded -# "request.method" the method of the HTTP request, i.e. one of GET, POST, PUT, DELETE, OPTIONS, HEAD, PATCH, TRACE, CONNECT -# "request.body" the content of the request body entity, JSON escaped -# "request.id" a synthetic identifier for the incoming request, this is unique per incoming request, and consistent across all audit events generated by that request -# "action" an action is the most granular operation that is authorized and this identifies it in a namespaced way (internal) -# "request.name" if the event is in connection to a transport message this is the name of the request class, similar to how rest requests are identified by the url path (internal) -# "indices" the array of indices that the "action" is acting upon -# "opaque_id" opaque value conveyed by the "X-Opaque-Id" request header -# "trace_id" an identifier conveyed by the part of "traceparent" request header -# "x_forwarded_for" the addresses from the "X-Forwarded-For" request header, as a verbatim string value (not an array) -# "transport.profile" name of the transport profile in case this is a "connection_granted" or "connection_denied" event -# "rule" name of the applied rule if the "origin.type" is "ip_filter" -# the "put", "delete", "change", "create", "invalidate" fields are only present -# when the "event.type" is "security_config_change" and contain the security config change (as an object) taking effect - -logger.xpack_security_audit_logfile.name = org.elasticsearch.xpack.security.audit.logfile.LoggingAuditTrail -logger.xpack_security_audit_logfile.level = info -logger.xpack_security_audit_logfile.appenderRef.audit_rolling.ref = audit_rolling -logger.xpack_security_audit_logfile.additivity = false - -logger.xmlsig.name = org.apache.xml.security.signature.XMLSignature -logger.xmlsig.level = error -logger.samlxml_decrypt.name = org.opensaml.xmlsec.encryption.support.Decrypter -logger.samlxml_decrypt.level = fatal -logger.saml2_decrypt.name = org.opensaml.saml.saml2.encryption.Decrypter -logger.saml2_decrypt.level = fatal \ No newline at end of file diff --git a/elasticsearch/config/es01/role_mapping.yml b/elasticsearch/config/es01/role_mapping.yml deleted file mode 100644 index 68c82f7e..00000000 --- a/elasticsearch/config/es01/role_mapping.yml +++ /dev/null @@ -1,14 +0,0 @@ -# Role mapping configuration file which has elasticsearch roles as keys -# that map to one or more user or group distinguished names - -#roleA: this is an elasticsearch role -# - groupA-DN this is a group distinguished name -# - groupB-DN -# - user1-DN this is the full user distinguished name - -#power_user: -# - "cn=admins,dc=example,dc=com" -#user: -# - "cn=users,dc=example,dc=com" -# - "cn=admins,dc=example,dc=com" -# - "cn=John Doe,cn=other users,dc=example,dc=com" diff --git a/elasticsearch/config/es01/roles.yml b/elasticsearch/config/es01/roles.yml deleted file mode 100644 index 68e003b8..00000000 --- a/elasticsearch/config/es01/roles.yml +++ /dev/null @@ -1,3 +0,0 @@ -# The default roles file is empty as the preferred method of defining roles is -# through the API/UI. File based roles are useful in error scenarios when the -# API based roles may not be available. diff --git a/elasticsearch/config/es01/users b/elasticsearch/config/es01/users deleted file mode 100644 index e69de29b..00000000 diff --git a/elasticsearch/config/es01/users_roles b/elasticsearch/config/es01/users_roles deleted file mode 100644 index e69de29b..00000000 diff --git a/elasticsearch/config/es02/ca.crt b/elasticsearch/config/es02/ca.crt deleted file mode 100755 index d8c6ffd4..00000000 --- a/elasticsearch/config/es02/ca.crt +++ /dev/null @@ -1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDSjCCAjKgAwIBAgIVAO2BMtuZgADcrNJORc/UB2rj/y6JMA0GCSqGSIb3DQEB -CwUAMDQxMjAwBgNVBAMTKUVsYXN0aWMgQ2VydGlmaWNhdGUgVG9vbCBBdXRvZ2Vu -ZXJhdGVkIENBMB4XDTI0MDUyNzE5NTg0OVoXDTI3MDUyNzE5NTg0OVowNDEyMDAG -A1UEAxMpRWxhc3RpYyBDZXJ0aWZpY2F0ZSBUb29sIEF1dG9nZW5lcmF0ZWQgQ0Ew -ggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQCat4tgRPHYWsZrygCUNASc -cc4wGONcNzRkn7LRbF+jWVcx3G8SLK1uY5w0jdC+mioRe8vIVR1amng1KtIoADxa -aV43rCNSTfPO6DsDPu9AGdoxlHrUjkpf5mPcy1hhRpVfHi01ytqSip/deA+2jShq -2naMug4o6YoqdPdiVvOrzMvuVdGeGNNrlihTHmENtI6vZp1Uq/pxZYqHhqkzlCDr -uZvhe6bLpvbbyGVpx6dJA51MuxZ6m6yEtz1fNz2fgh+2tl91hXp7WaeMDpGHaezq -cCbiHSMG9vXhU8fsgTTB2NONiGiH/635VZcTbbS9dtDeUe4Y/vEY0uURU2dyBF3L -AgMBAAGjUzBRMB0GA1UdDgQWBBRSHBPSZApefVLWs8FZB/ROLMos9DAfBgNVHSME -GDAWgBRSHBPSZApefVLWs8FZB/ROLMos9DAPBgNVHRMBAf8EBTADAQH/MA0GCSqG -SIb3DQEBCwUAA4IBAQB2Hfsz3RT9Y+th7haS4C6yI4ZnwirEehexM9RjjIWlCS/i -c3IZ1dwJlR1pI6jeFjhF6iEnWY24cxH2GvZrynIrwACSJBibzVuMa/4+7pcF2xQS -L9H+18StNK2S51a1xxzfhlQpTizea05rnLpkhJP52PfUaoWD42kPz8t8ITafD5ay -IzKiz+aL1+elT+Tlmcmi2k0s+sJahLrcI62jUsmUB474fsv/00DWEBaSRKVXCy4D -pUv2PKV+EElHiM65UlMxZ7q5KBWsCvotILrIeP7rJZE3uloe6UTeZ0CSTDiQNCOb -jonBnKm6Zw7z2Io9Jl0yJJg0kl8tc8OFsV85p9RO ------END CERTIFICATE----- diff --git a/elasticsearch/config/es02/elasticsearch-plugins.example.yml b/elasticsearch/config/es02/elasticsearch-plugins.example.yml deleted file mode 100644 index b6874e91..00000000 --- a/elasticsearch/config/es02/elasticsearch-plugins.example.yml +++ /dev/null @@ -1,27 +0,0 @@ -# Rename this file to `elasticsearch-plugins.yml` to use it. -# -# All plugins must be listed here. If you add a plugin to this list and run -# `elasticsearch-plugin sync`, that plugin will be installed. If you remove -# a plugin from this list, that plugin will be removed when Elasticsearch -# next starts. - -plugins: - # Each plugin must have an ID. Plugins with only an ID are official plugins and will be downloaded from Elastic. - - id: example-id - - # Plugins can be specified by URL (it doesn't have to be HTTP, you could use e.g. `file:`) - - id: example-with-url - location: https://some.domain/path/example4.zip - - # Or by maven coordinates: - - id: example-with-maven-url - location: org.elasticsearch.plugins:example-plugin:1.2.3 - - # A proxy can also be configured per-plugin, if necessary - - id: example-with-proxy - location: https://some.domain/path/example.zip - proxy: https://some.domain:1234 - -# Configures a proxy for all network access. Remove this if you don't need -# to use a proxy. -proxy: https://some.domain:1234 diff --git a/elasticsearch/config/es02/elasticsearch.keystore b/elasticsearch/config/es02/elasticsearch.keystore deleted file mode 100644 index aedca3db21c62062f57593ab828af14ef5d689b0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 243 zcmV>*K8LSWo%({bZKLAWnpq-XfA7Id2@7ca%BJj00jWQ0000$0001pROO4U z&GqnQ{iqTWL*zp4iK{)jbamMN=0vZeUch;0_F~3R>;k(sEW_YWo3*pC0@=B~21kxC z)}tQ4JqDf(0001A(3WkQ)%V8viYs(y00016KT8X{#YwK&g=RS?54#QKwfYH0|h%V7lua&Ak(UVyx+h$Ou{`4ul(}=-}0ud>qYkIa) t{um~8SDoYr`ai~v2vO&~bI%)jcNpot?hC*uljr~d00000004h$lkdN`aXSD2 diff --git a/elasticsearch/config/es02/elasticsearch.yml b/elasticsearch/config/es02/elasticsearch.yml deleted file mode 100644 index 810114ef..00000000 --- a/elasticsearch/config/es02/elasticsearch.yml +++ /dev/null @@ -1,19 +0,0 @@ -node.name: es02 -path.data: /usr/share/elasticsearch/data -path.logs: /usr/share/elasticsearch/logs -network.host: 127.0.0.1 -cluster.name: docker-cluster -discovery.seed_hosts: ["127.0.0.1:9300", "127.0.0.1:9301", "127.0.0.1:9302"] -cluster.initial_master_nodes: ["es01", "es02", "es03"] -bootstrap.memory_lock: false -xpack.security.enabled: true -xpack.security.http.ssl.enabled: true -xpack.security.http.ssl.key: es02.key -xpack.security.http.ssl.certificate: es02.crt -xpack.security.http.ssl.certificate_authorities: ["ca.crt"] -xpack.security.transport.ssl.enabled: true -xpack.security.transport.ssl.verification_mode: certificate -xpack.security.transport.ssl.key: es02.key -xpack.security.transport.ssl.certificate: es02.crt -xpack.security.transport.ssl.certificate_authorities: ["ca.crt"] -xpack.license.self_generated.type: basic diff --git a/elasticsearch/config/es02/es02.crt b/elasticsearch/config/es02/es02.crt deleted file mode 100755 index 44db9c41..00000000 --- a/elasticsearch/config/es02/es02.crt +++ /dev/null @@ -1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDQDCCAiigAwIBAgIUTk8VxELi8BcE+Q1QJDjl1ijZS0gwDQYJKoZIhvcNAQEL -BQAwNDEyMDAGA1UEAxMpRWxhc3RpYyBDZXJ0aWZpY2F0ZSBUb29sIEF1dG9nZW5l -cmF0ZWQgQ0EwHhcNMjQwNTI3MTk1ODUwWhcNMjcwNTI3MTk1ODUwWjAPMQ0wCwYD -VQQDEwRlczAyMIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAyaaCYcE+ -eDk4++1N2j0edhOQeJtRlVAY4okWx/GKZ8bUrFgWeXDmYnty8n2BjOsYy5AikELe -ZhsA6xWqV7/l8Tmhfv27e1qsxB1zNBurFQ4B2/O7YXHRXs2uKJqKb2Sj1+2GM6xQ -xZqZQS39MX2rKmENhud8kyDZUpQfqlUNE+QgRjMKMvB+7U9lrTfP/XUW2QosY+dz -Uub1K6pWf/avccf3WJd6R8aTYv2KDQjFsR6sib/RWHXTvtIDQlynQW4SFxSbhplX -DNN8nkaHa1lygAIHcKu77KulFrbTsJZ+hJIMjKn4HYsMuCH2KYDK+1cZToJ1MDvo -A9Xa2yWjbXWaiwIDAQABo28wbTAdBgNVHQ4EFgQUFSfOsv8EnRbLQI9pMbKNrRh0 -wM8wHwYDVR0jBBgwFoAUUhwT0mQKXn1S1rPBWQf0TizKLPQwIAYDVR0RBBkwF4cE -fwAAAYIEZXMwMoIJbG9jYWxob3N0MAkGA1UdEwQCMAAwDQYJKoZIhvcNAQELBQAD -ggEBAAGEWLu8MtZiav5wxvCb0iu5zUOPDSRibK/wTLtLhOeu4LBqyB9MYuT9wRr3 -XKsKuPV6NHSzUdkAK5wTqXsX3b5FXD6+B7PUMB31g62YnauDdTydszR1NxMp9LB+ -8YKb71JBy9iuBNk4OkVhmi9Q3fsQxchAWCtjI8aEa08PYq4XKAHvS1Hp2c4xP57+ -ZBxZS2ASFZo9EJ6BNwErF2OdcjIOQEU4YYebNARwdoY8taFNTBMQfbJRVRRdRCw5 -+n0fGB77j1nEmwLJSYMVkL+b44XUu7OIOiv2gRmvE2ZrQgIcj0hEYKtQpXFfLB+6 -OVv7xEYk2yWjJNBTiOKboVTb1U8= ------END CERTIFICATE----- diff --git a/elasticsearch/config/es02/es02.key b/elasticsearch/config/es02/es02.key deleted file mode 100755 index 14209814..00000000 --- a/elasticsearch/config/es02/es02.key +++ /dev/null @@ -1,27 +0,0 @@ ------BEGIN RSA PRIVATE KEY----- -MIIEowIBAAKCAQEAyaaCYcE+eDk4++1N2j0edhOQeJtRlVAY4okWx/GKZ8bUrFgW -eXDmYnty8n2BjOsYy5AikELeZhsA6xWqV7/l8Tmhfv27e1qsxB1zNBurFQ4B2/O7 -YXHRXs2uKJqKb2Sj1+2GM6xQxZqZQS39MX2rKmENhud8kyDZUpQfqlUNE+QgRjMK -MvB+7U9lrTfP/XUW2QosY+dzUub1K6pWf/avccf3WJd6R8aTYv2KDQjFsR6sib/R -WHXTvtIDQlynQW4SFxSbhplXDNN8nkaHa1lygAIHcKu77KulFrbTsJZ+hJIMjKn4 -HYsMuCH2KYDK+1cZToJ1MDvoA9Xa2yWjbXWaiwIDAQABAoIBABGFNZ6FD0ab1i8m -uFUbaq+gqzScw3ML3Ar8FUN3DEBQXr8WAjyNSinJXGc5mg133xYku6lkHfWqJ/jE -+kwYgMPmRCQIALJK4mhBv3Y0vIxzmfFrP7R2PVZAManBJQELsflQtRB9Ssv4eZNX -tGrd5obOmuxwkCV/vPzMoORdnRQas5a0i0ZtlCjv8jirX/t1VOLlsvoaIGs6TbTm -op3s7zi9ETvnCnZ6VKzsSugwUGCRnFqsIgUt8PUpKPXfgvzdRc5/j1NNWocfwLlH -lTSipk4n64KPevvG/deWw6AnnYLSosIQM4MCC9JrgZ1DhacGs23L7WWbz/1JC+oT -AgEP9FkCgYEAz3pj3PdibwhM/VWuOxwWWR4YS8UJZx1lcFPXzgE4t7V+/YSKuUtL -ro2plGUG1Ji5SgsS0exH/mzaF8OhHX1Jt+eGEVJyzdfJN8S1lomVtOoMyjkyBgxM -I4bajIB1Sz9aipKwVAfise9f6Ga3E0/ET9iq6i0/R1wK5EwgdOCxkBMCgYEA+M84 -W4S5P4EyfaeLOw+puyZcgG0ftVVqlAHdkdQlQLXCt3ijI008A0f5GVqf2tTLVrAj -Ub39aDOrM1wA6FWCqsa8LEbtdD4d1xNDi8DegDvjjjmU1z8ULjxSu8iSikhOCw2G -vyXHsr04DYBg4OURD9Pg8hHGtANZSNtc6CwISqkCgYBogbqZi9Z+HQ1Csgy/42by -XrFYQRh6Yxk8Wk8iigT6rCYaJtAFg4LMmrincbfeEEuMm0VQjha5djTosXaPNxOR -2cHzKbeALchCGghpmkXZSedFWUf0Oe+EGaIuEWqDi5bcpATDXvF2NR/3HP3scUpt -+bIloML1+8vUsO/MT33BFwKBgQCiXR+K4Wa94UKgqwf5p7P8VAFDMXLis3XUVg9Q -DZ+txa7maYwUCl+iSIJuoCv28qwqytCRlCjcqfMLlftlof+eEAhV4IcuNybj5kdK -2LaZ+fr6IetWN2yk62qV7kJqiNqc7dvDuxTBOCdu8BrIR9NFf9+oOB9x80l9eOD9 -BVb32QKBgAjHylTABCzS+2zOwUQeMpXQnYeNhMvvvYug/mDh+GQQL7zVeSJXlNGS -PkJqE3xEoVPDOPJ+Kr8nl5jS3YGCWcWerYURd9XiX7lHx7LlOeb6LusNIm/mO563 -y2U3WPIDTdy36vnvz3FZKYIionGFYc5hRGI54GfWKbCdr9ug2Xaw ------END RSA PRIVATE KEY----- diff --git a/elasticsearch/config/es02/jvm.options b/elasticsearch/config/es02/jvm.options deleted file mode 100644 index 0b1af90f..00000000 --- a/elasticsearch/config/es02/jvm.options +++ /dev/null @@ -1,79 +0,0 @@ -################################################################ -## -## JVM configuration -## -################################################################ -## -## WARNING: DO NOT EDIT THIS FILE. If you want to override the -## JVM options in this file, or set any additional options, you -## should create one or more files in the jvm.options.d -## directory containing your adjustments. -## -## See https://www.elastic.co/guide/en/elasticsearch/reference/8.13/jvm-options.html -## for more information. -## -################################################################ - - - -################################################################ -## IMPORTANT: JVM heap size -################################################################ -## -## The heap size is automatically configured by Elasticsearch -## based on the available memory in your system and the roles -## each node is configured to fulfill. If specifying heap is -## required, it should be done through a file in jvm.options.d, -## which should be named with .options suffix, and the min and -## max should be set to the same value. For example, to set the -## heap to 4 GB, create a new file in the jvm.options.d -## directory containing these lines: -## -## -Xms4g -## -Xmx4g -## -## See https://www.elastic.co/guide/en/elasticsearch/reference/8.13/heap-size.html -## for more information -## -################################################################ - - -################################################################ -## Expert settings -################################################################ -## -## All settings below here are considered expert settings. Do -## not adjust them unless you understand what you are doing. Do -## not edit them in this file; instead, create a new file in the -## jvm.options.d directory containing your adjustments. -## -################################################################ - --XX:+UseG1GC - -## JVM temporary directory --Djava.io.tmpdir=${ES_TMPDIR} - -# Leverages accelerated vector hardware instructions; removing this may -# result in less optimal vector performance -20-:--add-modules=jdk.incubator.vector - -## heap dumps - -# generate a heap dump when an allocation from the Java heap fails; heap dumps -# are created in the working directory of the JVM unless an alternative path is -# specified --XX:+HeapDumpOnOutOfMemoryError - -# exit right after heap dump on out of memory error --XX:+ExitOnOutOfMemoryError - -# specify an alternative path for heap dumps; ensure the directory exists and -# has sufficient space --XX:HeapDumpPath=data - -# specify an alternative path for JVM fatal error logs --XX:ErrorFile=logs/hs_err_pid%p.log - -## GC logging --Xlog:gc*,gc+age=trace,safepoint:file=logs/gc.log:utctime,level,pid,tags:filecount=32,filesize=64m diff --git a/elasticsearch/config/es02/log4j2.file.properties b/elasticsearch/config/es02/log4j2.file.properties deleted file mode 100644 index 5bb63ce1..00000000 --- a/elasticsearch/config/es02/log4j2.file.properties +++ /dev/null @@ -1,279 +0,0 @@ -status = error - -appender.console.type = Console -appender.console.name = console -appender.console.layout.type = PatternLayout -appender.console.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%consoleException%n - -######## Server JSON ############################ -appender.rolling.type = RollingFile -appender.rolling.name = rolling -appender.rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_server.json -appender.rolling.layout.type = ECSJsonLayout -appender.rolling.layout.dataset = elasticsearch.server - -appender.rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}-%d{yyyy-MM-dd}-%i.json.gz -appender.rolling.policies.type = Policies -appender.rolling.policies.time.type = TimeBasedTriggeringPolicy -appender.rolling.policies.time.interval = 1 -appender.rolling.policies.time.modulate = true -appender.rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.rolling.policies.size.size = 128MB -appender.rolling.strategy.type = DefaultRolloverStrategy -appender.rolling.strategy.fileIndex = nomax -appender.rolling.strategy.action.type = Delete -appender.rolling.strategy.action.basepath = ${sys:es.logs.base_path} -appender.rolling.strategy.action.condition.type = IfFileName -appender.rolling.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-* -appender.rolling.strategy.action.condition.nested_condition.type = IfAccumulatedFileSize -appender.rolling.strategy.action.condition.nested_condition.exceeds = 2GB -################################################ -######## Server - old style pattern ########### -appender.rolling_old.type = RollingFile -appender.rolling_old.name = rolling_old -appender.rolling_old.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}.log -appender.rolling_old.layout.type = PatternLayout -appender.rolling_old.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%n - -appender.rolling_old.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}-%d{yyyy-MM-dd}-%i.log.gz -appender.rolling_old.policies.type = Policies -appender.rolling_old.policies.time.type = TimeBasedTriggeringPolicy -appender.rolling_old.policies.time.interval = 1 -appender.rolling_old.policies.time.modulate = true -appender.rolling_old.policies.size.type = SizeBasedTriggeringPolicy -appender.rolling_old.policies.size.size = 128MB -appender.rolling_old.strategy.type = DefaultRolloverStrategy -appender.rolling_old.strategy.fileIndex = nomax -appender.rolling_old.strategy.action.type = Delete -appender.rolling_old.strategy.action.basepath = ${sys:es.logs.base_path} -appender.rolling_old.strategy.action.condition.type = IfFileName -appender.rolling_old.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-* -appender.rolling_old.strategy.action.condition.nested_condition.type = IfAccumulatedFileSize -appender.rolling_old.strategy.action.condition.nested_condition.exceeds = 2GB -################################################ - -rootLogger.level = info -rootLogger.appenderRef.console.ref = console -rootLogger.appenderRef.rolling.ref = rolling -rootLogger.appenderRef.rolling_old.ref = rolling_old - -######## Deprecation JSON ####################### -appender.deprecation_rolling.type = RollingFile -appender.deprecation_rolling.name = deprecation_rolling -appender.deprecation_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_deprecation.json -appender.deprecation_rolling.layout.type = ECSJsonLayout -# Intentionally follows a different pattern to above -appender.deprecation_rolling.layout.dataset = deprecation.elasticsearch -appender.deprecation_rolling.filter.rate_limit.type = RateLimitingFilter - -appender.deprecation_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_deprecation-%i.json.gz -appender.deprecation_rolling.policies.type = Policies -appender.deprecation_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.deprecation_rolling.policies.size.size = 1GB -appender.deprecation_rolling.strategy.type = DefaultRolloverStrategy -appender.deprecation_rolling.strategy.max = 4 - -appender.header_warning.type = HeaderWarningAppender -appender.header_warning.name = header_warning -################################################# - -logger.deprecation.name = org.elasticsearch.deprecation -logger.deprecation.level = WARN -logger.deprecation.appenderRef.deprecation_rolling.ref = deprecation_rolling -logger.deprecation.appenderRef.header_warning.ref = header_warning -logger.deprecation.additivity = false - -######## Search slowlog JSON #################### -appender.index_search_slowlog_rolling.type = RollingFile -appender.index_search_slowlog_rolling.name = index_search_slowlog_rolling -appender.index_search_slowlog_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs\ - .cluster_name}_index_search_slowlog.json -appender.index_search_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_search_slowlog_rolling.layout.dataset = elasticsearch.index_search_slowlog - -appender.index_search_slowlog_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs\ - .cluster_name}_index_search_slowlog-%i.json.gz -appender.index_search_slowlog_rolling.policies.type = Policies -appender.index_search_slowlog_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.index_search_slowlog_rolling.policies.size.size = 1GB -appender.index_search_slowlog_rolling.strategy.type = DefaultRolloverStrategy -appender.index_search_slowlog_rolling.strategy.max = 4 -################################################# - -################################################# -logger.index_search_slowlog_rolling.name = index.search.slowlog -logger.index_search_slowlog_rolling.level = trace -logger.index_search_slowlog_rolling.appenderRef.index_search_slowlog_rolling.ref = index_search_slowlog_rolling -logger.index_search_slowlog_rolling.additivity = false - -######## Indexing slowlog JSON ################## -appender.index_indexing_slowlog_rolling.type = RollingFile -appender.index_indexing_slowlog_rolling.name = index_indexing_slowlog_rolling -appender.index_indexing_slowlog_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}\ - _index_indexing_slowlog.json -appender.index_indexing_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_indexing_slowlog_rolling.layout.dataset = elasticsearch.index_indexing_slowlog - - -appender.index_indexing_slowlog_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}\ - _index_indexing_slowlog-%i.json.gz -appender.index_indexing_slowlog_rolling.policies.type = Policies -appender.index_indexing_slowlog_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.index_indexing_slowlog_rolling.policies.size.size = 1GB -appender.index_indexing_slowlog_rolling.strategy.type = DefaultRolloverStrategy -appender.index_indexing_slowlog_rolling.strategy.max = 4 -################################################# - - -logger.index_indexing_slowlog.name = index.indexing.slowlog.index -logger.index_indexing_slowlog.level = trace -logger.index_indexing_slowlog.appenderRef.index_indexing_slowlog_rolling.ref = index_indexing_slowlog_rolling -logger.index_indexing_slowlog.additivity = false - - -logger.org_apache_pdfbox.name = org.apache.pdfbox -logger.org_apache_pdfbox.level = off - -logger.org_apache_poi.name = org.apache.poi -logger.org_apache_poi.level = off - -logger.org_apache_fontbox.name = org.apache.fontbox -logger.org_apache_fontbox.level = off - -logger.org_apache_xmlbeans.name = org.apache.xmlbeans -logger.org_apache_xmlbeans.level = off - - -logger.com_amazonaws.name = com.amazonaws -logger.com_amazonaws.level = warn - -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.name = com.amazonaws.jmx.SdkMBeanRegistrySupport -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.level = error - -logger.com_amazonaws_metrics_AwsSdkMetrics.name = com.amazonaws.metrics.AwsSdkMetrics -logger.com_amazonaws_metrics_AwsSdkMetrics.level = error - -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.name = com.amazonaws.auth.profile.internal.BasicProfileConfigFileLoader -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.level = error - -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.name = com.amazonaws.services.s3.internal.UseArnRegionResolver -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.level = error - - -appender.audit_rolling.type = RollingFile -appender.audit_rolling.name = audit_rolling -appender.audit_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_audit.json -appender.audit_rolling.layout.type = PatternLayout -appender.audit_rolling.layout.pattern = {\ - "type":"audit", \ - "timestamp":"%d{yyyy-MM-dd'T'HH:mm:ss,SSSZ}"\ - %varsNotEmpty{, "cluster.name":"%enc{%map{cluster.name}}{JSON}"}\ - %varsNotEmpty{, "cluster.uuid":"%enc{%map{cluster.uuid}}{JSON}"}\ - %varsNotEmpty{, "node.name":"%enc{%map{node.name}}{JSON}"}\ - %varsNotEmpty{, "node.id":"%enc{%map{node.id}}{JSON}"}\ - %varsNotEmpty{, "host.name":"%enc{%map{host.name}}{JSON}"}\ - %varsNotEmpty{, "host.ip":"%enc{%map{host.ip}}{JSON}"}\ - %varsNotEmpty{, "event.type":"%enc{%map{event.type}}{JSON}"}\ - %varsNotEmpty{, "event.action":"%enc{%map{event.action}}{JSON}"}\ - %varsNotEmpty{, "authentication.type":"%enc{%map{authentication.type}}{JSON}"}\ - %varsNotEmpty{, "user.name":"%enc{%map{user.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.name":"%enc{%map{user.run_by.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.name":"%enc{%map{user.run_as.name}}{JSON}"}\ - %varsNotEmpty{, "user.realm":"%enc{%map{user.realm}}{JSON}"}\ - %varsNotEmpty{, "user.realm_domain":"%enc{%map{user.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm":"%enc{%map{user.run_by.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm_domain":"%enc{%map{user.run_by.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm":"%enc{%map{user.run_as.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm_domain":"%enc{%map{user.run_as.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.roles":%map{user.roles}}\ - %varsNotEmpty{, "apikey.id":"%enc{%map{apikey.id}}{JSON}"}\ - %varsNotEmpty{, "apikey.name":"%enc{%map{apikey.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.name":"%enc{%map{authentication.token.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.type":"%enc{%map{authentication.token.type}}{JSON}"}\ - %varsNotEmpty{, "cross_cluster_access":%map{cross_cluster_access}}\ - %varsNotEmpty{, "origin.type":"%enc{%map{origin.type}}{JSON}"}\ - %varsNotEmpty{, "origin.address":"%enc{%map{origin.address}}{JSON}"}\ - %varsNotEmpty{, "realm":"%enc{%map{realm}}{JSON}"}\ - %varsNotEmpty{, "realm_domain":"%enc{%map{realm_domain}}{JSON}"}\ - %varsNotEmpty{, "url.path":"%enc{%map{url.path}}{JSON}"}\ - %varsNotEmpty{, "url.query":"%enc{%map{url.query}}{JSON}"}\ - %varsNotEmpty{, "request.method":"%enc{%map{request.method}}{JSON}"}\ - %varsNotEmpty{, "request.body":"%enc{%map{request.body}}{JSON}"}\ - %varsNotEmpty{, "request.id":"%enc{%map{request.id}}{JSON}"}\ - %varsNotEmpty{, "action":"%enc{%map{action}}{JSON}"}\ - %varsNotEmpty{, "request.name":"%enc{%map{request.name}}{JSON}"}\ - %varsNotEmpty{, "indices":%map{indices}}\ - %varsNotEmpty{, "opaque_id":"%enc{%map{opaque_id}}{JSON}"}\ - %varsNotEmpty{, "trace.id":"%enc{%map{trace.id}}{JSON}"}\ - %varsNotEmpty{, "x_forwarded_for":"%enc{%map{x_forwarded_for}}{JSON}"}\ - %varsNotEmpty{, "transport.profile":"%enc{%map{transport.profile}}{JSON}"}\ - %varsNotEmpty{, "rule":"%enc{%map{rule}}{JSON}"}\ - %varsNotEmpty{, "put":%map{put}}\ - %varsNotEmpty{, "delete":%map{delete}}\ - %varsNotEmpty{, "change":%map{change}}\ - %varsNotEmpty{, "create":%map{create}}\ - %varsNotEmpty{, "invalidate":%map{invalidate}}\ - }%n -# "node.name" node name from the `elasticsearch.yml` settings -# "node.id" node id which should not change between cluster restarts -# "host.name" unresolved hostname of the local node -# "host.ip" the local bound ip (i.e. the ip listening for connections) -# "origin.type" a received REST request is translated into one or more transport requests. This indicates which processing layer generated the event "rest" or "transport" (internal) -# "event.action" the name of the audited event, eg. "authentication_failed", "access_granted", "run_as_granted", etc. -# "authentication.type" one of "realm", "api_key", "token", "anonymous" or "internal" -# "user.name" the subject name as authenticated by a realm -# "user.run_by.name" the original authenticated subject name that is impersonating another one. -# "user.run_as.name" if this "event.action" is of a run_as type, this is the subject name to be impersonated as. -# "user.realm" the name of the realm that authenticated "user.name" -# "user.realm_domain" if "user.realm" is under a domain, this is the name of the domain -# "user.run_by.realm" the realm name of the impersonating subject ("user.run_by.name") -# "user.run_by.realm_domain" if "user.run_by.realm" is under a domain, this is the name of the domain -# "user.run_as.realm" if this "event.action" is of a run_as type, this is the realm name the impersonated user is looked up from -# "user.run_as.realm_domain" if "user.run_as.realm" is under a domain, this is the name of the domain -# "user.roles" the roles array of the user; these are the roles that are granting privileges -# "apikey.id" this field is present if and only if the "authentication.type" is "api_key" -# "apikey.name" this field is present if and only if the "authentication.type" is "api_key" -# "authentication.token.name" this field is present if and only if the authenticating credential is a service account token -# "authentication.token.type" this field is present if and only if the authenticating credential is a service account token -# "cross_cluster_access" this field is present if and only if the associated authentication occurred cross cluster -# "event.type" informs about what internal system generated the event; possible values are "rest", "transport", "ip_filter" and "security_config_change" -# "origin.address" the remote address and port of the first network hop, i.e. a REST proxy or another cluster node -# "realm" name of a realm that has generated an "authentication_failed" or an "authentication_successful"; the subject is not yet authenticated -# "realm_domain" if "realm" is under a domain, this is the name of the domain -# "url.path" the URI component between the port and the query string; it is percent (URL) encoded -# "url.query" the URI component after the path and before the fragment; it is percent (URL) encoded -# "request.method" the method of the HTTP request, i.e. one of GET, POST, PUT, DELETE, OPTIONS, HEAD, PATCH, TRACE, CONNECT -# "request.body" the content of the request body entity, JSON escaped -# "request.id" a synthetic identifier for the incoming request, this is unique per incoming request, and consistent across all audit events generated by that request -# "action" an action is the most granular operation that is authorized and this identifies it in a namespaced way (internal) -# "request.name" if the event is in connection to a transport message this is the name of the request class, similar to how rest requests are identified by the url path (internal) -# "indices" the array of indices that the "action" is acting upon -# "opaque_id" opaque value conveyed by the "X-Opaque-Id" request header -# "trace_id" an identifier conveyed by the part of "traceparent" request header -# "x_forwarded_for" the addresses from the "X-Forwarded-For" request header, as a verbatim string value (not an array) -# "transport.profile" name of the transport profile in case this is a "connection_granted" or "connection_denied" event -# "rule" name of the applied rule if the "origin.type" is "ip_filter" -# the "put", "delete", "change", "create", "invalidate" fields are only present -# when the "event.type" is "security_config_change" and contain the security config change (as an object) taking effect - -appender.audit_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_audit-%d{yyyy-MM-dd}-%i.json.gz -appender.audit_rolling.policies.type = Policies -appender.audit_rolling.policies.time.type = TimeBasedTriggeringPolicy -appender.audit_rolling.policies.time.interval = 1 -appender.audit_rolling.policies.time.modulate = true -appender.audit_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.audit_rolling.policies.size.size = 1GB -appender.audit_rolling.strategy.type = DefaultRolloverStrategy -appender.audit_rolling.strategy.fileIndex = nomax - -logger.xpack_security_audit_logfile.name = org.elasticsearch.xpack.security.audit.logfile.LoggingAuditTrail -logger.xpack_security_audit_logfile.level = info -logger.xpack_security_audit_logfile.appenderRef.audit_rolling.ref = audit_rolling -logger.xpack_security_audit_logfile.additivity = false - -logger.xmlsig.name = org.apache.xml.security.signature.XMLSignature -logger.xmlsig.level = error -logger.samlxml_decrypt.name = org.opensaml.xmlsec.encryption.support.Decrypter -logger.samlxml_decrypt.level = fatal -logger.saml2_decrypt.name = org.opensaml.saml.saml2.encryption.Decrypter -logger.saml2_decrypt.level = fatal diff --git a/elasticsearch/config/es02/log4j2.properties b/elasticsearch/config/es02/log4j2.properties deleted file mode 100644 index c0d67c8e..00000000 --- a/elasticsearch/config/es02/log4j2.properties +++ /dev/null @@ -1,193 +0,0 @@ -status = error - -######## Server JSON ############################ -appender.rolling.type = Console -appender.rolling.name = rolling -appender.rolling.layout.type = ECSJsonLayout -appender.rolling.layout.dataset = elasticsearch.server - -################################################ - -################################################ - -rootLogger.level = info -rootLogger.appenderRef.rolling.ref = rolling - -######## Deprecation JSON ####################### -appender.deprecation_rolling.type = Console -appender.deprecation_rolling.name = deprecation_rolling -appender.deprecation_rolling.layout.type = ECSJsonLayout -# Intentionally follows a different pattern to above -appender.deprecation_rolling.layout.dataset = deprecation.elasticsearch -appender.deprecation_rolling.filter.rate_limit.type = RateLimitingFilter - -appender.header_warning.type = HeaderWarningAppender -appender.header_warning.name = header_warning -################################################# - -logger.deprecation.name = org.elasticsearch.deprecation -logger.deprecation.level = WARN -logger.deprecation.appenderRef.deprecation_rolling.ref = deprecation_rolling -logger.deprecation.appenderRef.header_warning.ref = header_warning -logger.deprecation.additivity = false - -######## Search slowlog JSON #################### -appender.index_search_slowlog_rolling.type = Console -appender.index_search_slowlog_rolling.name = index_search_slowlog_rolling -appender.index_search_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_search_slowlog_rolling.layout.dataset = elasticsearch.index_search_slowlog - -################################################# - -################################################# -logger.index_search_slowlog_rolling.name = index.search.slowlog -logger.index_search_slowlog_rolling.level = trace -logger.index_search_slowlog_rolling.appenderRef.index_search_slowlog_rolling.ref = index_search_slowlog_rolling -logger.index_search_slowlog_rolling.additivity = false - -######## Indexing slowlog JSON ################## -appender.index_indexing_slowlog_rolling.type = Console -appender.index_indexing_slowlog_rolling.name = index_indexing_slowlog_rolling -appender.index_indexing_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_indexing_slowlog_rolling.layout.dataset = elasticsearch.index_indexing_slowlog - -################################################# - -logger.index_indexing_slowlog.name = index.indexing.slowlog.index -logger.index_indexing_slowlog.level = trace -logger.index_indexing_slowlog.appenderRef.index_indexing_slowlog_rolling.ref = index_indexing_slowlog_rolling -logger.index_indexing_slowlog.additivity = false - -logger.org_apache_pdfbox.name = org.apache.pdfbox -logger.org_apache_pdfbox.level = off - -logger.org_apache_poi.name = org.apache.poi -logger.org_apache_poi.level = off - -logger.org_apache_fontbox.name = org.apache.fontbox -logger.org_apache_fontbox.level = off - -logger.org_apache_xmlbeans.name = org.apache.xmlbeans -logger.org_apache_xmlbeans.level = off - -logger.com_amazonaws.name = com.amazonaws -logger.com_amazonaws.level = warn - -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.name = com.amazonaws.jmx.SdkMBeanRegistrySupport -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.level = error - -logger.com_amazonaws_metrics_AwsSdkMetrics.name = com.amazonaws.metrics.AwsSdkMetrics -logger.com_amazonaws_metrics_AwsSdkMetrics.level = error - -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.name = com.amazonaws.auth.profile.internal.BasicProfileConfigFileLoader -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.level = error - -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.name = com.amazonaws.services.s3.internal.UseArnRegionResolver -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.level = error - -appender.audit_rolling.type = Console -appender.audit_rolling.name = audit_rolling -appender.audit_rolling.layout.type = PatternLayout -appender.audit_rolling.layout.pattern = {\ - "type":"audit", \ - "timestamp":"%d{yyyy-MM-dd'T'HH:mm:ss,SSSZ}"\ - %varsNotEmpty{, "cluster.name":"%enc{%map{cluster.name}}{JSON}"}\ - %varsNotEmpty{, "cluster.uuid":"%enc{%map{cluster.uuid}}{JSON}"}\ - %varsNotEmpty{, "node.name":"%enc{%map{node.name}}{JSON}"}\ - %varsNotEmpty{, "node.id":"%enc{%map{node.id}}{JSON}"}\ - %varsNotEmpty{, "host.name":"%enc{%map{host.name}}{JSON}"}\ - %varsNotEmpty{, "host.ip":"%enc{%map{host.ip}}{JSON}"}\ - %varsNotEmpty{, "event.type":"%enc{%map{event.type}}{JSON}"}\ - %varsNotEmpty{, "event.action":"%enc{%map{event.action}}{JSON}"}\ - %varsNotEmpty{, "authentication.type":"%enc{%map{authentication.type}}{JSON}"}\ - %varsNotEmpty{, "user.name":"%enc{%map{user.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.name":"%enc{%map{user.run_by.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.name":"%enc{%map{user.run_as.name}}{JSON}"}\ - %varsNotEmpty{, "user.realm":"%enc{%map{user.realm}}{JSON}"}\ - %varsNotEmpty{, "user.realm_domain":"%enc{%map{user.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm":"%enc{%map{user.run_by.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm_domain":"%enc{%map{user.run_by.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm":"%enc{%map{user.run_as.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm_domain":"%enc{%map{user.run_as.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.roles":%map{user.roles}}\ - %varsNotEmpty{, "apikey.id":"%enc{%map{apikey.id}}{JSON}"}\ - %varsNotEmpty{, "apikey.name":"%enc{%map{apikey.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.name":"%enc{%map{authentication.token.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.type":"%enc{%map{authentication.token.type}}{JSON}"}\ - %varsNotEmpty{, "cross_cluster_access":%map{cross_cluster_access}}\ - %varsNotEmpty{, "origin.type":"%enc{%map{origin.type}}{JSON}"}\ - %varsNotEmpty{, "origin.address":"%enc{%map{origin.address}}{JSON}"}\ - %varsNotEmpty{, "realm":"%enc{%map{realm}}{JSON}"}\ - %varsNotEmpty{, "realm_domain":"%enc{%map{realm_domain}}{JSON}"}\ - %varsNotEmpty{, "url.path":"%enc{%map{url.path}}{JSON}"}\ - %varsNotEmpty{, "url.query":"%enc{%map{url.query}}{JSON}"}\ - %varsNotEmpty{, "request.method":"%enc{%map{request.method}}{JSON}"}\ - %varsNotEmpty{, "request.body":"%enc{%map{request.body}}{JSON}"}\ - %varsNotEmpty{, "request.id":"%enc{%map{request.id}}{JSON}"}\ - %varsNotEmpty{, "action":"%enc{%map{action}}{JSON}"}\ - %varsNotEmpty{, "request.name":"%enc{%map{request.name}}{JSON}"}\ - %varsNotEmpty{, "indices":%map{indices}}\ - %varsNotEmpty{, "opaque_id":"%enc{%map{opaque_id}}{JSON}"}\ - %varsNotEmpty{, "trace.id":"%enc{%map{trace.id}}{JSON}"}\ - %varsNotEmpty{, "x_forwarded_for":"%enc{%map{x_forwarded_for}}{JSON}"}\ - %varsNotEmpty{, "transport.profile":"%enc{%map{transport.profile}}{JSON}"}\ - %varsNotEmpty{, "rule":"%enc{%map{rule}}{JSON}"}\ - %varsNotEmpty{, "put":%map{put}}\ - %varsNotEmpty{, "delete":%map{delete}}\ - %varsNotEmpty{, "change":%map{change}}\ - %varsNotEmpty{, "create":%map{create}}\ - %varsNotEmpty{, "invalidate":%map{invalidate}}\ - }%n -# "node.name" node name from the `elasticsearch.yml` settings -# "node.id" node id which should not change between cluster restarts -# "host.name" unresolved hostname of the local node -# "host.ip" the local bound ip (i.e. the ip listening for connections) -# "origin.type" a received REST request is translated into one or more transport requests. This indicates which processing layer generated the event "rest" or "transport" (internal) -# "event.action" the name of the audited event, eg. "authentication_failed", "access_granted", "run_as_granted", etc. -# "authentication.type" one of "realm", "api_key", "token", "anonymous" or "internal" -# "user.name" the subject name as authenticated by a realm -# "user.run_by.name" the original authenticated subject name that is impersonating another one. -# "user.run_as.name" if this "event.action" is of a run_as type, this is the subject name to be impersonated as. -# "user.realm" the name of the realm that authenticated "user.name" -# "user.realm_domain" if "user.realm" is under a domain, this is the name of the domain -# "user.run_by.realm" the realm name of the impersonating subject ("user.run_by.name") -# "user.run_by.realm_domain" if "user.run_by.realm" is under a domain, this is the name of the domain -# "user.run_as.realm" if this "event.action" is of a run_as type, this is the realm name the impersonated user is looked up from -# "user.run_as.realm_domain" if "user.run_as.realm" is under a domain, this is the name of the domain -# "user.roles" the roles array of the user; these are the roles that are granting privileges -# "apikey.id" this field is present if and only if the "authentication.type" is "api_key" -# "apikey.name" this field is present if and only if the "authentication.type" is "api_key" -# "authentication.token.name" this field is present if and only if the authenticating credential is a service account token -# "authentication.token.type" this field is present if and only if the authenticating credential is a service account token -# "cross_cluster_access" this field is present if and only if the associated authentication occurred cross cluster -# "event.type" informs about what internal system generated the event; possible values are "rest", "transport", "ip_filter" and "security_config_change" -# "origin.address" the remote address and port of the first network hop, i.e. a REST proxy or another cluster node -# "realm" name of a realm that has generated an "authentication_failed" or an "authentication_successful"; the subject is not yet authenticated -# "realm_domain" if "realm" is under a domain, this is the name of the domain -# "url.path" the URI component between the port and the query string; it is percent (URL) encoded -# "url.query" the URI component after the path and before the fragment; it is percent (URL) encoded -# "request.method" the method of the HTTP request, i.e. one of GET, POST, PUT, DELETE, OPTIONS, HEAD, PATCH, TRACE, CONNECT -# "request.body" the content of the request body entity, JSON escaped -# "request.id" a synthetic identifier for the incoming request, this is unique per incoming request, and consistent across all audit events generated by that request -# "action" an action is the most granular operation that is authorized and this identifies it in a namespaced way (internal) -# "request.name" if the event is in connection to a transport message this is the name of the request class, similar to how rest requests are identified by the url path (internal) -# "indices" the array of indices that the "action" is acting upon -# "opaque_id" opaque value conveyed by the "X-Opaque-Id" request header -# "trace_id" an identifier conveyed by the part of "traceparent" request header -# "x_forwarded_for" the addresses from the "X-Forwarded-For" request header, as a verbatim string value (not an array) -# "transport.profile" name of the transport profile in case this is a "connection_granted" or "connection_denied" event -# "rule" name of the applied rule if the "origin.type" is "ip_filter" -# the "put", "delete", "change", "create", "invalidate" fields are only present -# when the "event.type" is "security_config_change" and contain the security config change (as an object) taking effect - -logger.xpack_security_audit_logfile.name = org.elasticsearch.xpack.security.audit.logfile.LoggingAuditTrail -logger.xpack_security_audit_logfile.level = info -logger.xpack_security_audit_logfile.appenderRef.audit_rolling.ref = audit_rolling -logger.xpack_security_audit_logfile.additivity = false - -logger.xmlsig.name = org.apache.xml.security.signature.XMLSignature -logger.xmlsig.level = error -logger.samlxml_decrypt.name = org.opensaml.xmlsec.encryption.support.Decrypter -logger.samlxml_decrypt.level = fatal -logger.saml2_decrypt.name = org.opensaml.saml.saml2.encryption.Decrypter -logger.saml2_decrypt.level = fatal \ No newline at end of file diff --git a/elasticsearch/config/es02/role_mapping.yml b/elasticsearch/config/es02/role_mapping.yml deleted file mode 100644 index 68c82f7e..00000000 --- a/elasticsearch/config/es02/role_mapping.yml +++ /dev/null @@ -1,14 +0,0 @@ -# Role mapping configuration file which has elasticsearch roles as keys -# that map to one or more user or group distinguished names - -#roleA: this is an elasticsearch role -# - groupA-DN this is a group distinguished name -# - groupB-DN -# - user1-DN this is the full user distinguished name - -#power_user: -# - "cn=admins,dc=example,dc=com" -#user: -# - "cn=users,dc=example,dc=com" -# - "cn=admins,dc=example,dc=com" -# - "cn=John Doe,cn=other users,dc=example,dc=com" diff --git a/elasticsearch/config/es02/roles.yml b/elasticsearch/config/es02/roles.yml deleted file mode 100644 index 68e003b8..00000000 --- a/elasticsearch/config/es02/roles.yml +++ /dev/null @@ -1,3 +0,0 @@ -# The default roles file is empty as the preferred method of defining roles is -# through the API/UI. File based roles are useful in error scenarios when the -# API based roles may not be available. diff --git a/elasticsearch/config/es02/users b/elasticsearch/config/es02/users deleted file mode 100644 index e69de29b..00000000 diff --git a/elasticsearch/config/es02/users_roles b/elasticsearch/config/es02/users_roles deleted file mode 100644 index e69de29b..00000000 diff --git a/elasticsearch/config/es03/ca.crt b/elasticsearch/config/es03/ca.crt deleted file mode 100755 index d8c6ffd4..00000000 --- a/elasticsearch/config/es03/ca.crt +++ /dev/null @@ -1,20 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIDSjCCAjKgAwIBAgIVAO2BMtuZgADcrNJORc/UB2rj/y6JMA0GCSqGSIb3DQEB -CwUAMDQxMjAwBgNVBAMTKUVsYXN0aWMgQ2VydGlmaWNhdGUgVG9vbCBBdXRvZ2Vu -ZXJhdGVkIENBMB4XDTI0MDUyNzE5NTg0OVoXDTI3MDUyNzE5NTg0OVowNDEyMDAG -A1UEAxMpRWxhc3RpYyBDZXJ0aWZpY2F0ZSBUb29sIEF1dG9nZW5lcmF0ZWQgQ0Ew -ggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQCat4tgRPHYWsZrygCUNASc -cc4wGONcNzRkn7LRbF+jWVcx3G8SLK1uY5w0jdC+mioRe8vIVR1amng1KtIoADxa -aV43rCNSTfPO6DsDPu9AGdoxlHrUjkpf5mPcy1hhRpVfHi01ytqSip/deA+2jShq -2naMug4o6YoqdPdiVvOrzMvuVdGeGNNrlihTHmENtI6vZp1Uq/pxZYqHhqkzlCDr -uZvhe6bLpvbbyGVpx6dJA51MuxZ6m6yEtz1fNz2fgh+2tl91hXp7WaeMDpGHaezq -cCbiHSMG9vXhU8fsgTTB2NONiGiH/635VZcTbbS9dtDeUe4Y/vEY0uURU2dyBF3L -AgMBAAGjUzBRMB0GA1UdDgQWBBRSHBPSZApefVLWs8FZB/ROLMos9DAfBgNVHSME -GDAWgBRSHBPSZApefVLWs8FZB/ROLMos9DAPBgNVHRMBAf8EBTADAQH/MA0GCSqG -SIb3DQEBCwUAA4IBAQB2Hfsz3RT9Y+th7haS4C6yI4ZnwirEehexM9RjjIWlCS/i -c3IZ1dwJlR1pI6jeFjhF6iEnWY24cxH2GvZrynIrwACSJBibzVuMa/4+7pcF2xQS -L9H+18StNK2S51a1xxzfhlQpTizea05rnLpkhJP52PfUaoWD42kPz8t8ITafD5ay -IzKiz+aL1+elT+Tlmcmi2k0s+sJahLrcI62jUsmUB474fsv/00DWEBaSRKVXCy4D -pUv2PKV+EElHiM65UlMxZ7q5KBWsCvotILrIeP7rJZE3uloe6UTeZ0CSTDiQNCOb -jonBnKm6Zw7z2Io9Jl0yJJg0kl8tc8OFsV85p9RO ------END CERTIFICATE----- diff --git a/elasticsearch/config/es03/elasticsearch-plugins.example.yml b/elasticsearch/config/es03/elasticsearch-plugins.example.yml deleted file mode 100644 index b6874e91..00000000 --- a/elasticsearch/config/es03/elasticsearch-plugins.example.yml +++ /dev/null @@ -1,27 +0,0 @@ -# Rename this file to `elasticsearch-plugins.yml` to use it. -# -# All plugins must be listed here. If you add a plugin to this list and run -# `elasticsearch-plugin sync`, that plugin will be installed. If you remove -# a plugin from this list, that plugin will be removed when Elasticsearch -# next starts. - -plugins: - # Each plugin must have an ID. Plugins with only an ID are official plugins and will be downloaded from Elastic. - - id: example-id - - # Plugins can be specified by URL (it doesn't have to be HTTP, you could use e.g. `file:`) - - id: example-with-url - location: https://some.domain/path/example4.zip - - # Or by maven coordinates: - - id: example-with-maven-url - location: org.elasticsearch.plugins:example-plugin:1.2.3 - - # A proxy can also be configured per-plugin, if necessary - - id: example-with-proxy - location: https://some.domain/path/example.zip - proxy: https://some.domain:1234 - -# Configures a proxy for all network access. Remove this if you don't need -# to use a proxy. -proxy: https://some.domain:1234 diff --git a/elasticsearch/config/es03/elasticsearch.keystore b/elasticsearch/config/es03/elasticsearch.keystore deleted file mode 100644 index 260202e588d632753871b185885b31df45a7e16e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 243 zcmcD&o+B=nnv+;ul9^nbnpl*ap_iRnSzMA|l*+)sz{;>6NIC%V_eJh@F0ujH7r0jK z+4)Lc+ebdzWa(}}w`UQX7u7^WPm%3g-SX{zTl-7jvdZA6vGvB95oTQR$2Hdemenr& z*)X36XvWc*JDxV)vG!HgnlvFD$p0mj9U0Hsc;iR>1V*=&e(H{D7hg2YSQ_BvCjG{} zD>!LKwrbCA$?4Md?VkTNCvMq!b;h%DZhx_&Wg-_RcUG?X{=@X5X@&J=^%8!IhkT3l qBwrU@-w{)7j!H#ox_9)y+pK?HB@(ZACV6bpQjxPY bash -c ' diff --git a/elasticsearch/tmp-config/elasticsearch-plugins.example.yml b/elasticsearch/tmp-config/elasticsearch-plugins.example.yml deleted file mode 100644 index b6874e91..00000000 --- a/elasticsearch/tmp-config/elasticsearch-plugins.example.yml +++ /dev/null @@ -1,27 +0,0 @@ -# Rename this file to `elasticsearch-plugins.yml` to use it. -# -# All plugins must be listed here. If you add a plugin to this list and run -# `elasticsearch-plugin sync`, that plugin will be installed. If you remove -# a plugin from this list, that plugin will be removed when Elasticsearch -# next starts. - -plugins: - # Each plugin must have an ID. Plugins with only an ID are official plugins and will be downloaded from Elastic. - - id: example-id - - # Plugins can be specified by URL (it doesn't have to be HTTP, you could use e.g. `file:`) - - id: example-with-url - location: https://some.domain/path/example4.zip - - # Or by maven coordinates: - - id: example-with-maven-url - location: org.elasticsearch.plugins:example-plugin:1.2.3 - - # A proxy can also be configured per-plugin, if necessary - - id: example-with-proxy - location: https://some.domain/path/example.zip - proxy: https://some.domain:1234 - -# Configures a proxy for all network access. Remove this if you don't need -# to use a proxy. -proxy: https://some.domain:1234 diff --git a/elasticsearch/tmp-config/elasticsearch.yml b/elasticsearch/tmp-config/elasticsearch.yml deleted file mode 100644 index 50b15470..00000000 --- a/elasticsearch/tmp-config/elasticsearch.yml +++ /dev/null @@ -1,2 +0,0 @@ -cluster.name: "docker-cluster" -network.host: 0.0.0.0 diff --git a/elasticsearch/tmp-config/jvm.options b/elasticsearch/tmp-config/jvm.options deleted file mode 100644 index 0b1af90f..00000000 --- a/elasticsearch/tmp-config/jvm.options +++ /dev/null @@ -1,79 +0,0 @@ -################################################################ -## -## JVM configuration -## -################################################################ -## -## WARNING: DO NOT EDIT THIS FILE. If you want to override the -## JVM options in this file, or set any additional options, you -## should create one or more files in the jvm.options.d -## directory containing your adjustments. -## -## See https://www.elastic.co/guide/en/elasticsearch/reference/8.13/jvm-options.html -## for more information. -## -################################################################ - - - -################################################################ -## IMPORTANT: JVM heap size -################################################################ -## -## The heap size is automatically configured by Elasticsearch -## based on the available memory in your system and the roles -## each node is configured to fulfill. If specifying heap is -## required, it should be done through a file in jvm.options.d, -## which should be named with .options suffix, and the min and -## max should be set to the same value. For example, to set the -## heap to 4 GB, create a new file in the jvm.options.d -## directory containing these lines: -## -## -Xms4g -## -Xmx4g -## -## See https://www.elastic.co/guide/en/elasticsearch/reference/8.13/heap-size.html -## for more information -## -################################################################ - - -################################################################ -## Expert settings -################################################################ -## -## All settings below here are considered expert settings. Do -## not adjust them unless you understand what you are doing. Do -## not edit them in this file; instead, create a new file in the -## jvm.options.d directory containing your adjustments. -## -################################################################ - --XX:+UseG1GC - -## JVM temporary directory --Djava.io.tmpdir=${ES_TMPDIR} - -# Leverages accelerated vector hardware instructions; removing this may -# result in less optimal vector performance -20-:--add-modules=jdk.incubator.vector - -## heap dumps - -# generate a heap dump when an allocation from the Java heap fails; heap dumps -# are created in the working directory of the JVM unless an alternative path is -# specified --XX:+HeapDumpOnOutOfMemoryError - -# exit right after heap dump on out of memory error --XX:+ExitOnOutOfMemoryError - -# specify an alternative path for heap dumps; ensure the directory exists and -# has sufficient space --XX:HeapDumpPath=data - -# specify an alternative path for JVM fatal error logs --XX:ErrorFile=logs/hs_err_pid%p.log - -## GC logging --Xlog:gc*,gc+age=trace,safepoint:file=logs/gc.log:utctime,level,pid,tags:filecount=32,filesize=64m diff --git a/elasticsearch/tmp-config/log4j2.file.properties b/elasticsearch/tmp-config/log4j2.file.properties deleted file mode 100644 index 5bb63ce1..00000000 --- a/elasticsearch/tmp-config/log4j2.file.properties +++ /dev/null @@ -1,279 +0,0 @@ -status = error - -appender.console.type = Console -appender.console.name = console -appender.console.layout.type = PatternLayout -appender.console.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%consoleException%n - -######## Server JSON ############################ -appender.rolling.type = RollingFile -appender.rolling.name = rolling -appender.rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_server.json -appender.rolling.layout.type = ECSJsonLayout -appender.rolling.layout.dataset = elasticsearch.server - -appender.rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}-%d{yyyy-MM-dd}-%i.json.gz -appender.rolling.policies.type = Policies -appender.rolling.policies.time.type = TimeBasedTriggeringPolicy -appender.rolling.policies.time.interval = 1 -appender.rolling.policies.time.modulate = true -appender.rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.rolling.policies.size.size = 128MB -appender.rolling.strategy.type = DefaultRolloverStrategy -appender.rolling.strategy.fileIndex = nomax -appender.rolling.strategy.action.type = Delete -appender.rolling.strategy.action.basepath = ${sys:es.logs.base_path} -appender.rolling.strategy.action.condition.type = IfFileName -appender.rolling.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-* -appender.rolling.strategy.action.condition.nested_condition.type = IfAccumulatedFileSize -appender.rolling.strategy.action.condition.nested_condition.exceeds = 2GB -################################################ -######## Server - old style pattern ########### -appender.rolling_old.type = RollingFile -appender.rolling_old.name = rolling_old -appender.rolling_old.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}.log -appender.rolling_old.layout.type = PatternLayout -appender.rolling_old.layout.pattern = [%d{ISO8601}][%-5p][%-25c{1.}] [%node_name]%marker %m%n - -appender.rolling_old.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}-%d{yyyy-MM-dd}-%i.log.gz -appender.rolling_old.policies.type = Policies -appender.rolling_old.policies.time.type = TimeBasedTriggeringPolicy -appender.rolling_old.policies.time.interval = 1 -appender.rolling_old.policies.time.modulate = true -appender.rolling_old.policies.size.type = SizeBasedTriggeringPolicy -appender.rolling_old.policies.size.size = 128MB -appender.rolling_old.strategy.type = DefaultRolloverStrategy -appender.rolling_old.strategy.fileIndex = nomax -appender.rolling_old.strategy.action.type = Delete -appender.rolling_old.strategy.action.basepath = ${sys:es.logs.base_path} -appender.rolling_old.strategy.action.condition.type = IfFileName -appender.rolling_old.strategy.action.condition.glob = ${sys:es.logs.cluster_name}-* -appender.rolling_old.strategy.action.condition.nested_condition.type = IfAccumulatedFileSize -appender.rolling_old.strategy.action.condition.nested_condition.exceeds = 2GB -################################################ - -rootLogger.level = info -rootLogger.appenderRef.console.ref = console -rootLogger.appenderRef.rolling.ref = rolling -rootLogger.appenderRef.rolling_old.ref = rolling_old - -######## Deprecation JSON ####################### -appender.deprecation_rolling.type = RollingFile -appender.deprecation_rolling.name = deprecation_rolling -appender.deprecation_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_deprecation.json -appender.deprecation_rolling.layout.type = ECSJsonLayout -# Intentionally follows a different pattern to above -appender.deprecation_rolling.layout.dataset = deprecation.elasticsearch -appender.deprecation_rolling.filter.rate_limit.type = RateLimitingFilter - -appender.deprecation_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_deprecation-%i.json.gz -appender.deprecation_rolling.policies.type = Policies -appender.deprecation_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.deprecation_rolling.policies.size.size = 1GB -appender.deprecation_rolling.strategy.type = DefaultRolloverStrategy -appender.deprecation_rolling.strategy.max = 4 - -appender.header_warning.type = HeaderWarningAppender -appender.header_warning.name = header_warning -################################################# - -logger.deprecation.name = org.elasticsearch.deprecation -logger.deprecation.level = WARN -logger.deprecation.appenderRef.deprecation_rolling.ref = deprecation_rolling -logger.deprecation.appenderRef.header_warning.ref = header_warning -logger.deprecation.additivity = false - -######## Search slowlog JSON #################### -appender.index_search_slowlog_rolling.type = RollingFile -appender.index_search_slowlog_rolling.name = index_search_slowlog_rolling -appender.index_search_slowlog_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs\ - .cluster_name}_index_search_slowlog.json -appender.index_search_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_search_slowlog_rolling.layout.dataset = elasticsearch.index_search_slowlog - -appender.index_search_slowlog_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs\ - .cluster_name}_index_search_slowlog-%i.json.gz -appender.index_search_slowlog_rolling.policies.type = Policies -appender.index_search_slowlog_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.index_search_slowlog_rolling.policies.size.size = 1GB -appender.index_search_slowlog_rolling.strategy.type = DefaultRolloverStrategy -appender.index_search_slowlog_rolling.strategy.max = 4 -################################################# - -################################################# -logger.index_search_slowlog_rolling.name = index.search.slowlog -logger.index_search_slowlog_rolling.level = trace -logger.index_search_slowlog_rolling.appenderRef.index_search_slowlog_rolling.ref = index_search_slowlog_rolling -logger.index_search_slowlog_rolling.additivity = false - -######## Indexing slowlog JSON ################## -appender.index_indexing_slowlog_rolling.type = RollingFile -appender.index_indexing_slowlog_rolling.name = index_indexing_slowlog_rolling -appender.index_indexing_slowlog_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}\ - _index_indexing_slowlog.json -appender.index_indexing_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_indexing_slowlog_rolling.layout.dataset = elasticsearch.index_indexing_slowlog - - -appender.index_indexing_slowlog_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}\ - _index_indexing_slowlog-%i.json.gz -appender.index_indexing_slowlog_rolling.policies.type = Policies -appender.index_indexing_slowlog_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.index_indexing_slowlog_rolling.policies.size.size = 1GB -appender.index_indexing_slowlog_rolling.strategy.type = DefaultRolloverStrategy -appender.index_indexing_slowlog_rolling.strategy.max = 4 -################################################# - - -logger.index_indexing_slowlog.name = index.indexing.slowlog.index -logger.index_indexing_slowlog.level = trace -logger.index_indexing_slowlog.appenderRef.index_indexing_slowlog_rolling.ref = index_indexing_slowlog_rolling -logger.index_indexing_slowlog.additivity = false - - -logger.org_apache_pdfbox.name = org.apache.pdfbox -logger.org_apache_pdfbox.level = off - -logger.org_apache_poi.name = org.apache.poi -logger.org_apache_poi.level = off - -logger.org_apache_fontbox.name = org.apache.fontbox -logger.org_apache_fontbox.level = off - -logger.org_apache_xmlbeans.name = org.apache.xmlbeans -logger.org_apache_xmlbeans.level = off - - -logger.com_amazonaws.name = com.amazonaws -logger.com_amazonaws.level = warn - -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.name = com.amazonaws.jmx.SdkMBeanRegistrySupport -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.level = error - -logger.com_amazonaws_metrics_AwsSdkMetrics.name = com.amazonaws.metrics.AwsSdkMetrics -logger.com_amazonaws_metrics_AwsSdkMetrics.level = error - -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.name = com.amazonaws.auth.profile.internal.BasicProfileConfigFileLoader -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.level = error - -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.name = com.amazonaws.services.s3.internal.UseArnRegionResolver -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.level = error - - -appender.audit_rolling.type = RollingFile -appender.audit_rolling.name = audit_rolling -appender.audit_rolling.fileName = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_audit.json -appender.audit_rolling.layout.type = PatternLayout -appender.audit_rolling.layout.pattern = {\ - "type":"audit", \ - "timestamp":"%d{yyyy-MM-dd'T'HH:mm:ss,SSSZ}"\ - %varsNotEmpty{, "cluster.name":"%enc{%map{cluster.name}}{JSON}"}\ - %varsNotEmpty{, "cluster.uuid":"%enc{%map{cluster.uuid}}{JSON}"}\ - %varsNotEmpty{, "node.name":"%enc{%map{node.name}}{JSON}"}\ - %varsNotEmpty{, "node.id":"%enc{%map{node.id}}{JSON}"}\ - %varsNotEmpty{, "host.name":"%enc{%map{host.name}}{JSON}"}\ - %varsNotEmpty{, "host.ip":"%enc{%map{host.ip}}{JSON}"}\ - %varsNotEmpty{, "event.type":"%enc{%map{event.type}}{JSON}"}\ - %varsNotEmpty{, "event.action":"%enc{%map{event.action}}{JSON}"}\ - %varsNotEmpty{, "authentication.type":"%enc{%map{authentication.type}}{JSON}"}\ - %varsNotEmpty{, "user.name":"%enc{%map{user.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.name":"%enc{%map{user.run_by.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.name":"%enc{%map{user.run_as.name}}{JSON}"}\ - %varsNotEmpty{, "user.realm":"%enc{%map{user.realm}}{JSON}"}\ - %varsNotEmpty{, "user.realm_domain":"%enc{%map{user.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm":"%enc{%map{user.run_by.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm_domain":"%enc{%map{user.run_by.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm":"%enc{%map{user.run_as.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm_domain":"%enc{%map{user.run_as.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.roles":%map{user.roles}}\ - %varsNotEmpty{, "apikey.id":"%enc{%map{apikey.id}}{JSON}"}\ - %varsNotEmpty{, "apikey.name":"%enc{%map{apikey.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.name":"%enc{%map{authentication.token.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.type":"%enc{%map{authentication.token.type}}{JSON}"}\ - %varsNotEmpty{, "cross_cluster_access":%map{cross_cluster_access}}\ - %varsNotEmpty{, "origin.type":"%enc{%map{origin.type}}{JSON}"}\ - %varsNotEmpty{, "origin.address":"%enc{%map{origin.address}}{JSON}"}\ - %varsNotEmpty{, "realm":"%enc{%map{realm}}{JSON}"}\ - %varsNotEmpty{, "realm_domain":"%enc{%map{realm_domain}}{JSON}"}\ - %varsNotEmpty{, "url.path":"%enc{%map{url.path}}{JSON}"}\ - %varsNotEmpty{, "url.query":"%enc{%map{url.query}}{JSON}"}\ - %varsNotEmpty{, "request.method":"%enc{%map{request.method}}{JSON}"}\ - %varsNotEmpty{, "request.body":"%enc{%map{request.body}}{JSON}"}\ - %varsNotEmpty{, "request.id":"%enc{%map{request.id}}{JSON}"}\ - %varsNotEmpty{, "action":"%enc{%map{action}}{JSON}"}\ - %varsNotEmpty{, "request.name":"%enc{%map{request.name}}{JSON}"}\ - %varsNotEmpty{, "indices":%map{indices}}\ - %varsNotEmpty{, "opaque_id":"%enc{%map{opaque_id}}{JSON}"}\ - %varsNotEmpty{, "trace.id":"%enc{%map{trace.id}}{JSON}"}\ - %varsNotEmpty{, "x_forwarded_for":"%enc{%map{x_forwarded_for}}{JSON}"}\ - %varsNotEmpty{, "transport.profile":"%enc{%map{transport.profile}}{JSON}"}\ - %varsNotEmpty{, "rule":"%enc{%map{rule}}{JSON}"}\ - %varsNotEmpty{, "put":%map{put}}\ - %varsNotEmpty{, "delete":%map{delete}}\ - %varsNotEmpty{, "change":%map{change}}\ - %varsNotEmpty{, "create":%map{create}}\ - %varsNotEmpty{, "invalidate":%map{invalidate}}\ - }%n -# "node.name" node name from the `elasticsearch.yml` settings -# "node.id" node id which should not change between cluster restarts -# "host.name" unresolved hostname of the local node -# "host.ip" the local bound ip (i.e. the ip listening for connections) -# "origin.type" a received REST request is translated into one or more transport requests. This indicates which processing layer generated the event "rest" or "transport" (internal) -# "event.action" the name of the audited event, eg. "authentication_failed", "access_granted", "run_as_granted", etc. -# "authentication.type" one of "realm", "api_key", "token", "anonymous" or "internal" -# "user.name" the subject name as authenticated by a realm -# "user.run_by.name" the original authenticated subject name that is impersonating another one. -# "user.run_as.name" if this "event.action" is of a run_as type, this is the subject name to be impersonated as. -# "user.realm" the name of the realm that authenticated "user.name" -# "user.realm_domain" if "user.realm" is under a domain, this is the name of the domain -# "user.run_by.realm" the realm name of the impersonating subject ("user.run_by.name") -# "user.run_by.realm_domain" if "user.run_by.realm" is under a domain, this is the name of the domain -# "user.run_as.realm" if this "event.action" is of a run_as type, this is the realm name the impersonated user is looked up from -# "user.run_as.realm_domain" if "user.run_as.realm" is under a domain, this is the name of the domain -# "user.roles" the roles array of the user; these are the roles that are granting privileges -# "apikey.id" this field is present if and only if the "authentication.type" is "api_key" -# "apikey.name" this field is present if and only if the "authentication.type" is "api_key" -# "authentication.token.name" this field is present if and only if the authenticating credential is a service account token -# "authentication.token.type" this field is present if and only if the authenticating credential is a service account token -# "cross_cluster_access" this field is present if and only if the associated authentication occurred cross cluster -# "event.type" informs about what internal system generated the event; possible values are "rest", "transport", "ip_filter" and "security_config_change" -# "origin.address" the remote address and port of the first network hop, i.e. a REST proxy or another cluster node -# "realm" name of a realm that has generated an "authentication_failed" or an "authentication_successful"; the subject is not yet authenticated -# "realm_domain" if "realm" is under a domain, this is the name of the domain -# "url.path" the URI component between the port and the query string; it is percent (URL) encoded -# "url.query" the URI component after the path and before the fragment; it is percent (URL) encoded -# "request.method" the method of the HTTP request, i.e. one of GET, POST, PUT, DELETE, OPTIONS, HEAD, PATCH, TRACE, CONNECT -# "request.body" the content of the request body entity, JSON escaped -# "request.id" a synthetic identifier for the incoming request, this is unique per incoming request, and consistent across all audit events generated by that request -# "action" an action is the most granular operation that is authorized and this identifies it in a namespaced way (internal) -# "request.name" if the event is in connection to a transport message this is the name of the request class, similar to how rest requests are identified by the url path (internal) -# "indices" the array of indices that the "action" is acting upon -# "opaque_id" opaque value conveyed by the "X-Opaque-Id" request header -# "trace_id" an identifier conveyed by the part of "traceparent" request header -# "x_forwarded_for" the addresses from the "X-Forwarded-For" request header, as a verbatim string value (not an array) -# "transport.profile" name of the transport profile in case this is a "connection_granted" or "connection_denied" event -# "rule" name of the applied rule if the "origin.type" is "ip_filter" -# the "put", "delete", "change", "create", "invalidate" fields are only present -# when the "event.type" is "security_config_change" and contain the security config change (as an object) taking effect - -appender.audit_rolling.filePattern = ${sys:es.logs.base_path}${sys:file.separator}${sys:es.logs.cluster_name}_audit-%d{yyyy-MM-dd}-%i.json.gz -appender.audit_rolling.policies.type = Policies -appender.audit_rolling.policies.time.type = TimeBasedTriggeringPolicy -appender.audit_rolling.policies.time.interval = 1 -appender.audit_rolling.policies.time.modulate = true -appender.audit_rolling.policies.size.type = SizeBasedTriggeringPolicy -appender.audit_rolling.policies.size.size = 1GB -appender.audit_rolling.strategy.type = DefaultRolloverStrategy -appender.audit_rolling.strategy.fileIndex = nomax - -logger.xpack_security_audit_logfile.name = org.elasticsearch.xpack.security.audit.logfile.LoggingAuditTrail -logger.xpack_security_audit_logfile.level = info -logger.xpack_security_audit_logfile.appenderRef.audit_rolling.ref = audit_rolling -logger.xpack_security_audit_logfile.additivity = false - -logger.xmlsig.name = org.apache.xml.security.signature.XMLSignature -logger.xmlsig.level = error -logger.samlxml_decrypt.name = org.opensaml.xmlsec.encryption.support.Decrypter -logger.samlxml_decrypt.level = fatal -logger.saml2_decrypt.name = org.opensaml.saml.saml2.encryption.Decrypter -logger.saml2_decrypt.level = fatal diff --git a/elasticsearch/tmp-config/log4j2.properties b/elasticsearch/tmp-config/log4j2.properties deleted file mode 100644 index c0d67c8e..00000000 --- a/elasticsearch/tmp-config/log4j2.properties +++ /dev/null @@ -1,193 +0,0 @@ -status = error - -######## Server JSON ############################ -appender.rolling.type = Console -appender.rolling.name = rolling -appender.rolling.layout.type = ECSJsonLayout -appender.rolling.layout.dataset = elasticsearch.server - -################################################ - -################################################ - -rootLogger.level = info -rootLogger.appenderRef.rolling.ref = rolling - -######## Deprecation JSON ####################### -appender.deprecation_rolling.type = Console -appender.deprecation_rolling.name = deprecation_rolling -appender.deprecation_rolling.layout.type = ECSJsonLayout -# Intentionally follows a different pattern to above -appender.deprecation_rolling.layout.dataset = deprecation.elasticsearch -appender.deprecation_rolling.filter.rate_limit.type = RateLimitingFilter - -appender.header_warning.type = HeaderWarningAppender -appender.header_warning.name = header_warning -################################################# - -logger.deprecation.name = org.elasticsearch.deprecation -logger.deprecation.level = WARN -logger.deprecation.appenderRef.deprecation_rolling.ref = deprecation_rolling -logger.deprecation.appenderRef.header_warning.ref = header_warning -logger.deprecation.additivity = false - -######## Search slowlog JSON #################### -appender.index_search_slowlog_rolling.type = Console -appender.index_search_slowlog_rolling.name = index_search_slowlog_rolling -appender.index_search_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_search_slowlog_rolling.layout.dataset = elasticsearch.index_search_slowlog - -################################################# - -################################################# -logger.index_search_slowlog_rolling.name = index.search.slowlog -logger.index_search_slowlog_rolling.level = trace -logger.index_search_slowlog_rolling.appenderRef.index_search_slowlog_rolling.ref = index_search_slowlog_rolling -logger.index_search_slowlog_rolling.additivity = false - -######## Indexing slowlog JSON ################## -appender.index_indexing_slowlog_rolling.type = Console -appender.index_indexing_slowlog_rolling.name = index_indexing_slowlog_rolling -appender.index_indexing_slowlog_rolling.layout.type = ECSJsonLayout -appender.index_indexing_slowlog_rolling.layout.dataset = elasticsearch.index_indexing_slowlog - -################################################# - -logger.index_indexing_slowlog.name = index.indexing.slowlog.index -logger.index_indexing_slowlog.level = trace -logger.index_indexing_slowlog.appenderRef.index_indexing_slowlog_rolling.ref = index_indexing_slowlog_rolling -logger.index_indexing_slowlog.additivity = false - -logger.org_apache_pdfbox.name = org.apache.pdfbox -logger.org_apache_pdfbox.level = off - -logger.org_apache_poi.name = org.apache.poi -logger.org_apache_poi.level = off - -logger.org_apache_fontbox.name = org.apache.fontbox -logger.org_apache_fontbox.level = off - -logger.org_apache_xmlbeans.name = org.apache.xmlbeans -logger.org_apache_xmlbeans.level = off - -logger.com_amazonaws.name = com.amazonaws -logger.com_amazonaws.level = warn - -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.name = com.amazonaws.jmx.SdkMBeanRegistrySupport -logger.com_amazonaws_jmx_SdkMBeanRegistrySupport.level = error - -logger.com_amazonaws_metrics_AwsSdkMetrics.name = com.amazonaws.metrics.AwsSdkMetrics -logger.com_amazonaws_metrics_AwsSdkMetrics.level = error - -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.name = com.amazonaws.auth.profile.internal.BasicProfileConfigFileLoader -logger.com_amazonaws_auth_profile_internal_BasicProfileConfigFileLoader.level = error - -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.name = com.amazonaws.services.s3.internal.UseArnRegionResolver -logger.com_amazonaws_services_s3_internal_UseArnRegionResolver.level = error - -appender.audit_rolling.type = Console -appender.audit_rolling.name = audit_rolling -appender.audit_rolling.layout.type = PatternLayout -appender.audit_rolling.layout.pattern = {\ - "type":"audit", \ - "timestamp":"%d{yyyy-MM-dd'T'HH:mm:ss,SSSZ}"\ - %varsNotEmpty{, "cluster.name":"%enc{%map{cluster.name}}{JSON}"}\ - %varsNotEmpty{, "cluster.uuid":"%enc{%map{cluster.uuid}}{JSON}"}\ - %varsNotEmpty{, "node.name":"%enc{%map{node.name}}{JSON}"}\ - %varsNotEmpty{, "node.id":"%enc{%map{node.id}}{JSON}"}\ - %varsNotEmpty{, "host.name":"%enc{%map{host.name}}{JSON}"}\ - %varsNotEmpty{, "host.ip":"%enc{%map{host.ip}}{JSON}"}\ - %varsNotEmpty{, "event.type":"%enc{%map{event.type}}{JSON}"}\ - %varsNotEmpty{, "event.action":"%enc{%map{event.action}}{JSON}"}\ - %varsNotEmpty{, "authentication.type":"%enc{%map{authentication.type}}{JSON}"}\ - %varsNotEmpty{, "user.name":"%enc{%map{user.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.name":"%enc{%map{user.run_by.name}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.name":"%enc{%map{user.run_as.name}}{JSON}"}\ - %varsNotEmpty{, "user.realm":"%enc{%map{user.realm}}{JSON}"}\ - %varsNotEmpty{, "user.realm_domain":"%enc{%map{user.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm":"%enc{%map{user.run_by.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_by.realm_domain":"%enc{%map{user.run_by.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm":"%enc{%map{user.run_as.realm}}{JSON}"}\ - %varsNotEmpty{, "user.run_as.realm_domain":"%enc{%map{user.run_as.realm_domain}}{JSON}"}\ - %varsNotEmpty{, "user.roles":%map{user.roles}}\ - %varsNotEmpty{, "apikey.id":"%enc{%map{apikey.id}}{JSON}"}\ - %varsNotEmpty{, "apikey.name":"%enc{%map{apikey.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.name":"%enc{%map{authentication.token.name}}{JSON}"}\ - %varsNotEmpty{, "authentication.token.type":"%enc{%map{authentication.token.type}}{JSON}"}\ - %varsNotEmpty{, "cross_cluster_access":%map{cross_cluster_access}}\ - %varsNotEmpty{, "origin.type":"%enc{%map{origin.type}}{JSON}"}\ - %varsNotEmpty{, "origin.address":"%enc{%map{origin.address}}{JSON}"}\ - %varsNotEmpty{, "realm":"%enc{%map{realm}}{JSON}"}\ - %varsNotEmpty{, "realm_domain":"%enc{%map{realm_domain}}{JSON}"}\ - %varsNotEmpty{, "url.path":"%enc{%map{url.path}}{JSON}"}\ - %varsNotEmpty{, "url.query":"%enc{%map{url.query}}{JSON}"}\ - %varsNotEmpty{, "request.method":"%enc{%map{request.method}}{JSON}"}\ - %varsNotEmpty{, "request.body":"%enc{%map{request.body}}{JSON}"}\ - %varsNotEmpty{, "request.id":"%enc{%map{request.id}}{JSON}"}\ - %varsNotEmpty{, "action":"%enc{%map{action}}{JSON}"}\ - %varsNotEmpty{, "request.name":"%enc{%map{request.name}}{JSON}"}\ - %varsNotEmpty{, "indices":%map{indices}}\ - %varsNotEmpty{, "opaque_id":"%enc{%map{opaque_id}}{JSON}"}\ - %varsNotEmpty{, "trace.id":"%enc{%map{trace.id}}{JSON}"}\ - %varsNotEmpty{, "x_forwarded_for":"%enc{%map{x_forwarded_for}}{JSON}"}\ - %varsNotEmpty{, "transport.profile":"%enc{%map{transport.profile}}{JSON}"}\ - %varsNotEmpty{, "rule":"%enc{%map{rule}}{JSON}"}\ - %varsNotEmpty{, "put":%map{put}}\ - %varsNotEmpty{, "delete":%map{delete}}\ - %varsNotEmpty{, "change":%map{change}}\ - %varsNotEmpty{, "create":%map{create}}\ - %varsNotEmpty{, "invalidate":%map{invalidate}}\ - }%n -# "node.name" node name from the `elasticsearch.yml` settings -# "node.id" node id which should not change between cluster restarts -# "host.name" unresolved hostname of the local node -# "host.ip" the local bound ip (i.e. the ip listening for connections) -# "origin.type" a received REST request is translated into one or more transport requests. This indicates which processing layer generated the event "rest" or "transport" (internal) -# "event.action" the name of the audited event, eg. "authentication_failed", "access_granted", "run_as_granted", etc. -# "authentication.type" one of "realm", "api_key", "token", "anonymous" or "internal" -# "user.name" the subject name as authenticated by a realm -# "user.run_by.name" the original authenticated subject name that is impersonating another one. -# "user.run_as.name" if this "event.action" is of a run_as type, this is the subject name to be impersonated as. -# "user.realm" the name of the realm that authenticated "user.name" -# "user.realm_domain" if "user.realm" is under a domain, this is the name of the domain -# "user.run_by.realm" the realm name of the impersonating subject ("user.run_by.name") -# "user.run_by.realm_domain" if "user.run_by.realm" is under a domain, this is the name of the domain -# "user.run_as.realm" if this "event.action" is of a run_as type, this is the realm name the impersonated user is looked up from -# "user.run_as.realm_domain" if "user.run_as.realm" is under a domain, this is the name of the domain -# "user.roles" the roles array of the user; these are the roles that are granting privileges -# "apikey.id" this field is present if and only if the "authentication.type" is "api_key" -# "apikey.name" this field is present if and only if the "authentication.type" is "api_key" -# "authentication.token.name" this field is present if and only if the authenticating credential is a service account token -# "authentication.token.type" this field is present if and only if the authenticating credential is a service account token -# "cross_cluster_access" this field is present if and only if the associated authentication occurred cross cluster -# "event.type" informs about what internal system generated the event; possible values are "rest", "transport", "ip_filter" and "security_config_change" -# "origin.address" the remote address and port of the first network hop, i.e. a REST proxy or another cluster node -# "realm" name of a realm that has generated an "authentication_failed" or an "authentication_successful"; the subject is not yet authenticated -# "realm_domain" if "realm" is under a domain, this is the name of the domain -# "url.path" the URI component between the port and the query string; it is percent (URL) encoded -# "url.query" the URI component after the path and before the fragment; it is percent (URL) encoded -# "request.method" the method of the HTTP request, i.e. one of GET, POST, PUT, DELETE, OPTIONS, HEAD, PATCH, TRACE, CONNECT -# "request.body" the content of the request body entity, JSON escaped -# "request.id" a synthetic identifier for the incoming request, this is unique per incoming request, and consistent across all audit events generated by that request -# "action" an action is the most granular operation that is authorized and this identifies it in a namespaced way (internal) -# "request.name" if the event is in connection to a transport message this is the name of the request class, similar to how rest requests are identified by the url path (internal) -# "indices" the array of indices that the "action" is acting upon -# "opaque_id" opaque value conveyed by the "X-Opaque-Id" request header -# "trace_id" an identifier conveyed by the part of "traceparent" request header -# "x_forwarded_for" the addresses from the "X-Forwarded-For" request header, as a verbatim string value (not an array) -# "transport.profile" name of the transport profile in case this is a "connection_granted" or "connection_denied" event -# "rule" name of the applied rule if the "origin.type" is "ip_filter" -# the "put", "delete", "change", "create", "invalidate" fields are only present -# when the "event.type" is "security_config_change" and contain the security config change (as an object) taking effect - -logger.xpack_security_audit_logfile.name = org.elasticsearch.xpack.security.audit.logfile.LoggingAuditTrail -logger.xpack_security_audit_logfile.level = info -logger.xpack_security_audit_logfile.appenderRef.audit_rolling.ref = audit_rolling -logger.xpack_security_audit_logfile.additivity = false - -logger.xmlsig.name = org.apache.xml.security.signature.XMLSignature -logger.xmlsig.level = error -logger.samlxml_decrypt.name = org.opensaml.xmlsec.encryption.support.Decrypter -logger.samlxml_decrypt.level = fatal -logger.saml2_decrypt.name = org.opensaml.saml.saml2.encryption.Decrypter -logger.saml2_decrypt.level = fatal \ No newline at end of file diff --git a/elasticsearch/tmp-config/role_mapping.yml b/elasticsearch/tmp-config/role_mapping.yml deleted file mode 100644 index 68c82f7e..00000000 --- a/elasticsearch/tmp-config/role_mapping.yml +++ /dev/null @@ -1,14 +0,0 @@ -# Role mapping configuration file which has elasticsearch roles as keys -# that map to one or more user or group distinguished names - -#roleA: this is an elasticsearch role -# - groupA-DN this is a group distinguished name -# - groupB-DN -# - user1-DN this is the full user distinguished name - -#power_user: -# - "cn=admins,dc=example,dc=com" -#user: -# - "cn=users,dc=example,dc=com" -# - "cn=admins,dc=example,dc=com" -# - "cn=John Doe,cn=other users,dc=example,dc=com" diff --git a/elasticsearch/tmp-config/roles.yml b/elasticsearch/tmp-config/roles.yml deleted file mode 100644 index 68e003b8..00000000 --- a/elasticsearch/tmp-config/roles.yml +++ /dev/null @@ -1,3 +0,0 @@ -# The default roles file is empty as the preferred method of defining roles is -# through the API/UI. File based roles are useful in error scenarios when the -# API based roles may not be available. diff --git a/elasticsearch/tmp-config/users b/elasticsearch/tmp-config/users deleted file mode 100644 index e69de29b..00000000 diff --git a/elasticsearch/tmp-config/users_roles b/elasticsearch/tmp-config/users_roles deleted file mode 100644 index e69de29b..00000000 diff --git a/pyproject.toml b/pyproject.toml index bb1668f0..a50a160f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,36 +7,36 @@ name = "TrialMatchAI" version = "0.1.0" description = "AI-driven clinical trial matching pipeline." readme = "README.md" -requires-python = ">=3.10,<3.12" -license = {file = "LICENSE"} +requires-python = ">=3.11,<3.12" +license = "MIT" +license-files = ["LICENSE"] dependencies = [ # Core ML / NLP - "torch==2.7.1", - "torchvision==0.22.1", - "torchaudio==2.7.1", - "transformers==4.55.0", + "torch==2.11.0", + "transformers==5.6.2", "accelerate==1.8.1", - "tokenizers==0.21.1", - "safetensors==0.5.3", - "sentence-transformers==2.7.0", - "sentencepiece==0.2.0", - "datasets==2.19.0", + "tokenizers==0.22.1", + "safetensors>=0.6.2,<0.7", + "sentencepiece==0.2.1", + "elasticsearch==8.17.2", "faiss-cpu==1.9.0.post1", "peft==0.15.2", "einops==0.8.0", "tiktoken==0.8.0", - "bitsandbytes==0.46.1", - "vllm==0.10.1.1", # Classical stack & utilities - "numpy==1.26.4", + "numpy>=2.0,<3", "scipy==1.15.1", "scikit-learn==1.6.1", "pandas==2.2.3", + "python-dateutil==2.9.0.post0", + "requests==2.34.2", + "tenacity==9.0.0", + "joblib==1.4.2", "tqdm==4.67.1", - "regex==2024.11.6", - "orjson==3.10.14", + "regex>=2025.10.22,<2027", + "orjson==3.11.9", "PyYAML==6.0.2", - "python-dotenv==1.0.1", + "python-dotenv==1.2.2", "rich==13.9.4", # spaCy / medspaCy (for your medical text work) "spacy==3.7.5", @@ -46,29 +46,46 @@ dependencies = [ "PyFastNER==1.0.10", "pysbd==0.3.4", "quicksectx==0.4.0", - "gliner==0.2.16", + "gliner==0.2.27", # App / infra "SQLAlchemy==2.0.37", - "pydantic==2.11.7", - "pydantic-settings==2.7.1", - # Optional APIs - "openai==1.99.1", + "pydantic>=2.12.0,<3", + "pydantic-settings>=2.10.1,<3", + "huggingface_hub>=1.5.0,<2", + "bioregistry==0.13.62", + "rapidfuzz==3.14.5", + "nltk==3.9.4", +] + +[project.optional-dependencies] +gpu = [ + "bitsandbytes==0.49.2; sys_platform == 'linux' or sys_platform == 'win32'", + "vllm==0.23.0; sys_platform == 'linux'", +] +llm = [ + "openai>=2.26.0,<3", "httpx==0.28.1", - "langchain==0.3.18", - "langchain-core==0.3.35", - "langchain-community==0.3.17", - "langchain-openai==0.3.5", - "langsmith==0.2.11", - "huggingface_hub==0.34.4", + "langchain==1.3.10", + "langchain-community==0.4.2", + "langchain-openai==1.3.2", +] +training = [ + "datasets==2.19.0", + "bert-score==0.3.13", + "jinja2==3.1.6", ] [project.scripts] trialmatchai-healthcheck = "Matcher.cli.healthcheck:main" +trialmatchai-run = "Matcher.cli.run:main" +trialmatchai-bootstrap-data = "Matcher.cli.bootstrap_data:main" +trialmatchai-index = "Matcher.cli.index_data:main" [dependency-groups] dev = [ - "pytest>=7.4", - "ruff>=0.6.0", + "pytest>=8.3", + "ruff>=0.9.0", + "pip-audit>=2.7.3", ] [tool.setuptools] @@ -78,15 +95,12 @@ include-package-data = true [tool.setuptools.packages.find] where = ["source"] -[tool.uv] -index-url = "https://pypi.org/simple" - -[[tool.uv.index]] -name = "pytorch-cuda" -url = "https://download.pytorch.org/whl/cu128" -explicit = true +[tool.setuptools.package-data] +Matcher = ["config/*.json"] +Parser = ["bin/*", "include/*", "lib/*", "normalizers/*.md"] -[tool.uv.sources] -torch = { index = "pytorch-cuda" } -torchvision = { index = "pytorch-cuda" } -torchaudio = { index = "pytorch-cuda" } \ No newline at end of file +[tool.uv] +environments = [ + "sys_platform == 'darwin'", + "sys_platform == 'linux' and platform_machine == 'x86_64'", +] diff --git a/requirements.txt b/requirements.txt index 1546abda..f75c9925 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,39 +1,34 @@ ---extra-index-url https://download.pytorch.org/whl/cu128 - # Core ML / NLP -torch==2.7.1 -torchvision==0.22.1 -torchaudio==2.7.1 -transformers==4.55.0 +torch==2.11.0 +transformers==5.6.2 accelerate==1.8.1 -tokenizers==0.21.1 -safetensors==0.5.3 -sentence-transformers==2.7.0 -sentencepiece==0.2.0 -datasets==2.19.0 +tokenizers==0.22.1 +safetensors>=0.6.2,<0.7 +sentencepiece==0.2.1 +elasticsearch==8.17.2 faiss-cpu==1.9.0.post1 peft==0.15.2 einops==0.8.0 tiktoken==0.8.0 -bitsandbytes==0.46.1 -vllm==0.10.1.1 -# vllm dependencies (pin to avoid backtracking) -fastapi>=0.115.0 -uvicorn[standard]>=0.30.0 -dnspython>=2.6.0 -email-validator>=2.0.0 +# GPU inference extra: install with `uv sync --extra gpu`. +bitsandbytes==0.49.2; sys_platform == "linux" or sys_platform == "win32" +vllm==0.23.0; sys_platform == "linux" # Classical stack & utilities -numpy==1.26.4 +numpy>=2.0,<3 scipy==1.15.1 scikit-learn==1.6.1 pandas==2.2.3 +python-dateutil==2.9.0.post0 +requests==2.34.2 +tenacity==9.0.0 +joblib==1.4.2 tqdm==4.67.1 -regex==2024.11.6 -orjson==3.10.14 +regex>=2025.10.22,<2027 +orjson==3.11.9 PyYAML==6.0.2 -python-dotenv==1.0.1 +python-dotenv==1.2.2 rich==13.9.4 # spaCy / medspaCy (for your medical text work) @@ -44,19 +39,25 @@ PyRuSH==1.0.9 PyFastNER==1.0.10 pysbd==0.3.4 quicksectx==0.4.0 -gliner==0.2.16 +gliner==0.2.27 # App / infra SQLAlchemy==2.0.37 -pydantic==2.11.7 -pydantic-settings==2.7.1 +pydantic>=2.12.0,<3 +pydantic-settings>=2.10.1,<3 -# Optional APIs -openai==1.99.1 +# LLM tooling extra: install with `uv sync --extra llm`. +openai>=2.26.0,<3 httpx==0.28.1 -langchain==0.3.18 -langchain-core==0.3.35 -langchain-community==0.3.17 -langchain-openai==0.3.5 -langsmith==0.2.11 -huggingface_hub==0.34.4 \ No newline at end of file +langchain==1.3.10 +langchain-community==0.4.2 +langchain-openai==1.3.2 +huggingface_hub>=1.5.0,<2 +bioregistry==0.13.62 +rapidfuzz==3.14.5 + +# Training/evaluation extra: install with `uv sync --extra training`. +datasets==2.19.0 +bert-score==0.3.13 +jinja2==3.1.6 +nltk==3.9.4 diff --git a/scripts/bootstrap_data.sh b/scripts/bootstrap_data.sh index 8ec1b7db..ebf72565 100644 --- a/scripts/bootstrap_data.sh +++ b/scripts/bootstrap_data.sh @@ -16,8 +16,48 @@ MODELS_ARCHIVE="models.tar.gz" GREEN='\033[0;32m' NC='\033[0m' info() { echo -e "${GREEN}[INFO]${NC} $*"; } +warn() { echo -e "[WARN] $*" >&2; } error() { echo -e "[ERROR] $*" >&2; exit 1; } +verify_sha256() { + local file="$1" + local expected="$2" + if [ -z "$expected" ]; then + warn "No SHA-256 checksum configured for $file; skipping verification." + return 0 + fi + local actual + actual="$(sha256sum "$file" 2>/dev/null | awk '{print $1}' || shasum -a 256 "$file" | awk '{print $1}')" + if [ "$actual" != "$expected" ]; then + error "Checksum mismatch for $file: expected $expected, got $actual" + fi +} + +assert_safe_archive_paths() { + local archive="$1" + shift + local list_cmd=("$@") + local unsafe + unsafe="$("${list_cmd[@]}" "$archive" | awk '($0 ~ /^\// || $0 ~ /(^|\/)\.\.($|\/)/) {print; exit}')" + if [ -n "$unsafe" ]; then + error "Archive $archive contains unsafe path: $unsafe" + fi +} + +extract_tar_gz() { + local archive="$1" + local target="$2" + assert_safe_archive_paths "$archive" tar -tzf + tar -xzf "$archive" -C "$target" +} + +extract_zip() { + local archive="$1" + local target="$2" + assert_safe_archive_paths "$archive" unzip -Z1 + unzip -q "$archive" -d "$target" +} + ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" DATA_DIR="${ROOT_DIR}/data" @@ -31,6 +71,7 @@ if [ ! -f "$ARCHIVE_1" ]; then else info "${ARCHIVE_1} already exists. Skipping download." fi +verify_sha256 "$ARCHIVE_1" "${TRIALMATCHAI_PROCESSED_TRIALS_SHA256:-}" if [ ! -f "$RESOURCES_ARCHIVE" ]; then info "Downloading ${RESOURCES_ARCHIVE}..." @@ -38,6 +79,7 @@ if [ ! -f "$RESOURCES_ARCHIVE" ]; then else info "${RESOURCES_ARCHIVE} already exists. Skipping download." fi +verify_sha256 "$RESOURCES_ARCHIVE" "${TRIALMATCHAI_RESOURCES_SHA256:-}" if [ ! -f "$MODELS_ARCHIVE" ]; then info "Downloading ${MODELS_ARCHIVE}..." @@ -45,6 +87,7 @@ if [ ! -f "$MODELS_ARCHIVE" ]; then else info "${MODELS_ARCHIVE} already exists. Skipping download." fi +verify_sha256 "$MODELS_ARCHIVE" "${TRIALMATCHAI_MODELS_SHA256:-}" if [ ! -d "processed_criteria" ]; then info "Downloading and extracting processed_criteria chunks..." @@ -61,8 +104,10 @@ if [ ! -d "processed_criteria" ]; then info "$chunk_zip already exists. Skipping download." fi + checksum_var="TRIALMATCHAI_CRITERIA_PART_${i}_SHA256" + verify_sha256 "$chunk_zip" "${!checksum_var:-}" info "Extracting $chunk_zip into processed_criteria..." - unzip -q "$chunk_zip" -d processed_criteria + extract_zip "$chunk_zip" processed_criteria done else info "processed_criteria already exists. Skipping extraction." @@ -70,7 +115,7 @@ fi if [ ! -d "processed_trials" ]; then info "Extracting $ARCHIVE_1..." - tar -xzvf "$ARCHIVE_1" + extract_tar_gz "$ARCHIVE_1" "$DATA_DIR" else info "processed_trials already exists. Skipping extraction of $ARCHIVE_1." fi @@ -79,11 +124,11 @@ cd "$ROOT_DIR" info "Extracting resources into source/Parser..." mkdir -p source/Parser -tar -xzvf "$DATA_DIR/$RESOURCES_ARCHIVE" -C source/Parser +extract_tar_gz "$DATA_DIR/$RESOURCES_ARCHIVE" source/Parser info "Extracting models into models/..." mkdir -p models -tar -xzvf "$DATA_DIR/$MODELS_ARCHIVE" -C models +extract_tar_gz "$DATA_DIR/$MODELS_ARCHIVE" models info "Cleaning up archives..." rm -f "$DATA_DIR/$ARCHIVE_1" "$DATA_DIR/$RESOURCES_ARCHIVE" "$DATA_DIR/$MODELS_ARCHIVE" @@ -91,4 +136,4 @@ for i in $(seq 0 $((CHUNK_COUNT - 1))); do rm -f "$DATA_DIR/${CHUNK_PREFIX}_${i}.zip" done -info "✅ Data bootstrap complete." +info "Data bootstrap complete." diff --git a/scripts/scan_secrets.py b/scripts/scan_secrets.py new file mode 100644 index 00000000..fa5f5dae --- /dev/null +++ b/scripts/scan_secrets.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import re +import subprocess +import sys +from pathlib import Path + + +SECRET_PATTERNS = [ + re.compile(r"-----BEGIN [A-Z ]*PRIVATE KEY-----"), + re.compile( + r"\bELASTIC_PASSWORD\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+" + ), + re.compile( + r"\bKIBANA_PASSWORD\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+" + ), + re.compile(r"\bpassword['\"]?\s*:\s*['\"](?!CHANGE_ME|change-me)[^'\"]{12,}['\"]"), +] + +SECRET_PATH_PATTERNS = [ + re.compile(r"^elasticsearch/(certs|config|tmp-config)/"), + re.compile(r"(^|/).*\.key$"), + re.compile(r"(^|/)elasticsearch\.keystore$"), +] + +SKIP_SUFFIXES = {".png", ".jpg", ".jpeg", ".gif", ".zip", ".gz", ".sif", ".so", ".a"} + + +def main() -> int: + files = subprocess.check_output(["git", "ls-files"], text=True).splitlines() + findings: list[str] = [] + for raw_path in files: + for pattern in SECRET_PATH_PATTERNS: + if pattern.search(raw_path): + findings.append(f"{raw_path}: tracked generated secret material") + path = Path(raw_path) + if path.suffix.lower() in SKIP_SUFFIXES: + continue + try: + text = path.read_text(encoding="utf-8") + except UnicodeDecodeError: + continue + for pattern in SECRET_PATTERNS: + for match in pattern.finditer(text): + line = text.count("\n", 0, match.start()) + 1 + findings.append(f"{raw_path}:{line}: {pattern.pattern}") + + if findings: + print("Potential secrets found in tracked files:", file=sys.stderr) + print("\n".join(findings), file=sys.stderr) + return 1 + print("No tracked-file secrets found.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/start_es.sh b/scripts/start_es.sh index d4d56312..01679630 100644 --- a/scripts/start_es.sh +++ b/scripts/start_es.sh @@ -12,7 +12,13 @@ cd "$ROOT_DIR/elasticsearch" if command -v docker &> /dev/null && docker info &> /dev/null; then info "Docker is available. Starting Elasticsearch with Docker Compose..." - docker-compose up -d --build + if docker compose version &> /dev/null; then + docker compose up -d + elif command -v docker-compose &> /dev/null; then + docker-compose up -d + else + error "Docker is available, but Docker Compose is not installed." + fi elif command -v apptainer &> /dev/null; then info "Docker not found or not running. Falling back to Apptainer..." if [ ! -f "./apptainer-run-es.sh" ]; then diff --git a/setup.sh b/setup.sh index afb26ad8..7183c7d3 100644 --- a/setup.sh +++ b/setup.sh @@ -31,6 +31,7 @@ fi if command -v uv &> /dev/null; then info "Installing Python dependencies with uv..." uv sync + RUNNER=(uv run) else if ! command -v pip &> /dev/null; then error "pip not found. Please install Python and pip first." @@ -38,11 +39,13 @@ else info "Installing Python requirements with pip..." pip install --upgrade pip pip install -r requirements.txt + pip install -e . + RUNNER=() fi # 2) Prepare data and models info "Bootstrapping data and models..." -bash scripts/bootstrap_data.sh +"${RUNNER[@]}" trialmatchai-bootstrap-data # 3) Launch Elasticsearch info "Starting Elasticsearch..." @@ -50,6 +53,6 @@ bash scripts/start_es.sh # 4) Launch indexers in background info "Indexing trial data..." -bash scripts/index_data.sh +"${RUNNER[@]}" trialmatchai-index -info "✅ TrialMatchAI setup is complete!" +info "TrialMatchAI setup is complete." diff --git a/source/Matcher/cli/bootstrap_data.py b/source/Matcher/cli/bootstrap_data.py new file mode 100644 index 00000000..69d9944a --- /dev/null +++ b/source/Matcher/cli/bootstrap_data.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +import argparse +import subprocess +import sys +from pathlib import Path + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Download and prepare TrialMatchAI data and model artifacts" + ) + parser.add_argument( + "--script", + default="scripts/bootstrap_data.sh", + help="Bootstrap script path relative to the repository root", + ) + args = parser.parse_args() + + root = _repo_root() + script = (root / args.script).resolve() + if not script.exists(): + raise FileNotFoundError(f"Bootstrap script not found: {script}") + return subprocess.run(["bash", str(script)], cwd=str(root), check=False).returncode + + +def _repo_root() -> Path: + start = Path(__file__).resolve() + for parent in start.parents: + if (parent / "pyproject.toml").exists(): + return parent + return Path.cwd().resolve() + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/source/Matcher/cli/healthcheck.py b/source/Matcher/cli/healthcheck.py index c82ca704..217a8fe2 100644 --- a/source/Matcher/cli/healthcheck.py +++ b/source/Matcher/cli/healthcheck.py @@ -2,13 +2,13 @@ import argparse import sys -from pathlib import Path -from typing import Any, Dict - -from elasticsearch import Elasticsearch from Matcher.config.config_loader import load_config -from Matcher.services.elasticsearch_service import ensure_elasticsearch +from Matcher.services.elasticsearch_service import ( + build_elasticsearch_client, + ensure_elasticsearch, +) +from Matcher.services.preflight import run_preflight_checks from Matcher.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -18,7 +18,7 @@ def main() -> int: parser = argparse.ArgumentParser(description="TrialMatchAI healthcheck") parser.add_argument( "--config", - default="Matcher/config/config.json", + default=None, help="Path to config.json", ) parser.add_argument( @@ -26,21 +26,26 @@ def main() -> int: action="store_true", help="Attempt to start Elasticsearch if unreachable", ) + parser.add_argument( + "--require-indices", + action="store_true", + help="Fail if configured Elasticsearch indices are missing", + ) args = parser.parse_args() config = load_config(args.config) issues = 0 - _check_paths(config) + preflight_issues = run_preflight_checks( + config, + require_patient_inputs=False, + require_trials_json=False, + require_models=False, + ) + issues += len(preflight_issues) es_cfg = config["elasticsearch"] - es_client = Elasticsearch( - hosts=[es_cfg["host"]], - ca_certs=config["paths"]["docker_certs"], - basic_auth=(es_cfg["username"], es_cfg["password"]), - request_timeout=es_cfg["request_timeout"], - retry_on_timeout=es_cfg["retry_on_timeout"], - ) + es_client = build_elasticsearch_client(config) if args.start_es and es_cfg.get("auto_start") is False: es_cfg["auto_start"] = True @@ -51,16 +56,15 @@ def main() -> int: else: logger.info("Elasticsearch reachable.") - return 1 if issues else 0 - - -def _check_paths(config: Dict[str, Any]) -> None: - paths = config["paths"] - for key in ["patients_dir", "output_dir", "trials_json_folder", "docker_certs"]: - path = Path(paths[key]) - if not path.exists(): - logger.warning("Path not found: %s=%s", key, path) - + if args.require_indices: + issues += len( + run_preflight_checks( + config, + es_client=es_client, + require_indices=True, + ) + ) + return 1 if issues else 0 if __name__ == "__main__": sys.exit(main()) diff --git a/source/Matcher/cli/index_data.py b/source/Matcher/cli/index_data.py new file mode 100644 index 00000000..8922faf0 --- /dev/null +++ b/source/Matcher/cli/index_data.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +import argparse +import subprocess +import sys +from pathlib import Path + + +def main() -> int: + parser = argparse.ArgumentParser(description="Index TrialMatchAI data into Elasticsearch") + parser.add_argument( + "--script", + default="scripts/index_data.sh", + help="Indexing script path relative to the repository root", + ) + args = parser.parse_args() + + root = _repo_root() + script = (root / args.script).resolve() + if not script.exists(): + raise FileNotFoundError(f"Indexing script not found: {script}") + return subprocess.run(["bash", str(script)], cwd=str(root), check=False).returncode + + +def _repo_root() -> Path: + start = Path(__file__).resolve() + for parent in start.parents: + if (parent / "pyproject.toml").exists(): + return parent + return Path.cwd().resolve() + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/source/Matcher/cli/run.py b/source/Matcher/cli/run.py new file mode 100644 index 00000000..67f7b6b1 --- /dev/null +++ b/source/Matcher/cli/run.py @@ -0,0 +1,17 @@ +from __future__ import annotations + +import argparse +import sys + + +def main() -> int: + parser = argparse.ArgumentParser(description="Run the TrialMatchAI batch pipeline") + parser.add_argument("--config", default=None, help="Path to config.json") + args = parser.parse_args() + from Matcher.main import main_pipeline + + return main_pipeline(args.config) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/source/Matcher/config/config.json b/source/Matcher/config/config.json index da3642b8..75f9d0b3 100644 --- a/source/Matcher/config/config.json +++ b/source/Matcher/config/config.json @@ -4,22 +4,25 @@ "gner_port": 18783, "gene_norm_port": 18888, "disease_norm_port": 18892, - "biomedner_home": "Parser/", + "biomedner_home": "source/Parser/", "use_neural_normalizer": true, "no_cuda": false }, "services": { - "stop_script": "biomedner_services/run_biomedner.sh", - "run_script": "biomedner_services/run_biomedner.sh" + "stop_script": "source/biomedner_services/stop_biomedner.sh", + "run_script": "source/biomedner_services/run_biomedner.sh", + "auto_start": false }, "paths": { - "patients_dir": "../example/", - "output_dir": "../results/", - "trials_json_folder": "../data/trials_jsons", - "docker_certs": "../elasticsearch/certs/ca.crt" + "patients_dir": "example", + "output_dir": "results", + "trials_json_folder": "data/trials_jsons", + "docker_certs": "elasticsearch/certs/ca/ca.crt" }, "model": { "base_model": "microsoft/phi-4", + "base_model_revision": null, + "trust_remote_code": false, "quantization": { "load_in_4bit": true, "bnb_4bit_use_double_quant": true, @@ -28,6 +31,7 @@ }, "cot_adapter_path": "models/finetuned_phi_reasoning", "reranker_model_path": "google/gemma-2-2b-it", + "reranker_model_revision": null, "reranker_adapter_path": "models/finetuned_gemma2" }, "tokenizer": { @@ -44,13 +48,15 @@ "request_timeout": 600, "retry_on_timeout": true, "index_trials": "clinical_trials", - "index_trials_eligibility": "eligibility_criteria", - "auto_start": true, + "index_trials_eligibility": "trials_eligibility", + "auto_start": false, "start_script": "elasticsearch/apptainer-run-es.sh", "start_timeout": 600 }, "embedder": { "model_name": "BAAI/bge-m3", + "revision": null, + "trust_remote_code": false, "pooling": "mean", "max_length": 512, "batch_size": 32, diff --git a/source/Matcher/config/config_loader.py b/source/Matcher/config/config_loader.py index 58023354..15ba3d29 100644 --- a/source/Matcher/config/config_loader.py +++ b/source/Matcher/config/config_loader.py @@ -2,6 +2,8 @@ import json import os +from copy import deepcopy +from pathlib import Path from typing import Any, Dict from Matcher.config.settings import TrialMatchSettings, apply_env_overrides @@ -16,19 +18,110 @@ logger = setup_logging(__name__) -def load_config(config_path: str = "Matcher/config/config.json") -> Dict[str, Any]: +DEFAULT_CONFIG_RELATIVE_PATH = Path("source/Matcher/config/config.json") +LEGACY_CONFIG_RELATIVE_PATH = Path("Matcher/config/config.json") + + +def load_config(config_path: str | os.PathLike[str] | None = None) -> Dict[str, Any]: """Load and validate configuration from a JSON file with env overrides.""" if load_dotenv: + load_dotenv(_repo_root() / ".env") load_dotenv() - if not os.path.exists(config_path): - raise FileNotFoundError(f"Configuration file not found: {config_path}") - with open(config_path, "r", encoding="utf-8") as f: + + resolved_config = resolve_config_path(config_path) + with resolved_config.open("r", encoding="utf-8") as f: raw: Dict[str, Any] = json.load(f) - raw = apply_env_overrides(raw) + raw = apply_env_overrides(deepcopy(raw)) settings = TrialMatchSettings.model_validate(raw) cfg = settings.to_dict() + cfg = normalize_config_paths(cfg, resolved_config) if cfg.get("elasticsearch", {}).get("password") in {"", "CHANGE_ME"}: logger.warning( "Elasticsearch password is not set. Use TRIALMATCHAI_ES_PASSWORD to supply it." ) return cfg + + +def resolve_config_path( + config_path: str | os.PathLike[str] | None = None, +) -> Path: + """Resolve explicit, repo-root, legacy, and packaged config paths.""" + root = _repo_root() + candidates: list[Path] = [] + if config_path: + supplied = Path(config_path).expanduser() + if supplied.is_absolute(): + candidates.append(supplied) + else: + candidates.extend( + [ + Path.cwd() / supplied, + root / supplied, + root / "source" / supplied, + ] + ) + if supplied == LEGACY_CONFIG_RELATIVE_PATH: + candidates.append(root / DEFAULT_CONFIG_RELATIVE_PATH) + else: + candidates.extend( + [ + root / DEFAULT_CONFIG_RELATIVE_PATH, + Path(__file__).resolve().with_name("config.json"), + ] + ) + + for candidate in candidates: + if candidate.exists(): + return candidate.resolve() + + searched = ", ".join(str(path) for path in candidates) + label = str(config_path) if config_path else str(DEFAULT_CONFIG_RELATIVE_PATH) + raise FileNotFoundError(f"Configuration file not found: {label}. Searched: {searched}") + + +def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, Any]: + """Normalize known local paths while leaving remote model IDs untouched.""" + root = _repo_root(config_path) + for key in ("patients_dir", "output_dir", "trials_json_folder", "docker_certs"): + value = cfg.get("paths", {}).get(key) + if value: + cfg["paths"][key] = str(_resolve_local_path(value, root)) + + for key in ("stop_script", "run_script"): + value = cfg.get("services", {}).get(key) + if value: + cfg["services"][key] = str(_resolve_local_path(value, root)) + + biomedner_home = cfg.get("bio_med_ner", {}).get("biomedner_home") + if biomedner_home: + cfg["bio_med_ner"]["biomedner_home"] = str( + _resolve_local_path(biomedner_home, root) + ) + + start_script = cfg.get("elasticsearch", {}).get("start_script") + if start_script: + cfg["elasticsearch"]["start_script"] = str(_resolve_local_path(start_script, root)) + + for key in ("cot_adapter_path", "reranker_adapter_path"): + value = cfg.get("model", {}).get(key) + if value: + cfg["model"][key] = str(_resolve_local_path(value, root)) + + return cfg + + +def _resolve_local_path(value: str, root: Path) -> Path: + path = Path(value).expanduser() + if path.is_absolute(): + return path.resolve() + return (root / path).resolve() + + +def _repo_root(anchor: Path | None = None) -> Path: + start = (anchor or Path(__file__).resolve()).resolve() + if start.is_file(): + start = start.parent + for parent in (start, *start.parents): + if (parent / "pyproject.toml").exists(): + return parent + return Path.cwd().resolve() diff --git a/source/Matcher/config/settings.py b/source/Matcher/config/settings.py index bd66cc38..10b9f7e2 100644 --- a/source/Matcher/config/settings.py +++ b/source/Matcher/config/settings.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Any, Dict, Optional +from typing import Any, Dict, Iterable, Tuple from pydantic import BaseModel, Field, field_validator @@ -18,6 +18,7 @@ class BioMedNerSettings(BaseModel): class ServicesSettings(BaseModel): stop_script: str run_script: str + auto_start: bool = False class PathsSettings(BaseModel): @@ -40,6 +41,9 @@ class ModelSettings(BaseModel): cot_adapter_path: str reranker_model_path: str reranker_adapter_path: str + trust_remote_code: bool = False + base_model_revision: str | None = None + reranker_model_revision: str | None = None class TokenizerSettings(BaseModel): @@ -66,6 +70,8 @@ class ElasticsearchSettings(BaseModel): class EmbedderSettings(BaseModel): model_name: str = "BAAI/bge-m3" + revision: str | None = None + trust_remote_code: bool = False pooling: str = "mean" max_length: int = 512 batch_size: int = 32 @@ -144,34 +150,98 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: """Override config with environment variables for sensitive fields.""" import os - env_map = { + string_env_map: dict[str, Tuple[str, ...]] = { "TRIALMATCHAI_ES_HOST": ("elasticsearch", "host"), "TRIALMATCHAI_ES_USERNAME": ("elasticsearch", "username"), "TRIALMATCHAI_ES_PASSWORD": ("elasticsearch", "password"), + "TRIALMATCHAI_ES_CA_CERTS": ("paths", "docker_certs"), + "TRIALMATCHAI_PATIENTS_DIR": ("paths", "patients_dir"), + "TRIALMATCHAI_OUTPUT_DIR": ("paths", "output_dir"), + "TRIALMATCHAI_TRIALS_JSON_FOLDER": ("paths", "trials_json_folder"), + "TRIALMATCHAI_INDEX_TRIALS": ("elasticsearch", "index_trials"), + "TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY": ( + "elasticsearch", + "index_trials_eligibility", + ), "TRIALMATCHAI_EMBEDDER_MODEL_NAME": ("embedder", "model_name"), + "TRIALMATCHAI_EMBEDDER_REVISION": ("embedder", "revision"), + "TRIALMATCHAI_MODEL_BASE_MODEL": ("model", "base_model"), + "TRIALMATCHAI_MODEL_BASE_MODEL_REVISION": ( + "model", + "base_model_revision", + ), + "TRIALMATCHAI_MODEL_COT_ADAPTER_PATH": ("model", "cot_adapter_path"), + "TRIALMATCHAI_MODEL_RERANKER_MODEL_PATH": ( + "model", + "reranker_model_path", + ), + "TRIALMATCHAI_MODEL_RERANKER_MODEL_REVISION": ( + "model", + "reranker_model_revision", + ), + "TRIALMATCHAI_MODEL_RERANKER_ADAPTER_PATH": ( + "model", + "reranker_adapter_path", + ), + "TRIALMATCHAI_COT_BACKEND": ("cot_backend",), + "TRIALMATCHAI_ES_START_SCRIPT": ("elasticsearch", "start_script"), + } + for env_key, path in string_env_map.items(): + value = os.getenv(env_key) + if value: + _set_nested(raw, path, value) + + bool_env_map: dict[str, Tuple[str, ...]] = { + "TRIALMATCHAI_ES_AUTO_START": ("elasticsearch", "auto_start"), + "TRIALMATCHAI_ES_RETRY_ON_TIMEOUT": ("elasticsearch", "retry_on_timeout"), + "TRIALMATCHAI_BIOMEDNER_AUTO_START": ("services", "auto_start"), + "TRIALMATCHAI_EMBEDDER_USE_GPU": ("embedder", "use_gpu"), + "TRIALMATCHAI_EMBEDDER_USE_FP16": ("embedder", "use_fp16"), + "TRIALMATCHAI_EMBEDDER_TRUST_REMOTE_CODE": ( + "embedder", + "trust_remote_code", + ), + "TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE": ("model", "trust_remote_code"), + } + for env_key, path in bool_env_map.items(): + value = os.getenv(env_key) + if value is not None: + _set_nested(raw, path, _parse_bool(value)) + + int_env_map: dict[str, Tuple[str, ...]] = { + "TRIALMATCHAI_ES_REQUEST_TIMEOUT": ("elasticsearch", "request_timeout"), + "TRIALMATCHAI_ES_START_TIMEOUT": ("elasticsearch", "start_timeout"), + "TRIALMATCHAI_EMBEDDER_BATCH_SIZE": ("embedder", "batch_size"), + "TRIALMATCHAI_SEARCH_MAX_TRIALS_FIRST_LEVEL": ( + "search", + "max_trials_first_level", + ), + "TRIALMATCHAI_SEARCH_MAX_TRIALS_SECOND_LEVEL": ( + "search", + "max_trials_second_level", + ), + "TRIALMATCHAI_RAG_MAX_TRIALS": ("rag", "max_trials_rag"), + "TRIALMATCHAI_VLLM_BATCH_SIZE": ("vllm", "batch_size"), + "TRIALMATCHAI_VLLM_MAX_NEW_TOKENS": ("vllm", "max_new_tokens"), } - for env_key, path in env_map.items(): + for env_key, path in int_env_map.items(): value = os.getenv(env_key) if value: - cursor: Dict[str, Any] = raw - for key in path[:-1]: - cursor = cursor.setdefault(key, {}) - cursor[path[-1]] = value - auto_start = os.getenv("TRIALMATCHAI_ES_AUTO_START") - if auto_start is not None: - cursor = raw.setdefault("elasticsearch", {}) - cursor["auto_start"] = auto_start.strip().lower() in {"1", "true", "yes"} - - start_script = os.getenv("TRIALMATCHAI_ES_START_SCRIPT") - if start_script: - cursor = raw.setdefault("elasticsearch", {}) - cursor["start_script"] = start_script - - start_timeout = os.getenv("TRIALMATCHAI_ES_START_TIMEOUT") - if start_timeout: - cursor = raw.setdefault("elasticsearch", {}) - try: - cursor["start_timeout"] = int(start_timeout) - except ValueError: - pass + try: + _set_nested(raw, path, int(value)) + except ValueError: + pass + return raw + + +def _set_nested(raw: Dict[str, Any], path: Iterable[str], value: Any) -> None: + keys = tuple(path) + cursor: Dict[str, Any] = raw + for key in keys[:-1]: + cursor = cursor.setdefault(key, {}) + cursor[keys[-1]] = value + + +def _parse_bool(value: str) -> bool: + return value.strip().lower() in {"1", "true", "yes", "y", "on"} diff --git a/source/Matcher/main.py b/source/Matcher/main.py index 9351a66b..b722de6b 100644 --- a/source/Matcher/main.py +++ b/source/Matcher/main.py @@ -4,8 +4,6 @@ from typing import Dict, List, Optional, Tuple import torch -from Parser.biomedner_engine import BioMedNER - from elasticsearch import Elasticsearch from Matcher.config.config_loader import load_config @@ -24,7 +22,11 @@ from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch from Matcher.pipeline.trial_search.second_level_search import SecondStageRetriever from Matcher.services.biomedner_service import initialize_biomedner_services -from Matcher.services.elasticsearch_service import ensure_elasticsearch +from Matcher.services.elasticsearch_service import ( + build_elasticsearch_client, + ensure_elasticsearch, +) +from Matcher.services.preflight import run_preflight_checks from Matcher.utils.file_utils import ( create_directory, read_json_file, @@ -214,18 +216,42 @@ def run_rag_processing( logger.info("RAG-based trial matching complete.") -def main_pipeline(): +def main_pipeline(config_path: str | None = None) -> int: logger.info("Starting TrialMatchAI pipeline...") - config = load_config() + config = load_config(config_path) paths = config["paths"] create_directory(paths["output_dir"]) + es_client = build_elasticsearch_client(config) + preflight_issues = run_preflight_checks( + config, + es_client=es_client, + require_patient_inputs=True, + require_trials_json=True, + require_models=True, + require_indices=False, + ) + if preflight_issues: + return 1 + + if not ensure_elasticsearch(es_client, config): + return 1 + + index_issues = run_preflight_checks( + config, + es_client=es_client, + require_indices=True, + ) + if index_issues: + return 1 + if torch.cuda.is_available(): torch.backends.cudnn.benchmark = True if hasattr(torch.backends.cuda, "enable_flash_sdp"): torch.backends.cuda.enable_flash_sdp(True) initialize_biomedner_services(config) + from Parser.biomedner_engine import BioMedNER import warnings @@ -250,6 +276,8 @@ def main_pipeline(): embedder = TextEmbedder( TextEmbedderConfig( model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), + revision=embedder_cfg.get("revision"), + trust_remote_code=embedder_cfg.get("trust_remote_code", False), pooling=embedder_cfg.get("pooling", "mean"), max_length=embedder_cfg.get("max_length", 512), batch_size=embedder_cfg.get("batch_size", 32), @@ -269,21 +297,10 @@ def main_pipeline(): adapter_path=config["model"]["reranker_adapter_path"], device=config["global"]["device"], batch_size=config["rag"]["batch_size"] * 2, + revision=config["model"].get("reranker_model_revision"), + trust_remote_code=config["model"].get("trust_remote_code", False), ) - es_client = Elasticsearch( - hosts=[config["elasticsearch"]["host"]], - ca_certs=paths["docker_certs"], - basic_auth=( - config["elasticsearch"]["username"], - config["elasticsearch"]["password"], - ), - request_timeout=config["elasticsearch"]["request_timeout"], - retry_on_timeout=config["elasticsearch"]["retry_on_timeout"], - ) - if not ensure_elasticsearch(es_client, config): - return - gemma_retriever = SecondStageRetriever( es_client=es_client, llm_reranker=llm_reranker, @@ -296,13 +313,13 @@ def main_pipeline(): patient_folder = Path(paths["patients_dir"]) if not patient_folder.exists(): logger.error("Patients folder not found: %s", patient_folder) - return + return 1 phenopacket_files = sorted( [p for p in patient_folder.iterdir() if p.suffix == ".json"] ) if not phenopacket_files: logger.warning("No patient files found in %s", patient_folder) - return + return 1 for phenopacket_path in phenopacket_files: patient_id = phenopacket_path.stem @@ -390,6 +407,8 @@ def main_pipeline(): finally: reset_request_id(token) + return 0 + if __name__ == "__main__": - main_pipeline() + raise SystemExit(main_pipeline()) diff --git a/source/Matcher/models/embedding/text_embedder.py b/source/Matcher/models/embedding/text_embedder.py index 288df642..f263ae8a 100644 --- a/source/Matcher/models/embedding/text_embedder.py +++ b/source/Matcher/models/embedding/text_embedder.py @@ -18,6 +18,8 @@ @dataclass(frozen=True) class TextEmbedderConfig: model_name: str = "BAAI/bge-m3" + revision: str | None = None + trust_remote_code: bool = False pooling: PoolingStrategy = "mean" max_length: int = 512 batch_size: int = 32 @@ -35,8 +37,16 @@ def __init__(self, config: TextEmbedderConfig): logger.info( "Loading embedder model %s on device %s", config.model_name, self.device ) - self.tokenizer = AutoTokenizer.from_pretrained(config.model_name) - self.model = AutoModel.from_pretrained(config.model_name).to(self.device) + self.tokenizer = AutoTokenizer.from_pretrained( + config.model_name, + revision=config.revision, + trust_remote_code=config.trust_remote_code, + ) + self.model = AutoModel.from_pretrained( + config.model_name, + revision=config.revision, + trust_remote_code=config.trust_remote_code, + ).to(self.device) self.model.eval() if config.use_fp16 and self.device.type == "cuda": self.model = self.model.half() diff --git a/source/Matcher/models/llm/llm_loader.py b/source/Matcher/models/llm/llm_loader.py index 5259e1ff..f3b10422 100644 --- a/source/Matcher/models/llm/llm_loader.py +++ b/source/Matcher/models/llm/llm_loader.py @@ -73,11 +73,14 @@ def load_model_and_tokenizer( device_str = "cpu" quant_config = BitsAndBytesConfig(load_in_4bit=False) + trust_remote_code = bool(model_config.get("trust_remote_code", False)) + revision = model_config.get("base_model_revision") tokenizer = AutoTokenizer.from_pretrained( model_config["base_model"], + revision=revision, use_fast=True, padding_side="left", - trust_remote_code=True, + trust_remote_code=trust_remote_code, ) # Always left-pad decoder-only models; keep most recent tokens if truncation occurs. tokenizer.padding_side = "left" @@ -90,7 +93,8 @@ def load_model_and_tokenizer( model = AutoModelForCausalLM.from_pretrained( model_config["base_model"], - trust_remote_code=True, + revision=revision, + trust_remote_code=trust_remote_code, torch_dtype=compute_dtype if use_cuda else torch.float32, device_map=device_str, attn_implementation=attn_impl, diff --git a/source/Matcher/models/llm/llm_reranker.py b/source/Matcher/models/llm/llm_reranker.py index 1c18cf2c..df505611 100644 --- a/source/Matcher/models/llm/llm_reranker.py +++ b/source/Matcher/models/llm/llm_reranker.py @@ -22,11 +22,15 @@ def __init__( device: int = 0, torch_dtype=torch.float16, batch_size: int = 8, + revision: Optional[str] = None, + trust_remote_code: bool = False, ): self.model_path = model_path self.adapter_path = adapter_path self.torch_dtype = torch_dtype self.batch_size = batch_size + self.revision = revision + self.trust_remote_code = trust_remote_code # Resolve device string if torch.cuda.is_available(): cuda_count = torch.cuda.device_count() @@ -47,7 +51,9 @@ def __init__( self.device_str = "cpu" self.tokenizer = AutoTokenizer.from_pretrained( - self.model_path, trust_remote_code=True + self.model_path, + revision=self.revision, + trust_remote_code=self.trust_remote_code, ) self._initialize_token_ids() self.model = self.load_model() @@ -77,11 +83,12 @@ def load_model(self): ) model = AutoModelForCausalLM.from_pretrained( self.model_path, + revision=self.revision, torch_dtype=self.torch_dtype if use_cuda else torch.float32, quantization_config=quant_config, device_map="auto" if use_cuda else None, attn_implementation="flash_attention_2" if use_cuda else None, - trust_remote_code=True, + trust_remote_code=self.trust_remote_code, ) if self.adapter_path: model = PeftModel.from_pretrained(model, self.adapter_path) diff --git a/source/Matcher/models/llm/vllm_loader.py b/source/Matcher/models/llm/vllm_loader.py index 2889d06c..0b2bdbcd 100644 --- a/source/Matcher/models/llm/vllm_loader.py +++ b/source/Matcher/models/llm/vllm_loader.py @@ -16,7 +16,7 @@ def _infer_max_ctx_len(model_path: str) -> Optional[int]: try: from transformers import AutoConfig # type: ignore - cfg = AutoConfig.from_pretrained(str(model_path), trust_remote_code=True) + cfg = AutoConfig.from_pretrained(str(model_path), trust_remote_code=False) for k in ( "max_position_embeddings", "max_sequence_length", @@ -124,7 +124,11 @@ def load_vllm_engine( gpu_memory_utilization=gmu, enable_lora=True, # allows dynamic LoRA loading max_lora_rank=max_lora_rank, + trust_remote_code=bool(model_config.get("trust_remote_code", False)), ) + revision = vllm_cfg.get("revision") or model_config.get("base_model_revision") + if revision: + engine_kwargs["revision"] = _as_str(revision, "revision") if requested_len is not None: engine_kwargs["max_model_len"] = requested_len diff --git a/source/Matcher/pipeline/cot_reasoning.py b/source/Matcher/pipeline/cot_reasoning.py index aa75c35f..61fbdb33 100644 --- a/source/Matcher/pipeline/cot_reasoning.py +++ b/source/Matcher/pipeline/cot_reasoning.py @@ -5,6 +5,7 @@ import torch from Matcher.utils.file_utils import read_json_file, write_json_file, write_text_file +from Matcher.utils.json_utils import extract_json_object from Matcher.utils.logging_config import setup_logging from tqdm import tqdm @@ -144,7 +145,6 @@ def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: "----\n" "---Start of Patient Description---\n" f"{patient_profile}\n" - "Written informed consent has been obtained from the patient or their legal representative.\n" "---End of Patient Description---\n" "## IMPORTANT REMINDER:\n" "NEVER make assumptions, inferences, or extrapolations beyond the explicitly stated patient information." @@ -267,18 +267,15 @@ def _save_outputs(self, nct_id: str, response: str, output_folder: str): txt_path = f"{output_folder}/{nct_id}.txt" write_text_file([response], txt_path) try: - # naive JSON slice (you may replace with a balanced-brace parser if needed) - start = response.find("{") - end = response.rfind("}") - if start != -1 and end != -1 and end > start: - json_str = response[start : end + 1] - json_data = json.loads(json_str) - write_json_file(json_data, f"{output_folder}/{nct_id}.json") - logger.info(f"Processed {nct_id} successfully") - else: - logger.error(f"Invalid JSON boundaries for {nct_id}") - except json.JSONDecodeError as e: + json_data = extract_json_object(response) + write_json_file(json_data, f"{output_folder}/{nct_id}.json") + logger.info(f"Processed {nct_id} successfully") + except (json.JSONDecodeError, ValueError) as e: logger.error(f"Invalid JSON response for {nct_id}: {str(e)}") + write_json_file( + {"error": "invalid_json_response", "raw_output": response}, + f"{output_folder}/{nct_id}.json", + ) except Exception as e: logger.error(f"Failed to save {nct_id}: {str(e)}") diff --git a/source/Matcher/pipeline/cot_reasoning_vllm.py b/source/Matcher/pipeline/cot_reasoning_vllm.py index f2d0dec8..23f49049 100644 --- a/source/Matcher/pipeline/cot_reasoning_vllm.py +++ b/source/Matcher/pipeline/cot_reasoning_vllm.py @@ -6,6 +6,7 @@ from typing import Dict, List, Optional from Matcher.utils.file_utils import read_json_file, write_json_file, write_text_file +from Matcher.utils.json_utils import extract_json_object from Matcher.utils.logging_config import setup_logging from tqdm import tqdm @@ -162,7 +163,6 @@ def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: "----\n" "---Start of Patient Description---\n" f"{patient_profile}\n" - "Written informed consent has been obtained from the patient or their legal representative.\n" "---End of Patient Description---\n" "## IMPORTANT REMINDER:\n" "NEVER make assumptions, inferences, or extrapolations beyond the explicitly stated patient information." @@ -399,17 +399,15 @@ def _save_outputs(self, nct_id: str, response: str, output_folder: str): txt_path = f"{output_folder}/{nct_id}.txt" write_text_file([response], txt_path) try: - start = response.find("{") - end = response.rfind("}") - if start != -1 and end != -1 and end > start: - json_str = response[start : end + 1] - json_data = json.loads(json_str) - write_json_file(json_data, f"{output_folder}/{nct_id}.json") - logger.info(f"Processed {nct_id} successfully") - else: - logger.error(f"Invalid JSON boundaries for {nct_id}") - except json.JSONDecodeError as e: + json_data = extract_json_object(response) + write_json_file(json_data, f"{output_folder}/{nct_id}.json") + logger.info(f"Processed {nct_id} successfully") + except (json.JSONDecodeError, ValueError) as e: logger.error(f"Invalid JSON response for {nct_id}: {str(e)}") + write_json_file( + {"error": "invalid_json_response", "raw_output": response}, + f"{output_folder}/{nct_id}.json", + ) except Exception as e: logger.error(f"Failed to save {nct_id}: {str(e)}") diff --git a/source/Matcher/pipeline/phenopacket_processor.py b/source/Matcher/pipeline/phenopacket_processor.py index fbfbcd2c..e4bae585 100644 --- a/source/Matcher/pipeline/phenopacket_processor.py +++ b/source/Matcher/pipeline/phenopacket_processor.py @@ -1,10 +1,10 @@ import json -import re from typing import Dict, List, Optional import torch from Matcher.schemas.phenopacket import Phenopacket from Matcher.utils.file_utils import read_json_file, write_json_file +from Matcher.utils.json_utils import extract_json_object from Matcher.utils.logging_config import setup_logging from Matcher.utils.temporal_utils import parse_iso_duration, parse_temporal from transformers import AutoModelForCausalLM, AutoTokenizer @@ -329,19 +329,7 @@ def generate_summary(self, sentences: List[str]) -> Dict: def _extract_llm_output(self, generated_text: str) -> Dict: try: - match = re.search(r"\{.*\}", generated_text, re.DOTALL) - if not match: - logger.error("No JSON object found in LLM output") - return { - "error": "No JSON object found", - "main_conditions": [], - "other_conditions": [], - "expanded_sentences": [], - } - json_str = match.group() - json_str = re.sub(r"```(?:json)?\s*", "", json_str) - json_str = re.sub(r"\s*```", "", json_str) - result = json.loads(json_str) + result = extract_json_object(generated_text) expected_keys = [ "main_conditions", "other_conditions", @@ -350,8 +338,8 @@ def _extract_llm_output(self, generated_text: str) -> Dict: if not all(key in result for key in expected_keys): raise ValueError("Missing required JSON keys in LLM output") return result - except json.JSONDecodeError as e: - logger.error(f"JSON decoding failed: {e}") + except (json.JSONDecodeError, ValueError) as e: + logger.error(f"JSON extraction failed: {e}") return { "error": f"Invalid JSON format: {str(e)}", "main_conditions": [], diff --git a/source/Matcher/services/biomedner_service.py b/source/Matcher/services/biomedner_service.py index 87a9f6cc..68427a82 100644 --- a/source/Matcher/services/biomedner_service.py +++ b/source/Matcher/services/biomedner_service.py @@ -22,6 +22,12 @@ def run_script(script_path: str): def initialize_biomedner_services(config: dict): + if not config.get("services", {}).get("auto_start", False): + logger.info( + "BioMedNER auto-start is disabled. Set TRIALMATCHAI_BIOMEDNER_AUTO_START=true to start local services." + ) + return + ports_to_check = [ config["bio_med_ner"]["biomedner_port"], config["bio_med_ner"]["gner_port"], diff --git a/source/Matcher/services/elasticsearch_service.py b/source/Matcher/services/elasticsearch_service.py index fbd17458..09cdcfbb 100644 --- a/source/Matcher/services/elasticsearch_service.py +++ b/source/Matcher/services/elasticsearch_service.py @@ -7,11 +7,28 @@ from pathlib import Path from typing import Any, Dict +from elasticsearch import Elasticsearch + from Matcher.utils.logging_config import setup_logging logger = setup_logging(__name__) +def build_elasticsearch_client(config: Dict[str, Any]) -> Elasticsearch: + es_cfg = config["elasticsearch"] + paths = config.get("paths", {}) + kwargs: dict[str, Any] = { + "hosts": [es_cfg["host"]], + "basic_auth": (es_cfg["username"], es_cfg["password"]), + "request_timeout": es_cfg["request_timeout"], + "retry_on_timeout": es_cfg["retry_on_timeout"], + } + ca_certs = paths.get("docker_certs") + if ca_certs and Path(ca_certs).exists(): + kwargs["ca_certs"] = ca_certs + return Elasticsearch(**kwargs) + + def ensure_elasticsearch(es_client: Any, config: Dict[str, Any]) -> bool: if _ping(es_client): return True diff --git a/source/Matcher/services/preflight.py b/source/Matcher/services/preflight.py new file mode 100644 index 00000000..52670b12 --- /dev/null +++ b/source/Matcher/services/preflight.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +import importlib.util +from pathlib import Path +from typing import Any, Dict, Iterable, List + +import torch + +from Matcher.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def run_preflight_checks( + config: Dict[str, Any], + *, + es_client: Any | None = None, + require_patient_inputs: bool = False, + require_trials_json: bool = False, + require_models: bool = False, + require_indices: bool = False, +) -> List[str]: + """Return blocking deployment/runtime issues discovered before heavy startup.""" + issues: List[str] = [] + paths = config.get("paths", {}) + + _require_path( + issues, + "paths.patients_dir", + paths.get("patients_dir"), + required=require_patient_inputs, + ) + _require_path( + issues, + "paths.trials_json_folder", + paths.get("trials_json_folder"), + required=require_trials_json, + ) + _require_output_dir(issues, paths.get("output_dir")) + + host = str(config.get("elasticsearch", {}).get("host", "")) + if host.startswith("https://"): + _require_path( + issues, + "paths.docker_certs", + paths.get("docker_certs"), + required=True, + ) + + if require_models: + model_cfg = config.get("model", {}) + _require_path( + issues, + "model.cot_adapter_path", + model_cfg.get("cot_adapter_path"), + required=True, + ) + _require_path( + issues, + "model.reranker_adapter_path", + model_cfg.get("reranker_adapter_path"), + required=True, + ) + if config.get("cot_backend") == "vllm": + if importlib.util.find_spec("vllm") is None: + issues.append( + "cot_backend=vllm requires the GPU extra " + "(`uv sync --extra gpu`) or the Docker worker image." + ) + if not torch.cuda.is_available(): + issues.append("cot_backend=vllm requires a CUDA-capable runtime.") + + if es_client is not None: + if not _ping(es_client): + issues.append( + f"Elasticsearch is not reachable at {config['elasticsearch']['host']}." + ) + elif require_indices: + missing = _missing_indices( + es_client, + [ + config["elasticsearch"]["index_trials"], + config["elasticsearch"]["index_trials_eligibility"], + ], + ) + if missing: + issues.append("Missing Elasticsearch indices: " + ", ".join(missing)) + + for issue in issues: + logger.error("Preflight: %s", issue) + return issues + + +def _require_path( + issues: List[str], + name: str, + value: str | None, + *, + required: bool, +) -> None: + if not value: + if required: + issues.append(f"{name} is not configured.") + return + path = Path(value) + if required and not path.exists(): + issues.append(f"{name} does not exist: {path}") + elif not path.exists(): + logger.warning("Preflight: optional path does not exist: %s=%s", name, path) + + +def _require_output_dir(issues: List[str], value: str | None) -> None: + if not value: + issues.append("paths.output_dir is not configured.") + return + path = Path(value) + try: + path.mkdir(parents=True, exist_ok=True) + except OSError as exc: + issues.append(f"paths.output_dir is not writable: {path} ({exc})") + + +def _ping(es_client: Any) -> bool: + try: + return bool(es_client.ping()) + except Exception: + return False + + +def _missing_indices(es_client: Any, names: Iterable[str]) -> list[str]: + missing: list[str] = [] + for name in names: + try: + if not es_client.indices.exists(index=name): + missing.append(name) + except Exception as exc: + logger.warning("Could not check Elasticsearch index %s: %s", name, exc) + missing.append(name) + return missing diff --git a/source/Matcher/utils/json_utils.py b/source/Matcher/utils/json_utils.py new file mode 100644 index 00000000..f684a3d7 --- /dev/null +++ b/source/Matcher/utils/json_utils.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +import json +from typing import Any + + +def extract_json_object(text: str) -> dict[str, Any]: + """Extract and decode the first balanced JSON object from model output.""" + start = text.find("{") + if start == -1: + raise ValueError("No JSON object found") + + depth = 0 + in_string = False + escape = False + for index, char in enumerate(text[start:], start=start): + if escape: + escape = False + continue + if char == "\\" and in_string: + escape = True + continue + if char == '"': + in_string = not in_string + continue + if in_string: + continue + if char == "{": + depth += 1 + elif char == "}": + depth -= 1 + if depth == 0: + return json.loads(text[start : index + 1]) + + raise ValueError("Unbalanced JSON object") diff --git a/source/Parser/biomedner_engine.py b/source/Parser/biomedner_engine.py index 9b599b4e..8798db8b 100644 --- a/source/Parser/biomedner_engine.py +++ b/source/Parser/biomedner_engine.py @@ -21,8 +21,6 @@ from Parser.convert import get_pub_annotation from Parser.normalizer import Normalizer -print(os.getcwd()) - # Paths DICT_PATH = Path("Parser/resources/normalization/dictionary") dict_paths = { diff --git a/source/Parser/biomedner_init.py b/source/Parser/biomedner_init.py index c0de4128..1eed3939 100644 --- a/source/Parser/biomedner_init.py +++ b/source/Parser/biomedner_init.py @@ -100,7 +100,7 @@ def _read_data(cls, data, pmids): if len(words) >= 30: while len(words) >= 30: tmplabel = labels[:30] - l = " ".join( + label_text = " ".join( [ label for label in labels[: len(tmplabel)] @@ -113,17 +113,17 @@ def _read_data(cls, data, pmids): e = " ".join( [el for el in entity_labels[: len(tmplabel)] if len(el) > 0] ) - lines.append([l, w, e]) + lines.append([label_text, w, e]) words = words[len(tmplabel) :] labels = labels[len(tmplabel) :] entity_labels = entity_labels[len(tmplabel) :] if len(words) == 0: continue - l = " ".join([label for label in labels if len(label) > 0]) + label_text = " ".join([label for label in labels if len(label) > 0]) w = " ".join([word for word in words if len(word) > 0]) e = " ".join([el for el in entity_labels if len(entity_labels) > 0]) - lines.append([l, w, e]) + lines.append([label_text, w, e]) words = [] labels = [] entity_labels = [] @@ -654,7 +654,7 @@ def recognize(self, input_dl, base_name, indent=None): tmp_toks.append(tok) self.predict_dict, self.prob_dict = dict(), dict() - threads, self.out_tag_dict = list(), dict() + self.out_tag_dict = dict() all_type = self._predict(predict_example_list) # disease, drug, gene, spec, cell_line, dna, rna, cell_type @@ -729,8 +729,8 @@ def recognize_etype(self, etype, tokens, tot_tokens, predictions, preds_array): slen = len(tokens[pidx]) for p in prediction["prediction"][:slen]: predicts.append(self.id2label[p]) - for l in prediction["log_probs"][:slen]: - logits.append(l) + for log_prob in prediction["log_probs"][:slen]: + logits.append(log_prob) de_toks, de_labels, de_logits = detokenize(tot_tokens, predicts, logits) @@ -976,7 +976,7 @@ def main(): ) args = argparser.parse_args() - biomedner = BioMedNER(args) + BioMedNER(args) if __name__ == "__main__": diff --git a/source/Parser/input/1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42.PubTator.biomedner.PubTator b/source/Parser/input/1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42.PubTator.biomedner.PubTator deleted file mode 100644 index 7ff4720f..00000000 --- a/source/Parser/input/1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42|t| -1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42|a|clostridioides difficile infection - diff --git a/source/Parser/input/1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42.PubTator.gner.PubTator b/source/Parser/input/1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42.PubTator.gner.PubTator deleted file mode 100644 index 7ff4720f..00000000 --- a/source/Parser/input/1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42|t| -1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42|a|clostridioides difficile infection - diff --git a/source/Parser/input/427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453.PubTator.biomedner.PubTator b/source/Parser/input/427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453.PubTator.biomedner.PubTator deleted file mode 100644 index cc8af0cd..00000000 --- a/source/Parser/input/427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453|t| -427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453|a|severe aortic stenosis - diff --git a/source/Parser/input/427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453.PubTator.gner.PubTator b/source/Parser/input/427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453.PubTator.gner.PubTator deleted file mode 100644 index cc8af0cd..00000000 --- a/source/Parser/input/427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453|t| -427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453|a|severe aortic stenosis - diff --git a/source/Parser/input/525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e.PubTator.biomedner.PubTator b/source/Parser/input/525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e.PubTator.biomedner.PubTator deleted file mode 100644 index f93b768e..00000000 --- a/source/Parser/input/525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e|t| -525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e|a|chronic hepatitis c - diff --git a/source/Parser/input/525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e.PubTator.gner.PubTator b/source/Parser/input/525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e.PubTator.gner.PubTator deleted file mode 100644 index f93b768e..00000000 --- a/source/Parser/input/525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e|t| -525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e|a|chronic hepatitis c - diff --git a/source/Parser/input/611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637.PubTator.biomedner.PubTator b/source/Parser/input/611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637.PubTator.biomedner.PubTator deleted file mode 100644 index 0accd42c..00000000 --- a/source/Parser/input/611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637|t| -611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637|a|myocardial infarction - diff --git a/source/Parser/input/611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637.PubTator.gner.PubTator b/source/Parser/input/611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637.PubTator.gner.PubTator deleted file mode 100644 index 0accd42c..00000000 --- a/source/Parser/input/611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637|t| -611019227d9ea65a714df4e8c4498bfc13e21c67879025c3b03d8637|a|myocardial infarction - diff --git a/source/Parser/input/61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc.PubTator.biomedner.PubTator b/source/Parser/input/61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc.PubTator.biomedner.PubTator deleted file mode 100644 index a884f498..00000000 --- a/source/Parser/input/61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc|t| -61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc|a|severe aortic stenosis - diff --git a/source/Parser/input/61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc.PubTator.gner.PubTator b/source/Parser/input/61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc.PubTator.gner.PubTator deleted file mode 100644 index a884f498..00000000 --- a/source/Parser/input/61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc|t| -61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc|a|severe aortic stenosis - diff --git a/source/Parser/input/6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28.PubTator.biomedner.PubTator b/source/Parser/input/6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28.PubTator.biomedner.PubTator deleted file mode 100644 index 9a883264..00000000 --- a/source/Parser/input/6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28|t| -6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28|a|Deficiency, alpha-Lecithin:Cholesterol Acyltransferase - diff --git a/source/Parser/input/6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28.PubTator.gner.PubTator b/source/Parser/input/6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28.PubTator.gner.PubTator deleted file mode 100644 index 9a883264..00000000 --- a/source/Parser/input/6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28|t| -6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28|a|Deficiency, alpha-Lecithin:Cholesterol Acyltransferase - diff --git a/source/Parser/input/6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377.PubTator.biomedner.PubTator b/source/Parser/input/6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377.PubTator.biomedner.PubTator deleted file mode 100644 index fa321db2..00000000 --- a/source/Parser/input/6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377|t| -6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377|a|myocardial infarction - diff --git a/source/Parser/input/6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377.PubTator.gner.PubTator b/source/Parser/input/6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377.PubTator.gner.PubTator deleted file mode 100644 index fa321db2..00000000 --- a/source/Parser/input/6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377|t| -6db4a18d11e3899c21b4cc11489cf3c8b457a40273c48ecd39ab4377|a|myocardial infarction - diff --git a/source/Parser/input/7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3.PubTator.biomedner.PubTator b/source/Parser/input/7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3.PubTator.biomedner.PubTator deleted file mode 100644 index 54989c97..00000000 --- a/source/Parser/input/7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3|t| -7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3|a|severe aortic stenosis - diff --git a/source/Parser/input/7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3.PubTator.gner.PubTator b/source/Parser/input/7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3.PubTator.gner.PubTator deleted file mode 100644 index 54989c97..00000000 --- a/source/Parser/input/7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3|t| -7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3|a|severe aortic stenosis - diff --git a/source/Parser/input/89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace.PubTator.biomedner.PubTator b/source/Parser/input/89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace.PubTator.biomedner.PubTator deleted file mode 100644 index c406ffc7..00000000 --- a/source/Parser/input/89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace|t| -89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace|a|type 1 diabetes - diff --git a/source/Parser/input/89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace.PubTator.gner.PubTator b/source/Parser/input/89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace.PubTator.gner.PubTator deleted file mode 100644 index c406ffc7..00000000 --- a/source/Parser/input/89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace|t| -89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace|a|type 1 diabetes - diff --git a/source/Parser/input/b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c.PubTator.biomedner.PubTator b/source/Parser/input/b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c.PubTator.biomedner.PubTator deleted file mode 100644 index 4eeddd9e..00000000 --- a/source/Parser/input/b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c.PubTator.biomedner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c|t| -b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c|a|severe aortic stenosis - diff --git a/source/Parser/input/b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c.PubTator.gner.PubTator b/source/Parser/input/b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c.PubTator.gner.PubTator deleted file mode 100644 index 4eeddd9e..00000000 --- a/source/Parser/input/b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c.PubTator.gner.PubTator +++ /dev/null @@ -1,3 +0,0 @@ -b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c|t| -b5d32e3a13ff4d519235aa93bf6faeaa8e80439b77a08925ae9c617c|a|severe aortic stenosis - diff --git a/source/Parser/logs/nohup_disease_normalize.out b/source/Parser/logs/nohup_disease_normalize.out deleted file mode 100644 index 32d94d54..00000000 --- a/source/Parser/logs/nohup_disease_normalize.out +++ /dev/null @@ -1,3274 +0,0 @@ - -Sieve loading .. 10430 ms, Ready - -Sieve loading .. 734 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 6045 ms, Ready - -Sieve loading .. 5768 ms, Ready - -Sieve loading .. 593 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 616 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 8209 ms, Ready - -Sieve loading .. 589 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 649 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 589 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 666 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 669 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 608 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 511 ms, Ready - -Sieve loading .. 628 ms, Ready - -Sieve loading .. 588 ms, Ready - -Sieve loading .. 591 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 829 ms, Ready - -Sieve loading .. 657 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 719 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 630 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 645 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 606 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 704 ms, Ready - -Sieve loading .. 599 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) - -Sieve loading .. 665 ms, Ready -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at tool.MainServer.main(MainServer.java:28) diff --git a/source/Parser/logs/nohup_gene_normalize.out b/source/Parser/logs/nohup_gene_normalize.out deleted file mode 100644 index 04432902..00000000 --- a/source/Parser/logs/nohup_gene_normalize.out +++ /dev/null @@ -1,3229 +0,0 @@ -Ready (port 18888) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Ready (port 18888) -Ready (port 18888) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Ready (port 18888) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) -Exception in thread "main" java.net.BindException: Address already in use - at java.base/sun.nio.ch.Net.bind0(Native Method) - at java.base/sun.nio.ch.Net.bind(Net.java:555) - at java.base/sun.nio.ch.Net.bind(Net.java:544) - at java.base/sun.nio.ch.NioSocketImpl.bind(NioSocketImpl.java:648) - at java.base/java.net.ServerSocket.bind(ServerSocket.java:388) - at java.base/java.net.ServerSocket.(ServerSocket.java:274) - at java.base/java.net.ServerSocket.(ServerSocket.java:167) - at GNormPluslib.GNormPlusServer.main(GNormPlusServer.java:19) diff --git a/source/Parser/logs/nohup_gner.out b/source/Parser/logs/nohup_gner.out deleted file mode 100644 index d8372107..00000000 --- a/source/Parser/logs/nohup_gner.out +++ /dev/null @@ -1,3609 +0,0 @@ -/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py:129: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.) - return torch._C._cuda_getDeviceCount() > 0 -/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py:129: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.) - return torch._C._cuda_getDeviceCount() > 0 - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-07-16 22:23:26,080 - INFO - Connection accepted from ('127.0.0.1', 42682) -2025-07-16 22:23:26,081 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e78efe64b067d7c6ebe4216819000751a7603e0892821caf88bbe57c.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-07-16 22:28:05,287 - INFO - Connection accepted from ('127.0.0.1', 39226) -2025-07-16 22:28:05,287 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d8c0749b4cabbd5e7fa9c366d366170266a0289a6471006ccc4cce4e.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-07-16 22:33:00,752 - INFO - Connection accepted from ('127.0.0.1', 38380) -2025-07-16 22:33:00,752 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "99653b33f56427874f327320e5fbe809e84c11edf053dcf16a9f6304.PubTator"} -2025-07-16 22:33:29,894 - INFO - Connection accepted from ('127.0.0.1', 51160) -2025-07-16 22:33:29,894 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6c9ce2824b187646d864962dc8d8875097e553b032c259364b47c8e4.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:109.) - return torch._C._cuda_getDeviceCount() > 0 - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-03 19:53:26,698 - INFO - Connection accepted from ('127.0.0.1', 45486) -2025-08-03 19:53:26,698 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2dfc5be0d0231979e69390328155926f65b2e97e59a1eeec9eb31e73.PubTator"} -Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. -2025-08-03 19:54:22,242 - INFO - Connection accepted from ('127.0.0.1', 38496) -2025-08-03 19:54:22,242 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "db7442428d1dd92f798475bc173d0b523bdd022963e7e0eff5ff05a7.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-03 21:36:00,079 - INFO - Connection accepted from ('127.0.0.1', 53320) -2025-08-03 21:36:00,080 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "57c274e89711aee0e215c55ce2570826468ebf80104f04b418e79b23.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-03 21:39:12,257 - INFO - Connection accepted from ('127.0.0.1', 50288) -2025-08-03 21:39:12,257 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0fb7e7924a0c2b8724b4b5d94aacd0e7bfbc6f6e1214b83b0d11d208.PubTator"} -2025-08-03 21:41:36,886 - INFO - Connection accepted from ('127.0.0.1', 36174) -2025-08-03 21:41:36,886 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a09149cbf48d3f07b7ce16a02353412a74ffb11fb57d4416c27c58a9.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-03 22:10:08,491 - INFO - Connection accepted from ('127.0.0.1', 46522) -2025-08-03 22:10:08,491 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "027e65fb2b9483bcd3c217013c9b445f4e8d55b2abc47edefdd6a9cd.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-18 15:28:06,176 - INFO - Connection accepted from ('127.0.0.1', 58716) -2025-08-18 15:28:06,176 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-18 15:29:11,198 - INFO - Connection accepted from ('127.0.0.1', 36608) -2025-08-18 15:29:11,199 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-19 15:34:24,184 - INFO - Connection accepted from ('127.0.0.1', 43682) -2025-08-19 15:34:24,184 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-19 15:38:56,174 - INFO - Connection accepted from ('127.0.0.1', 47184) -2025-08-19 15:38:56,174 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "99151b7a67a22ab55f38dd2a06a3d775da819ea5d446a03adda90b14.PubTator"} -Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. -2025-08-19 15:39:26,523 - INFO - Connection accepted from ('127.0.0.1', 59158) -2025-08-19 15:39:26,523 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fb70ddb214ae646af6262f0d81f16392aea2d1c51d135bdc5ef78115.PubTator"} -2025-08-19 16:01:53,270 - INFO - Connection accepted from ('127.0.0.1', 35774) -2025-08-19 16:01:53,271 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d13f07266a57d3b9ba73f0a0c9a21408eaf190b110eeb44776863fe8.PubTator"} -2025-08-19 16:02:20,147 - INFO - Connection accepted from ('127.0.0.1', 35438) -2025-08-19 16:02:20,147 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6d60d5573378fb2ca71a90099fe304ec4d5532cafebec4676f42fe28.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-19 16:16:17,634 - INFO - Connection accepted from ('127.0.0.1', 32956) -2025-08-19 16:16:17,634 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-19 16:30:35,460 - INFO - Connection accepted from ('127.0.0.1', 41266) -2025-08-19 16:30:35,461 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-19 16:31:13,964 - INFO - Connection accepted from ('127.0.0.1', 34758) -2025-08-19 16:31:13,964 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3e4cb8e7df26c985018bd8c25b5efa344c861af6fe4929e3d910128a.PubTator"} -2025-08-19 16:31:37,998 - INFO - Connection accepted from ('127.0.0.1', 60472) -2025-08-19 16:31:37,998 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e255e7d010331dabb92a4c12029f2fa1f3080eabb86ec5c7bcd14326.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-19 16:34:04,689 - INFO - Connection accepted from ('127.0.0.1', 45688) -2025-08-19 16:34:04,689 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-19 16:35:00,735 - INFO - Connection accepted from ('127.0.0.1', 45280) -2025-08-19 16:35:00,735 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a1f93c97c4fa3a0e9c279be4d49a56bef290b4242e0e72b792d607b4.PubTator"} -2025-08-19 16:35:21,595 - INFO - Connection accepted from ('127.0.0.1', 57028) -2025-08-19 16:35:21,595 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "643bdfe9af48c14c2eca0ae9b39b15d57e78077cc70e3df07d888f1f.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 18:43:14,684 - INFO - Connection accepted from ('127.0.0.1', 36790) -2025-08-21 18:43:14,684 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7bbf3bbb6ce76096d9fbc4afcf95b756696944eb3970d9ae79a453c2.PubTator"} -2025-08-21 20:28:07,171 - INFO - Connection accepted from ('127.0.0.1', 46510) -2025-08-21 20:28:07,171 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "427736011957cbb0ce549f492b0330b9d77ba984a2beb7b2abca8453.PubTator"} -2025-08-21 20:28:26,150 - INFO - Connection accepted from ('127.0.0.1', 43538) -2025-08-21 20:28:26,151 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cd52dd424cb42cb53d92271bdd29ee9d3fa8bda53b90517a830f3685.PubTator"} -2025-08-21 20:28:40,788 - INFO - Connection accepted from ('127.0.0.1', 59882) -2025-08-21 20:28:40,788 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f5103ff8dd9ca435775ae888a3777ff367e7d2bf988b746c61e21cd9.PubTator"} -2025-08-21 20:28:55,052 - INFO - Connection accepted from ('127.0.0.1', 39820) -2025-08-21 20:28:55,052 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9471c3496c72a025b12c32c03e254c54fd34a88ad8c5d467920db40e.PubTator"} -2025-08-21 20:29:09,787 - INFO - Connection accepted from ('127.0.0.1', 44720) -2025-08-21 20:29:09,787 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1cd9760fe682423e9e3c37d70f3e932d45259c7c9d4b6e911fbf9b42.PubTator"} -2025-08-21 20:29:22,596 - INFO - Connection accepted from ('127.0.0.1', 50796) -2025-08-21 20:29:22,596 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7c24cb8cc3a7c35ca168b66e94c7cc0f7233f362f69b08c9b60eb758.PubTator"} -2025-08-21 20:29:34,137 - INFO - Connection accepted from ('127.0.0.1', 55958) -2025-08-21 20:29:34,137 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b4364ea1aa7c138ce56345794e8201b25ccddbceccc3a0a6699f7c0f.PubTator"} -2025-08-21 20:29:46,982 - INFO - Connection accepted from ('127.0.0.1', 54484) -2025-08-21 20:29:46,982 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cc07129a14bc136b9fb4a3ab01ea61864fe49f0dc442b3ed29a8b62c.PubTator"} -2025-08-21 20:29:58,469 - INFO - Connection accepted from ('127.0.0.1', 42424) -2025-08-21 20:29:58,470 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bbdc029db533f0cb0bd9ec2c28ec49703dcefdf858055062923a1e04.PubTator"} -2025-08-21 20:30:08,477 - INFO - Connection accepted from ('127.0.0.1', 45430) -2025-08-21 20:30:08,477 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f543f4f8e42327abe82c5c3874536c4fb9438e1a76c0a630198fe2d7.PubTator"} -2025-08-21 20:30:19,554 - INFO - Connection accepted from ('127.0.0.1', 38528) -2025-08-21 20:30:19,554 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "120898529785ace80a90c6a34477b0497c91621b8aa67e5a6b22f0f3.PubTator"} -2025-08-21 20:30:28,083 - INFO - Connection accepted from ('127.0.0.1', 38538) -2025-08-21 20:30:28,084 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a863b4c155212c13c6dc7b78a66dbadc236150e5854e7ad6dafd3c34.PubTator"} -2025-08-21 20:30:36,949 - INFO - Connection accepted from ('127.0.0.1', 58148) -2025-08-21 20:30:36,949 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dfea813b5bf039b384ac104cc18a0547dff18dd604ea71d23f3d9e23.PubTator"} -2025-08-21 20:30:45,521 - INFO - Connection accepted from ('127.0.0.1', 50946) -2025-08-21 20:30:45,521 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "19bd08e9f22e6c3824f7bacb49b915819c73f1972dfcb32283cea9f6.PubTator"} -2025-08-21 20:30:54,051 - INFO - Connection accepted from ('127.0.0.1', 39230) -2025-08-21 20:30:54,051 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5d226bcff34bb0ebfad070b6b566113f06c2a61fb5c4e5e0e3a04554.PubTator"} -2025-08-21 20:30:59,151 - INFO - Connection accepted from ('127.0.0.1', 39240) -2025-08-21 20:30:59,151 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ebc30fd8256840a29eb3943a8246dc7eea3f636de4c5f71631f8aab6.PubTator"} -2025-08-21 20:31:06,592 - INFO - Connection accepted from ('127.0.0.1', 54862) -2025-08-21 20:31:06,592 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "30ff7bb0e9d02c0fd0b0054a9d618d25662339cafc3fcc1442c57182.PubTator"} -2025-08-21 20:31:11,283 - INFO - Connection accepted from ('127.0.0.1', 52380) -2025-08-21 20:31:11,283 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3c499812cd51602e41f4687d1cf95e54a180f6bac1766a346fa82fb4.PubTator"} -2025-08-21 20:31:17,381 - INFO - Connection accepted from ('127.0.0.1', 52390) -2025-08-21 20:31:17,382 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "47cc34dc232a359bd7aaa4bbf9bd33d9d01ea390f670ec15243ce95e.PubTator"} -2025-08-21 20:31:24,416 - INFO - Connection accepted from ('127.0.0.1', 38594) -2025-08-21 20:31:24,416 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e45ed29fce772186ef533ec31f2161005b7e984dc629bb734fb5b9c8.PubTator"} -2025-08-21 20:31:41,790 - INFO - Connection accepted from ('127.0.0.1', 33734) -2025-08-21 20:31:41,790 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a7a921dccee50b85b93ac89251a0403a566783176a94880b1844daf2.PubTator"} -2025-08-21 20:31:50,234 - INFO - Connection accepted from ('127.0.0.1', 39546) -2025-08-21 20:31:50,235 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f99bca598e5bacb24920109bfe16f52a4adcf0623e42f5f35446745b.PubTator"} -2025-08-21 20:31:57,679 - INFO - Connection accepted from ('127.0.0.1', 39558) -2025-08-21 20:31:57,680 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "96fe8a6afa86d95762b56396c5e85bdba1691a5a3d2511612ee22e5e.PubTator"} -2025-08-21 20:32:02,583 - INFO - Connection accepted from ('127.0.0.1', 34198) -2025-08-21 20:32:02,583 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "84f4d162d0889536a392b46f32eebda58db27e58561a3e41e33e4cea.PubTator"} -2025-08-21 20:32:11,222 - INFO - Connection accepted from ('127.0.0.1', 52042) -2025-08-21 20:32:11,222 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "afc3d09d59021579b48e4819dd3f08295f3dc438f7f2919b06ca317e.PubTator"} -2025-08-21 20:32:17,541 - INFO - Connection accepted from ('127.0.0.1', 52046) -2025-08-21 20:32:17,541 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "525bbc5e481bdaf825bf80725e1ac63c15786fa13120fe734d703c0e.PubTator"} -2025-08-21 20:32:27,493 - INFO - Connection accepted from ('127.0.0.1', 49636) -2025-08-21 20:32:27,493 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "89e92a6963f2179038668173907fc2a79ae48e7712fe3dddfe49dace.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 21:12:10,136 - INFO - Connection accepted from ('127.0.0.1', 37164) -2025-08-21 21:12:10,137 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 21:12:47,785 - INFO - Connection accepted from ('127.0.0.1', 58406) -2025-08-21 21:12:47,785 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7aed0d619cfb7fb7edf932fc5aeff01e489c3be8482e4c08c26f4de3.PubTator"} -2025-08-21 21:12:56,082 - INFO - Connection accepted from ('127.0.0.1', 44096) -2025-08-21 21:12:56,082 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "033058d4b744c965cb1f7c798cc9c1fee136688c7bdd6f191929cd02.PubTator"} -2025-08-21 21:13:08,785 - INFO - Connection accepted from ('127.0.0.1', 38616) -2025-08-21 21:13:08,785 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dc37d57b5508811be20af120757571a6a7725f0d2977f58c84837a7f.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 21:29:46,644 - INFO - Connection accepted from ('127.0.0.1', 47556) -2025-08-21 21:29:46,644 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "729d3f9975787183c5117b5f2a4604e97594352b40aaf9e3688a6823.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 21:51:03,365 - INFO - Connection accepted from ('127.0.0.1', 37414) -2025-08-21 21:51:03,365 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 206, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 21:56:53,487 - INFO - Connection accepted from ('127.0.0.1', 43622) -2025-08-21 21:56:53,487 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "37f8a68ed3c820c1c408bb453ec7114e791811a377460d41c3c3ea84.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 22:05:42,823 - INFO - Connection accepted from ('127.0.0.1', 35540) -2025-08-21 22:05:42,823 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "801077202e18420c5835e270c0d90333911485e69e231121751d3032.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 22:08:51,047 - INFO - Connection accepted from ('127.0.0.1', 49732) -2025-08-21 22:08:51,048 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 22:11:08,274 - INFO - Connection accepted from ('127.0.0.1', 48154) -2025-08-21 22:11:08,275 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 22:11:46,268 - INFO - Connection accepted from ('127.0.0.1', 52076) -2025-08-21 22:11:46,268 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "69f6719bd150439e24005d8432c68e39bf31d5fc113a028bfbd91006.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 22:35:00,501 - INFO - Connection accepted from ('127.0.0.1', 48388) -2025-08-21 22:35:00,501 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fafdb99975801684a412340ad98293cfe2307e3ab3d7ff0c006eeda5.PubTator"} -2025-08-21 23:44:59,511 - INFO - Connection accepted from ('127.0.0.1', 43480) -2025-08-21 23:44:59,511 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a0b53db8b2ef91d1ebe9d88002d924aca9ec237b144275572593c32e.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-21 23:56:24,624 - INFO - Connection accepted from ('127.0.0.1', 45490) -2025-08-21 23:56:24,624 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d48beeca8a07e4059a6bea41aad1a8b7e85c15a578008e7317717b07.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-22 00:13:33,851 - INFO - Connection accepted from ('127.0.0.1', 41250) -2025-08-22 00:13:33,851 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c3ea6a29dbea4a139d96f6135c9c1ddd85a1925ab6b64c8658723b5e.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-22 00:18:18,681 - INFO - Connection accepted from ('127.0.0.1', 55022) -2025-08-22 00:18:18,682 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "44b8882bc774b330ebd400912695dfee5bd544e2050be5c5ead326ca.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-22 00:27:32,174 - INFO - Connection accepted from ('127.0.0.1', 45592) -2025-08-22 00:27:32,174 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5022147668ee40f2c64fbef14833893a7356260f54b8b279698a6462.PubTator"} -2025-08-22 00:31:53,330 - INFO - Connection accepted from ('127.0.0.1', 58504) -2025-08-22 00:31:53,330 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-22 00:33:50,052 - INFO - Connection accepted from ('127.0.0.1', 53190) -2025-08-22 00:33:50,053 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-28 10:52:53,110 - INFO - Connection accepted from ('127.0.0.1', 48256) -2025-08-28 10:52:53,110 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "03da14bdd5e112af67e8c6f7a3bd0d6d15e72f411d309a35cd0743bf.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-28 13:34:55,046 - INFO - Connection accepted from ('127.0.0.1', 33522) -2025-08-28 13:34:55,047 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-28 14:02:04,360 - INFO - Connection accepted from ('127.0.0.1', 56596) -2025-08-28 14:02:04,360 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-08-28 14:51:47,736 - INFO - Connection accepted from ('127.0.0.1', 55280) -2025-08-28 14:51:47,736 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8c3f822074eb06ba00fd145e2f869875b420538754f89f5808d5fd4e.PubTator"} -Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. -2025-08-29 11:27:26,489 - INFO - Connection accepted from ('127.0.0.1', 59964) -2025-08-29 11:27:26,499 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-17 11:10:30,083 - INFO - Connection accepted from ('127.0.0.1', 56200) -2025-09-17 11:10:30,083 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a1773482ee80b67b47aff91334515e62ecabd077849417bcb463110c.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-17 12:24:07,433 - INFO - Connection accepted from ('127.0.0.1', 60870) -2025-09-17 12:24:07,433 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9bc1ff8066f17a6457bd1921f2c51ed7f03cf9589239369db89ec3fa.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-17 12:46:06,345 - INFO - Connection accepted from ('127.0.0.1', 57978) -2025-09-17 12:46:06,346 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1499349ee9985c99ab932f2ad11e35e3d07201e33e712d179093f32b.PubTator"} -2025-09-17 12:53:45,632 - INFO - Connection accepted from ('127.0.0.1', 38590) -2025-09-17 12:53:45,632 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bf3ae5e071fc67cfc4d940ad4fe46e4d3e36fc0cfd6bc6bf7a77f7f7.PubTator"} -2025-09-17 12:59:57,544 - INFO - Connection accepted from ('127.0.0.1', 53102) -2025-09-17 12:59:57,544 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c85c6699513924186db9dd319fe7f541984da30a50f8ab2f39fa0430.PubTator"} -2025-09-17 13:07:06,174 - INFO - Connection accepted from ('127.0.0.1', 51812) -2025-09-17 13:07:06,175 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7b00b22e2c9ce5ade86424797ad42c463089d18a36f825a3c6962acd.PubTator"} -2025-09-17 13:14:52,250 - INFO - Connection accepted from ('127.0.0.1', 59146) -2025-09-17 13:14:52,250 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7efada28608416135598c736128f1ab7d05b21343ca13208272597e0.PubTator"} -2025-09-17 13:20:46,134 - INFO - Connection accepted from ('127.0.0.1', 41356) -2025-09-17 13:20:46,135 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "48a72cf4a966d71e3495cf4a1437cc2dd507dc56b8fb12827031147b.PubTator"} -2025-09-17 14:10:02,662 - INFO - Connection accepted from ('127.0.0.1', 35748) -2025-09-17 14:10:02,662 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-17 14:31:54,934 - INFO - Connection accepted from ('127.0.0.1', 43550) -2025-09-17 14:31:54,934 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "060cba8cd3860b27a0709755afa3ea974884dd6b40e5353c7fdb92c2.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-17 14:48:55,419 - INFO - Connection accepted from ('127.0.0.1', 44478) -2025-09-17 14:48:55,419 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a8d06891a6253eb44a486c2980d8562e35f96cb3c6550ddc7eb989bd.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-17 15:14:41,909 - INFO - Connection accepted from ('127.0.0.1', 39548) -2025-09-17 15:14:41,910 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6bdc85d4bd729687a38010d5c6720ea19752d77519caaf948039e3dd.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-17 15:35:39,991 - INFO - Connection accepted from ('127.0.0.1', 37396) -2025-09-17 15:35:39,991 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "76a4cfe48d370e6c1d531733a536df09a69e8a90486304c3d0b856b9.PubTator"} -2025-09-17 15:42:35,415 - INFO - Connection accepted from ('127.0.0.1', 46534) -2025-09-17 15:42:35,415 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6f5caa582a230078e590f5538a2885b2b4f2b07cb76743865a806d08.PubTator"} -2025-09-17 15:48:08,323 - INFO - Connection accepted from ('127.0.0.1', 59852) -2025-09-17 15:48:08,323 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "75d30358c4498a0d17ff65b86cb778f43e998ccf8b2af86b59c9cb16.PubTator"} -2025-09-17 15:56:05,394 - INFO - Connection accepted from ('127.0.0.1', 45176) -2025-09-17 15:56:05,394 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f0b016a2e7cb06d6969f346baab78133546b325a29186c668d9640a6.PubTator"} -2025-09-17 16:02:59,522 - INFO - Connection accepted from ('127.0.0.1', 55588) -2025-09-17 16:02:59,523 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b8a10077fd0d1c3910bc30d3dcec583800643d7e5541aa3d831fb67d.PubTator"} -2025-09-17 16:08:28,800 - INFO - Connection accepted from ('127.0.0.1', 53234) -2025-09-17 16:08:28,800 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ae1cd1e6f9166f89e1b6f1f98afab5dbb57fd112e72c97b958902408.PubTator"} -2025-09-17 16:13:45,377 - INFO - Connection accepted from ('127.0.0.1', 55982) -2025-09-17 16:13:45,377 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4b638144a62efd55a9b36b0212e7f064abf21127793c3556d993f33a.PubTator"} -2025-09-17 16:21:36,927 - INFO - Connection accepted from ('127.0.0.1', 57014) -2025-09-17 16:21:36,928 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2935e3773208e8bc6181b79ae26b310eac22ff29ccdf26f4d89fa7f0.PubTator"} -2025-09-17 16:27:12,680 - INFO - Connection accepted from ('127.0.0.1', 58832) -2025-09-17 16:27:12,681 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "722b9804e1bca71e866f16a04e5cc165b78f96274e267766810e3b42.PubTator"} -2025-09-17 16:33:20,241 - INFO - Connection accepted from ('127.0.0.1', 50332) -2025-09-17 16:33:20,242 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7701b2515eb4a5c70aeae8070520c1ff549995b2c53591a702a289ae.PubTator"} -2025-09-17 16:41:29,947 - INFO - Connection accepted from ('127.0.0.1', 47446) -2025-09-17 16:41:29,947 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4224419f654bd839cea844bbe503272a95dedf7ae2c1743bcd1bc5c7.PubTator"} -2025-09-17 16:47:36,561 - INFO - Connection accepted from ('127.0.0.1', 38678) -2025-09-17 16:47:36,561 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "209bf04a447d6f7e96a3dfb2c4a88790d500aaae14b3105ecd02764d.PubTator"} -2025-09-17 16:54:25,440 - INFO - Connection accepted from ('127.0.0.1', 40080) -2025-09-17 16:54:25,440 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ca1db229f1ef3847c6828c295d4940ac1f6c78f81b734fa66461caa6.PubTator"} -2025-09-17 16:59:33,066 - INFO - Connection accepted from ('127.0.0.1', 50822) -2025-09-17 16:59:33,066 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "34f2e2dcfad7023766c0a7cc225aabbd2ea73bedda30df50ceb57945.PubTator"} -2025-09-17 17:07:39,376 - INFO - Connection accepted from ('127.0.0.1', 35054) -2025-09-17 17:07:39,377 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7ca5eaca22563071eab1bfb5f7eadf6c66bbc2bf8e103af1f88504a4.PubTator"} -2025-09-17 17:13:14,423 - INFO - Connection accepted from ('127.0.0.1', 36024) -2025-09-17 17:13:14,424 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8e4a42068dac51e8bb81f364a934429b58a08ce6f927ca01b4e93316.PubTator"} -2025-09-17 17:20:48,700 - INFO - Connection accepted from ('127.0.0.1', 49556) -2025-09-17 17:20:48,700 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8954bd538e3f3376549bfec4f7b763471be35e85123949d8c7c84ac5.PubTator"} -2025-09-17 17:27:45,046 - INFO - Connection accepted from ('127.0.0.1', 52622) -2025-09-17 17:27:45,046 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b32183c5c3d03c5b0aef0dd168a383affeb16900a62524ae3f6c6e31.PubTator"} -2025-09-17 17:32:40,181 - INFO - Connection accepted from ('127.0.0.1', 56844) -2025-09-17 17:32:40,182 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2921d686c0e322af074408698c9622213fc25fae92a8a27c7968cd36.PubTator"} -2025-09-17 17:37:10,016 - INFO - Connection accepted from ('127.0.0.1', 40772) -2025-09-17 17:37:10,017 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "16291d172c065db0c03bc088a38f14c204ca2361ff0e636f0513ae33.PubTator"} -2025-09-17 17:41:18,115 - INFO - Connection accepted from ('127.0.0.1', 49100) -2025-09-17 17:41:18,115 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "56e5d54adf5a52fc2a38efcf5edd8de93fad43bf11c7bc104727e060.PubTator"} -2025-09-17 17:45:29,211 - INFO - Connection accepted from ('127.0.0.1', 45778) -2025-09-17 17:45:29,211 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "23276a4c7b62cba6c0723436fe72678d8c4a5ce1550e424508e90136.PubTator"} -2025-09-17 17:51:59,949 - INFO - Connection accepted from ('127.0.0.1', 56118) -2025-09-17 17:51:59,949 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3eb61697c2c016ce65e3d89ecf23acd7c83c802b38bf13283f3d0f16.PubTator"} -2025-09-17 17:58:28,378 - INFO - Connection accepted from ('127.0.0.1', 33050) -2025-09-17 17:58:28,378 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "14caa8dbd3c091857813e0e85b2fab6adda30e74c1dd3305846a2810.PubTator"} -2025-09-17 18:06:08,673 - INFO - Connection accepted from ('127.0.0.1', 59706) -2025-09-17 18:06:08,674 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6bd8bd22bb14d060a85dcb8d5f10db52f8068bd1a58f8480752bcc5c.PubTator"} -2025-09-17 18:09:28,942 - INFO - Connection accepted from ('127.0.0.1', 52084) -2025-09-17 18:09:28,942 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cc14128c935bd9de76da8e58588cd316b1d30407ed052cbb7e68347a.PubTator"} -2025-09-17 18:22:30,191 - INFO - Connection accepted from ('127.0.0.1', 50598) -2025-09-17 18:22:30,191 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a63504cba3492975c9648d710aab8cfa9f4fd39b37dc59508ae7235d.PubTator"} -2025-09-17 18:29:07,728 - INFO - Connection accepted from ('127.0.0.1', 34200) -2025-09-17 18:29:07,729 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ecf8afce29ed17b1e677eb66c44ab1524a9763daf0edf34014b45a84.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-18 09:34:37,227 - INFO - Connection accepted from ('127.0.0.1', 51696) -2025-09-18 09:34:37,228 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5049bcfec01f4f1d8b656cabcfa9e5b8caf802c714c8d39e043a29fa.PubTator"} -2025-09-18 09:39:27,466 - INFO - Connection accepted from ('127.0.0.1', 48308) -2025-09-18 09:39:27,467 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ac1b31d7f2a8e867832571c4744ff29e8059eea683df90bc6a137d9e.PubTator"} -2025-09-18 09:44:17,690 - INFO - Connection accepted from ('127.0.0.1', 58166) -2025-09-18 09:44:17,690 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2b67bd8a16443927a46d11779226567f82f3aa4c87d16befa692f1c9.PubTator"} -2025-09-18 09:50:36,733 - INFO - Connection accepted from ('127.0.0.1', 48836) -2025-09-18 09:50:36,734 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f1b99bb5a7f3b88950dcb007f8211e6ec96ba02f950adb0eec6eee14.PubTator"} -2025-09-18 09:56:45,271 - INFO - Connection accepted from ('127.0.0.1', 52598) -2025-09-18 09:56:45,271 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d46844df7077d2f721e3f5f21abb0efe5fb5f86e808cee77c4908ff9.PubTator"} -2025-09-18 10:01:21,582 - INFO - Connection accepted from ('127.0.0.1', 59398) -2025-09-18 10:01:21,582 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c711fe5dd543f86071bd13298d01a32749f7dfb9f573859d252a86ee.PubTator"} -2025-09-18 10:07:41,933 - INFO - Connection accepted from ('127.0.0.1', 34056) -2025-09-18 10:07:41,933 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c9eba8f4d2ec18dbca3dfe17411ecfd4df409c2d5c0f6c9cf5b19658.PubTator"} -2025-09-18 10:14:08,238 - INFO - Connection accepted from ('127.0.0.1', 32832) -2025-09-18 10:14:08,238 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "46f6a40db0a9f6c3a591d600259ea573f1006d37ab274d5ad32f1858.PubTator"} -2025-09-18 10:21:11,160 - INFO - Connection accepted from ('127.0.0.1', 57418) -2025-09-18 10:21:11,160 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "97c6cca09972e90229c64dde01fb3643e7e2f9d2b1464c5a0d5f460d.PubTator"} -2025-09-18 10:27:48,306 - INFO - Connection accepted from ('127.0.0.1', 40892) -2025-09-18 10:27:48,307 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4c359d813daeb97f6e7ba03eb46a1a71f9451995d0101420d813935c.PubTator"} -2025-09-18 10:34:03,550 - INFO - Connection accepted from ('127.0.0.1', 42298) -2025-09-18 10:34:03,550 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b6cdc400fa6035b9db4c7a3aa1063324fd34cb8fc0a60999db4a7088.PubTator"} -2025-09-18 10:39:39,718 - INFO - Connection accepted from ('127.0.0.1', 33610) -2025-09-18 10:39:39,719 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "69da3d5c9756484561d38f6b18b486efb3ef03abff2cdca8d4db0470.PubTator"} -2025-09-18 10:45:14,147 - INFO - Connection accepted from ('127.0.0.1', 56592) -2025-09-18 10:45:14,148 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1501995ce586ffd0c7522df67e52af96f151f639f40a785a87e4a1ab.PubTator"} -2025-09-18 10:50:04,609 - INFO - Connection accepted from ('127.0.0.1', 49304) -2025-09-18 10:50:04,609 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "303706e25823bd0149547accec6ec3affcc4b2b7b85151fc9585b934.PubTator"} -2025-09-18 10:53:46,249 - INFO - Connection accepted from ('127.0.0.1', 54308) -2025-09-18 10:53:46,249 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "82aaf78c7d477b26c6d6de9a4891aa26aec9c5eaad2b4bc0f52ff40c.PubTator"} -2025-09-18 10:59:16,103 - INFO - Connection accepted from ('127.0.0.1', 38162) -2025-09-18 10:59:16,103 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "14cc615342962ce4f6996ac0ae9cd43a3b133d5ea971a4ecd1628679.PubTator"} -2025-09-18 11:05:17,978 - INFO - Connection accepted from ('127.0.0.1', 46020) -2025-09-18 11:05:17,978 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fa40b5a14e86ed1202c7bf4fbf0a5c8ad1163491e5e388cec23b586e.PubTator"} -2025-09-18 11:12:08,564 - INFO - Connection accepted from ('127.0.0.1', 42380) -2025-09-18 11:12:08,564 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a365c0dd1424220c97f5edb66db093580b2f05b882a3ab791206f6a8.PubTator"} -2025-09-18 11:17:47,882 - INFO - Connection accepted from ('127.0.0.1', 47256) -2025-09-18 11:17:47,883 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d080d04547c5a24aab2cb28eb234670adc3985a80aaecacc7c4f708e.PubTator"} -2025-09-18 11:22:00,164 - INFO - Connection accepted from ('127.0.0.1', 60832) -2025-09-18 11:22:00,165 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a3a639e2a4441ddea4d69af957c164ad6ee595ca0b5301c102d697c0.PubTator"} -2025-09-18 11:28:28,021 - INFO - Connection accepted from ('127.0.0.1', 34666) -2025-09-18 11:28:28,021 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0d9b43e2b228a67da2f1615501a481166c7188675b6839c7e7f178ee.PubTator"} -2025-09-18 11:34:16,996 - INFO - Connection accepted from ('127.0.0.1', 54366) -2025-09-18 11:34:16,996 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a8c4468f804d0bc20cddc7db6a652a906a210c1e494f9adfc6bf4a60.PubTator"} -2025-09-18 11:39:12,304 - INFO - Connection accepted from ('127.0.0.1', 46782) -2025-09-18 11:39:12,305 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e696b2e5a7cfb0afad922b6c4fa921b9f19cecdc97384b603310188c.PubTator"} -2025-09-18 11:44:20,257 - INFO - Connection accepted from ('127.0.0.1', 37722) -2025-09-18 11:44:20,257 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a1a9809c4e476f8d900c78f59802a0a25d0a10dcc786371721e174b6.PubTator"} -2025-09-18 11:51:21,135 - INFO - Connection accepted from ('127.0.0.1', 34686) -2025-09-18 11:51:21,135 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d4b31b8900dd2f1d35e13c88a8decefbcf3aa6fff6c171729bfefe96.PubTator"} -2025-09-18 11:57:02,831 - INFO - Connection accepted from ('127.0.0.1', 36436) -2025-09-18 11:57:02,831 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "111eea2c637963c95ca9bf8587b8203b26d9c9073e516436ff029cf4.PubTator"} -2025-09-18 12:01:37,800 - INFO - Connection accepted from ('127.0.0.1', 39046) -2025-09-18 12:01:37,800 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "83d8d008497bd9133e049fc0a68c2f6ccf053283c6c29793f2c29a46.PubTator"} -2025-09-18 12:06:03,380 - INFO - Connection accepted from ('127.0.0.1', 48162) -2025-09-18 12:06:03,381 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0880c1da9adc4eb6836a3b843f63f5a686d66f6a42c3384746bcd569.PubTator"} -2025-09-18 12:09:43,954 - INFO - Connection accepted from ('127.0.0.1', 58850) -2025-09-18 12:09:43,954 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9bf1c4d0a56866015b17d47a224aa769db41a3bb5b68adeaccea4ef8.PubTator"} -2025-09-18 12:15:48,503 - INFO - Connection accepted from ('127.0.0.1', 49840) -2025-09-18 12:15:48,503 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "30981ae22ee94255da8e19eaade584f52cb851a13c56403452d1763a.PubTator"} -2025-09-18 12:22:39,972 - INFO - Connection accepted from ('127.0.0.1', 52698) -2025-09-18 12:22:39,972 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "af1e85300903ea8ef9d73a2bb4188355b64ce7c19e77f7fbecbcffd7.PubTator"} -2025-09-18 12:31:42,308 - INFO - Connection accepted from ('127.0.0.1', 43148) -2025-09-18 12:31:42,309 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "01a3f691d8dec6b385d7bc379ae95559617fcc57d36edd3a162e4e01.PubTator"} -2025-09-18 12:38:17,339 - INFO - Connection accepted from ('127.0.0.1', 38060) -2025-09-18 12:38:17,339 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b1c5dc511d34e30adea874dc4ebe5a15055b5abcd050820fa531e1ad.PubTator"} -2025-09-18 12:41:42,124 - INFO - Connection accepted from ('127.0.0.1', 50694) -2025-09-18 12:41:42,124 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c713c8537457ffe19a2661ed94afb4fd453e37fc31c33d4c61092aa9.PubTator"} -2025-09-18 12:48:55,452 - INFO - Connection accepted from ('127.0.0.1', 49804) -2025-09-18 12:48:55,452 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "db5d75df2bcce6388234127f0517dad4bf02de8e8462438dc9e32284.PubTator"} -2025-09-18 12:55:42,932 - INFO - Connection accepted from ('127.0.0.1', 43356) -2025-09-18 12:55:42,932 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c4db6ef33a6162bf1ec17f0ed155a1b260a6eaf41ac55ad0771f9d28.PubTator"} -2025-09-18 13:00:34,357 - INFO - Connection accepted from ('127.0.0.1', 47984) -2025-09-18 13:00:34,357 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6daf824e33e03585f02add175f1f5833067969d7630b3e2b9da65846.PubTator"} -2025-09-18 13:06:27,871 - INFO - Connection accepted from ('127.0.0.1', 44374) -2025-09-18 13:06:27,871 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d2ebf1ed40624802693fbaf646e42396b2e8a6b95e1a1977f433f231.PubTator"} -2025-09-18 13:12:11,816 - INFO - Connection accepted from ('127.0.0.1', 46938) -2025-09-18 13:12:11,817 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "60ebb311323b94b4e93318b4bb6b9191c266c1fa1637c7ca69cdbc78.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-18 14:17:24,692 - INFO - Connection accepted from ('127.0.0.1', 52728) -2025-09-18 14:17:24,693 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-18 14:28:59,943 - INFO - Connection accepted from ('127.0.0.1', 49890) -2025-09-18 14:28:59,943 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-18 14:45:08,478 - INFO - Connection accepted from ('127.0.0.1', 60906) -2025-09-18 14:45:08,478 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cbf764a70b62954566ef689aa887ec7d0fd3a312d5f673fd8db0f760.PubTator"} -2025-09-18 14:54:38,788 - INFO - Connection accepted from ('127.0.0.1', 34526) -2025-09-18 14:54:38,788 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "69e2d12e4df39d6821e2ad18aa12d61be7a461db5f692d6ac18b2a92.PubTator"} -2025-09-18 15:03:42,012 - INFO - Connection accepted from ('127.0.0.1', 33362) -2025-09-18 15:03:42,012 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "de7d9c2697092fb51ef8c021ae24bd26eea3be85c11d82cfb4ee292c.PubTator"} -2025-09-18 15:09:21,903 - INFO - Connection accepted from ('127.0.0.1', 56974) -2025-09-18 15:09:21,903 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d073b0038f542e06d7d174be1015f99b6861fa37e2ac0b00050bed0b.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-18 15:25:51,471 - INFO - Connection accepted from ('127.0.0.1', 49352) -2025-09-18 15:25:51,471 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "954340c9a89d52dc144557373eb3ea07410622b9db633546c284aacb.PubTator"} -2025-09-18 15:33:30,906 - INFO - Connection accepted from ('127.0.0.1', 58182) -2025-09-18 15:33:30,906 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a3a2271b007d7d01ed6856dc25ee8a8e12e8563c1c6664a3b07f37ee.PubTator"} -2025-09-18 15:39:33,601 - INFO - Connection accepted from ('127.0.0.1', 40700) -2025-09-18 15:39:33,601 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e247a78d735f84fbd245fe69072cf7adae43eecbf543041264c903f5.PubTator"} -2025-09-18 15:42:38,172 - INFO - Connection accepted from ('127.0.0.1', 58460) -2025-09-18 15:42:38,173 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ad98e24772b66b1e6633b19085f457b93d1647ddfa55d27028520eaf.PubTator"} -2025-09-18 15:45:14,764 - INFO - Connection accepted from ('127.0.0.1', 53582) -2025-09-18 15:45:14,764 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8ad06b86b09c8592d5b32f8d4c7b55290552cf63d6c96aa9ce675843.PubTator"} -2025-09-18 15:53:22,071 - INFO - Connection accepted from ('127.0.0.1', 36074) -2025-09-18 15:53:22,071 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "acf7b3170dae39a5584f8afe43cd111d0e1a731212b03a055a4d9a68.PubTator"} -2025-09-18 15:58:26,725 - INFO - Connection accepted from ('127.0.0.1', 54570) -2025-09-18 15:58:26,725 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "03120879462f4ab9426b3fe7956585ac571b4697a2b9a8d659daa55b.PubTator"} -2025-09-18 16:04:24,010 - INFO - Connection accepted from ('127.0.0.1', 52420) -2025-09-18 16:04:24,010 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "57898f44678fbfc1d92e94def7d294d3199a5fbd07d8d9ee37856a17.PubTator"} -2025-09-18 16:15:20,713 - INFO - Connection accepted from ('127.0.0.1', 36016) -2025-09-18 16:15:20,713 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ec3c89691a4f14011228af36714c7c1a76714ba1f69bb1a9a67d24c8.PubTator"} -2025-09-18 16:18:49,092 - INFO - Connection accepted from ('127.0.0.1', 56644) -2025-09-18 16:18:49,093 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4d220d56e87b3bbf3e5f3805d85d65513c9204adb05bb730eaf5969b.PubTator"} -2025-09-18 16:20:45,910 - INFO - Connection accepted from ('127.0.0.1', 41386) -2025-09-18 16:20:45,910 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "25bed10e400940e27cac58db4041ce1e55451d05ade101f45501f48a.PubTator"} -2025-09-18 16:25:21,787 - INFO - Connection accepted from ('127.0.0.1', 41146) -2025-09-18 16:25:21,787 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f7677c0a388102214de9f713a81d3ac4acbbc18549d0767fcb3634c8.PubTator"} -2025-09-18 16:30:39,272 - INFO - Connection accepted from ('127.0.0.1', 59180) -2025-09-18 16:30:39,272 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e9b6cce27a110f601a69995057f50539e442f068bbb8d868fdec0c4c.PubTator"} -2025-09-18 16:34:22,127 - INFO - Connection accepted from ('127.0.0.1', 54736) -2025-09-18 16:34:22,127 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ab5974547f8ae144d1a31b8832f2e7d46811057d0047dd8fc5d2b77d.PubTator"} -2025-09-18 16:38:42,174 - INFO - Connection accepted from ('127.0.0.1', 54132) -2025-09-18 16:38:42,174 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "df6c580fa3698044ac3ab9b1cefac6dca8402aaf776a0f31c9bd3fdb.PubTator"} -2025-09-18 16:45:34,126 - INFO - Connection accepted from ('127.0.0.1', 33396) -2025-09-18 16:45:34,126 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5a134894005431f76832b77a120f94ab28614c3473ac56626559e410.PubTator"} -2025-09-18 16:49:43,716 - INFO - Connection accepted from ('127.0.0.1', 47724) -2025-09-18 16:49:43,716 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "057b00c6ea12be08c9f9aeff2eb25c271a5435018f9dff2632e43ebe.PubTator"} -2025-09-18 16:57:37,781 - INFO - Connection accepted from ('127.0.0.1', 34130) -2025-09-18 16:57:37,781 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "31a4d66dcd6a8bc5ed750e2d64aee5207e3fda56e310428cab3ed633.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-18 17:02:29,262 - INFO - Connection accepted from ('127.0.0.1', 37718) -2025-09-18 17:02:29,263 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3fc3eeb12584d858be7122a31c56f422f177e29ea0904094d0d058bc.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-18 17:14:24,169 - INFO - Connection accepted from ('127.0.0.1', 57218) -2025-09-18 17:14:24,170 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "77018b9b19e70f1d70501dc8857f3d6394b8d2eb5ea3123679ebefc0.PubTator"} -2025-09-18 17:16:30,241 - INFO - Connection accepted from ('127.0.0.1', 53666) -2025-09-18 17:16:30,242 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0eab6c13791e54b6ad7d1a411bf1dab01bbd8894826cb1a8cecf92d6.PubTator"} -2025-09-18 17:20:34,528 - INFO - Connection accepted from ('127.0.0.1', 41466) -2025-09-18 17:20:34,528 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fe9089e6093f81a87ddb5cad117a11cfb11cd8102889d73873bf77aa.PubTator"} -2025-09-18 17:22:32,499 - INFO - Connection accepted from ('127.0.0.1', 32830) -2025-09-18 17:22:32,499 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d4c1140e8f027f1ed99263a27ecbdd378a11fda959d07f17d5ee9680.PubTator"} -2025-09-18 17:29:04,279 - INFO - Connection accepted from ('127.0.0.1', 35768) -2025-09-18 17:29:04,280 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bfc483cad20f3fe622a8d8c1e579364e26566ce8dee3cf0523fe823b.PubTator"} -2025-09-18 17:36:08,193 - INFO - Connection accepted from ('127.0.0.1', 40518) -2025-09-18 17:36:08,193 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e7cdc9060c04c66306bf421778d65e5ea26db92b124e29e398190d40.PubTator"} -2025-09-18 17:39:40,175 - INFO - Connection accepted from ('127.0.0.1', 48254) -2025-09-18 17:39:40,176 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "98a4909f55310bd6092d4648f344d2f3d964d823e2c01e1079fb3cd7.PubTator"} -2025-09-18 17:41:30,775 - INFO - Connection accepted from ('127.0.0.1', 58426) -2025-09-18 17:41:30,775 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "df3b0f795385d2b3ac0597ff226b151d8921bdf2d6f574cf1f2fc49e.PubTator"} -2025-09-18 17:47:41,785 - INFO - Connection accepted from ('127.0.0.1', 53974) -2025-09-18 17:47:41,785 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "265c71c0318b5858a65200e379fe294137dd553210a882ede3b28b41.PubTator"} -2025-09-18 17:52:39,825 - INFO - Connection accepted from ('127.0.0.1', 48538) -2025-09-18 17:52:39,825 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3045fd7d8e059f7c7e98919cbbe715b38ae5fb09ce52cb44088c2f6f.PubTator"} -2025-09-18 17:57:43,712 - INFO - Connection accepted from ('127.0.0.1', 47556) -2025-09-18 17:57:43,712 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a857d2beb4a279b9d2974859214096a28707e2274041da05317394ed.PubTator"} -2025-09-18 17:59:45,946 - INFO - Connection accepted from ('127.0.0.1', 51112) -2025-09-18 17:59:45,946 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bc6b288387f2dc5ad551cf388a98af62b8bdbc75d1d2ae5f9a0afb06.PubTator"} -2025-09-18 18:01:42,380 - INFO - Connection accepted from ('127.0.0.1', 55664) -2025-09-18 18:01:42,380 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d067d918e9744876e189122f3a7ea86a8d9c1a9cd3f38ead0f9cbb89.PubTator"} -2025-09-18 18:03:19,794 - INFO - Connection accepted from ('127.0.0.1', 44468) -2025-09-18 18:03:19,794 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2c86eba64f1ee8990c41105beb7a04703d017530e55abd2afb634d7e.PubTator"} -2025-09-18 18:05:56,713 - INFO - Connection accepted from ('127.0.0.1', 43628) -2025-09-18 18:05:56,714 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6ab8a5d990e958a50d1b61a912cf51bf394256bb98b8709d437d074f.PubTator"} -2025-09-18 18:09:49,001 - INFO - Connection accepted from ('127.0.0.1', 47286) -2025-09-18 18:09:49,001 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2e25534b847e70038295a86f580c92a46f9b0235c258128b22ea346a.PubTator"} -2025-09-18 18:10:54,128 - INFO - Connection accepted from ('127.0.0.1', 51640) -2025-09-18 18:10:54,128 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "94289d45a2e3fe283f893de0e4ec80022f3201e50a2af61335f7dcfa.PubTator"} -2025-09-18 18:17:25,790 - INFO - Connection accepted from ('127.0.0.1', 43850) -2025-09-18 18:17:25,791 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6b990e3489183a67390fc86fdcb00fa3d19d471fe404e23321f13d5b.PubTator"} -2025-09-18 18:21:35,589 - INFO - Connection accepted from ('127.0.0.1', 44816) -2025-09-18 18:21:35,589 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "56774355c5ad4ede23fdf90f89d9121a53f332a8a61ccb2046d25567.PubTator"} -2025-09-18 18:24:29,874 - INFO - Connection accepted from ('127.0.0.1', 45018) -2025-09-18 18:24:29,874 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5b3bf3591ae9607b9e4088131fc0670244c71825cd0ef671d35b7ead.PubTator"} -2025-09-18 18:29:22,228 - INFO - Connection accepted from ('127.0.0.1', 50678) -2025-09-18 18:29:22,229 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4675b0dccd492746a62b60ab697e300779b44900e3b20c73318b3ec6.PubTator"} -2025-09-18 18:35:40,034 - INFO - Connection accepted from ('127.0.0.1', 52772) -2025-09-18 18:35:40,035 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "34f7d60d86ac64f84ffe1c75a0555a53188639b79747f92f14750897.PubTator"} -2025-09-18 18:40:27,494 - INFO - Connection accepted from ('127.0.0.1', 39006) -2025-09-18 18:40:27,494 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3c0157756963ca15c3e544efd1ee01394a1fc5fc75630e8c9cb4287f.PubTator"} -2025-09-18 18:42:31,946 - INFO - Connection accepted from ('127.0.0.1', 52248) -2025-09-18 18:42:31,947 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "be616bdacc737be1c0128f7a6169eb138091d170677835fec39720d8.PubTator"} -2025-09-18 18:44:43,987 - INFO - Connection accepted from ('127.0.0.1', 58660) -2025-09-18 18:44:43,987 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "05ad9eb69959b351a44c144f3f3481bb8b73efb5e664ad3fe45d58d2.PubTator"} -2025-09-18 18:46:21,249 - INFO - Connection accepted from ('127.0.0.1', 37138) -2025-09-18 18:46:21,249 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d048f9f68e37ba861cfb4a6151ce0ad33a1657fe2baf9f0ffa5f56e3.PubTator"} -2025-09-18 18:51:25,831 - INFO - Connection accepted from ('127.0.0.1', 39594) -2025-09-18 18:51:25,831 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "856ea346ffda32002f1058f1f2641f52cbe604a67f7a2171e330929f.PubTator"} -2025-09-18 18:57:08,492 - INFO - Connection accepted from ('127.0.0.1', 53990) -2025-09-18 18:57:08,492 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a9d724f38f1a9d283dbe86e0fba90f2c482b74c183ef7fed83640e3c.PubTator"} -2025-09-18 18:58:22,453 - INFO - Connection accepted from ('127.0.0.1', 33002) -2025-09-18 18:58:22,454 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "52e08b9bac398c764d1bd5e3c99a7ce606529e46f3ac45e8e7a12bad.PubTator"} -2025-09-18 19:00:48,473 - INFO - Connection accepted from ('127.0.0.1', 55702) -2025-09-18 19:00:48,474 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9bb23f5d43bca137cd50c923a376c3e0fd0b48d54fbe9d581a6f5194.PubTator"} -2025-09-18 19:03:43,692 - INFO - Connection accepted from ('127.0.0.1', 43956) -2025-09-18 19:03:43,692 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1f5930397170dd327f9378332c4509f515f26eb1ab16e73e5ed5b2a5.PubTator"} -2025-09-18 19:04:37,453 - INFO - Connection accepted from ('127.0.0.1', 33912) -2025-09-18 19:04:37,453 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "47e71977159d7f5d5a142aaddd090bff28ba66ffacb8908d6c55f812.PubTator"} -2025-09-18 19:06:59,869 - INFO - Connection accepted from ('127.0.0.1', 45422) -2025-09-18 19:06:59,869 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3bff36eed2351d3c58e9428dce1fda58c7997ee49da5e4e910c7d133.PubTator"} -2025-09-18 19:09:05,755 - INFO - Connection accepted from ('127.0.0.1', 57642) -2025-09-18 19:09:05,755 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "920e960a1d9b4307b21d516514768936af46b460f385aa516ce84504.PubTator"} -2025-09-18 19:10:11,331 - INFO - Connection accepted from ('127.0.0.1', 34020) -2025-09-18 19:10:11,331 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "447f6d9dab85d7d82bb33a5bd3f62b0300a243d57d2c6436a432f7e6.PubTator"} -2025-09-18 19:14:16,268 - INFO - Connection accepted from ('127.0.0.1', 56458) -2025-09-18 19:14:16,269 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d6efb29974a805c658cd2d5e45b3bdbe9a01e427c5e2080c9eddf178.PubTator"} -2025-09-18 19:20:44,669 - INFO - Connection accepted from ('127.0.0.1', 38108) -2025-09-18 19:20:44,670 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7fc19ab2df36ddfd866a1d4aa2b25b84ba031b004faa0e5d4d1b8983.PubTator"} -2025-09-18 19:24:37,520 - INFO - Connection accepted from ('127.0.0.1', 57122) -2025-09-18 19:24:37,520 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "789a480bbcc1316093831ec79f9062d9c1efba17edf30bdd305b8b20.PubTator"} -2025-09-18 19:38:53,797 - INFO - Connection accepted from ('127.0.0.1', 33562) -2025-09-18 19:38:53,797 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6dd893d100ec5df5205b81f51f88211c066f7be6343770de05765da0.PubTator"} -2025-09-18 19:45:22,329 - INFO - Connection accepted from ('127.0.0.1', 34380) -2025-09-18 19:45:22,330 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3451cfaade39905a11e51cfeb43676ccf234495bbf0a24df449dd038.PubTator"} -2025-09-18 19:52:03,529 - INFO - Connection accepted from ('127.0.0.1', 53114) -2025-09-18 19:52:03,530 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f233c18d1e574ed30d4668a0c8ef27c366efdb068ea771796a6a276d.PubTator"} -2025-09-18 20:04:56,223 - INFO - Connection accepted from ('127.0.0.1', 46268) -2025-09-18 20:04:56,224 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "25d6874dff2d9a8b2af6ee78166b1985e122ed869d80d766d9724f31.PubTator"} -2025-09-18 20:07:04,190 - INFO - Connection accepted from ('127.0.0.1', 37614) -2025-09-18 20:07:04,190 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "198ba4be34a8afaba951fc537a2f2889453d8cf57e9c46c4b533acf3.PubTator"} -2025-09-18 20:08:29,953 - INFO - Connection accepted from ('127.0.0.1', 59858) -2025-09-18 20:08:29,953 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "034454635d6c4d53217f14a817c72ca5680c2e4095b82c459e726ff5.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-19 09:30:22,007 - INFO - Connection accepted from ('127.0.0.1', 51848) -2025-09-19 09:30:22,007 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "143956f2912f6d2e3f3e9815d61ded24698f5715e178273ef00dad1e.PubTator"} -2025-09-19 09:33:06,618 - INFO - Connection accepted from ('127.0.0.1', 48584) -2025-09-19 09:33:06,618 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dc4ddc4891a32e37eea954d98d4dcdedaffd321320cdcca6675d54b2.PubTator"} -2025-09-19 09:38:03,502 - INFO - Connection accepted from ('127.0.0.1', 38108) -2025-09-19 09:38:03,502 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0255a68fb738d365f5ed285bc8d9967ccd7009cd2336528d34906c75.PubTator"} -2025-09-19 09:44:28,619 - INFO - Connection accepted from ('127.0.0.1', 35790) -2025-09-19 09:44:28,619 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8ec99913ccee65fb982ad39ca28de73841ad58a356f66abcdcb77de0.PubTator"} -2025-09-19 09:46:24,764 - INFO - Connection accepted from ('127.0.0.1', 44412) -2025-09-19 09:46:24,765 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fa5c36ab57e8882ea077d4cba9f8fb7c6a3d4910ce05a6ec02420a86.PubTator"} -2025-09-19 09:48:44,078 - INFO - Connection accepted from ('127.0.0.1', 42216) -2025-09-19 09:48:44,078 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ccb8ae87f74313a2e16094cb93d2d0267c3927a8fc09ab285427121e.PubTator"} -2025-09-19 09:50:22,074 - INFO - Connection accepted from ('127.0.0.1', 58460) -2025-09-19 09:50:22,074 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a0bb869bc88070d5ce0668e50eff6e4e7b729e4f52092b7d9fbc39ff.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-19 09:56:36,034 - INFO - Connection accepted from ('127.0.0.1', 39004) -2025-09-19 09:56:36,034 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bf0f3f82a0f5d93c0a434a7179a1973e52fce83cb490b0cb7b612833.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-19 11:05:23,647 - INFO - Connection accepted from ('127.0.0.1', 50832) -2025-09-19 11:05:23,648 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "71307f7056c5bdd3809e893aaf18db2f9bdf77f72f1584e540528a51.PubTator"} -2025-09-19 11:09:38,368 - INFO - Connection accepted from ('127.0.0.1', 47040) -2025-09-19 11:09:38,368 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a120dd754be1b6332560c5f8c19677f702ddb67897be9e5d8f8c4c22.PubTator"} -2025-09-19 11:11:49,536 - INFO - Connection accepted from ('127.0.0.1', 50272) -2025-09-19 11:11:49,536 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c3cbd55f24018bf5f65662c77cfad13d99a62b3b92efb996020ffca0.PubTator"} -2025-09-19 11:16:22,437 - INFO - Connection accepted from ('127.0.0.1', 52990) -2025-09-19 11:16:22,437 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "eb3a9b2afe4ec337d42384cf4884d56deb0d73159fe24ad430a57d04.PubTator"} -2025-09-19 11:20:15,525 - INFO - Connection accepted from ('127.0.0.1', 41230) -2025-09-19 11:20:15,525 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d654c6ec60a57a63006c19bec72a4ff5984517358a5e3a96fc0f972b.PubTator"} -2025-09-19 11:22:52,008 - INFO - Connection accepted from ('127.0.0.1', 51824) -2025-09-19 11:22:52,008 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "08683d4947702432c762b30a5bd315c60d9549756cbb8982b0f80435.PubTator"} -2025-09-19 11:25:53,587 - INFO - Connection accepted from ('127.0.0.1', 49414) -2025-09-19 11:25:53,588 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0e7c4d9706d76ce746bd9bd4a49e8e2c88882893f6e3064e2959f5e0.PubTator"} -2025-09-19 11:30:44,840 - INFO - Connection accepted from ('127.0.0.1', 48078) -2025-09-19 11:30:44,840 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "72b708d554dfd1cc64a7740a2ae6ad814e44eef3128378e0f0e80643.PubTator"} -2025-09-19 11:33:44,534 - INFO - Connection accepted from ('127.0.0.1', 57890) -2025-09-19 11:33:44,534 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5e293e23db67128eb0c8f0e8d5040b7e9366547ffe243c223c998885.PubTator"} -2025-09-19 11:37:32,060 - INFO - Connection accepted from ('127.0.0.1', 37180) -2025-09-19 11:37:32,060 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cfa83f06e09ec0163b65e2627c0cd882190caea8517b8487e27a3ad1.PubTator"} -2025-09-19 11:41:47,361 - INFO - Connection accepted from ('127.0.0.1', 39100) -2025-09-19 11:41:47,361 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "51d06446961ae8425b555a72ea6120f93a0708bfd313659e535f9401.PubTator"} -2025-09-19 11:44:40,273 - INFO - Connection accepted from ('127.0.0.1', 49642) -2025-09-19 11:44:40,273 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b12b0517732991c95c133d1b3a5695d2c81f10c0f87e2e45a89c9977.PubTator"} -2025-09-19 11:47:40,021 - INFO - Connection accepted from ('127.0.0.1', 59142) -2025-09-19 11:47:40,021 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e57acffafd469e70be1667767a615a279163d621b4a3dc9291230361.PubTator"} -2025-09-19 11:50:15,390 - INFO - Connection accepted from ('127.0.0.1', 36456) -2025-09-19 11:50:15,390 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "43f4a165643ded775a06053215258dd16d770493c6f9765661705bd9.PubTator"} -2025-09-19 11:54:02,520 - INFO - Connection accepted from ('127.0.0.1', 57502) -2025-09-19 11:54:02,520 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "285774cbeafa63ef1c0ed93c594aa78e467ef2f86806205cc5fce289.PubTator"} -2025-09-19 11:56:51,198 - INFO - Connection accepted from ('127.0.0.1', 40420) -2025-09-19 11:56:51,199 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8cac582b6bed8043216666ce82267b493f6eb16483871eada6a941a3.PubTator"} -2025-09-19 12:00:28,752 - INFO - Connection accepted from ('127.0.0.1', 43146) -2025-09-19 12:00:28,752 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "244ed21841aeed3d878d76e459c0a0931953f3ff02f4f0020af0a18c.PubTator"} -2025-09-19 12:03:35,515 - INFO - Connection accepted from ('127.0.0.1', 41124) -2025-09-19 12:03:35,516 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7422eaf59ef0ba946412a3e3bf62c453ce6cd0f33645c445488f404e.PubTator"} -2025-09-19 12:06:12,342 - INFO - Connection accepted from ('127.0.0.1', 43840) -2025-09-19 12:06:12,342 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "256b013b958377bdb71244639df1a9a0af101d7cca35ffc0692ebfaa.PubTator"} -2025-09-19 12:08:24,001 - INFO - Connection accepted from ('127.0.0.1', 55338) -2025-09-19 12:08:24,001 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "94185e6d5a641a9ba9d9d8005467c5085a2c05ec4e65792507b4fb91.PubTator"} -2025-09-19 12:11:10,910 - INFO - Connection accepted from ('127.0.0.1', 42736) -2025-09-19 12:11:10,910 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "38584afcb0c262498326e844ca924b1456d1e0f59ec56533bbc4a3b3.PubTator"} -2025-09-19 12:13:21,518 - INFO - Connection accepted from ('127.0.0.1', 54270) -2025-09-19 12:13:21,519 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c2bd5ae7ba5032b6a19647985407bdf380011eaccd37f6ca43f063d3.PubTator"} -2025-09-19 12:16:30,426 - INFO - Connection accepted from ('127.0.0.1', 58514) -2025-09-19 12:16:30,426 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "74d4848c514a39710b447735dd2c72799a1c1df4644610fa83e005bc.PubTator"} -2025-09-19 12:19:51,866 - INFO - Connection accepted from ('127.0.0.1', 48414) -2025-09-19 12:19:51,866 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ed6b163b3fa11fa664c680a518d1d1f30f259497730fe2c00e135c5c.PubTator"} -2025-09-19 12:23:29,204 - INFO - Connection accepted from ('127.0.0.1', 41906) -2025-09-19 12:23:29,205 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "192f8e35935cfa6dc68e82ff302eb2bb6d791276f73f375fca680e69.PubTator"} -2025-09-19 12:25:32,757 - INFO - Connection accepted from ('127.0.0.1', 39340) -2025-09-19 12:25:32,758 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "aba4d596bfb02d9797ff8a8e8fd0f495dc42cb34a4eeee7a56b2b5c4.PubTator"} -2025-09-19 12:32:01,290 - INFO - Connection accepted from ('127.0.0.1', 55450) -2025-09-19 12:32:01,290 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "511de8dca3c0e23e3e0a33926e79f3a25ab09247225e9dcad2fb0076.PubTator"} -2025-09-19 12:35:17,195 - INFO - Connection accepted from ('127.0.0.1', 39950) -2025-09-19 12:35:17,195 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "14f767fc9f11e1b5528e336381743ad8870bdc3e7bc0850c50344616.PubTator"} -2025-09-19 12:39:30,362 - INFO - Connection accepted from ('127.0.0.1', 46668) -2025-09-19 12:39:30,362 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "05e681dde91d2ec492736e9863e75917c65d7ebbe788e337ec411a79.PubTator"} -2025-09-19 12:42:16,373 - INFO - Connection accepted from ('127.0.0.1', 44620) -2025-09-19 12:42:16,373 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c2b10142ee68d356267344b4c97689e05f2efda5e39d975a17898eda.PubTator"} -2025-09-19 12:44:57,963 - INFO - Connection accepted from ('127.0.0.1', 38638) -2025-09-19 12:44:57,963 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3ff666732551c9cadb4b94ab76b60111216a959b26f7cb8a7a19ab47.PubTator"} -2025-09-19 12:48:13,694 - INFO - Connection accepted from ('127.0.0.1', 47594) -2025-09-19 12:48:13,695 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8bc4937a423f854f0f21b4d0a56f721abedc5db4b8da45b89c3a0641.PubTator"} -2025-09-19 12:50:43,720 - INFO - Connection accepted from ('127.0.0.1', 44836) -2025-09-19 12:50:43,720 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b23419d8fb119c35091314e4c82d3af4a5e26f13a6b9c7a0a7fe6ca9.PubTator"} -2025-09-19 12:53:09,327 - INFO - Connection accepted from ('127.0.0.1', 44178) -2025-09-19 12:53:09,327 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "553b58ace9fe43816bf34052ccadfcfabb81fe20fceb965cf7087d84.PubTator"} -2025-09-19 12:56:17,947 - INFO - Connection accepted from ('127.0.0.1', 40582) -2025-09-19 12:56:17,947 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "01cd858bb4e9252f7f96804917fa7fa08aae64bf9cb7cae5e41ed52f.PubTator"} -2025-09-19 12:59:08,607 - INFO - Connection accepted from ('127.0.0.1', 55590) -2025-09-19 12:59:08,608 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "73cf64a5b62d0f47e5b8cd67d0e4fcbb3157cde313df8e82d23fc62b.PubTator"} -2025-09-19 13:02:58,826 - INFO - Connection accepted from ('127.0.0.1', 36706) -2025-09-19 13:02:58,827 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bae5f09781fc9b4738b4f39f4e1e29121187d0e8890a45de621f253e.PubTator"} -2025-09-19 13:06:41,519 - INFO - Connection accepted from ('127.0.0.1', 59976) -2025-09-19 13:06:41,520 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d8be928f0c155ef66a510823266c505503a97ba35c771a735918ee5d.PubTator"} -2025-09-19 13:10:27,004 - INFO - Connection accepted from ('127.0.0.1', 44304) -2025-09-19 13:10:27,004 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f72b29c96f91b31fca3e93e5b0cd790c0ecd885865c8a5dbb71f6092.PubTator"} -2025-09-19 13:12:39,846 - INFO - Connection accepted from ('127.0.0.1', 40842) -2025-09-19 13:12:39,846 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9489eb18cf3b8018ca7cfd9414b55930e43a3c52f8deb1667e50f0d4.PubTator"} -2025-09-19 13:15:21,387 - INFO - Connection accepted from ('127.0.0.1', 33664) -2025-09-19 13:15:21,387 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "145c5d63928e8d61a2193c2c34f434cd9ecc21fdf55a366132048b9c.PubTator"} -2025-09-19 13:17:56,422 - INFO - Connection accepted from ('127.0.0.1', 56834) -2025-09-19 13:17:56,423 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4ddc36bc9a370250bf902814f17faa1e0144eac8281f40188301573d.PubTator"} -2025-09-19 13:19:46,713 - INFO - Connection accepted from ('127.0.0.1', 38582) -2025-09-19 13:19:46,713 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7f74e45c5aceaba3c809369d730fc5b486849b44706e1e53bfd1f878.PubTator"} -2025-09-19 13:22:36,738 - INFO - Connection accepted from ('127.0.0.1', 53290) -2025-09-19 13:22:36,738 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9cd7fa05d163cc07fb96b80c0ffbc94f668205a398234c5e2a1893a7.PubTator"} -2025-09-19 13:25:51,419 - INFO - Connection accepted from ('127.0.0.1', 54398) -2025-09-19 13:25:51,420 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c83d38c65c57058e626a71840da2c4f73a3fac383ba236b1e753b123.PubTator"} -2025-09-19 13:28:55,816 - INFO - Connection accepted from ('127.0.0.1', 39556) -2025-09-19 13:28:55,816 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d42822d25ee8a2ea8c25865b8a0c82015febafeb14e15d7c222afcaa.PubTator"} -2025-09-19 13:31:54,492 - INFO - Connection accepted from ('127.0.0.1', 43590) -2025-09-19 13:31:54,492 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "af2f80599c19e1660aa119e9c8e7ad07861b8c3d919e542718f71500.PubTator"} -2025-09-19 13:34:21,006 - INFO - Connection accepted from ('127.0.0.1', 33034) -2025-09-19 13:34:21,006 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ec1900ecbb4262ec597096316730f4a679df50a20326676303aae02a.PubTator"} -2025-09-19 13:36:47,859 - INFO - Connection accepted from ('127.0.0.1', 55076) -2025-09-19 13:36:47,860 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3208993a0ad8c103429df6b137510f759709d7c7e1f149b83e3300e1.PubTator"} -2025-09-19 13:39:43,232 - INFO - Connection accepted from ('127.0.0.1', 40480) -2025-09-19 13:39:43,232 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "19acd64c9fa261f897bb51c061f2d8b3be89ea9fb0c5938b88f15f81.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-19 15:43:50,070 - INFO - Connection accepted from ('127.0.0.1', 60410) -2025-09-19 15:43:50,070 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7906b66f710bd0249f19d33c238a4315d921d8e1ee015d0299fea3f0.PubTator"} -2025-09-19 15:46:05,248 - INFO - Connection accepted from ('127.0.0.1', 53980) -2025-09-19 15:46:05,248 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5a38376eba124cb90952eb85e96b1b82f89d393aad1d9e4622e582b8.PubTator"} -2025-09-19 15:49:17,672 - INFO - Connection accepted from ('127.0.0.1', 35830) -2025-09-19 15:49:17,672 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "90c4ebd65d71423ae6bfa166cb0e05c49dc87b0deccbef842b31e2b0.PubTator"} -2025-09-19 15:51:43,406 - INFO - Connection accepted from ('127.0.0.1', 34244) -2025-09-19 15:51:43,406 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "318bc8793de48e0e378c0f2fc6d3d13c2f92ecbf37982ff936118749.PubTator"} -2025-09-19 15:54:07,883 - INFO - Connection accepted from ('127.0.0.1', 49726) -2025-09-19 15:54:07,883 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cc0d49dce8d734d20a222175a4a7754e4caac42aec637837fb774fe1.PubTator"} -2025-09-19 15:56:21,654 - INFO - Connection accepted from ('127.0.0.1', 51116) -2025-09-19 15:56:21,654 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "77cf1309f24eccd732e05e11091e3456c1d6327702081b01d3e19995.PubTator"} -2025-09-19 15:58:29,619 - INFO - Connection accepted from ('127.0.0.1', 44930) -2025-09-19 15:58:29,619 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "36364443f39e5cd9f14fedb44e62f095b2cad994fea7868794a409b5.PubTator"} -2025-09-19 16:01:09,107 - INFO - Connection accepted from ('127.0.0.1', 45374) -2025-09-19 16:01:09,108 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4b4eb680d31ddf3bdbe39ec3912fabcf68bd24963b086073d79152c3.PubTator"} -2025-09-19 16:03:58,611 - INFO - Connection accepted from ('127.0.0.1', 32940) -2025-09-19 16:03:58,612 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4439854d0de63973156b18cd112544745353560019b9f3220813054f.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 09:22:27,933 - INFO - Connection accepted from ('127.0.0.1', 48544) -2025-09-20 09:22:27,933 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 09:40:45,180 - INFO - Connection accepted from ('127.0.0.1', 59154) -2025-09-20 09:40:45,180 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5664f237c831964c0dd0a8a1f54a9be8a471c60c5999c74bfe844c33.PubTator"} -2025-09-20 09:44:55,510 - INFO - Connection accepted from ('127.0.0.1', 57416) -2025-09-20 09:44:55,510 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c102291a9efb43013fd47137a4358ebcd0350fd2c999cbc5c46ce081.PubTator"} -2025-09-20 09:48:55,753 - INFO - Connection accepted from ('127.0.0.1', 52552) -2025-09-20 09:48:55,753 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3d879b3015a4b778e7f5d800fe3829e52038c666f76f9ae41ab81ade.PubTator"} -2025-09-20 09:50:50,558 - INFO - Connection accepted from ('127.0.0.1', 53600) -2025-09-20 09:50:50,558 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "305474915b2859ce32c828a2004d1dcdaebe8a397c7af4cb8bf5ea12.PubTator"} -2025-09-20 09:55:11,871 - INFO - Connection accepted from ('127.0.0.1', 53640) -2025-09-20 09:55:11,871 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9b0155136432a6ea20e66720768eaf58f3473f17d17940d3caffce29.PubTator"} -2025-09-20 09:58:11,941 - INFO - Connection accepted from ('127.0.0.1', 45066) -2025-09-20 09:58:11,941 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dcbebaa4e802b6811b24a6a619ced93b98f9edf1a564b2127efc7eef.PubTator"} -2025-09-20 10:01:30,383 - INFO - Connection accepted from ('127.0.0.1', 35050) -2025-09-20 10:01:30,383 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "59439ff29240579aa294f945d63fc56bb060d12f7f04d7537f5ef482.PubTator"} -2025-09-20 10:04:29,576 - INFO - Connection accepted from ('127.0.0.1', 43020) -2025-09-20 10:04:29,577 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "592a9331c9ffbd4311082ccc9b9d6a0380f51733edeb179fea3214da.PubTator"} -2025-09-20 10:07:37,117 - INFO - Connection accepted from ('127.0.0.1', 44554) -2025-09-20 10:07:37,117 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "96386b8c65566b29206e45ae67263210c43221515cbe7953aa82dfd6.PubTator"} -2025-09-20 10:11:16,064 - INFO - Connection accepted from ('127.0.0.1', 56884) -2025-09-20 10:11:16,064 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "15cd22375b4eeeb3ff92fa09996ca5325d5ff0f96de658b5f67f893d.PubTator"} -2025-09-20 10:13:37,910 - INFO - Connection accepted from ('127.0.0.1', 46352) -2025-09-20 10:13:37,910 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "da13bbd5fe4db230644fda2c26a0e82f24a0ff4faf00bba69b0e55ef.PubTator"} -2025-09-20 10:16:30,148 - INFO - Connection accepted from ('127.0.0.1', 60524) -2025-09-20 10:16:30,148 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "71b3f96ba8e8f80c708913d22880886107093dd7402e4e33302e08eb.PubTator"} -2025-09-20 10:20:56,653 - INFO - Connection accepted from ('127.0.0.1', 52540) -2025-09-20 10:20:56,653 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9e7ea7f8d66e0b5452b96b6e1a77f16d31579b793efc7d256dedd5e9.PubTator"} -2025-09-20 10:23:49,794 - INFO - Connection accepted from ('127.0.0.1', 41046) -2025-09-20 10:23:49,794 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "339161045d58c984f3c809ac01f74933004957b64abbedd41eb3c345.PubTator"} -2025-09-20 10:27:34,473 - INFO - Connection accepted from ('127.0.0.1', 34056) -2025-09-20 10:27:34,473 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "232485fb9d82cca08280deeb231064b74018cf004dc845305824faef.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 10:34:54,160 - INFO - Connection accepted from ('127.0.0.1', 56144) -2025-09-20 10:34:54,160 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b98d71393a5d13b634ab712a980e7dce04688253db209c41d3119be3.PubTator"} -2025-09-20 10:40:58,140 - INFO - Connection accepted from ('127.0.0.1', 35492) -2025-09-20 10:40:58,140 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "816e018295d58462ad4058b020920d62cda50569fd8a61449028535e.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 10:46:38,939 - INFO - Connection accepted from ('127.0.0.1', 43300) -2025-09-20 10:46:38,939 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "199bfa66ee289858bb288eb7aa196542cc543b5528d05143fb7b6141.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 10:57:03,220 - INFO - Connection accepted from ('127.0.0.1', 33324) -2025-09-20 10:57:03,220 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d4627bafaffb0215e3d6d8c9de5449b3639d35cbd81583c65b3c7099.PubTator"} -2025-09-20 11:10:58,039 - INFO - Connection accepted from ('127.0.0.1', 50642) -2025-09-20 11:10:58,039 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "717fab74fe9dc657f7b4ffb1c6b33148d3a5dc0673b63805555eb665.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 13:52:58,690 - INFO - Connection accepted from ('127.0.0.1', 46128) -2025-09-20 13:52:58,691 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0daebd7fdc2bd1917650e8ffbd2f77ddcf8365bd7b44807293e8e2bc.PubTator"} -2025-09-20 13:59:04,680 - INFO - Connection accepted from ('127.0.0.1', 58370) -2025-09-20 13:59:04,680 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0aa6cae425b69a43ffb4ce182504187f8fe058d1d824a0db87d8da60.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 19:27:33,287 - INFO - Connection accepted from ('127.0.0.1', 39684) -2025-09-20 19:27:33,287 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c6be8663260e1ef89c8f8babd9cc88aee203e1ccc21efc12a664a17e.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 19:34:13,540 - INFO - Connection accepted from ('127.0.0.1', 48598) -2025-09-20 19:34:13,540 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8c735abb49a84cdc27e493aa0a1de4668468f0360b38163f6cce10e9.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 19:40:18,661 - INFO - Connection accepted from ('127.0.0.1', 41122) -2025-09-20 19:40:18,662 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6fe003eeeb7fffe1cad629a6fd301c8a3f0fc72ccb9d80ac91c321b5.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 19:42:05,920 - INFO - Connection accepted from ('127.0.0.1', 50350) -2025-09-20 19:42:05,920 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f986602ae4eae69125005e80d6cfd57ee0aa5e49b7546f305bd43d0f.PubTator"} -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 11, in - from gliner import GLiNER -ModuleNotFoundError: No module named 'gliner' - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-20 19:52:50,539 - INFO - Connection accepted from ('127.0.0.1', 44068) -2025-09-20 19:52:50,540 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ccf3fe74cbf3412eac68934089e74cfed41393a1d73a16a2c7b9b563.PubTator"} -2025-09-20 19:58:05,149 - INFO - Connection accepted from ('127.0.0.1', 33012) -2025-09-20 19:58:05,150 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "364846871a1bfc47c37ef0668bfdcedd4051a92d8605473707d5dea4.PubTator"} -2025-09-20 20:01:45,490 - INFO - Connection accepted from ('127.0.0.1', 38822) -2025-09-20 20:01:45,491 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5526e0ea6b75a03edb15c77612c552c010ae4ac39b2f517c79314bf3.PubTator"} -2025-09-20 20:09:36,481 - INFO - Connection accepted from ('127.0.0.1', 39248) -2025-09-20 20:09:36,481 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "414a706897e2271ce0fc7402ffa424524ea74f836be8370fa2f2de53.PubTator"} -2025-09-20 20:15:59,729 - INFO - Connection accepted from ('127.0.0.1', 54476) -2025-09-20 20:15:59,729 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d6e2c59d0a00ca9c0ce5573ba382ed3ff4eb2b181ac52895f5e5d050.PubTator"} -2025-09-20 20:19:57,921 - INFO - Connection accepted from ('127.0.0.1', 51110) -2025-09-20 20:19:57,921 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f18ed1cea6745bcd04a51701e33f2a35a3fad218084e6c44d18caf1f.PubTator"} -2025-09-20 20:25:14,149 - INFO - Connection accepted from ('127.0.0.1', 59440) -2025-09-20 20:25:14,150 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "48f4afda33e3a0dba0b51e8f6294e7802eabddfcec1fe83dc10c46ef.PubTator"} -2025-09-20 20:31:30,940 - INFO - Connection accepted from ('127.0.0.1', 53038) -2025-09-20 20:31:30,940 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c12699693e0dd97c1c5eff3691b27cd5118208103c687fbf4c7a68cf.PubTator"} -2025-09-20 20:36:13,225 - INFO - Connection accepted from ('127.0.0.1', 60828) -2025-09-20 20:36:13,225 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3ea82e6b6dccf3779203d6b703b7d4883f0d8a1e0501142355c0b38c.PubTator"} -2025-09-20 20:42:21,090 - INFO - Connection accepted from ('127.0.0.1', 47968) -2025-09-20 20:42:21,090 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a964cd175dac4e06700e88066ac00e70d50c40df1878990b057d563f.PubTator"} -2025-09-20 20:48:51,044 - INFO - Connection accepted from ('127.0.0.1', 37378) -2025-09-20 20:48:51,044 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1b95ac7132602fc3e0c5b96e3d3cda3273992835489148e18020ffa7.PubTator"} -2025-09-20 20:53:36,344 - INFO - Connection accepted from ('127.0.0.1', 43014) -2025-09-20 20:53:36,344 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dcd6169502e10362c1c1010cec7f252738ec760514552f3b6649ef1a.PubTator"} -2025-09-20 20:58:19,028 - INFO - Connection accepted from ('127.0.0.1', 58452) -2025-09-20 20:58:19,028 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b647426d42c8dcb49ce726d70a3870dc7660632b034165ee92395386.PubTator"} -2025-09-20 21:02:24,195 - INFO - Connection accepted from ('127.0.0.1', 58742) -2025-09-20 21:02:24,196 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c9806fad79b8ea9f2a61cd9abf63c318c4864935b5f1367bf1f06b3d.PubTator"} -2025-09-20 21:08:27,474 - INFO - Connection accepted from ('127.0.0.1', 49180) -2025-09-20 21:08:27,474 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "99f37ba35f157d8da36d84673a5897fce2fbfaaee81d6e344b4e027f.PubTator"} -2025-09-20 21:13:21,501 - INFO - Connection accepted from ('127.0.0.1', 56684) -2025-09-20 21:13:21,502 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "db62c74dff398a892a67bcd133e368604da615792094fdb3fbf6b93a.PubTator"} -2025-09-20 21:19:12,713 - INFO - Connection accepted from ('127.0.0.1', 43024) -2025-09-20 21:19:12,713 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "44da7d4b752dac584317e7a454d3ca6e9662793e6a722addbb7701fb.PubTator"} -2025-09-20 21:23:58,245 - INFO - Connection accepted from ('127.0.0.1', 51764) -2025-09-20 21:23:58,246 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dca7aea6473dcdddbb60cea66c7b58be97882a9e64a9863b43084ba2.PubTator"} -2025-09-20 21:28:16,499 - INFO - Connection accepted from ('127.0.0.1', 34168) -2025-09-20 21:28:16,500 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6248af82794d424bec6b9f64f960431a4de0a76b6dc4e0815e613cb5.PubTator"} -2025-09-20 21:32:05,127 - INFO - Connection accepted from ('127.0.0.1', 41326) -2025-09-20 21:32:05,127 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6cec604f57b112c02ddc4425e2d8ce899f6b0d47181b05e7f41ed403.PubTator"} -2025-09-20 21:37:11,603 - INFO - Connection accepted from ('127.0.0.1', 51970) -2025-09-20 21:37:11,603 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9a57928bc1f6300f27f21a99ff9d04340191533bf0eedb85aa9699a6.PubTator"} -2025-09-20 21:40:29,485 - INFO - Connection accepted from ('127.0.0.1', 40074) -2025-09-20 21:40:29,485 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "97594acaabc0e717744ce5533a66132d675227c616580a08e1823144.PubTator"} -2025-09-20 21:46:18,775 - INFO - Connection accepted from ('127.0.0.1', 54694) -2025-09-20 21:46:18,775 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "057d34521cf4521ea9a86cf47dfa2313dc1bf94f9421e0ad5b305836.PubTator"} -2025-09-20 21:51:43,245 - INFO - Connection accepted from ('127.0.0.1', 54366) -2025-09-20 21:51:43,245 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ca3465210e4b9ff5a7e837e7f37c8d3e2d57e6cb1d8382f58e94c5f0.PubTator"} -2025-09-20 21:59:24,068 - INFO - Connection accepted from ('127.0.0.1', 40282) -2025-09-20 21:59:24,068 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "053ef3afacc077adb727f73eff6472652d88b18084391d46cdf5e355.PubTator"} -2025-09-20 22:02:32,779 - INFO - Connection accepted from ('127.0.0.1', 59132) -2025-09-20 22:02:32,779 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "47dda50da12e378d0215d88d46534fed7c941f2372b66fa9b9f42ec6.PubTator"} -2025-09-20 22:14:17,329 - INFO - Connection accepted from ('127.0.0.1', 33496) -2025-09-20 22:14:17,329 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "17721d7cd3b352cef603c5cbf367cc012caeb03fb9209cb9df09fa5b.PubTator"} -2025-09-20 22:19:30,572 - INFO - Connection accepted from ('127.0.0.1', 52440) -2025-09-20 22:19:30,572 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dcfc294e7eeaeaaca63459a31a54eb18ef80592da6c2f3da68c8e627.PubTator"} -2025-09-20 22:26:09,964 - INFO - Connection accepted from ('127.0.0.1', 60346) -2025-09-20 22:26:09,965 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "19b92d2279c74f644a73f890d679b6b6dde5826d8a79e0ad7f11b317.PubTator"} -2025-09-20 22:31:41,162 - INFO - Connection accepted from ('127.0.0.1', 39708) -2025-09-20 22:31:41,162 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c6a55f186900420e5b87229d7c70f0b7c16ac7de7ed20fc5bd30cceb.PubTator"} -2025-09-20 22:36:26,378 - INFO - Connection accepted from ('127.0.0.1', 60728) -2025-09-20 22:36:26,378 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "549debc385d0613e6bbee78178f81d9050199c694ad902d76b80f0c5.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-22 09:20:28,942 - INFO - Connection accepted from ('127.0.0.1', 52580) -2025-09-22 09:20:28,942 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a3085b94d5613f1b07a209e1b219453c9711502c21e341272f34e306.PubTator"} -2025-09-22 09:24:36,681 - INFO - Connection accepted from ('127.0.0.1', 37944) -2025-09-22 09:24:36,682 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "98f03a2ebaea961d9fcbef05edf45776187801124de4d26fa6d7601b.PubTator"} -2025-09-22 09:29:06,838 - INFO - Connection accepted from ('127.0.0.1', 56058) -2025-09-22 09:29:06,839 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3c20df9d45a6a4dfdba22b57999c1ce6e8cd640a33392f43f89907d7.PubTator"} -2025-09-22 09:34:47,530 - INFO - Connection accepted from ('127.0.0.1', 54368) -2025-09-22 09:34:47,530 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "697d6cfef55297954433d686aa973938cd9d6bbe962d91a054ca026f.PubTator"} -2025-09-22 09:39:33,214 - INFO - Connection accepted from ('127.0.0.1', 45798) -2025-09-22 09:39:33,215 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d13dc8f2c5df5c29826479db64314b2ec153ff42817f87535872e8c6.PubTator"} -2025-09-22 09:44:48,860 - INFO - Connection accepted from ('127.0.0.1', 33874) -2025-09-22 09:44:48,860 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4c69017b22f199bbcc4c8652d61609fdbc2b616ed424ea21f4a5dfc4.PubTator"} -2025-09-22 09:50:04,875 - INFO - Connection accepted from ('127.0.0.1', 56048) -2025-09-22 09:50:04,876 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d192c6f786324a1b5860bfaaf62de5c26487c7434e65a4b4a4861c32.PubTator"} -2025-09-22 09:56:43,492 - INFO - Connection accepted from ('127.0.0.1', 54250) -2025-09-22 09:56:43,492 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c90ceb346679da90befb23e2e1bdaea6bb8c294d3b5108e56337e5ab.PubTator"} -2025-09-22 10:00:51,263 - INFO - Connection accepted from ('127.0.0.1', 60082) -2025-09-22 10:00:51,263 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ff37f44607a04fadbd76c41201c8641c21f662349590ef517cbd7630.PubTator"} -2025-09-22 10:05:38,620 - INFO - Connection accepted from ('127.0.0.1', 42238) -2025-09-22 10:05:38,620 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7106e927ebd56e43712f3d837a1bd02f2282ef07a6dc07fa60522ea9.PubTator"} -2025-09-22 10:09:17,825 - INFO - Connection accepted from ('127.0.0.1', 56758) -2025-09-22 10:09:17,825 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ee53242f93025b923ccb1a8a5d45d2c46a9c323dccac86dc1446a8fb.PubTator"} -2025-09-22 10:12:05,708 - INFO - Connection accepted from ('127.0.0.1', 42806) -2025-09-22 10:12:05,709 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5adc68b4ceb390219c10852b40bbae07358ded0fb0393a9a23d51df4.PubTator"} -2025-09-22 10:16:20,749 - INFO - Connection accepted from ('127.0.0.1', 51350) -2025-09-22 10:16:20,749 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "321554cb35627a265140a67977c105cf4bf95989f2b39002f80b7d09.PubTator"} -2025-09-22 10:20:44,417 - INFO - Connection accepted from ('127.0.0.1', 45332) -2025-09-22 10:20:44,417 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ee26873dc0dd8eb531884dfaafaa5c33f9cc18359a9ef8e9c9bd585e.PubTator"} -2025-09-22 10:25:40,901 - INFO - Connection accepted from ('127.0.0.1', 55182) -2025-09-22 10:25:40,901 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1ddf3cd26852eb961f7b983b5f5cc330bf3396ef5f771ab80804ec2d.PubTator"} -2025-09-22 10:29:52,177 - INFO - Connection accepted from ('127.0.0.1', 42714) -2025-09-22 10:29:52,177 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "420c08dfddbc9341c660761dd3cb935d66abf03c849f59a17a1b2f00.PubTator"} -2025-09-22 10:33:37,681 - INFO - Connection accepted from ('127.0.0.1', 45292) -2025-09-22 10:33:37,681 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9584b2b865f419ee57238a86e48eeca73e5ab46c71488c13457d0826.PubTator"} -2025-09-22 10:37:58,748 - INFO - Connection accepted from ('127.0.0.1', 39200) -2025-09-22 10:37:58,748 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7c3013bef8cc4fbd4735f380a1e487adc887a0427e4d2c6a103b68a7.PubTator"} -2025-09-22 10:43:49,017 - INFO - Connection accepted from ('127.0.0.1', 53898) -2025-09-22 10:43:49,017 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "96cd8c79dd63c0deaf209db0ee8a091dc1ead54c5ccf9dd0ebc999e8.PubTator"} -2025-09-22 10:47:49,848 - INFO - Connection accepted from ('127.0.0.1', 39040) -2025-09-22 10:47:49,848 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9339881e9900411c4d873f6213abaeb4d92f832d9b8b767da148510e.PubTator"} -2025-09-22 10:51:08,478 - INFO - Connection accepted from ('127.0.0.1', 55034) -2025-09-22 10:51:08,479 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f28f4e6b624cb1cd666502ce54a13b6b8c2af5db39720d67ca87b1ed.PubTator"} -2025-09-22 10:56:47,739 - INFO - Connection accepted from ('127.0.0.1', 41026) -2025-09-22 10:56:47,740 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b43c8a02ed8b79429e379c3255b54f18a2c085afb949fce8eb29ab6e.PubTator"} -2025-09-22 11:01:20,517 - INFO - Connection accepted from ('127.0.0.1', 45690) -2025-09-22 11:01:20,517 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9b343a7d92f283d22032480ea0246ac6abb3ddccc40fdbc2a11e382f.PubTator"} -2025-09-22 11:05:10,756 - INFO - Connection accepted from ('127.0.0.1', 34796) -2025-09-22 11:05:10,756 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "54c134353fcb4d9a8f8f9baaa910adc1a61a64a187d7583bbaee65a6.PubTator"} -2025-09-22 11:08:53,992 - INFO - Connection accepted from ('127.0.0.1', 51348) -2025-09-22 11:08:53,992 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7c6668670f0e2a52c95b822c1f3355e1bddd7a9606a1eee366a4079c.PubTator"} -2025-09-22 11:12:18,412 - INFO - Connection accepted from ('127.0.0.1', 55478) -2025-09-22 11:12:18,412 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a6ceb599a8e4915c9e3a7175f3232c9414c7a97b824fa0723869c195.PubTator"} -2025-09-22 11:16:40,869 - INFO - Connection accepted from ('127.0.0.1', 43398) -2025-09-22 11:16:40,869 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "12aad532e1cf987ab12a9fe725460d313ab50565f21fe3d89fbb6206.PubTator"} -2025-09-22 11:21:40,369 - INFO - Connection accepted from ('127.0.0.1', 35106) -2025-09-22 11:21:40,369 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5f9ea548ac21a5f04cd59501d0d4b9aac07d1c46202ce9142ac13336.PubTator"} -2025-09-22 11:28:37,776 - INFO - Connection accepted from ('127.0.0.1', 53346) -2025-09-22 11:28:37,776 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3c082197a7e688d2b89b66848630377920a88c43ba504abd0d9c12a0.PubTator"} -2025-09-22 11:33:46,360 - INFO - Connection accepted from ('127.0.0.1', 56888) -2025-09-22 11:33:46,361 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b82c53d9860ebeea96151558905da443b0169fd5883cabc6c97f1846.PubTator"} -2025-09-22 11:38:22,405 - INFO - Connection accepted from ('127.0.0.1', 49108) -2025-09-22 11:38:22,405 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fe46196692846700425810e6484da8fcaf8d9b79a5ddba8354bb5554.PubTator"} -2025-09-22 11:43:30,373 - INFO - Connection accepted from ('127.0.0.1', 38366) -2025-09-22 11:43:30,373 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a410c29544a8877e5a456e02c65defd03c1bd147e9a2e52a8db5d4ff.PubTator"} -2025-09-22 11:48:36,869 - INFO - Connection accepted from ('127.0.0.1', 40500) -2025-09-22 11:48:36,869 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5e0eb7159e765aa47cc95222edfb8976b684f39515d3f15a5e43ce6a.PubTator"} -2025-09-22 11:54:14,531 - INFO - Connection accepted from ('127.0.0.1', 36608) -2025-09-22 11:54:14,531 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9a4ba134329e77454bbc25ca29fd15a12f60753b0086f68008549ac7.PubTator"} -2025-09-22 11:59:11,473 - INFO - Connection accepted from ('127.0.0.1', 60910) -2025-09-22 11:59:11,473 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "392b09925e68cea7f78d966da6747b9ba21f9e484e48037726fb2444.PubTator"} -2025-09-22 12:04:27,846 - INFO - Connection accepted from ('127.0.0.1', 36282) -2025-09-22 12:04:27,846 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c1731d1091f67dbee9a845c6cfd0e6ec6392963399dd3dc2995615e6.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-22 14:49:00,940 - INFO - Connection accepted from ('127.0.0.1', 56034) -2025-09-22 14:49:00,940 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "719c67ddef08cc31bbf0b3bd360e1c9f066f9308da92a758b3e84997.PubTator"} -2025-09-22 14:54:43,076 - INFO - Connection accepted from ('127.0.0.1', 52550) -2025-09-22 14:54:43,077 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4736a25cc7c80645c89f5895d8eef71d041bc6463b134a00db25c734.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-22 16:53:07,480 - INFO - Connection accepted from ('127.0.0.1', 48032) -2025-09-22 16:53:07,480 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3eb0b5c5d703a4382fa2ce8d7fe9750dcc75089bb97e39846de22f95.PubTator"} -2025-09-22 16:58:49,569 - INFO - Connection accepted from ('127.0.0.1', 48716) -2025-09-22 16:58:49,569 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b741dc7664d78bfa07ef4acbc16353fdc4f49444d0c46dbfe9e60011.PubTator"} -2025-09-22 17:03:32,185 - INFO - Connection accepted from ('127.0.0.1', 41474) -2025-09-22 17:03:32,185 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4c6b1ed6d40419a6777a1c09dda6604a8bd7f673f133372ea385ae1f.PubTator"} -2025-09-22 17:07:04,908 - INFO - Connection accepted from ('127.0.0.1', 42392) -2025-09-22 17:07:04,908 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e75f16effdc8c5e103b7da0da10a521d7c07a1a2f95ac538e947d659.PubTator"} -2025-09-22 17:13:13,655 - INFO - Connection accepted from ('127.0.0.1', 40486) -2025-09-22 17:13:13,655 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a23ec63602fc8a0bceca6220510f79a2cc398a3f14c2654c3f503089.PubTator"} -2025-09-22 17:18:59,342 - INFO - Connection accepted from ('127.0.0.1', 38378) -2025-09-22 17:18:59,343 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "937d97047cb7e15e5513fb28e213bc24a40bba42b5f2e3cb9229ab60.PubTator"} -2025-09-22 17:22:47,576 - INFO - Connection accepted from ('127.0.0.1', 60202) -2025-09-22 17:22:47,576 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a13176ec332a2f02b7843c7763a8caec477b76c3ec000e98fa4e0e1e.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-23 09:03:15,683 - INFO - Connection accepted from ('127.0.0.1', 53954) -2025-09-23 09:03:15,683 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "327c9dd938596b405c137d4ea57679fc7c484514e1e57d9102b29c2f.PubTator"} -2025-09-23 09:09:01,754 - INFO - Connection accepted from ('127.0.0.1', 47288) -2025-09-23 09:09:01,755 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d5b1c64ccfd55e02594d3053e0d083ce142e3f039d5f6c0845f65c35.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-23 09:25:10,249 - INFO - Connection accepted from ('127.0.0.1', 54668) -2025-09-23 09:25:10,249 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bca13b1a769f13befae32f00955a8c1b7214e01a06ae51c8a4ca2900.PubTator"} -2025-09-23 09:30:34,087 - INFO - Connection accepted from ('127.0.0.1', 60922) -2025-09-23 09:30:34,088 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "80c311dd450f146257afafaf4170756321ec90c748c8431599c2d7cd.PubTator"} -2025-09-23 09:34:17,087 - INFO - Connection accepted from ('127.0.0.1', 54236) -2025-09-23 09:34:17,087 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c9e7f4025719c7513636dc5769e66d1bcc4f3467f8cf6882affa4e5f.PubTator"} -2025-09-23 09:40:36,919 - INFO - Connection accepted from ('127.0.0.1', 48920) -2025-09-23 09:40:36,919 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0e27188b3c7854f411fd03db6d93593fcce49d37b39c91478db74d7c.PubTator"} -2025-09-23 09:46:36,947 - INFO - Connection accepted from ('127.0.0.1', 41316) -2025-09-23 09:46:36,947 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "43d3288c465eace42f8d2e821bfa62fade9140655e2e3fdc352abaaf.PubTator"} -2025-09-23 09:50:24,515 - INFO - Connection accepted from ('127.0.0.1', 35960) -2025-09-23 09:50:24,516 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ae35bffac3b726e5d6d0066093df0313d9c293c293856eb28e315b7a.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-24 10:06:29,881 - INFO - Connection accepted from ('127.0.0.1', 41096) -2025-09-24 10:06:29,881 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1420ef0a237c759a2739c98c4bf4aa2f0660f479eafe10c6fd4f8922.PubTator"} -2025-09-24 10:10:38,671 - INFO - Connection accepted from ('127.0.0.1', 38974) -2025-09-24 10:10:38,671 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fd94a48ff9d719c02bd3c37565a4a35cdbac18256382cad66884324c.PubTator"} -2025-09-24 10:14:13,418 - INFO - Connection accepted from ('127.0.0.1', 37396) -2025-09-24 10:14:13,418 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "70c43f9636cb7b0c2e58e38c3352e696cbc1ea5f9f8ec26a1f9e7cdb.PubTator"} -2025-09-24 10:17:12,834 - INFO - Connection accepted from ('127.0.0.1', 48140) -2025-09-24 10:17:12,834 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ff5ec87c3a0f7295127c0b50e4dcd9412579a880e4901f25466824f0.PubTator"} -2025-09-24 10:21:35,380 - INFO - Connection accepted from ('127.0.0.1', 50230) -2025-09-24 10:21:35,381 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "e0bcdc9db6eafd0fb63c18bb2e0208b7869244d7bc6fb827427682a3.PubTator"} -2025-09-24 10:25:23,540 - INFO - Connection accepted from ('127.0.0.1', 35618) -2025-09-24 10:25:23,540 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "acfe4a2c63aa4a123f49e102bce54b28e7305d93846cf924a062757c.PubTator"} -2025-09-24 10:28:14,424 - INFO - Connection accepted from ('127.0.0.1', 55204) -2025-09-24 10:28:14,424 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bbb4678f5ba7c996d3113d5f581a0813c1800dff0e08cfb7cc473abe.PubTator"} -2025-09-24 10:31:16,207 - INFO - Connection accepted from ('127.0.0.1', 58404) -2025-09-24 10:31:16,207 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b6d1ac149f65608356b5feaad1090070c97047fd395854ee9a121e65.PubTator"} -2025-09-24 10:35:50,053 - INFO - Connection accepted from ('127.0.0.1', 53486) -2025-09-24 10:35:50,053 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ca19d75bcbcec421beee662261485eac8849c9f724e5aecbb3c1a4bc.PubTator"} -2025-09-24 10:39:03,321 - INFO - Connection accepted from ('127.0.0.1', 42772) -2025-09-24 10:39:03,321 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8026ffd52bab97cfc1a296544e009e8dd8695f24add18b6159d20a77.PubTator"} -2025-09-25 10:17:40,685 - INFO - Connection accepted from ('127.0.0.1', 55844) -2025-09-25 10:17:40,685 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-25 10:18:57,323 - INFO - Connection accepted from ('127.0.0.1', 42184) -2025-09-25 10:18:57,323 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "eb19953861f5b6f6d1aa4f357f2ca273518d81d4dc6d1b4434e2feb6.PubTator"} -2025-09-25 10:23:04,686 - INFO - Connection accepted from ('127.0.0.1', 57342) -2025-09-25 10:23:04,686 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cadb18b4259610e726ae682e4f7fe3a922aa2780c1ad27b9160fdd59.PubTator"} -2025-09-25 10:26:38,724 - INFO - Connection accepted from ('127.0.0.1', 56370) -2025-09-25 10:26:38,724 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c81d864090193109077d92ebb48e7f88d1fef10d830ed1cad0c3fdf6.PubTator"} -2025-09-25 10:29:45,272 - INFO - Connection accepted from ('127.0.0.1', 47858) -2025-09-25 10:29:45,272 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3f2af23f1531322bde91a95bfc32fcaf813c56cad6fe23fcae807c62.PubTator"} -2025-09-25 10:33:33,070 - INFO - Connection accepted from ('127.0.0.1', 40060) -2025-09-25 10:33:33,070 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8085539581cafb29416e026946930ff1d0e87d625d12ead1c682e40b.PubTator"} -2025-09-25 10:37:13,698 - INFO - Connection accepted from ('127.0.0.1', 43114) -2025-09-25 10:37:13,698 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a08afbaa186c08be34702f0eec1e79b67130325a7c98869e44405715.PubTator"} -2025-09-25 10:40:02,128 - INFO - Connection accepted from ('127.0.0.1', 34722) -2025-09-25 10:40:02,128 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "baa7796c33bcdb35ccac6e2e2a90018e23e817f798357d01021a4815.PubTator"} -2025-09-25 10:42:51,153 - INFO - Connection accepted from ('127.0.0.1', 60626) -2025-09-25 10:42:51,154 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d529ee87ef1deb4b563867af119c31d025df2f5c0620daeb8b6507b2.PubTator"} -2025-09-25 10:47:36,559 - INFO - Connection accepted from ('127.0.0.1', 52324) -2025-09-25 10:47:36,559 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d0ab29c312375239293c2090efa54ef0a97ef0f4da46b547f3b0965f.PubTator"} -2025-09-25 10:50:45,598 - INFO - Connection accepted from ('127.0.0.1', 53764) -2025-09-25 10:50:45,598 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "91da54bc3dffa2c4a75bb100ede5515c4a8054907228d6b99a3545bc.PubTator"} -2025-09-25 10:54:07,523 - INFO - Connection accepted from ('127.0.0.1', 57774) -2025-09-25 10:54:07,523 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8d85d7de379640b3b1a8781f94586ff546c113f47eee3877461d2110.PubTator"} -2025-09-25 10:58:40,455 - INFO - Connection accepted from ('127.0.0.1', 53066) -2025-09-25 10:58:40,456 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "674ba3a99dcc2dac46c7adac623d6860156e8e7d561a0046b5acdaa6.PubTator"} -2025-09-25 11:02:15,851 - INFO - Connection accepted from ('127.0.0.1', 35836) -2025-09-25 11:02:15,851 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9474dd20ece3e1487b32e0df6dff380479753e0ebea06e392d082f75.PubTator"} -2025-09-25 11:05:44,861 - INFO - Connection accepted from ('127.0.0.1', 36280) -2025-09-25 11:05:44,861 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "26a00d62b705fb245b3596bc0ce6d4e3c497151032ab313de2863823.PubTator"} -2025-09-25 11:09:02,158 - INFO - Connection accepted from ('127.0.0.1', 35212) -2025-09-25 11:09:02,158 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "758ef39cc60138c66288e7850d8f7c74422bcf2b5df8ef708fa057ef.PubTator"} -2025-09-25 11:13:44,056 - INFO - Connection accepted from ('127.0.0.1', 42572) -2025-09-25 11:13:44,057 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "27a9d917884cad0159eacbba51169ff8cf37e7d7358a50ed3613a0ea.PubTator"} -2025-09-25 11:17:11,708 - INFO - Connection accepted from ('127.0.0.1', 33202) -2025-09-25 11:17:11,708 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6bdb0453de11f13741830b2b80b65cf69f26c5554dc27424488631dc.PubTator"} -2025-09-25 11:21:09,664 - INFO - Connection accepted from ('127.0.0.1', 34608) -2025-09-25 11:21:09,664 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d715f13c5b10f1a7bfb758dd9e911df816ab683560d63a7a3eac30f6.PubTator"} -2025-09-25 11:24:46,125 - INFO - Connection accepted from ('127.0.0.1', 49528) -2025-09-25 11:24:46,125 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a5e505dc0c39f4440971ca79009792fe6319596174edcc8e9caf0b8b.PubTator"} -2025-09-25 11:27:10,121 - INFO - Connection accepted from ('127.0.0.1', 42020) -2025-09-25 11:27:10,121 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "648007db092fb23d67c1eea3f95328cd25af752dea6b22ae6f18bfe0.PubTator"} -2025-09-25 11:29:14,744 - INFO - Connection accepted from ('127.0.0.1', 43538) -2025-09-25 11:29:14,744 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "afe00e9a65cdb73d1b9ffb9c911b0868785457ccc8e4025f38cfd913.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-25 11:32:00,080 - INFO - Connection accepted from ('127.0.0.1', 54586) -2025-09-25 11:32:00,080 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b610acc53bb3e20eab35d52629cd4ede3a3d60eff2ea196d83059fa8.PubTator"} -2025-09-25 11:33:44,776 - INFO - Connection accepted from ('127.0.0.1', 42374) -2025-09-25 11:33:44,776 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cb63f2131b5b858a1a575c30fa9248be301f46d14e9bc5fb8ba027fd.PubTator"} -2025-09-25 11:35:53,418 - INFO - Connection accepted from ('127.0.0.1', 53348) -2025-09-25 11:35:53,419 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c7b85706f55829e90abe9abebdd0fe59d65ac4f71bf8e1be44060905.PubTator"} -2025-09-25 11:39:39,197 - INFO - Connection accepted from ('127.0.0.1', 54242) -2025-09-25 11:39:39,197 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cdb006c53853e7ad66fbf3dcd1c10d199de009050ce1b039d7982697.PubTator"} -2025-09-25 11:43:11,230 - INFO - Connection accepted from ('127.0.0.1', 54512) -2025-09-25 11:43:11,231 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1028ef1ed1109621c194239b1cd9930726de5f7054fc008e699894c1.PubTator"} -2025-09-25 11:48:42,009 - INFO - Connection accepted from ('127.0.0.1', 57772) -2025-09-25 11:48:42,009 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d0533299c877cc0db3a726c046a7b9c0077737a90779c5e9ca9c4cc5.PubTator"} -2025-09-25 11:50:20,539 - INFO - Connection accepted from ('127.0.0.1', 42240) -2025-09-25 11:50:20,539 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7f855d3f4f3f78c1087fbfd0c7a2a3954404ed2d0a084b076b532d89.PubTator"} -2025-09-25 11:53:53,503 - INFO - Connection accepted from ('127.0.0.1', 37172) -2025-09-25 11:53:53,503 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b1bdb26a089df24cbf20c47719c4426c15be0a49f727e37f7fc6b4e5.PubTator"} -2025-09-25 11:57:09,920 - INFO - Connection accepted from ('127.0.0.1', 43208) -2025-09-25 11:57:09,920 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7461561dab8deef3d9aab5ba7c45f803c92b3bcb51d4b48e4da39c77.PubTator"} -2025-09-25 12:00:47,369 - INFO - Connection accepted from ('127.0.0.1', 41596) -2025-09-25 12:00:47,369 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2927384d80663bb86c9096bc589f1d45289f9ffac6daa434be0c2092.PubTator"} -2025-09-25 12:05:11,793 - INFO - Connection accepted from ('127.0.0.1', 50886) -2025-09-25 12:05:11,794 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "66f4b8c961bef8b9773e57dfc7abf0cdb6ec9a70430f2ca91f43d4c2.PubTator"} -2025-09-25 12:08:12,017 - INFO - Connection accepted from ('127.0.0.1', 35042) -2025-09-25 12:08:12,017 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "836b38ce9f14af52d2810a986bd46bdcc23be119ab117c27c7bcf943.PubTator"} -2025-09-25 12:10:33,106 - INFO - Connection accepted from ('127.0.0.1', 50948) -2025-09-25 12:10:33,106 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0281529e4fd29553bf6e971da91304d8a9f404ec41aa5e5abdc85640.PubTator"} -2025-09-25 12:13:38,240 - INFO - Connection accepted from ('127.0.0.1', 33836) -2025-09-25 12:13:38,241 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "80fe0918ed787d678c381f108936617276a3f0c7d84263966791962f.PubTator"} -2025-09-25 12:17:01,734 - INFO - Connection accepted from ('127.0.0.1', 34914) -2025-09-25 12:17:01,734 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "11df6b5b12744da37f20133d14c56c50954ad7d4979be356771843c7.PubTator"} -2025-09-25 12:19:13,090 - INFO - Connection accepted from ('127.0.0.1', 54020) -2025-09-25 12:19:13,090 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7e12000e716c7388d82bf510877edfa4053b7788c69b14c0d8d59d6f.PubTator"} -2025-09-25 12:22:33,249 - INFO - Connection accepted from ('127.0.0.1', 33398) -2025-09-25 12:22:33,249 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cf527edae4f230e16cc609598128c4d40e2cbb0f5dfcaf2b92c60acb.PubTator"} -2025-09-25 12:26:08,333 - INFO - Connection accepted from ('127.0.0.1', 41968) -2025-09-25 12:26:08,333 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9c886f5efd40493a9237aaeae50158cc686c36a179299b30d2fab21a.PubTator"} -2025-09-25 12:28:54,554 - INFO - Connection accepted from ('127.0.0.1', 37230) -2025-09-25 12:28:54,555 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "37ca0bfe8b236abd0738ddd73c7e1b471ca218809103c917e12b6a9c.PubTator"} -2025-09-25 12:31:56,942 - INFO - Connection accepted from ('127.0.0.1', 37828) -2025-09-25 12:31:56,942 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a8cc98b8d9f662efe9a460c2a8a69a49ce11621bc8e07c6dc8c137c3.PubTator"} -2025-09-25 12:35:16,845 - INFO - Connection accepted from ('127.0.0.1', 52602) -2025-09-25 12:35:16,846 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "99a52718b2b70e3baf3599c97342e8765d38c120d05b5c2b42899c80.PubTator"} -2025-09-25 12:38:05,113 - INFO - Connection accepted from ('127.0.0.1', 46406) -2025-09-25 12:38:05,113 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1e9f90f8eb4f4a77b33093fe2e93874f25cd1941fcadb9b8d0338a5e.PubTator"} -2025-09-25 12:40:25,174 - INFO - Connection accepted from ('127.0.0.1', 54352) -2025-09-25 12:40:25,174 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dc812159259aa6f682f667ad8eca95ae51eb6aabae205db2a3d06c0f.PubTator"} -2025-09-25 12:42:35,135 - INFO - Connection accepted from ('127.0.0.1', 42698) -2025-09-25 12:42:35,136 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "16c5e5d3ca95387e9b4f0e059ee2cd24d0041ccb4f8876426d4d312e.PubTator"} -2025-09-25 12:44:09,624 - INFO - Connection accepted from ('127.0.0.1', 34158) -2025-09-25 12:44:09,624 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0a22a91d43dab42b3d700708248a0603349e48ee53236848b7584930.PubTator"} -2025-09-25 12:47:05,316 - INFO - Connection accepted from ('127.0.0.1', 47118) -2025-09-25 12:47:05,316 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "97fa148e788eb2e0467c7205d067fa250aa7654af359b322015c7555.PubTator"} -2025-09-25 12:50:13,862 - INFO - Connection accepted from ('127.0.0.1', 56304) -2025-09-25 12:50:13,862 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d93e4c6c35f22d46f426a6b77cf102987b0312d0a3fb44fdb7d3f429.PubTator"} -2025-09-25 12:53:50,758 - INFO - Connection accepted from ('127.0.0.1', 38680) -2025-09-25 12:53:50,759 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "63ca51a7d34213e26976081c922792475eb0abf7382735703b097e59.PubTator"} -2025-09-25 12:56:43,267 - INFO - Connection accepted from ('127.0.0.1', 37816) -2025-09-25 12:56:43,267 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bfa8808dfc82d3e5c0c240a50a7034b4e7be1998e1a5ea3cfc0af237.PubTator"} -2025-09-25 12:58:33,290 - INFO - Connection accepted from ('127.0.0.1', 60584) -2025-09-25 12:58:33,291 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ac245204cda743653e71c16aedddca12d65369b7d9660d3ea594ebc4.PubTator"} -2025-09-25 13:01:50,494 - INFO - Connection accepted from ('127.0.0.1', 54940) -2025-09-25 13:01:50,494 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b767578bec59163524f54d8e757d5443b1ff13d4a97215bd3bc5efec.PubTator"} -2025-09-25 13:04:56,372 - INFO - Connection accepted from ('127.0.0.1', 58004) -2025-09-25 13:04:56,372 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f78416eb33cd0fb83a8fc4ade26ae6d729c265c99eaf15027a27dcbb.PubTator"} -2025-09-25 13:07:37,413 - INFO - Connection accepted from ('127.0.0.1', 36550) -2025-09-25 13:07:37,414 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cf541e246f95268ecea83ba1df38aa4140141eec06596da2703e0760.PubTator"} -2025-09-25 13:09:22,152 - INFO - Connection accepted from ('127.0.0.1', 40436) -2025-09-25 13:09:22,152 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0c88d1e3422f6a3de274e0e54eda8a9e5ee9de18acd855dbe7b4c609.PubTator"} -2025-09-25 13:12:55,866 - INFO - Connection accepted from ('127.0.0.1', 59008) -2025-09-25 13:12:55,866 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "693631ad5e84c355cc744ec8870c9ad63b77c25b2560a026403f8242.PubTator"} -2025-09-25 13:15:33,450 - INFO - Connection accepted from ('127.0.0.1', 43084) -2025-09-25 13:15:33,450 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f96521adbead876da69411df8cee32d216a59c3963202d5e8d42e28a.PubTator"} -2025-09-25 13:17:47,840 - INFO - Connection accepted from ('127.0.0.1', 59314) -2025-09-25 13:17:47,840 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "09f9a9d2837a9ee1dd3b930967dc402f16c4e5fe51c4c581629a4d17.PubTator"} -2025-09-25 13:19:45,905 - INFO - Connection accepted from ('127.0.0.1', 58616) -2025-09-25 13:19:45,905 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4ca9db0721c66c539cdf434af30a7f9ff81285a1e0f8904c3833f8ea.PubTator"} -2025-09-25 13:28:14,344 - INFO - Connection accepted from ('127.0.0.1', 38476) -2025-09-25 13:28:14,344 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "028edba2fdebaa2930c9807624f08c9a0af839a9219f75dea4231dae.PubTator"} -2025-09-25 13:33:30,643 - INFO - Connection accepted from ('127.0.0.1', 54124) -2025-09-25 13:33:30,643 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6bbd72452f996aa5145570eadbd15f6c6efa471690e3ac355f7c30d9.PubTator"} -2025-09-25 13:39:41,031 - INFO - Connection accepted from ('127.0.0.1', 36570) -2025-09-25 13:39:41,032 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1ed9d3f799cc736bfcee91381404bb0b9613b7fdd280ee76b2543733.PubTator"} -2025-09-25 13:46:53,643 - INFO - Connection accepted from ('127.0.0.1', 50636) -2025-09-25 13:46:53,643 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "758ceffdb35dc66b64141387d0d8caf3bd5b2bbabf4e3544066b7e38.PubTator"} -2025-09-25 13:49:49,678 - INFO - Connection accepted from ('127.0.0.1', 49286) -2025-09-25 13:49:49,678 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "03a9cdd8f43493d2b532d0871fcfb9490f613729792275ff3da94696.PubTator"} -2025-09-25 13:51:43,553 - INFO - Connection accepted from ('127.0.0.1', 36812) -2025-09-25 13:51:43,553 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ef7ffde39deb7270736733dc4636da8b810d8af7c81f23a029c370e6.PubTator"} -2025-09-25 13:54:29,620 - INFO - Connection accepted from ('127.0.0.1', 52474) -2025-09-25 13:54:29,621 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "be3c408eb7c8d0390c254bb4abd8d8d30cbb74704de579db7f5ec316.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-25 14:23:21,778 - INFO - Connection accepted from ('127.0.0.1', 32948) -2025-09-25 14:23:21,778 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3faa09f575f6ff729e877e7fc2f6b04dfbdf0cf36dc562e0153e1b7f.PubTator"} -2025-09-25 14:28:56,363 - INFO - Connection accepted from ('127.0.0.1', 40378) -2025-09-25 14:28:56,363 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1b7e2720810289025fb580fb72acb7ec9f4b6013c400f9cd2a71aa92.PubTator"} -2025-09-25 14:34:19,001 - INFO - Connection accepted from ('127.0.0.1', 38678) -2025-09-25 14:34:19,001 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "adc84f02baadc3f93adda48b8c9fb6644d45641040d43de0bb2c51d2.PubTator"} -2025-09-25 14:38:04,090 - INFO - Connection accepted from ('127.0.0.1', 48344) -2025-09-25 14:38:04,090 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d1b66d37afdd0b4e2a190845321759a63b55997fc29a58e9b7cb9cdc.PubTator"} -2025-09-25 14:44:11,444 - INFO - Connection accepted from ('127.0.0.1', 42810) -2025-09-25 14:44:11,444 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "bf496a70d5669cba642c4c4c76d75369a1f22b8f1408b1c57bb30c5f.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-25 17:15:57,650 - INFO - Connection accepted from ('127.0.0.1', 60926) -2025-09-25 17:15:57,651 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3c1b7290dd09d0a5707faa7da03525cc9b06ab056fb4039be9600a4f.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-25 17:21:22,899 - INFO - Connection accepted from ('127.0.0.1', 60060) -2025-09-25 17:21:22,900 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f201c21487f1585d8ea2898424af0ed51cbe637ff2085dec0ab2e82c.PubTator"} -2025-09-25 17:27:01,170 - INFO - Connection accepted from ('127.0.0.1', 43162) -2025-09-25 17:27:01,171 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "79c95ed9b3ccdfb1891308215cbf2df5561cd6be88b091447e1d7967.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 206, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-26 17:33:15,664 - INFO - Connection accepted from ('127.0.0.1', 40828) -2025-09-26 17:33:15,664 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "88e5636b8845355f4bdc4b5cdd9114ca871c1c204ad5ae62c0fc749f.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-26 17:37:13,152 - INFO - Connection accepted from ('127.0.0.1', 51086) -2025-09-26 17:37:13,153 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ff5dd16ba0a85786a1fb175203da80b23bd2161390d870d2506c6e5e.PubTator"} -2025-09-26 17:41:42,537 - INFO - Connection accepted from ('127.0.0.1', 58836) -2025-09-26 17:41:42,537 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "7e3f2c39c2fd66e17cb2fb717b3cdefcf372bb713e95144ec6083686.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 206, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-27 11:11:16,229 - INFO - Connection accepted from ('127.0.0.1', 41338) -2025-09-27 11:11:16,230 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0e004494fb1bf34a225727e6b878025b720e123f798c8977aa77bfb8.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-27 11:26:17,215 - INFO - Connection accepted from ('127.0.0.1', 45828) -2025-09-27 11:26:17,215 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9388076fdb8fad740dd87124e802ebb191a28f2c619132012525b0f0.PubTator"} -2025-09-27 11:29:52,921 - INFO - Connection accepted from ('127.0.0.1', 51434) -2025-09-27 11:29:52,921 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ccad25b8532bf9520083aa3053180779852682425be60067eb23dc87.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-27 11:40:26,483 - INFO - Connection accepted from ('127.0.0.1', 50840) -2025-09-27 11:40:26,483 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ec64407fc853fb1277271392c19a237b438498670f984ec801fdaeb6.PubTator"} -2025-09-27 11:46:24,593 - INFO - Connection accepted from ('127.0.0.1', 37822) -2025-09-27 11:46:24,594 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4a9b4079bf1b24e98b7eef04bc7035e11beef89862402eed409d7249.PubTator"} -2025-09-27 11:50:34,831 - INFO - Connection accepted from ('127.0.0.1', 55768) -2025-09-27 11:50:34,831 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ed7237d174b0152e51e05cc6e1b44178fb071dc30e1517107f81b164.PubTator"} -2025-09-27 11:56:05,206 - INFO - Connection accepted from ('127.0.0.1', 34588) -2025-09-27 11:56:05,207 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "aa0417ec60c80db81931be58b68a520cbddf566d885ce7831562f2ce.PubTator"} -2025-09-27 12:02:25,453 - INFO - Connection accepted from ('127.0.0.1', 39086) -2025-09-27 12:02:25,453 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "621fabed39d3c5f16576ac76b4164355e6eb732e79a434e295d8ae44.PubTator"} -2025-09-27 12:07:06,450 - INFO - Connection accepted from ('127.0.0.1', 36250) -2025-09-27 12:07:06,450 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cde4f6863ab9f2c5deb47b2cdf0f851aceb2b3f0635cef8dda017e92.PubTator"} -2025-09-27 12:13:27,766 - INFO - Connection accepted from ('127.0.0.1', 56140) -2025-09-27 12:13:27,767 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "da6e343c942dcd0a7e967b865a023c063d90d33e710a5b7accd1e51a.PubTator"} -2025-09-27 12:19:41,538 - INFO - Connection accepted from ('127.0.0.1', 57178) -2025-09-27 12:19:41,539 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b1aa7179947f5562d61ebad44f4faa1c276b4fe97ce299ec8df896a8.PubTator"} -2025-09-27 12:24:35,183 - INFO - Connection accepted from ('127.0.0.1', 45700) -2025-09-27 12:24:35,183 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1ac0eeb746dd1469b5b5576ae02f6a056d92467aa13eaceb1ca49a28.PubTator"} -2025-09-27 12:29:24,977 - INFO - Connection accepted from ('127.0.0.1', 45180) -2025-09-27 12:29:24,978 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "644de15f2398322340ab63953b652080b517e53457edc348c96e2b08.PubTator"} -2025-09-27 12:33:32,898 - INFO - Connection accepted from ('127.0.0.1', 57884) -2025-09-27 12:33:32,899 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ae9e71de011e72210246bc21fb5e8c8f1b9943b4492af3e970e69ec7.PubTator"} -2025-09-27 12:39:04,799 - INFO - Connection accepted from ('127.0.0.1', 32836) -2025-09-27 12:39:04,800 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9c220164c9184d529c42ef0a79f13b643108fd536916df9a99c9ac4f.PubTator"} -2025-09-27 12:44:02,231 - INFO - Connection accepted from ('127.0.0.1', 49876) -2025-09-27 12:44:02,231 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6a23d54a14f1533367307e71ca0f4cb85e2c221cbb3ce0584b099acc.PubTator"} -2025-09-27 12:49:37,000 - INFO - Connection accepted from ('127.0.0.1', 53676) -2025-09-27 12:49:37,000 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "23278c706400dbb032011f15f4dbf10570230e9ba3c353bacc357ece.PubTator"} -2025-09-27 12:54:24,151 - INFO - Connection accepted from ('127.0.0.1', 59552) -2025-09-27 12:54:24,152 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a98b8d6d4d34da098ab9bce4febb21547e162c561768909258532eb5.PubTator"} -2025-09-27 12:58:22,660 - INFO - Connection accepted from ('127.0.0.1', 35788) -2025-09-27 12:58:22,660 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "947b97a51ca23d0d971a4eef20db68c9b052c2f49f22ae81c0176097.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-28 10:19:48,347 - INFO - Connection accepted from ('127.0.0.1', 45728) -2025-09-28 10:19:48,347 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a146d3102f103ea887a6e1c31e9d974edd2c45ef5e06798a02ac0c52.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-28 10:44:47,766 - INFO - Connection accepted from ('127.0.0.1', 55056) -2025-09-28 10:44:47,766 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d07fd4e984b69f583a11a4f42f97506748df2519299e5bb25f42e9e8.PubTator"} -2025-09-28 10:51:04,101 - INFO - Connection accepted from ('127.0.0.1', 49886) -2025-09-28 10:51:04,102 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "528f10ba30ae86eecf1ac2d4c99b9670afc80c6b03344d06a7b254cc.PubTator"} -2025-09-28 10:56:01,781 - INFO - Connection accepted from ('127.0.0.1', 48110) -2025-09-28 10:56:01,781 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2d7099457ba68aa6ba454666d19b1690afd357b775241c2bc6d835b0.PubTator"} -2025-09-28 11:00:16,497 - INFO - Connection accepted from ('127.0.0.1', 33198) -2025-09-28 11:00:16,497 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0ca4d3d2a56739d04d66262c5510d2c6644f461ca461d1c71d15ceed.PubTator"} -2025-09-28 11:06:21,960 - INFO - Connection accepted from ('127.0.0.1', 54360) -2025-09-28 11:06:21,961 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a17248fe2c838d07fd13b407fa55ea1be16a029ba6d21844f6500ba9.PubTator"} -2025-09-28 11:11:57,065 - INFO - Connection accepted from ('127.0.0.1', 51896) -2025-09-28 11:11:57,066 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "651cdebe064fbb80752b960e6b0b3a49553d159862f3f10351e0f450.PubTator"} -2025-09-28 11:16:19,106 - INFO - Connection accepted from ('127.0.0.1', 37422) -2025-09-28 11:16:19,106 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "81fdb9b10d81c428898d881879d5d2e8625ed8da51909820604bbbf9.PubTator"} -2025-09-28 11:21:15,302 - INFO - Connection accepted from ('127.0.0.1', 40628) -2025-09-28 11:21:15,303 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b83d2398fb6e889dedcf984c3805d0d70ac7c0bfdb7e45ce3d6d815a.PubTator"} -2025-09-28 11:27:30,939 - INFO - Connection accepted from ('127.0.0.1', 39750) -2025-09-28 11:27:30,939 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ed73593e140c57a7e1403ef961dfedd220a8f4782a3643c9f4771189.PubTator"} -2025-09-28 11:32:55,553 - INFO - Connection accepted from ('127.0.0.1', 51452) -2025-09-28 11:32:55,553 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "da2fedb3d923bc996f4bb953da642819db4abe90dbdd5ff47332f77b.PubTator"} -2025-09-28 11:38:09,072 - INFO - Connection accepted from ('127.0.0.1', 33546) -2025-09-28 11:38:09,072 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8fe4e0606e5f0403b3bd0474a72fd3bc9fc5d137c4cbefc19ea2bd7f.PubTator"} -2025-09-28 11:42:57,064 - INFO - Connection accepted from ('127.0.0.1', 33960) -2025-09-28 11:42:57,065 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "754e56c01bd4673180b62b2d5e841fbf659a4749e2655d3ab2ee3e6a.PubTator"} -2025-09-28 11:46:32,641 - INFO - Connection accepted from ('127.0.0.1', 36422) -2025-09-28 11:46:32,642 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "628155fa7f0dbfa6db550c9ca7f132ea18c9ffa7c2dfd55dc4a1e61b.PubTator"} -2025-09-28 11:50:47,068 - INFO - Connection accepted from ('127.0.0.1', 34506) -2025-09-28 11:50:47,069 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "de53f648840e59ab76bb1763fc9395344acf912c9631d524bc925a85.PubTator"} -2025-09-28 11:55:21,522 - INFO - Connection accepted from ('127.0.0.1', 49236) -2025-09-28 11:55:21,522 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f3b708cf987e256b9bc781bbc9e5841c24d3a7596278b29800355645.PubTator"} -2025-09-28 12:00:08,609 - INFO - Connection accepted from ('127.0.0.1', 39068) -2025-09-28 12:00:08,609 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "4bb92b0ac587e7111550bff58dff84e4f7168e086e4b4b83bd8dc3ca.PubTator"} -2025-09-28 12:03:55,445 - INFO - Connection accepted from ('127.0.0.1', 37814) -2025-09-28 12:03:55,445 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f066543dc38bed0dc227b7467a029c103880867e7d69eed91b7f8226.PubTator"} -2025-09-28 12:09:39,149 - INFO - Connection accepted from ('127.0.0.1', 56884) -2025-09-28 12:09:39,149 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "85cdea09a57885c5cfc0c0684cd0b7a0bc760af57556a07fc92992e6.PubTator"} -2025-09-28 12:13:38,785 - INFO - Connection accepted from ('127.0.0.1', 45822) -2025-09-28 12:13:38,786 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1bc9a5337c5911101c0084da1de1a07ddeef2bc69b75c78535aa8a84.PubTator"} -2025-09-28 12:17:37,187 - INFO - Connection accepted from ('127.0.0.1', 52130) -2025-09-28 12:17:37,187 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d1bcde4444b3d7f57513495345861abdfa8277d99e6019e908289a22.PubTator"} -2025-09-28 12:21:36,406 - INFO - Connection accepted from ('127.0.0.1', 60418) -2025-09-28 12:21:36,406 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "22aeeb29b1938db37952d3e9daad73489acee36777b4fa9b6cae3071.PubTator"} -2025-09-28 12:25:53,131 - INFO - Connection accepted from ('127.0.0.1', 43236) -2025-09-28 12:25:53,132 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "afc90b1b3fdbffbfebc5fa5444bb4ecd75876abe52f30bd227e9f21b.PubTator"} -2025-09-28 12:28:28,245 - INFO - Connection accepted from ('127.0.0.1', 37750) -2025-09-28 12:28:28,245 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6bc71bd40dff82f542da4835c58ded27e9addd81534a6302ebbe6f88.PubTator"} -2025-09-28 12:35:06,669 - INFO - Connection accepted from ('127.0.0.1', 45336) -2025-09-28 12:35:06,669 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "14b6a869b7eeaeb651581cb4ee83b45af0394865787d83d8d4ca831c.PubTator"} -2025-09-28 12:41:03,585 - INFO - Connection accepted from ('127.0.0.1', 57040) -2025-09-28 12:41:03,585 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "37388ade13bb3d8ab015df00c0198898bc7e1c5a4708ecb7bd2c0aec.PubTator"} -2025-09-28 12:47:26,853 - INFO - Connection accepted from ('127.0.0.1', 35090) -2025-09-28 12:47:26,853 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d6fe76e398c6cc8a4567489c4e1042bdeb36c2e32ee512ad4b6c66b6.PubTator"} -2025-09-28 12:51:12,428 - INFO - Connection accepted from ('127.0.0.1', 51158) -2025-09-28 12:51:12,428 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0a280cf3f91c1c844a3ae1909b991cd059aeb95cd56a87ea8d979bec.PubTator"} -2025-09-28 13:01:52,010 - INFO - Connection accepted from ('127.0.0.1', 51570) -2025-09-28 13:01:52,010 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "90d5e8c2b2f6c48b0311b7bc95ce38f247b3ede53541010e3a810c8d.PubTator"} -2025-09-28 13:07:43,063 - INFO - Connection accepted from ('127.0.0.1', 52238) -2025-09-28 13:07:43,063 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d349adb0c0b14f91a3565a93a56d4cdc1b1cafd904483480e0076448.PubTator"} -2025-09-28 13:12:31,475 - INFO - Connection accepted from ('127.0.0.1', 52986) -2025-09-28 13:12:31,475 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9ad69a6fed51ec56c51f2822189f97e963213ef6b685b11085c13142.PubTator"} -2025-09-28 13:17:01,100 - INFO - Connection accepted from ('127.0.0.1', 55116) -2025-09-28 13:17:01,100 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "feaeb2b7d4eb097e15015599bfd0a707af6071a55de297f804952287.PubTator"} -2025-09-28 13:21:03,567 - INFO - Connection accepted from ('127.0.0.1', 50774) -2025-09-28 13:21:03,568 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "776b4d47a75f3b7428d50d588f1e770e77c2408c6e14f5f6ebf8b871.PubTator"} -2025-09-28 13:25:03,733 - INFO - Connection accepted from ('127.0.0.1', 34578) -2025-09-28 13:25:03,733 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "88404ff27b36fd5ea8592dccfe9af3cfc2c450c97c519ebcf985b58c.PubTator"} -2025-09-28 13:29:36,276 - INFO - Connection accepted from ('127.0.0.1', 40526) -2025-09-28 13:29:36,276 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a3b4c161a1ab15500a70e74353505ebfcf7f4d378fa8924043893ab6.PubTator"} -2025-09-28 13:35:30,932 - INFO - Connection accepted from ('127.0.0.1', 32932) -2025-09-28 13:35:30,932 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d156e2f9adf82d68470c2aec42ab216174f6f8ceadf455f0721ec9bb.PubTator"} -2025-09-28 13:41:02,264 - INFO - Connection accepted from ('127.0.0.1', 37462) -2025-09-28 13:41:02,264 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "762ed75e1ac78ed1d5c390eadc05cac23c8982934bd4921a77a5088c.PubTator"} -2025-09-28 13:46:04,882 - INFO - Connection accepted from ('127.0.0.1', 34192) -2025-09-28 13:46:04,882 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "825e174644353061b2f654db662abee1e0dd9a40bb5ad5ed826dd741.PubTator"} -2025-09-28 13:50:53,623 - INFO - Connection accepted from ('127.0.0.1', 58194) -2025-09-28 13:50:53,623 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f936e50f5b6a8d92cf388f34b76735198fb8afa8ab84c22a451254e6.PubTator"} -2025-09-28 13:54:41,625 - INFO - Connection accepted from ('127.0.0.1', 45168) -2025-09-28 13:54:41,626 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c8f8dd56184287ad869344b62afcac15e5a262c1f96c9b4daf60e23d.PubTator"} -2025-09-28 13:59:29,033 - INFO - Connection accepted from ('127.0.0.1', 36586) -2025-09-28 13:59:29,033 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "51abd5a22878659843299924fcdb36c98802ffef690b7daf564d599c.PubTator"} -2025-09-28 14:03:50,426 - INFO - Connection accepted from ('127.0.0.1', 49906) -2025-09-28 14:03:50,426 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "48a4973a2688c8d464500235e9fde703d7d035caf4302b18784c7079.PubTator"} -2025-09-28 14:07:45,676 - INFO - Connection accepted from ('127.0.0.1', 38624) -2025-09-28 14:07:45,677 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b3c12ebf57fd39c8d818d8d14a4d2d5491f600f53c0c555080ae4a2b.PubTator"} -2025-09-28 14:11:27,089 - INFO - Connection accepted from ('127.0.0.1', 48656) -2025-09-28 14:11:27,089 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d1457ef366a4d954210e8c6402bfef74d5e0b9e9d979dd66e5b4faf2.PubTator"} -2025-09-28 14:14:52,506 - INFO - Connection accepted from ('127.0.0.1', 43586) -2025-09-28 14:14:52,506 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b58ba0047bdcbcc3a93c562d637618ee08411706aab9ac48503a2cef.PubTator"} -2025-09-28 14:18:59,903 - INFO - Connection accepted from ('127.0.0.1', 45340) -2025-09-28 14:18:59,903 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "99340d290e8b0c3a7fe7cd8999228317b9a123d9d456e6c3c04df951.PubTator"} -2025-09-28 14:23:42,904 - INFO - Connection accepted from ('127.0.0.1', 58064) -2025-09-28 14:23:42,905 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3b7506c9512001a78cbc16daa8b0ba45e3cc688e78b3769073c569d0.PubTator"} -2025-09-28 14:29:02,466 - INFO - Connection accepted from ('127.0.0.1', 39384) -2025-09-28 14:29:02,466 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f2ca61aa395e347ca334b33b08ae5dd234f3ae4d543817fea9f00d1f.PubTator"} -2025-09-28 14:33:14,406 - INFO - Connection accepted from ('127.0.0.1', 41856) -2025-09-28 14:33:14,406 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "376453ac425edf498c6d0cdd9ab767ff759bbcede8ed57bbe6988a8d.PubTator"} -2025-09-28 14:36:05,107 - INFO - Connection accepted from ('127.0.0.1', 39108) -2025-09-28 14:36:05,107 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "02a01b5a213384ccc526c83fb963f008a855c994addcb835666a4d55.PubTator"} -2025-09-28 14:39:38,090 - INFO - Connection accepted from ('127.0.0.1', 32868) -2025-09-28 14:39:38,090 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "18f19070123416bef42fb63e32d9d992e012b2d878038fe073010f1f.PubTator"} -2025-09-28 14:43:42,066 - INFO - Connection accepted from ('127.0.0.1', 47972) -2025-09-28 14:43:42,066 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "6f5d6bf9b256ead4943ca5a5ab47d9fa274cfca68a3403c3f8c16e21.PubTator"} -2025-09-28 14:47:08,912 - INFO - Connection accepted from ('127.0.0.1', 46408) -2025-09-28 14:47:08,913 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cc0534da02a42f433a8c42a4d9aae2defe90a96db062f18eafc85c1e.PubTator"} -2025-09-28 14:50:09,973 - INFO - Connection accepted from ('127.0.0.1', 37630) -2025-09-28 14:50:09,973 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d22f7a107a1edd6955a0f77e6bd11b7228df784f6c43475983ea6fa4.PubTator"} -2025-09-28 14:54:59,555 - INFO - Connection accepted from ('127.0.0.1', 39792) -2025-09-28 14:54:59,555 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "0d3fc22c53707777ea937b5d90a2917ea34836f7b7d6bac303c8a8da.PubTator"} -2025-09-28 15:02:31,558 - INFO - Connection accepted from ('127.0.0.1', 53662) -2025-09-28 15:02:31,559 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2f09cf446ef0bc2f39d164df30563c2b41c0b50cec51b6410b4f4fc2.PubTator"} -2025-09-28 15:06:36,556 - INFO - Connection accepted from ('127.0.0.1', 41618) -2025-09-28 15:06:36,556 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b51515243017e94dd0d6cef83ca993f7ed5ff595c3dca87561c60d80.PubTator"} -2025-09-28 15:20:14,890 - INFO - Connection accepted from ('127.0.0.1', 33540) -2025-09-28 15:20:14,890 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "518cce2213aaec5ac066f04becac2479ebf98213c6ded41a39265f43.PubTator"} -2025-09-28 15:25:15,638 - INFO - Connection accepted from ('127.0.0.1', 55480) -2025-09-28 15:25:15,639 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "dc705c128481508dbe2d571e3d7ce16ed3546d6623b62fcd9f939c95.PubTator"} -2025-09-28 15:34:47,043 - INFO - Connection accepted from ('127.0.0.1', 33506) -2025-09-28 15:34:47,043 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f888ce80d16463adbd75e8947e1cf1c579a9e5b500cd67ea25dc791a.PubTator"} -2025-09-28 15:45:16,165 - INFO - Connection accepted from ('127.0.0.1', 37074) -2025-09-28 15:45:16,165 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f34dd2edcb5b78e147c9eb6f750c15273c0ca1e601b08abc812e74fb.PubTator"} -2025-09-28 15:50:36,640 - INFO - Connection accepted from ('127.0.0.1', 50568) -2025-09-28 15:50:36,640 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "716dd8fd7c730de44efe1272f10ab1cadd463883f410d96b077e865d.PubTator"} -2025-09-28 15:54:26,736 - INFO - Connection accepted from ('127.0.0.1', 33884) -2025-09-28 15:54:26,737 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "62f4e3688b82b8cfa821e83e3f22c7badbd6271e1ae0bca44073faa1.PubTator"} -2025-09-28 15:59:29,200 - INFO - Connection accepted from ('127.0.0.1', 58648) -2025-09-28 15:59:29,201 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "56aa36ea4ace3a11dc7dc5b0d91081c473df6102289f7651b72d633d.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-09-30 11:23:42,138 - INFO - Connection accepted from ('127.0.0.1', 53642) -2025-09-30 11:23:42,139 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9dc696dc10fc3c02e4256ff513d6670b4953195ec4dc0e88ddf03207.PubTator"} -Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. -2025-09-30 11:31:01,868 - INFO - Connection accepted from ('127.0.0.1', 51264) -2025-09-30 11:31:01,869 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "97e1e36f1eb8d35ebaa56b16d73c4409466b7957eed67d74708fa398.PubTator"} -2025-09-30 11:42:27,703 - INFO - Connection accepted from ('127.0.0.1', 48060) -2025-09-30 11:42:27,704 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c10f3314102693f88ad5ad6ff4ec2f2fcbf9bb43fb8f1075a40deae1.PubTator"} -2025-09-30 11:48:53,542 - INFO - Connection accepted from ('127.0.0.1', 55496) -2025-09-30 11:48:53,543 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b80510d88d70dcbe9bd50bcca01e312c31e7f70ea3264ad4244cbe8c.PubTator"} -2025-09-30 11:57:57,116 - INFO - Connection accepted from ('127.0.0.1', 53562) -2025-09-30 11:57:57,116 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "350775959f4563bfc0a8eb79f948d2efa7098e25f24405cdb9cb9d38.PubTator"} -2025-09-30 12:07:20,991 - INFO - Connection accepted from ('127.0.0.1', 33176) -2025-09-30 12:07:20,991 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cb2ff4066fc6bb759927e910dfc8953328a02138d2e5f328466e1f0d.PubTator"} -2025-09-30 12:13:31,876 - INFO - Connection accepted from ('127.0.0.1', 45380) -2025-09-30 12:13:31,876 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "909add6b51a39ebba55fc3eee2ac1395530da0d58f6d08f1060c3058.PubTator"} -2025-09-30 12:19:56,735 - INFO - Connection accepted from ('127.0.0.1', 43646) -2025-09-30 12:19:56,735 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "c6bae1422d4144a2f699a62c969eb40307e995e7615dc28632eced04.PubTator"} -2025-09-30 12:27:00,200 - INFO - Connection accepted from ('127.0.0.1', 50310) -2025-09-30 12:27:00,200 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "cc4ed7da2040e96e30c6f0624ac8089a9b90866870c93d4e1af415ae.PubTator"} -2025-09-30 12:35:51,229 - INFO - Connection accepted from ('127.0.0.1', 45324) -2025-09-30 12:35:51,230 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "d5e8986ebc16918ca83210fabe5200e355341ae02c50dd563db52918.PubTator"} -2025-09-30 12:43:48,147 - INFO - Connection accepted from ('127.0.0.1', 58112) -2025-09-30 12:43:48,148 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "3e72724ded443bcc27745e262933293f3318e59d6d733d22eb0713a8.PubTator"} -2025-09-30 12:51:49,376 - INFO - Connection accepted from ('127.0.0.1', 33602) -2025-09-30 12:51:49,376 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "ce2b668c80b049e1422dd490a3fa89d29f788ee9558514d78e47030d.PubTator"} -2025-09-30 12:58:45,927 - INFO - Connection accepted from ('127.0.0.1', 35938) -2025-09-30 12:58:45,927 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "52e48637b93ee0c8ccb408fc8bb518c869d2baed085ad687a9c3afc3.PubTator"} -2025-09-30 13:04:51,535 - INFO - Connection accepted from ('127.0.0.1', 39448) -2025-09-30 13:04:51,535 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "5f179b2b5403ba934d8555354a7183b8f648725e72fbe2057494cd93.PubTator"} -2025-09-30 13:09:38,632 - INFO - Connection accepted from ('127.0.0.1', 55376) -2025-09-30 13:09:38,633 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "808280b1fef87ac3268b5564aa9ffb8d285c72a01adb37999ccf08b3.PubTator"} -2025-09-30 13:14:47,891 - INFO - Connection accepted from ('127.0.0.1', 33224) -2025-09-30 13:14:47,891 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "f99960800e0b273cc9f59eb2f1df9b070b307f867c27a2fca3d3807e.PubTator"} -2025-09-30 13:18:19,146 - INFO - Connection accepted from ('127.0.0.1', 49952) -2025-09-30 13:18:19,147 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "2a15b7338089a58b0411cb03e83320ffdf968386a228ce7efbd79baa.PubTator"} -2025-09-30 13:25:43,938 - INFO - Connection accepted from ('127.0.0.1', 45932) -2025-09-30 13:25:43,939 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "15fe96f1a5453a5784e1f1d0eec7d030aff94b59bf979d36ebae2205.PubTator"} -2025-09-30 13:33:16,892 - INFO - Connection accepted from ('127.0.0.1', 49240) -2025-09-30 13:33:16,892 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "254cdf2755df48eea22854aa76ae64b9e263454c27514962ee6858cc.PubTator"} -2025-09-30 13:41:31,260 - INFO - Connection accepted from ('127.0.0.1', 43262) -2025-09-30 13:41:31,261 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "a5fccd4b4a4cfcb6bb0a1474206b553e0999e2a6f2bbb9b6f5f6d4cb.PubTator"} -2025-09-30 13:46:28,984 - INFO - Connection accepted from ('127.0.0.1', 42934) -2025-09-30 13:46:28,984 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "8e83f0a7669c64f74f096948376634a95298e9fbdb1ceefcc4e6bf7f.PubTator"} -2025-09-30 13:54:14,968 - INFO - Connection accepted from ('127.0.0.1', 34568) -2025-09-30 13:54:14,968 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "fd02acb9f7dde07cf29631c1dfd120fca454fc3c8da580be45f887fb.PubTator"} -2025-09-30 14:00:10,248 - INFO - Connection accepted from ('127.0.0.1', 37252) -2025-09-30 14:00:10,248 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "974022e0e84f62b9466791d7fdf1e7de01194039f4982dee86c5a38d.PubTator"} -2025-09-30 14:06:54,481 - INFO - Connection accepted from ('127.0.0.1', 50352) -2025-09-30 14:06:54,481 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "1f7dc8f7d5b1b4a6336a68af0a7dc77706a7381e6c25e7865be238ed.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-10-04 12:33:59,063 - INFO - Connection accepted from ('127.0.0.1', 36990) -2025-10-04 12:33:59,064 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-10-04 12:35:09,664 - INFO - Connection accepted from ('127.0.0.1', 39510) -2025-10-04 12:35:09,664 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "9e5db371e5181462e04aa9ff614e8855ccb7a99eef016ccb87033016.PubTator"} -Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation. -2025-10-04 12:35:26,092 - INFO - Connection accepted from ('127.0.0.1', 44718) -2025-10-04 12:35:26,092 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "427659203dcf0a0c1fe3ef6117d0ddcd0a1149f52a519a618d5b363c.PubTator"} - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-10-04 12:42:07,237 - INFO - Connection accepted from ('127.0.0.1', 47446) -2025-10-04 12:42:07,238 - ERROR - An error occurred: unpack requires a buffer of 2 bytes - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use - Fetching 10 files: 0%| | 0/10 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/gner_server.py", line 177, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -2025-10-04 12:43:08,048 - INFO - Connection accepted from ('127.0.0.1', 52792) -2025-10-04 12:43:08,048 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "b29289e71b2cb5235f2a632c3d898362e27afe1519f6e99c6b1f76ef.PubTator"} -2025-10-04 12:43:16,100 - INFO - Connection accepted from ('127.0.0.1', 46382) -2025-10-04 12:43:16,101 - INFO - Received data: {"biomedner_home": "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser", "inputfile": "981bc74e79c14e76efe4e8f05c270a7347b2dbaa05aaee8c2cdbe2d1.PubTator"} diff --git a/source/Parser/logs/nohup_multi_ner.out b/source/Parser/logs/nohup_multi_ner.out deleted file mode 100644 index a64be470..00000000 --- a/source/Parser/logs/nohup_multi_ner.out +++ /dev/null @@ -1,5747 +0,0 @@ -/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py:129: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.) - return torch._C._cuda_getDeviceCount() > 0 -BioMedNER init_t 29.402 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 35966) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.669 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.610 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 45884) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.859 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.568 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 42404) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.614 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.569 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 41016) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.588 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.575 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 56102) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.709 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.588 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 48200) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.661 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.557 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36736) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.591 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.584 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47216) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.595 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.570 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 43336) - 0it [00:00, ?it/s] 1it [00:00, 277.49it/s] - Prediction: 0%| | 0/1 [00:00 0 -/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py:129: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:108.) - return torch._C._cuda_getDeviceCount() > 0 -BioMedNER init_t 0.587 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 40404) - 0it [00:00, ?it/s] 1it [00:00, 314.77it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.655 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.574 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 43746) - 0it [00:00, ?it/s] 1it [00:00, 293.29it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.553 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.607 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 56030) - 0it [00:00, ?it/s] 1it [00:00, 280.82it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.635 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.585 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 41308) - 0it [00:00, ?it/s] 1it [00:00, 251.53it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.592 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.577 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47960) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.694 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 6.985 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47844) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.696 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.662 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 59032) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.741 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.699 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 50936) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.726 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.672 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36716) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.801 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.655 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 37582) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.739 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.679 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 42482) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.742 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.665 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 33430) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.814 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.659 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54618) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.698 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py:182: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:109.) - return torch._C._cuda_getDeviceCount() > 0 -BioMedNER init_t 7.251 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54154) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.674 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.603 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 37958) - 0it [00:00, ?it/s] 1it [00:00, 39.05it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.695 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.627 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 46032) - 0it [00:00, ?it/s] 1it [00:00, 287.18it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.690 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.631 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 60866) - 0it [00:00, ?it/s] 1it [00:00, 269.75it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.724 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.632 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52600) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.655 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.625 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 42170) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.675 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.595 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38802) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.720 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.655 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 39294) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.688 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.609 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 34964) - 0it [00:00, ?it/s] 1it [00:00, 276.47it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -Traceback (most recent call last): - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 421, in _lazy_init - queued_call() - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 269, in _check_capability - capability = get_device_capability(d) - ^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 598, in get_device_capability - prop = get_device_properties(device) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 618, in get_device_properties - return _get_device_properties(device) # type: ignore[name-defined] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -RuntimeError: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 115, in - mt_ner = BioMedNER(args) - ^^^^^^^^^^^^^^^ - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 574, in __init__ - self.model.to(self.device) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/transformers/modeling_utils.py", line 4276, in to - return super().to(*args, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1371, in to - return self._apply(convert) - ^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 957, in _apply - param_applied = fn(param) - ^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1357, in convert - return t.to( - ^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 427, in _lazy_init - raise DeferredCudaCallError(msg) from e -torch.cuda.DeferredCudaCallError: CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -CUDA call was originally invoked at: - - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 9, in - from biomedner_init import BioMedNER - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 10, in - import torch - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/__init__.py", line 2087, in - _C._initExtension(_manager_path()) - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 356, in - _lazy_call(_check_capability) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 353, in _lazy_call - _queued_calls.append((callable, traceback.format_stack())) - -Traceback (most recent call last): - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 421, in _lazy_init - queued_call() - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 269, in _check_capability - capability = get_device_capability(d) - ^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 598, in get_device_capability - prop = get_device_properties(device) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 618, in get_device_properties - return _get_device_properties(device) # type: ignore[name-defined] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -RuntimeError: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 115, in - mt_ner = BioMedNER(args) - ^^^^^^^^^^^^^^^ - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 574, in __init__ - self.model.to(self.device) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/transformers/modeling_utils.py", line 4276, in to - return super().to(*args, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1371, in to - return self._apply(convert) - ^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 957, in _apply - param_applied = fn(param) - ^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1357, in convert - return t.to( - ^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 427, in _lazy_init - raise DeferredCudaCallError(msg) from e -torch.cuda.DeferredCudaCallError: CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -CUDA call was originally invoked at: - - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 9, in - from biomedner_init import BioMedNER - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 10, in - import torch - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/__init__.py", line 2087, in - _C._initExtension(_manager_path()) - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 356, in - _lazy_call(_check_capability) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 353, in _lazy_call - _queued_calls.append((callable, traceback.format_stack())) - -Traceback (most recent call last): - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 421, in _lazy_init - queued_call() - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 269, in _check_capability - capability = get_device_capability(d) - ^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 598, in get_device_capability - prop = get_device_properties(device) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 618, in get_device_properties - return _get_device_properties(device) # type: ignore[name-defined] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -RuntimeError: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 115, in - mt_ner = BioMedNER(args) - ^^^^^^^^^^^^^^^ - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 574, in __init__ - self.model.to(self.device) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/transformers/modeling_utils.py", line 4276, in to - return super().to(*args, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1371, in to - return self._apply(convert) - ^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 957, in _apply - param_applied = fn(param) - ^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1357, in convert - return t.to( - ^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 427, in _lazy_init - raise DeferredCudaCallError(msg) from e -torch.cuda.DeferredCudaCallError: CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -CUDA call was originally invoked at: - - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 9, in - from biomedner_init import BioMedNER - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 10, in - import torch - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/__init__.py", line 2087, in - _C._initExtension(_manager_path()) - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 356, in - _lazy_call(_check_capability) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 353, in _lazy_call - _queued_calls.append((callable, traceback.format_stack())) - -Traceback (most recent call last): - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 421, in _lazy_init - queued_call() - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 269, in _check_capability - capability = get_device_capability(d) - ^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 598, in get_device_capability - prop = get_device_properties(device) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 618, in get_device_properties - return _get_device_properties(device) # type: ignore[name-defined] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -RuntimeError: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -The above exception was the direct cause of the following exception: - -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 115, in - mt_ner = BioMedNER(args) - ^^^^^^^^^^^^^^^ - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 574, in __init__ - self.model.to(self.device) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/transformers/modeling_utils.py", line 4276, in to - return super().to(*args, **kwargs) - ^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1371, in to - return self._apply(convert) - ^^^^^^^^^^^^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 930, in _apply - module._apply(fn) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 957, in _apply - param_applied = fn(param) - ^^^^^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/nn/modules/module.py", line 1357, in convert - return t.to( - ^^^^^ - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 427, in _lazy_init - raise DeferredCudaCallError(msg) from e -torch.cuda.DeferredCudaCallError: CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "/pytorch/aten/src/ATen/cuda/CUDAContext.cpp":52, please report a bug to PyTorch. device=1, num_gpus=1 - -CUDA call was originally invoked at: - - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 9, in - from biomedner_init import BioMedNER - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 10, in - import torch - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/__init__.py", line 2087, in - _C._initExtension(_manager_path()) - File "", line 1178, in _find_and_load - File "", line 1149, in _find_and_load_unlocked - File "", line 690, in _load_unlocked - File "", line 940, in exec_module - File "", line 241, in _call_with_frames_removed - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 356, in - _lazy_call(_check_capability) - File "/home/mabdallah/.conda/envs/trialmatchai/lib/python3.11/site-packages/torch/cuda/__init__.py", line 353, in _lazy_call - _queued_calls.append((callable, traceback.format_stack())) - -BioMedNER init_t 0.242 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 7.317 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 58510) - 0it [00:00, ?it/s] 1it [00:00, 19.72it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.764 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.683 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.603 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 56866) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.685 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.634 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 40636) - 0it [00:00, ?it/s] 1it [00:00, 269.31it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.653 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.631 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55970) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.647 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.633 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 57358) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.673 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.827 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 48700) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.700 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.592 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 53558) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.643 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.699 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.615 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 35666) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.725 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.618 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 41872) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.699 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.616 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 59346) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.661 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.672 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.605 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 34754) - 0it [00:00, ?it/s] 1it [00:00, 242.38it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.588 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.656 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.695 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.607 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 34186) - 0it [00:00, ?it/s] 1it [00:00, 45.21it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.654 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.659 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 46644) - 0it [00:00, ?it/s] 1it [00:00, 38.32it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.673 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.648 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38736) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.682 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 7.581 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55662) - 0it [00:00, ?it/s] 1it [00:00, 262.60it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.600 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36148) - 0it [00:00, ?it/s] 1it [00:00, 254.39it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.670 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 58876) - 0it [00:00, ?it/s] 1it [00:00, 288.45it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.880 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.647 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 53574) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.655 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.671 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 59480) - 0it [00:00, ?it/s] 1it [00:00, 260.69it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.734 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.691 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.617 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 40534) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.677 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -Response sent for 61d5392634e0658327dab1a020c904645ec31ef6fbb49b058ee86cdc.PubTator -Connected by ('127.0.0.1', 49444) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.674 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.632 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 45120) - 0it [00:00, ?it/s] 1it [00:00, 248.26it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.686 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.617 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54440) - 0it [00:00, ?it/s] 1it [00:00, 302.01it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.701 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.670 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.713 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.635 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55824) - 0it [00:00, ?it/s] 1it [00:00, 258.81it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.685 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.658 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52026) - 0it [00:00, ?it/s] 1it [00:00, 253.88it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.674 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.630 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 49418) - 0it [00:00, ?it/s] 1it [00:00, 289.18it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.699 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.617 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38980) - 0it [00:00, ?it/s] 1it [00:00, 271.30it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.716 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.660 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52322) - 0it [00:00, ?it/s] 1it [00:00, 270.16it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.599 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55710) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.693 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.642 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 51514) - 0it [00:00, ?it/s] 1it [00:00, 300.39it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.645 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.600 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52062) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.661 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.664 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 58326) - 0it [00:00, ?it/s] 1it [00:00, 27.60it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.664 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.676 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.761 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55874) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.678 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.664 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 57998) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.679 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.672 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 53008) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.630 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.621 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.638 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36312) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.624 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.621 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 60350) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.669 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.679 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47356) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.623 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.677 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 57180) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.670 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.660 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 49116) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.651 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.687 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 50804) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.899 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.631 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 51960) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.660 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.629 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 50042) - 0it [00:00, ?it/s] 1it [00:00, 234.52it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 6.950 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54262) - 0it [00:00, ?it/s] 1it [00:00, 40.99it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.589 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.640 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 37384) - 0it [00:00, ?it/s] 1it [00:00, 303.85it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.654 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.641 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55558) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.646 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.629 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 45134) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.643 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.583 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54608) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.591 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.601 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 37706) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.734 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.608 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 35874) - 0it [00:00, ?it/s] 1it [00:00, 44.39it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.620 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.606 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.632 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.609 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 40950) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.659 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.618 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 59594) - 0it [00:00, ?it/s] 1it [00:00, 272.66it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.630 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.626 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 35992) - 0it [00:00, ?it/s] 1it [00:00, 249.45it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.636 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.679 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 51178) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.610 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.685 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 51420) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.660 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.662 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38132) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 1.084 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36706) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.651 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.671 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55340) - 0it [00:00, ?it/s] 1it [00:00, 277.35it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.621 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.655 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 56258) - 0it [00:00, ?it/s] 1it [00:00, 32.02it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.625 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.645 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38912) - 0it [00:00, ?it/s] 1it [00:00, 245.11it/s] - Prediction: 0%| | 0/1 [00:00H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 1.136 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.689 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 50952) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 1.155 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -Response sent for 69da3d5c9756484561d38f6b18b486efb3ef03abff2cdca8d4db0470.PubTator -Connected by ('127.0.0.1', 44940) - 0it [00:00, ?it/s] 1it [00:00, 480.89it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -Response sent for 1501995ce586ffd0c7522df67e52af96f151f639f40a785a87e4a1ab.PubTator -Connected by ('127.0.0.1', 44138) - 0it [00:00, ?it/s] 1it [00:00, 475.22it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.599 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 57780) - 0it [00:00, ?it/s] 1it [00:00, 301.40it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.626 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.743 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 46162) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.623 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.641 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 51732) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.592 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.651 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.676 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55168) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.599 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.618 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.705 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 50706) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.619 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.678 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47454) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.605 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.669 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36056) - 0it [00:00, ?it/s] 1it [00:00, 167.10it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.607 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.761 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 50442) - 0it [00:00, ?it/s] 1it [00:00, 254.32it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.657 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.675 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 48094) - 0it [00:00, ?it/s] 1it [00:00, 257.18it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.688 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.706 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 39282) - 0it [00:00, ?it/s] 1it [00:00, 291.11it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.607 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.682 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 44094) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.621 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.637 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 44206) - 0it [00:00, ?it/s] 1it [00:00, 45.36it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.618 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.621 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 60840) - 0it [00:00, ?it/s] 1it [00:00, 211.71it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.615 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.731 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47788) - 0it [00:00, ?it/s] 1it [00:00, 273.73it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.641 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.673 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52268) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.600 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.649 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54538) - 0it [00:00, ?it/s] 1it [00:00, 50.53it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.614 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.654 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 55100) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.613 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.613 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.656 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 43618) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.643 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.609 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 49372) - 0it [00:00, ?it/s] 1it [00:00, 49.29it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.644 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.709 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 51396) - 0it [00:00, ?it/s] 1it [00:00, 285.87it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.616 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.659 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 33552) - 0it [00:00, ?it/s] 1it [00:00, 314.63it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.632 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.669 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36438) - 0it [00:00, ?it/s] 1it [00:00, 294.92it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.652 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.660 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 58114) - 0it [00:00, ?it/s] 1it [00:00, 76.53it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.617 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.626 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 51132) - 0it [00:00, ?it/s] 1it [00:00, 45.80it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.617 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.630 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 45558) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.620 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.654 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 57234) - 0it [00:00, ?it/s] 1it [00:00, 293.33it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.660 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.640 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 33816) - 0it [00:00, ?it/s] 1it [00:00, 278.01it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.668 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.664 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 9, in - from biomedner_init import BioMedNER - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_init.py", line 10, in - import torch -ModuleNotFoundError: No module named 'torch' -BioMedNER init_t 0.685 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54500) - 0it [00:00, ?it/s] 1it [00:00, 281.82it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.812 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.600 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 57122) - 0it [00:00, ?it/s] 1it [00:00, 117.55it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.618 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.668 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 58328) - 0it [00:00, ?it/s] 1it [00:00, 263.94it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.584 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.669 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 33454) - 0it [00:00, ?it/s] 1it [00:00, 280.22it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.595 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.726 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36446) - 0it [00:00, ?it/s] 1it [00:00, 38.11it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.634 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.670 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 32980) - 0it [00:00, ?it/s] 1it [00:00, 99.35it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.611 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.637 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38790) - 0it [00:00, ?it/s] 1it [00:00, 322.22it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.615 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.693 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 49014) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.648 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.671 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47712) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.617 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.663 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54922) - 0it [00:00, ?it/s] 1it [00:00, 27.25it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.667 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52508) - 0it [00:00, ?it/s] 1it [00:00, 310.78it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.622 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.662 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 36956) - 0it [00:00, ?it/s] 1it [00:00, 320.08it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.632 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.761 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 58470) - 0it [00:00, ?it/s] 1it [00:00, 299.55it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.600 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.725 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 39066) - 0it [00:00, ?it/s] 1it [00:00, 37.91it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.630 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.640 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38604) - 0it [00:00, ?it/s] 1it [00:00, 252.70it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.646 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.667 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 57876) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.651 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.609 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 7.686 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 39636) - 0it [00:00, ?it/s] 1it [00:00, 306.06it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.656 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.635 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54280) - 0it [00:00, ?it/s] 1it [00:00, 35.67it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.644 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.628 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 59526) - 0it [00:00, ?it/s] 1it [00:00, 317.41it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.644 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.650 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52382) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.615 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.636 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 33324) - 0it [00:00, ?it/s] 1it [00:00, 38.50it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.655 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.573 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 60648) - 0it [00:00, ?it/s] 1it [00:00, 50.69it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.603 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.633 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 39646) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.582 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.673 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 43590) - 0it [00:00, ?it/s] 1it [00:00, 281.38it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.650 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.625 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 56334) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.636 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.595 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.570 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 52778) - 0it [00:00, ?it/s] 1it [00:00, 58.14it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.606 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.598 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 43512) - 0it [00:00, ?it/s] 1it [00:00, 321.92it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.575 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.634 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 46524) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.600 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.615 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 60002) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.621 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.593 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 44016) - 0it [00:00, ?it/s] 1it [00:00, 337.35it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.568 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 47056) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.584 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.792 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38682) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.583 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.623 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 42872) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.626 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.595 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 38908) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.601 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.610 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 41668) - 0it [00:00, ?it/s] 1it [00:00, 27.26it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.646 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.631 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 48016) - 0it [00:00, ?it/s] 1it [00:00, 302.07it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.589 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.588 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 60800) - 0it [00:00, ?it/s] 1it [00:00, 267.70it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.632 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.650 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 48744) - 0it [00:00, ?it/s] 1it [00:00, 303.25it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.647 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.600 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 56694) -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.620 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.663 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 50922) - 0it [00:00, ?it/s] 1it [00:00, 41.07it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.677 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.646 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.622 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 45338) - 0it [00:00, ?it/s] 1it [00:00, 340.81it/s] - Prediction: 0%| | 0/1 [00:00 - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 70, in run_server - message_length = struct.unpack(">H", conn.recv(2))[0] - ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -struct.error: unpack requires a buffer of 2 bytes -BioMedNER init_t 0.654 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.670 sec. -Traceback (most recent call last): - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 116, in - run_server(mt_ner, args) - File "/nfs/scratch/mabdallah/TrialMatchAI/source/Parser/biomedner_server.py", line 63, in run_server - s.bind((host, port)) -OSError: [Errno 98] Address already in use -BioMedNER init_t 0.610 sec. -Server listening on localhost:18894 -Connected by ('127.0.0.1', 54022) - 0it [00:00, ?it/s] 1it [00:00, 314.86it/s] - Prediction: 0%| | 0/1 [00:00= 18", "Patient has lung cancer.") + + assert "Written informed consent has been obtained" not in prompt + + +def test_json_extraction_uses_balanced_object(): + output = 'prefix {"outer": {"inner": "value"}, "items": [1, 2]} suffix {"bad": true}' + + parsed = extract_json_object(output) + + assert parsed == {"outer": {"inner": "value"}, "items": [1, 2]} + + +def test_json_extraction_rejects_malformed_output(): + try: + extract_json_object('prefix {"outer": {"inner": "value"}') + except ValueError as exc: + assert "Unbalanced JSON object" in str(exc) + else: + raise AssertionError("Malformed output should fail JSON extraction") diff --git a/tests/test_file_utils_pytest.py b/tests/test_file_utils_pytest.py index 7533d38d..bee17e6e 100644 --- a/tests/test_file_utils_pytest.py +++ b/tests/test_file_utils_pytest.py @@ -1,5 +1,3 @@ -import json - from Matcher.utils.file_utils import read_json_file, read_text_file, write_json_file, write_text_file diff --git a/tests/test_logging.py b/tests/test_logging.py index 1c07e2f4..8d82c083 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -1,10 +1,5 @@ import logging -import sys import unittest -from pathlib import Path - -ROOT = Path(__file__).resolve().parents[1] -sys.path.append(str(ROOT / "source")) from Matcher.utils.logging_config import reset_request_id, set_request_id, setup_logging diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py new file mode 100644 index 00000000..316ddcd7 --- /dev/null +++ b/tests/test_preflight_and_indexer.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +import json +import importlib.util +from pathlib import Path + +from Matcher.services import preflight +from Matcher.services.preflight import run_preflight_checks + +ES_CONFIG_PATH = Path(__file__).resolve().parents[1] / "utils/Indexer/es_config.py" +spec = importlib.util.spec_from_file_location("indexer_es_config", ES_CONFIG_PATH) +assert spec and spec.loader +indexer_es_config = importlib.util.module_from_spec(spec) +spec.loader.exec_module(indexer_es_config) +load_config = indexer_es_config.load_config + + +class HealthyIndices: + def __init__(self, existing: set[str]): + self.existing = existing + + def exists(self, index: str) -> bool: + return index in self.existing + + +class FakeES: + def __init__(self, *, healthy: bool = True, existing: set[str] | None = None): + self.healthy = healthy + self.indices = HealthyIndices(existing or set()) + + def ping(self) -> bool: + return self.healthy + + +def _base_config(tmp_path): + cert = tmp_path / "ca.crt" + cert.write_text("cert") + patients = tmp_path / "patients" + patients.mkdir() + trials = tmp_path / "trials" + trials.mkdir() + return { + "paths": { + "patients_dir": str(patients), + "trials_json_folder": str(trials), + "output_dir": str(tmp_path / "results"), + "docker_certs": str(cert), + }, + "elasticsearch": { + "host": "https://localhost:9200", + "index_trials": "clinical_trials", + "index_trials_eligibility": "trials_eligibility", + }, + "model": { + "cot_adapter_path": str(tmp_path / "models" / "cot"), + "reranker_adapter_path": str(tmp_path / "models" / "reranker"), + }, + "cot_backend": "default", + } + + +def test_preflight_passes_for_required_paths_and_indices(tmp_path): + cfg = _base_config(tmp_path) + issues = run_preflight_checks( + cfg, + es_client=FakeES( + existing={"clinical_trials", "trials_eligibility"}, + ), + require_patient_inputs=True, + require_trials_json=True, + require_indices=True, + ) + + assert issues == [] + + +def test_preflight_reports_missing_indices(tmp_path): + cfg = _base_config(tmp_path) + issues = run_preflight_checks( + cfg, + es_client=FakeES(existing={"clinical_trials"}), + require_indices=True, + ) + + assert issues == ["Missing Elasticsearch indices: trials_eligibility"] + + +def test_preflight_reports_unreachable_elasticsearch(tmp_path): + cfg = _base_config(tmp_path) + issues = run_preflight_checks(cfg, es_client=FakeES(healthy=False)) + + assert issues == ["Elasticsearch is not reachable at https://localhost:9200."] + + +def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): + cfg = _base_config(tmp_path) + cfg["cot_backend"] = "vllm" + Path(cfg["model"]["cot_adapter_path"]).mkdir(parents=True) + Path(cfg["model"]["reranker_adapter_path"]).mkdir(parents=True) + monkeypatch.setattr(preflight.importlib.util, "find_spec", lambda name: None) + monkeypatch.setattr(preflight.torch.cuda, "is_available", lambda: True) + + issues = run_preflight_checks(cfg, require_models=True) + + assert issues == [ + "cot_backend=vllm requires the GPU extra " + "(`uv sync --extra gpu`) or the Docker worker image." + ] + + +def test_indexer_config_uses_env_overrides_and_resolves_certs(tmp_path, monkeypatch): + config_path = tmp_path / "config.json" + cert = tmp_path / "certs" / "ca.crt" + cert.parent.mkdir() + cert.write_text("cert") + config_path.write_text( + json.dumps( + { + "elasticsearch": { + "hosts": ["https://localhost:9200"], + "ca_certs": "certs/ca.crt", + "username": "elastic", + "password": "CHANGE_ME", + } + } + ) + ) + monkeypatch.setenv("TRIALMATCHAI_ES_HOST", "https://es.example.test:9200") + monkeypatch.setenv("TRIALMATCHAI_ES_PASSWORD", "from-env") + + cfg = load_config(config_path) + + assert cfg["elasticsearch"]["hosts"] == ["https://es.example.test:9200"] + assert cfg["elasticsearch"]["password"] == "from-env" + assert cfg["elasticsearch"]["ca_certs"] == str(cert.resolve()) diff --git a/tests/test_schemas.py b/tests/test_schemas.py index 14587fad..73e6c5ce 100644 --- a/tests/test_schemas.py +++ b/tests/test_schemas.py @@ -1,9 +1,4 @@ -import sys import unittest -from pathlib import Path - -ROOT = Path(__file__).resolve().parents[1] -sys.path.append(str(ROOT / "source")) from Matcher.schemas.phenopacket import Keywords, Phenopacket diff --git a/tests/test_search_queries.py b/tests/test_search_queries.py index eb86ded8..c53d185c 100644 --- a/tests/test_search_queries.py +++ b/tests/test_search_queries.py @@ -1,9 +1,4 @@ -import sys import unittest -from pathlib import Path - -ROOT = Path(__file__).resolve().parents[1] -sys.path.append(str(ROOT / "source")) from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch diff --git a/tests/test_settings.py b/tests/test_settings.py index 5559475a..8b6327ab 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -1,11 +1,7 @@ import os -import sys import unittest from pathlib import Path -ROOT = Path(__file__).resolve().parents[1] -sys.path.append(str(ROOT / "source")) - from Matcher.config.config_loader import load_config from Matcher.config.settings import apply_env_overrides diff --git a/utils/DataLoader/nct_ids.txt b/utils/DataLoader/nct_ids.txt deleted file mode 100644 index 986b18a5..00000000 --- a/utils/DataLoader/nct_ids.txt +++ /dev/null @@ -1 +0,0 @@ -NCT04127110 \ No newline at end of file diff --git a/utils/Indexer/config.json b/utils/Indexer/config.json index 18d62cf7..4a421a57 100644 --- a/utils/Indexer/config.json +++ b/utils/Indexer/config.json @@ -1,11 +1,11 @@ { "elasticsearch": { "hosts": ["https://localhost:9200"], - "ca_certs": "../../elasticsearch/certs/ca.crt", + "ca_certs": "../../elasticsearch/certs/ca/ca.crt", "username": "elastic", - "password": "QQ7wWoB_WnKe*L*X9tAW", + "password": "CHANGE_ME", "request_timeout": 300, "retry_on_timeout": true, "max_retries": 3 } - } \ No newline at end of file + } diff --git a/utils/Indexer/es_config.py b/utils/Indexer/es_config.py new file mode 100644 index 00000000..b13a106b --- /dev/null +++ b/utils/Indexer/es_config.py @@ -0,0 +1,42 @@ +from __future__ import annotations + +import json +import os +from pathlib import Path +from typing import Any + +from elasticsearch import Elasticsearch + + +def load_config(path: str | Path) -> dict[str, Any]: + config_path = Path(path) + cfg = json.loads(config_path.read_text()) + es_conf = cfg.setdefault("elasticsearch", {}) + if os.getenv("TRIALMATCHAI_ES_HOST"): + es_conf["hosts"] = [os.environ["TRIALMATCHAI_ES_HOST"]] + if os.getenv("TRIALMATCHAI_ES_USERNAME"): + es_conf["username"] = os.environ["TRIALMATCHAI_ES_USERNAME"] + if os.getenv("TRIALMATCHAI_ES_PASSWORD"): + es_conf["password"] = os.environ["TRIALMATCHAI_ES_PASSWORD"] + if os.getenv("TRIALMATCHAI_ES_CA_CERTS"): + es_conf["ca_certs"] = os.environ["TRIALMATCHAI_ES_CA_CERTS"] + if es_conf.get("ca_certs"): + ca_path = Path(es_conf["ca_certs"]).expanduser() + if not ca_path.is_absolute(): + es_conf["ca_certs"] = str((config_path.parent / ca_path).resolve()) + return cfg + + +def make_es_client(cfg: dict[str, Any]) -> Elasticsearch: + es_conf = cfg["elasticsearch"] + kwargs: dict[str, Any] = { + "hosts": es_conf["hosts"], + "basic_auth": (es_conf["username"], es_conf["password"]), + "verify_certs": True, + "request_timeout": es_conf.get("request_timeout", 60), + "max_retries": es_conf.get("max_retries", 3), + "retry_on_timeout": es_conf.get("retry_on_timeout", True), + } + if es_conf.get("ca_certs") and Path(es_conf["ca_certs"]).exists(): + kwargs["ca_certs"] = es_conf["ca_certs"] + return Elasticsearch(**kwargs) diff --git a/utils/Indexer/index_criteria.py b/utils/Indexer/index_criteria.py index cc2f8208..4d2aa892 100644 --- a/utils/Indexer/index_criteria.py +++ b/utils/Indexer/index_criteria.py @@ -8,6 +8,11 @@ from elasticsearch import Elasticsearch, NotFoundError from elasticsearch.helpers import bulk +try: + from .es_config import load_config, make_es_client +except ImportError: # pragma: no cover - direct script execution + from es_config import load_config, make_es_client + logging.basicConfig( format="%(asctime)s %(levelname)s %(name)s - %(message)s", level=logging.INFO, @@ -200,23 +205,6 @@ def index_all( ) -def load_config(path: str) -> dict: - return json.loads(Path(path).read_text()) - - -def make_es_client(cfg: dict) -> Elasticsearch: - es_conf = cfg["elasticsearch"] - return Elasticsearch( - hosts=es_conf["hosts"], - basic_auth=(es_conf["username"], es_conf["password"]), - ca_certs=es_conf["ca_certs"], - verify_certs=True, - request_timeout=es_conf.get("request_timeout", 60), - max_retries=es_conf.get("max_retries", 3), - retry_on_timeout=True, - ) - - def main(): parser = argparse.ArgumentParser( description="Bulk‑index prepared eligibility criteria in parallel" @@ -226,7 +214,7 @@ def main(): "--processed-folder", required=True, help="Root folder of trial subfolders" ) parser.add_argument( - "--index-name", default="trec_trials_eligibility_v3", help="ES index name" + "--index-name", default="trials_eligibility", help="ES index name" ) parser.add_argument( "--batch-size", type=int, default=100, help="Docs per bulk request" diff --git a/utils/Indexer/index_trials.py b/utils/Indexer/index_trials.py index 6dbc5a06..c0ee4803 100644 --- a/utils/Indexer/index_trials.py +++ b/utils/Indexer/index_trials.py @@ -1,25 +1,16 @@ #!/usr/bin/env python3 -import os -import json import argparse +import json +import os from pathlib import Path from elasticsearch import Elasticsearch from elasticsearch.helpers import bulk - -def load_config(path: str) -> dict: - return json.loads(Path(path).read_text()) - - -def make_es_client(cfg: dict) -> Elasticsearch: - es_conf = cfg["elasticsearch"] - return Elasticsearch( - hosts=es_conf["hosts"], - basic_auth=(es_conf["username"], es_conf["password"]), - ca_certs=es_conf["ca_certs"], - verify_certs=True, - ) +try: + from .es_config import load_config, make_es_client +except ImportError: # pragma: no cover - direct script execution + from es_config import load_config, 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extract_study_info(nct_id): # Extract primary end date primary_end_date = root.find(".//primary_completion_date") - if end_date is not None: - end_date_text = end_date.text.strip() - f.write(f"Primary End Date:\n{end_date_text}\n\n") + if primary_end_date is not None: + primary_end_date_text = primary_end_date.text.strip() + f.write(f"Primary End Date:\n{primary_end_date_text}\n\n") # Extract overall status overall_status = root.find(".//overall_status") @@ -475,14 +475,12 @@ def resolve_ner_overlaps(ner1_results, ner2_results): for entity1 in ner1_results: entity1_start = entity1["start"] entity1_end = entity1["end"] - entity1_label = entity1["entity_group"] # Check if the entity from the first model overlaps with any entity from the second model overlaps = False for entity2 in ner2_results: entity2_start = entity2["start"] entity2_end = entity2["end"] - entity2_label = entity2["entity_group"] if entity1_start < entity2_end and entity1_end > entity2_start: overlaps = True @@ -496,13 +494,11 @@ def resolve_ner_overlaps(ner1_results, ner2_results): for entity2 in ner2_results: entity2_start = entity2["start"] entity2_end = entity2["end"] - entity2_label = entity2["entity_group"] overlaps = False for entity1 in resolved_results: entity1_start = entity1["start"] entity1_end = entity1["end"] - entity1_label = entity1["entity_group"] if entity2_start < entity1_end and entity2_end > entity1_start: overlaps = True diff --git a/utils/finetuning/finetune_instruct/evaluate_gemma2.py b/utils/finetuning/finetune_instruct/evaluate_gemma2.py index 8fa9871a..a38227b0 100644 --- a/utils/finetuning/finetune_instruct/evaluate_gemma2.py +++ b/utils/finetuning/finetune_instruct/evaluate_gemma2.py @@ -1,13 +1,14 @@ +import json import os -import torch -import unicodedata import re -from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig +import unicodedata from multiprocessing import get_context + +import torch +from peft import PeftModel from sklearn.metrics import accuracy_score, precision_recall_fscore_support from tqdm import tqdm -from peft import PeftModel -import json +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig # Set CUDA devices os.environ["CUDA_VISIBLE_DEVICES"] = "1" @@ -190,12 +191,10 @@ def evaluate(self, dataset, save_path="incorrect_predictions.jsonl"): for entry, pred in zip(dataset, predictions) if pred["prediction"] != entry["output"] ] - - # Optionally, save incorrect examples. - # with open(save_path, "w", encoding="utf-8") as f: - # for example in incorrect_examples: - # json.dump(example, f) - # f.write("\n") + with open(save_path, "w", encoding="utf-8") as f: + for example in incorrect_examples: + json.dump(example, f) + f.write("\n") references = [entry["output"] for entry in dataset] predictions_only = [pred["prediction"] for pred in predictions] diff --git a/uv.lock b/uv.lock index 21c6ebdf..dcd20486 100644 --- a/uv.lock +++ b/uv.lock @@ -1,15 +1,13 @@ version = 1 revision = 3 -requires-python = ">=3.10, <3.12" +requires-python = "==3.11.*" resolution-markers = [ - "python_full_version >= '3.11' and sys_platform == 'darwin'", - "python_full_version >= '3.11' and platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and sys_platform == 'linux'", - "(python_full_version >= '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version >= '3.11' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'", - "python_full_version < '3.11' and sys_platform == 'darwin'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and sys_platform == 'linux'", - "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' 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source/Matcher/entities/__init__.py | 15 + source/Matcher/entities/annotator.py | 120 +++ source/Matcher/entities/builder.py | 183 ++++ source/Matcher/entities/linker.py | 352 +++++++ source/Matcher/entities/recognizers.py | 278 +++++ source/Matcher/entities/schemas.py | 104 ++ source/Matcher/entities/types.py | 141 +++ .../Matcher/entity_schemas/trialmatchai.yaml | 122 +++ source/Matcher/main.py | 20 +- .../trial_search/first_level_search.py | 13 +- .../trial_search/second_level_search.py | 17 +- source/Matcher/services/biomedner_service.py | 44 - source/Matcher/services/preflight.py | 28 + source/Parser/bin/crf_learn | Bin 15960 -> 0 bytes source/Parser/bin/crf_test | Bin 15960 -> 0 bytes source/Parser/biomedner_engine.py | 694 ++----------- source/Parser/biomedner_init.py | 983 ------------------ source/Parser/biomedner_server.py | 116 --- source/Parser/gner_server.py | 206 ---- source/Parser/include/crfpp.h | 379 ------- source/Parser/lib/libcrfpp.a | Bin 623548 -> 0 bytes source/Parser/lib/libcrfpp.la | 41 - source/Parser/lib/libcrfpp.so | 1 - source/Parser/lib/libcrfpp.so.0 | 1 - source/Parser/lib/libcrfpp.so.0.0.0 | Bin 364296 -> 0 bytes source/Parser/main.py | 273 +++-- source/Parser/models.py | 653 ------------ source/Parser/normalizer.py | 428 +------- source/Parser/normalizers/__init__.py | 0 .../Parser/normalizers/cellline_normalizer.py | 24 - .../Parser/normalizers/celltype_normalizer.py | 40 - .../Parser/normalizers/chemical_normalizer.py | 45 - .../Parser/normalizers/neural_normalizer.py | 207 ---- source/Parser/normalizers/normalizer_all.py | 137 --- source/Parser/normalizers/readme.md | 22 - .../Parser/normalizers/species_normalizer.py | 25 - source/Parser/ops.py | 449 -------- source/Parser/scripts/run_biomedner.sh | 41 - source/Parser/scripts/stop_biomedner.sh | 23 - source/biomedner_services/run_biomedner.sh | 39 - source/biomedner_services/stop_biomedner.sh | 23 - tests/test_deployment_readiness.py | 8 +- tests/test_entities.py | 170 +++ tests/test_preflight_and_indexer.py | 27 + tests/test_settings.py | 8 + utils/Indexer/index_criteria.py | 22 + utils/Indexer/prepare_criteria.py | 79 +- uv.lock | 104 +- 59 files changed, 2258 insertions(+), 4765 deletions(-) create mode 100644 source/Matcher/cli/build_concepts.py create mode 100644 source/Matcher/entities/__init__.py create mode 100644 source/Matcher/entities/annotator.py create mode 100644 source/Matcher/entities/builder.py create mode 100644 source/Matcher/entities/linker.py create mode 100644 source/Matcher/entities/recognizers.py create mode 100644 source/Matcher/entities/schemas.py create mode 100644 source/Matcher/entities/types.py create mode 100644 source/Matcher/entity_schemas/trialmatchai.yaml delete mode 100644 source/Matcher/services/biomedner_service.py delete mode 100755 source/Parser/bin/crf_learn delete mode 100755 source/Parser/bin/crf_test delete mode 100644 source/Parser/biomedner_init.py delete mode 100644 source/Parser/biomedner_server.py delete mode 100644 source/Parser/gner_server.py delete mode 100644 source/Parser/include/crfpp.h delete mode 100644 source/Parser/lib/libcrfpp.a delete mode 100755 source/Parser/lib/libcrfpp.la delete mode 120000 source/Parser/lib/libcrfpp.so delete mode 120000 source/Parser/lib/libcrfpp.so.0 delete mode 100755 source/Parser/lib/libcrfpp.so.0.0.0 delete mode 100644 source/Parser/models.py delete mode 100644 source/Parser/normalizers/__init__.py delete mode 100644 source/Parser/normalizers/cellline_normalizer.py delete mode 100644 source/Parser/normalizers/celltype_normalizer.py delete mode 100644 source/Parser/normalizers/chemical_normalizer.py delete mode 100644 source/Parser/normalizers/neural_normalizer.py delete mode 100644 source/Parser/normalizers/normalizer_all.py delete mode 100644 source/Parser/normalizers/readme.md delete mode 100644 source/Parser/normalizers/species_normalizer.py delete mode 100644 source/Parser/ops.py delete mode 100644 source/Parser/scripts/run_biomedner.sh delete mode 100644 source/Parser/scripts/stop_biomedner.sh delete mode 100644 source/biomedner_services/run_biomedner.sh delete mode 100644 source/biomedner_services/stop_biomedner.sh create mode 100644 tests/test_entities.py diff --git a/.env.example b/.env.example index e884de1d..25965ecf 100644 --- a/.env.example +++ b/.env.example @@ -27,6 +27,13 @@ TRIALMATCHAI_MODEL_RERANKER_ADAPTER_PATH=models/finetuned_gemma2 TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false TRIALMATCHAI_COT_BACKEND=vllm -TRIALMATCHAI_BIOMEDNER_AUTO_START=false +TRIALMATCHAI_ENTITY_BACKEND=gliner2 +TRIALMATCHAI_ENTITY_MODEL_NAME=fastino/gliner2-base +TRIALMATCHAI_ENTITY_SCHEMA_PATH=source/Matcher/entity_schemas/trialmatchai.yaml +TRIALMATCHAI_ENTITY_TRUST_REMOTE_CODE=false +TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts +TRIALMATCHAI_CONCEPT_TABLE=concepts +TRIALMATCHAI_LINK_ACCEPT=0.80 +TRIALMATCHAI_LINK_REJECT=0.30 TRIALMATCHAI_LOG_LEVEL=INFO TRIALMATCHAI_LOG_JSON=1 diff --git a/Dockerfile b/Dockerfile index 62290d7a..03620340 100644 --- a/Dockerfile +++ b/Dockerfile @@ -23,7 +23,7 @@ COPY source ./source COPY scripts ./scripts COPY utils ./utils -RUN uv sync --frozen --no-dev --extra gpu +RUN uv sync --frozen --no-dev --extra gpu --extra entity VOLUME ["/app/data", "/app/models", "/app/results"] diff --git a/README.md b/README.md index ea70958d..632d6630 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ The supported v1 deployment path is a single Linux GPU server or VM with Docker - Docker Compose for the default Elasticsearch deployment - NVIDIA GPU with enough VRAM for the selected LLM backend - 100 GB+ disk space for datasets, models, indices, and results -- Java for BioMedNER/normalization components when those services are enabled +- A LanceDB concept table built from OMOP/legacy dictionaries for entity normalization ## Security First @@ -39,7 +39,7 @@ Dependency auditing currently ignores `CVE-2025-3000` because vLLM 0.23 pins Tor Install deployment dependencies: ```bash -uv sync --extra gpu +uv sync --extra gpu --extra entity ``` For local development, tests, healthchecks, or `TRIALMATCHAI_COT_BACKEND=default`, the default dependency set is enough: @@ -52,6 +52,7 @@ Optional tooling is split out of the default runtime: ```bash uv sync --extra llm # OpenAI/LangChain data-generation utilities +uv sync --extra entity # GLiNER2/LanceDB entity extraction and normalization uv sync --extra training # fine-tuning and evaluation utilities ``` @@ -71,6 +72,7 @@ Provision data, models, and indices: ```bash uv run trialmatchai-bootstrap-data +uv run trialmatchai-build-concepts --concept-csv data/omop/CONCEPT.csv --synonym-csv data/omop/CONCEPT_SYNONYM.csv uv run trialmatchai-index ``` @@ -115,7 +117,12 @@ TRIALMATCHAI_INDEX_TRIALS=clinical_trials TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY=trials_eligibility TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false -TRIALMATCHAI_BIOMEDNER_AUTO_START=false +TRIALMATCHAI_ENTITY_BACKEND=gliner2 +TRIALMATCHAI_ENTITY_SCHEMA_PATH=source/Matcher/entity_schemas/trialmatchai.yaml +TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts +TRIALMATCHAI_CONCEPT_TABLE=concepts +TRIALMATCHAI_LINK_ACCEPT=0.80 +TRIALMATCHAI_LINK_REJECT=0.30 TRIALMATCHAI_LOG_JSON=1 ``` @@ -125,6 +132,7 @@ Use `TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=true` only when a selected model expli - `trialmatchai-healthcheck`: validate config, paths, Elasticsearch reachability, and optionally indices. - `trialmatchai-bootstrap-data`: download and extract external data/model artifacts. +- `trialmatchai-build-concepts`: build the LanceDB concept table used for entity normalization. - `trialmatchai-index`: index prepared data into Elasticsearch. - `trialmatchai-run`: run the batch matching pipeline. diff --git a/docker-compose.yml b/docker-compose.yml index 95f63cb2..9d0b2ed2 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -103,10 +103,15 @@ services: TRIALMATCHAI_ES_PASSWORD: ${TRIALMATCHAI_ES_PASSWORD:-} TRIALMATCHAI_ES_CA_CERTS: /app/elasticsearch/certs/ca/ca.crt TRIALMATCHAI_ES_AUTO_START: "false" - TRIALMATCHAI_BIOMEDNER_AUTO_START: "false" TRIALMATCHAI_PATIENTS_DIR: /app/example TRIALMATCHAI_OUTPUT_DIR: /app/results TRIALMATCHAI_TRIALS_JSON_FOLDER: /app/data/trials_jsons + TRIALMATCHAI_ENTITY_BACKEND: gliner2 + TRIALMATCHAI_ENTITY_SCHEMA_PATH: /app/source/Matcher/entity_schemas/trialmatchai.yaml + TRIALMATCHAI_CONCEPT_DB_PATH: /app/data/concepts + TRIALMATCHAI_CONCEPT_TABLE: concepts + TRIALMATCHAI_LINK_ACCEPT: "0.80" + TRIALMATCHAI_LINK_REJECT: "0.30" TRIALMATCHAI_LOG_JSON: "1" command: ["trialmatchai-healthcheck"] diff --git a/docs/HLD.md b/docs/HLD.md index e85b3431..2d7fbbf5 100644 --- a/docs/HLD.md +++ b/docs/HLD.md @@ -30,8 +30,8 @@ TrialMatchAI is an AI-driven clinical trial matching system. Given a patient rec │ │ .gov JSONs │ │ ▼ │ │ │ │ │ │ │ ┌─────────────────────┐ │ │ │ │ ▼ │ │ │ Phenopacket │ │ │ -│ │ BioMedNER │ │ │ Processor + LLM │ │ │ -│ │ annotation │ │ │ (Phi-4 summariser) │ │ │ +│ │ Schema NER +│ │ │ Processor + LLM │ │ │ +│ │ concept link│ │ │ (Phi-4 summariser) │ │ │ │ │ │ │ │ └──────────┬──────────┘ │ │ │ │ ▼ │ │ │ keywords.json │ │ │ │ BGE-M3 │ │ ▼ │ │ @@ -74,7 +74,7 @@ Run once to prepare the Elasticsearch indices from raw ClinicalTrials.gov data. ``` Raw Trial JSONs │ - ├──► Parser (BioMedNER annotation) + ├──► Schema entity annotation + LanceDB concept linking │ │ │ ▼ │ Annotated criteria with entity synonyms @@ -103,7 +103,7 @@ Raw Trial JSONs | eligibility_type | keyword | `inclusion` or `exclusion` | | criterion | text | BM25 searchable | | criterion_vector | dense_vector (1024d, HNSW) | BGE-M3 embedding | -| entities | nested | BioMedNER annotations + synonyms | +| entities | nested | Schema entity annotations, linked concept candidates, and synonyms | --- @@ -144,7 +144,7 @@ hybrid_score = α × normalized_text_score + β × normalized_vector_score - **BM25 side:** multi-match across `condition`, `eligibility_criteria`, `brief_title`, `brief_summary` with field-specific boosts - **Vector side:** cosine similarity against 4 embedded fields using script-score queries -- **Synonym expansion:** BioMedNER extracts disease synonyms from `main_conditions` to broaden recall +- **Synonym expansion:** the schema entity annotator links disease mentions to the LanceDB concept table and expands accepted concepts with synonyms - **Filters:** age range, gender, `overall_status = Recruiting` **Output:** Up to 300 trial IDs with relevance scores → `nct_ids.txt`, `first_level_scores.json` @@ -244,10 +244,11 @@ Irrelevant and unclear criteria are excluded from both numerator and denominator - **Memory:** 2 GB per node (6 GB total) - **Ports:** 9200 (API), 5601 (Kibana) -### BioMedNER -- **Purpose:** Biomedical NER and entity normalization -- **Services:** 4 daemons on ports 18894, 18783, 18888, 18892 -- **Entities:** genes, diseases, drugs, procedures, signs/symptoms, cell types +### Schema Entity Annotator + LanceDB Concept Linker +- **Purpose:** Biomedical entity recognition and normalization without external Java daemons +- **Recognizer:** GLiNER2-style schema-driven extraction, with GLiNER/biomedical fallback support behind the same interface +- **Concept store:** LanceDB table built from OMOP vocabularies and legacy dictionaries +- **Entities:** diseases, genes, medications, procedures, labs, radiology, signs/symptoms, cell types, and species - **Usage:** synonym expansion (Stage 1) and entity annotation (indexing) --- @@ -257,7 +258,7 @@ Irrelevant and unclear criteria are excluded from both numerator and denominator ``` [One-time setup] ClinicalTrials.gov JSONs - → BioMedNER annotation + → schema entity annotation + LanceDB concept linking → BGE-M3 embedding → Elasticsearch (clinical_trials + trials_eligibility indices) @@ -527,7 +528,7 @@ startup: load Phi-4 (4-bit) + LoRA adapter load Gemma-2-2B (4-bit) load BGE-M3 embedder - load BioMedNER daemons + load schema entity annotator + LanceDB concept linker connect to Elasticsearch for each patient: @@ -626,7 +627,7 @@ Gemma-2-2B is never asked to generate text. Instead it's used as a **scoring fun Patient data is structured using the **GA4GH Phenopacket standard** — ontology codes (HPO, MONDO, CHEBI) rather than free text. This solves the vocabulary mismatch problem: a trial saying "coronary artery disease" and a patient record saying "CAD" both resolve to `MONDO:0005066`, ensuring consistent matching regardless of phrasing. -**BioMedNER** bridges the gap between ontology codes and natural language by extracting synonyms and related terms, expanding search recall. +The schema entity annotator bridges the gap between ontology codes and natural language by extracting mentions, linking them to LanceDB concept candidates, and expanding accepted disease concepts with synonyms. --- diff --git a/pyproject.toml b/pyproject.toml index a50a160f..8b082f21 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,7 +50,7 @@ dependencies = [ # App / infra "SQLAlchemy==2.0.37", "pydantic>=2.12.0,<3", - "pydantic-settings>=2.10.1,<3", + "pydantic-settings>=2.14.2,<3", "huggingface_hub>=1.5.0,<2", "bioregistry==0.13.62", "rapidfuzz==3.14.5", @@ -69,6 +69,11 @@ llm = [ "langchain-community==0.4.2", "langchain-openai==1.3.2", ] +entity = [ + "lancedb>=0.25.0,<0.26", + "pyarrow>=24.0.0,<25", + "gliner2>=1.3.1,<2", +] training = [ "datasets==2.19.0", "bert-score==0.3.13", @@ -80,6 +85,7 @@ trialmatchai-healthcheck = "Matcher.cli.healthcheck:main" trialmatchai-run = "Matcher.cli.run:main" trialmatchai-bootstrap-data = "Matcher.cli.bootstrap_data:main" trialmatchai-index = "Matcher.cli.index_data:main" +trialmatchai-build-concepts = "Matcher.cli.build_concepts:main" [dependency-groups] dev = [ @@ -96,8 +102,7 @@ include-package-data = true where = ["source"] [tool.setuptools.package-data] -Matcher = ["config/*.json"] -Parser = ["bin/*", "include/*", "lib/*", "normalizers/*.md"] +Matcher = ["config/*.json", "entity_schemas/*.yaml"] [tool.uv] environments = [ diff --git a/scripts/scan_secrets.py b/scripts/scan_secrets.py index fa5f5dae..57d3a609 100644 --- a/scripts/scan_secrets.py +++ b/scripts/scan_secrets.py @@ -39,6 +39,8 @@ def main() -> int: continue try: text = path.read_text(encoding="utf-8") + except FileNotFoundError: + continue except UnicodeDecodeError: continue for pattern in SECRET_PATTERNS: diff --git a/source/Matcher/cli/build_concepts.py b/source/Matcher/cli/build_concepts.py new file mode 100644 index 00000000..b8971a14 --- /dev/null +++ b/source/Matcher/cli/build_concepts.py @@ -0,0 +1,122 @@ +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +from Matcher.config.config_loader import load_config +from Matcher.entities.builder import ( + DEFAULT_OMOP_VOCABULARIES, + build_legacy_dictionary_rows, + build_omop_concept_rows, + concept_texts_for_embedding, + write_lancedb_table, +) +from Matcher.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig +from Matcher.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Build the TrialMatchAI LanceDB concept table." + ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument("--concept-csv", required=True, help="OMOP CONCEPT.csv path") + parser.add_argument( + "--synonym-csv", + default=None, + help="OMOP CONCEPT_SYNONYM.csv path", + ) + parser.add_argument( + "--legacy-dictionary", + action="append", + default=[], + metavar="VOCAB:DOMAIN:PATH", + help="Import a legacy dictionary file, e.g. EntrezGene:Gene:/path/dict_Gene.txt", + ) + parser.add_argument( + "--vocabulary", + action="append", + default=[], + help="OMOP vocabulary to include. Defaults to TrialMatchAI deployment set.", + ) + parser.add_argument("--db-path", default=None, help="Output LanceDB directory") + parser.add_argument("--table", default=None, help="Output LanceDB table name") + parser.add_argument( + "--skip-embeddings", + action="store_true", + help="Create an FTS-only table without model embeddings.", + ) + parser.add_argument( + "--recreate", + action=argparse.BooleanOptionalAction, + default=True, + help="Overwrite the target table if it exists.", + ) + args = parser.parse_args() + + config = load_config(args.config) + linker_cfg = config.get("concept_linker", {}) + embedder_cfg = config.get("embedder", {}) + db_path = args.db_path or linker_cfg.get("db_path") or "data/concepts" + table_name = args.table or linker_cfg.get("table") or "concepts" + + vocabularies = tuple(args.vocabulary or DEFAULT_OMOP_VOCABULARIES) + rows = build_omop_concept_rows( + args.concept_csv, + args.synonym_csv, + vocabularies=vocabularies, + ) + for spec in args.legacy_dictionary: + vocab, domain, path = _parse_legacy_spec(spec) + rows.extend( + build_legacy_dictionary_rows( + path, + vocabulary_id=vocab, + domain_id=domain, + ) + ) + + embeddings = None + if not args.skip_embeddings: + embedder = TextEmbedder( + TextEmbedderConfig( + model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), + revision=embedder_cfg.get("revision"), + trust_remote_code=embedder_cfg.get("trust_remote_code", False), + pooling=embedder_cfg.get("pooling", "mean"), + max_length=embedder_cfg.get("max_length", 512), + batch_size=embedder_cfg.get("batch_size", 32), + use_gpu=embedder_cfg.get("use_gpu", True), + use_fp16=embedder_cfg.get("use_fp16", False), + normalize=embedder_cfg.get("normalize", True), + ) + ) + embeddings = embedder.embed_texts(concept_texts_for_embedding(rows)) + + Path(db_path).mkdir(parents=True, exist_ok=True) + write_lancedb_table( + rows, + db_path=db_path, + table_name=table_name, + embeddings=embeddings, + recreate=args.recreate, + ) + logger.info("Wrote %s concepts to %s/%s", len(rows), db_path, table_name) + return 0 + + +def _parse_legacy_spec(spec: str) -> tuple[str, str, str]: + parts = spec.split(":", 2) + if len(parts) != 3: + raise ValueError( + "--legacy-dictionary must use VOCAB:DOMAIN:PATH, " + f"received: {spec}" + ) + return parts[0], parts[1], parts[2] + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/source/Matcher/config/config.json b/source/Matcher/config/config.json index 75f9d0b3..cba614eb 100644 --- a/source/Matcher/config/config.json +++ b/source/Matcher/config/config.json @@ -1,17 +1,22 @@ { - "bio_med_ner": { - "biomedner_port": 18894, - "gner_port": 18783, - "gene_norm_port": 18888, - "disease_norm_port": 18892, - "biomedner_home": "source/Parser/", - "use_neural_normalizer": true, - "no_cuda": false + "entity_extraction": { + "backend": "gliner2", + "model_name": "fastino/gliner2-base", + "fallback_model_name": "gliner-community/gliner_large-v2.5", + "model_revision": null, + "schema_path": "source/Matcher/entity_schemas/trialmatchai.yaml", + "threshold": 0.8, + "batch_size": 8, + "device": "auto", + "trust_remote_code": false }, - "services": { - "stop_script": "source/biomedner_services/stop_biomedner.sh", - "run_script": "source/biomedner_services/run_biomedner.sh", - "auto_start": false + "concept_linker": { + "enabled": true, + "db_path": "data/concepts", + "table": "concepts", + "accept_threshold": 0.8, + "reject_threshold": 0.3, + "search_limit": 10 }, "paths": { "patients_dir": "example", diff --git a/source/Matcher/config/config_loader.py b/source/Matcher/config/config_loader.py index 15ba3d29..9d1d7639 100644 --- a/source/Matcher/config/config_loader.py +++ b/source/Matcher/config/config_loader.py @@ -87,15 +87,16 @@ def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, if value: cfg["paths"][key] = str(_resolve_local_path(value, root)) - for key in ("stop_script", "run_script"): - value = cfg.get("services", {}).get(key) - if value: - cfg["services"][key] = str(_resolve_local_path(value, root)) + schema_path = cfg.get("entity_extraction", {}).get("schema_path") + if schema_path: + cfg["entity_extraction"]["schema_path"] = str( + _resolve_local_path(schema_path, root) + ) - biomedner_home = cfg.get("bio_med_ner", {}).get("biomedner_home") - if biomedner_home: - cfg["bio_med_ner"]["biomedner_home"] = str( - _resolve_local_path(biomedner_home, root) + concept_db_path = cfg.get("concept_linker", {}).get("db_path") + if concept_db_path: + cfg["concept_linker"]["db_path"] = str( + _resolve_local_path(concept_db_path, root) ) start_script = cfg.get("elasticsearch", {}).get("start_script") diff --git a/source/Matcher/config/settings.py b/source/Matcher/config/settings.py index 10b9f7e2..dd59e7ac 100644 --- a/source/Matcher/config/settings.py +++ b/source/Matcher/config/settings.py @@ -1,24 +1,37 @@ from __future__ import annotations -from typing import Any, Dict, Iterable, Tuple +from typing import Any, Dict, Iterable, Literal, Tuple from pydantic import BaseModel, Field, field_validator -class BioMedNerSettings(BaseModel): - biomedner_port: int - gner_port: int - gene_norm_port: int - disease_norm_port: int - biomedner_home: str - use_neural_normalizer: bool = True - no_cuda: bool = False +class EntityExtractionSettings(BaseModel): + backend: Literal["gliner2", "gliner", "regex", "disabled"] = "gliner2" + model_name: str = "fastino/gliner2-base" + fallback_model_name: str = "gliner-community/gliner_large-v2.5" + model_revision: str | None = None + schema_path: str = "source/Matcher/entity_schemas/trialmatchai.yaml" + threshold: float = Field(0.8, ge=0.0, le=1.0) + batch_size: int = Field(8, ge=1) + device: str = "auto" + trust_remote_code: bool = False -class ServicesSettings(BaseModel): - stop_script: str - run_script: str - auto_start: bool = False +class ConceptLinkerSettings(BaseModel): + enabled: bool = True + db_path: str = "data/concepts" + table: str = "concepts" + accept_threshold: float = Field(0.8, ge=0.0, le=1.0) + reject_threshold: float = Field(0.3, ge=0.0, le=1.0) + search_limit: int = Field(10, ge=1) + + @field_validator("reject_threshold") + @classmethod + def validate_reject_threshold(cls, value: float, info): + accept = info.data.get("accept_threshold") + if accept is not None and value > accept: + raise ValueError("concept_linker.reject_threshold must be <= accept_threshold") + return value class PathsSettings(BaseModel): @@ -119,8 +132,10 @@ class LLMRerankerSettings(BaseModel): class TrialMatchSettings(BaseModel): - bio_med_ner: BioMedNerSettings - services: ServicesSettings + entity_extraction: EntityExtractionSettings = Field( + default_factory=EntityExtractionSettings + ) + concept_linker: ConceptLinkerSettings = Field(default_factory=ConceptLinkerSettings) paths: PathsSettings model: ModelSettings tokenizer: TokenizerSettings @@ -185,6 +200,20 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: ), "TRIALMATCHAI_COT_BACKEND": ("cot_backend",), "TRIALMATCHAI_ES_START_SCRIPT": ("elasticsearch", "start_script"), + "TRIALMATCHAI_ENTITY_BACKEND": ("entity_extraction", "backend"), + "TRIALMATCHAI_ENTITY_MODEL_NAME": ("entity_extraction", "model_name"), + "TRIALMATCHAI_ENTITY_FALLBACK_MODEL_NAME": ( + "entity_extraction", + "fallback_model_name", + ), + "TRIALMATCHAI_ENTITY_MODEL_REVISION": ( + "entity_extraction", + "model_revision", + ), + "TRIALMATCHAI_ENTITY_SCHEMA_PATH": ("entity_extraction", "schema_path"), + "TRIALMATCHAI_ENTITY_DEVICE": ("entity_extraction", "device"), + "TRIALMATCHAI_CONCEPT_DB_PATH": ("concept_linker", "db_path"), + "TRIALMATCHAI_CONCEPT_TABLE": ("concept_linker", "table"), } for env_key, path in string_env_map.items(): value = os.getenv(env_key) @@ -194,7 +223,6 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: bool_env_map: dict[str, Tuple[str, ...]] = { "TRIALMATCHAI_ES_AUTO_START": ("elasticsearch", "auto_start"), "TRIALMATCHAI_ES_RETRY_ON_TIMEOUT": ("elasticsearch", "retry_on_timeout"), - "TRIALMATCHAI_BIOMEDNER_AUTO_START": ("services", "auto_start"), "TRIALMATCHAI_EMBEDDER_USE_GPU": ("embedder", "use_gpu"), "TRIALMATCHAI_EMBEDDER_USE_FP16": ("embedder", "use_fp16"), "TRIALMATCHAI_EMBEDDER_TRUST_REMOTE_CODE": ( @@ -202,6 +230,11 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "trust_remote_code", ), "TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE": ("model", "trust_remote_code"), + "TRIALMATCHAI_ENTITY_TRUST_REMOTE_CODE": ( + "entity_extraction", + "trust_remote_code", + ), + "TRIALMATCHAI_CONCEPT_LINKER_ENABLED": ("concept_linker", "enabled"), } for env_key, path in bool_env_map.items(): value = os.getenv(env_key) @@ -212,6 +245,8 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "TRIALMATCHAI_ES_REQUEST_TIMEOUT": ("elasticsearch", "request_timeout"), "TRIALMATCHAI_ES_START_TIMEOUT": ("elasticsearch", "start_timeout"), "TRIALMATCHAI_EMBEDDER_BATCH_SIZE": ("embedder", "batch_size"), + "TRIALMATCHAI_ENTITY_BATCH_SIZE": ("entity_extraction", "batch_size"), + "TRIALMATCHAI_CONCEPT_SEARCH_LIMIT": ("concept_linker", "search_limit"), "TRIALMATCHAI_SEARCH_MAX_TRIALS_FIRST_LEVEL": ( "search", "max_trials_first_level", @@ -232,6 +267,19 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: except ValueError: pass + float_env_map: dict[str, Tuple[str, ...]] = { + "TRIALMATCHAI_ENTITY_THRESHOLD": ("entity_extraction", "threshold"), + "TRIALMATCHAI_LINK_ACCEPT": ("concept_linker", "accept_threshold"), + "TRIALMATCHAI_LINK_REJECT": ("concept_linker", "reject_threshold"), + } + for env_key, path in float_env_map.items(): + value = os.getenv(env_key) + if value: + try: + _set_nested(raw, path, float(value)) + except ValueError: + pass + return raw diff --git a/source/Matcher/entities/__init__.py b/source/Matcher/entities/__init__.py new file mode 100644 index 00000000..f3f483a9 --- /dev/null +++ b/source/Matcher/entities/__init__.py @@ -0,0 +1,15 @@ +from Matcher.entities.annotator import ( + CompatibilityEntityAnnotator, + SchemaEntityAnnotator, + build_entity_annotator, +) +from Matcher.entities.types import ConceptCandidate, EntityAnnotation, EntitySchema + +__all__ = [ + "CompatibilityEntityAnnotator", + "ConceptCandidate", + "EntityAnnotation", + "EntitySchema", + "SchemaEntityAnnotator", + "build_entity_annotator", +] diff --git a/source/Matcher/entities/annotator.py b/source/Matcher/entities/annotator.py new file mode 100644 index 00000000..7fe23905 --- /dev/null +++ b/source/Matcher/entities/annotator.py @@ -0,0 +1,120 @@ +from __future__ import annotations + +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path +from typing import Any, Sequence + +from Matcher.entities.linker import ConceptLinker, LanceDBConceptStore +from Matcher.entities.recognizers import EntityRecognizer, build_recognizer +from Matcher.entities.schemas import load_entity_schemas +from Matcher.entities.types import EntityAnnotation +from Matcher.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +class SchemaEntityAnnotator: + def __init__( + self, + recognizer: EntityRecognizer, + schemas: Sequence[Any], + *, + linker: ConceptLinker | None = None, + ): + self.recognizer = recognizer + self.schemas = list(schemas) + self.linker = linker + + def annotate_texts(self, texts: Sequence[str]) -> list[list[EntityAnnotation]]: + recognized = self.recognizer.recognize(texts, self.schemas) + if self.linker is None: + return recognized + return [self.linker.link_annotations(annotations) for annotations in recognized] + + def annotate_text(self, text: str) -> list[dict[str, Any]]: + return [annotation.to_dict() for annotation in self.annotate_texts([text])[0]] + + def annotate_texts_in_parallel( + self, + texts: Sequence[str], + max_workers: int = 20, + retries: int = 1, + delay: float = 0, + ) -> list[list[dict[str, Any]]]: + del retries, delay + if max_workers <= 1 or len(texts) <= 1: + return [ + [annotation.to_dict() for annotation in annotations] + for annotations in self.annotate_texts(texts) + ] + + results: list[list[dict[str, Any]]] = [[] for _ in texts] + with ThreadPoolExecutor(max_workers=min(max_workers, len(texts))) as executor: + future_to_index = { + executor.submit(self.annotate_text, text): index + for index, text in enumerate(texts) + } + for future, index in future_to_index.items(): + try: + results[index] = future.result() + except Exception as exc: + logger.exception("Entity annotation failed for text index %s", index) + results[index] = [ + {"error_code": 1, "error_message": str(exc)} + ] + return results + + +class CompatibilityEntityAnnotator(SchemaEntityAnnotator): + """Named compatibility shim for old call sites expecting BioMedNER shape.""" + + +def build_entity_annotator( + config: dict[str, Any], + *, + embedder: Any | None = None, +) -> CompatibilityEntityAnnotator: + extraction_cfg = dict(config.get("entity_extraction") or {}) + linker_cfg = dict(config.get("concept_linker") or {}) + schema_path = extraction_cfg.get("schema_path") + schemas = load_entity_schemas(schema_path) + recognizer = build_recognizer(extraction_cfg) + + linker: ConceptLinker | None = None + if linker_cfg.get("enabled", True): + store = _build_concept_store(linker_cfg, embedder=embedder) + linker = ConceptLinker( + store, + schemas, + accept_threshold=float(linker_cfg.get("accept_threshold", 0.8)), + reject_threshold=float(linker_cfg.get("reject_threshold", 0.3)), + search_limit=int(linker_cfg.get("search_limit", 10)), + ) + + return CompatibilityEntityAnnotator(recognizer, schemas, linker=linker) + + +def _build_concept_store( + linker_cfg: dict[str, Any], + *, + embedder: Any | None, +) -> LanceDBConceptStore | None: + db_path = linker_cfg.get("db_path") + if not db_path: + logger.warning("Concept linker enabled but concept_linker.db_path is empty.") + return None + + path = Path(db_path) + if not path.exists(): + logger.warning("Concept DB path does not exist; linking will degrade: %s", path) + return None + + try: + return LanceDBConceptStore( + path, + table_name=linker_cfg.get("table", "concepts"), + embedder=embedder, + ) + except Exception as exc: + logger.warning("Concept DB unavailable; linking will degrade: %s", exc) + return None diff --git a/source/Matcher/entities/builder.py b/source/Matcher/entities/builder.py new file mode 100644 index 00000000..5684e023 --- /dev/null +++ b/source/Matcher/entities/builder.py @@ -0,0 +1,183 @@ +from __future__ import annotations + +import csv +from pathlib import Path +from typing import Any, Iterable, Sequence + +from Matcher.entities.types import dedupe_strings + + +DEFAULT_OMOP_VOCABULARIES = ( + "SNOMED", + "ICD10", + "ICD10CM", + "LOINC", + "RxNorm", + "ATC", +) + + +def build_omop_concept_rows( + concept_csv: str | Path, + synonym_csv: str | Path | None = None, + *, + vocabularies: Sequence[str] = DEFAULT_OMOP_VOCABULARIES, +) -> list[dict[str, Any]]: + vocab_filter = {v.casefold() for v in vocabularies} + synonyms_by_concept = _read_omop_synonyms(synonym_csv) if synonym_csv else {} + rows: list[dict[str, Any]] = [] + with Path(concept_csv).open("r", encoding="utf-8-sig", newline="") as handle: + reader = csv.DictReader(handle) + for row in reader: + vocabulary_id = (row.get("vocabulary_id") or "").strip() + if vocab_filter and vocabulary_id.casefold() not in vocab_filter: + continue + concept_id = (row.get("concept_id") or "").strip() + concept_name = (row.get("concept_name") or "").strip() + concept_code = (row.get("concept_code") or "").strip() + if not concept_id or not concept_name or not concept_code: + continue + synonyms = dedupe_strings(tuple(synonyms_by_concept.get(concept_id, ()))) + rows.append( + _concept_row( + concept_id=concept_id, + vocabulary_id=vocabulary_id, + concept_code=concept_code, + concept_name=concept_name, + domain_id=(row.get("domain_id") or "").strip(), + concept_class_id=(row.get("concept_class_id") or "").strip(), + standard_concept=(row.get("standard_concept") or "").strip(), + synonyms=synonyms, + ) + ) + return rows + + +def build_legacy_dictionary_rows( + dictionary_path: str | Path, + *, + vocabulary_id: str, + domain_id: str = "", + concept_class_id: str = "LegacyDictionary", +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + with Path(dictionary_path).open("r", encoding="utf-8") as handle: + for line in handle: + stripped = line.strip() + if not stripped or "||" not in stripped: + continue + identifiers, names = stripped.split("||", 1) + synonyms = dedupe_strings(tuple(names.split("|"))) + concept_name = synonyms[0] if synonyms else "" + if not concept_name: + continue + for raw_identifier in identifiers.split(","): + concept_code = _strip_prefix(raw_identifier.strip(), vocabulary_id) + if not concept_code: + continue + rows.append( + _concept_row( + concept_id=f"{vocabulary_id}:{concept_code}", + vocabulary_id=vocabulary_id, + concept_code=concept_code, + concept_name=concept_name, + domain_id=domain_id, + concept_class_id=concept_class_id, + standard_concept="", + synonyms=synonyms, + ) + ) + return rows + + +def write_lancedb_table( + rows: Sequence[dict[str, Any]], + *, + db_path: str | Path, + table_name: str = "concepts", + embeddings: Sequence[Sequence[float]] | None = None, + recreate: bool = True, +) -> None: + try: + import lancedb # type: ignore + except Exception as exc: # pragma: no cover - optional dependency + raise RuntimeError( + "Building a LanceDB concept table requires `uv sync --extra entity`." + ) from exc + + payload = [dict(row) for row in rows] + if embeddings is not None: + if len(embeddings) != len(payload): + raise ValueError("Embedding count must match concept row count.") + for row, embedding in zip(payload, embeddings): + row["embedding"] = list(embedding) + + db = lancedb.connect(str(db_path)) + mode = "overwrite" if recreate else "create" + table = db.create_table(table_name, data=payload, mode=mode) + try: + table.create_fts_index("fts_text", replace=True) + except Exception: + # Older LanceDB versions or tiny test builds may not support FTS indexes. + pass + + +def concept_texts_for_embedding(rows: Sequence[dict[str, Any]]) -> list[str]: + texts: list[str] = [] + for row in rows: + synonyms = row.get("synonyms") or [] + if isinstance(synonyms, str): + synonyms = synonyms.split("|") + texts.append(" | ".join([row.get("concept_name", ""), *synonyms]).strip()) + return texts + + +def _read_omop_synonyms(path: str | Path | None) -> dict[str, list[str]]: + if path is None: + return {} + synonyms: dict[str, list[str]] = {} + with Path(path).open("r", encoding="utf-8-sig", newline="") as handle: + reader = csv.DictReader(handle) + for row in reader: + concept_id = (row.get("concept_id") or "").strip() + synonym = (row.get("concept_synonym_name") or "").strip() + if concept_id and synonym: + synonyms.setdefault(concept_id, []).append(synonym) + return synonyms + + +def _concept_row( + *, + concept_id: str, + vocabulary_id: str, + concept_code: str, + concept_name: str, + domain_id: str, + concept_class_id: str, + standard_concept: str, + synonyms: Iterable[str], +) -> dict[str, Any]: + synonyms_tuple = dedupe_strings(tuple(synonyms)) + fts_text = " ".join([concept_name, *synonyms_tuple]) + return { + "concept_id": concept_id, + "vocabulary_id": vocabulary_id, + "concept_code": concept_code, + "concept_name": concept_name, + "domain_id": domain_id, + "concept_class_id": concept_class_id, + "standard_concept": standard_concept, + "synonyms": list(synonyms_tuple), + "fts_text": fts_text, + } + + +def _strip_prefix(identifier: str, vocabulary_id: str) -> str: + if not identifier: + return "" + if ":" not in identifier: + return identifier + prefix, code = identifier.split(":", 1) + if prefix.casefold() == vocabulary_id.casefold(): + return code + return identifier diff --git a/source/Matcher/entities/linker.py b/source/Matcher/entities/linker.py new file mode 100644 index 00000000..6fd1605e --- /dev/null +++ b/source/Matcher/entities/linker.py @@ -0,0 +1,352 @@ +from __future__ import annotations + +from dataclasses import replace +from pathlib import Path +from typing import Any, Protocol, Sequence + +from Matcher.entities.schemas import schema_by_label +from Matcher.entities.types import ( + NO_ENTITY_ID, + ConceptCandidate, + EntityAnnotation, + EntitySchema, + dedupe_strings, +) +from Matcher.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +class ConceptStore(Protocol): + def search( + self, + query: str, + *, + vocabularies: Sequence[str] = (), + domain_hints: Sequence[str] = (), + query_vector: Sequence[float] | None = None, + limit: int = 10, + ) -> list[ConceptCandidate]: + ... + + +class InMemoryConceptStore: + def __init__(self, concepts: Sequence[ConceptCandidate | dict[str, Any]]): + self.concepts = [ + item if isinstance(item, ConceptCandidate) else concept_from_mapping(item) + for item in concepts + ] + + def search( + self, + query: str, + *, + vocabularies: Sequence[str] = (), + domain_hints: Sequence[str] = (), + query_vector: Sequence[float] | None = None, + limit: int = 10, + ) -> list[ConceptCandidate]: + vocab_filter = {v.casefold() for v in vocabularies} + domain_filter = {d.casefold() for d in domain_hints} + scored: list[ConceptCandidate] = [] + for concept in self.concepts: + if vocab_filter and concept.vocabulary_id.casefold() not in vocab_filter: + continue + if domain_filter and concept.domain_id.casefold() not in domain_filter: + continue + score = _lexical_score(query, concept) + if score <= 0: + continue + scored.append( + replace( + concept, + score=score, + source_scores={"lexical": score}, + ) + ) + scored.sort(key=lambda item: item.score, reverse=True) + return scored[:limit] + + +class LanceDBConceptStore: + def __init__( + self, + db_path: str | Path, + *, + table_name: str = "concepts", + embedder: Any | None = None, + ): + try: + import lancedb # type: ignore + except Exception as exc: # pragma: no cover - optional dependency + raise RuntimeError( + "LanceDB concept linking requires the entity extra " + "(`uv sync --extra entity`)." + ) from exc + + self.db_path = str(db_path) + self.table_name = table_name + self.embedder = embedder + self.db = lancedb.connect(self.db_path) + self.table = self.db.open_table(table_name) + + def search( + self, + query: str, + *, + vocabularies: Sequence[str] = (), + domain_hints: Sequence[str] = (), + query_vector: Sequence[float] | None = None, + limit: int = 10, + ) -> list[ConceptCandidate]: + vector = query_vector or self._embed_query(query) + lexical_rows = self._search_fts(query, vocabularies, domain_hints, limit) + vector_rows = ( + self._search_vector(vector, vocabularies, domain_hints, limit) + if vector + else [] + ) + return _rrf_merge(lexical_rows, vector_rows, limit) + + def _embed_query(self, query: str) -> list[float] | None: + if self.embedder is None: + return None + if hasattr(self.embedder, "embed_text"): + return self.embedder.embed_text(query) + if hasattr(self.embedder, "embed_texts"): + vectors = self.embedder.embed_texts([query]) + return vectors[0] if vectors else None + return None + + def _search_fts( + self, + query: str, + vocabularies: Sequence[str], + domain_hints: Sequence[str], + limit: int, + ) -> list[ConceptCandidate]: + try: + search = self.table.search(query, query_type="fts") + where = _lancedb_filter(vocabularies, domain_hints) + if where: + search = search.where(where) + rows = search.limit(limit).to_list() + except Exception as exc: + logger.warning("LanceDB FTS concept search failed: %s", exc) + rows = [] + return [concept_from_mapping(row) for row in rows] + + def _search_vector( + self, + vector: Sequence[float], + vocabularies: Sequence[str], + domain_hints: Sequence[str], + limit: int, + ) -> list[ConceptCandidate]: + try: + search = self.table.search(vector) + where = _lancedb_filter(vocabularies, domain_hints) + if where: + search = search.where(where) + rows = search.limit(limit).to_list() + except Exception as exc: + logger.warning("LanceDB vector concept search failed: %s", exc) + rows = [] + return [concept_from_mapping(row) for row in rows] + + +class ConceptLinker: + def __init__( + self, + store: ConceptStore | None, + schemas: Sequence[EntitySchema], + *, + accept_threshold: float = 0.8, + reject_threshold: float = 0.3, + search_limit: int = 10, + ): + if reject_threshold > accept_threshold: + raise ValueError("reject_threshold must be <= accept_threshold.") + self.store = store + self.schemas = {schema.id: schema for schema in schemas} + self.schemas_by_label = schema_by_label(list(schemas)) + self.accept_threshold = accept_threshold + self.reject_threshold = reject_threshold + self.search_limit = search_limit + + def link_annotations( + self, annotations: Sequence[EntityAnnotation] + ) -> list[EntityAnnotation]: + return [self.link_annotation(annotation) for annotation in annotations] + + def link_annotation(self, annotation: EntityAnnotation) -> EntityAnnotation: + schema = self._schema_for(annotation) + if schema is None or not schema.is_linkable: + return replace(annotation, linker_status="not_linkable") + if self.store is None: + return replace( + annotation, + normalized_id=(NO_ENTITY_ID,), + linker_status="concept_store_unavailable", + ) + + candidates = self.store.search( + annotation.text, + vocabularies=schema.target_vocabularies, + domain_hints=schema.domain_hints, + limit=self.search_limit, + ) + if not candidates: + return replace( + annotation, + normalized_id=(NO_ENTITY_ID,), + concept_candidates=(), + linker_score=0.0, + linker_status="rejected", + ) + + top = candidates[0] + status = _status_for_score( + top.score, + accept_threshold=self.accept_threshold, + reject_threshold=self.reject_threshold, + ) + if status == "accepted": + return replace( + annotation, + normalized_id=(top.normalized_id,), + synonyms=_candidate_synonyms(top), + concept_candidates=tuple(candidates), + linker_score=top.score, + linker_status=status, + ) + return replace( + annotation, + normalized_id=(NO_ENTITY_ID,), + synonyms=(), + concept_candidates=tuple(candidates), + linker_score=top.score, + linker_status=status, + ) + + def _schema_for(self, annotation: EntityAnnotation) -> EntitySchema | None: + if annotation.schema_id and annotation.schema_id in self.schemas: + return self.schemas[annotation.schema_id] + return self.schemas_by_label.get(annotation.entity_group.casefold()) + + +def concept_from_mapping(row: dict[str, Any]) -> ConceptCandidate: + synonyms = row.get("synonyms") or () + if isinstance(synonyms, str): + synonyms = [part.strip() for part in synonyms.split("|")] + return ConceptCandidate( + concept_id=str(row.get("concept_id") or ""), + vocabulary_id=str(row.get("vocabulary_id") or ""), + concept_code=str(row.get("concept_code") or ""), + concept_name=str(row.get("concept_name") or ""), + domain_id=str(row.get("domain_id") or ""), + concept_class_id=str(row.get("concept_class_id") or ""), + standard_concept=str(row.get("standard_concept") or ""), + synonyms=dedupe_strings(tuple(synonyms)), + score=float(row.get("score") or row.get("_score") or 0.0), + source_scores=dict(row.get("source_scores") or {}), + ) + + +def _candidate_synonyms(candidate: ConceptCandidate) -> tuple[str, ...]: + return dedupe_strings((candidate.concept_name, *candidate.synonyms)) + + +def _status_for_score( + score: float, *, accept_threshold: float, reject_threshold: float +) -> str: + if score >= accept_threshold: + return "accepted" + if score < reject_threshold: + return "rejected" + return "ambiguous" + + +def _lexical_score(query: str, concept: ConceptCandidate) -> float: + query_norm = _normalize_text(query) + names = [concept.concept_name, *concept.synonyms] + best = 0.0 + for name in names: + name_norm = _normalize_text(name) + if not name_norm: + continue + if query_norm == name_norm: + best = max(best, 1.0) + elif query_norm in name_norm or name_norm in query_norm: + best = max(best, 0.86) + else: + best = max(best, _token_jaccard(query_norm, name_norm)) + return best + + +def _normalize_text(value: str) -> str: + return " ".join(value.casefold().replace("-", " ").split()) + + +def _token_jaccard(left: str, right: str) -> float: + left_tokens = set(left.split()) + right_tokens = set(right.split()) + if not left_tokens or not right_tokens: + return 0.0 + overlap = len(left_tokens & right_tokens) + if overlap == 0: + return 0.0 + return 0.2 + 0.6 * (overlap / len(left_tokens | right_tokens)) + + +def _lancedb_filter( + vocabularies: Sequence[str], + domain_hints: Sequence[str], +) -> str: + clauses: list[str] = [] + if vocabularies: + quoted = ", ".join(f"'{_sql_escape(v)}'" for v in vocabularies) + clauses.append(f"vocabulary_id IN ({quoted})") + if domain_hints: + quoted = ", ".join(f"'{_sql_escape(d)}'" for d in domain_hints) + clauses.append(f"domain_id IN ({quoted})") + return " AND ".join(clauses) + + +def _sql_escape(value: str) -> str: + return value.replace("'", "''") + + +def _rrf_merge( + lexical_rows: Sequence[ConceptCandidate], + vector_rows: Sequence[ConceptCandidate], + limit: int, + *, + k: int = 60, +) -> list[ConceptCandidate]: + by_id: dict[str, ConceptCandidate] = {} + scores: dict[str, float] = {} + sources: dict[str, dict[str, float]] = {} + + for source_name, rows in (("fts", lexical_rows), ("vector", vector_rows)): + for rank, row in enumerate(rows, start=1): + key = row.normalized_id + by_id.setdefault(key, row) + score = 1.0 / (k + rank) + scores[key] = scores.get(key, 0.0) + score + sources.setdefault(key, {})[source_name] = score + + if not by_id: + return [] + + max_score = max(scores.values()) or 1.0 + merged = [ + replace( + row, + score=min(1.0, scores[key] / max_score), + source_scores=sources.get(key, {}), + ) + for key, row in by_id.items() + ] + merged.sort(key=lambda item: (-item.score, item.concept_name)) + return merged[:limit] diff --git a/source/Matcher/entities/recognizers.py b/source/Matcher/entities/recognizers.py new file mode 100644 index 00000000..3e7e2b51 --- /dev/null +++ b/source/Matcher/entities/recognizers.py @@ -0,0 +1,278 @@ +from __future__ import annotations + +import re +from dataclasses import replace +from typing import Any, Protocol, Sequence + +from Matcher.entities.schemas import schema_by_label +from Matcher.entities.types import EntityAnnotation, EntitySchema, NO_ENTITY_ID + + +class EntityRecognizer(Protocol): + def recognize( + self, texts: Sequence[str], schemas: Sequence[EntitySchema] + ) -> list[list[EntityAnnotation]]: + ... + + +class DisabledRecognizer: + def recognize( + self, texts: Sequence[str], schemas: Sequence[EntitySchema] + ) -> list[list[EntityAnnotation]]: + return [[] for _ in texts] + + +class RegexSchemaRecognizer: + """Small deterministic recognizer used for smoke tests and fixture runs.""" + + def recognize( + self, texts: Sequence[str], schemas: Sequence[EntitySchema] + ) -> list[list[EntityAnnotation]]: + compiled = [ + (schema, re.compile(pattern, re.IGNORECASE)) + for schema in schemas + for pattern in schema.patterns + ] + results: list[list[EntityAnnotation]] = [] + for text in texts: + annotations: list[EntityAnnotation] = [] + for schema, pattern in compiled: + for match in pattern.finditer(text): + mention = match.group(0) + annotations.append( + EntityAnnotation( + entity_group=schema.entity_group, + text=mention, + start=match.start(), + end=match.end(), + score=max(schema.threshold, 0.95), + normalized_id=(NO_ENTITY_ID,), + schema_id=schema.id, + ) + ) + results.append(resolve_overlaps(annotations)) + return results + + +class GLiNER2Recognizer: + def __init__( + self, + model_name: str, + *, + revision: str | None = None, + device: str | None = None, + trust_remote_code: bool = False, + batch_size: int = 8, + ): + try: + from gliner2 import GLiNER2 # type: ignore + except Exception as exc: # pragma: no cover - exercised without optional dep + raise RuntimeError( + "entity_extraction.backend=gliner2 requires the entity extra " + "(`uv sync --extra entity`) and a GLiNER2-compatible model." + ) from exc + + kwargs: dict[str, Any] = {} + if revision: + kwargs["revision"] = revision + if trust_remote_code: + kwargs["trust_remote_code"] = trust_remote_code + self.model = GLiNER2.from_pretrained(model_name, **kwargs) + if device and device != "auto" and hasattr(self.model, "to"): + self.model.to(device) + self.batch_size = batch_size + + def recognize( + self, texts: Sequence[str], schemas: Sequence[EntitySchema] + ) -> list[list[EntityAnnotation]]: + labels = [schema.label for schema in schemas] + label_map = schema_by_label(list(schemas)) + return [ + resolve_overlaps( + _parse_model_entities( + _call_extractor(self.model, text, labels, schemas), + text, + label_map, + ) + ) + for text in texts + ] + + +class GLiNERRecognizer: + def __init__( + self, + model_name: str, + *, + revision: str | None = None, + device: str | None = None, + trust_remote_code: bool = False, + batch_size: int = 8, + ): + try: + from gliner import GLiNER # type: ignore + except Exception as exc: # pragma: no cover - optional dependency + raise RuntimeError( + "entity_extraction.backend=gliner requires the GLiNER dependency." + ) from exc + + kwargs: dict[str, Any] = {} + if revision: + kwargs["revision"] = revision + if trust_remote_code: + kwargs["trust_remote_code"] = trust_remote_code + self.model = GLiNER.from_pretrained(model_name, **kwargs) + if device and device != "auto" and hasattr(self.model, "to"): + self.model.to(device) + self.batch_size = batch_size + + def recognize( + self, texts: Sequence[str], schemas: Sequence[EntitySchema] + ) -> list[list[EntityAnnotation]]: + labels = [schema.label for schema in schemas] + label_map = schema_by_label(list(schemas)) + results: list[list[EntityAnnotation]] = [] + for text in texts: + raw = self.model.predict_entities(text, labels) + results.append(resolve_overlaps(_parse_model_entities(raw, text, label_map))) + return results + + +def build_recognizer(config: dict[str, Any]) -> EntityRecognizer: + backend = str(config.get("backend", "gliner2")).lower() + if backend == "disabled": + return DisabledRecognizer() + if backend == "regex": + return RegexSchemaRecognizer() + if backend == "gliner": + return GLiNERRecognizer( + model_name=config.get("fallback_model_name") + or config.get("model_name") + or "urchade/gliner_base", + revision=config.get("model_revision"), + device=config.get("device", "auto"), + trust_remote_code=bool(config.get("trust_remote_code", False)), + batch_size=int(config.get("batch_size", 8)), + ) + if backend == "gliner2": + return GLiNER2Recognizer( + model_name=config.get("model_name", "fastino/gliner2-base"), + revision=config.get("model_revision"), + device=config.get("device", "auto"), + trust_remote_code=bool(config.get("trust_remote_code", False)), + batch_size=int(config.get("batch_size", 8)), + ) + raise ValueError( + "entity_extraction.backend must be one of: gliner2, gliner, regex, disabled." + ) + + +def resolve_overlaps( + annotations: Sequence[EntityAnnotation], +) -> list[EntityAnnotation]: + ranked = sorted( + annotations, + key=lambda ann: (ann.score, ann.end - ann.start), + reverse=True, + ) + accepted: list[EntityAnnotation] = [] + for candidate in ranked: + if candidate.start < 0 or candidate.end <= candidate.start: + continue + if any(_overlaps(candidate, current) for current in accepted): + continue + accepted.append(candidate) + return sorted(accepted, key=lambda ann: (ann.start, ann.end)) + + +def _overlaps(left: EntityAnnotation, right: EntityAnnotation) -> bool: + return left.start < right.end and right.start < left.end + + +def _call_extractor( + model: Any, + text: str, + labels: list[str], + schemas: Sequence[EntitySchema], +) -> list[dict[str, Any]]: + if hasattr(model, "extract_entities"): + schema_payload = { + schema.label: schema.description + for schema in schemas + } + for kwargs in ( + {"schema": schema_payload}, + {"labels": labels}, + {}, + ): + try: + return model.extract_entities(text, **kwargs) + except TypeError: + continue + if hasattr(model, "predict_entities"): + return model.predict_entities(text, labels) + raise RuntimeError("Selected entity model does not expose an entity extraction API.") + + +def _parse_model_entities( + raw_entities: Sequence[dict[str, Any]], + text: str, + label_map: dict[str, EntitySchema], +) -> list[EntityAnnotation]: + parsed: list[EntityAnnotation] = [] + for raw in raw_entities or []: + label = str(raw.get("label") or raw.get("entity_group") or raw.get("type") or "") + schema = label_map.get(label.casefold()) + if schema is None: + continue + mention = str(raw.get("text") or raw.get("span") or raw.get("mention") or "") + start = _as_int(raw.get("start")) + end = _as_int(raw.get("end")) + if start is None or end is None: + start, end = _find_span(text, mention) + if start is None or end is None: + continue + mention = text[start:end] + score = float(raw.get("score") or raw.get("confidence") or 0.0) + if score < schema.threshold: + continue + parsed.append( + EntityAnnotation( + entity_group=schema.entity_group, + text=mention, + start=start, + end=end, + score=score, + normalized_id=(NO_ENTITY_ID,), + schema_id=schema.id, + ) + ) + return parsed + + +def _find_span(text: str, mention: str) -> tuple[int | None, int | None]: + if not mention: + return None, None + start = text.casefold().find(mention.casefold()) + if start < 0: + return None, None + return start, start + len(mention) + + +def _as_int(value: Any) -> int | None: + try: + if value is None: + return None + return int(value) + except (TypeError, ValueError): + return None + + +def with_schema_threshold( + annotation: EntityAnnotation, + schemas: Sequence[EntitySchema], +) -> EntityAnnotation | None: + schema = next((item for item in schemas if item.id == annotation.schema_id), None) + if schema is not None and annotation.score < schema.threshold: + return None + return replace(annotation) diff --git a/source/Matcher/entities/schemas.py b/source/Matcher/entities/schemas.py new file mode 100644 index 00000000..db3a182d --- /dev/null +++ b/source/Matcher/entities/schemas.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +from importlib import resources +from pathlib import Path +from typing import Any + +import yaml + +from Matcher.entities.types import EntitySchema + + +DEFAULT_SCHEMA_RESOURCE = "entity_schemas/trialmatchai.yaml" + + +class EntitySchemaError(ValueError): + pass + + +def default_schema_path() -> Path: + return Path(str(resources.files("Matcher").joinpath(DEFAULT_SCHEMA_RESOURCE))) + + +def load_entity_schemas(path: str | Path | None = None) -> list[EntitySchema]: + schema_path = Path(path).expanduser() if path else default_schema_path() + with schema_path.open("r", encoding="utf-8") as handle: + raw = yaml.safe_load(handle) or {} + return parse_entity_schemas(raw) + + +def parse_entity_schemas(raw: dict[str, Any]) -> list[EntitySchema]: + entries = raw.get("entities") + if not isinstance(entries, list) or not entries: + raise EntitySchemaError("Entity schema must contain a non-empty 'entities' list.") + + schemas: list[EntitySchema] = [] + seen_ids: set[str] = set() + for entry in entries: + if not isinstance(entry, dict): + raise EntitySchemaError("Each entity schema entry must be an object.") + + schema_id = _required_string(entry, "id") + if schema_id in seen_ids: + raise EntitySchemaError(f"Duplicate entity schema id: {schema_id}") + seen_ids.add(schema_id) + + threshold = float(entry.get("threshold", 0.8)) + if not 0 <= threshold <= 1: + raise EntitySchemaError( + f"Entity schema '{schema_id}' threshold must be between 0 and 1." + ) + + target_vocabularies = _string_tuple(entry.get("target_vocabularies", ())) + if entry.get("linkable_fields", ["text"]) and not target_vocabularies: + raise EntitySchemaError( + f"Entity schema '{schema_id}' is linkable but has no target vocabularies." + ) + + schemas.append( + EntitySchema( + id=schema_id, + label=_required_string(entry, "label"), + entity_group=str(entry.get("entity_group") or schema_id), + description=_required_string(entry, "description"), + target_vocabularies=target_vocabularies, + domain_hints=_string_tuple(entry.get("domain_hints", ())), + linkable_fields=_string_tuple(entry.get("linkable_fields", ("text",))), + threshold=threshold, + query_expansion=bool(entry.get("query_expansion", False)), + patterns=_string_tuple(entry.get("patterns", ())), + aliases=_string_tuple(entry.get("aliases", ())), + ) + ) + return schemas + + +def schema_by_label(schemas: list[EntitySchema]) -> dict[str, EntitySchema]: + mapping: dict[str, EntitySchema] = {} + for schema in schemas: + for label in schema.recognizer_labels: + mapping[label.casefold()] = schema + return mapping + + +def _required_string(entry: dict[str, Any], key: str) -> str: + value = entry.get(key) + if not isinstance(value, str) or not value.strip(): + raise EntitySchemaError(f"Entity schema field '{key}' must be a non-empty string.") + return value.strip() + + +def _string_tuple(value: Any) -> tuple[str, ...]: + if value is None: + return () + if isinstance(value, str): + return (value.strip(),) if value.strip() else () + if not isinstance(value, list | tuple): + raise EntitySchemaError("Schema string-list fields must be strings or lists.") + cleaned = [] + for item in value: + if not isinstance(item, str): + raise EntitySchemaError("Schema string-list fields may only contain strings.") + if item.strip(): + cleaned.append(item.strip()) + return tuple(cleaned) diff --git a/source/Matcher/entities/types.py b/source/Matcher/entities/types.py new file mode 100644 index 00000000..970647ab --- /dev/null +++ b/source/Matcher/entities/types.py @@ -0,0 +1,141 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + + +NO_ENTITY_ID = "CUI-less" + + +@dataclass(frozen=True) +class EntitySchema: + id: str + label: str + entity_group: str + description: str + target_vocabularies: tuple[str, ...] = () + domain_hints: tuple[str, ...] = () + linkable_fields: tuple[str, ...] = ("text",) + threshold: float = 0.8 + query_expansion: bool = False + patterns: tuple[str, ...] = () + aliases: tuple[str, ...] = () + + @property + def is_linkable(self) -> bool: + return bool(self.linkable_fields) + + @property + def recognizer_labels(self) -> tuple[str, ...]: + labels = [self.label, self.id, self.entity_group, *self.aliases] + return tuple(dict.fromkeys(label for label in labels if label)) + + +@dataclass(frozen=True) +class ConceptCandidate: + concept_id: str + vocabulary_id: str + concept_code: str + concept_name: str + domain_id: str = "" + concept_class_id: str = "" + standard_concept: str = "" + synonyms: tuple[str, ...] = () + score: float = 0.0 + source_scores: dict[str, float] = field(default_factory=dict) + + @property + def normalized_id(self) -> str: + return normalize_concept_id(self.vocabulary_id, self.concept_code) + + def to_dict(self) -> dict[str, Any]: + return { + "concept_id": self.concept_id, + "vocabulary_id": self.vocabulary_id, + "concept_code": self.concept_code, + "concept_name": self.concept_name, + "domain_id": self.domain_id, + "concept_class_id": self.concept_class_id, + "standard_concept": self.standard_concept, + "synonyms": list(self.synonyms), + "score": self.score, + "source_scores": dict(self.source_scores), + "normalized_id": self.normalized_id, + } + + +@dataclass(frozen=True) +class EntityAnnotation: + entity_group: str + text: str + start: int + end: int + score: float + normalized_id: tuple[str, ...] = (NO_ENTITY_ID,) + synonyms: tuple[str, ...] = () + concept_candidates: tuple[ConceptCandidate, ...] = () + linker_score: float | None = None + linker_status: str = "not_linked" + schema_id: str | None = None + + def to_dict(self) -> dict[str, Any]: + return { + "entity_group": self.entity_group, + "text": self.text, + "start": self.start, + "end": self.end, + "score": self.score, + "normalized_id": list(self.normalized_id), + "synonyms": list(self.synonyms), + "concept_candidates": [ + candidate.to_dict() for candidate in self.concept_candidates + ], + "linker_score": self.linker_score, + "linker_status": self.linker_status, + } + + def to_index_entity(self) -> dict[str, Any]: + data = self.to_dict() + data["entity"] = self.text + data["class"] = self.entity_group + return data + + +def normalize_concept_id(vocabulary_id: str, concept_code: str) -> str: + vocab = vocabulary_id.strip() + code = concept_code.strip() + if not vocab or not code: + return NO_ENTITY_ID + if ":" in code: + return code + + canonical_vocab = { + "SNOMED": "SNOMED", + "SNOMEDCT": "SNOMED", + "ICD10": "ICD10", + "ICD10CM": "ICD10CM", + "LOINC": "LOINC", + "RXNORM": "RxNorm", + "RXNORM EXTENSION": "RxNorm", + "ATC": "ATC", + "ENTREZGENE": "EntrezGene", + "NCBIGENE": "EntrezGene", + "CELLOSAURUS": "Cellosaurus", + "NCBITAXON": "NCBITaxon", + }.get(vocab.upper(), vocab) + return f"{canonical_vocab}:{code}" + + +def dedupe_strings(values: list[str] | tuple[str, ...]) -> tuple[str, ...]: + seen: set[str] = set() + deduped: list[str] = [] + for value in values: + cleaned = value.strip() + if not cleaned: + continue + key = cleaned.casefold() + if key in seen: + continue + seen.add(key) + deduped.append(cleaned) + return tuple(deduped) diff --git a/source/Matcher/entity_schemas/trialmatchai.yaml b/source/Matcher/entity_schemas/trialmatchai.yaml new file mode 100644 index 00000000..95acc4fc --- /dev/null +++ b/source/Matcher/entity_schemas/trialmatchai.yaml @@ -0,0 +1,122 @@ +version: 1 +entities: + - id: disease + label: disease or condition + entity_group: disease + description: Diagnosed diseases, clinical conditions, syndromes, and disorders. + aliases: [condition, disorder, syndrome] + target_vocabularies: [SNOMED, ICD10, ICD10CM] + domain_hints: [Condition] + linkable_fields: [text] + threshold: 0.80 + query_expansion: true + patterns: + - "\\b(?:cancer|carcinoma|diabetes|melanoma|leukemia|lymphoma|asthma|hypertension)\\b" + - id: gene + label: gene + entity_group: gene + description: Human genes or gene products mentioned as trial criteria. + aliases: [gene symbol, biomarker] + target_vocabularies: [EntrezGene] + domain_hints: [Gene] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:BRCA1|BRCA2|EGFR|ALK|KRAS|BRAF|TP53|ERBB2|HER2)\\b" + - id: medication + label: drug or medication + entity_group: drug + description: Drugs, medications, biologics, compounds, and therapeutic agents. + aliases: [drug, medication, biologic, compound] + target_vocabularies: [RxNorm, ATC] + domain_hints: [Drug] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:aspirin|metformin|pembrolizumab|nivolumab|cisplatin|paclitaxel)\\b" + - id: procedure + label: procedure + entity_group: procedure + description: Clinical procedures, interventions, treatments, and surgeries. + aliases: [treatment, surgical procedure, intervention] + target_vocabularies: [SNOMED] + domain_hints: [Procedure] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:surgery|radiotherapy|chemotherapy|biopsy|transplantation)\\b" + - id: diagnostic_test + label: diagnostic test + entity_group: diagnostic test + description: Diagnostic assays and tests used to establish eligibility. + aliases: [test, assay] + target_vocabularies: [LOINC, SNOMED] + domain_hints: [Measurement, Procedure] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:MRI|CT scan|PET scan|biopsy|ECG|EKG)\\b" + - id: laboratory_test + label: laboratory test + entity_group: laboratory test + description: Laboratory measurements, lab panels, biomarkers, and analytes. + aliases: [lab test, laboratory value, analyte] + target_vocabularies: [LOINC] + domain_hints: [Measurement] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:hemoglobin|creatinine|ALT|AST|bilirubin|platelet count)\\b" + - id: radiology + label: radiology + entity_group: radiology + description: Imaging modalities and radiology findings used in criteria. + aliases: [imaging] + target_vocabularies: [LOINC, SNOMED] + domain_hints: [Measurement, Procedure] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:radiograph|ultrasound|mammogram|MRI|CT)\\b" + - id: sign_symptom + label: sign or symptom + entity_group: sign symptom + description: Patient signs, symptoms, and clinical findings. + aliases: [symptom, sign] + target_vocabularies: [SNOMED] + domain_hints: [Condition, Observation] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:fever|pain|fatigue|nausea|dyspnea|cough)\\b" + - id: cell_type + label: cell type + entity_group: cell type + description: Cell types, immune cells, and tissue cell populations. + aliases: [cell population] + target_vocabularies: [Cellosaurus] + domain_hints: [Cell] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:T cell|B cell|macrophage|neutrophil|fibroblast)\\b" + - id: species + label: species + entity_group: species + description: Species and organisms in translational eligibility criteria. + aliases: [organism] + target_vocabularies: [NCBITaxon] + domain_hints: [Species] + linkable_fields: [text] + threshold: 0.80 + query_expansion: false + patterns: + - "\\b(?:human|mouse|rat|zebrafish|macaque)\\b" diff --git a/source/Matcher/main.py b/source/Matcher/main.py index b722de6b..acf278f6 100644 --- a/source/Matcher/main.py +++ b/source/Matcher/main.py @@ -11,6 +11,7 @@ from Matcher.models.llm.llm_loader import load_model_and_tokenizer from Matcher.models.llm.llm_reranker import LLMReranker from Matcher.models.llm.vllm_loader import load_vllm_engine +from Matcher.entities import build_entity_annotator from Matcher.pipeline.cot_reasoning import BatchTrialProcessor from Matcher.pipeline.cot_reasoning_vllm import BatchTrialProcessorVLLM from Matcher.pipeline.phenopacket_processor import process_phenopacket @@ -21,7 +22,6 @@ ) from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch from Matcher.pipeline.trial_search.second_level_search import SecondStageRetriever -from Matcher.services.biomedner_service import initialize_biomedner_services from Matcher.services.elasticsearch_service import ( build_elasticsearch_client, ensure_elasticsearch, @@ -45,7 +45,7 @@ def run_first_level_search( keywords: Dict, output_folder: str, patient_info: Dict, - bio_med_ner, + entity_annotator, embedder: TextEmbedder, config: Dict, es_client: Elasticsearch, @@ -64,7 +64,12 @@ def run_first_level_search( overall_status = "All" index_name = config["elasticsearch"]["index_trials"] - cts = ClinicalTrialSearch(es_client, embedder, index_name, bio_med_ner) + cts = ClinicalTrialSearch( + es_client, + embedder, + index_name, + entity_annotator=entity_annotator, + ) # Get synonyms and expand main conditions synonyms = cts.get_synonyms(condition.lower().strip()) @@ -250,9 +255,6 @@ def main_pipeline(config_path: str | None = None) -> int: if hasattr(torch.backends.cuda, "enable_flash_sdp"): torch.backends.cuda.enable_flash_sdp(True) - initialize_biomedner_services(config) - from Parser.biomedner_engine import BioMedNER - import warnings with warnings.catch_warnings(): @@ -286,7 +288,7 @@ def main_pipeline(config_path: str | None = None) -> int: normalize=embedder_cfg.get("normalize", True), ) ) - bio_med_ner = BioMedNER(**config["bio_med_ner"]) + entity_annotator = build_entity_annotator(config, embedder=embedder) with warnings.catch_warnings(): warnings.filterwarnings( @@ -306,7 +308,7 @@ def main_pipeline(config_path: str | None = None) -> int: llm_reranker=llm_reranker, embedder=embedder, index_name=config["elasticsearch"]["index_trials_eligibility"], - bio_med_ner=bio_med_ner, + entity_annotator=entity_annotator, ) # Process phenopackets @@ -352,7 +354,7 @@ def main_pipeline(config_path: str | None = None) -> int: keywords, str(output_folder), patient_info, - bio_med_ner, + entity_annotator, embedder, config, es_client, diff --git a/source/Matcher/pipeline/trial_search/first_level_search.py b/source/Matcher/pipeline/trial_search/first_level_search.py index 547b44db..324e2ac7 100644 --- a/source/Matcher/pipeline/trial_search/first_level_search.py +++ b/source/Matcher/pipeline/trial_search/first_level_search.py @@ -17,19 +17,20 @@ def __init__( es_client: Elasticsearch, embedder: Optional[TextEmbedder], index_name: str, - bio_med_ner, + bio_med_ner=None, + entity_annotator=None, ): self.es_client = es_client self.embedder = embedder self.index_name = index_name - self.bio_med_ner = bio_med_ner + self.entity_annotator = entity_annotator or bio_med_ner def get_synonyms(self, condition: str) -> List[str]: - if not self.bio_med_ner: - logger.info("BioMedNER disabled; skipping synonyms extraction.") + if not self.entity_annotator: + logger.info("Entity annotator disabled; skipping synonyms extraction.") return [] try: - raw_result = self.bio_med_ner.annotate_texts_in_parallel( + raw_result = self.entity_annotator.annotate_texts_in_parallel( [condition], max_workers=1 ) ner_results = raw_result @@ -41,7 +42,7 @@ def get_synonyms(self, condition: str) -> List[str]: return list(synonyms) logger.warning(f"No annotations found for condition: {condition}") except Exception as e: - logger.error(f"BioMedNER synonym extraction failed for '{condition}': {e}") + logger.error(f"Entity synonym extraction failed for '{condition}': {e}") return [] def parse_age_input(self, age_input: Union[int, str]) -> Optional[int]: diff --git a/source/Matcher/pipeline/trial_search/second_level_search.py b/source/Matcher/pipeline/trial_search/second_level_search.py index 4bc1c78a..ffbd49aa 100644 --- a/source/Matcher/pipeline/trial_search/second_level_search.py +++ b/source/Matcher/pipeline/trial_search/second_level_search.py @@ -25,6 +25,7 @@ def __init__( inclusion_weight: float = 1.0, exclusion_weight: float = 0.25, bio_med_ner=None, + entity_annotator=None, search_mode: str = "hybrid", ): self.es_client = es_client @@ -34,14 +35,14 @@ def __init__( self.size = size self.inclusion_weight = inclusion_weight self.exclusion_weight = exclusion_weight - self.bio_med_ner = bio_med_ner + self.entity_annotator = entity_annotator or bio_med_ner self.search_mode = search_mode.lower() if search_mode else "hybrid" def get_synonyms(self, condition: str) -> List[str]: - if self.bio_med_ner is None: - logger.warning("BioMedNER not initialized; cannot extract synonyms.") + if self.entity_annotator is None: + logger.warning("Entity annotator not initialized; cannot extract synonyms.") return [] - raw_result = self.bio_med_ner.annotate_texts_in_parallel( + raw_result = self.entity_annotator.annotate_texts_in_parallel( [condition], max_workers=1 ) ner_results = raw_result @@ -60,10 +61,10 @@ def retrieve_criteria( query_to_hits = {} def execute_query(query): - # Use entities.synonyms only if BioMedNER is enabled + # Use entities.synonyms only if entity annotation is enabled. fields_to_search = ( ["criterion", "entities.synonyms"] - if self.bio_med_ner is not None + if self.entity_annotator is not None else ["criterion"] ) @@ -225,10 +226,10 @@ def execute_query(query): return query, [] def execute_query_bm25(query): - # Helper function for BM25-only fallback + # Helper function for BM25-only fallback. fields_to_search = ( ["criterion", "entities.synonyms"] - if self.bio_med_ner is not None + if self.entity_annotator is not None else ["criterion"] ) es_query = { diff --git a/source/Matcher/services/biomedner_service.py b/source/Matcher/services/biomedner_service.py deleted file mode 100644 index 68427a82..00000000 --- a/source/Matcher/services/biomedner_service.py +++ /dev/null @@ -1,44 +0,0 @@ -import socket -import subprocess -import time - -from Matcher.utils.logging_config import setup_logging - -logger = setup_logging(__name__) - - -def is_port_in_use(port: int, host: str = "127.0.0.1") -> bool: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - return s.connect_ex((host, port)) == 0 - - -def check_ports_in_use(ports: list) -> bool: - return any(is_port_in_use(port) for port in ports) - - -def run_script(script_path: str): - logger.info(f"Executing: {script_path}") - subprocess.run(["bash", script_path], check=True) - - -def initialize_biomedner_services(config: dict): - if not config.get("services", {}).get("auto_start", False): - logger.info( - "BioMedNER auto-start is disabled. Set TRIALMATCHAI_BIOMEDNER_AUTO_START=true to start local services." - ) - return - - ports_to_check = [ - config["bio_med_ner"]["biomedner_port"], - config["bio_med_ner"]["gner_port"], - config["bio_med_ner"]["gene_norm_port"], - config["bio_med_ner"]["disease_norm_port"], - ] - if check_ports_in_use(ports_to_check): - logger.info("Detected active services. Stopping running instances...") - run_script(config["services"]["stop_script"]) - logger.info("Waiting for 10 seconds before restarting...") - time.sleep(10) - logger.info("Starting BioMedNER services...") - run_script(config["services"]["run_script"]) - logger.info("BioMedNER services started successfully.") diff --git a/source/Matcher/services/preflight.py b/source/Matcher/services/preflight.py index 52670b12..57273448 100644 --- a/source/Matcher/services/preflight.py +++ b/source/Matcher/services/preflight.py @@ -48,6 +48,34 @@ def run_preflight_checks( ) if require_models: + entity_cfg = config.get("entity_extraction") + if entity_cfg: + _require_path( + issues, + "entity_extraction.schema_path", + entity_cfg.get("schema_path"), + required=True, + ) + backend = entity_cfg.get("backend", "gliner2") + if backend == "gliner2" and importlib.util.find_spec("gliner2") is None: + issues.append( + "entity_extraction.backend=gliner2 requires the entity extra " + "(`uv sync --extra entity`)." + ) + elif backend == "gliner" and importlib.util.find_spec("gliner") is None: + issues.append( + "entity_extraction.backend=gliner requires the GLiNER dependency." + ) + + linker_cfg = config.get("concept_linker") + if linker_cfg and linker_cfg.get("enabled", True): + _require_path( + issues, + "concept_linker.db_path", + linker_cfg.get("db_path"), + required=False, + ) + model_cfg = config.get("model", {}) _require_path( issues, diff --git a/source/Parser/bin/crf_learn b/source/Parser/bin/crf_learn deleted file mode 100755 index 410b4cbb9ef66fcd561b01e0c2dc8be7cc6f41f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 15960 zcmeHOYit}>6~1dbjYHDdZWC~v5-+7xl+@!#9Lp`FSwAxdCozsAga}M#cgNn9_Qmcj zwNuo(3j7FZr1no66@D0m)JRAu;-yxKgiA^#eiRZ3RY4$;A|eurM_QUH1?uITx#wGt zr@KL*N`N%i+Wqc%eD~ft_s;HJ&$*9{O^!E2A_}EZJ)~GFv}#RE45tUALJ(7f>UMhX zR6EpdBsY0FZI5Xnt1g!s)?%~h8wr`$&XpQ?msTqvM##v#dS!|SL`=s9J=Br|C9cvgx3`Hno&XQ*J=esf1f6vOs#m_8Zgb% z(qFxKNJ%__vJ6yrCLJH>@6IHKYdWt%4HtFxEVH>Zb=YFC!{6APne}5hQH0kfI(T=hvX7*c2r|8V3 zORiI#**}uW=bRZko^h0A&1LgBtK`~6*Rr&0O~*uWwotG#j$O{i{WeAemC+VP~F$=E4g7<3(t zGz0oi&rXD9iDv@q?gvjjO;ggSPKvJFk>0>F`Lx6Up9i=Pr`oh(N$z*(TSea-({|_~ zU)rnnJpWh&thwyd^BkgF_35!z*>>Hh=XHp3)2H`7KgDoC^y^915db5^Falu&!U%*B z2qO?iAdEm5f&b+Q{IzAr-^}BGY&K7>d#zh3^Q+5lL*;^b{H5me-mojXpC$Dhm5+R# zpruo>e~K+{TzjojsXWOtSx29T=8eFHLZm3+AcFzlv z{{x=??mmY5R?HLE%@_Z?*L?A2qZxVGytv|Sp$6VDbOX(m%d;(=G;Z*De2aT-kXe;? 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Please enter your text in English.", "" - - pubtator_file = f"{base_name}.PubTator" - input_data = f"{base_name}|t|\n{base_name}|a|{text}\n\n" - - input_biomedner = os.path.join( - self.input_dir, f"{pubtator_file}.biomedner.PubTator" - ) - output_biomedner = os.path.join( - self.output_dir, f"{pubtator_file}.biomedner.json" - ) - input_gner = os.path.join(self.input_dir, f"{pubtator_file}.gner.PubTator") - output_gner = os.path.join(self.output_dir, f"{pubtator_file}.gner.json") - - self.create_directories([self.input_dir, self.output_dir]) - - # Write input files sequentially - for path in [input_biomedner, input_gner]: - self.write_to_file(path, input_data) - - ner_start_time = time.time() - - async_result = asyncio.run( - self.async_ner( - [ - ("biomedner", pubtator_file, output_biomedner, biomedner_home), - ("gner", pubtator_file, output_gner, biomedner_home), - ] - ) - ) - - ner_elapse_time = time.time() - ner_start_time - async_result["ner_elapse_time"] = ner_elapse_time + def load_dictionary_file(file_path: str | Path) -> list[str]: + with Path(file_path).open("r", encoding="utf-8") as handle: + return handle.read().splitlines() - tagged_docs = async_result["biomedner_tagged_docs"] - gner_entities = async_result["gner_tagged_docs"] + def annotate_text(self, text: str) -> list[dict[str, Any]]: + return self._annotator.annotate_text(text) - r_norm_start_time = time.time() - tagged_docs = self.normalize_entities( - tagged_docs, async_result["biomedner_num_entities"], base_name - ) - gner_entities = self.normalize_entities( - gner_entities, async_result["gner_num_entities"], base_name - ) - r_norm_elapse_time = time.time() - r_norm_start_time - async_result["r_norm_elapse_time"] = r_norm_elapse_time - - n_norm_start_time = time.time() - if self.normalizer.use_neural_normalizer: - if async_result["biomedner_num_entities"] > 0: - tagged_docs = self.normalizer.neural_normalize( - ent_type="disease", tagged_docs=tagged_docs - ) - tagged_docs = self.normalizer.neural_normalize( - ent_type="drug", tagged_docs=tagged_docs - ) - tagged_docs = self.normalizer.neural_normalize( - ent_type="gene", tagged_docs=tagged_docs - ) - n_norm_elapse_time = time.time() - n_norm_start_time - async_result["n_norm_elapse_time"] = n_norm_elapse_time - - tagged_docs[0] = get_pub_annotation(tagged_docs[0]) - tagged_docs[0]["elapse_time"] = self.get_elapsed_times(async_result) - - gner_entities[0] = get_pub_annotation(gner_entities[0]) - gner_entities[0]["elapse_time"] = { - "gner_elapse_time": async_result["gner_elapse_time"] - } - - self.cleanup_temp_files( - [input_biomedner, output_biomedner, input_gner, output_gner] + def annotate_texts_in_parallel( + self, + texts: Sequence[str], + max_workers: int = 20, + retries: int = 1, + delay: float = 0, + ) -> list[list[dict[str, Any]]]: + return self._annotator.annotate_texts_in_parallel( + texts, + max_workers=max_workers, + retries=retries, + delay=delay, ) - gc.collect() - return tagged_docs[0], gner_entities[0] - - def get_elapsed_times(self, async_result): - return { - "biomedner_elapse_time": async_result["biomedner_elapse_time"], - "gner_elapse_time": async_result["gner_elapse_time"], - "ner_elapse_time": async_result["ner_elapse_time"], - "r_norm_elapse_time": async_result["r_norm_elapse_time"], - "n_norm_elapse_time": async_result["n_norm_elapse_time"], - "norm_elapse_time": async_result["r_norm_elapse_time"] - + async_result["n_norm_elapse_time"], - } - - def normalize_entities(self, entities, num_entities, base_name): - if num_entities > 0: - entities = self.normalizer.normalize(base_name, entities) - return entities - - def write_to_file(self, path, data): - with open(path, "w", encoding="utf-8") as f: - f.write(data) - - def generate_base_name(self, text): - return hashlib.sha224((text + str(time.time())).encode("utf-8")).hexdigest() - - async def async_ner(self, arguments): - coroutines = [self._ner_wrap(arg) for arg in arguments] - result = await asyncio.gather(*coroutines) - return {k: v for e in result if e is not None for k, v in e.items()} - - async def _ner_wrap(self, ner_type_info): - ner_type, pubtator_file, output_file, biomedner_home = ner_type_info - retries = 1 - for _ in range(retries): - try: - start_time = time.time() - await async_tell_inputfile( - self.biomedner_host, - self.biomedner_port if ner_type == "biomedner" else self.gner_port, - biomedner_home, - pubtator_file, - asyncio.get_event_loop(), - ) - with open(output_file, "r", encoding="utf-8") as f: - content = f.read() - try: - tagged_docs = [json.loads(content)] - except json.JSONDecodeError as e: - self.print_log(f"JSON decode error: {e}") - continue - - num_entities = tagged_docs[0]["num_entities"] - if tagged_docs is None: - return None - - elapse_time = time.time() - start_time - return { - f"{ner_type}_elapse_time": elapse_time, - f"{ner_type}_tagged_docs": tagged_docs, - f"{ner_type}_num_entities": num_entities, - } - - except json.JSONDecodeError as e: - self.print_log(f"JSON decode error: {e}") - time.sleep(0) - - raise Exception("Failed to decode JSON after multiple attempts") - - def cleanup_temp_files(self, files): - for f in files: - if os.path.exists(f): - os.remove(f) - - def annotate_single_text_with_retry(self, text, retries=1, delay=0): - for attempt in range(retries): - try: - return self.annotate_text(text) - except (ConnectionResetError, ConnectionRefusedError) as e: - self.print_log( - f"Error: {e}. Retrying {attempt + 1}/{retries} in {delay} seconds..." - ) - time.sleep(delay) - raise Exception(f"Failed to annotate text after {retries} attempts: {text}") +def _default_config() -> dict[str, Any]: + return { + "entity_extraction": { + "backend": "gliner2", + "model_name": "fastino/gliner2-base", + "fallback_model_name": "gliner-community/gliner_large-v2.5", + "schema_path": str(_repo_root() / "source/Matcher/entity_schemas/trialmatchai.yaml"), + "batch_size": 8, + "device": "auto", + "trust_remote_code": False, + }, + "concept_linker": { + "enabled": True, + "db_path": str(_repo_root() / "data/concepts"), + "table": "concepts", + "accept_threshold": 0.8, + "reject_threshold": 0.3, + "search_limit": 10, + }, + } - def annotate_texts_in_parallel(self, texts, max_workers=20, retries=1, delay=0): - # Reduced max_workers to avoid overwhelming the system - from typing import Any - results: list[Any] = [None] * len(texts) - with ThreadPoolExecutor(max_workers=max_workers) as executor: - future_to_index = { - executor.submit( - self.annotate_single_text_with_retry, text, retries, delay - ): i - for i, text in enumerate(texts) +def _repo_root() -> Path: + start = Path(__file__).resolve() + for parent in start.parents: + if (parent / "pyproject.toml").exists(): + return parent + return Path.cwd().resolve() + + +def transform_results(data: dict[str, Any]) -> list[dict[str, Any]]: + """Best-effort adapter for callers that still pass PubTator-style JSON.""" + annotations = data.get("annotations", []) if isinstance(data, dict) else [] + results: list[dict[str, Any]] = [] + for annotation in annotations: + span = annotation.get("span", {}) + results.append( + { + "entity_group": annotation.get("obj", ""), + "score": annotation.get("prob", 0.0), + "text": annotation.get("mention", ""), + "start": span.get("begin", 0), + "end": span.get("end", 0), + "normalized_id": annotation.get("id", ["CUI-less"]), + "synonyms": [], + "concept_candidates": [], + "linker_score": None, + "linker_status": "not_linked", } - while future_to_index: - done, _ = wait(future_to_index, return_when=ALL_COMPLETED) - for future in done: - index = future_to_index.pop(future) - try: - results[index] = future.result() - except Exception as exc: - self.print_log( - f"Text at index {index} generated an exception: {exc}" - ) - results[index] = {"error_code": 1, "error_message": str(exc)} - gc.collect() - return results - - -async def async_tell_inputfile(host, port, biomedner_home, inputfile, loop): - sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) - try: - sock.connect((host, port)) - message = json.dumps({"biomedner_home": biomedner_home, "inputfile": inputfile}) - input_stream = struct.pack(">H", len(message)) + message.encode("utf-8") - sock.send(input_stream) - output_stream = await loop.run_in_executor(None, sock.recv, 512) - resp = output_stream.decode("utf-8") - sock.close() - return resp - except (ConnectionRefusedError, TimeoutError, ConnectionResetError) as e: - print(e) - return None - - -def append_synonyms(ner_results, dict_paths): - """ - Append synonyms to NER results based on normalized IDs and dictionary paths. - - Args: - ner_results (list): List of dictionaries containing NER results with entity groups and normalized IDs. - dict_paths (dict): Mapping of dictionary types (e.g., 'gene', 'disease') to file paths. + ) + return results - Returns: - None: Updates ner_results in place by appending 'synonyms' to each entity. - """ - # Map entity groups to the corresponding dictionary keys - entity_group_to_dict = { - "gene": "gene", - "disease": "disease", - "sign symptom": "sign symptom", - "cell type": "cell type", - "drug": "drug", - "diagnostic test": "procedure", - "treatment": "procedure", - "laboratory test": "procedure", - "radiology": "procedure", - "surgical procedure": "procedure", - "genomic analysis technique": "procedure", - } +def append_synonyms( + ner_results: list[dict[str, Any]], dict_paths: dict[str, str | Path] +) -> None: + del dict_paths for entity in ner_results: - entity_group = entity["entity_group"].lower() - dict_key = entity_group_to_dict.get(entity_group) - if dict_key and dict_key in dict_paths: - file_path = dict_paths[dict_key] - if dict_key == "gene": - # Normalize gene IDs to match expected formats - entity["normalized_id"] = [ - nid.replace("NCBIGene:", "EntrezGene:") - for nid in entity["normalized_id"] - ] - entity["synonyms"] = get_synonyms_from_file( - file_path, entity["normalized_id"] - ) - else: - entity["synonyms"] = [] - - -def get_synonyms_from_file(file_path, entity_ids): - """ - Retrieve synonyms from the dictionary file based on normalized IDs. - - Args: - file_path (str): Path to the dictionary file. - entity_ids (list): List of normalized IDs to find synonyms for. - - Returns: - list: List of unique synonyms associated with the given IDs. - """ - entity_dict = {} - - # Load the dictionary - with open(file_path, "r", encoding="utf-8") as file: - for line in file: - parts = line.strip().split("||") - if len(parts) > 1: - # First part is the ID(s), second part is the synonyms - ids = parts[0].split(",") # Handles potential multiple IDs - synonyms = parts[1].split("|") # Synonyms are separated by '|' - for nid in ids: - nid = nid.strip().upper() # Normalize to uppercase - if nid not in entity_dict: - entity_dict[nid] = set() - entity_dict[nid].update(name.strip() for name in synonyms) - - # Collect synonyms for the provided entity IDs - synonyms = set() - for nid in entity_ids: - nid = nid.strip().upper() # Normalize input ID to uppercase - synonyms.update(entity_dict.get(nid, set())) # Add synonyms if the ID exists - - return list(synonyms) - - -def transform_results(data): - relevant_objs = { - "gene", - "disease", - "drug", - "cell type", - "sign symptom", - "diagnostic test", - "treatment", - "laboratory test", - "surgical procedure", - "radiology", - "genomic analysis technique", - } - - # Filter and transform the data into the desired format using a list comprehension - all_entities = [ - { - "entity_group": annotation["obj"], - "score": annotation["prob"], - "text": annotation["mention"], - "start": annotation["span"]["begin"], - "end": annotation["span"]["end"], - "normalized_id": annotation["id"], - } - for annotation in data["annotations"] - if annotation["prob"] >= 0.8 and annotation["obj"] in relevant_objs - ] - - # Sort entities by descending score - all_entities.sort(key=lambda x: -x["score"]) - - # Initialize structures for efficient overlap detection - accepted_starts = [] - accepted_ends = [] - non_overlapping_entities = [] - - for entity in all_entities: - start, end = entity["start"], entity["end"] - - # Use bisect to find the insertion point for the current entity's start - i = bisect.bisect_left(accepted_starts, start) - - overlap = False - # Check for overlap with the previous accepted entity - if i > 0 and accepted_ends[i - 1] > start: - overlap = True - # Check for overlap with the next accepted entity - if i < len(accepted_starts) and accepted_starts[i] < end: - overlap = True - - if not overlap: - # Insert the current entity into the accepted lists - bisect.insort(accepted_starts, start) - bisect.insort(accepted_ends, end) - non_overlapping_entities.append(entity) - - # Sort the final entities by start and end positions - non_overlapping_entities.sort(key=lambda x: (x["start"], x["end"])) - - return non_overlapping_entities - - -if __name__ == "__main__": - import argparse - - argparser = argparse.ArgumentParser() - argparser.add_argument( - "--max_word_len", type=int, help="word max chars", default=50 - ) - argparser.add_argument("--seed", type=int, help="seed value", default=2019) - argparser.add_argument("--biomedner_home", help="biomedical language model home") - argparser.add_argument( - "--biomedner_host", help="biomedical language model host", default="localhost" - ) - argparser.add_argument( - "--biomedner_port", - type=int, - help="biomedical language model port", - default=18894, - ) - argparser.add_argument("--gner_host", help="gner host", default="localhost") - argparser.add_argument("--gner_port", type=int, help="gner port", default=18783) - argparser.add_argument( - "--gene_norm_port", type=int, help="Gene port", default=18888 - ) - argparser.add_argument( - "--disease_norm_port", type=int, help="Sieve port", default=18892 - ) - argparser.add_argument( - "--time_format", help="time format", default="[%d/%b/%Y %H:%M:%S.%f]" - ) - argparser.add_argument("--use_neural_normalizer", action="store_true") - argparser.add_argument( - "--no_cuda", action="store_false", help="Avoid using CUDA when available" - ) - args = argparser.parse_args() + entity.setdefault("synonyms", []) - biomedner = BioMedNER( - max_word_len=args.max_word_len, - seed=args.seed, - gene_norm_port=args.gene_norm_port, - disease_norm_port=args.disease_norm_port, - biomedner_home=os.path.abspath(args.biomedner_home), - biomedner_host=args.biomedner_host, - biomedner_port=args.biomedner_port, - gner_host=args.gner_host, - gner_port=args.gner_port, - time_format=args.time_format, - use_neural_normalizer=args.use_neural_normalizer, - no_cuda=args.no_cuda, - ) - texts = ["colorectal cancer"] - results = biomedner.annotate_texts_in_parallel(texts, max_workers=5) - for result in results: - print(result) +def get_synonyms_from_file( + file_path: str | Path, entity_ids: Sequence[str] +) -> list[str]: + del file_path, entity_ids + return [] diff --git a/source/Parser/biomedner_init.py b/source/Parser/biomedner_init.py deleted file mode 100644 index 1eed3939..00000000 --- a/source/Parser/biomedner_init.py +++ /dev/null @@ -1,983 +0,0 @@ -import argparse -import json -import logging -import os -import time -from dataclasses import dataclass -from typing import Any, Callable, Dict, List, NamedTuple, NewType, Optional, Tuple - -import numpy as np -import torch -from models import BERTMultiNER2, RoBERTaMultiNER2 -from ops import ( - Profile, - detokenize, - get_prob, - input_form, - json_to_sent, - preprocess, -) -from torch import nn -from torch.utils.data.dataloader import DataLoader -from torch.utils.data.dataset import Dataset -from torch.utils.data.sampler import SequentialSampler -from tqdm import tqdm -from transformers import ( - AutoConfig, - AutoTokenizer, - PreTrainedTokenizer, - set_seed, -) - -logger = logging.getLogger(__name__) - -InputDataClass = NewType("InputDataClass", Any) -DataCollator = NewType( - "DataCollator", Callable[[List[InputDataClass]], Dict[str, torch.Tensor]] -) - - -@dataclass -class InputExample: - """ - A single training/test example for token classification. - - Args: - guid: Unique id for the example. - words: list. The words of the sequence. - labels: (Optional) list. The labels for each word of the sequence. This should be - specified for train and dev examples, but not for test examples. - """ - - guid: str - words: List[str] - labels: Optional[List[str]] - entity_labels: Optional[List[int]] - - -@dataclass -class InputFeatures: - """ - A single set of features of data. - Property names are the same names as the corresponding inputs to a model. - """ - - input_ids: List[int] - attention_mask: List[int] - token_type_ids: Optional[List[int]] = None - label_ids: Optional[List[int]] = None - entity_type_ids: Optional[List[int]] = None - - -class DataProcessor(object): - """Base class for data converters for sequence classification data sets.""" - - def get_train_examples(self, data_dir): - """Gets a collection of `InputExample`s for the train set.""" - raise NotImplementedError() - - def get_dev_examples(self, data_dir): - """Gets a collection of `InputExample`s for the dev set.""" - raise NotImplementedError() - - def get_labels(self): - """Gets the list of labels for this data set.""" - raise NotImplementedError() - - @classmethod - def _read_data(cls, data, pmids): - """Reads a BIO data.""" - lines = [] - words = [] - labels = [] - entity_labels = [] - for pmid in pmids: - for sent in data[pmid]["words"]: - words = sent[:] - labels = ["O"] * len(words) - entity_labels = [str(0)] * len(words) - - if len(words) >= 30: - while len(words) >= 30: - tmplabel = labels[:30] - label_text = " ".join( - [ - label - for label in labels[: len(tmplabel)] - if len(label) > 0 - ] - ) - w = " ".join( - [word for word in words[: len(tmplabel)] if len(word) > 0] - ) - e = " ".join( - [el for el in entity_labels[: len(tmplabel)] if len(el) > 0] - ) - lines.append([label_text, w, e]) - words = words[len(tmplabel) :] - labels = labels[len(tmplabel) :] - entity_labels = entity_labels[len(tmplabel) :] - if len(words) == 0: - continue - - label_text = " ".join([label for label in labels if len(label) > 0]) - w = " ".join([word for word in words if len(word) > 0]) - e = " ".join([el for el in entity_labels if len(entity_labels) > 0]) - lines.append([label_text, w, e]) - words = [] - labels = [] - entity_labels = [] - continue - - return lines - - -class NerDataset(Dataset): - """ - This will be superseded by a framework-agnostic approach soon. - """ - - features: List[InputFeatures] - pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index - - def __init__( - self, - predict_examples, - labels: List[str], - tokenizer: PreTrainedTokenizer, - config, - params, - base_name, - ): - logger.info("Creating features from dataset file") - self.labels = labels - self.predict_examples = predict_examples - self.tokenizer = tokenizer - self.config = config - self.params = params - - self.features = convert_examples_to_features( - self.predict_examples, - self.labels, - self.params.max_seq_length, - self.tokenizer, - cls_token_at_end=bool(self.config.model_type in ["xlnet"]), - cls_token=self.tokenizer.cls_token, - cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0, - sep_token=self.tokenizer.sep_token, - sep_token_extra=False, - pad_on_left=bool(self.tokenizer.padding_side == "left"), - pad_token=self.tokenizer.pad_token_id, - pad_token_segment_id=self.tokenizer.pad_token_type_id, - pad_token_label_id=self.pad_token_label_id, - base_name=base_name, - ) - - def __len__(self): - return len(self.features) - - def __getitem__(self, i) -> InputFeatures: - return self.features[i] - - -class PredictionOutput(NamedTuple): - predictions: np.ndarray - label_ids: Optional[np.ndarray] - - -def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Tensor]: - """ - Very simple data collator that: - - simply collates batches of dict-like objects - - Performs special handling for potential keys named: - - `label`: handles a single value (int or float) per object - - `label_ids`: handles a list of values per object - - does not do any additional preprocessing - - i.e., Property names of the input object will be used as corresponding inputs to the model. - See glue and ner for example of how it's useful. - """ - - # In this function we'll make the assumption that all `features` in the batch - # have the same attributes. - # So we will look at the first element as a proxy for what attributes exist - # on the whole batch. - if not isinstance(features[0], dict): - features = [vars(f) for f in features] - - first = features[0] - batch = {} - - # Special handling for labels. - # Ensure that tensor is created with the correct type - # (it should be automatically the case, but let's make sure of it.) - if "label" in first and first["label"] is not None: - dtype = torch.long if type(first["label"]) is int else torch.float - batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype) - elif "label_ids" in first and first["label_ids"] is not None: - if isinstance(first["label_ids"], torch.Tensor): - batch["labels"] = torch.stack([f["label_ids"] for f in features]) - else: - dtype = torch.long if type(first["label_ids"][0]) is int else torch.float - batch["labels"] = torch.tensor( - [f["label_ids"] for f in features], dtype=dtype - ) - - # Handling of all other possible keys. - # Again, we will use the first element to figure out which key/values are not None for this model. - for k, v in first.items(): - if k not in ("label", "label_ids") and v is not None and not isinstance(v, str): - if isinstance(v, torch.Tensor): - batch[k] = torch.stack([f[k] for f in features]) - else: - batch[k] = torch.tensor([f[k] for f in features], dtype=torch.long) - - return batch - - -def convert_examples_to_features( - examples: List[InputExample], - label_list: List[str], - max_seq_length: int, - tokenizer: PreTrainedTokenizer, - cls_token_at_end=False, - cls_token="[CLS]", - cls_token_segment_id=1, - sep_token="[SEP]", - sep_token_extra=False, - pad_on_left=False, - pad_token=0, - pad_token_segment_id=0, - pad_token_label_id=-100, - sequence_a_segment_id=0, - mask_padding_with_zero=True, - base_name="", -) -> List[InputFeatures]: - """Loads a data file into a list of `InputFeatures` - `cls_token_at_end` define the location of the CLS token: - - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP] - - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] - `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet) - """ - # TODO clean up all this to leverage built-in features of tokenizers - - label_map = {label: i for i, label in enumerate(label_list)} - features = [] - - for ex_index, example in tqdm(enumerate(examples)): - if ex_index % 10_000 == 0: - logger.info("Writing example %d of %d", ex_index, len(examples)) - - ( - tokens, - label_ids, - ) = [], [] - det_tokens = [] - - for word_idx, (word, label) in enumerate( - zip(example.words.split(), example.labels.split()) - ): - word_tokens = tokenizer.tokenize(word) - - # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space. - if len(word_tokens) > 0: - tokens.extend(word_tokens) - # Use the real label id for the first token of the word, and padding ids for the remaining tokens - label_ids.extend( - [label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1) - ) - - if len(word_tokens) == 1: - det_tokens.extend(word_tokens) - elif len(word_tokens) > 1: - for det_idx, det_word in enumerate(word_tokens): - if det_idx > 0: - det_word = "##" + det_word - det_tokens.append(det_word) - else: - det_tokens.append(det_word) - - # calculate temperature with length : temp = 1 - 0.02 * length - # temperature = [1 - sharpening * i if i > 1 else i for _, i in enumerate(entity_length)] - - # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa. - special_tokens_count = tokenizer.num_special_tokens_to_add() - ## truncating tokens with max_seq_length - # if len(tokens) > max_seq_length - special_tokens_count: - # tokens = tokens[: (max_seq_length - special_tokens_count)] - # label_ids = label_ids[: (max_seq_length - special_tokens_count)] - # det_tokens = det_tokens[: (max_seq_length - special_tokens_count)] - - # for sliding window tokens - update 23.11.13 - for i in range(0, (len(tokens) // max_seq_length) + 1): - if i == 0: - window_tokens = tokens[ - i * max_seq_length : (i + 1) * max_seq_length - special_tokens_count - ] - window_label_ids = label_ids[ - i * max_seq_length : (i + 1) * max_seq_length - special_tokens_count - ] - window_det_tokens = det_tokens[ - i * max_seq_length : (i + 1) * max_seq_length - special_tokens_count - ] - elif i >= 1: - window_tokens = tokens[ - i * max_seq_length - special_tokens_count : (i + 1) * max_seq_length - - special_tokens_count - ] - window_label_ids = label_ids[ - i * max_seq_length - special_tokens_count : (i + 1) * max_seq_length - - special_tokens_count - ] - window_det_tokens = det_tokens[ - i * max_seq_length - special_tokens_count : (i + 1) * max_seq_length - - special_tokens_count - ] - - # The convention in BERT is: - # (a) For sequence pairs: - # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] - # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 - # (b) For single sequences: - # tokens: [CLS] the dog is hairy . [SEP] - # type_ids: 0 0 0 0 0 0 0 - # - # Where "type_ids" are used to indicate whether this is the first - # sequence or the second sequence. The embedding vectors for `type=0` and - # `type=1` were learned during pre-training and are added to the wordpiece - # embedding vector (and position vector). This is not *strictly* necessary - # since the [SEP] token unambiguously separates the sequences, but it makes - # it easier for the model to learn the concept of sequences. - # - # For classification tasks, the first vector (corresponding to [CLS]) is - # used as as the "sentence vector". Note that this only makes sense because - # the entire model is fine-tuned. - window_tokens += [sep_token] - window_label_ids += [pad_token_label_id] - window_det_tokens += [sep_token] - - if sep_token_extra: - # roberta uses an extra separator b/w pairs of sentences - window_tokens += [sep_token] - window_label_ids += [pad_token_label_id] - window_det_tokens += [sep_token] - - # make entity type label index for multiner - entity_type_ids = [int(example.entity_labels[0])] * len(window_tokens) - segment_ids = [sequence_a_segment_id] * len(window_tokens) - if cls_token_at_end: - window_tokens += [cls_token] - window_label_ids += [pad_token_label_id] - segment_ids += [cls_token_segment_id] - entity_type_ids += [int(example.entity_labels[0])] - window_det_tokens += [cls_token] - else: - window_tokens = [cls_token] + window_tokens - window_label_ids = [pad_token_label_id] + window_label_ids - segment_ids = [cls_token_segment_id] + segment_ids - entity_type_ids = [int(example.entity_labels[0])] + entity_type_ids - window_det_tokens = [cls_token] + window_det_tokens - - input_ids = tokenizer.convert_tokens_to_ids(window_tokens) - # The mask has 1 for real tokens and 0 for padding tokens. Only real - # tokens are attended to. - input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) - - # Zero-pad up to the sequence length. - padding_length = max_seq_length - len(input_ids) - - if pad_on_left: - input_ids = ([pad_token] * padding_length) + input_ids - input_mask = ( - [0 if mask_padding_with_zero else 1] * padding_length - ) + input_mask - segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids - window_label_ids = ( - [pad_token_label_id] * padding_length - ) + window_label_ids - entity_type_ids = ( - [int(example.entity_labels[0])] * padding_length - ) + entity_type_ids - window_tokens = (["**NULL**"] * padding_length) + window_tokens - window_det_tokens = (["**NULL**"] * padding_length) + window_det_tokens - else: - input_ids += [pad_token] * padding_length - input_mask += [0 if mask_padding_with_zero else 1] * padding_length - segment_ids += [pad_token_segment_id] * padding_length - window_label_ids += [pad_token_label_id] * padding_length - entity_type_ids += [int(example.entity_labels[0])] * padding_length - window_tokens += ["**NULL**"] * padding_length - window_det_tokens += ["**NULL**"] * padding_length - - assert len(input_ids) == max_seq_length - assert len(input_mask) == max_seq_length - assert len(segment_ids) == max_seq_length - assert len(window_label_ids) == max_seq_length - assert len(entity_type_ids) == max_seq_length - assert len(window_tokens) == max_seq_length - - if ex_index < 1: - logger.info("*** Example ***") - logger.info("guid: %s", example.guid) - logger.info("tokens: %s", " ".join([str(x) for x in window_tokens])) - logger.info("input_ids: %s", " ".join([str(x) for x in input_ids])) - logger.info("input_mask: %s", " ".join([str(x) for x in input_mask])) - logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids])) - logger.info( - "label_ids: %s", " ".join([str(x) for x in window_label_ids]) - ) - logger.info( - "entity_type_ids: %s", " ".join([str(x) for x in entity_type_ids]) - ) - - if "token_type_ids" not in tokenizer.model_input_names: - segment_ids = None - - features.append( - InputFeatures( - input_ids=input_ids, - attention_mask=input_mask, - token_type_ids=segment_ids, - label_ids=window_label_ids, - entity_type_ids=entity_type_ids, - ) - ) - write_tokens(window_tokens, window_det_tokens, "test", base_name) - - return features - - -def write_tokens(tokens, det_tokens, mode, base_name): - if mode == "test": - tmp_path = os.path.join("multi_ner", "tmp") - if not os.path.exists(tmp_path): - os.makedirs(tmp_path) - - path = os.path.join( - "multi_ner", "tmp", "token_{}_{}.txt".format(mode, base_name) - ) - with open(path, "a") as wf: - for token in tokens: - if token != "**NULL**": - wf.write(token + "\n") - - det_path = os.path.join( - "multi_ner", "tmp", "det_token_{}_{}.txt".format(mode, base_name) - ) - with open(det_path, "a") as wf: - for token in det_tokens: - if token != "**NULL**": - wf.write(token + "\n") - - -class NerProcessor(DataProcessor): - def get_test_examples(self, data_dir): - data = list() - pmids = list() - with open(data_dir, "r") as in_: - for line in in_: - line = line.strip() - tmp = json.loads(line) - tmp["title"] = preprocess(tmp["title"]) - tmp["abstract"] = preprocess(tmp["abstract"]) - data.append(tmp) - pmids.append(tmp["pmid"]) - - json_file = input_form(json_to_sent(data)) - - return ( - self._create_example(self._read_data(json_file, pmids), "test"), - json_file, - data, - ) - - def get_test_dict_list(self, dict_list): - pmids = list() - for d in dict_list: - pmids.append(d["pmid"]) - - json_file = input_form(json_to_sent(dict_list)) - - return self._create_example( - self._read_data(json_file, pmids), "test" - ), json_file - - def get_labels(self): - return ["B", "I", "O"] - - def _create_example(self, lines, set_type): - examples = [] - for i, line in enumerate(lines): - guid = "%s-%s" % (set_type, i) - text = line[1] - label = line[0] - entity_labels = line[2] - examples.append( - InputExample( - guid=guid, words=text, labels=label, entity_labels=entity_labels - ) - ) - - return examples - - -class BioMedNER: - def __init__(self, params): - init_start_t = time.time() - - # Set ner processor - self.processor = NerProcessor() - - # Setup parsing - self.params = params - self.prediction_loss_only = False - - # Set seed - set_seed(self.params.seed) - - # Prepare Labels - self.labels = self.processor.get_labels() - self.id2label: Dict[int, str] = { - i: label for i, label in enumerate(self.labels) - } - self.label2id = {label: i for i, label in enumerate(self.labels)} - self.num_labels = len(self.labels) - - self.config = AutoConfig.from_pretrained( - self.params.model_name_or_path, - num_labels=self.num_labels, - id2label=self.id2label, - label2id=self.label2id, - ) - - self.tokenizer = AutoTokenizer.from_pretrained( - self.params.model_name_or_path, - ) - if ( - "roberta" in self.params.model_name_or_path - or "biolm" in self.params.model_name_or_path - ): - # roberta - self.model = RoBERTaMultiNER2.from_pretrained( - self.params.model_name_or_path, - num_labels=self.num_labels, - config=self.config, - ) - else: - self.model = BERTMultiNER2.from_pretrained( - self.params.model_name_or_path, - num_labels=self.num_labels, - config=self.config, - ) - # Determine the device - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.model.to(self.device) - self.entity_types = [ - "disease", - "drug", - "gene", - "species", - "cell line", - "DNA", - "RNA", - "cell type", - ] - self.estimator_dict = {} - for etype in self.entity_types: - self.estimator_dict[etype] = {} - self.estimator_dict[etype]["prediction"] = [] - self.estimator_dict[etype]["log_probs"] = [] - - self.counter = 0 - self.pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index - init_end_t = time.time() - print("BioMedNER init_t {:.3f} sec.".format(init_end_t - init_start_t)) - - @Profile(__name__) - def recognize(self, input_dl, base_name, indent=None): - if type(input_dl) is str: - predict_examples, self.json_dict, self.data_list = ( - self.processor.get_test_examples(input_dl) - ) - elif type(input_dl) is list: - predict_examples, self.json_dict = self.processor.get_test_dict_list( - input_dl - ) - self.data_list = input_dl - else: - raise ValueError("Wrong type") - - token_path = os.path.join( - "multi_ner", "tmp", "token_test_{}.txt".format(base_name) - ) - det_token_path = os.path.join( - "multi_ner", "tmp", "det_token_test_{}.txt".format(base_name) - ) - - if os.path.exists(token_path): - os.remove(token_path) - if os.path.exists(det_token_path): - os.remove(det_token_path) - - predict_example_list = NerDataset( - predict_examples, - self.labels, - self.tokenizer, - self.config, - self.params, - base_name, - ) - - tokens, tot_tokens = list(), list() - - """ - Aggregate label results with detokenized tokens - - words: Auto phagy main tain s tumour growth ... - label: O O O O O O B O ... O - - detok_words: Authophagy maintains tumour growth ... - detok_label: O O O B O ... - """ - - with open(det_token_path, "r") as reader: - for line_idx, line in enumerate(reader): - tok = line.strip() - tot_tokens.append(tok) - - if tok == "[CLS]" or tok == "": - tmp_toks = [tok] - elif tok == "[SEP]" or tok == "": - tmp_toks.append(tok) - tokens.append(tmp_toks) - else: - tmp_toks.append(tok) - - self.predict_dict, self.prob_dict = dict(), dict() - self.out_tag_dict = dict() - - all_type = self._predict(predict_example_list) - # disease, drug, gene, spec, cell_line, dna, rna, cell_type - for etype_idx, etype in enumerate(self.entity_types): - predictions, label_ids = all_type[etype_idx] # batch, seq, labels - preds_array = self.align_predictions(predictions) # batch, seq - - self.out_tag_dict[etype] = (False, None) - self.recognize_etype(etype, tokens, tot_tokens, predictions, preds_array) - - for etype in self.entity_types: - if self.out_tag_dict[etype][0]: - if type(input_dl) is str: - print( - os.path.split(input_dl)[1], - "Found an error:", - self.out_tag_dict[etype][1], - ) - else: - print("Found an error:", self.out_tag_dict[etype][1]) - if os.path.exists(token_path): - os.remove(token_path) - return None - - # get probability of all mentions - data_list = get_prob( - self.data_list, - self.json_dict, - self.predict_dict, - self.prob_dict, - entity_types=self.entity_types, - ) - - if type(input_dl) is str: - output_path = os.path.join( - "result/", - os.path.splitext(os.path.basename(input_dl))[0] - + "_NER_{}.json".format(base_name), - ) - print("pred", output_path) - - with open(output_path, "w") as resultf: - for paper in data_list: - paper["ner_model"] = "MULTI-TASK NER v.20210707" - resultf.write( - json.dumps(paper, sort_keys=True, indent=indent) + "\n" - ) - # delete temp files - if os.path.exists(token_path): - os.remove(token_path) - if os.path.exists(det_token_path): - os.remove(det_token_path) - - return data_list - - @Profile(__name__) - def recognize_etype(self, etype, tokens, tot_tokens, predictions, preds_array): - result = [] - - for one_batch in range(predictions.shape[0]): - result.append( - { - "prediction": preds_array[one_batch], - "log_probs": predictions[one_batch], - } - ) - - predicts = list() - logits = list() - - for pidx, prediction in enumerate(result): - slen = len(tokens[pidx]) - for p in prediction["prediction"][:slen]: - predicts.append(self.id2label[p]) - for log_prob in prediction["log_probs"][:slen]: - logits.append(log_prob) - - de_toks, de_labels, de_logits = detokenize(tot_tokens, predicts, logits) - - self.predict_dict[etype] = dict() - self.prob_dict[etype] = dict() - piv = 0 - for data in self.data_list: - pmid = data["pmid"] - self.predict_dict[etype][pmid] = list() - self.prob_dict[etype][pmid] = list() - - sent_lens = list() - for sent in self.json_dict[pmid]["words"]: - sent_lens.append(len(sent)) - sent_idx = 0 - de_i = 0 - overlen = False - while True: - if overlen: - try: - self.predict_dict[etype][pmid][-1].extend(de_labels[piv + de_i]) - except Exception as e: - self.out_tag_dict[etype] = (True, e) - break - self.prob_dict[etype][pmid][-1].extend(de_logits[piv + de_i]) - de_i += 1 - if len(self.predict_dict[etype][pmid][-1]) == len( - self.json_dict[pmid]["words"][ - len(self.predict_dict[etype][pmid]) - 1 - ] - ): - sent_idx += 1 - overlen = False - - else: - self.predict_dict[etype][pmid].append(de_labels[piv + de_i]) - self.prob_dict[etype][pmid].append(de_logits[piv + de_i]) - de_i += 1 - if len(self.predict_dict[etype][pmid][-1]) == len( - self.json_dict[pmid]["words"][ - len(self.predict_dict[etype][pmid]) - 1 - ] - ): - sent_idx += 1 - overlen = False - else: - overlen = True - - if sent_idx == len(self.json_dict[pmid]["words"]): - piv += de_i - break - - if self.out_tag_dict[etype][0]: - break - - def _predict(self, test_dataset: Dataset): - sampler = SequentialSampler(test_dataset) - data_loader = DataLoader( - test_dataset, - sampler=sampler, - batch_size=32, # you can adjust evaluation batch size, we prefer using 32 - collate_fn=default_data_collator, - drop_last=False, - ) - return self._prediction_loop(data_loader, description="Prediction") - - def _prediction_loop( - self, - dataloader: DataLoader, - description: str, - prediction_loss_only: Optional[bool] = None, - ) -> Tuple[ - PredictionOutput, - PredictionOutput, - PredictionOutput, - PredictionOutput, - PredictionOutput, - PredictionOutput, - PredictionOutput, - PredictionOutput, - ]: - """ - Prediction/evaluation loop, shared by `evaluate()` and `predict()`. - - Works both with or without labels. - """ - - prediction_loss_only = ( - prediction_loss_only - if prediction_loss_only is not None - else self.prediction_loss_only - ) - - model = self.model - device = self.device # Use the dedicated device attribute - - eval_losses: List[float] = [] - dise_preds = chem_preds = gene_preds = spec_preds = cl_preds = dna_preds = ( - rna_preds - ) = ct_preds = None - label_ids = None - model.eval() - - with torch.no_grad(): - for inputs in tqdm(dataloader, desc=description): - # Determine if labels are present - has_labels = any( - inputs.get(k) is not None - for k in ["labels", "lm_labels", "masked_lm_labels"] - ) - - # Move all tensor inputs to the appropriate device - inputs = { - k: v.to(device) if isinstance(v, torch.Tensor) else v - for k, v in inputs.items() - } - - # Forward pass - outputs = model(**inputs) - - if has_labels: - step_eval_loss, logits = outputs[:2] - eval_losses.append(step_eval_loss.mean().item()) - else: - logits = outputs[0] - - if not prediction_loss_only: - # Unpack logits for each entity type - ( - dise_logits, - chem_logits, - gene_logits, - spec_logits, - cl_logits, - dna_logits, - rna_logits, - _, - ct_logits, - ) = logits - - # Initialize prediction tensors if they are None - if dise_preds is None: - dise_preds = dise_logits.detach() - chem_preds = chem_logits.detach() - gene_preds = gene_logits.detach() - spec_preds = spec_logits.detach() - cl_preds = cl_logits.detach() - dna_preds = dna_logits.detach() - rna_preds = rna_logits.detach() - ct_preds = ct_logits.detach() - else: - # Concatenate predictions along the batch dimension - dise_preds = torch.cat( - (dise_preds, dise_logits.detach()), dim=0 - ) - chem_preds = torch.cat( - (chem_preds, chem_logits.detach()), dim=0 - ) - gene_preds = torch.cat( - (gene_preds, gene_logits.detach()), dim=0 - ) - spec_preds = torch.cat( - (spec_preds, spec_logits.detach()), dim=0 - ) - cl_preds = torch.cat((cl_preds, cl_logits.detach()), dim=0) - dna_preds = torch.cat((dna_preds, dna_logits.detach()), dim=0) - rna_preds = torch.cat((rna_preds, rna_logits.detach()), dim=0) - ct_preds = torch.cat((ct_preds, ct_logits.detach()), dim=0) - - if has_labels and not prediction_loss_only: - # Concatenate label IDs - if label_ids is None: - label_ids = inputs["labels"].detach().clone() - else: - label_ids = torch.cat( - (label_ids, inputs["labels"].detach().clone()), dim=0 - ) - - # Convert all prediction tensors to numpy arrays - if all( - pred is not None - for pred in [ - dise_preds, - chem_preds, - gene_preds, - spec_preds, - cl_preds, - dna_preds, - rna_preds, - ct_preds, - ] - ): - dise_preds = dise_preds.cpu().numpy() - chem_preds = chem_preds.cpu().numpy() - gene_preds = gene_preds.cpu().numpy() - spec_preds = spec_preds.cpu().numpy() - cl_preds = cl_preds.cpu().numpy() - dna_preds = dna_preds.cpu().numpy() - rna_preds = rna_preds.cpu().numpy() - ct_preds = ct_preds.cpu().numpy() - - if label_ids is not None: - label_ids = label_ids.cpu().numpy() - - # Create PredictionOutput tuples for each entity type - return_output = ( - PredictionOutput(predictions=dise_preds, label_ids=label_ids), - PredictionOutput(predictions=chem_preds, label_ids=label_ids), - PredictionOutput(predictions=gene_preds, label_ids=label_ids), - PredictionOutput(predictions=spec_preds, label_ids=label_ids), - PredictionOutput(predictions=cl_preds, label_ids=label_ids), - PredictionOutput(predictions=dna_preds, label_ids=label_ids), - PredictionOutput(predictions=rna_preds, label_ids=label_ids), - PredictionOutput(predictions=ct_preds, label_ids=label_ids), - ) - - return return_output - - def align_predictions(self, predictions: np.ndarray) -> List[int]: - preds = np.argmax(predictions, axis=2) - batch_size, seq_len = preds.shape - - preds_list = [[] for _ in range(batch_size)] - - for i in range(batch_size): - for j in range(seq_len): - preds_list[i].append(preds[i][j]) - - return np.array(preds_list) - - -def main(): - argparser = argparse.ArgumentParser() - argparser.add_argument("--model_name_or_path", default="dmis-lab/biobert-v1.1") - argparser.add_argument( - "--max_seq_length", - type=int, - help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.", - default=128, - ) - argparser.add_argument( - "--seed", type=int, help="random seed for initialization", default=1 - ) - args = argparser.parse_args() - - BioMedNER(args) - - -if __name__ == "__main__": - main() diff --git a/source/Parser/biomedner_server.py b/source/Parser/biomedner_server.py deleted file mode 100644 index 6017b805..00000000 --- a/source/Parser/biomedner_server.py +++ /dev/null @@ -1,116 +0,0 @@ -# server_script.py -import argparse -import json -import os -import socket -import struct -from datetime import datetime - -from biomedner_init import BioMedNER -from ops import filter_entities, pubtator2dict_list - - -def count_entities(data): - num_entities = 0 - for d in data: - if "entities" not in d: - continue - for ent_type, entities in d["entities"].items(): - num_entities += len(entities) - return num_entities - - -def biomedner_recognize(model, dict_path, base_name, biomedner_home, args): - # Ensure input and output directories exist within biomedner_home - input_dir = os.path.join(biomedner_home, "input") - output_dir = os.path.join(biomedner_home, "output") - os.makedirs(input_dir, exist_ok=True) - os.makedirs(output_dir, exist_ok=True) - - input_mt_ner = os.path.join( - biomedner_home, "input", f"{dict_path}.biomedner.PubTator" - ) - output_mt_ner = os.path.join( - biomedner_home, "output", f"{dict_path}.biomedner.json" - ) - - dict_list = pubtator2dict_list(input_mt_ner) - - res = model.recognize(input_dl=dict_list, base_name=base_name) - - if res is None: - return None, 0 - - num_filtered_species_per_doc = filter_entities(res) - for n_f_spcs in num_filtered_species_per_doc: - if n_f_spcs[1] > 0: - print( - datetime.now().strftime(args.time_format), - "[{}] Filtered {} species".format(base_name, n_f_spcs[1]), - ) - num_entities = count_entities(res) - - res[0]["num_entities"] = num_entities - # Write output str to a .PubTator format file - with open(output_mt_ner, "w", encoding="utf-8") as f: - json.dump(res[0], f) - - -def run_server(model, args): - host = args.biomedner_host - port = args.biomedner_port - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind((host, port)) - s.listen(600) - print(f"Server listening on {host}:{port}") - while True: - conn, addr = s.accept() - with conn: - print(f"Connected by {addr}") - message_length = struct.unpack(">H", conn.recv(2))[0] - message = conn.recv(message_length).decode("utf-8") - request = json.loads(message) - biomedner_home = request["biomedner_home"] - inputfile = request["inputfile"] - base_name = inputfile.split(".")[0] - base_name = base_name.replace("\x00A", "") - - biomedner_recognize(model, inputfile, base_name, biomedner_home, args) - - output_stream = struct.pack(">H", len(inputfile)) + inputfile.encode( - "utf-8" - ) - conn.send(output_stream) - print(f"Response sent for {inputfile}") - - -if __name__ == "__main__": - argparser = argparse.ArgumentParser() - argparser.add_argument( - "--seed", type=int, help="random seed for initialization", default=1 - ) - argparser.add_argument("--model_name_or_path") - argparser.add_argument( - "--max_seq_length", - type=int, - help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.", - default=512, - ) - argparser.add_argument( - "--biomedner_host", help="biomedical language model host", default="localhost" - ) - argparser.add_argument( - "--biomedner_port", - type=int, - help="biomedical language model port", - default=18894, - ) - argparser.add_argument( - "--time_format", help="time format", default="[%d/%b/%Y %H:%M:%S.%f]" - ) - argparser.add_argument( - "--no_cuda", action="store_false", help="Avoid using CUDA when available" - ) - args = argparser.parse_args() - mt_ner = BioMedNER(args) - run_server(mt_ner, args) diff --git a/source/Parser/gner_server.py b/source/Parser/gner_server.py deleted file mode 100644 index 0155b771..00000000 --- a/source/Parser/gner_server.py +++ /dev/null @@ -1,206 +0,0 @@ -# server_script.py -import argparse -import json -import logging -import os -import socket -import struct -import warnings -from concurrent.futures import ThreadPoolExecutor - -from gliner import GLiNER -from ops import pubtator2dict_list - -warnings.filterwarnings( - "ignore", category=UserWarning, message="TypedStorage is deprecated" -) - -# Setup logging -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) - - -def entities_highly_overlap(entity1, entity2, threshold=0.5): - start1, end1 = entity1["start"], entity1["end"] - start2, end2 = entity2["start"], entity2["end"] - overlap_start = max(start1, start2) - overlap_end = min(end1, end2) - overlap_length = max(0, overlap_end - overlap_start) - length1 = end1 - start1 - length2 = end2 - start2 - shorter_length = min(length1, length2) - if shorter_length == 0: - return False - overlap_ratio = overlap_length / shorter_length - return overlap_ratio >= threshold - - -def filter_overlapping_entities(entities, overlap_threshold=0.5): - # Sort entities by score descending - entities = sorted(entities, key=lambda x: x.get("score", 0), reverse=True) - selected_entities = [] - for entity in entities: - overlaps = False - for sel_entity in selected_entities: - if entities_highly_overlap(entity, sel_entity, threshold=overlap_threshold): - overlaps = True - break - if not overlaps: - selected_entities.append(entity) - return selected_entities - - -class GNER: - def __init__(self, params): - self.params = params - self.entities = [ - "diagnostic test", - "treatment", - "laboratory test", - "surgical procedure", - "sign symptom", - "radiology", - "genomic analysis technique", - ] - self.model = GLiNER.from_pretrained( - self.params.model_name_or_path, map_location="cpu" - ) - - def recognize(self, text, base_name): - entities = self.model.predict_entities( - text, self.entities, flat_ner=False, threshold=0.8 - ) - - clean_entities = [ - { - "entity_group": d["label"], - "score": float(d.get("score", 0)), - "text": d["text"], - "start": int(d["start"]), - "end": int(d["end"]), - } - for d in entities - ] - - # clean_entities.extend(aberration_entities) - - # Filter overlapping entities - filtered_entities = filter_overlapping_entities( - clean_entities, overlap_threshold=0.75 - ) - - entity_dict = {entity: [] for entity in self.entities} - prob_dict = {entity: [] for entity in self.entities} - - for entity in filtered_entities: - entity_type = entity["entity_group"] - if entity_type in entity_dict: - entity_dict[entity_type].append( - {"start": entity["start"], "end": entity["end"]} - ) - prob_dict[entity_type].append( - [{"start": entity["start"], "end": entity["end"]}, entity["score"]] - ) - - return { - "pmid": base_name, - "entities": entity_dict, - "title": "", - "abstract": text, - "prob": prob_dict, - "num_entities": len(filtered_entities), - } - - -def gner_recognize(model, input_file, output_file, base_name): - try: - dict_list = pubtator2dict_list(input_file) - texts = [ - entry["abstract"] - for entry in dict_list - if isinstance(entry, dict) and "abstract" in entry - ] - - results = [model.recognize(text, base_name) for text in texts] - - if not results: - return None, 0 - - num_entities = sum(r["num_entities"] for r in results) - results[0]["num_entities"] = ( - num_entities # Total number of entities across all texts - ) - - with open(output_file, "w", encoding="utf-8") as f: - json.dump(results[0], f) - - return results, num_entities - except Exception as e: - logging.error(f"An error occurred in gner_recognize: {e}") - return None, 0 - - -def handle_connection(conn, addr, model, args): - try: - with conn: - logging.info(f"Connection accepted from {addr}") - message_length = struct.unpack(">H", conn.recv(2))[0] - message = conn.recv(message_length).decode("utf-8").replace("\x00", "") - logging.info(f"Received data: {message}") - data = json.loads(message) - biomedner_home = data["biomedner_home"] - inputfile = data["inputfile"] - base_name = os.path.splitext(os.path.basename(inputfile))[0] - - input_file = os.path.join( - biomedner_home, "input", f"{inputfile}.gner.PubTator" - ) - output_file = os.path.join( - biomedner_home, "output", f"{inputfile}.gner.json" - ) - - gner_recognize(model, input_file, output_file, base_name) - - output_stream = struct.pack(">H", len(inputfile)) + inputfile.encode( - "utf-8" - ) - conn.send(output_stream) - except Exception as e: - logging.error(f"An error occurred: {e}") - - -def run_server(model, args): - host = args.gner_host - port = args.gner_port - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - s.bind((host, port)) - s.listen(600) - logging.info(f"Server started on {host}:{port}") - with ThreadPoolExecutor(max_workers=10) as executor: - while True: - try: - conn, addr = s.accept() - executor.submit(handle_connection, conn, addr, model, args) - except Exception as e: - logging.error(f"An error occurred: {e}") - - -if __name__ == "__main__": - argparser = argparse.ArgumentParser() - argparser.add_argument( - "--model_name_or_path", type=str, required=True, help="Path to the model" - ) - argparser.add_argument( - "--gner_host", default="localhost", help="biomedical language model host" - ) - argparser.add_argument( - "--gner_port", type=int, default=18894, help="biomedical language model port" - ) - argparser.add_argument( - "--time_format", default="[%d/%b/%Y %H:%M:%S.%f]", help="time format" - ) - args = argparser.parse_args() - - mt_ner = GNER(args) - run_server(mt_ner, args) diff --git a/source/Parser/include/crfpp.h b/source/Parser/include/crfpp.h deleted file mode 100644 index aa2dbda2..00000000 --- a/source/Parser/include/crfpp.h +++ /dev/null @@ -1,379 +0,0 @@ -/* - CRF++ -- Yet Another CRF toolkit - - $Id: crfpp.h 1592 2007-02-12 09:40:53Z taku $; - - Copyright(C) 2005-2007 Taku Kudo -*/ -#ifndef CRFPP_CRFPP_H_ -#define CRFPP_CRFPP_H_ - -/* C interface */ -#ifdef __cplusplus -#include -#else -#include -#endif - -#ifdef __cplusplus -extern "C" { -#endif - -#ifdef _WIN32 -#include -# ifdef DLL_EXPORT -# define CRFPP_DLL_EXTERN __declspec(dllexport) -# define CRFPP_DLL_CLASS_EXTERN __declspec(dllexport) -# else -# define CRFPP_DLL_EXTERN __declspec(dllimport) -# endif -#endif - -#ifndef CRFPP_DLL_EXTERN -# define CRFPP_DLL_EXTERN extern -#endif - -#ifndef CRFPP_DLL_CLASS_EXTERN -# define CRFPP_DLL_CLASS_EXTERN -#endif - -#ifndef SWIG - typedef struct crfpp_t crfpp_t; - typedef struct crfpp_model_t crfpp_model_t; - - /* C interface */ - CRFPP_DLL_EXTERN crfpp_model_t* crfpp_model_new(int, char**); - CRFPP_DLL_EXTERN crfpp_model_t* crfpp_model_new2(const char*); - CRFPP_DLL_EXTERN crfpp_model_t* crfpp_model_from_array_new(int, char**, const char *, size_t); - CRFPP_DLL_EXTERN crfpp_model_t* crfpp_model_from_array_new2(const char*, const char *, size_t); - CRFPP_DLL_EXTERN const char * crfpp_model_get_template(crfpp_model_t*); - CRFPP_DLL_EXTERN void crfpp_model_destroy(crfpp_model_t*); - CRFPP_DLL_EXTERN const char * crfpp_model_strerror(crfpp_model_t *); - CRFPP_DLL_EXTERN crfpp_t* crfpp_model_new_tagger(crfpp_model_t *); - - CRFPP_DLL_EXTERN crfpp_t* crfpp_new(int, char**); - CRFPP_DLL_EXTERN crfpp_t* crfpp_new2(const char*); - CRFPP_DLL_EXTERN void crfpp_destroy(crfpp_t*); - CRFPP_DLL_EXTERN int crfpp_set_model(crfpp_t *, crfpp_model_t *); - CRFPP_DLL_EXTERN int crfpp_add2(crfpp_t*, size_t, const char **); - CRFPP_DLL_EXTERN int crfpp_add(crfpp_t*, const char*); - CRFPP_DLL_EXTERN size_t crfpp_size(crfpp_t*); - CRFPP_DLL_EXTERN size_t crfpp_xsize(crfpp_t*); - CRFPP_DLL_EXTERN size_t crfpp_dsize(crfpp_t*); - CRFPP_DLL_EXTERN const float* crfpp_weight_vector(crfpp_t*); - CRFPP_DLL_EXTERN size_t crfpp_result(crfpp_t*, size_t); - CRFPP_DLL_EXTERN size_t crfpp_answer(crfpp_t*, size_t); - CRFPP_DLL_EXTERN size_t crfpp_y(crfpp_t*, size_t); - CRFPP_DLL_EXTERN size_t crfpp_ysize(crfpp_t*); - CRFPP_DLL_EXTERN double crfpp_prob(crfpp_t*, size_t, size_t); - CRFPP_DLL_EXTERN double crfpp_prob2(crfpp_t*, size_t); - CRFPP_DLL_EXTERN double crfpp_prob3(crfpp_t*); - CRFPP_DLL_EXTERN void crfpp_set_penalty(crfpp_t *, size_t i, size_t j, double penalty); - CRFPP_DLL_EXTERN double crfpp_penalty(crfpp_t *, size_t i, size_t j); - CRFPP_DLL_EXTERN double crfpp_alpha(crfpp_t*, size_t, size_t); - CRFPP_DLL_EXTERN double crfpp_beta(crfpp_t*, size_t, size_t); - CRFPP_DLL_EXTERN double crfpp_emisstion_cost(crfpp_t*, size_t, size_t); - CRFPP_DLL_EXTERN double crfpp_next_transition_cost(crfpp_t*, size_t, - size_t, size_t); - CRFPP_DLL_EXTERN double crfpp_prev_transition_cost(crfpp_t*, size_t, - size_t, size_t); - CRFPP_DLL_EXTERN double crfpp_best_cost(crfpp_t*, size_t, size_t); - CRFPP_DLL_EXTERN const int* crfpp_emittion_vector(crfpp_t*, size_t, size_t); - CRFPP_DLL_EXTERN const int* crfpp_next_transition_vector(crfpp_t*, size_t, - size_t, size_t); - CRFPP_DLL_EXTERN const int* crfpp_prev_transition_vector(crfpp_t*, size_t, - size_t, size_t); - CRFPP_DLL_EXTERN double crfpp_Z(crfpp_t*); - CRFPP_DLL_EXTERN int crfpp_parse(crfpp_t*); - CRFPP_DLL_EXTERN int crfpp_empty(crfpp_t*); - CRFPP_DLL_EXTERN int crfpp_clear(crfpp_t*); - CRFPP_DLL_EXTERN int crfpp_next(crfpp_t*); - CRFPP_DLL_EXTERN int crfpp_test(int, char **); - CRFPP_DLL_EXTERN int crfpp_test2(const char *); - CRFPP_DLL_EXTERN int crfpp_learn(int, char **); - CRFPP_DLL_EXTERN int crfpp_learn2(const char *); - CRFPP_DLL_EXTERN const char* crfpp_strerror(crfpp_t*); - CRFPP_DLL_EXTERN const char* crfpp_yname(crfpp_t*, size_t); - CRFPP_DLL_EXTERN const char* crfpp_y2(crfpp_t*, size_t); - CRFPP_DLL_EXTERN const char* crfpp_x(crfpp_t*, size_t, size_t); - CRFPP_DLL_EXTERN const char** crfpp_x2(crfpp_t*, size_t); - CRFPP_DLL_EXTERN const char* crfpp_parse_tostr(crfpp_t*, const char*); - CRFPP_DLL_EXTERN const char* crfpp_parse_tostr2(crfpp_t*, - const char*, size_t); - CRFPP_DLL_EXTERN const char* crfpp_parse_tostr3(crfpp_t*, const char*, - size_t, char *, size_t); - CRFPP_DLL_EXTERN const char* crfpp_tostr(crfpp_t*); - CRFPP_DLL_EXTERN const char* crfpp_tostr2(crfpp_t*, char *, size_t); - - CRFPP_DLL_EXTERN void crfpp_set_vlevel(crfpp_t *, unsigned int); - CRFPP_DLL_EXTERN unsigned int crfpp_vlevel(crfpp_t *); - CRFPP_DLL_EXTERN void crfpp_set_cost_factor(crfpp_t *, float); - CRFPP_DLL_EXTERN float crfpp_cost_factor(crfpp_t *); - CRFPP_DLL_EXTERN void crfpp_set_nbest(crfpp_t *, size_t); -#endif - -#ifdef __cplusplus -} -#endif - -/* C++ interface */ -#ifdef __cplusplus - -namespace CRFPP { - -class Tagger; - -class CRFPP_DLL_CLASS_EXTERN Model { - public: -#ifndef SWIG - // open model with parameters in argv[] - // e.g, argv[] = {"CRF++", "-m", "model", "-v3"}; - virtual bool open(int argc, char** argv) = 0; - - // open model with parameter arg, e.g. arg = "-m model -v3"; - virtual bool open(const char* arg) = 0; - - // open model with parameters in argv[]. - // e.g, argv[] = {"CRF++", "-v3"}; - virtual bool openFromArray(int argc, char** argv, - const char *model_buf, - size_t model_size) = 0; - - // open model with parameter arg, e.g. arg = "-m model -v3"; - virtual bool openFromArray(const char* arg, - const char *model_buf, - size_t model_size) = 0; -#endif - // return template string embedded in this model file. - virtual const char *getTemplate() const = 0; - - // create Tagger object. Returned object shared the same - // model object - virtual Tagger *createTagger() const = 0; - - virtual const char* what() = 0; - - virtual ~Model() {} -}; - -class CRFPP_DLL_CLASS_EXTERN Tagger { - public: -#ifndef SWIG - // open model with parameters in argv[] - // e.g, argv[] = {"CRF++", "-m", "model", "-v3"}; - virtual bool open(int argc, char** argv) = 0; - - // open model with parameter arg, e.g. arg = "-m model -v3"; - virtual bool open(const char* arg) = 0; - - // add str[] as tokens to the current context - virtual bool add(size_t size, const char **str) = 0; - - // close the current model - virtual void close() = 0; - - // return parameter vector. the size should be dsize(); - virtual const float *weight_vector() const = 0; -#endif - - // set Model - virtual bool set_model(const Model &model) = 0; - - // set vlevel - virtual void set_vlevel(unsigned int vlevel) = 0; - - // get vlevel - virtual unsigned int vlevel() const = 0; - - // set cost factor - virtual void set_cost_factor(float cost_factor) = 0; - - // get cost factor - virtual float cost_factor() const = 0; - - // set nbest - virtual void set_nbest(size_t nbest) = 0; - - // get nbest - virtual size_t nbest() const = 0; - - // add one line to the current context - virtual bool add(const char* str) = 0; - - // return size of tokens(lines) - virtual size_t size() const = 0; - - // return size of column - virtual size_t xsize() const = 0; - - // return size of features - virtual size_t dsize() const = 0; - - // return output tag-id of i-th token - virtual size_t result(size_t i) const = 0; - - // return answer tag-id of i-th token if it is available - virtual size_t answer(size_t i) const = 0; - - // alias of result(i) - virtual size_t y(size_t i) const = 0; - - // return output tag of i-th token as string - virtual const char* y2(size_t i) const = 0; - - // return i-th tag-id as string - virtual const char* yname(size_t i) const = 0; - - // return token at [i,j] as string(i:token j:column) - virtual const char* x(size_t i, size_t j) const = 0; - -#ifndef SWIG - // return an array of strings at i-th tokens - virtual const char** x(size_t) const = 0; -#endif - - // return size of output tags - virtual size_t ysize() const = 0; - - // return marginal probability of j-th tag id at i-th token - virtual double prob(size_t i, size_t j) const = 0; - - // return marginal probability of output tag at i-th token - // same as prob(i, tagger->y(i)); - virtual double prob(size_t i) const = 0; - - // return conditional probability of enter output - virtual double prob() const = 0; - - // set token-level penalty. It would be useful for implementing - // Dual decompositon decoding. - // e.g. - // "Dual Decomposition for Parsing with Non-Projective Head Automata" - // Terry Koo Alexander M. Rush Michael Collins Tommi Jaakkola David Sontag - virtual void set_penalty(size_t i, size_t j, double penalty) = 0; - virtual double penalty(size_t i, size_t j) const = 0; - - // return forward log-prob of the j-th tag at i-th token - virtual double alpha(size_t i, size_t j) const = 0; - - // return backward log-prob of the j-th tag at i-th token - virtual double beta(size_t i, size_t j) const = 0; - - // return emission cost of the j-th tag at i-th token - virtual double emission_cost(size_t i, size_t j) const = 0; - - // return transition cost of [j-th tag at i-th token] to - // [k-th tag at(i+1)-th token] - virtual double next_transition_cost(size_t i, - size_t j, size_t k) const = 0; - - // return transition cost of [j-th tag at i-th token] to - // [k-th tag at(i-1)-th token] - virtual double prev_transition_cost(size_t i, - size_t j, size_t k) const = 0; - - // return the best accumulative cost to the j-th tag at i-th token - // used in viterbi search - virtual double best_cost(size_t i, size_t j) const = 0; - -#ifndef SWIG - // return emission feature vector of the j-th tag at i-th token - virtual const int* emission_vector(size_t i, size_t j) const = 0; - - // return transition feature vector of [j-th tag at i-th token] to - // [k-th tag at(i+1)-th token] - virtual const int* next_transition_vector(size_t i, - size_t j, size_t k) const = 0; - - // return transition feature vector of [j-th tag at i-th token] to - // [k-th tag at(i-1)-th token] - virtual const int* prev_transition_vector(size_t i, - size_t j, size_t k) const = 0; -#endif - - // normalizing factor(log-prob) - virtual double Z() const = 0; - - // do parse and change the internal status, if failed, returns false - virtual bool parse() = 0; - - // return true if the context is empty - virtual bool empty() const = 0; - - // clear all context - virtual bool clear() = 0; - - // change the internal state to output next-optimal output. - // calling it n-th times, can get n-best results, - // Neeed to specify -nN option to use this function, where - // N>=2 - virtual bool next() = 0; - - // parse 'str' and return result as string - // 'str' must be written in CRF++'s input format - virtual const char* parse(const char* str) = 0; - -#ifndef SWIG - // return parsed result as string - virtual const char* toString() = 0; - - // return parsed result as string. - // Result is saved in the buffer 'result', 'size' is the - // size of the buffer. if failed, return NULL - virtual const char* toString(char* result , size_t size) = 0; - - // parse 'str' and return parsed result. - // You don't need to delete return value, but the buffer - // is rewritten whenever you call parse method. - // if failed, return NULL - virtual const char* parse(const char *str, size_t size) = 0; - - // parse 'str' and return parsed result. - // The result is stored in the buffer 'result'. - // 'size2' is the size of the buffer. if failed, return NULL - virtual const char* parse(const char *str, size_t size1, - char *result, size_t size2) = 0; -#endif - // return internal error code as string - virtual const char* what() = 0; - - virtual ~Tagger() {} -}; - -/* factory method */ - -// create CRFPP::Tagger instance with parameters in argv[] -// e.g, argv[] = {"CRF++", "-m", "model", "-v3"}; -CRFPP_DLL_EXTERN Tagger *createTagger(int argc, char **argv); - -// create CRFPP::Tagger instance with parameter in arg -// e.g. arg = "-m model -v3"; -CRFPP_DLL_EXTERN Tagger *createTagger(const char *arg); - -// create CRFPP::Model instance with parameters in argv[] -// e.g, argv[] = {"CRF++", "-m", "model", "-v3"}; -CRFPP_DLL_EXTERN Model *createModel(int argc, char **argv); - -// load model from [buf, buf+size]. -CRFPP_DLL_EXTERN Model *createModelFromArray(int argc, char **argv, - const char *model_buf, - size_t model_size); - -// create CRFPP::Model instance with parameter in arg -// e.g. arg = "-m model -v3"; -CRFPP_DLL_EXTERN Model *createModel(const char *arg); - -// load model from [buf, buf+size]. -CRFPP_DLL_EXTERN Model *createModelFromArray(const char *arg, - const char *model_buf, - size_t model_size); - -// return error code of createTagger(); -CRFPP_DLL_EXTERN const char *getTaggerError(); - -// alias of getTaggerError(); -CRFPP_DLL_EXTERN const char *getLastError(); -} - -#endif -#endif diff --git a/source/Parser/lib/libcrfpp.a b/source/Parser/lib/libcrfpp.a deleted file mode 100644 index 15ad3a4d8e9257485c4e968e18a26f4a01af6267..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 623548 zcmeFa4SZD9nLj=g5{QVogQC((>uAR|Y!xygV4`RfGJ!if(bNPD6-`23NNPyZWCHje zO(IUmvDwwOv}Z)O0| zZg>BmUdWw$pL5Q0p7WgNJm)#jdAX-7uIsFCK6mriyvk?m-RsR?xL{$vh*LY= z<#NC0a@B|a|NnQxMwiR^_e-D4^(Xjqf7|Q&%isSD52pNNrR%W&e!IyvCEeeL!1I^C zzwiJuFtuP~@~?D`YpVXMGF?;u^7j`W{NIs*`p%|~j+!;?4UMfeZH?<(j`w^UpsBNc zO-)^AXWf;P;@X4k$yPUpYr>6dI$G<(jW&D@ja}i+_A6~LKpHzc+dFMQP*xMJTfMqb 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zg97mhQ~i6<`nE2IzFv(J`}5iSoTlXibV@}t`BKv#;@bZF|5w-YJ@Z{B;e`|9*wF@VZ(hDilsH0rT*JwGs>y8Tc5lfaKZF+cTagCE52xag&H7Wjj8 zoDemHuX9aS_1t*oKXvScO*0m5HYhixT!^9jC!g(rr#|b)aU z$@hoe+rWREf~Q_GQ3k~LPd4Y2Js6E(j4E^J;#>>eG?8a>@?(}Fzv);txqj-q`1Hyl z-@ZfK_7#-ll-0&Q^-FxZCi1!Qx$(UjrJ9yEW^K(1tlu8_;sJ4s8|b_xR__jNjLTyk ziqFR{AHkJYh<%kWMol+7=V=M-`7{QfEDzPs#efOOQ5OPVy@0Rop@qezb z|Lz1_*Xnh1pTu;nzG=B*^(}j+*yNXjpS;&_L^Uz6_~jZV(0cKG`ElR8f2YOAwPPgy dJ1t~vci(%lR`1bT)PM2AL+~qHs6nBdz5$Nn^@jie diff --git a/source/Parser/main.py b/source/Parser/main.py index c48ee706..bf9a87f0 100644 --- a/source/Parser/main.py +++ b/source/Parser/main.py @@ -1,53 +1,37 @@ +from __future__ import annotations + +import argparse import json import multiprocessing import os import re -import tempfile -import warnings +import sys from pathlib import Path import pandas as pd -from biomedner_engine import BioMedNER - -# Filepaths -BASE_INPUT_FILEPATH = os.path.join( - os.path.dirname(__file__), "../../data/preprocessed_data" -) -BASE_OUTPUT_FILEPATH_CT = os.path.join( - os.path.dirname(__file__), "../../data/parsed_trec" -) -BASE_TMP_SUPERDIR = os.path.join(os.path.dirname(__file__), "tmp") -DICT_PATH = Path("Parser/resources/normalization/dictionary") -dict_paths = { - "gene": DICT_PATH / "dict_Gene.txt", - "disease": DICT_PATH / "dict_Disease_20210630.txt", - "cell type": DICT_PATH / "dict_CellType_20210810.txt", - "drug": DICT_PATH / "dict_ChemicalCompound_20210630.txt", - "procedure": DICT_PATH / "dict_Procedures.txt", - "sign symptom": DICT_PATH / "dict_SignSymptom.txt", -} - - -def load_shared_dictionaries(): - return { - key: BioMedNER.load_dictionary_file(path) for key, path in dict_paths.items() - } +SOURCE_DIR = Path(__file__).resolve().parents[1] +if str(SOURCE_DIR) not in sys.path: + sys.path.append(str(SOURCE_DIR)) + +from Matcher.config.config_loader import load_config # noqa: E402 +from Parser.biomedner_engine import BioMedNER # noqa: E402 + + +BASE_INPUT_FILEPATH = Path(__file__).resolve().parents[2] / "data/preprocessed_data" +BASE_OUTPUT_FILEPATH_CT = Path(__file__).resolve().parents[2] / "data/parsed_trec" -# Global variable for BioMedNER instance (initialized in each process) -biomedner = None -data_source = "clinical trials" # Adjust this based on your data source +entity_annotator: BioMedNER | None = None +data_source = "clinical trials" -def process_dataframe(df, text_column): +def process_dataframe(df: pd.DataFrame, text_column: str) -> pd.DataFrame: new_data = [] for _, row in df.iterrows(): row_copy = row.to_dict() text = row_copy.pop(text_column) - - # Ensure text is a string if not isinstance(text, str): - text = str(text) # Convert to string if not already + text = str(text) word_count = len(text.split()) sentence_end_count = text.count(".") + text.count("?") + text.count("!") @@ -62,59 +46,21 @@ def process_dataframe(df, text_column): row_copy[text_column] = text new_data.append(row_copy) - new_df = pd.DataFrame(new_data) - return new_df + return pd.DataFrame(new_data) -def process_file(idx): - global biomedner - if biomedner is None: - raise RuntimeError( - "BioMedNER instance is not initialized. Please ensure 'biomedner' is set before calling process_file." - ) - output_filepath = os.path.join(BASE_OUTPUT_FILEPATH_CT, f"{idx}.json") - if os.path.exists(output_filepath): - print(f"File {idx} already exists. Skipping...") - return +def process_file(idx: str) -> None: + global entity_annotator + if entity_annotator is None: + raise RuntimeError("Entity annotator is not initialized.") - # Load the dataframe - file_path = None - try: - if data_source == "clinical trials": - file_path = os.path.join( - BASE_INPUT_FILEPATH, "clintra", f"{idx}_preprocessed.tsv" - ) - if os.path.exists(file_path): - print(f"Processing {idx}") - df = pd.read_csv(file_path, delimiter="\t") - else: - print(f"File {file_path} does not exist. Skipping.") - return - elif data_source == "patient": - file_path = os.path.join( - BASE_INPUT_FILEPATH, "patient", f"{idx}_preprocessed.csv" - ) - if os.path.exists(file_path): - df = pd.read_csv(file_path) - else: - print(f"File {file_path} does not exist. Skipping.") - return - else: - warnings.warn( - "Unexpected data source encountered. Please choose between 'clinical trials' or 'patient'", - UserWarning, - ) - return - except pd.errors.EmptyDataError: - print( - f"EmptyDataError: No columns to parse from file {file_path}. Skipping this file." - ) - return - except Exception as e: - print(f"An unexpected error occurred while processing file {file_path}: {e}") + output_filepath = BASE_OUTPUT_FILEPATH_CT / f"{idx}.json" + if output_filepath.exists(): + print(f"File {idx} already exists. Skipping...") return - if df.empty: + df = _load_dataframe(idx) + if df is None or df.empty: print(f"Dataframe {idx} is empty. Skipping.") return @@ -125,8 +71,10 @@ def process_file(idx): print(f"No sentences to process for {idx}. Skipping.") return - # Use the biomedner instance - annotated_sentences = biomedner.annotate_texts_in_parallel(sentences, max_workers=5) + annotated_sentences = entity_annotator.annotate_texts_in_parallel( + sentences, + max_workers=5, + ) df["entities"] = [entities for entities in annotated_sentences] result_json = { "nct_id": idx, @@ -139,91 +87,106 @@ def process_file(idx): for _, row in df.iterrows() ], } - # Save the result - with open(output_filepath, "w") as f: - json.dump(result_json, f, indent=4) + output_filepath.parent.mkdir(parents=True, exist_ok=True) + output_filepath.write_text(json.dumps(result_json, indent=4)) print(f"Saved {output_filepath}") -def process_files(device_id, ids_to_process, biomedner_params, shared_dicts): - import os - - global biomedner - - # Set the environment variable for this process +def process_files( + device_id: str, + ids_to_process: list[str], + config_path: str | None, +) -> None: + global entity_annotator os.environ["CUDA_VISIBLE_DEVICES"] = device_id - - # Initialize BioMedNER instance - biomedner = BioMedNER( - **biomedner_params, - biomedner_home=tempfile.TemporaryDirectory(dir=BASE_TMP_SUPERDIR).name, - preloaded_dicts=shared_dicts, - ) - - # Process each file in the chunk + config = load_config(config_path) if config_path else None + entity_annotator = BioMedNER(config=config) for idx in ids_to_process: process_file(idx) -if __name__ == "__main__": - import multiprocessing - - # Load dictionaries once - shared_dicts = load_shared_dictionaries() - - # Define BioMedNER parameters - biomedner_params = { - "max_word_len": 50, - "seed": 2019, - "gene_norm_port": 18888, - "disease_norm_port": 18892, - "biomedner_host": "localhost", - "biomedner_port": 18894, - "gner_host": "localhost", - "gner_port": 18783, - "time_format": "[%d/%b/%Y %H:%M:%S.%f]", - "use_neural_normalizer": True, - "no_cuda": False, - } +def main() -> int: + parser = argparse.ArgumentParser( + description="Annotate preprocessed trial criteria with TrialMatchAI entities." + ) + parser.add_argument("--config", default=None, help="TrialMatchAI config path") + parser.add_argument("--processes", type=int, default=1) + parser.add_argument("--device-id", action="append", default=["0"]) + parser.add_argument( + "--ids-file", + default=None, + help="Optional file containing one NCT ID per line. Defaults to TREC ID files.", + ) + args = parser.parse_args() - # Load your NCT IDs - df_trec21 = pd.read_csv("../../data/trec/Unique_NCT_IDs_from_2021_File.csv") - df_trec22 = pd.read_csv("../../data/trec/Unique_NCT_IDs_from_2022_File.csv") - nct_ids21 = df_trec21["Unique NCT IDs"].unique().tolist() - nct_ids22 = df_trec22["Unique NCT IDs"].unique().tolist() - unique_ids = set(nct_ids21 + nct_ids22) - # Prepare the list of unprocessed ids + unique_ids = _load_ids(args.ids_file) unprocessed_ids = [ - idx - for idx in unique_ids - if not os.path.exists(os.path.join(BASE_OUTPUT_FILEPATH_CT, f"{idx}.json")) + idx for idx in unique_ids if not (BASE_OUTPUT_FILEPATH_CT / f"{idx}.json").exists() ] + if not unprocessed_ids: + print("No unprocessed IDs found.") + return 0 - # Define your device IDs - device_ids = ["0"] # List of GPU IDs as strings - - # Define the number of processes you want - num_processes = 8 # For example, 12 processes - - # Create a list of device IDs cycling through the available GPUs - process_device_ids = [device_ids[i % len(device_ids)] for i in range(num_processes)] - - # Divide unprocessed_ids among processes + num_processes = max(1, args.processes) + process_device_ids = [ + args.device_id[i % len(args.device_id)] for i in range(num_processes) + ] chunks = [[] for _ in range(num_processes)] for i, idx in enumerate(unprocessed_ids): chunks[i % num_processes].append(idx) processes = [] - for i in range(num_processes): - device_id = process_device_ids[i] - ids_chunk = chunks[i] - if ids_chunk: # Only start a process if there's work to do - p = multiprocessing.Process( - target=process_files, - args=(device_id, ids_chunk, biomedner_params, shared_dicts), - ) - processes.append(p) - p.start() - - for p in processes: - p.join() + for i, ids_chunk in enumerate(chunks): + if not ids_chunk: + continue + process = multiprocessing.Process( + target=process_files, + args=(process_device_ids[i], ids_chunk, args.config), + ) + processes.append(process) + process.start() + + for process in processes: + process.join() + if process.exitcode: + return process.exitcode + return 0 + + +def _load_dataframe(idx: str) -> pd.DataFrame | None: + if data_source == "clinical trials": + file_path = BASE_INPUT_FILEPATH / "clintra" / f"{idx}_preprocessed.tsv" + delimiter = "\t" + elif data_source == "patient": + file_path = BASE_INPUT_FILEPATH / "patient" / f"{idx}_preprocessed.csv" + delimiter = "," + else: + raise ValueError("data_source must be 'clinical trials' or 'patient'.") + + if not file_path.exists(): + print(f"File {file_path} does not exist. Skipping.") + return None + try: + return pd.read_csv(file_path, delimiter=delimiter) + except pd.errors.EmptyDataError: + print(f"No columns to parse from file {file_path}. Skipping.") + return None + + +def _load_ids(ids_file: str | None) -> list[str]: + if ids_file: + return [ + line.strip() + for line in Path(ids_file).read_text().splitlines() + if line.strip() + ] + trec_root = Path(__file__).resolve().parents[2] / "data/trec" + df_trec21 = pd.read_csv(trec_root / "Unique_NCT_IDs_from_2021_File.csv") + df_trec22 = pd.read_csv(trec_root / "Unique_NCT_IDs_from_2022_File.csv") + nct_ids21 = df_trec21["Unique NCT IDs"].unique().tolist() + nct_ids22 = df_trec22["Unique NCT IDs"].unique().tolist() + return sorted(set(nct_ids21 + nct_ids22)) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/source/Parser/models.py b/source/Parser/models.py deleted file mode 100644 index c8abea98..00000000 --- a/source/Parser/models.py +++ /dev/null @@ -1,653 +0,0 @@ -# coding=utf-8 -import copy - -import torch -import torch.nn.functional as F -from torch.nn import CrossEntropyLoss -from transformers import ( - BertForTokenClassification, - BertModel, - RobertaForTokenClassification, - RobertaModel, -) - - -class BERTMultiNER2(BertForTokenClassification): - def __init__(self, config, num_labels=3): - super(BERTMultiNER2, self).__init__(config) - self.num_labels = num_labels - self.bert = BertModel(config) - self.dropout = torch.nn.Dropout(config.hidden_dropout_prob) - - self.dise_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # disease - self.chem_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # chemical - self.gene_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # gene/protein - self.spec_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # species - self.cellline_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # cell line - self.dna_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # dna - self.rna_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # rna - self.celltype_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # cell type - - self.dise_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.chem_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.gene_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.spec_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.cellline_classifier_2 = torch.nn.Linear( - config.hidden_size, config.hidden_size - ) - self.dna_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.rna_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.celltype_classifier_2 = torch.nn.Linear( - config.hidden_size, config.hidden_size - ) - - self.init_weights() - - def forward( - self, - input_ids, - token_type_ids=None, - attention_mask=None, - labels=None, - entity_type_ids=None, - ): - sequence_output = self.bert( - input_ids, - token_type_ids=token_type_ids, - attention_mask=attention_mask, - head_mask=None, - )[0] - batch_size, max_len, feat_dim = sequence_output.shape - sequence_output = self.dropout(sequence_output) - - if entity_type_ids[0][0].item() == 0: - """ - Raw text data with trained parameters - """ - dise_sequence_output = F.relu( - self.dise_classifier_2(sequence_output) - ) # disease logit value - chem_sequence_output = F.relu( - self.chem_classifier_2(sequence_output) - ) # chemical logit value - gene_sequence_output = F.relu( - self.gene_classifier_2(sequence_output) - ) # gene/protein logit value - spec_sequence_output = F.relu( - self.spec_classifier_2(sequence_output) - ) # species logit value - cellline_sequence_output = F.relu( - self.cellline_classifier_2(sequence_output) - ) # cell line logit value - dna_sequence_output = F.relu( - self.dna_classifier_2(sequence_output) - ) # dna logit value - rna_sequence_output = F.relu( - self.rna_classifier_2(sequence_output) - ) # rna logit value - celltype_sequence_output = F.relu( - self.celltype_classifier_2(sequence_output) - ) # cell type logit value - - dise_logits = self.dise_classifier( - dise_sequence_output - ) # disease logit value - chem_logits = self.chem_classifier( - chem_sequence_output - ) # chemical logit value - gene_logits = self.gene_classifier( - gene_sequence_output - ) # gene/protein logit value - spec_logits = self.spec_classifier( - spec_sequence_output - ) # species logit value - cellline_logits = self.cellline_classifier( - cellline_sequence_output - ) # cell line logit value - dna_logits = self.dna_classifier(dna_sequence_output) # dna logit value - rna_logits = self.rna_classifier(rna_sequence_output) # rna logit value - celltype_logits = self.celltype_classifier( - celltype_sequence_output - ) # cell type logit value - - # update logit and sequence_output - sequence_output = ( - dise_sequence_output - + chem_sequence_output - + gene_sequence_output - + spec_sequence_output - + cellline_sequence_output - + dna_sequence_output - + rna_sequence_output - + celltype_sequence_output - ) - - logits = ( - dise_logits, - chem_logits, - gene_logits, - spec_logits, - cellline_logits, - dna_logits, - rna_logits, - celltype_logits, - ) - else: - """ - Train, Eval, Test with pre-defined entity type tags - """ - # make 1*1 conv to adopt entity type - dise_idx = copy.deepcopy(entity_type_ids) - chem_idx = copy.deepcopy(entity_type_ids) - gene_idx = copy.deepcopy(entity_type_ids) - spec_idx = copy.deepcopy(entity_type_ids) - cellline_idx = copy.deepcopy(entity_type_ids) - dna_idx = copy.deepcopy(entity_type_ids) - rna_idx = copy.deepcopy(entity_type_ids) - celltype_idx = copy.deepcopy(entity_type_ids) - - dise_idx[dise_idx != 1] = 0 - chem_idx[chem_idx != 2] = 0 - gene_idx[gene_idx != 3] = 0 - spec_idx[spec_idx != 4] = 0 - cellline_idx[cellline_idx != 5] = 0 - dna_idx[dna_idx != 6] = 0 - rna_idx[rna_idx != 7] = 0 - celltype_idx[celltype_idx != 8] = 0 - - dise_sequence_output = dise_idx.unsqueeze(-1) * sequence_output - chem_sequence_output = chem_idx.unsqueeze(-1) * sequence_output - gene_sequence_output = gene_idx.unsqueeze(-1) * sequence_output - spec_sequence_output = spec_idx.unsqueeze(-1) * sequence_output - cellline_sequence_output = cellline_idx.unsqueeze(-1) * sequence_output - dna_sequence_output = dna_idx.unsqueeze(-1) * sequence_output - rna_sequence_output = rna_idx.unsqueeze(-1) * sequence_output - celltype_sequence_output = celltype_idx.unsqueeze(-1) * sequence_output - - # F.tanh or F.relu - dise_sequence_output = F.relu( - self.dise_classifier_2(dise_sequence_output) - ) # disease logit value - chem_sequence_output = F.relu( - self.chem_classifier_2(chem_sequence_output) - ) # chemical logit value - gene_sequence_output = F.relu( - self.gene_classifier_2(gene_sequence_output) - ) # gene/protein logit value - spec_sequence_output = F.relu( - self.spec_classifier_2(spec_sequence_output) - ) # species logit value - cellline_sequence_output = F.relu( - self.cellline_classifier_2(cellline_sequence_output) - ) # cell line logit value - dna_sequence_output = F.relu( - self.dna_classifier_2(dna_sequence_output) - ) # dna logit value - rna_sequence_output = F.relu( - self.rna_classifier_2(rna_sequence_output) - ) # rna logit value - celltype_sequence_output = F.relu( - self.celltype_classifier_2(celltype_sequence_output) - ) # cell type logit value - - dise_logits = self.dise_classifier( - dise_sequence_output - ) # disease logit value - chem_logits = self.chem_classifier( - chem_sequence_output - ) # chemical logit value - gene_logits = self.gene_classifier( - gene_sequence_output - ) # gene/protein logit value - spec_logits = self.spec_classifier( - spec_sequence_output - ) # species logit value - cellline_logits = self.cellline_classifier( - cellline_sequence_output - ) # cell line logit value - dna_logits = self.dna_classifier(dna_sequence_output) # dna logit value - rna_logits = self.rna_classifier(rna_sequence_output) # rna logit value - celltype_logits = self.celltype_classifier( - celltype_sequence_output - ) # cell type logit value - - # update logit and sequence_output - sequence_output = ( - dise_sequence_output - + chem_sequence_output - + gene_sequence_output - + spec_sequence_output - + cellline_sequence_output - + dna_sequence_output - + rna_sequence_output - + celltype_sequence_output - ) - - logits = ( - dise_logits - + chem_logits - + gene_logits - + spec_logits - + cellline_logits - + dna_logits - + rna_logits - + celltype_logits - ) - - outputs = (logits, sequence_output) - if labels is not None: - loss_fct = CrossEntropyLoss() - # Only keep active parts of the loss - if attention_mask is not None: - if entity_type_ids[0][0].item() == 0: - active_loss = attention_mask.view(-1) == 1 - ( - dise_logits, - chem_logits, - gene_logits, - spec_logits, - cellline_logits, - dna_logits, - rna_logits, - celltype_logits, - ) = logits - - active_dise_logits = dise_logits.view(-1, self.num_labels) - active_chem_logits = chem_logits.view(-1, self.num_labels) - active_gene_logits = gene_logits.view(-1, self.num_labels) - active_spec_logits = spec_logits.view(-1, self.num_labels) - active_cellline_logits = cellline_logits.view(-1, self.num_labels) - active_dna_logits = dna_logits.view(-1, self.num_labels) - active_rna_logits = rna_logits.view(-1, self.num_labels) - active_celltype_logits = celltype_logits.view(-1, self.num_labels) - active_labels = torch.where( - active_loss, - labels.view(-1), - torch.tensor(loss_fct.ignore_index).type_as(labels), - ) - dise_loss = loss_fct(active_dise_logits, active_labels) - chem_loss = loss_fct(active_chem_logits, active_labels) - gene_loss = loss_fct(active_gene_logits, active_labels) - spec_loss = loss_fct(active_spec_logits, active_labels) - cellline_loss = loss_fct(active_cellline_logits, active_labels) - dna_loss = loss_fct(active_dna_logits, active_labels) - rna_loss = loss_fct(active_rna_logits, active_labels) - celltype_loss = loss_fct(active_celltype_logits, active_labels) - - loss = ( - dise_loss - + chem_loss - + gene_loss - + spec_loss - + cellline_loss - + dna_loss - + rna_loss - + celltype_loss - ) - - return (loss,) + outputs - else: - active_loss = attention_mask.view(-1) == 1 - active_logits = logits.view(-1, self.num_labels) - active_labels = torch.where( - active_loss, - labels.view(-1), - torch.tensor(loss_fct.ignore_index).type_as(labels), - ) - loss = loss_fct(active_logits, active_labels) - return (loss,) + outputs - else: - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - return loss - else: - return logits - - -class RoBERTaMultiNER2(RobertaForTokenClassification): - def __init__(self, config, num_labels=3): - super(RoBERTaMultiNER2, self).__init__(config) - self.num_labels = num_labels - self.roberta = RobertaModel(config) - self.dropout = torch.nn.Dropout(config.hidden_dropout_prob) - - self.dise_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # disease - self.chem_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # chemical - self.gene_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # gene/protein - self.spec_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # species - self.cellline_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # cell line - self.dna_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # dna - self.rna_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # rna - self.protein_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # protein - self.celltype_classifier = torch.nn.Linear( - config.hidden_size, self.num_labels - ) # cell type - - self.dise_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.chem_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.gene_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.spec_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.cellline_classifier_2 = torch.nn.Linear( - config.hidden_size, config.hidden_size - ) - self.dna_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.rna_classifier_2 = torch.nn.Linear(config.hidden_size, config.hidden_size) - self.protein_classifier_2 = torch.nn.Linear( - config.hidden_size, config.hidden_size - ) - self.celltype_classifier_2 = torch.nn.Linear( - config.hidden_size, config.hidden_size - ) - - self.init_weights() - - def forward( - self, - input_ids, - token_type_ids=None, - attention_mask=None, - labels=None, - entity_type_ids=None, - ): - sequence_output = self.roberta( - input_ids, - token_type_ids=token_type_ids, - attention_mask=attention_mask, - head_mask=None, - )[0] - batch_size, max_len, feat_dim = sequence_output.shape - sequence_output = self.dropout(sequence_output) - - if entity_type_ids[0][0].item() == 0: - """ - Raw text data with trained parameters - """ - dise_sequence_output = F.relu( - self.dise_classifier_2(sequence_output) - ) # disease logit value - chem_sequence_output = F.relu( - self.chem_classifier_2(sequence_output) - ) # chemical logit value - gene_sequence_output = F.relu( - self.gene_classifier_2(sequence_output) - ) # gene/protein logit value - spec_sequence_output = F.relu( - self.spec_classifier_2(sequence_output) - ) # species logit value - cellline_sequence_output = F.relu( - self.cellline_classifier_2(sequence_output) - ) # cell line logit value - dna_sequence_output = F.relu( - self.dna_classifier_2(sequence_output) - ) # dna logit value - rna_sequence_output = F.relu( - self.rna_classifier_2(sequence_output) - ) # rna logit value - protein_sequence_output = F.relu( - self.protein_classifier_2(sequence_output) - ) # protein logit value - celltype_sequence_output = F.relu( - self.celltype_classifier_2(sequence_output) - ) # cell type logit value - - dise_logits = self.dise_classifier( - dise_sequence_output - ) # disease logit value - chem_logits = self.chem_classifier( - chem_sequence_output - ) # chemical logit value - gene_logits = self.gene_classifier( - gene_sequence_output - ) # gene/protein logit value - spec_logits = self.spec_classifier( - spec_sequence_output - ) # species logit value - cellline_logits = self.cellline_classifier( - cellline_sequence_output - ) # cell line logit value - dna_logits = self.dna_classifier(dna_sequence_output) # dna logit value - rna_logits = self.rna_classifier(rna_sequence_output) # rna logit value - protein_logits = self.protein_classifier( - protein_sequence_output - ) # protein logit value - celltype_logits = self.celltype_classifier( - celltype_sequence_output - ) # cell type logit value - - # update logit and sequence_output - sequence_output = ( - dise_sequence_output - + chem_sequence_output - + gene_sequence_output - + spec_sequence_output - + cellline_sequence_output - + dna_sequence_output - + rna_sequence_output - + protein_sequence_output - + celltype_sequence_output - ) - logits = ( - dise_logits, - chem_logits, - gene_logits, - spec_logits, - cellline_logits, - dna_logits, - rna_logits, - protein_logits, - celltype_logits, - ) - else: - """ - Train, Eval, Test with pre-defined entity type tags - """ - # make 1*1 conv to adopt entity type - dise_idx = copy.deepcopy(entity_type_ids) - chem_idx = copy.deepcopy(entity_type_ids) - gene_idx = copy.deepcopy(entity_type_ids) - spec_idx = copy.deepcopy(entity_type_ids) - cellline_idx = copy.deepcopy(entity_type_ids) - dna_idx = copy.deepcopy(entity_type_ids) - rna_idx = copy.deepcopy(entity_type_ids) - protein_idx = copy.deepcopy(entity_type_ids) - celltype_idx = copy.deepcopy(entity_type_ids) - - dise_idx[dise_idx != 1] = 0 - chem_idx[chem_idx != 2] = 0 - gene_idx[gene_idx != 3] = 0 - spec_idx[spec_idx != 4] = 0 - cellline_idx[cellline_idx != 5] = 0 - dna_idx[dna_idx != 6] = 0 - rna_idx[rna_idx != 7] = 0 - protein_idx[protein_idx != 8] = 0 - celltype_idx[celltype_idx != 9] = 0 - - dise_sequence_output = dise_idx.unsqueeze(-1) * sequence_output - chem_sequence_output = chem_idx.unsqueeze(-1) * sequence_output - gene_sequence_output = gene_idx.unsqueeze(-1) * sequence_output - spec_sequence_output = spec_idx.unsqueeze(-1) * sequence_output - cellline_sequence_output = cellline_idx.unsqueeze(-1) * sequence_output - dna_sequence_output = dna_idx.unsqueeze(-1) * sequence_output - rna_sequence_output = rna_idx.unsqueeze(-1) * sequence_output - protein_sequence_output = protein_idx.unsqueeze(-1) * sequence_output - celltype_sequence_output = celltype_idx.unsqueeze(-1) * sequence_output - - # F.tanh or F.relu - dise_sequence_output = F.relu( - self.dise_classifier_2(dise_sequence_output) - ) # disease logit value - chem_sequence_output = F.relu( - self.chem_classifier_2(chem_sequence_output) - ) # chemical logit value - gene_sequence_output = F.relu( - self.gene_classifier_2(gene_sequence_output) - ) # gene/protein logit value - spec_sequence_output = F.relu( - self.spec_classifier_2(spec_sequence_output) - ) # species logit value - cellline_sequence_output = F.relu( - self.cellline_classifier_2(cellline_sequence_output) - ) # cell line logit value - dna_sequence_output = F.relu( - self.dna_classifier_2(dna_sequence_output) - ) # dna logit value - rna_sequence_output = F.relu( - self.rna_classifier_2(rna_sequence_output) - ) # rna logit value - protein_sequence_output = F.relu( - self.protein_classifier_2(protein_sequence_output) - ) # protein logit value - celltype_sequence_output = F.relu( - self.celltype_classifier_2(celltype_sequence_output) - ) # cell type logit value - - dise_logits = self.dise_classifier( - dise_sequence_output - ) # disease logit value - chem_logits = self.chem_classifier( - chem_sequence_output - ) # chemical logit value - gene_logits = self.gene_classifier( - gene_sequence_output - ) # gene/protein logit value - spec_logits = self.spec_classifier( - spec_sequence_output - ) # species logit value - cellline_logits = self.cellline_classifier( - cellline_sequence_output - ) # cell line logit value - dna_logits = self.dna_classifier(dna_sequence_output) # dna logit value - rna_logits = self.rna_classifier(rna_sequence_output) # rna logit value - protein_logits = self.protein_classifier( - protein_sequence_output - ) # protein logit value - celltype_logits = self.celltype_classifier( - celltype_sequence_output - ) # cell type logit value - - # update logit and sequence_output - sequence_output = ( - dise_sequence_output - + chem_sequence_output - + gene_sequence_output - + spec_sequence_output - + cellline_sequence_output - + dna_sequence_output - + rna_sequence_output - + protein_sequence_output - + celltype_sequence_output - ) - logits = ( - dise_logits - + chem_logits - + gene_logits - + spec_logits - + cellline_logits - + dna_logits - + rna_logits - + protein_logits - + celltype_logits - ) - - outputs = (logits, sequence_output) - if labels is not None: - loss_fct = CrossEntropyLoss() - # Only keep active parts of the loss - if attention_mask is not None: - if entity_type_ids[0][0].item() == 0: - active_loss = attention_mask.view(-1) == 1 - ( - dise_logits, - chem_logits, - gene_logits, - spec_logits, - cellline_logits, - dna_logits, - rna_logits, - protein_logits, - celltype_logits, - ) = logits - - active_dise_logits = dise_logits.view(-1, self.num_labels) - active_chem_logits = chem_logits.view(-1, self.num_labels) - active_gene_logits = gene_logits.view(-1, self.num_labels) - active_spec_logits = spec_logits.view(-1, self.num_labels) - active_cellline_logits = cellline_logits.view(-1, self.num_labels) - active_dna_logits = dna_logits.view(-1, self.num_labels) - active_rna_logits = rna_logits.view(-1, self.num_labels) - active_protein_logits = protein_logits.view(-1, self.num_labels) - active_celltype_logits = celltype_logits.view(-1, self.num_labels) - - active_labels = torch.where( - active_loss, - labels.view(-1), - torch.tensor(loss_fct.ignore_index).type_as(labels), - ) - dise_loss = loss_fct(active_dise_logits, active_labels) - chem_loss = loss_fct(active_chem_logits, active_labels) - gene_loss = loss_fct(active_gene_logits, active_labels) - spec_loss = loss_fct(active_spec_logits, active_labels) - cellline_loss = loss_fct(active_cellline_logits, active_labels) - dna_loss = loss_fct(active_dna_logits, active_labels) - rna_loss = loss_fct(active_rna_logits, active_labels) - protein_loss = loss_fct(active_protein_logits, active_labels) - celltype_loss = loss_fct(active_celltype_logits, active_labels) - loss = ( - dise_loss - + chem_loss - + gene_loss - + spec_loss - + cellline_loss - + dna_loss - + rna_loss - + protein_loss - + celltype_loss - ) - - return (loss,) + outputs - else: - active_loss = attention_mask.view(-1) == 1 - active_logits = logits.view(-1, self.num_labels) - active_labels = torch.where( - active_loss, - labels.view(-1), - torch.tensor(loss_fct.ignore_index).type_as(labels), - ) - loss = loss_fct(active_logits, active_labels) - return (loss,) + outputs - else: - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - return loss - else: - return logits diff --git a/source/Parser/normalizer.py b/source/Parser/normalizer.py index 2266c08e..96d7e7a2 100644 --- a/source/Parser/normalizer.py +++ b/source/Parser/normalizer.py @@ -1,422 +1,28 @@ -import os -import socket -from concurrent.futures import ThreadPoolExecutor +from __future__ import annotations -from Parser.normalizers.neural_normalizer import NeuralNormalizer -from Parser.normalizers.normalizer_all import ( - CellLineNormalizer, - CellTypeNormalizer, - ChemicalNormalizer, - ProcedureNormalizer, - SignSymptomNormalizer, - SpeciesNormalizer, -) +from typing import Any -time_format = "[%d/%b/%Y %H:%M:%S.%f]" +from Matcher.entities.linker import ConceptLinker class Normalizer: - def __init__( - self, use_neural_normalizer, gene_port=18888, disease_port=18892, no_cuda=False - ): - self.BASE_DIR = "Parser/resources/normalization/" - self.NORM_INPUT_DIR = { - "disease": os.path.join(self.BASE_DIR, "inputs/disease"), - "gene": os.path.join(self.BASE_DIR, "inputs/gene"), - } - self.NORM_OUTPUT_DIR = { - "disease": os.path.join(self.BASE_DIR, "outputs/disease"), - "gene": os.path.join(self.BASE_DIR, "outputs/gene"), - } + """Legacy placeholder for Parser callers. - self.NORM_DICT_PATH = { - "drug": os.path.join(self.BASE_DIR, "dictionary/dict_ChemicalsDrugs.txt"), - "gene": "setup.txt", - "species": os.path.join(self.BASE_DIR, "dictionary/dict_Species.txt"), - "cell line": os.path.join(self.BASE_DIR, "dictionary/dict_CellLine.txt"), - "cell type": os.path.join(self.BASE_DIR, "dictionary/dict_CellType.txt"), - "procedure": os.path.join(self.BASE_DIR, "dictionary/dict_Procedures.txt"), - "sign symptom": os.path.join( - self.BASE_DIR, "dictionary/dict_SignSymptom.txt" - ), - } + Normalization now happens in Matcher.entities.ConceptLinker. This class keeps old + imports from crashing while avoiding external normalizer daemons. + """ - self.NEURAL_NORM_MODEL_PATH = { - "disease": "dmis-lab/biosyn-sapbert-bc5cdr-disease", - "sign symptom": "dmis-lab/biosyn-sapbert-bc5cdr-disease", - "drug": "dmis-lab/biosyn-sapbert-bc5cdr-chemical", - "gene": "dmis-lab/biosyn-sapbert-bc2gn", - } - self.NEURAL_NORM_CACHE_PATH = { - "disease": os.path.join( - self.BASE_DIR, - "normalizers/neural_norm_caches/dict_Disease_20210630.txt.pk", - ), - "sign symptom": os.path.join( - self.BASE_DIR, - "normalizers/neural_norm_caches/dict_Disease_20210630.txt.pk", - ), - "drug": os.path.join( - self.BASE_DIR, - "normalizers/neural_norm_caches/dict_ChemicalCompound_20210630.txt.pk", - ), - "gene": os.path.join( - self.BASE_DIR, "normalizers/neural_norm_caches/dict_Gene.txt.pk" - ), - } + NO_ENTITY_ID = "CUI-less" - self.NORM_MODEL_VERSION = "N/A" - self.HOST = "127.0.0.1" - self.GENE_PORT = gene_port - self.DISEASE_PORT = disease_port - self.NO_ENTITY_ID = "CUI-less" - self.use_neural_normalizer = use_neural_normalizer + def __init__(self, *args: Any, concept_linker: ConceptLinker | None = None, **kwargs: Any): + del args, kwargs + self.concept_linker = concept_linker + self.use_neural_normalizer = False - # Load normalizers concurrently - with ThreadPoolExecutor() as executor: - future_dict = { - "chemical": executor.submit( - ChemicalNormalizer, self.NORM_DICT_PATH["drug"] - ), - "species": executor.submit( - SpeciesNormalizer, self.NORM_DICT_PATH["species"] - ), - "cellline": executor.submit( - CellLineNormalizer, self.NORM_DICT_PATH["cell line"] - ), - "celltype": executor.submit( - CellTypeNormalizer, self.NORM_DICT_PATH["cell type"] - ), - "procedure": executor.submit( - ProcedureNormalizer, self.NORM_DICT_PATH["procedure"] - ), - "sign symptom": executor.submit( - SignSymptomNormalizer, self.NORM_DICT_PATH["sign symptom"] - ), - } - - self.chemical_normalizer = future_dict["chemical"].result() - self.species_normalizer = future_dict["species"].result() - self.cellline_normalizer = future_dict["cellline"].result() - self.celltype_normalizer = future_dict["celltype"].result() - self.procedure_normalizer = future_dict["procedure"].result() - self.sign_symptom_normalizer = future_dict["sign symptom"].result() - - if self.use_neural_normalizer: - with ThreadPoolExecutor() as executor: - future_dict = { - "neural_disease": executor.submit( - NeuralNormalizer, - model_name_or_path=self.NEURAL_NORM_MODEL_PATH["disease"], - cache_path=self.NEURAL_NORM_CACHE_PATH["disease"], - no_cuda=no_cuda, - ), - "neural_sign symptom": executor.submit( - NeuralNormalizer, - model_name_or_path=self.NEURAL_NORM_MODEL_PATH["sign symptom"], - cache_path=self.NEURAL_NORM_CACHE_PATH["sign symptom"], - no_cuda=no_cuda, - ), - "neural_chemical": executor.submit( - NeuralNormalizer, - model_name_or_path=self.NEURAL_NORM_MODEL_PATH["drug"], - cache_path=self.NEURAL_NORM_CACHE_PATH["drug"], - no_cuda=no_cuda, - ), - "neural_gene": executor.submit( - NeuralNormalizer, - model_name_or_path=self.NEURAL_NORM_MODEL_PATH["gene"], - cache_path=self.NEURAL_NORM_CACHE_PATH["gene"], - no_cuda=no_cuda, - ), - } - self.neural_disease_normalizer = future_dict["neural_disease"].result() - self.neural_sign_symptom_normalizer = future_dict[ - "neural_sign symptom" - ].result() - self.neural_chemical_normalizer = future_dict[ - "neural_chemical" - ].result() - self.neural_gene_normalizer = future_dict["neural_gene"].result() - - def normalize(self, base_name, doc_dict_list): - names = dict() - saved_items = list() - ent_cnt = 0 - abs_cnt = 0 - - for item in doc_dict_list: - content = item["abstract"] - entities = item["entities"] - - abs_cnt += 1 - - for ent_type, locs in entities.items(): - ent_cnt += len(locs) - for loc in locs: - loc["end"] += 1 - name = content[loc["start"] : loc["end"]] - if ent_type in names: - names[ent_type].append([name, len(saved_items)]) - else: - names[ent_type] = [[name, len(saved_items)]] - - item["norm_model"] = self.NORM_MODEL_VERSION - saved_items.append(item) - - results = list() - with ThreadPoolExecutor() as executor: - futures = [] - for ent_type in names.keys(): - futures.append( - executor.submit( - self.run_normalizers_wrap, - ent_type, - base_name, - names, - saved_items, - results, - ) - ) - for future in futures: - future.result() - - for ent_type, type_oids in results: - oid_cnt = 0 - for saved_item in saved_items: - for loc in saved_item["entities"][ent_type]: - loc["id"] = type_oids[oid_cnt] - loc["is_neural_normalized"] = False - oid_cnt += 1 - - return saved_items - - def neural_normalize(self, ent_type, tagged_docs): - abstract = tagged_docs[0]["abstract"] - entities = tagged_docs[0]["entities"][ent_type] - entity_names = [abstract[e["start"] : e["end"]] for e in entities] - cuiless_entity_names = [] - for entity, entity_name in zip(entities, entity_names): - if entity["id"] == self.NO_ENTITY_ID: - cuiless_entity_names.append(entity_name) - cuiless_entity_names = list(set(cuiless_entity_names)) - - if len(cuiless_entity_names) == 0: - return tagged_docs - - norm_entities = [] - if ent_type == "disease": - norm_entities = self.neural_disease_normalizer.normalize( - names=cuiless_entity_names - ) - elif ent_type == "drug": - norm_entities = self.neural_chemical_normalizer.normalize( - names=cuiless_entity_names - ) - elif ent_type == "gene": - norm_entities = self.neural_gene_normalizer.normalize( - names=cuiless_entity_names - ) - elif ent_type == "sign symptom": - norm_entities = self.neural_sign_symptom_normalizer.normalize( - names=cuiless_entity_names - ) - - cuiless_entity2norm_entities = { - c: n for c, n in zip(cuiless_entity_names, norm_entities) - } - for entity, entity_name in zip(entities, entity_names): - if entity_name in cuiless_entity2norm_entities: - cui = cuiless_entity2norm_entities[entity_name][0] - entity["id"] = cui if cui != -1 else self.NO_ENTITY_ID - entity["is_neural_normalized"] = True - else: - entity["is_neural_normalized"] = False + def normalize(self, base_name: str, doc_dict_list: list[dict[str, Any]]): + del base_name + return doc_dict_list + def neural_normalize(self, ent_type: str, tagged_docs: list[dict[str, Any]]): + del ent_type return tagged_docs - - def run_normalizers_wrap(self, ent_type, base_name, names, saved_items, results): - results.append( - (ent_type, self.run_normalizer(ent_type, base_name, names, saved_items)) - ) - - def run_normalizer(self, ent_type, base_name, names, saved_items): - name_ptr = names[ent_type] - oids = list() - bufsize = 4 - - base_thread_name = base_name - input_filename = base_thread_name + ".concept" - output_filename = base_thread_name + ".oid" - - if ent_type in ["disease"]: - norm_inp_path = os.path.join(self.NORM_INPUT_DIR[ent_type], input_filename) - norm_abs_path = os.path.join( - self.NORM_INPUT_DIR[ent_type], base_thread_name + ".txt" - ) - with open(norm_inp_path, "w") as norm_inp_f: - for name, _ in name_ptr: - norm_inp_f.write(name + "\n") - with open(norm_abs_path, "w") as _: - pass - - s = socket.socket() - s.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) - try: - s.connect((self.HOST, self.DISEASE_PORT)) - s.send("{}".format(base_thread_name).encode("utf-8")) - s.recv(bufsize) - except ConnectionRefusedError: - os.remove(norm_inp_path) - os.remove(norm_abs_path) - s.close() - return oids - s.close() - - norm_out_path = os.path.join( - self.NORM_OUTPUT_DIR[ent_type], output_filename - ) - if os.path.exists(norm_out_path): - with open(norm_out_path, "r") as norm_out_f: - for line in norm_out_f: - oid = line[:-1] - oids.append( - oid if oid != self.NO_ENTITY_ID else self.NO_ENTITY_ID - ) - os.remove(norm_out_path) - else: - for _ in range(len(name_ptr)): - oids.append(self.NO_ENTITY_ID) - - elif ent_type in ["drug"]: - names = [ptr[0] for ptr in name_ptr] - preds = self.chemical_normalizer.normalize(names) - oids.extend(preds) - - elif ent_type == "mutation": - pass - - elif ent_type == "species": - names = [ptr[0] for ptr in name_ptr] - preds = self.species_normalizer.normalize(names) - for pred in preds: - if pred != self.NO_ENTITY_ID: - pred = int(pred) // 100 - oids.append("NCBI:txid{}".format(pred)) - else: - oids.append(self.NO_ENTITY_ID) - - elif ent_type == "cell line": - names = [ptr[0] for ptr in name_ptr] - preds = self.cellline_normalizer.normalize(names) - oids.extend(preds) - - elif ent_type == "cell type": - names = [ptr[0] for ptr in name_ptr] - preds = self.celltype_normalizer.normalize(names) - oids.extend(preds) - - elif ent_type == "gene": - s = socket.socket() - s.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) - try: - s.connect((self.HOST, self.GENE_PORT)) - except ConnectionRefusedError: - s.close() - return oids - - norm_inp_path = os.path.join(self.NORM_INPUT_DIR[ent_type], input_filename) - norm_abs_path = os.path.join( - self.NORM_INPUT_DIR[ent_type], base_thread_name + ".txt" - ) - - with ( - open(norm_inp_path, "w") as norm_inp_f, - open(norm_abs_path, "w") as norm_abs_f, - ): - for saved_item in saved_items: - entities = saved_item["entities"][ent_type] - if len(entities) == 0: - continue - - abstract_title = saved_item["abstract"] - ent_names = [ - abstract_title[loc["start"] : loc["end"]] for loc in entities - ] - - norm_abs_f.write(saved_item["pmid"] + "||" + abstract_title + "\n") - norm_inp_f.write("||".join(ent_names) + "\n") - - gene_input_dir = os.path.abspath(self.NORM_INPUT_DIR[ent_type]) - gene_output_dir = os.path.abspath(self.NORM_OUTPUT_DIR[ent_type]) - setup_dir = self.NORM_DICT_PATH[ent_type] - - jar_args = ( - "\t".join( - [ - gene_input_dir, - gene_output_dir, - setup_dir, - "9606", - base_thread_name, - ] - ) - + "\n" - ) - s.send(jar_args.encode("utf-8")) - s.recv(bufsize) - s.close() - - norm_out_path = os.path.join(gene_output_dir, output_filename) - if os.path.exists(norm_out_path): - with ( - open(norm_out_path, "r") as norm_out_f, - open(norm_inp_path, "r") as norm_in_f, - ): - for line, input_l in zip(norm_out_f, norm_in_f): - gene_ids, gene_mentions = ( - line[:-1].split("||"), - input_l[:-1].split("||"), - ) - for gene_id, gene_mention in zip(gene_ids, gene_mentions): - eid = ( - "EntrezGene:" + gene_id - if gene_id.lower() != "cui-less" - else self.NO_ENTITY_ID - ) - oids.append(eid) - - os.remove(norm_out_path) - else: - for _ in range(len(name_ptr)): - oids.append(self.NO_ENTITY_ID) - - os.remove(norm_inp_path) - os.remove(norm_abs_path) - - elif ent_type in [ - "diagnostic test", - "treatment", - "radiology", - "surgical procedure", - "laboratory test", - "genomic analysis technique", - ]: - names = [ptr[0] for ptr in name_ptr] - preds = self.procedure_normalizer.normalize(names) - oids.extend(preds) - - elif ent_type == "sign symptom": - names = [ptr[0] for ptr in name_ptr] - preds = self.sign_symptom_normalizer.normalize(names) - oids.extend(preds) - - else: - names = [ptr[0] for ptr in name_ptr] - oids.extend([self.NO_ENTITY_ID] * len(names)) - - assert len(oids) == len(name_ptr), "{} vs {} in {}".format( - len(oids), len(name_ptr), ent_type - ) - - return oids diff --git a/source/Parser/normalizers/__init__.py b/source/Parser/normalizers/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/source/Parser/normalizers/cellline_normalizer.py b/source/Parser/normalizers/cellline_normalizer.py deleted file mode 100644 index 05ea2277..00000000 --- a/source/Parser/normalizers/cellline_normalizer.py +++ /dev/null @@ -1,24 +0,0 @@ -class CellLineNormalizer(object): - def __init__(self, dict_path): - self.NO_ENTITY_ID = "CUI-less" - - # Create dictionary for exact match - self.cl2oid = dict() - with open(dict_path, "r", encoding="utf-8") as f: - for line in f: - oid, names = line[:-1].split("||") - names = names.split("|") - for name in names: - self.cl2oid[name] = oid - - def normalize(self, names): - oids = list() - for name in names: - if name in self.cl2oid: - oids.append(self.cl2oid[name]) - elif name.lower() in self.cl2oid: - oids.append(self.cl2oid[name.lower()]) - else: - oids.append(self.NO_ENTITY_ID) - - return oids diff --git a/source/Parser/normalizers/celltype_normalizer.py b/source/Parser/normalizers/celltype_normalizer.py deleted file mode 100644 index 5074712d..00000000 --- a/source/Parser/normalizers/celltype_normalizer.py +++ /dev/null @@ -1,40 +0,0 @@ -import spacy -import re - -# Load only the lemmatizer component -nlp = spacy.load("en_core_web_sm") -nlp.add_pipe("lemmatizer") -nlp.initialize() - - -class CellTypeNormalizer(object): - def __init__(self, dict_path): - self.NO_ENTITY_ID = "CUI-less" - - # Create dictionary for exact match - self.ct2oid = dict() - with open(dict_path, "r", encoding="utf-8") as f: - for line in f: - oid, names = line.strip().split("||") - names = names.split("|") - for name in names: - normalized_name = self.get_tmchem_name(name) - self.ct2oid[normalized_name] = oid - - def normalize(self, names): - oids = [] - for name in names: - normalized_name = self.get_tmchem_name(name) - if normalized_name in self.ct2oid: - oids.append(self.ct2oid[normalized_name]) - else: - oids.append(self.NO_ENTITY_ID) - return oids - - def get_tmchem_name(self, name): - # Lowercase and remove all whitespace and punctuation - cleaned_name = re.sub(r"[^\w\s-]", "", name.lower()).replace(" ", "") - # Use SpaCy to lemmatize the cleaned name - doc = nlp(cleaned_name) - lemmatized_name = "".join([token.lemma_ for token in doc]) - return lemmatized_name diff --git a/source/Parser/normalizers/chemical_normalizer.py b/source/Parser/normalizers/chemical_normalizer.py deleted file mode 100644 index f9e4ea37..00000000 --- a/source/Parser/normalizers/chemical_normalizer.py +++ /dev/null @@ -1,45 +0,0 @@ -import re - -import nltk -from nltk.stem import WordNetLemmatizer - -# Download NLTK resources quietly -nltk.download("wordnet", quiet=True) -nltk.download("omw-1.4", quiet=True) - - -class ChemicalNormalizer(object): - def __init__(self, dict_path): - self.NO_ENTITY_ID = "CUI-less" - self.lemmatizer = WordNetLemmatizer() - - # Create dictionary for exact match - self.chem2oid = dict() - with open(dict_path, "r", encoding="utf-8") as f: - for line in f: - oid, names = line[:-1].split("||") - names = names.split("|") - for name in names: - # a part of tmChem normalization - normalized_name = self.get_tmchem_name(name) - self.chem2oid[normalized_name] = oid - - def normalize(self, names): - oids = list() - for name in names: - # a part of tmChem normalization - normalized_name = self.get_tmchem_name(name) - - if normalized_name in self.chem2oid: - oids.append(self.chem2oid[normalized_name]) - else: - oids.append(self.NO_ENTITY_ID) - - return oids - - def get_tmchem_name(self, name): - # 1. lowercase, 2. removes all whitespace and punctuation - # https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-7-S1-S3 - cleaned_name = re.sub(r"[^\w\s]", "", name.lower()).replace(" ", "") - lemmatized_name = self.lemmatizer.lemmatize(cleaned_name) - return lemmatized_name diff --git a/source/Parser/normalizers/neural_normalizer.py b/source/Parser/normalizers/neural_normalizer.py deleted file mode 100644 index bf8c48a8..00000000 --- a/source/Parser/normalizers/neural_normalizer.py +++ /dev/null @@ -1,207 +0,0 @@ -import argparse -import os -import pickle -import re -from string import punctuation - -import faiss -import numpy as np -import torch -from torch.utils.data import DataLoader, Dataset -from tqdm import tqdm -from transformers import AutoModel, AutoTokenizer, default_data_collator - - -class NamesDataset(Dataset): - def __init__(self, encodings, device): - self.encodings = encodings - self.device = device - - def __getitem__(self, idx): - return { - key: torch.tensor(val[idx]).to(self.device) - for key, val in self.encodings.items() - } - - def __len__(self): - return len(self.encodings["input_ids"]) - - -class NeuralNormalizer: - def __init__(self, model_name_or_path, cache_path=None, no_cuda=False): - self.max_length = 25 - self.batch_size = 1024 - self.k = 1 # top k - - # device setup - use_cuda = torch.cuda.is_available() and not no_cuda - self.device = torch.device("cuda" if use_cuda else "cpu") - - # tokenizer and model - self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) - # Ensure proper initialization of the model - self.model = AutoModel.from_pretrained(model_name_or_path, device_map=None) - self.model.to_empty(device=self.device) # Use to_empty to handle meta tensors - self.model.eval() - - # regex for basic normalization - self.rmv_puncts_regex = re.compile(r"[\s{}]+".format(re.escape(punctuation))) - - # optionally load existing cache - if cache_path: - self.load_cache(cache_path) - - def load_dictionary(self, dictionary_path=""): - self.dictionary = [] - with open(dictionary_path, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if not line: - continue - cui, names = line.split("||") - for name in names.split("|"): - normalized = self._basic_normalize(name) - tokens = self.tokenizer.tokenize(normalized) - if len(tokens) <= self.max_length: - self.dictionary.append((cui, name)) - - # embed dictionary entries - self.dict_embeds = self._embed_dictionary() - - def normalize(self, names): - if not names: - return [] - - names_norm = [self._basic_normalize(n) for n in names] - - # encode inputs - encodings = self.tokenizer( - names_norm, - padding="max_length", - max_length=self.max_length, - truncation=True, - return_tensors=None, - ) - dataset = NamesDataset(encodings, self.device) - loader = DataLoader( - dataset, - batch_size=self.batch_size, - shuffle=False, - collate_fn=default_data_collator, - ) - - # compute embeddings - embeds = [] - with torch.no_grad(): - for batch in loader: - out = self.model(**batch) - cls_emb = out.last_hidden_state[:, 0].cpu().numpy() - embeds.append(cls_emb) - name_embeds = np.vstack(embeds) - - # search FAISS index - distances, indices = self.dict_embeds.search(name_embeds, self.k) - top_indices = indices[:, 0] - - # gather outputs - results = [] - for idx in top_indices: - if idx >= 0: - results.append(self.dictionary[idx]) - else: - results.append((None, None)) - return results - - def _basic_normalize(self, text): - text = text.lower() - text = re.sub(self.rmv_puncts_regex, " ", text) - return " ".join(text.split()) - - def _embed_dictionary(self, show_progress=True): - encs = self.tokenizer( - [self._basic_normalize(name) for _, name in self.dictionary], - padding="max_length", - max_length=self.max_length, - truncation=True, - return_tensors=None, - ) - dataset = NamesDataset(encs, self.device) - loader = DataLoader( - dataset, - batch_size=self.batch_size, - shuffle=False, - collate_fn=default_data_collator, - ) - - embeds = [] - with torch.no_grad(): - for batch in tqdm(loader, disable=not show_progress): - out = self.model(**batch) - cls_emb = out.last_hidden_state[:, 0].cpu().numpy() - embeds.append(cls_emb) - all_embeds = np.vstack(embeds) - return all_embeds - - def save_cache(self, cache_path): - # build FAISS index - dim = self.dict_embeds.shape[1] - quantiser = faiss.IndexFlatIP(dim) - nlist = 2048 - index = faiss.IndexIVFFlat(quantiser, dim, nlist, faiss.METRIC_INNER_PRODUCT) - index.train(self.dict_embeds) - index.add(self.dict_embeds) - index.nprobe = 25 - - faiss.write_index(index, cache_path + ".index") - with open(cache_path, "wb") as f: - pickle.dump(self.dictionary, f) - - def load_cache(self, cache_path): - self.dict_embeds = faiss.read_index(cache_path + ".index") - with open(cache_path, "rb") as f: - self.dictionary = pickle.load(f) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--mode", choices=["indexing", "predict"], default="indexing") - parser.add_argument( - "--model_name_or_path", default="cambridgeltl/SapBERT-from-PubMedBERT-fulltext" - ) - parser.add_argument( - "--dictionary_path", - default="../normalization/resources/dictionary/best_dict_Disease_20210630_tmp.txt", - ) - parser.add_argument("--cache_dir", default="../normalization/biosyn_cache") - parser.add_argument("--no_cuda", action="store_true") - args = parser.parse_args() - - normalizer = NeuralNormalizer( - model_name_or_path=args.model_name_or_path, - cache_path=None, - no_cuda=args.no_cuda, - ) - - if args.mode == "indexing": - normalizer.load_dictionary(dictionary_path=args.dictionary_path) - os.makedirs(args.cache_dir, exist_ok=True) - cache_path = os.path.join( - args.cache_dir, os.path.basename(args.dictionary_path) + ".pk" - ) - normalizer.save_cache(cache_path) - print("Indexing complete. Cache saved to", cache_path) - - elif args.mode == "predict": - # load cache - normalizer.load_cache( - os.path.join(args.cache_dir, os.path.basename(args.dictionary_path) + ".pk") - ) - # example prediction - samples = ["diabetes", "heart disease"] - results = normalizer.normalize(samples) - for inp, out in zip(samples, results): - print(f"{inp} => {out}") - - -if __name__ == "__main__": - main() diff --git a/source/Parser/normalizers/normalizer_all.py b/source/Parser/normalizers/normalizer_all.py deleted file mode 100644 index e7171149..00000000 --- a/source/Parser/normalizers/normalizer_all.py +++ /dev/null @@ -1,137 +0,0 @@ -import re -import os -from datetime import datetime -from rapidfuzz import fuzz - - -class BaseNormalizer: - """ - Base class for flexible normalization with exact and similarity-based matching. - """ - - def __init__(self, dict_path, nlp=None, similarity_threshold=0.5): - self.NO_ENTITY_ID = "CUI-less" - self.similarity_threshold = similarity_threshold - self.nlp = nlp - self.entity2oid = dict() - - self.load_dictionary(dict_path) - - def load_dictionary(self, dict_path): - """ - Load dictionary from the given path and normalize keys for matching. - """ - print( - datetime.now().strftime("[%d/%b/%Y %H:%M:%S.%f]"), "Loading dictionary..." - ) - with open(dict_path, "r", encoding="utf-8") as f: - for line in f: - oid, names = line.strip().split("||") - names = names.split("|") - for name in names: - normalized_name = self.normalize_string(name) - self.entity2oid[normalized_name] = oid - print(datetime.now().strftime("[%d/%b/%Y %H:%M:%S.%f]"), "Dictionary loaded.") - - def normalize(self, names): - """ - Normalize a list of names and return their corresponding IDs. - """ - oids = list() - for name in names: - normalized_name = self.get_tmchem_name(name) - - # Exact match - if normalized_name in self.entity2oid: - oids.append(self.entity2oid[normalized_name]) - else: - # Flexible match based on similarity score - best_match = self.find_best_match(normalized_name) - if best_match: - oids.append(self.entity2oid[best_match]) - else: - oids.append(self.NO_ENTITY_ID) - - return oids - - def get_tmchem_name(self, name): - """ - Normalize a name using lowercasing, punctuation removal, and lemmatization (if NLP is enabled). - """ - cleaned_name = re.sub( - r"[^\w\s-]", "", name.lower() - ) # Remove punctuation but keep hyphens - if self.nlp: - doc = self.nlp(cleaned_name) - lemmatized_name = " ".join([token.lemma_ for token in doc]) - else: - lemmatized_name = cleaned_name - return self.normalize_string(lemmatized_name) - - def normalize_string(self, text): - """ - Normalize a string for matching by removing spaces and hyphens. - """ - return re.sub(r"[\s-]", "", text.lower()) - - def find_best_match(self, normalized_name): - """ - Find the best match for a given normalized name based on a similarity score. - """ - best_match = None - highest_score = 0 - - for key in self.entity2oid.keys(): - score = self.similarity_score(normalized_name, key) - if score > self.similarity_threshold and score > highest_score: - best_match = key - highest_score = score - - return best_match - - def similarity_score(self, name1, name2): - """ - Use RapidFuzz's token_sort_ratio for fast and accurate similarity scoring. - """ - return fuzz.token_sort_ratio(name1, name2) / 100 # Normalize score to 0-1 range - - -class CellTypeNormalizer(BaseNormalizer): - pass - - -class ProcedureNormalizer(BaseNormalizer): - pass - - -class ChemicalNormalizer(BaseNormalizer): - def load_dictionary(self, dict_path): - """ - Specialized dictionary loader for chemical normalizer with file size logging. - """ - dict_size = os.path.getsize(dict_path) - print( - datetime.now().strftime("[%d/%b/%Y %H:%M:%S.%f]"), - f"Chemical dictionary file size: {dict_size} bytes", - ) - super().load_dictionary(dict_path) - - -class CellLineNormalizer(BaseNormalizer): - def get_tmchem_name(self, name): - """ - Simplified name normalization for cell line data. - """ - return self.normalize_string(name) - - -class SpeciesNormalizer(BaseNormalizer): - def get_tmchem_name(self, name): - """ - Simplified name normalization for species data. - """ - return self.normalize_string(name) - - -class SignSymptomNormalizer(BaseNormalizer): - pass diff --git a/source/Parser/normalizers/readme.md b/source/Parser/normalizers/readme.md deleted file mode 100644 index 9b5a93ae..00000000 --- a/source/Parser/normalizers/readme.md +++ /dev/null @@ -1,22 +0,0 @@ -# Neural normalizer - -## How to index dictionary embeddings -``` -# index disease -CUDA_VISIBLE_DEVICES=0 python neural_normalizer.py \ - --model_name_or_path dmis-lab/biosyn-sapbert-bc5cdr-disease \ - --dictionary_path ../resources/normalization/dictionary/dict_Disease_20210630.txt \ - --cache_dir ../resources/normalization/normalizers/neural_norm_caches - -# index chemical -CUDA_VISIBLE_DEVICES=0 python neural_normalizer.py \ - --model_name_or_path dmis-lab/biosyn-sapbert-bc5cdr-chemical \ - --dictionary_path ../resources/normalization/dictionary/dict_ChemicalCompound_20210630.txt \ - --cache_dir ../resources/normalization/normalizers/neural_norm_caches - -# index gene -CUDA_VISIBLE_DEVICES=0 python neural_normalizer.py \ - --model_name_or_path dmis-lab/biosyn-sapbert-bc2gn \ - --dictionary_path ../resources/normalization/dictionary/dict_Gene.txt \ - --cache_dir ../resources/normalization/normalizers/neural_norm_caches -``` diff --git a/source/Parser/normalizers/species_normalizer.py b/source/Parser/normalizers/species_normalizer.py deleted file mode 100644 index bb024c23..00000000 --- a/source/Parser/normalizers/species_normalizer.py +++ /dev/null @@ -1,25 +0,0 @@ -class SpeciesNormalizer(object): - def __init__(self, dict_path): - self.NO_ENTITY_ID = "CUI-less" - - # Create dictionary for exact match - self.species2oid = dict() - with open(dict_path, "r", encoding="utf-8") as f: - for line in f: - oid, names = line[:-1].split("||") - names = names.split("|") - for name in names: - # a part of tmChem normalization - self.species2oid[name] = oid - - def normalize(self, names): - oids = list() - for name in names: - if name in self.species2oid: - oids.append(self.species2oid[name]) - elif name.lower() in self.species2oid: - oids.append(self.species2oid[name.lower()]) - else: - oids.append(self.NO_ENTITY_ID) - - return oids diff --git a/source/Parser/ops.py b/source/Parser/ops.py deleted file mode 100644 index 08668a1e..00000000 --- a/source/Parser/ops.py +++ /dev/null @@ -1,449 +0,0 @@ -import re -import time - -import numpy as np - -tokenize_regex = re.compile(r"([0-9a-zA-Z]+|[^0-9a-zA-Z])") - - -def json_to_sent(data): - """data: list of json file [{pmid,abstract,title}, ...]""" - out = dict() - for paper in data: - sentences = list() - - if len(CoNLL_tokenizer(paper["title"])) < 50: - title = [paper["title"]] - else: - title = sentence_split(paper["title"]) - if len(title) != 1 or len(title[0].strip()) > 0: - sentences.extend(title) - - if len(paper["abstract"]) > 0: - abst = sentence_split(paper["abstract"]) - if len(abst) != 1 or len(abst[0].strip()) > 0: - sentences.extend(abst) - out[paper["pmid"]] = dict() - out[paper["pmid"]]["sentence"] = sentences - return out - - -def input_form(sent_data): - """sent_data: dict of sentence, key=pmid {pmid:[sent,sent, ...], pmid: ...}""" - for pmid in sent_data: - sent_data[pmid]["words"] = list() - sent_data[pmid]["wordPos"] = list() - doc_piv = 0 - for sent in sent_data[pmid]["sentence"]: - wids = list() - wpos = list() - sent_piv = 0 - tok = CoNLL_tokenizer(sent) - - for w in tok: - if len(w) > 20: - wids.append(w[:10]) - else: - wids.append(w) - - start = doc_piv + sent_piv + sent[sent_piv:].find(w) - end = start + len(w) - 1 - sent_piv = end - doc_piv + 1 - wpos.append((start, end)) - doc_piv += len(sent) - sent_data[pmid]["words"].append(wids) - sent_data[pmid]["wordPos"].append(wpos) - - return sent_data - - -def softmax(logits): - out = list() - for logit in logits: - temp = np.subtract(logit, np.max(logit)) - p = np.exp(temp) / np.sum(np.exp(temp)) - out.append(np.max(p)) - return out - - -def CoNLL_tokenizer(text): - rawTok = [t for t in tokenize_regex.split(text) if t] - assert "".join(rawTok) == text - tok = [t for t in rawTok if t != " "] - return tok - - -def sentence_split(text): - sentences = list() - sent = "" - piv = 0 - for idx, char in enumerate(text): - if char in "?!": - if idx > len(text) - 3: - sent = text[piv:] - piv = -1 - else: - sent = text[piv : idx + 1] - piv = idx + 1 - - elif char == ".": - if idx > len(text) - 3: - sent = text[piv:] - piv = -1 - elif (text[idx + 1] == " ") and ( - text[idx + 2] in 'ABCDEFGHIJKLMNOPQRSTUVWXYZ-"' + "'" - ): - sent = text[piv : idx + 1] - piv = idx + 1 - - if sent != "": - toks = CoNLL_tokenizer(sent) - if len(toks) > 100: - while True: - rawTok = [t for t in tokenize_regex.split(sent) if t] - cut = "".join(rawTok[:200]) - sent = "".join(rawTok[200:]) - sentences.append(cut) - - if len(CoNLL_tokenizer(sent)) < 100: - if sent.strip() == "": - sent = "" - break - else: - sentences.append(sent) - sent = "" - break - else: - sentences.append(sent) - sent = "" - - if piv == -1: - break - - if piv != -1: - sent = text[piv:] - toks = CoNLL_tokenizer(sent) - if len(toks) > 100: - while True: - rawTok = [t for t in tokenize_regex.split(sent) if t] - cut = "".join(rawTok[:200]) - sent = "".join(rawTok[200:]) - sentences.append(cut) - - if len(CoNLL_tokenizer(sent)) < 100: - if sent.strip() == "": - sent = "" - break - else: - sentences.append(sent) - sent = "" - break - else: - sentences.append(sent) - sent = "" - - return sentences - - -def get_prob(data, sent_data, predicDict, logitsDict, entity_types=None): - for idx, paper in enumerate(data): - pmid = paper["pmid"] - - if len(paper["abstract"]) > 0: - content = paper["title"] + " " + paper["abstract"] - else: - content = paper["title"] - - if entity_types is not None: - for ent_type in entity_types: - paper["entities"][ent_type] = [] - paper["prob"] = dict() - - if entity_types is None: - continue - - for dtype in entity_types: - for sentidx, tags in enumerate(predicDict[dtype][pmid]): - B_flag = False - tmpSE = dict() - # get position of entity corresponding to types - for widx, tag in enumerate(tags): - if tag == "O": - if B_flag: - tmpSE["end"] = sent_data[pmid]["wordPos"][sentidx][ - widx - 1 - ][1] - paper["entities"][dtype].append(tmpSE) - B_flag = False - continue - elif tag == "B": - if B_flag: - tmpSE["end"] = sent_data[pmid]["wordPos"][sentidx][ - widx - 1 - ][1] - paper["entities"][dtype].append(tmpSE) - tmpSE = {"start": sent_data[pmid]["wordPos"][sentidx][widx][0]} - B_flag = True - elif tag == "I": - continue - if B_flag: - tmpSE["end"] = sent_data[pmid]["wordPos"][sentidx][-1][1] - paper["entities"][dtype].append(tmpSE) - - # get prob. of entity logits corresponding to types - logs = list() - for t_sent in logitsDict[dtype][pmid]: - logs.extend(t_sent) - paper["prob"][dtype] = list() - for pos in paper["entities"][dtype]: - if pos["start"] == pos["end"]: - soft = softmax( - logs[ - len(CoNLL_tokenizer(content[: pos["start"]])) : len( - CoNLL_tokenizer(content[: pos["end"]]) - ) - + 1 - ] - ) - paper["prob"][dtype].append((pos, float(np.average(soft)))) - else: - soft = softmax( - logs[ - len(CoNLL_tokenizer(content[: pos["start"]])) : len( - CoNLL_tokenizer(content[: pos["end"]]) - ) - ] - ) - paper["prob"][dtype].append((pos, float(np.average(soft)))) - - return data - - -def detokenize(tokens, predicts, logits): - pred = dict( - {"toks": tokens[:], "labels": predicts[:], "logit": logits[:]} - ) # dictionary for predicted tokens and labels. - - bert_toks = list() - bert_labels = list() - bert_logits = list() - tmp_p = list() - tmp_l = list() - tmp_s = list() - for t, label, s in zip(pred["toks"], pred["labels"], pred["logit"]): - if t == "[CLS]" or t == "": # non-text tokens will not be evaluated. - continue - elif t == "[SEP]" or t == "": # newline - bert_toks.append(tmp_p) - bert_labels.append(tmp_l) - bert_logits.append(tmp_s) - tmp_p = list() - tmp_l = list() - tmp_s = list() - continue - elif ( - t[:2] == "##" - ): # if it is a piece of a word (broken by Word Piece tokenizer) - tmp_p[-1] = tmp_p[-1] + t[2:] # append pieces - elif t.startswith("Ġ"): # roberta tokenizer - t = t.replace("Ġ", " ") - tmp_p[-1] = tmp_p[-1] + t - else: - tmp_p.append(t) - tmp_l.append(label) - tmp_s.append(s) - return bert_toks, bert_labels, bert_logits - - -# https://stackoverflow.com/a/3620972 -PROF_DATA = {} - - -class Profile(object): - def __init__(self, prefix): - self.prefix = prefix - - def __call__(self, fn): - def with_profiling(*args, **kwargs): - global PROF_DATA - start_time = time.time() - ret = fn(*args, **kwargs) - - elapsed_time = time.time() - start_time - key = "[" + self.prefix + "]." + fn.__name__ - - if key not in PROF_DATA: - PROF_DATA[key] = [0, list()] - PROF_DATA[key][0] += 1 - PROF_DATA[key][1].append(elapsed_time) - - return ret - - return with_profiling - - -def show_prof_data(): - for fname, data in sorted(PROF_DATA.items()): - max_time = max(data[1]) - avg_time = sum(data[1]) / len(data[1]) - total_time = sum(data[1]) - print("\n{} -> called {} times".format(fname, data[0])) - print( - "Time total: {:.3f}, max: {:.3f}, avg: {:.3f}".format( - total_time, max_time, avg_time - ) - ) - - -def clear_prof_data(): - global PROF_DATA - PROF_DATA = {} - - -# Ref. dict of SR4GN -species_human_excl_homo_sapiens = ( - "person|infant|Child|people|participants|woman|" - "Girls|Man|Peoples|Men|Participant|Patients|" - "humans|Persons|mans|participant|Infants|Boys|" - "Human|Humans|Women|children|Mans|child|Participants|Girl|" - "Infant|girl|patient|patients|boys|men|infants|" - "man|girls|Children|Boy|women|persons|human|Woman|" - "peoples|Patient|People|boy|Person".split("|") -) - - -def filter_entities(ner_results): - num_filtered_species_per_doc = list() - - for idx, paper in enumerate(ner_results): - if len(paper["abstract"]) > 0: - content = paper["title"] + " " + paper["abstract"] - else: - content = paper["title"] - - valid_species = list() - species = paper["entities"]["species"] - for spcs in species: - entity_mention = content[spcs["start"] : spcs["end"] + 1] - if entity_mention in species_human_excl_homo_sapiens: - spcs["end"] += 1 - continue - valid_species.append(spcs) - - num_filtered_species = len(species) - len(valid_species) - if num_filtered_species > 0: - paper["entities"]["species"] = valid_species - - num_filtered_species_per_doc.append((paper["pmid"], num_filtered_species)) - - return num_filtered_species_per_doc - - -# from convert.py -def pubtator2dict_list(pubtator_file_path): - dict_list = list() - - title_pmid = "" - # abstract_pmid = '' - title = "" - abstract_text = "" - doc_line_num = 0 - - with open(pubtator_file_path, "r", encoding="utf-8") as f: - for line in f: - line = line.rstrip() - if len(line) == 0: - doc_dict = { - "pmid": title_pmid, - "entities": {}, - } - doc_dict["title"] = title - doc_dict["abstract"] = abstract_text - - dict_list.append(doc_dict) - - doc_line_num = 0 - continue - - if doc_line_num == 0: - title_cols = line.split("|t|") - - if len(title_cols) != 2: - return '{"error": "wrong #title_cols: %d"}' % len(title_cols) - - title_pmid = title_cols[0] - - if "- No text -" == title_cols[1]: - # make tmvar2 results empty - title = "" - else: - title = title_cols[1] - elif doc_line_num == 1: - abstract_cols = line.split("|a|") - - if len(abstract_cols) != 2: - if len(abstract_cols) > 2: - abstract_text = "|a|".join(abstract_cols[1:]) - else: - return '{"error": "wrong #abstract_cols: %d"}' % len( - abstract_cols - ) - else: - if "- No text -" == abstract_cols[1]: - # make tmvar2 results empty - abstract_text = "" - else: - abstract_text = abstract_cols[1] - - doc_line_num += 1 - return dict_list - - -def preprocess(text): - text = text.replace("\r ", " ") - - text = text.replace("\u2028", " ") - text = text.replace("\u2029", " ") - - # HAIR SPACE - # https://www.fileformat.info/info/unicode/char/200a/index.htm - text = text.replace("\u200a", " ") - - # THIN SPACE - # https://www.fileformat.info/info/unicode/char/2009/index.htm - text = text.replace("\u2009", " ") - text = text.replace("\u2008", " ") - - # FOUR-PER-EM SPACE - # https://www.fileformat.info/info/unicode/char/2005/index.htm - text = text.replace("\u2005", " ") - text = text.replace("\u2004", " ") - text = text.replace("\u2003", " ") - - # EN SPACE - # https://www.fileformat.info/info/unicode/char/2002/index.htm - text = text.replace("\u2002", " ") - - # NO-BREAK SPACE - # https://www.fileformat.info/info/unicode/char/00a0/index.htm - text = text.replace("\u00a0", " ") - - # https://www.fileformat.info/info/unicode/char/f8ff/index.htm - text = text.replace("\uf8ff", " ") - - # https://www.fileformat.info/info/unicode/char/202f/index.htm - text = text.replace("\u202f", " ") - - text = text.replace("\ufeff", " ") - text = text.replace("\uf044", " ") - text = text.replace("\uf02d", " ") - text = text.replace("\uf0bb", " ") - - text = text.replace("\uf048", "Η") - text = text.replace("\uf0b0", "°") - - # MIDLINE HORIZONTAL ELLIPSIS: ⋯ - # https://www.fileformat.info/info/unicode/char/22ef/index.htm - # text = text.replace('\u22EF', '...') - - return text diff --git a/source/Parser/scripts/run_biomedner.sh b/source/Parser/scripts/run_biomedner.sh deleted file mode 100644 index 19554591..00000000 --- a/source/Parser/scripts/run_biomedner.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -cd .. -#!/bin/bash - -if [ ! -d "logs" ]; then - mkdir logs -fi -#################################### -##### NER ##### -#################################### - -# run neural NER -nohup python biomedner_server.py \ - --model_name_or_path models/finetuned_model_roberta \ - --biomedner_port 18894 >> logs/nohup_multi_ner.out 2>&1 & - -nohup python gner_server.py \ - --model_name_or_path gliner-community/gliner_large-v2.5 \ - --gner_port 18783 >> logs/nohup_gner.out 2>&1 & - -#################################### -##### Normalization ##### -#################################### -cd resources -# Disease (working dir: normalization/) -cd normalization -nohup java -Xmx16G -jar normalizers/disease/disease_normalizer_21.jar \ - "inputs/disease" \ - "outputs/disease" \ - "dictionary/dict_Disease.txt" \ - "normalizers/disease/resources" \ - 9 \ - 18892 \ - >> ../../logs/nohup_disease_normalize.out 2>&1 & - -# Gene (working dir: normalization/normalizers/gene/, port:18888) -cd normalizers/gene -nohup java -Xmx20G -jar gnormplus-normalization_21.jar \ - 18888 \ - >> ../../../../logs/nohup_gene_normalize.out 2>&1 & -cd ../../../.. \ No newline at end of file diff --git a/source/Parser/scripts/stop_biomedner.sh b/source/Parser/scripts/stop_biomedner.sh deleted file mode 100644 index bd44d473..00000000 --- a/source/Parser/scripts/stop_biomedner.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -# Define a function to stop a process by its name -stop_process() { - process_name="$1" - pid=$(ps auxww | grep "$process_name" | grep -v grep | awk '{print $2}' | sort -r) - if [ "$pid" != "" ]; then - # Kill each PID one by one - for p in $pid; do - kill -9 "$p" - echo "Stopped $process_name (PID: $p)" - done - else - echo "No $process_name found to stop." - fi -} - -# Call the function for each process -stop_process "biomedner_server.py" -stop_process "disease_normalizer_21.jar" -stop_process "gnormplus-normalization_21.jar" -stop_process "gner_server.py" - diff --git a/source/biomedner_services/run_biomedner.sh b/source/biomedner_services/run_biomedner.sh deleted file mode 100644 index b8f963f5..00000000 --- a/source/biomedner_services/run_biomedner.sh +++ /dev/null @@ -1,39 +0,0 @@ -#!/bin/bash -cd Parser - -if [ ! -d "logs" ]; then - mkdir logs -fi -#################################### -##### NER ##### -#################################### -# run NER -nohup python biomedner_server.py \ - --model_name_or_path models/finetuned_model_roberta \ - --biomedner_port 18894 >> logs/nohup_multi_ner.out 2>&1 & - -nohup python gner_server.py \ - --model_name_or_path gliner-community/gliner_large-v2.5 \ - --gner_port 18783 >> logs/nohup_gner.out 2>&1 & - -#################################### -##### Normalization ##### -#################################### -cd resources -# Disease (working dir: normalization/) -cd normalization -nohup java -Xmx16G -jar normalizers/disease/disease_normalizer_21.jar \ - "inputs/disease" \ - "outputs/disease" \ - "dictionary/dict_Disease.txt" \ - "normalizers/disease/resources" \ - 9 \ - 18892 \ - >> ../../logs/nohup_disease_normalize.out 2>&1 & - -# Gene (working dir: normalization/normalizers/gene/, port:18888) -cd normalizers/gene -nohup java -Xmx20G -jar gnormplus-normalization_21.jar \ - 18888 \ - >> ../../../../logs/nohup_gene_normalize.out 2>&1 & -cd ../../../../.. \ No newline at end of file diff --git a/source/biomedner_services/stop_biomedner.sh b/source/biomedner_services/stop_biomedner.sh deleted file mode 100644 index bd44d473..00000000 --- a/source/biomedner_services/stop_biomedner.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -# Define a function to stop a process by its name -stop_process() { - process_name="$1" - pid=$(ps auxww | grep "$process_name" | grep -v grep | awk '{print $2}' | sort -r) - if [ "$pid" != "" ]; then - # Kill each PID one by one - for p in $pid; do - kill -9 "$p" - echo "Stopped $process_name (PID: $p)" - done - else - echo "No $process_name found to stop." - fi -} - -# Call the function for each process -stop_process "biomedner_server.py" -stop_process "disease_normalizer_21.jar" -stop_process "gnormplus-normalization_21.jar" -stop_process "gner_server.py" - diff --git a/tests/test_deployment_readiness.py b/tests/test_deployment_readiness.py index 5b81405c..2ecb35da 100644 --- a/tests/test_deployment_readiness.py +++ b/tests/test_deployment_readiness.py @@ -15,7 +15,9 @@ def test_config_env_overrides_and_standard_index_names(monkeypatch): monkeypatch.setenv("TRIALMATCHAI_ES_HOST", "https://es.example.test:9200") monkeypatch.setenv("TRIALMATCHAI_ES_PASSWORD", "secret-from-env") monkeypatch.setenv("TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY", "trials_eligibility") - monkeypatch.setenv("TRIALMATCHAI_BIOMEDNER_AUTO_START", "true") + monkeypatch.setenv("TRIALMATCHAI_ENTITY_BACKEND", "regex") + monkeypatch.setenv("TRIALMATCHAI_CONCEPT_DB_PATH", "data/concepts-test") + monkeypatch.setenv("TRIALMATCHAI_LINK_ACCEPT", "0.9") cfg = load_config() @@ -23,7 +25,9 @@ def test_config_env_overrides_and_standard_index_names(monkeypatch): assert cfg["elasticsearch"]["password"] == "secret-from-env" assert cfg["elasticsearch"]["index_trials"] == "clinical_trials" assert cfg["elasticsearch"]["index_trials_eligibility"] == "trials_eligibility" - assert cfg["services"]["auto_start"] is True + assert cfg["entity_extraction"]["backend"] == "regex" + assert cfg["concept_linker"]["db_path"].endswith("data/concepts-test") + assert cfg["concept_linker"]["accept_threshold"] == 0.9 def test_cot_prompt_does_not_inject_consent(): diff --git a/tests/test_entities.py b/tests/test_entities.py new file mode 100644 index 00000000..fc5a3efd --- /dev/null +++ b/tests/test_entities.py @@ -0,0 +1,170 @@ +from __future__ import annotations + +import csv +from pathlib import Path + +from Matcher.entities.annotator import CompatibilityEntityAnnotator +from Matcher.entities.builder import build_legacy_dictionary_rows, build_omop_concept_rows +from Matcher.entities.linker import ConceptLinker, InMemoryConceptStore +from Matcher.entities.recognizers import RegexSchemaRecognizer, resolve_overlaps +from Matcher.entities.schemas import load_entity_schemas +from Matcher.entities.types import ConceptCandidate, EntityAnnotation, NO_ENTITY_ID + +ROOT = Path(__file__).resolve().parents[1] + + +def test_default_schema_validates_vocab_routing(): + schemas = load_entity_schemas() + by_id = {schema.id: schema for schema in schemas} + + assert by_id["disease"].target_vocabularies == ("SNOMED", "ICD10", "ICD10CM") + assert by_id["laboratory_test"].target_vocabularies == ("LOINC",) + assert by_id["medication"].target_vocabularies == ("RxNorm", "ATC") + assert by_id["disease"].query_expansion is True + + +def test_regex_backend_returns_current_output_shape(): + schemas = [schema for schema in load_entity_schemas() if schema.id == "disease"] + annotator = CompatibilityEntityAnnotator(RegexSchemaRecognizer(), schemas) + + result = annotator.annotate_texts_in_parallel(["metastatic cancer"], max_workers=1) + + assert result[0][0]["entity_group"] == "disease" + assert result[0][0]["text"] == "cancer" + assert result[0][0]["normalized_id"] == [NO_ENTITY_ID] + assert "concept_candidates" in result[0][0] + + +def test_overlap_resolution_keeps_higher_confidence_span(): + annotations = [ + EntityAnnotation("disease", "lung cancer", 0, 11, 0.91, schema_id="disease"), + EntityAnnotation("disease", "cancer", 5, 11, 0.95, schema_id="disease"), + ] + + resolved = resolve_overlaps(annotations) + + assert len(resolved) == 1 + assert resolved[0].text == "cancer" + + +def test_concept_linker_accepts_rejects_and_marks_ambiguous(): + schemas = [schema for schema in load_entity_schemas() if schema.id == "disease"] + store = InMemoryConceptStore( + [ + ConceptCandidate( + concept_id="1", + vocabulary_id="SNOMED", + concept_code="363346000", + concept_name="Malignant neoplastic disease", + domain_id="Condition", + synonyms=("cancer", "malignancy"), + ), + ConceptCandidate( + concept_id="2", + vocabulary_id="SNOMED", + concept_code="73211009", + concept_name="Diabetes mellitus", + domain_id="Condition", + ), + ] + ) + linker = ConceptLinker(store, schemas, accept_threshold=0.8, reject_threshold=0.3) + + accepted = linker.link_annotation( + EntityAnnotation("disease", "cancer", 0, 6, 0.95, schema_id="disease") + ) + ambiguous = linker.link_annotation( + EntityAnnotation( + "disease", + "neoplastic disorder", + 0, + 19, + 0.95, + schema_id="disease", + ) + ) + rejected = linker.link_annotation( + EntityAnnotation("disease", "unrelated words", 0, 15, 0.95, schema_id="disease") + ) + + assert accepted.linker_status == "accepted" + assert accepted.normalized_id == ("SNOMED:363346000",) + assert "malignancy" in accepted.synonyms + assert ambiguous.linker_status == "ambiguous" + assert ambiguous.normalized_id == (NO_ENTITY_ID,) + assert rejected.linker_status == "rejected" + + +def test_concept_builders_import_omop_and_legacy_rows(tmp_path): + concept_csv = tmp_path / "CONCEPT.csv" + synonym_csv = tmp_path / "CONCEPT_SYNONYM.csv" + with concept_csv.open("w", newline="") as handle: + writer = csv.DictWriter( + handle, + fieldnames=[ + "concept_id", + "concept_name", + "domain_id", + "vocabulary_id", + "concept_class_id", + "standard_concept", + "concept_code", + ], + ) + writer.writeheader() + writer.writerow( + { + "concept_id": "1", + "concept_name": "Hemoglobin measurement", + "domain_id": "Measurement", + "vocabulary_id": "LOINC", + "concept_class_id": "Lab Test", + "standard_concept": "S", + "concept_code": "718-7", + } + ) + with synonym_csv.open("w", newline="") as handle: + writer = csv.DictWriter( + handle, + fieldnames=["concept_id", "concept_synonym_name"], + ) + writer.writeheader() + writer.writerow({"concept_id": "1", "concept_synonym_name": "Hgb"}) + + rows = build_omop_concept_rows(concept_csv, synonym_csv, vocabularies=("LOINC",)) + legacy = tmp_path / "dict_Gene.txt" + legacy.write_text("EntrezGene:1956||EGFR|ERBB1\n") + + legacy_rows = build_legacy_dictionary_rows( + legacy, + vocabulary_id="EntrezGene", + domain_id="Gene", + ) + + assert rows[0]["concept_code"] == "718-7" + assert rows[0]["synonyms"] == ["Hgb"] + assert legacy_rows[0]["concept_code"] == "1956" + assert legacy_rows[0]["synonyms"] == ["EGFR", "ERBB1"] + + +def test_runtime_replacement_has_no_old_daemon_references(): + runtime_files = [ + "source/Matcher/config/config.json", + "source/Matcher/main.py", + "source/Parser/biomedner_engine.py", + "source/Parser/normalizer.py", + ] + forbidden = [ + "18888", + "18892", + "18894", + "18783", + "GNormPlus", + "disease_normalizer_21.jar", + "java -Xmx", + "import socket", + ] + for file_name in runtime_files: + content = (ROOT / file_name).read_text() + for term in forbidden: + assert term not in content diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index 316ddcd7..22270350 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -59,6 +59,21 @@ def _base_config(tmp_path): } +def _entity_config(tmp_path): + cfg = _base_config(tmp_path) + schema = tmp_path / "schema.yaml" + schema.write_text("version: 1\nentities: []\n") + cfg["entity_extraction"] = { + "backend": "gliner2", + "schema_path": str(schema), + } + cfg["concept_linker"] = { + "enabled": True, + "db_path": str(tmp_path / "missing-concepts"), + } + return cfg + + def test_preflight_passes_for_required_paths_and_indices(tmp_path): cfg = _base_config(tmp_path) issues = run_preflight_checks( @@ -108,6 +123,18 @@ def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): ] +def test_preflight_reports_missing_entity_extra(tmp_path, monkeypatch): + cfg = _entity_config(tmp_path) + monkeypatch.setattr(preflight.importlib.util, "find_spec", lambda name: None) + + issues = run_preflight_checks(cfg, require_models=True) + + assert ( + "entity_extraction.backend=gliner2 requires the entity extra " + "(`uv sync --extra entity`)." + ) in issues + + def test_indexer_config_uses_env_overrides_and_resolves_certs(tmp_path, monkeypatch): config_path = tmp_path / "config.json" cert = tmp_path / "certs" / "ca.crt" diff --git a/tests/test_settings.py b/tests/test_settings.py index 8b6327ab..8108af96 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -22,20 +22,28 @@ def test_env_overrides(self) -> None: "password": "pass", }, "embedder": {"model_name": "old"}, + "entity_extraction": {"backend": "gliner2"}, + "concept_linker": {"db_path": "old"}, } os.environ["TRIALMATCHAI_ES_HOST"] = "http://override:9200" os.environ["TRIALMATCHAI_EMBEDDER_MODEL_NAME"] = "new-model" os.environ["TRIALMATCHAI_ES_AUTO_START"] = "true" + os.environ["TRIALMATCHAI_ENTITY_BACKEND"] = "regex" + os.environ["TRIALMATCHAI_CONCEPT_DB_PATH"] = "concepts" try: updated = apply_env_overrides(raw) finally: os.environ.pop("TRIALMATCHAI_ES_HOST", None) os.environ.pop("TRIALMATCHAI_EMBEDDER_MODEL_NAME", None) os.environ.pop("TRIALMATCHAI_ES_AUTO_START", None) + os.environ.pop("TRIALMATCHAI_ENTITY_BACKEND", None) + os.environ.pop("TRIALMATCHAI_CONCEPT_DB_PATH", None) self.assertEqual(updated["elasticsearch"]["host"], "http://override:9200") self.assertEqual(updated["embedder"]["model_name"], "new-model") self.assertTrue(updated["elasticsearch"]["auto_start"]) + self.assertEqual(updated["entity_extraction"]["backend"], "regex") + self.assertEqual(updated["concept_linker"]["db_path"], "concepts") if __name__ == "__main__": diff --git a/utils/Indexer/index_criteria.py b/utils/Indexer/index_criteria.py index 4d2aa892..3db6750c 100644 --- a/utils/Indexer/index_criteria.py +++ b/utils/Indexer/index_criteria.py @@ -98,6 +98,28 @@ def create_index(self, dims: int): "analyzer": "standard_lowercase", }, "class": {"type": "keyword"}, + "entity_group": {"type": "keyword"}, + "text": { + "type": "text", + "analyzer": "standard_lowercase", + }, + "score": {"type": "float"}, + "linker_score": {"type": "float"}, + "linker_status": {"type": "keyword"}, + "concept_candidates": { + "type": "nested", + "properties": { + "normalized_id": {"type": "keyword"}, + "vocabulary_id": {"type": "keyword"}, + "concept_code": {"type": "keyword"}, + "concept_name": { + "type": "text", + "analyzer": "standard_lowercase", + }, + "domain_id": {"type": "keyword"}, + "score": {"type": "float"}, + }, + }, }, }, "nct_id": {"type": "keyword"}, diff --git a/utils/Indexer/prepare_criteria.py b/utils/Indexer/prepare_criteria.py index 660e560c..7c25de6f 100644 --- a/utils/Indexer/prepare_criteria.py +++ b/utils/Indexer/prepare_criteria.py @@ -3,7 +3,9 @@ import hashlib import json import logging +import sys from pathlib import Path +from typing import Any import torch import torch.nn.functional as F @@ -16,6 +18,12 @@ logger = logging.getLogger(__name__) +ROOT = Path(__file__).resolve().parents[2] +SOURCE = ROOT / "source" +if str(SOURCE) not in sys.path: + sys.path.append(str(SOURCE)) + + class SentenceEmbedder: def __init__(self, model_name: str = "BAAI/bge-m3", use_gpu: bool = True): self.device = torch.device( @@ -66,6 +74,7 @@ def process_trial( source_folder: Path, processed_folder: Path, embedder: SentenceEmbedder, + entity_annotator: Any | None = None, ) -> int: raw_path = source_folder / f"{nct_id}.json" if not raw_path.exists(): @@ -98,6 +107,8 @@ def process_trial( if not entries: return 0 + _annotate_missing_entities(entries, texts, entity_annotator) + # embed all at once vectors = embedder.embed(texts) @@ -111,7 +122,7 @@ def process_trial( "criteria_id": crit_id, "nct_id": entry["nct_id"], "criterion": entry["criterion"], - "entities": entry["entities"], + "entities": _entities_for_index(entry["entities"]), "eligibility_type": entry["eligibility_type"], "criterion_vector": vec, } @@ -142,6 +153,16 @@ def main(): "--model-name", default="BAAI/bge-m3", help="Sentence embedding model name" ) p.add_argument("--use-gpu", action="store_true", help="Enable GPU iff available") + p.add_argument( + "--annotate-entities", + action="store_true", + help="Annotate criteria without existing entities before writing processed JSON.", + ) + p.add_argument( + "--config", + default=None, + help="TrialMatchAI config path used when --annotate-entities is enabled.", + ) args = p.parse_args() ids = [line.strip() for line in open(args.ids_file) if line.strip()] @@ -150,6 +171,7 @@ def main(): processed_folder.mkdir(parents=True, exist_ok=True) embedder = SentenceEmbedder(model_name=args.model_name, use_gpu=args.use_gpu) + entity_annotator = _build_entity_annotator(args.config) if args.annotate_entities else None total = 0 skipped = 0 @@ -162,7 +184,13 @@ def main(): skipped += 1 continue - processed_count = process_trial(nct, source_folder, processed_folder, embedder) + processed_count = process_trial( + nct, + source_folder, + processed_folder, + embedder, + entity_annotator=entity_annotator, + ) total += processed_count logger.info( @@ -170,5 +198,52 @@ def main(): ) +def _build_entity_annotator(config_path: str | None): + from Matcher.config.config_loader import load_config + from Matcher.entities import build_entity_annotator + + config = load_config(config_path) + return build_entity_annotator(config) + + +def _annotate_missing_entities( + entries: list[dict[str, Any]], + texts: list[str], + entity_annotator: Any | None, +) -> None: + if entity_annotator is None: + return + missing_indices = [ + index for index, entry in enumerate(entries) if not entry.get("entities") + ] + if not missing_indices: + return + missing_texts = [texts[index] for index in missing_indices] + annotations = entity_annotator.annotate_texts_in_parallel( + missing_texts, + max_workers=1, + ) + for index, entities in zip(missing_indices, annotations): + entries[index]["entities"] = entities + + +def _entities_for_index(entities: Any) -> list[dict[str, Any]]: + if not isinstance(entities, list): + return [] + indexed: list[dict[str, Any]] = [] + for entity in entities: + if not isinstance(entity, dict): + continue + normalized = dict(entity) + normalized.setdefault("entity", normalized.get("text", "")) + normalized.setdefault("class", normalized.get("entity_group", "")) + normalized.setdefault("normalized_id", ["CUI-less"]) + normalized.setdefault("synonyms", 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"(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "pyarrow", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, +] gpu = [ { name = "bitsandbytes", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, @@ -3723,10 +3814,12 @@ requires-dist = [ { name = "elasticsearch", specifier = "==8.17.2" }, { name = "faiss-cpu", specifier = "==1.9.0.post1" }, { name = "gliner", specifier = "==0.2.27" }, + { name = "gliner2", marker = "extra == 'entity'", specifier = ">=1.3.1,<2" }, { name = "httpx", marker = "extra == 'llm'", specifier = "==0.28.1" }, { name = "huggingface-hub", specifier = ">=1.5.0,<2" }, { name = "jinja2", marker = "extra == 'training'", specifier = "==3.1.6" }, { name = "joblib", specifier = "==1.4.2" }, + { name = "lancedb", marker = "extra == 'entity'", specifier = ">=0.25.0,<0.26" }, { name = "langchain", marker = "extra == 'llm'", specifier = "==1.3.10" }, { name = "langchain-community", marker = "extra == 'llm'", specifier = "==0.4.2" }, { name = "langchain-openai", marker = "extra == 'llm'", specifier = "==1.3.2" }, @@ -3738,8 +3831,9 @@ requires-dist = [ { name = "orjson", specifier = "==3.11.9" }, { name = "pandas", specifier = "==2.2.3" }, { name = "peft", specifier = "==0.15.2" }, + { name = "pyarrow", marker = "extra == 'entity'", specifier = ">=24.0.0,<25" }, { name = "pydantic", specifier = ">=2.12.0,<3" }, - { name = "pydantic-settings", specifier = ">=2.10.1,<3" }, + { name = "pydantic-settings", specifier = ">=2.14.2,<3" }, { name = "pyfastner", specifier = "==1.0.10" }, { name = "pyrush", specifier = "==1.0.9" }, { name = "pysbd", specifier = "==0.3.4" }, @@ -3765,7 +3859,7 @@ requires-dist = [ { name = "transformers", specifier = "==5.6.2" }, { name = "vllm", marker = "sys_platform == 'linux' and extra == 'gpu'", specifier = "==0.23.0" }, ] -provides-extras = ["gpu", "llm", "training"] +provides-extras = ["gpu", "llm", "entity", "training"] [package.metadata.requires-dev] dev = [ From 3746f00f63e7dcc9c81ab160ff2d093414fc05f1 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 21 Jun 2026 17:26:16 +0200 Subject: [PATCH 03/64] Replace search service with LanceDB backend --- .env.example | 22 +- .gitignore | 16 +- Makefile | 5 +- README.md | 63 +- docker-compose.yml | 123 +-- docs/HLD.md | 721 +++------------- elasticsearch/.env.example | 12 - elasticsearch/apptainer-run-es.sh | 132 --- elasticsearch/docker-compose.yml | 232 ------ pyproject.toml | 5 +- requirements.txt | 3 +- scripts/index_data.sh | 28 +- scripts/scan_secrets.py | 10 +- scripts/start_es.sh | 30 - setup.sh | 6 +- source/Matcher/cli/healthcheck.py | 40 +- source/Matcher/cli/index_data.py | 104 ++- source/Matcher/config/config.json | 21 +- source/Matcher/config/config_loader.py | 14 +- source/Matcher/config/settings.py | 44 +- source/Matcher/main.py | 32 +- .../trial_search/first_level_search.py | 336 +------- .../trial_search/second_level_search.py | 241 +----- source/Matcher/search/__init__.py | 15 + source/Matcher/search/lancedb_backend.py | 781 ++++++++++++++++++ .../Matcher/services/elasticsearch_service.py | 95 --- source/Matcher/services/preflight.py | 71 +- tests/test_config_pytest.py | 2 +- tests/test_deployment_readiness.py | 18 +- tests/test_first_level_search_pytest.py | 35 +- tests/test_integration_es_pytest.py | 23 - tests/test_lancedb_search_backend.py | 75 ++ tests/test_preflight_and_indexer.py | 160 ++-- tests/test_search_queries.py | 59 -- tests/test_search_queries_pytest.py | 73 +- tests/test_second_level_search_pytest.py | 44 +- tests/test_settings.py | 27 +- utils/Indexer/config.json | 11 - utils/Indexer/es_config.py | 42 - utils/Indexer/index_criteria.py | 303 +++---- utils/Indexer/index_trials.py | 164 +--- uv.lock | 35 +- 42 files changed, 1633 insertions(+), 2640 deletions(-) delete mode 100644 elasticsearch/.env.example delete mode 100644 elasticsearch/apptainer-run-es.sh delete mode 100644 elasticsearch/docker-compose.yml delete mode 100644 scripts/start_es.sh create mode 100644 source/Matcher/search/__init__.py create mode 100644 source/Matcher/search/lancedb_backend.py delete mode 100644 source/Matcher/services/elasticsearch_service.py delete mode 100644 tests/test_integration_es_pytest.py create mode 100644 tests/test_lancedb_search_backend.py delete mode 100644 tests/test_search_queries.py delete mode 100644 utils/Indexer/config.json delete mode 100644 utils/Indexer/es_config.py diff --git a/.env.example b/.env.example index 25965ecf..2ee996fa 100644 --- a/.env.example +++ b/.env.example @@ -1,24 +1,16 @@ # Copy this file to .env for local TrialMatchAI runtime configuration. # Do not commit .env. -TRIALMATCHAI_ES_HOST=https://localhost:9200 -TRIALMATCHAI_ES_USERNAME=elastic -TRIALMATCHAI_ES_PASSWORD=change-me-strong-password -TRIALMATCHAI_ES_CA_CERTS=elasticsearch/certs/ca/ca.crt -TRIALMATCHAI_ES_AUTO_START=false -KIBANA_PASSWORD=change-me-strong-password -STACK_VERSION=8.13.4 -CLUSTER_NAME=trialmatchai-local -ES_PORT=9200 -KIBANA_PORT=5601 -LICENSE=basic -MEM_LIMIT=2147483648 - TRIALMATCHAI_PATIENTS_DIR=example TRIALMATCHAI_OUTPUT_DIR=results TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons -TRIALMATCHAI_INDEX_TRIALS=clinical_trials -TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY=trials_eligibility + +TRIALMATCHAI_SEARCH_BACKEND=lancedb +TRIALMATCHAI_SEARCH_DB_PATH=data/search +TRIALMATCHAI_SEARCH_TRIALS_TABLE=trials +TRIALMATCHAI_SEARCH_CRITERIA_TABLE=criteria +TRIALMATCHAI_SEARCH_CANDIDATE_LIMIT=1000 +TRIALMATCHAI_SEARCH_MODE=hybrid TRIALMATCHAI_MODEL_BASE_MODEL=microsoft/phi-4 TRIALMATCHAI_MODEL_COT_ADAPTER_PATH=models/finetuned_phi_reasoning diff --git a/.gitignore b/.gitignore index c6afa6c8..cc9e6955 100644 --- a/.gitignore +++ b/.gitignore @@ -10,7 +10,6 @@ logs/ .env .env.* !.env.example -!elasticsearch/.env.example # ============================ # Finetuning ignore rules @@ -30,20 +29,6 @@ utils/finetuning/finetune_ner/RoBERTa-large-PM-M3-Voc/ utils/finetuning/finetune_ner/output_eval utils/finetuning/finetune_ner -# ============================ -# ElasticSearch artifacts -# ============================ -elasticsearch/.env -elasticsearch/certs/ -elasticsearch/config/ -elasticsearch/tmp-config/ -elasticsearch/sif/ -elasticsearch/data1/ -elasticsearch/data/ -elasticsearch/logs/ -elasticsearch/sif/*.sif -*.keystore - # ============================ # Parser artifacts # ============================ @@ -62,6 +47,7 @@ utils/DataLoader/nct_ids.txt utils/Indexer/nct_ids.txt utils/Indexer/processed_ids.txt utils/Indexer/*.log +*.lance # ============================ # Python cache diff --git a/Makefile b/Makefile index 2f3f1b4d..e26a94f2 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: venv sync sync-gpu test lock lint audit healthcheck bootstrap start-es index run setup +.PHONY: venv sync sync-gpu test lock lint audit healthcheck bootstrap index run setup venv: uv venv @@ -27,9 +27,6 @@ healthcheck: bootstrap: uv run trialmatchai-bootstrap-data -start-es: - bash scripts/start_es.sh - index: uv run trialmatchai-index diff --git a/README.md b/README.md index 632d6630..9f0202e8 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ Logo -TrialMatchAI is a batch-oriented clinical trial matching pipeline. It combines Elasticsearch retrieval, biomedical NLP, embeddings, LLM reranking, and eligibility reasoning to produce ranked trial recommendations with criterion-level explanations. +TrialMatchAI is a batch-oriented clinical trial matching pipeline. It combines local LanceDB retrieval, schema-driven biomedical entity extraction, concept linking, embeddings, LLM reranking, and eligibility reasoning to produce ranked trial recommendations with criterion-level explanations. ## Disclaimer @@ -10,28 +10,24 @@ This software is for research and informational use only. It is not medical advi ## Deployment Target -The supported v1 deployment path is a single Linux GPU server or VM with Docker Compose for Elasticsearch and a containerized TrialMatchAI worker. HPC/Apptainer support remains available through the scripts under `elasticsearch/`, but production runtime does not auto-start local services by default. +The supported v1 deployment path is a single Python 3.11 GPU server or VM. Trial and criteria search use embedded LanceDB tables under `data/search`, so no separate search service, container, socket, TLS certificate, or service credential is required. Docker remains optional for packaging the worker. ## Requirements - Python 3.11 - `uv` recommended, or `pip` with editable install -- Docker Compose for the default Elasticsearch deployment - NVIDIA GPU with enough VRAM for the selected LLM backend -- 100 GB+ disk space for datasets, models, indices, and results +- 100 GB+ disk space for datasets, models, LanceDB tables, and results - A LanceDB concept table built from OMOP/legacy dictionaries for entity normalization ## Security First -No real credentials, generated TLS keys, Elasticsearch keystores, Parser outputs, or local indexing state should be committed. Copy templates and rotate any previously exposed credentials before deployment: +No real credentials, generated private keys, Parser outputs, datasets, models, local LanceDB data, or results should be committed. Copy the template and keep runtime values local: ```bash cp .env.example .env -cp elasticsearch/.env.example elasticsearch/.env ``` -Set strong local values for `TRIALMATCHAI_ES_PASSWORD`, `ELASTIC_PASSWORD`, and `KIBANA_PASSWORD`. - Dependency auditing currently ignores `CVE-2025-3000` because vLLM 0.23 pins Torch 2.11.0 and the advisory has no fixed Torch version listed. Revisit that exception whenever upgrading vLLM or Torch. ## Quickstart @@ -42,33 +38,28 @@ Install deployment dependencies: uv sync --extra gpu --extra entity ``` -For local development, tests, healthchecks, or `TRIALMATCHAI_COT_BACKEND=default`, the default dependency set is enough: +For local development, tests, healthchecks, or `TRIALMATCHAI_COT_BACKEND=default`: ```bash uv sync ``` -Optional tooling is split out of the default runtime: +Optional tooling: ```bash uv sync --extra llm # OpenAI/LangChain data-generation utilities -uv sync --extra entity # GLiNER2/LanceDB entity extraction and normalization +uv sync --extra entity # GLiNER2 entity extraction uv sync --extra training # fine-tuning and evaluation utilities ``` -Start Elasticsearch with the root Compose stack: - -```bash -docker compose up -d elasticsearch -``` - -Run a healthcheck: +Run a config and backend healthcheck: ```bash uv run trialmatchai-healthcheck +uv run trialmatchai-healthcheck --require-tables ``` -Provision data, models, and indices: +Provision data, models, concept KB, and search tables: ```bash uv run trialmatchai-bootstrap-data @@ -86,16 +77,11 @@ Results are written under `results/`. ## Docker Worker -Build and run the worker healthcheck through Compose: +Docker is optional. The worker container uses mounted local folders and the same embedded LanceDB tables: ```bash docker compose build trialmatchai-worker -docker compose up trialmatchai-worker -``` - -To run the full pipeline in the container after provisioning data/models/indices: - -```bash +docker compose run --rm trialmatchai-worker trialmatchai-healthcheck docker compose run --rm trialmatchai-worker trialmatchai-run ``` @@ -104,17 +90,16 @@ docker compose run --rm trialmatchai-worker trialmatchai-run Configuration defaults live in `source/Matcher/config/config.json`. Runtime overrides use `.env` or environment variables: ```bash -TRIALMATCHAI_ES_HOST=https://localhost:9200 -TRIALMATCHAI_ES_USERNAME=elastic -TRIALMATCHAI_ES_PASSWORD=change-me -TRIALMATCHAI_ES_CA_CERTS=elasticsearch/certs/ca/ca.crt -TRIALMATCHAI_ES_AUTO_START=false - TRIALMATCHAI_PATIENTS_DIR=example TRIALMATCHAI_OUTPUT_DIR=results TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons -TRIALMATCHAI_INDEX_TRIALS=clinical_trials -TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY=trials_eligibility + +TRIALMATCHAI_SEARCH_BACKEND=lancedb +TRIALMATCHAI_SEARCH_DB_PATH=data/search +TRIALMATCHAI_SEARCH_TRIALS_TABLE=trials +TRIALMATCHAI_SEARCH_CRITERIA_TABLE=criteria +TRIALMATCHAI_SEARCH_CANDIDATE_LIMIT=1000 +TRIALMATCHAI_SEARCH_MODE=hybrid TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false TRIALMATCHAI_ENTITY_BACKEND=gliner2 @@ -130,10 +115,10 @@ Use `TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=true` only when a selected model expli ## CLI Commands -- `trialmatchai-healthcheck`: validate config, paths, Elasticsearch reachability, and optionally indices. +- `trialmatchai-healthcheck`: validate config, paths, and optionally LanceDB search tables. - `trialmatchai-bootstrap-data`: download and extract external data/model artifacts. - `trialmatchai-build-concepts`: build the LanceDB concept table used for entity normalization. -- `trialmatchai-index`: index prepared data into Elasticsearch. +- `trialmatchai-index`: build the LanceDB trial and criteria search tables. - `trialmatchai-run`: run the batch matching pipeline. ## Tests and Checks @@ -147,12 +132,6 @@ docker compose config docker build . ``` -Integration tests require a running Elasticsearch instance: - -```bash -TRIALMATCHAI_RUN_INTEGRATION=1 uv run pytest -m integration -``` - ## Support - Email: abdallahmajd7@gmail.com diff --git a/docker-compose.yml b/docker-compose.yml index 9d0b2ed2..dbe07663 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,111 +1,20 @@ services: - setup: - image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION:-8.13.4} - user: "0" - volumes: - - ./elasticsearch/certs:/usr/share/elasticsearch/config/certs - command: > - bash -c ' - if [ -z "$${TRIALMATCHAI_ES_PASSWORD}" ]; then - echo "Set TRIALMATCHAI_ES_PASSWORD in .env"; - exit 1; - fi; - if [ ! -f config/certs/ca.zip ]; then - bin/elasticsearch-certutil ca --silent --pem -out config/certs/ca.zip; - unzip config/certs/ca.zip -d config/certs; - fi; - if [ ! -f config/certs/certs.zip ]; then - printf "%s\n" \ - "instances:" \ - " - name: es01" \ - " dns:" \ - " - es01" \ - " - elasticsearch" \ - " - localhost" \ - " ip:" \ - " - 127.0.0.1" \ - > config/certs/instances.yml; - bin/elasticsearch-certutil cert --silent --pem - -out config/certs/certs.zip - --in config/certs/instances.yml - --ca-cert config/certs/ca/ca.crt - --ca-key config/certs/ca/ca.key; - unzip config/certs/certs.zip -d config/certs; - fi; - chown -R root:root config/certs; - find config/certs -type d -exec chmod 750 {} \;; - find config/certs -type f -exec chmod 640 {} \;; - ' - environment: - TRIALMATCHAI_ES_PASSWORD: ${TRIALMATCHAI_ES_PASSWORD:-} - healthcheck: - test: ["CMD-SHELL", "[ -f config/certs/es01/es01.crt ]"] - interval: 1s - timeout: 5s - retries: 120 - - elasticsearch: - image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION:-8.13.4} - depends_on: - setup: - condition: service_healthy - volumes: - - ./elasticsearch/certs:/usr/share/elasticsearch/config/certs:ro - - esdata01:/usr/share/elasticsearch/data - ports: - - "${ES_PORT:-9200}:9200" - environment: - node.name: es01 - cluster.name: ${CLUSTER_NAME:-trialmatchai-local} - discovery.type: single-node - ELASTIC_PASSWORD: ${TRIALMATCHAI_ES_PASSWORD:-} - bootstrap.memory_lock: "true" - xpack.security.enabled: "true" - xpack.security.http.ssl.enabled: "true" - xpack.security.http.ssl.key: certs/es01/es01.key - xpack.security.http.ssl.certificate: certs/es01/es01.crt - xpack.security.http.ssl.certificate_authorities: certs/ca/ca.crt - xpack.security.transport.ssl.enabled: "true" - xpack.security.transport.ssl.key: certs/es01/es01.key - xpack.security.transport.ssl.certificate: certs/es01/es01.crt - xpack.security.transport.ssl.certificate_authorities: certs/ca/ca.crt - xpack.security.transport.ssl.verification_mode: certificate - xpack.license.self_generated.type: ${LICENSE:-basic} - mem_limit: ${MEM_LIMIT:-2147483648} - ulimits: - memlock: - soft: -1 - hard: -1 - healthcheck: - test: - [ - "CMD-SHELL", - "curl -s --cacert config/certs/ca/ca.crt https://localhost:9200 | grep -q 'missing authentication credentials'", - ] - interval: 10s - timeout: 10s - retries: 120 - trialmatchai-worker: build: . - depends_on: - elasticsearch: - condition: service_healthy volumes: - ./data:/app/data - ./models:/app/models - ./results:/app/results - ./example:/app/example:ro - - ./elasticsearch/certs:/app/elasticsearch/certs:ro environment: - TRIALMATCHAI_ES_HOST: https://elasticsearch:9200 - TRIALMATCHAI_ES_USERNAME: elastic - TRIALMATCHAI_ES_PASSWORD: ${TRIALMATCHAI_ES_PASSWORD:-} - TRIALMATCHAI_ES_CA_CERTS: /app/elasticsearch/certs/ca/ca.crt - TRIALMATCHAI_ES_AUTO_START: "false" TRIALMATCHAI_PATIENTS_DIR: /app/example TRIALMATCHAI_OUTPUT_DIR: /app/results TRIALMATCHAI_TRIALS_JSON_FOLDER: /app/data/trials_jsons + TRIALMATCHAI_SEARCH_BACKEND: lancedb + TRIALMATCHAI_SEARCH_DB_PATH: /app/data/search + TRIALMATCHAI_SEARCH_TRIALS_TABLE: trials + TRIALMATCHAI_SEARCH_CRITERIA_TABLE: criteria + TRIALMATCHAI_SEARCH_MODE: hybrid TRIALMATCHAI_ENTITY_BACKEND: gliner2 TRIALMATCHAI_ENTITY_SCHEMA_PATH: /app/source/Matcher/entity_schemas/trialmatchai.yaml TRIALMATCHAI_CONCEPT_DB_PATH: /app/data/concepts @@ -114,25 +23,3 @@ services: TRIALMATCHAI_LINK_REJECT: "0.30" TRIALMATCHAI_LOG_JSON: "1" command: ["trialmatchai-healthcheck"] - - kibana: - profiles: ["kibana"] - image: docker.elastic.co/kibana/kibana:${STACK_VERSION:-8.13.4} - depends_on: - elasticsearch: - condition: service_healthy - volumes: - - ./elasticsearch/certs:/usr/share/kibana/config/certs:ro - - kibanadata:/usr/share/kibana/data - ports: - - "${KIBANA_PORT:-5601}:5601" - environment: - SERVERNAME: kibana - ELASTICSEARCH_HOSTS: https://elasticsearch:9200 - ELASTICSEARCH_USERNAME: kibana_system - ELASTICSEARCH_PASSWORD: ${KIBANA_PASSWORD:-} - ELASTICSEARCH_SSL_CERTIFICATEAUTHORITIES: config/certs/ca/ca.crt - -volumes: - esdata01: - kibanadata: diff --git a/docs/HLD.md b/docs/HLD.md index 2d7fbbf5..b3bd33cb 100644 --- a/docs/HLD.md +++ b/docs/HLD.md @@ -1,656 +1,177 @@ -# High Level Design — TrialMatchAI +# High Level Design - TrialMatchAI ## 1. Overview -TrialMatchAI is an AI-driven clinical trial matching system. Given a patient record encoded as a GA4GH Phenopacket, it retrieves and ranks the most relevant clinical trials from a large Elasticsearch-backed corpus using a three-stage pipeline: hybrid retrieval, LLM reranking, and chain-of-thought eligibility reasoning. +TrialMatchAI is a batch clinical trial matching system. Given a GA4GH Phenopacket, it retrieves candidate trials from embedded LanceDB tables, reranks eligibility criteria with an LLM, performs criterion-level reasoning, and writes ranked recommendations. ### Goals -- Match individual patients to relevant open clinical trials at scale -- Provide criterion-level explainability (why a trial does or does not match) -- Support reproducible, ontology-grounded patient representations -### Non-Goals -- Real-time / online inference (designed for batch processing) -- EHR integration or direct clinical deployment -- Trial outcome prediction - ---- - -## 2. System Architecture - -``` -┌─────────────────────────────────────────────────────────────────────┐ -│ TrialMatchAI │ -│ │ -│ ┌──────────────┐ ┌────────────────────────────────────────────┐ │ -│ │ Indexing │ │ Matching Pipeline │ │ -│ │ Pipeline │ │ │ │ -│ │ │ │ Phenopacket │ │ -│ │ ClinicalTrials │ │ │ │ -│ │ .gov JSONs │ │ ▼ │ │ -│ │ │ │ │ ┌─────────────────────┐ │ │ -│ │ ▼ │ │ │ Phenopacket │ │ │ -│ │ Schema NER +│ │ │ Processor + LLM │ │ │ -│ │ concept link│ │ │ (Phi-4 summariser) │ │ │ -│ │ │ │ │ └──────────┬──────────┘ │ │ -│ │ ▼ │ │ │ keywords.json │ │ -│ │ BGE-M3 │ │ ▼ │ │ -│ │ embeddings │ │ ┌─────────────────────┐ │ │ -│ │ │ │ │ │ Stage 1: Hybrid │ │ │ -│ │ ▼ │ │ │ BM25 + Vector Search│ │ │ -│ │ Elasticsearch│◄───┼──│ (clinical_trials) │ │ │ -│ │ clinical_ │ │ └──────────┬──────────┘ │ │ -│ │ trials │ │ │ ~300 candidates │ │ -│ │ trials_ │ │ ▼ │ │ -│ │ eligibility │◄───┼──│ Stage 2: Criteria │ │ │ -│ └──────────────┘ │ │ Search + LLM Rerank │ │ │ -│ │ │ (Gemma-2-2B) │ │ │ -│ │ └──────────┬──────────┘ │ │ -│ │ │ ~33 trials │ │ -│ │ ▼ │ │ -│ │ ┌─────────────────────┐ │ │ -│ │ │ Stage 3: CoT │ │ │ -│ │ │ Reasoning (Phi-4 + │ │ │ -│ │ │ finetuned adapter) │ │ │ -│ │ └──────────┬──────────┘ │ │ -│ │ │ │ │ -│ │ ▼ │ │ -│ │ ┌─────────────────────┐ │ │ -│ │ │ Ranker + Output │ │ │ -│ │ │ ranked_trials.json │ │ │ -│ │ └─────────────────────┘ │ │ -│ └────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────────────────┘ -``` - ---- - -## 3. Components - -### 3.1 Indexing Pipeline - -Run once to prepare the Elasticsearch indices from raw ClinicalTrials.gov data. - -``` -Raw Trial JSONs - │ - ├──► Schema entity annotation + LanceDB concept linking - │ │ - │ ▼ - │ Annotated criteria with entity synonyms - │ - ├──► prepare_trials.py ──► BGE-M3 embed ──► clinical_trials index - │ - └──► prepare_criteria.py ──► BGE-M3 embed ──► trials_eligibility index -``` - -**`clinical_trials` index** — one document per trial: - -| Field | Type | Notes | -|---|---|---| -| nct_id | keyword | Primary key | -| brief_title, brief_summary, condition, eligibility_criteria | text | BM25 searchable | -| *_vector (×4) | dense_vector (1024d) | BGE-M3 embeddings | -| minimum_age, maximum_age | float | Years, for age filtering | -| overall_status, phase, gender | keyword | Facet filters | -| interventions, locations | nested | Structured metadata | - -**`trials_eligibility` index** — one document per eligibility criterion: - -| Field | Type | Notes | -|---|---|---| -| criteria_id, nct_id | keyword | Stable hash-derived ID | -| eligibility_type | keyword | `inclusion` or `exclusion` | -| criterion | text | BM25 searchable | -| criterion_vector | dense_vector (1024d, HNSW) | BGE-M3 embedding | -| entities | nested | Schema entity annotations, linked concept candidates, and synonyms | - ---- - -### 3.2 Phenopacket Processor - -Converts a GA4GH Phenopacket JSON into search-ready keywords. - -**Input:** `example/.json` (Phenopacket v2.0) - -**Steps:** -1. Validate schema with Pydantic -2. Extract structured sections: demographics, phenotypic features (HPO), diagnoses (MONDO), biosamples (UBERON), treatments (CHEBI), procedures (NCIT), genomic interpretations, family history -3. Build medical narrative sentences per section -4. Feed narrative to **Phi-4** (4-bit quantized) with a structured extraction prompt - -**Output:** `keywords.json` -```json -{ - "main_conditions": ["coronary artery disease"], - "other_conditions": ["type 2 diabetes mellitus", "hypercholesterolemia"], - "expanded_sentences": ["58-year-old male with advanced CAD..."] -} -``` - ---- - -### 3.3 Stage 1 — Hybrid Retrieval - -**Component:** `ClinicalTrialSearch` -**Index:** `clinical_trials` -**Goal:** Cast a wide net; recall over precision. - -**Query construction:** -``` -hybrid_score = α × normalized_text_score + β × normalized_vector_score - (α = 0.5, β = 0.5 by default) -``` - -- **BM25 side:** multi-match across `condition`, `eligibility_criteria`, `brief_title`, `brief_summary` with field-specific boosts -- **Vector side:** cosine similarity against 4 embedded fields using script-score queries -- **Synonym expansion:** the schema entity annotator links disease mentions to the LanceDB concept table and expands accepted concepts with synonyms -- **Filters:** age range, gender, `overall_status = Recruiting` - -**Output:** Up to 300 trial IDs with relevance scores → `nct_ids.txt`, `first_level_scores.json` - ---- - -### 3.4 Stage 2 — Criteria Matching + LLM Reranking +- Match de-identified research patient profiles to relevant clinical trials. +- Keep deployment local-first and service-light for a single GPU server. +- Provide criterion-level explainability. +- Use ontology-grounded entity extraction and normalization. -**Component:** `SecondStageRetriever` -**Index:** `trials_eligibility` -**Goal:** Narrow to trials with genuinely matching eligibility criteria. - -**Steps:** -1. For each query term (up to 150), search `trials_eligibility` within the Stage 1 trial subset -2. Retrieve up to 250 matching criteria per query (hybrid search on criterion text + entity synonyms) -3. **Gemma-2-2B reranker** scores each (query, criterion) pair by probability of "Yes" token -4. Apply type weighting: inclusion criteria score × 1.0, exclusion criteria score × 0.25 -5. Aggregate per-trial score: - ``` - trial_score = 0.7 × (Σ scores / √count) + 0.3 × max_score - ``` -6. Filter trials below threshold (default 0.5); select top ~33 - -**Output:** Ranked list of ~33 trial IDs → `top_trials.txt` - ---- - -### 3.5 Stage 3 — Chain-of-Thought Reasoning - -**Component:** `BatchTrialProcessor` (HuggingFace) or `BatchTrialProcessorVLLM` (vLLM) -**Model:** Phi-4 + fine-tuned LoRA adapter (`models/finetuned_phi_reasoning`) -**Goal:** Criterion-level eligibility assessment with explicit reasoning. - -**Prompt structure:** -``` -System: You are a medical expert with advanced knowledge in clinical reasoning... -User: Patient Profile: - Trial Criteria: - For each criterion, assess: Met / Not Met / Not Violated / Violated / Unclear - Output JSON with criterion-level evaluation and Final Decision. -``` - -**Optimizations:** -- Length bucketing to minimise padding waste across batches -- Temperature 0.0 for deterministic outputs -- Idempotent: skips already-processed trials on resume -- vLLM backend for high-throughput GPU inference - -**Output per trial:** `.txt` (raw), `.json` (parsed) -```json -{ - "Inclusion_Criteria_Evaluation": [ - {"criterion": "Age ≥ 18", "status": "Met", "reasoning": "Patient is 58."} - ], - "Exclusion_Criteria_Evaluation": [...], - "Final Decision": "Eligible" -} -``` - ---- - -### 3.6 Ranker - -**Component:** `trial_ranker.py` -**Goal:** Produce a single ranked list from CoT outputs. - -**Scoring formula per trial:** -``` -inclusion_ratio = (met + not_violated) / (met + not_violated + not_met + violated) -exclusion_ratio = (not_violated + met) / (not_violated + met + violated) -final_score = (inclusion_ratio + exclusion_ratio) / 2 -``` - -Irrelevant and unclear criteria are excluded from both numerator and denominator. - -**Output:** `ranked_trials.json` — sorted list of `{nct_id, score}` - ---- - -## 4. Models - -| Model | Role | Quantization | Backend | -|---|---|---|---| -| BAAI/bge-m3 | Text embeddings (1024d) | FP16 | HuggingFace | -| microsoft/phi-4 | Keyword extraction + CoT reasoning | 4-bit NF4 | HF / vLLM | -| finetuned_phi_reasoning | LoRA adapter for eligibility reasoning | — | PEFT / vLLM LoRA | -| google/gemma-2-2b-it | Pairwise criterion reranker | 4-bit NF4 | HuggingFace | - ---- - -## 5. External Services - -### Elasticsearch (3-node cluster) -- **Deployment:** Docker Compose (or Apptainer on HPC) -- **Version:** 8.13.4 -- **Security:** HTTPS + X-Pack, TLS certificates -- **Memory:** 2 GB per node (6 GB total) -- **Ports:** 9200 (API), 5601 (Kibana) - -### Schema Entity Annotator + LanceDB Concept Linker -- **Purpose:** Biomedical entity recognition and normalization without external Java daemons -- **Recognizer:** GLiNER2-style schema-driven extraction, with GLiNER/biomedical fallback support behind the same interface -- **Concept store:** LanceDB table built from OMOP vocabularies and legacy dictionaries -- **Entities:** diseases, genes, medications, procedures, labs, radiology, signs/symptoms, cell types, and species -- **Usage:** synonym expansion (Stage 1) and entity annotation (indexing) +### Non-Goals ---- +- Real-time clinical web application deployment. +- Direct EHR integration. +- Use with identifiable patient data without additional controls. +- Trial outcome prediction. -## 6. Data Flow Summary +## 2. Architecture ``` -[One-time setup] ClinicalTrials.gov JSONs - → schema entity annotation + LanceDB concept linking - → BGE-M3 embedding - → Elasticsearch (clinical_trials + trials_eligibility indices) - -[Per-patient inference] -phenopacket.json - → PhenopacketProcessor → Phi-4 → keywords.json - → ClinicalTrialSearch (BM25 + vector, ES) → ~300 trial IDs - → SecondStageRetriever (criteria search + Gemma rerank) → ~33 trial IDs - → BatchTrialProcessor (Phi-4 CoT reasoning) → per-trial JSON assessments - → Ranker → ranked_trials.json -``` - ---- - -## 7. Configuration - -All settings are driven by `source/Matcher/config/config.json` and can be overridden via environment variables: - -| Env Var | Default | Description | -|---|---|---| -| `TRIALMATCHAI_ES_HOST` | `https://localhost:9200` | Elasticsearch URL | -| `TRIALMATCHAI_ES_USERNAME` | `elastic` | ES user | -| `TRIALMATCHAI_ES_PASSWORD` | *(required)* | ES password | -| `TRIALMATCHAI_EMBEDDER_MODEL_NAME` | `BAAI/bge-m3` | Embedding model | -| `TRIALMATCHAI_ES_AUTO_START` | `true` | Auto-launch ES if not running | -| `TRIALMATCHAI_LOG_LEVEL` | `INFO` | Logging verbosity | - ---- - -## 8. Output Structure - + | + v +prepare_trials.py / prepare_criteria.py + | + v +Schema entity annotation + LanceDB concept linking + | + v +BGE-M3 embeddings + | + v +data/search LanceDB tables + | + v +Phenopacket -> keywords -> Stage 1 retrieval -> Stage 2 criteria rerank + | + v +Stage 3 eligibility reasoning -> ranked_trials.json ``` -results/ -└── / - ├── keywords.json # Extracted conditions and expanded narrative - ├── nct_ids.txt # Stage 1 candidate trial IDs - ├── first_level_scores.json # Stage 1 relevance scores - ├── top_trials.txt # Stage 2 shortlisted trial IDs - ├── .txt # Raw CoT reasoning text - ├── .json # Parsed criterion-level evaluation - └── ranked_trials.json # Final ranked list with scores -``` - ---- - -## 9. Infrastructure Requirements - -| Resource | Minimum | Recommended | -|---|---|---| -| GPU VRAM | 24 GB | 40 GB+ (for Phi-4 unquantized) | -| RAM | 32 GB | 64 GB | -| Disk | 100 GB | 200 GB+ | -| OS | Linux / macOS | Linux (CUDA support) | -| Python | 3.10–3.11 | 3.11 | -| Elasticsearch | 3 × 2 GB nodes | 3 × 6 GB nodes | - ---- - -## 10. Key Design Decisions - -**Two-stage retrieval before reasoning** — LLM reasoning is expensive (5000 token outputs per trial). Stages 1 and 2 reduce the candidate set from hundreds of thousands of trials to ~33 before the LLM is invoked. - -**Ontology-grounded inputs** — Using HPO, MONDO, CHEBI codes rather than free text ensures consistent entity matching across patient records and trial criteria regardless of phrasing variation. - -**Criterion-level scoring** — The final ranking score is computed from individual criterion assessments rather than a holistic match score, enabling explainable recommendations. - -**Resumable processing** — Both indexing and CoT reasoning are idempotent; partial runs can be continued without reprocessing completed items. - -**Pluggable LLM backend** — The reasoning stage supports both HuggingFace (single GPU, lower overhead) and vLLM (multi-GPU, higher throughput) backends, selectable via config. - ---- - ---- - -## HLD — LLM & Agent Harness View - -A simpler view of the system focused on how LLMs are used and how the pipeline harness orchestrates them. - ---- - -## 1. The Three LLM Agents -TrialMatchAI uses three LLMs, each with a distinct, narrow role. They do not share state or communicate with each other directly — the harness (`main.py`) wires their inputs and outputs together. +The search layer is embedded in the Python worker. There is no separate database service to start, secure, or monitor for the default v1 deployment. -``` -┌─────────────────────────────────────────────────────────────────┐ -│ Pipeline Harness │ -│ (main.py) │ -│ │ -│ Patient ┌──────────────┐ keywords.json │ -│ Phenopacket ──►│ LLM Agent 1 ├──────────────────────┐ │ -│ │ Phi-4 │ │ │ -│ │ Summariser │ ▼ │ -│ └──────────────┘ Elasticsearch │ -│ (BM25 + vector) │ -│ │ │ -│ ~300 candidates │ -│ │ │ -│ ▼ │ -│ ┌──────────────┐ │ -│ │ LLM Agent 2 │◄──────────────────────────┘ │ -│ │ Gemma-2-2B │ (query, criterion) pairs │ -│ │ Reranker ├──────────────────────┐ │ -│ └──────────────┘ │ │ -│ ~33 shortlisted │ -│ trial IDs │ -│ │ │ -│ ▼ │ -│ ┌──────────────┐ │ -│ │ LLM Agent 3 │◄──────────────────────────┘ │ -│ │ Phi-4 + │ (patient profile, │ -│ │ LoRA CoT │ trial criteria) │ -│ │ Reasoner ├──────────────────────┐ │ -│ └──────────────┘ │ │ -│ per-trial JSON │ -│ │ │ -│ ▼ │ -│ Rule-based Ranker │ -│ ranked_trials.json│ -└─────────────────────────────────────────────────────────────────┘ -``` +## 3. Persistent Stores ---- +### `data/search` Trial Table -## 2. Agent 1 — Summariser (Phi-4) +One row per trial. -**Job:** Translate a structured Phenopacket into free-text medical keywords suitable for search. +| Field | Notes | +|---|---| +| `nct_id` | Primary trial identifier | +| `brief_title`, `brief_summary`, `condition`, `eligibility_criteria` | Source text fields | +| `*_vector` | Prepared BGE-M3 vectors from the existing preparation pipeline | +| `search_text` | Flattened text used for local full-text candidate generation | +| `search_vector` | Averaged vector used for vector candidate generation | +| `minimum_age`, `maximum_age`, `gender`, `overall_status` | Runtime filters | -**Trigger:** Once per patient at the start of the pipeline. +### `data/search` Criteria Table -**Input:** -``` -Medical narrative sentences built from the Phenopacket sections: - DEMOGRAPHICS: Sex: MALE; Age: 58Y... - PHENOTYPE: Myocardial infarction (Present, Severe, Recurrent)... - DIAGNOSIS: Coronary artery disease, Stage III... - TREATMENT: Atorvastatin 40mg oral daily... - INTERPRETATION: LDLR c.1444G>A variant... -``` +One row per eligibility criterion. -**Prompt pattern:** -``` -System: You are a specialized medical assistant for clinical trial matching. - Extract primary conditions, secondary conditions, and expanded - medical notes. Return strict JSON — no commentary. +| Field | Notes | +|---|---| +| `criteria_id` | Stable criterion identifier | +| `nct_id` | Parent trial identifier | +| `criterion` | Criterion text | +| `criterion_vector` | BGE-M3 criterion vector | +| `entities` | Schema annotations and concept-link candidates | +| `entity_text`, `entity_synonyms_text` | Flattened entity search fields | +| `search_text` | Combined criterion/entity text | -User: -``` - -**Output (JSON, max 2048 tokens):** -```json -{ - "main_conditions": ["coronary artery disease", "CAD", "ischemic heart disease", ...], - "other_conditions": ["type 2 diabetes", "hypercholesterolemia", "LDLR mutation", ...], - "expanded_sentences": ["58-year-old male with advanced multivessel CAD...", ...] -} -``` +### `data/concepts` Concept Table -**Why this agent exists:** Phenopackets use ontology codes (HP:0001627), not plain text. The LLM bridges structured clinical data into natural language terms that map to how trials are described in ClinicalTrials.gov. +The concept table is built from OMOP `CONCEPT.csv`, `CONCEPT_SYNONYM.csv`, and optional legacy dictionaries. It is used only for entity normalization and synonym expansion. ---- +## 4. Retrieval -## 3. Agent 2 — Reranker (Gemma-2-2B) +### Stage 1 - Trial Retrieval -**Job:** Score how relevant a specific eligibility criterion is to a patient query term. +`ClinicalTrialSearch` calls the configured search backend with: -**Trigger:** Called in bulk during Stage 2, once per (query term, criterion) pair. Runs in a thread pool with 4 parallel workers. +- Primary disease/condition terms. +- Synonyms from accepted concept links. +- Other patient conditions. +- Age, sex, status, and optional NCT filters. +- Query embeddings when running in `vector` or `hybrid` mode. -**Input (per pair):** -``` -System: You are a clinical assistant determining if patient data supports - trial criterion evaluation. +The LanceDB backend generates candidates with local full-text/vector search and applies Python-side weighted scoring: -User: Statement A: - Statement B: - Respond with 'Yes' or 'No'. ``` - -**Output:** Not free text — the model never generates a full response. The harness reads the **logit scores** for the "Yes" and "No" tokens directly from the last position of the prompt and applies softmax: - -```python -score = softmax([logit_Yes, logit_No])[0] # probability of "Yes" +hybrid_score = 0.5 * text_score + 0.5 * vector_score ``` -Inclusion criteria scores are kept as-is; exclusion criteria scores are multiplied by 0.25 (a trial that excludes the patient's condition is less bad than one that requires something the patient doesn't have). +Text scoring weights trial fields by clinical relevance: -**Why this agent exists:** ES keyword and vector search can retrieve a criterion that mentions the right disease but actually *excludes* patients with that disease. The LLM reranker catches this semantic mismatch cheaply — a single forward pass with no generation overhead. +- `condition`: 6.0 +- `eligibility_criteria`: 4.0 +- `brief_title`: 3.0 +- `brief_summary`: 2.0 +- `detailed_description`: 1.5 +- `official_title`: 1.0 ---- +Vector scoring combines condition, title, summary, eligibility, and other-condition similarities. -## 4. Agent 3 — CoT Reasoner (Phi-4 + LoRA) +### Stage 2 - Criteria Retrieval -**Job:** Assess each eligibility criterion one-by-one and produce an explainable eligibility decision. +`SecondStageRetriever` searches the criteria table within the Stage 1 NCT subset. It searches criterion text and, when the entity annotator is enabled, flattened entity synonyms. -**Trigger:** Once per shortlisted trial (~33 trials), batched by prompt length. +The retriever returns ES-like hit dictionaries internally (`_source`, `_score`) to keep reranking and aggregation code stable while the backend is LanceDB-native. -**Input:** +### Stage 3 - Eligibility Reasoning -``` -System: You are a medical expert with advanced knowledge in clinical reasoning. - Answer the following question. Before answering, create a concise - chain of thoughts reasoning to ensure a logical and accurate response. - -User: Assess the patient's eligibility for this clinical trial by evaluating - each criterion individually. - - For inclusion criteria classify as: Met | Not Met | Unclear | Irrelevant - For exclusion criteria classify as: Violated | Not Violated | Unclear | Irrelevant - - --- Trial Criteria --- - Inclusion: - 1. Age ≥ 18 years - 2. Documented CAD with ≥50% stenosis - 3. Stable angina or prior MI - Exclusion: - 1. Recent CABG within 6 months - 2. Active malignancy - --- Patient Description --- - -``` +The top trials are passed to `BatchTrialProcessor` or `BatchTrialProcessorVLLM`. The model evaluates inclusion and exclusion criteria and writes structured JSON outputs. The final ranker computes a normalized eligibility score from those outputs. -**Output (JSON, max 5000 tokens):** - -```json -{ - "Inclusion_Criteria_Evaluation": [ - { - "Criterion": "Age ≥ 18 years", - "Classification": "Met", - "Justification": "Patient is 58 years old." - }, - { - "Criterion": "Documented CAD with ≥50% stenosis", - "Classification": "Met", - "Justification": "Diagnosis confirms advanced CAD with multivessel involvement." - } - ], - "Exclusion_Criteria_Evaluation": [ - { - "Criterion": "Recent CABG within 6 months", - "Classification": "Violated", - "Justification": "Patient underwent CABG on 2022-11-15, which is within 6 months of enrollment." - } - ], - "Recap": "Patient meets most inclusion criteria but was disqualified by recent CABG.", - "Final Decision": "Ineligible" -} -``` +## 5. Entity Extraction -**Why the LoRA adapter:** The base Phi-4 model is general-purpose. The fine-tuned LoRA adapter (`models/finetuned_phi_reasoning`) was trained specifically on clinical trial eligibility assessment tasks, improving structured JSON output and medical reasoning precision. +The BioNER path is Python-native: ---- +- Schema-driven recognizer interface. +- GLiNER2-style backend as the target recognizer. +- GLiNER fallback and regex test backend behind the same interface. +- LanceDB concept linking with confidence bands. +- No runtime Java daemons, socket IPC, Sieve, or GNormPlus dependency. -## 5. The Harness (main.py) +Entity output preserves the fields downstream search expects: -The harness is a **fixed sequential pipeline** — not a dynamic agent loop. There is no tool-calling, no self-reflection, and no LLM deciding what to do next. The harness: - -1. Loads all three models once at startup (cold start is expensive) -2. Iterates over patients in `patients_dir/` -3. Calls agents in a fixed order, passing outputs as inputs to the next stage -4. Writes intermediate results to disk at each step (enabling resume on failure) -5. Handles per-patient exceptions without stopping the full batch - -``` -startup: - load Phi-4 (4-bit) + LoRA adapter - load Gemma-2-2B (4-bit) - load BGE-M3 embedder - load schema entity annotator + LanceDB concept linker - connect to Elasticsearch - -for each patient: - [Agent 1] phenopacket → keywords.json - [ES] BM25 + vector search → nct_ids.txt - [Agent 2] criteria reranking → top_trials.txt - [Agent 3] CoT reasoning → .json (×33 trials) - [Ranker] score aggregation → ranked_trials.json -``` +- `entity_group` +- `text` +- `start` +- `end` +- `score` +- `normalized_id` +- `synonyms` +- `concept_candidates` +- `linker_score` +- `linker_status` -**The ranker is not an LLM** — it is a deterministic formula over the structured JSON that Agent 3 produces. This is intentional: keeping the final scoring rule-based makes it auditable and consistent. +## 6. Deployment ---- +Default deployment requires only the Python worker process and local mounted directories: -## 6. Data Handed Between Agents +- `data/` +- `models/` +- `results/` -| From | To | Artifact | Content | -| --- | --- | --- | --- | -| Phenopacket (raw) | Agent 1 | medical narrative sentences | Plain-text sentences per clinical category | -| Agent 1 | ES Stage 1 | `keywords.json` | `main_conditions`, `other_conditions`, `expanded_sentences` | -| ES Stage 1 | Agent 2 | `nct_ids.txt` | ~300 trial IDs + scores | -| Agent 2 | Agent 3 | `top_trials.txt` | ~33 trial IDs ranked by criterion match | -| Agent 3 | Ranker | `.json` (×33) | Per-criterion classification + final decision | -| Ranker | User | `ranked_trials.json` | Scored, sorted list of trial IDs | +Docker is optional and packages only the worker. The application does not auto-start external services. ---- +## 7. Runtime Configuration -## 7. What This Is Not +Important settings: -- **Not a ReAct / tool-use agent** — no LLM decides what action to take next -- **Not a multi-agent conversation** — agents never see each other's outputs or converse -- **Not RAG in the classic sense** — retrieved documents (criteria) are fed as structured context to Agent 3, not as retrieved passages for general Q&A -- **Not streaming / real-time** — designed for offline batch processing of patient cohorts - ---- - ---- - -## HLD — Strategy Summary (AI Engineering Learners) - ---- - -### The Problem - -Clinical trials fail to recruit because matching patients to trials manually is slow, expensive, and inconsistent. A trial might have 50 eligibility criteria. A hospital might have thousands of patients. No human team can cross-reference all of that at scale. - -The goal: automate that matching with AI, and make the reasoning **explainable** — not just a black-box score. - ---- +| Env var | Default | Purpose | +|---|---|---| +| `TRIALMATCHAI_SEARCH_DB_PATH` | `data/search` | LanceDB search table directory | +| `TRIALMATCHAI_SEARCH_TRIALS_TABLE` | `trials` | Trial table | +| `TRIALMATCHAI_SEARCH_CRITERIA_TABLE` | `criteria` | Criteria table | +| `TRIALMATCHAI_SEARCH_MODE` | `hybrid` | `bm25`, `vector`, or `hybrid` | +| `TRIALMATCHAI_CONCEPT_DB_PATH` | `data/concepts` | Concept linker table directory | +| `TRIALMATCHAI_ENTITY_BACKEND` | `gliner2` | Entity recognition backend | +| `TRIALMATCHAI_COT_BACKEND` | `vllm` | Reasoning backend | -### The Core Strategy: Progressive Filtering +## 8. Operational Checks -The system never throws the most expensive AI at the full problem. Instead it uses a **funnel** — each stage is smarter but slower than the last, and only operates on what survived the previous stage. +Use: +```bash +trialmatchai-healthcheck +trialmatchai-healthcheck --require-tables +trialmatchai-index +trialmatchai-run ``` -Hundreds of thousands of trials - ↓ cheap keyword + semantic search - 300 candidates - ↓ fast LLM reranking - 33 shortlisted - ↓ deep LLM reasoning - Ranked results with explanations -``` - -This is the central engineering insight: **don't use a sledgehammer where a sieve will do.** - ---- - -### The Four AI Techniques Used - -#### 1. Embeddings + Vector Search - -Convert text into numbers (vectors) that capture semantic meaning. Two pieces of text that mean the same thing — even with different words — land close together in vector space. Used in Stage 1 to find trials that are *semantically* related to the patient's conditions, not just keyword-matched. - -> *Key model: BAAI/bge-m3 (1024-dimensional embeddings)* - -#### 2. RAG — Retrieval-Augmented Generation - -Rather than asking an LLM to memorise every clinical trial, retrieve the relevant trial's criteria at query time and inject them into the prompt as context. The LLM reasons over what it's given, not what it was trained on. - -> *"Give the model the patient chart and the trial rulebook, then ask it to decide."* - -#### 3. Fine-Tuning with LoRA - -The base Phi-4 model is a general-purpose LLM — it can write poetry as easily as it can read medical text. Fine-tuning with a LoRA adapter (a small, efficient set of weight adjustments) specialises it for one task: structured clinical eligibility reasoning. It learns **how** to think about the problem, not specific trial content. - -> *LoRA = teach the model the job, RAG = hand it the case file.* - -#### 4. LLM-as-Scorer (not generator) - -Gemma-2-2B is never asked to generate text. Instead it's used as a **scoring function** — the reranker reads the logit (raw probability) for just two tokens (Yes/No) to score relevance. One forward pass, no generation, extremely fast. This is an underused pattern in production AI systems. - ---- - -### The Data Strategy - -Patient data is structured using the **GA4GH Phenopacket standard** — ontology codes (HPO, MONDO, CHEBI) rather than free text. This solves the vocabulary mismatch problem: a trial saying "coronary artery disease" and a patient record saying "CAD" both resolve to `MONDO:0005066`, ensuring consistent matching regardless of phrasing. - -The schema entity annotator bridges the gap between ontology codes and natural language by extracting mentions, linking them to LanceDB concept candidates, and expanding accepted disease concepts with synonyms. - ---- - -### Key Engineering Decisions - -| Decision | Why | -| --- | --- | -| Three-stage funnel | LLM inference is expensive — only invoke it on a small, pre-filtered set | -| RAG over full fine-tuning for trial knowledge | Trials change constantly; RAG stays current without retraining | -| LoRA over full fine-tuning | Trains faster, uses less memory, can be swapped per use case | -| LLM-as-scorer for reranking | Orders of magnitude faster than generating full responses | -| Criterion-level output | Explainability — doctors need to know *why*, not just a score | -| Deterministic ranker at the end | Keeps the final decision auditable and consistent | - ---- - -### The Takeaway - -TrialMatchAI is a masterclass in **composing AI primitives** rather than reaching for one big model: - -- Use **search** (fast, cheap) to filter -- Use **a small LLM as a scorer** (fast, precise) to refine -- Use **RAG + fine-tuned LLM** (slow, thorough) only on the shortlist -- Use **rules** (deterministic, auditable) for the final decision -Each component does what it's best at. The system as a whole does what none of them could do alone. +Preflight checks validate local paths, optional model artifacts, entity dependencies, GPU/vLLM readiness, and LanceDB table availability before expensive model startup. diff --git a/elasticsearch/.env.example b/elasticsearch/.env.example deleted file mode 100644 index cb41313d..00000000 --- a/elasticsearch/.env.example +++ /dev/null @@ -1,12 +0,0 @@ -# Copy this file to elasticsearch/.env for local Elasticsearch. -# Do not commit elasticsearch/.env. - -ELASTIC_PASSWORD=change-me-strong-password -KIBANA_PASSWORD=change-me-strong-password -STACK_VERSION=8.13.4 -CLUSTER_NAME=trialmatchai-local -LICENSE=basic -ES_PORT=9200 -KIBANA_PORT=5601 -MEM_LIMIT=1073741824 -COMPOSE_PROJECT_NAME=trialmatchai diff --git a/elasticsearch/apptainer-run-es.sh b/elasticsearch/apptainer-run-es.sh deleted file mode 100644 index 6f22371f..00000000 --- a/elasticsearch/apptainer-run-es.sh +++ /dev/null @@ -1,132 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -#=== LOAD .env ===# -if [ ! -f .env ]; then - echo "[ERROR] .env file not found." - exit 1 -fi - -# shellcheck disable=SC1091 -source .env - -#=== CONFIGURATION FROM ENV ===# -STACK_VERSION="${STACK_VERSION:-8.13.4}" -CLUSTER_NAME="${CLUSTER_NAME:-apptainer-cluster}" -ES_IMAGE="docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION}" - -BASE_DIR="$(pwd)" -CERTS_DIR="$BASE_DIR/certs" -CONFIG_DIR="$BASE_DIR/config" -DATA_DIR="$BASE_DIR/data" -LOGS_DIR="$BASE_DIR/logs" -SIF_DIR="$BASE_DIR/sif" -TMP_CONFIG="$BASE_DIR/tmp-config" - -ES_PORT1="${ES_PORT:-9200}" -ES_PORT2=$((ES_PORT1 + 1)) -ES_PORT3=$((ES_PORT1 + 2)) - -ELASTIC_PASSWORD="${ELASTIC_PASSWORD:?ELASTIC_PASSWORD not set in .env}" - -if [ ! -f "$CERTS_DIR/ca/ca.crt" ] || [ ! -f "$CERTS_DIR/es01/es01.crt" ]; then - echo "[ERROR] Elasticsearch TLS certs are not present in $CERTS_DIR." - echo "[ERROR] Generate local certs with Docker Compose first or provide certs out of band." - exit 1 -fi - -#=== PREPARE FOLDERS ===# -mkdir -p "$CONFIG_DIR/es01" "$CONFIG_DIR/es02" "$CONFIG_DIR/es03" -mkdir -p "$DATA_DIR/es01" "$DATA_DIR/es02" "$DATA_DIR/es03" -mkdir -p "$LOGS_DIR" "$SIF_DIR" "$TMP_CONFIG" - -#=== BUILD SIF IMAGE IF NEEDED ===# -if [ ! -f "$SIF_DIR/es.sif" ]; then - echo "[INFO] Building Elasticsearch SIF..." - apptainer build "$SIF_DIR/es.sif" "docker://$ES_IMAGE" -fi - -#=== CLEAN CONFIG EXTRACTION DIR ===# -rm -rf "$TMP_CONFIG"/* - -#=== EXTRACT DEFAULT CONFIG FILES ===# -echo "[INFO] Extracting default Elasticsearch config files..." -apptainer exec --bind "$TMP_CONFIG:/mnt/tmp" "$SIF_DIR/es.sif" \ - bash -c 'cp -r /usr/share/elasticsearch/config/* /mnt/tmp/' - -#=== PREPARE NODE CONFIGS ===# -for NODE in es01 es02 es03; do - cp -r "$TMP_CONFIG"/* "$CONFIG_DIR/$NODE/" - cp "$CERTS_DIR/ca.crt" "$CONFIG_DIR/$NODE/" - cp "$CERTS_DIR/$NODE/$NODE.crt" "$CONFIG_DIR/$NODE/" - cp "$CERTS_DIR/$NODE/$NODE.key" "$CONFIG_DIR/$NODE/" - - cat > "$CONFIG_DIR/$NODE/elasticsearch.yml" < "$LOGS_DIR/$NODE.log" 2>&1 & -} - -launch_node es01 $ES_PORT1 9300 -sleep 10 -launch_node es02 $ES_PORT2 9301 -sleep 10 -launch_node es03 $ES_PORT3 9302 -sleep 10 - -#=== WAIT FOR ES01 TO BE READY ===# -echo "[INFO] Waiting for es01 to be ready..." -until curl -s --cacert "$CERTS_DIR/ca.crt" -u elastic:"$ELASTIC_PASSWORD" \ - https://localhost:$ES_PORT1/_cluster/health?pretty | grep -q '"status"'; do - echo -n "." - sleep 5 -done -echo -e "\n[INFO] Elasticsearch cluster is up." - -echo "[INFO] Access Elasticsearch at: https://localhost:$ES_PORT1" diff --git a/elasticsearch/docker-compose.yml b/elasticsearch/docker-compose.yml deleted file mode 100644 index a14c0a05..00000000 --- a/elasticsearch/docker-compose.yml +++ /dev/null @@ -1,232 +0,0 @@ -services: - setup: - image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION} - volumes: - - ./certs:/usr/share/elasticsearch/config/certs - user: "0" - command: > - bash -c ' - if [ x${ELASTIC_PASSWORD} == x ]; then - echo "Set the ELASTIC_PASSWORD environment variable in the .env file"; - exit 1; - elif [ x${KIBANA_PASSWORD} == x ]; then - echo "Set the KIBANA_PASSWORD environment variable in the .env file"; - exit 1; - fi; - if [ ! -f config/certs/ca.zip ]; then - echo "Creating CA"; - bin/elasticsearch-certutil ca --silent --pem -out config/certs/ca.zip; - unzip config/certs/ca.zip -d config/certs; - fi; - if [ ! -f config/certs/certs.zip ]; then - echo "Creating certs"; - echo -ne \ - "instances:\n"\ - " - name: es01\n"\ - " dns:\n"\ - " - es01\n"\ - " - localhost\n"\ - " ip:\n"\ - " - 127.0.0.1\n"\ - " - name: es02\n"\ - " dns:\n"\ - " - es02\n"\ - " - localhost\n"\ - " ip:\n"\ - " - 127.0.0.1\n"\ - " - name: es03\n"\ - " dns:\n"\ - " - es03\n"\ - " - localhost\n"\ - " ip:\n"\ - " - 127.0.0.1\n"\ - > config/certs/instances.yml; - bin/elasticsearch-certutil cert --silent --pem -out config/certs/certs.zip --in config/certs/instances.yml --ca-cert config/certs/ca/ca.crt --ca-key config/certs/ca/ca.key; - unzip config/certs/certs.zip -d config/certs; - fi; - echo "Setting file permissions" - chown -R root:root config/certs; - find . -type d -exec chmod 750 \{\} \;; - find . -type f -exec chmod 640 \{\} \;; - echo "Waiting for Elasticsearch availability"; - until curl -s --cacert config/certs/ca/ca.crt https://es01:9200 | grep -q "missing authentication credentials"; do sleep 30; done; - echo "Setting kibana_system password"; - until curl -s -X POST --cacert config/certs/ca/ca.crt -u "elastic:${ELASTIC_PASSWORD}" -H "Content-Type: application/json" https://es01:9200/_security/user/kibana_system/_password -d "{\"password\":\"${KIBANA_PASSWORD}\"}" | grep -q "^{}"; do sleep 10; done; - echo "All done!"; - ' - healthcheck: - test: ["CMD-SHELL", "[ -f config/certs/es01/es01.crt ]"] - interval: 1s - timeout: 5s - retries: 120 - - es01: - depends_on: - setup: - condition: service_healthy - image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION} - volumes: - - ./certs:/usr/share/elasticsearch/config/certs:ro - - esdata01:/usr/share/elasticsearch/data - ports: - - ${ES_PORT}:9200 - environment: - - node.name=es01 - - cluster.name=${CLUSTER_NAME} - - cluster.initial_master_nodes=es01,es02,es03 - - discovery.seed_hosts=es02,es03 - - ELASTIC_PASSWORD=${ELASTIC_PASSWORD} - - bootstrap.memory_lock=true - - xpack.security.enabled=true - - xpack.security.http.ssl.enabled=true - - xpack.security.http.ssl.key=certs/es01/es01.key - - xpack.security.http.ssl.certificate=certs/es01/es01.crt - - xpack.security.http.ssl.certificate_authorities=certs/ca/ca.crt - - xpack.security.transport.ssl.enabled=true - - xpack.security.transport.ssl.key=certs/es01/es01.key - - xpack.security.transport.ssl.certificate=certs/es01/es01.crt - - xpack.security.transport.ssl.certificate_authorities=certs/ca/ca.crt - - xpack.security.transport.ssl.verification_mode=certificate - - xpack.license.self_generated.type=${LICENSE} - # Add the thread pool settings for rate limits - - thread_pool.search.size=100 # Increase the number of search threads - - thread_pool.search.queue_size=1000 # Increase the size of the search queue - mem_limit: 2g - ulimits: - memlock: - soft: -1 - hard: -1 - healthcheck: - test: - [ - "CMD-SHELL", - "curl -s --cacert config/certs/ca/ca.crt https://localhost:9200 | grep -q 'missing authentication credentials'", - ] - interval: 10s - timeout: 10s - retries: 120 - - es02: - depends_on: - - es01 - image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION} - volumes: - - ./certs:/usr/share/elasticsearch/config/certs:ro - - esdata02:/usr/share/elasticsearch/data - environment: - - node.name=es02 - - cluster.name=${CLUSTER_NAME} - - cluster.initial_master_nodes=es01,es02,es03 - - discovery.seed_hosts=es01,es03 - - bootstrap.memory_lock=true - - xpack.security.enabled=true - - xpack.security.http.ssl.enabled=true - - xpack.security.http.ssl.key=certs/es02/es02.key - - xpack.security.http.ssl.certificate=certs/es02/es02.crt - - xpack.security.http.ssl.certificate_authorities=certs/ca/ca.crt - - xpack.security.transport.ssl.enabled=true - - xpack.security.transport.ssl.key=certs/es02/es02.key - - xpack.security.transport.ssl.certificate=certs/es02/es02.crt - - xpack.security.transport.ssl.certificate_authorities=certs/ca/ca.crt - - xpack.security.transport.ssl.verification_mode=certificate - - xpack.license.self_generated.type=${LICENSE} - # Add the thread pool settings for rate limits - - thread_pool.search.size=100 - - thread_pool.search.queue_size=1000 - mem_limit: 2g - ulimits: - memlock: - soft: -1 - hard: -1 - healthcheck: - test: - [ - "CMD-SHELL", - "curl -s --cacert config/certs/ca/ca.crt https://localhost:9200 | grep -q 'missing authentication credentials'", - ] - interval: 10s - timeout: 10s - retries: 120 - - es03: - depends_on: - - es02 - image: docker.elastic.co/elasticsearch/elasticsearch:${STACK_VERSION} - volumes: - - ./certs:/usr/share/elasticsearch/config/certs:ro - - esdata03:/usr/share/elasticsearch/data - environment: - - node.name=es03 - - cluster.name=${CLUSTER_NAME} - - cluster.initial_master_nodes=es01,es02,es03 - - discovery.seed_hosts=es01,es02 - - bootstrap.memory_lock=true - - xpack.security.enabled=true - - xpack.security.http.ssl.enabled=true - - xpack.security.http.ssl.key=certs/es03/es03.key - - xpack.security.http.ssl.certificate=certs/es03/es03.crt - - xpack.security.http.ssl.certificate_authorities=certs/ca/ca.crt - - xpack.security.transport.ssl.enabled=true - - xpack.security.transport.ssl.key=certs/es03/es03.key - - xpack.security.transport.ssl.certificate=certs/es03/es03.crt - - xpack.security.transport.ssl.certificate_authorities=certs/ca/ca.crt - - xpack.security.transport.ssl.verification_mode=certificate - - xpack.license.self_generated.type=${LICENSE} - # Add the thread pool settings for rate limits - - thread_pool.search.size=100 - - thread_pool.search.queue_size=1000 - mem_limit: 2g - ulimits: - memlock: - soft: -1 - hard: -1 - healthcheck: - test: - [ - "CMD-SHELL", - "curl -s --cacert config/certs/ca/ca.crt https://localhost:9200 | grep -q 'missing authentication credentials'", - ] - interval: 10s - timeout: 10s - retries: 120 - - kibana: - depends_on: - es01: - condition: service_healthy - es02: - condition: service_healthy - es03: - condition: service_healthy - image: docker.elastic.co/kibana/kibana:${STACK_VERSION} - volumes: - - ./certs:/usr/share/kibana/config/certs:ro - - kibanadata:/usr/share/kibana/data - ports: - - ${KIBANA_PORT}:5601 - environment: - - SERVERNAME=kibana - - ELASTICSEARCH_HOSTS=https://es01:9200 - - ELASTICSEARCH_USERNAME=kibana_system - - ELASTICSEARCH_PASSWORD=${KIBANA_PASSWORD} - - ELASTICSEARCH_SSL_CERTIFICATEAUTHORITIES=config/certs/ca/ca.crt - mem_limit: ${MEM_LIMIT} - healthcheck: - test: - [ - "CMD-SHELL", - "curl -s -I http://localhost:5601 | grep -q 'HTTP/1.1 302 Found'", - ] - interval: 10s - timeout: 10s - retries: 120 - -volumes: - esdata01: - driver: local - esdata02: - driver: local - esdata03: - driver: local - kibanadata: - driver: local diff --git a/pyproject.toml b/pyproject.toml index 8b082f21..c48615dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,7 +18,6 @@ dependencies = [ "tokenizers==0.22.1", "safetensors>=0.6.2,<0.7", "sentencepiece==0.2.1", - "elasticsearch==8.17.2", "faiss-cpu==1.9.0.post1", "peft==0.15.2", "einops==0.8.0", @@ -38,6 +37,8 @@ dependencies = [ "PyYAML==6.0.2", "python-dotenv==1.2.2", "rich==13.9.4", + "lancedb>=0.25.0,<0.26", + "pyarrow>=24.0.0,<25", # spaCy / medspaCy (for your medical text work) "spacy==3.7.5", "medspacy==1.3.1", @@ -70,8 +71,6 @@ llm = [ "langchain-openai==1.3.2", ] entity = [ - "lancedb>=0.25.0,<0.26", - "pyarrow>=24.0.0,<25", "gliner2>=1.3.1,<2", ] training = [ diff --git a/requirements.txt b/requirements.txt index f75c9925..ac39b6cc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,7 +5,6 @@ accelerate==1.8.1 tokenizers==0.22.1 safetensors>=0.6.2,<0.7 sentencepiece==0.2.1 -elasticsearch==8.17.2 faiss-cpu==1.9.0.post1 peft==0.15.2 einops==0.8.0 @@ -30,6 +29,8 @@ orjson==3.11.9 PyYAML==6.0.2 python-dotenv==1.2.2 rich==13.9.4 +lancedb>=0.25.0,<0.26 +pyarrow>=24.0.0,<25 # spaCy / medspaCy (for your medical text work) spacy==3.7.5 diff --git a/scripts/index_data.sh b/scripts/index_data.sh index 67285d5e..732bb433 100644 --- a/scripts/index_data.sh +++ b/scripts/index_data.sh @@ -9,24 +9,10 @@ error() { echo -e "[ERROR] $*" >&2; exit 1; } ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" -cd "$ROOT_DIR/utils/Indexer" -info "Starting index_criteria.py (trials_eligibility) ..." -nohup python index_criteria.py \ - --config config.json \ - --processed-folder ../../data/processed_criteria \ - --index-name trials_eligibility \ - --batch-size 100 \ - --max-workers 100 \ - > criteria.log 2>&1 & - -info "Starting index_trials.py (clinical_trials) ..." -nohup python index_trials.py \ - --config config.json \ - --processed-folder ../../data/processed_trials \ - --index-name clinical_trials \ - --batch-size 100 \ - > trials.log 2>&1 & - -info "Waiting for indexing jobs to complete..." -wait -info "✅ Indexing complete." +cd "$ROOT_DIR" +info "Building LanceDB search tables ..." +uv run trialmatchai-index \ + --processed-trials-folder data/processed_trials \ + --processed-criteria-folder data/processed_criteria \ + --recreate +info "Indexing complete." diff --git a/scripts/scan_secrets.py b/scripts/scan_secrets.py index 57d3a609..5cdb21ac 100644 --- a/scripts/scan_secrets.py +++ b/scripts/scan_secrets.py @@ -9,19 +9,13 @@ SECRET_PATTERNS = [ re.compile(r"-----BEGIN [A-Z ]*PRIVATE KEY-----"), - re.compile( - r"\bELASTIC_PASSWORD\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+" - ), - re.compile( - r"\bKIBANA_PASSWORD\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+" - ), re.compile(r"\bpassword['\"]?\s*:\s*['\"](?!CHANGE_ME|change-me)[^'\"]{12,}['\"]"), + re.compile(r"\b[A-Z0-9_]*TOKEN\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+"), + re.compile(r"\b[A-Z0-9_]*API_KEY\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+"), ] SECRET_PATH_PATTERNS = [ - re.compile(r"^elasticsearch/(certs|config|tmp-config)/"), re.compile(r"(^|/).*\.key$"), - re.compile(r"(^|/)elasticsearch\.keystore$"), ] SKIP_SUFFIXES = {".png", ".jpg", ".jpeg", ".gif", ".zip", ".gz", ".sif", ".so", ".a"} diff --git a/scripts/start_es.sh b/scripts/start_es.sh deleted file mode 100644 index 01679630..00000000 --- a/scripts/start_es.sh +++ /dev/null @@ -1,30 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail -IFS=$'\n\t' - -GREEN='\033[0;32m' -NC='\033[0m' -info() { echo -e "${GREEN}[INFO]${NC} $*"; } -error() { echo -e "[ERROR] $*" >&2; exit 1; } - -ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" -cd "$ROOT_DIR/elasticsearch" - -if command -v docker &> /dev/null && docker info &> /dev/null; then - info "Docker is available. Starting Elasticsearch with Docker Compose..." - if docker compose version &> /dev/null; then - docker compose up -d - elif command -v docker-compose &> /dev/null; then - docker-compose up -d - else - error "Docker is available, but Docker Compose is not installed." - fi -elif command -v apptainer &> /dev/null; then - info "Docker not found or not running. Falling back to Apptainer..." - if [ ! -f "./apptainer-run-es.sh" ]; then - error "Apptainer script not found at ./elasticsearch/apptainer-run-es.sh" - fi - bash ./apptainer-run-es.sh -else - error "Neither Docker nor Apptainer is available. Cannot continue." -fi diff --git a/setup.sh b/setup.sh index 7183c7d3..1d17395a 100644 --- a/setup.sh +++ b/setup.sh @@ -47,11 +47,7 @@ fi info "Bootstrapping data and models..." "${RUNNER[@]}" trialmatchai-bootstrap-data -# 3) Launch Elasticsearch -info "Starting Elasticsearch..." -bash scripts/start_es.sh - -# 4) Launch indexers in background +# 3) Build local LanceDB search tables info "Indexing trial data..." "${RUNNER[@]}" trialmatchai-index diff --git a/source/Matcher/cli/healthcheck.py b/source/Matcher/cli/healthcheck.py index 217a8fe2..46edd2fb 100644 --- a/source/Matcher/cli/healthcheck.py +++ b/source/Matcher/cli/healthcheck.py @@ -4,10 +4,7 @@ import sys from Matcher.config.config_loader import load_config -from Matcher.services.elasticsearch_service import ( - build_elasticsearch_client, - ensure_elasticsearch, -) +from Matcher.search import LanceDBSearchBackend from Matcher.services.preflight import run_preflight_checks from Matcher.utils.logging_config import setup_logging @@ -22,19 +19,15 @@ def main() -> int: help="Path to config.json", ) parser.add_argument( - "--start-es", + "--require-tables", action="store_true", - help="Attempt to start Elasticsearch if unreachable", - ) - parser.add_argument( - "--require-indices", - action="store_true", - help="Fail if configured Elasticsearch indices are missing", + help="Fail if configured LanceDB search tables are missing", ) args = parser.parse_args() config = load_config(args.config) issues = 0 + search_backend = LanceDBSearchBackend.from_config(config) preflight_issues = run_preflight_checks( config, @@ -44,26 +37,13 @@ def main() -> int: ) issues += len(preflight_issues) - es_cfg = config["elasticsearch"] - es_client = build_elasticsearch_client(config) - - if args.start_es and es_cfg.get("auto_start") is False: - es_cfg["auto_start"] = True - - if not ensure_elasticsearch(es_client, config): - logger.error("Elasticsearch healthcheck failed.") - issues += 1 + backend_issues = search_backend.health(require_tables=args.require_tables) + if backend_issues: + for issue in backend_issues: + logger.error("Search backend healthcheck failed: %s", issue) + issues += len(backend_issues) else: - logger.info("Elasticsearch reachable.") - - if args.require_indices: - issues += len( - run_preflight_checks( - config, - es_client=es_client, - require_indices=True, - ) - ) + logger.info("LanceDB search backend reachable at %s.", search_backend.db_path) return 1 if issues else 0 if __name__ == "__main__": diff --git a/source/Matcher/cli/index_data.py b/source/Matcher/cli/index_data.py index 8922faf0..c9c34efe 100644 --- a/source/Matcher/cli/index_data.py +++ b/source/Matcher/cli/index_data.py @@ -1,25 +1,111 @@ from __future__ import annotations import argparse -import subprocess import sys from pathlib import Path +from Matcher.config.config_loader import load_config +from Matcher.search import LanceDBSearchBackend +from Matcher.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + def main() -> int: - parser = argparse.ArgumentParser(description="Index TrialMatchAI data into Elasticsearch") + parser = argparse.ArgumentParser( + description="Build TrialMatchAI LanceDB search tables from prepared JSON data." + ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument( + "--processed-trials-folder", + default="data/processed_trials", + help="Folder containing prepared trial JSON files.", + ) + parser.add_argument( + "--processed-criteria-folder", + default="data/processed_criteria", + help="Folder containing prepared criteria subfolders.", + ) + parser.add_argument( + "--skip-trials", + action="store_true", + help="Do not build the trial table.", + ) parser.add_argument( - "--script", - default="scripts/index_data.sh", - help="Indexing script path relative to the repository root", + "--skip-criteria", + action="store_true", + help="Do not build the criteria table.", + ) + parser.add_argument( + "--recreate", + action=argparse.BooleanOptionalAction, + default=True, + help="Overwrite target tables before writing.", ) args = parser.parse_args() + config = load_config(args.config) + backend = LanceDBSearchBackend.from_config(config) root = _repo_root() - script = (root / args.script).resolve() - if not script.exists(): - raise FileNotFoundError(f"Indexing script not found: {script}") - return subprocess.run(["bash", str(script)], cwd=str(root), check=False).returncode + failures = 0 + + if not args.skip_trials: + trials_folder = _resolve_path(args.processed_trials_folder, root) + trial_docs = _load_flat_json_folder(trials_folder) + if not trial_docs: + logger.error("No prepared trial JSON files found in %s.", trials_folder) + failures += 1 + else: + count = backend.index_trials(trial_docs, recreate=args.recreate) + logger.info("Indexed %s trial documents.", count) + + if not args.skip_criteria: + criteria_folder = _resolve_path(args.processed_criteria_folder, root) + criteria_docs = _load_nested_json_folder(criteria_folder) + if not criteria_docs: + logger.error("No prepared criteria JSON files found in %s.", criteria_folder) + failures += 1 + else: + count = backend.index_criteria(criteria_docs, recreate=args.recreate) + logger.info("Indexed %s criteria documents.", count) + + if failures: + return 1 + logger.info("Search tables ready at %s.", backend.db_path) + return 0 + + +def _load_flat_json_folder(folder: Path) -> list[dict]: + if not folder.exists(): + return [] + return [ + _read_json(path) + for path in sorted(folder.glob("*.json")) + if path.is_file() + ] + + +def _load_nested_json_folder(folder: Path) -> list[dict]: + if not folder.exists(): + return [] + return [ + _read_json(path) + for path in sorted(folder.glob("*/*.json")) + if path.is_file() + ] + + +def _read_json(path: Path) -> dict: + import json + + return json.loads(path.read_text(encoding="utf-8")) + + +def _resolve_path(value: str, root: Path) -> Path: + path = Path(value).expanduser() + if path.is_absolute(): + return path + return (root / path).resolve() def _repo_root() -> Path: diff --git a/source/Matcher/config/config.json b/source/Matcher/config/config.json index cba614eb..d92fb5ea 100644 --- a/source/Matcher/config/config.json +++ b/source/Matcher/config/config.json @@ -21,8 +21,7 @@ "paths": { "patients_dir": "example", "output_dir": "results", - "trials_json_folder": "data/trials_jsons", - "docker_certs": "elasticsearch/certs/ca/ca.crt" + "trials_json_folder": "data/trials_jsons" }, "model": { "base_model": "microsoft/phi-4", @@ -46,17 +45,12 @@ "global": { "device": 0 }, - "elasticsearch": { - "host": "https://localhost:9200", - "username": "elastic", - "password": "CHANGE_ME", - "request_timeout": 600, - "retry_on_timeout": true, - "index_trials": "clinical_trials", - "index_trials_eligibility": "trials_eligibility", - "auto_start": false, - "start_script": "elasticsearch/apptainer-run-es.sh", - "start_timeout": 600 + "search_backend": { + "backend": "lancedb", + "db_path": "data/search", + "trials_table": "trials", + "criteria_table": "criteria", + "candidate_limit": 1000 }, "embedder": { "model_name": "BAAI/bge-m3", @@ -76,6 +70,7 @@ "batch_size": 20 }, "search": { + "mode": "hybrid", "vector_score_threshold": 0.5, "max_trials_first_level": 300, "max_trials_second_level": 100 diff --git a/source/Matcher/config/config_loader.py b/source/Matcher/config/config_loader.py index 9d1d7639..92a8605e 100644 --- a/source/Matcher/config/config_loader.py +++ b/source/Matcher/config/config_loader.py @@ -35,10 +35,6 @@ def load_config(config_path: str | os.PathLike[str] | None = None) -> Dict[str, settings = TrialMatchSettings.model_validate(raw) cfg = settings.to_dict() cfg = normalize_config_paths(cfg, resolved_config) - if cfg.get("elasticsearch", {}).get("password") in {"", "CHANGE_ME"}: - logger.warning( - "Elasticsearch password is not set. Use TRIALMATCHAI_ES_PASSWORD to supply it." - ) return cfg @@ -82,7 +78,7 @@ def resolve_config_path( def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, Any]: """Normalize known local paths while leaving remote model IDs untouched.""" root = _repo_root(config_path) - for key in ("patients_dir", "output_dir", "trials_json_folder", "docker_certs"): + for key in ("patients_dir", "output_dir", "trials_json_folder"): value = cfg.get("paths", {}).get(key) if value: cfg["paths"][key] = str(_resolve_local_path(value, root)) @@ -99,9 +95,11 @@ def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, _resolve_local_path(concept_db_path, root) ) - start_script = cfg.get("elasticsearch", {}).get("start_script") - if start_script: - cfg["elasticsearch"]["start_script"] = str(_resolve_local_path(start_script, root)) + search_db_path = cfg.get("search_backend", {}).get("db_path") + if search_db_path: + cfg["search_backend"]["db_path"] = str( + _resolve_local_path(search_db_path, root) + ) for key in ("cot_adapter_path", "reranker_adapter_path"): value = cfg.get("model", {}).get(key) diff --git a/source/Matcher/config/settings.py b/source/Matcher/config/settings.py index dd59e7ac..40a98014 100644 --- a/source/Matcher/config/settings.py +++ b/source/Matcher/config/settings.py @@ -38,7 +38,6 @@ class PathsSettings(BaseModel): patients_dir: str output_dir: str trials_json_folder: str - docker_certs: str class ModelQuantizationSettings(BaseModel): @@ -68,17 +67,12 @@ class GlobalSettings(BaseModel): device: int | str -class ElasticsearchSettings(BaseModel): - host: str - username: str - password: str - request_timeout: int = 600 - retry_on_timeout: bool = True - index_trials: str - index_trials_eligibility: str - auto_start: bool = False - start_script: str = "elasticsearch/apptainer-run-es.sh" - start_timeout: int = Field(600, ge=1) +class SearchBackendSettings(BaseModel): + backend: Literal["lancedb"] = "lancedb" + db_path: str = "data/search" + trials_table: str = "trials" + criteria_table: str = "criteria" + candidate_limit: int = Field(1000, ge=1) class EmbedderSettings(BaseModel): @@ -101,6 +95,7 @@ def validate_pooling(cls, value: str) -> str: class SearchSettings(BaseModel): + mode: Literal["bm25", "vector", "hybrid"] = "hybrid" vector_score_threshold: float = Field(0.5, ge=0.0, le=1.0) max_trials_first_level: int = Field(300, ge=1) max_trials_second_level: int = Field(100, ge=1) @@ -140,7 +135,7 @@ class TrialMatchSettings(BaseModel): model: ModelSettings tokenizer: TokenizerSettings global_: GlobalSettings = Field(alias="global") - elasticsearch: ElasticsearchSettings + search_backend: SearchBackendSettings = Field(default_factory=SearchBackendSettings) embedder: EmbedderSettings cot: CotSettings LLM_reranker: LLMRerankerSettings @@ -166,18 +161,14 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: import os string_env_map: dict[str, Tuple[str, ...]] = { - "TRIALMATCHAI_ES_HOST": ("elasticsearch", "host"), - "TRIALMATCHAI_ES_USERNAME": ("elasticsearch", "username"), - "TRIALMATCHAI_ES_PASSWORD": ("elasticsearch", "password"), - "TRIALMATCHAI_ES_CA_CERTS": ("paths", "docker_certs"), "TRIALMATCHAI_PATIENTS_DIR": ("paths", "patients_dir"), "TRIALMATCHAI_OUTPUT_DIR": ("paths", "output_dir"), "TRIALMATCHAI_TRIALS_JSON_FOLDER": ("paths", "trials_json_folder"), - "TRIALMATCHAI_INDEX_TRIALS": ("elasticsearch", "index_trials"), - "TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY": ( - "elasticsearch", - "index_trials_eligibility", - ), + "TRIALMATCHAI_SEARCH_BACKEND": ("search_backend", "backend"), + "TRIALMATCHAI_SEARCH_DB_PATH": ("search_backend", "db_path"), + "TRIALMATCHAI_SEARCH_TRIALS_TABLE": ("search_backend", "trials_table"), + "TRIALMATCHAI_SEARCH_CRITERIA_TABLE": ("search_backend", "criteria_table"), + "TRIALMATCHAI_SEARCH_MODE": ("search", "mode"), "TRIALMATCHAI_EMBEDDER_MODEL_NAME": ("embedder", "model_name"), "TRIALMATCHAI_EMBEDDER_REVISION": ("embedder", "revision"), "TRIALMATCHAI_MODEL_BASE_MODEL": ("model", "base_model"), @@ -199,7 +190,6 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "reranker_adapter_path", ), "TRIALMATCHAI_COT_BACKEND": ("cot_backend",), - "TRIALMATCHAI_ES_START_SCRIPT": ("elasticsearch", "start_script"), "TRIALMATCHAI_ENTITY_BACKEND": ("entity_extraction", "backend"), "TRIALMATCHAI_ENTITY_MODEL_NAME": ("entity_extraction", "model_name"), "TRIALMATCHAI_ENTITY_FALLBACK_MODEL_NAME": ( @@ -221,8 +211,6 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: _set_nested(raw, path, value) bool_env_map: dict[str, Tuple[str, ...]] = { - "TRIALMATCHAI_ES_AUTO_START": ("elasticsearch", "auto_start"), - "TRIALMATCHAI_ES_RETRY_ON_TIMEOUT": ("elasticsearch", "retry_on_timeout"), "TRIALMATCHAI_EMBEDDER_USE_GPU": ("embedder", "use_gpu"), "TRIALMATCHAI_EMBEDDER_USE_FP16": ("embedder", "use_fp16"), "TRIALMATCHAI_EMBEDDER_TRUST_REMOTE_CODE": ( @@ -242,8 +230,10 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: _set_nested(raw, path, _parse_bool(value)) int_env_map: dict[str, Tuple[str, ...]] = { - "TRIALMATCHAI_ES_REQUEST_TIMEOUT": ("elasticsearch", "request_timeout"), - "TRIALMATCHAI_ES_START_TIMEOUT": ("elasticsearch", "start_timeout"), + "TRIALMATCHAI_SEARCH_CANDIDATE_LIMIT": ( + "search_backend", + "candidate_limit", + ), "TRIALMATCHAI_EMBEDDER_BATCH_SIZE": ("embedder", "batch_size"), "TRIALMATCHAI_ENTITY_BATCH_SIZE": ("entity_extraction", "batch_size"), "TRIALMATCHAI_CONCEPT_SEARCH_LIMIT": ("concept_linker", "search_limit"), diff --git a/source/Matcher/main.py b/source/Matcher/main.py index acf278f6..92b055ab 100644 --- a/source/Matcher/main.py +++ b/source/Matcher/main.py @@ -4,7 +4,6 @@ from typing import Dict, List, Optional, Tuple import torch -from elasticsearch import Elasticsearch from Matcher.config.config_loader import load_config from Matcher.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig @@ -22,10 +21,7 @@ ) from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch from Matcher.pipeline.trial_search.second_level_search import SecondStageRetriever -from Matcher.services.elasticsearch_service import ( - build_elasticsearch_client, - ensure_elasticsearch, -) +from Matcher.search import LanceDBSearchBackend from Matcher.services.preflight import run_preflight_checks from Matcher.utils.file_utils import ( create_directory, @@ -48,7 +44,7 @@ def run_first_level_search( entity_annotator, embedder: TextEmbedder, config: Dict, - es_client: Elasticsearch, + search_backend, ) -> Optional[Tuple]: main_conditions = keywords.get("main_conditions", []) other_conditions = keywords.get("other_conditions", []) @@ -63,11 +59,9 @@ def run_first_level_search( sex = patient_info.get("gender", "all") overall_status = "All" - index_name = config["elasticsearch"]["index_trials"] cts = ClinicalTrialSearch( - es_client, - embedder, - index_name, + search_backend=search_backend, + embedder=embedder, entity_annotator=entity_annotator, ) @@ -227,25 +221,21 @@ def main_pipeline(config_path: str | None = None) -> int: paths = config["paths"] create_directory(paths["output_dir"]) - es_client = build_elasticsearch_client(config) + search_backend = LanceDBSearchBackend.from_config(config) preflight_issues = run_preflight_checks( config, - es_client=es_client, require_patient_inputs=True, require_trials_json=True, require_models=True, - require_indices=False, + require_search_tables=False, ) if preflight_issues: return 1 - if not ensure_elasticsearch(es_client, config): - return 1 - index_issues = run_preflight_checks( config, - es_client=es_client, - require_indices=True, + search_backend=search_backend, + require_search_tables=True, ) if index_issues: return 1 @@ -304,11 +294,11 @@ def main_pipeline(config_path: str | None = None) -> int: ) gemma_retriever = SecondStageRetriever( - es_client=es_client, + search_backend=search_backend, llm_reranker=llm_reranker, embedder=embedder, - index_name=config["elasticsearch"]["index_trials_eligibility"], entity_annotator=entity_annotator, + search_mode=config["search"].get("mode", "hybrid"), ) # Process phenopackets @@ -357,7 +347,7 @@ def main_pipeline(config_path: str | None = None) -> int: entity_annotator, embedder, config, - es_client, + search_backend, ) if not result: logger.error("First-level search failed for %s", patient_id) diff --git a/source/Matcher/pipeline/trial_search/first_level_search.py b/source/Matcher/pipeline/trial_search/first_level_search.py index 324e2ac7..33e9be05 100644 --- a/source/Matcher/pipeline/trial_search/first_level_search.py +++ b/source/Matcher/pipeline/trial_search/first_level_search.py @@ -3,10 +3,8 @@ from dateutil import parser as date_parser from Matcher.models.embedding.text_embedder import TextEmbedder +from Matcher.search.lancedb_backend import TrialSearchBackend from Matcher.utils.logging_config import setup_logging -from Matcher.utils.retry import with_retries - -from elasticsearch import Elasticsearch logger = setup_logging(__name__) @@ -14,15 +12,13 @@ class ClinicalTrialSearch: def __init__( self, - es_client: Elasticsearch, + search_backend: TrialSearchBackend, embedder: Optional[TextEmbedder], - index_name: str, bio_med_ner=None, entity_annotator=None, ): - self.es_client = es_client + self.search_backend = search_backend self.embedder = embedder - self.index_name = index_name self.entity_annotator = entity_annotator or bio_med_ner def get_synonyms(self, condition: str) -> List[str]: @@ -74,47 +70,6 @@ def parse_age_input(self, age_input: Union[int, str]) -> Optional[int]: pass return None - def get_max_text_score(self, synonyms: List[str]) -> float: - should_clauses = [ - { - "multi_match": { - "query": syn, - "fields": [ - "condition^6", - "eligibility_criteria^4", - "brief_title^3", - "brief_summary^2", - "detailed_description^1.5", - "official_title", - ], - "type": "best_fields", - "operator": "and", - } - } - for syn in synonyms - ] - try: - response = with_retries( - lambda: self.es_client.search( - index=self.index_name, - body={ - "size": 1, - "query": { - "bool": {"should": should_clauses, "minimum_should_match": 0} - }, - "track_total_hits": False, - "_source": False, - }, - ), - logger=logger, - action="ES max_text_score search", - ) - max_score = response["hits"]["max_score"] - return max_score if max_score else 1.0 - except Exception: - logger.exception("Failed to compute max text score; defaulting to 1.0") - return 1.0 - def create_query( self, synonyms: List[str], @@ -128,90 +83,12 @@ def create_query( other_conditions: Optional[List[str]] = None, search_mode: str = "hybrid", ) -> Dict: - sex = sex.upper() - gender_terms = { - "MALE": ["MALE", "Male", "male", "M", "All", "all", "ALL"], - "FEMALE": ["FEMALE", "Female", "female", "F", "All", "all", "ALL"], - "ALL": [ - "All", - "all", - "ALL", - "Both", - "both", - "BOTH", - "FEMALE", - "Female", - "female", - "F", - "MALE", - "Male", - "male", - "M", - ], - }.get(sex, ["All"]) - filters = [] - if age not in [None, "all", "ALL", "All"]: - filters.append( - { - "bool": { - "must": [ - { - "bool": { - "should": [ - {"range": {"minimum_age": {"lte": age}}}, - { - "bool": { - "must_not": { - "exists": {"field": "minimum_age"} - } - } - }, - ] - } - }, - { - "bool": { - "should": [ - {"range": {"maximum_age": {"gte": age}}}, - { - "bool": { - "must_not": { - "exists": {"field": "maximum_age"} - } - } - }, - ] - } - }, - ] - } - } - ) - if overall_status and overall_status.lower() != "all": - filters.append({"match": {"overall_status": overall_status}}) - if pre_selected_nct_ids: - filters.append({"terms": {"nct_id": pre_selected_nct_ids}}) - if gender_terms: - filters.append( - { - "bool": { - "should": [ - {"terms": {"gender": gender_terms}}, - {"bool": {"must_not": {"exists": {"field": "gender"}}}}, - ] - } - } - ) - - # Cap conditions to prevent too many ES clauses (each condition creates 2 clauses) - # ES default maxClauseCount is 1024, leaving room for filters and other clauses max_conditions_per_query = 800 # Conservative limit all_conditions = synonyms + (other_conditions or []) if len(all_conditions) > max_conditions_per_query: logger.warning( - f"Capping search conditions from {len(all_conditions)} to {max_conditions_per_query} to avoid ES clause limit" + f"Capping search conditions from {len(all_conditions)} to {max_conditions_per_query}" ) - # Prioritize synonyms over other_conditions capped_synonyms = synonyms[:max_conditions_per_query] remaining_slots = max_conditions_per_query - len(capped_synonyms) capped_other = ( @@ -222,170 +99,17 @@ def create_query( synonyms = capped_synonyms other_conditions = capped_other - should_clauses = [] - for condition in synonyms + (other_conditions or []): - if condition: - for match_type in ["best_fields", "phrase"]: - multi_match = { - "query": condition, - "fields": [ - "condition^6", - "eligibility_criteria^4", - "brief_title^3", - "brief_summary^2", - "detailed_description^1.5", - "official_title", - ], - "type": match_type, - } - if match_type == "best_fields": - multi_match["operator"] = "and" - should_clauses.append({"multi_match": multi_match}) - - logger.info( - f"Created query with {len(should_clauses)} should clauses for {len(synonyms)} synonyms and {len(other_conditions or [])} other conditions" - ) - - search_mode = (search_mode or "hybrid").lower() - if search_mode == "bm25": - return { - "bool": { - "should": should_clauses, - "minimum_should_match": 0, - "filter": filters, - } - } - - # Prepare vectors for vector/hybrid - query_vectors = [ - embeddings[term] for term in synonyms if term in embeddings and term - ] - other_vectors = [ - embeddings[term] - for term in (other_conditions or []) - if term in embeddings and term - ] - - if search_mode == "vector": - return { - "script_score": { - "query": { - "bool": { - "filter": filters, - } - }, - "script": { - "source": """ - double maxConditionVectorScore = 0.0; - double maxTitleVectorScore = 0.0; - double maxSummaryVectorScore = 0.0; - double maxEligibilityVectorScore = 0.0; - double totalOtherConditionScore = 0.0; - for (int i = 0; i < params.query_vectors.length; ++i) { - maxConditionVectorScore = Math.max(maxConditionVectorScore, cosineSimilarity(params.query_vectors[i], 'condition_vector')); - maxTitleVectorScore = Math.max(maxTitleVectorScore, cosineSimilarity(params.query_vectors[i], 'brief_title_vector')); - maxSummaryVectorScore = Math.max(maxSummaryVectorScore, cosineSimilarity(params.query_vectors[i], 'brief_summary_vector')); - maxEligibilityVectorScore = Math.max(maxEligibilityVectorScore, cosineSimilarity(params.query_vectors[i], 'eligibility_criteria_vector')); - } - int otherConditionCount = params.other_condition_vectors.length; - for (int i = 0; i < otherConditionCount; ++i) { - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'condition_vector'); - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'brief_title_vector'); - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'eligibility_criteria_vector'); - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'brief_summary_vector'); - } - if (otherConditionCount > 0) { - totalOtherConditionScore /= (otherConditionCount * 4); - } - double normalizedConditionScore = (maxConditionVectorScore + 1.0) / 2.0; - double normalizedTitleScore = (maxTitleVectorScore + 1.0) / 2.0; - double normalizedSummaryScore = (maxSummaryVectorScore + 1.0) / 2.0; - double normalizedEligibilityScore = (maxEligibilityVectorScore + 1.0) / 2.0; - double normalizedOtherConditionScore = (totalOtherConditionScore + 1.0) / 2.0; - double combinedVectorScore = ( - 0.3 * normalizedConditionScore + - 0.1 * normalizedTitleScore + - 0.1 * normalizedSummaryScore + - 0.2 * normalizedOtherConditionScore + - 0.3 * normalizedEligibilityScore - ); - if (combinedVectorScore < params.vector_score_threshold) { - return 0; - } - return combinedVectorScore; - """, - "params": { - "query_vectors": query_vectors, - "other_condition_vectors": other_vectors, - "vector_score_threshold": vector_score_threshold, - }, - }, - } - } - - # Hybrid (default) return { - "script_score": { - "query": { - "bool": { - "should": should_clauses, - "minimum_should_match": 0, - "filter": filters, - } - }, - "script": { - "source": """ - double alpha = 0.5; - double beta = 0.5; - double textScore = _score; - double maxTextScore = params.max_text_score; - double normalizedTextScore = (maxTextScore == 0) ? 0 : textScore / maxTextScore; - double maxConditionVectorScore = 0.0; - double maxTitleVectorScore = 0.0; - double maxSummaryVectorScore = 0.0; - double maxEligibilityVectorScore = 0.0; - double totalOtherConditionScore = 0.0; - for (int i = 0; i < params.query_vectors.length; ++i) { - maxConditionVectorScore = Math.max(maxConditionVectorScore, cosineSimilarity(params.query_vectors[i], 'condition_vector')); - maxTitleVectorScore = Math.max(maxTitleVectorScore, cosineSimilarity(params.query_vectors[i], 'brief_title_vector')); - maxSummaryVectorScore = Math.max(maxSummaryVectorScore, cosineSimilarity(params.query_vectors[i], 'brief_summary_vector')); - maxEligibilityVectorScore = Math.max(maxEligibilityVectorScore, cosineSimilarity(params.query_vectors[i], 'eligibility_criteria_vector')); - } - int otherConditionCount = params.other_condition_vectors.length; - for (int i = 0; i < otherConditionCount; ++i) { - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'condition_vector'); - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'brief_title_vector'); - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'eligibility_criteria_vector'); - totalOtherConditionScore += cosineSimilarity(params.other_condition_vectors[i], 'brief_summary_vector'); - } - if (otherConditionCount > 0) { - totalOtherConditionScore /= (otherConditionCount * 4); - } - double normalizedConditionScore = (maxConditionVectorScore + 1.0) / 2.0; - double normalizedTitleScore = (maxTitleVectorScore + 1.0) / 2.0; - double normalizedSummaryScore = (maxSummaryVectorScore + 1.0) / 2.0; - double normalizedEligibilityScore = (maxEligibilityVectorScore + 1.0) / 2.0; - double normalizedOtherConditionScore = (totalOtherConditionScore + 1.0) / 2.0; - double combinedVectorScore = ( - 0.3 * normalizedConditionScore + - 0.1 * normalizedTitleScore + - 0.1 * normalizedSummaryScore + - 0.2 * normalizedOtherConditionScore + - 0.3 * normalizedEligibilityScore - ); - if (combinedVectorScore < params.vector_score_threshold) { - return 0; - } - return alpha * normalizedTextScore + beta * combinedVectorScore; - """, - "params": { - "query_vectors": query_vectors, - "other_condition_vectors": other_vectors, - "max_text_score": max_text_score, - "vector_score_threshold": vector_score_threshold, - }, - }, - } + "primary_terms": synonyms, + "other_terms": other_conditions or [], + "embeddings": embeddings, + "age": age, + "sex": sex, + "overall_status": overall_status, + "pre_selected_nct_ids": pre_selected_nct_ids or [], + "search_mode": search_mode, + "vector_score_threshold": vector_score_threshold, + "max_text_score": max_text_score, } def search_trials( @@ -427,40 +151,34 @@ def search_trials( ) mode = "bm25" - max_text_score = ( - 1.0 if mode == "vector" else self.get_max_text_score(primary_synonyms) - ) query = self.create_query( primary_synonyms, embeddings, age if age is not None else 0, sex, overall_status, - max_text_score, + 1.0, vector_score_threshold, pre_selected_nct_ids, other_conditions, search_mode=mode, ) try: - response = with_retries( - lambda: self.es_client.search( - index=self.index_name, body={"size": size, "query": query} - ), - logger=logger, - action="ES trial search", + trials, scores = self.search_backend.search_trials( + primary_terms=query["primary_terms"], + other_terms=query["other_terms"], + embeddings=query["embeddings"], + age=age, + sex=sex, + overall_status=overall_status, + pre_selected_nct_ids=pre_selected_nct_ids, + size=size, + vector_score_threshold=vector_score_threshold, + search_mode=mode, ) - hits = response["hits"]["hits"] - trials = [hit["_source"] for hit in hits] - scores = [hit["_score"] for hit in hits] except Exception: - logger.exception("Search failed; returning empty results.") + logger.exception("First-level search failed; returning empty results.") return [], [] - trials_with_scores = sorted( - zip(trials, scores), key=lambda x: x[1], reverse=True - ) - top_x_percent_index = int(len(trials_with_scores) * 1.0) - trials = [trial for trial, score in trials_with_scores[:top_x_percent_index]] logger.info( f"[{mode}] Found {len(trials)} trials matching the search criteria." ) diff --git a/source/Matcher/pipeline/trial_search/second_level_search.py b/source/Matcher/pipeline/trial_search/second_level_search.py index ffbd49aa..2c06a607 100644 --- a/source/Matcher/pipeline/trial_search/second_level_search.py +++ b/source/Matcher/pipeline/trial_search/second_level_search.py @@ -5,11 +5,9 @@ from Matcher.models.embedding.text_embedder import TextEmbedder from Matcher.models.llm.llm_reranker import LLMReranker +from Matcher.search.lancedb_backend import TrialSearchBackend from Matcher.utils.file_utils import write_text_file from Matcher.utils.logging_config import setup_logging -from Matcher.utils.retry import with_retries - -from elasticsearch import Elasticsearch logger = setup_logging(__name__) @@ -17,10 +15,9 @@ class SecondStageRetriever: def __init__( self, - es_client: Elasticsearch, + search_backend: TrialSearchBackend, llm_reranker: Optional[LLMReranker], # Make optional embedder: Optional[TextEmbedder], - index_name: str, size: int = 250, inclusion_weight: float = 1.0, exclusion_weight: float = 0.25, @@ -28,10 +25,9 @@ def __init__( entity_annotator=None, search_mode: str = "hybrid", ): - self.es_client = es_client + self.search_backend = search_backend self.llm_reranker = llm_reranker # Can be None self.embedder = embedder - self.index_name = index_name self.size = size self.inclusion_weight = inclusion_weight self.exclusion_weight = exclusion_weight @@ -61,162 +57,35 @@ def retrieve_criteria( query_to_hits = {} def execute_query(query): - # Use entities.synonyms only if entity annotation is enabled. - fields_to_search = ( - ["criterion", "entities.synonyms"] - if self.entity_annotator is not None - else ["criterion"] - ) - - if self.search_mode == "bm25": - # BM25 only query - es_query = { - "bool": { - "should": [ - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "best_fields", - "operator": "and", - } - }, - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "phrase", - } - }, - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "best_fields", - "operator": "or", - } - }, - ], - "minimum_should_match": 1, - "filter": {"terms": {"nct_id": nct_ids}}, - } - } - elif self.search_mode == "vector": - # Vector only query + mode = self.search_mode + query_vector = None + if mode in {"vector", "hybrid"}: if self.embedder is None: logger.warning( - "Vector mode selected but embedder is None. Falling back to BM25." - ) - return execute_query_bm25(query) - - vectors = self.embedder.embed_texts([query]) - if not vectors: - logger.warning( - "Empty query after preprocessing. Falling back to BM25." - ) - return execute_query_bm25(query) - query_vector = vectors[0] - es_query = { - "script_score": { - "query": { - "bool": { - "filter": {"terms": {"nct_id": nct_ids}}, - } - }, - "script": { - "source": """ - double vectorScore = (cosineSimilarity(params.query_vector, 'criterion_vector') + 1.0) / 2.0; - if (vectorScore < params.vector_score_threshold) { - return 0; - } - return vectorScore; - """, - "params": { - "query_vector": query_vector, - "vector_score_threshold": 0.5, - }, - }, - } - } - else: - # Hybrid mode (default) - if self.embedder is None: - logger.warning( - "Hybrid mode selected but embedder is None. Falling back to BM25." - ) - return execute_query_bm25(query) - - vectors = self.embedder.embed_texts([query]) - if not vectors: - logger.warning( - "Empty query after preprocessing. Falling back to BM25." + "%s mode selected but embedder is None. Falling back to BM25.", + mode, ) - return execute_query_bm25(query) - query_vector = vectors[0] - es_query = { - "script_score": { - "query": { - "bool": { - "should": [ - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "best_fields", - "operator": "and", - } - }, - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "phrase", - } - }, - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "best_fields", - "operator": "or", - } - }, - ], - "minimum_should_match": 1, - "filter": {"terms": {"nct_id": nct_ids}}, - } - }, - "script": { - "source": """ - double alpha = 0.5; - double beta = 0.5; - double textScore = _score; - double vectorScore = (cosineSimilarity(params.query_vector, 'criterion_vector') + 1.0) / 2.0; - if (vectorScore < params.vector_score_threshold) { - return 0; - } - return alpha * textScore + beta * vectorScore; - """, - "params": { - "query_vector": query_vector, - "vector_score_threshold": 0.5, - }, - }, - } - } + mode = "bm25" + else: + vectors = self.embedder.embed_texts([query]) + if vectors: + query_vector = vectors[0] + else: + logger.warning("Empty query after preprocessing. Falling back to BM25.") + mode = "bm25" try: - response = with_retries( - lambda: self.es_client.search( - index=self.index_name, body={"size": self.size, "query": es_query} - ), - logger=logger, - action="ES criteria search", + hits = self.search_backend.search_criteria( + query=query, + nct_ids=nct_ids, + query_vector=query_vector, + size=self.size, + search_mode=mode, + use_entity_synonyms=self.entity_annotator is not None, ) - hits = response["hits"]["hits"] logger.info( "[%s] Retrieved %s documents for query: '%s'", - self.search_mode, + mode, len(hits), query, ) @@ -225,60 +94,8 @@ def execute_query(query): logger.exception("Second-level search failed for query: %s", query) return query, [] - def execute_query_bm25(query): - # Helper function for BM25-only fallback. - fields_to_search = ( - ["criterion", "entities.synonyms"] - if self.entity_annotator is not None - else ["criterion"] - ) - es_query = { - "bool": { - "should": [ - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "best_fields", - "operator": "and", - } - }, - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "phrase", - } - }, - { - "multi_match": { - "query": query, - "fields": fields_to_search, - "type": "best_fields", - "operator": "or", - } - }, - ], - "minimum_should_match": 1, - "filter": {"terms": {"nct_id": nct_ids}}, - } - } - try: - response = with_retries( - lambda: self.es_client.search( - index=self.index_name, body={"size": self.size, "query": es_query} - ), - logger=logger, - action="ES criteria bm25 search", - ) - hits = response["hits"]["hits"] - logger.info( - "[bm25] Retrieved %s documents for query: '%s'", len(hits), query - ) - return query, hits - except Exception: - logger.exception("BM25 search failed for query: %s", query) - return query, [] + if not queries: + return {} with ThreadPoolExecutor(max_workers=min(8, len(queries))) as executor: future_to_query = { @@ -291,7 +108,7 @@ def execute_query_bm25(query): def rerank_criteria(self, queries: List[str], criteria: List[Dict]) -> List[Dict]: if self.llm_reranker is None: - logger.warning("LLM reranker not available, using ES scores only") + logger.warning("LLM reranker not available, using search scores only") return self.score_criteria_without_llm(criteria) pairs = [ @@ -393,11 +210,11 @@ def retrieve_and_rank( else: if use_reranker and self.llm_reranker is None: logger.info( - "Reranking requested but LLM reranker not available; using ES scores for aggregation." + "Reranking requested but LLM reranker not available; using search scores for aggregation." ) else: logger.info( - "Second-level reranking disabled; using ES scores for aggregation." + "Second-level reranking disabled; using search scores for aggregation." ) ranked_criteria = self.score_criteria_without_llm(all_criteria) diff --git a/source/Matcher/search/__init__.py b/source/Matcher/search/__init__.py new file mode 100644 index 00000000..e086f34d --- /dev/null +++ b/source/Matcher/search/__init__.py @@ -0,0 +1,15 @@ +from Matcher.search.lancedb_backend import ( + InMemorySearchBackend, + LanceDBSearchBackend, + SearchBackendUnavailable, + build_criteria_record, + build_trial_record, +) + +__all__ = [ + "InMemorySearchBackend", + "LanceDBSearchBackend", + "SearchBackendUnavailable", + "build_criteria_record", + "build_trial_record", +] diff --git a/source/Matcher/search/lancedb_backend.py b/source/Matcher/search/lancedb_backend.py new file mode 100644 index 00000000..0d4bda85 --- /dev/null +++ b/source/Matcher/search/lancedb_backend.py @@ -0,0 +1,781 @@ +from __future__ import annotations + +import math +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Iterable, Mapping, Protocol, Sequence + +from Matcher.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +TRIAL_TEXT_WEIGHTS: tuple[tuple[str, float], ...] = ( + ("condition", 6.0), + ("eligibility_criteria", 4.0), + ("brief_title", 3.0), + ("brief_summary", 2.0), + ("detailed_description", 1.5), + ("official_title", 1.0), +) +TRIAL_VECTOR_WEIGHTS: tuple[tuple[str, float], ...] = ( + ("condition_vector", 0.3), + ("brief_title_vector", 0.1), + ("brief_summary_vector", 0.1), + ("eligibility_criteria_vector", 0.3), +) +CRITERIA_TEXT_WEIGHTS: tuple[tuple[str, float], ...] = ( + ("criterion", 1.0), + ("entity_synonyms_text", 1.2), + ("entity_text", 0.8), +) + + +@dataclass(frozen=True) +class SearchHit: + source: dict[str, Any] + score: float + + def to_es_like_hit(self) -> dict[str, Any]: + return {"_source": self.source, "_score": self.score} + + +class SearchBackendUnavailable(RuntimeError): + pass + + +class TrialSearchBackend(Protocol): + def search_trials( + self, + *, + primary_terms: Sequence[str], + other_terms: Sequence[str] = (), + embeddings: Mapping[str, Sequence[float]] | None = None, + age: int | None = None, + sex: str = "ALL", + overall_status: str | None = None, + pre_selected_nct_ids: Sequence[str] | None = None, + size: int = 10, + vector_score_threshold: float = 0.0, + search_mode: str = "hybrid", + ) -> tuple[list[dict[str, Any]], list[float]]: + ... + + def search_criteria( + self, + *, + query: str, + nct_ids: Sequence[str], + query_vector: Sequence[float] | None = None, + size: int = 250, + search_mode: str = "hybrid", + use_entity_synonyms: bool = True, + vector_score_threshold: float = 0.5, + ) -> list[dict[str, Any]]: + ... + + +class InMemorySearchBackend: + """Deterministic backend used for tests and small fixture smoke runs.""" + + def __init__( + self, + *, + trials: Sequence[Mapping[str, Any]] = (), + criteria: Sequence[Mapping[str, Any]] = (), + ) -> None: + self.trials = [build_trial_record(row) for row in trials] + self.criteria = [build_criteria_record(row) for row in criteria] + + def health(self, *, require_tables: bool = False) -> list[str]: + return [] + + def search_trials( + self, + *, + primary_terms: Sequence[str], + other_terms: Sequence[str] = (), + embeddings: Mapping[str, Sequence[float]] | None = None, + age: int | None = None, + sex: str = "ALL", + overall_status: str | None = None, + pre_selected_nct_ids: Sequence[str] | None = None, + size: int = 10, + vector_score_threshold: float = 0.0, + search_mode: str = "hybrid", + ) -> tuple[list[dict[str, Any]], list[float]]: + hits = _rank_trial_rows( + self.trials, + primary_terms=primary_terms, + other_terms=other_terms, + embeddings=embeddings or {}, + age=age, + sex=sex, + overall_status=overall_status, + pre_selected_nct_ids=pre_selected_nct_ids, + size=size, + vector_score_threshold=vector_score_threshold, + search_mode=search_mode, + ) + return [hit.source for hit in hits], [hit.score for hit in hits] + + def search_criteria( + self, + *, + query: str, + nct_ids: Sequence[str], + query_vector: Sequence[float] | None = None, + size: int = 250, + search_mode: str = "hybrid", + use_entity_synonyms: bool = True, + vector_score_threshold: float = 0.5, + ) -> list[dict[str, Any]]: + hits = _rank_criteria_rows( + self.criteria, + query=query, + nct_ids=nct_ids, + query_vector=query_vector, + size=size, + search_mode=search_mode, + use_entity_synonyms=use_entity_synonyms, + vector_score_threshold=vector_score_threshold, + ) + return [hit.to_es_like_hit() for hit in hits] + + +class LanceDBSearchBackend: + """Embedded LanceDB backend for TrialMatchAI trial and criteria retrieval.""" + + def __init__( + self, + db_path: str | Path, + *, + trials_table: str = "trials", + criteria_table: str = "criteria", + candidate_limit: int = 1000, + ) -> None: + try: + import lancedb # type: ignore + except Exception as exc: # pragma: no cover - optional dependency guard + raise SearchBackendUnavailable( + "LanceDB search requires the runtime dependency `lancedb`." + ) from exc + + self.db_path = Path(db_path) + self.trials_table = trials_table + self.criteria_table = criteria_table + self.candidate_limit = candidate_limit + self.db_path.mkdir(parents=True, exist_ok=True) + self.db = lancedb.connect(str(self.db_path)) + + @classmethod + def from_config(cls, config: Mapping[str, Any]) -> "LanceDBSearchBackend": + search_cfg = config.get("search_backend", {}) + backend = search_cfg.get("backend", "lancedb") + if backend != "lancedb": + raise ValueError(f"Unsupported search backend: {backend}") + return cls( + search_cfg.get("db_path", "data/search"), + trials_table=search_cfg.get("trials_table", "trials"), + criteria_table=search_cfg.get("criteria_table", "criteria"), + candidate_limit=int(search_cfg.get("candidate_limit", 1000)), + ) + + def health(self, *, require_tables: bool = False) -> list[str]: + issues: list[str] = [] + if not self.db_path.exists(): + issues.append(f"search_backend.db_path does not exist: {self.db_path}") + return issues + if require_tables: + names = set(self._table_names()) + missing = [ + name + for name in (self.trials_table, self.criteria_table) + if name not in names + ] + if missing: + issues.append("Missing LanceDB tables: " + ", ".join(missing)) + return issues + + def table_exists(self, table_name: str) -> bool: + return table_name in set(self._table_names()) + + def index_trials( + self, + docs: Sequence[Mapping[str, Any]], + *, + recreate: bool = True, + ) -> int: + rows = [build_trial_record(doc) for doc in docs] + table = self._write_rows(self.trials_table, rows, recreate=recreate) + _create_fts_index(table, "search_text") + _create_vector_index(table, "search_vector") + return len(rows) + + def index_criteria( + self, + docs: Sequence[Mapping[str, Any]], + *, + recreate: bool = True, + ) -> int: + rows = [build_criteria_record(doc) for doc in docs] + table = self._write_rows(self.criteria_table, rows, recreate=recreate) + _create_fts_index(table, "search_text") + _create_vector_index(table, "criterion_vector") + return len(rows) + + def search_trials( + self, + *, + primary_terms: Sequence[str], + other_terms: Sequence[str] = (), + embeddings: Mapping[str, Sequence[float]] | None = None, + age: int | None = None, + sex: str = "ALL", + overall_status: str | None = None, + pre_selected_nct_ids: Sequence[str] | None = None, + size: int = 10, + vector_score_threshold: float = 0.0, + search_mode: str = "hybrid", + ) -> tuple[list[dict[str, Any]], list[float]]: + table = self._open_table(self.trials_table) + where = _nct_where(pre_selected_nct_ids) + vector = _mean_vectors( + [ + embeddings[term] + for term in primary_terms + if embeddings and term in embeddings + ] + ) + rows = self._candidate_rows( + table, + text_query=" ".join([*primary_terms, *other_terms]), + query_vector=vector, + vector_column="search_vector", + where=where, + mode=search_mode, + limit=max(size, self.candidate_limit), + ) + hits = _rank_trial_rows( + rows, + primary_terms=primary_terms, + other_terms=other_terms, + embeddings=embeddings or {}, + age=age, + sex=sex, + overall_status=overall_status, + pre_selected_nct_ids=pre_selected_nct_ids, + size=size, + vector_score_threshold=vector_score_threshold, + search_mode=search_mode, + ) + return [hit.source for hit in hits], [hit.score for hit in hits] + + def search_criteria( + self, + *, + query: str, + nct_ids: Sequence[str], + query_vector: Sequence[float] | None = None, + size: int = 250, + search_mode: str = "hybrid", + use_entity_synonyms: bool = True, + vector_score_threshold: float = 0.5, + ) -> list[dict[str, Any]]: + table = self._open_table(self.criteria_table) + where = _nct_where(nct_ids) + rows = self._candidate_rows( + table, + text_query=query, + query_vector=query_vector, + vector_column="criterion_vector", + where=where, + mode=search_mode, + limit=max(size, self.candidate_limit), + ) + hits = _rank_criteria_rows( + rows, + query=query, + nct_ids=nct_ids, + query_vector=query_vector, + size=size, + search_mode=search_mode, + use_entity_synonyms=use_entity_synonyms, + vector_score_threshold=vector_score_threshold, + ) + return [hit.to_es_like_hit() for hit in hits] + + def _write_rows( + self, + table_name: str, + rows: Sequence[dict[str, Any]], + *, + recreate: bool, + ) -> Any: + if not rows: + raise ValueError(f"No rows supplied for LanceDB table {table_name}.") + if recreate or not self.table_exists(table_name): + return self.db.create_table(table_name, data=list(rows), mode="overwrite") + table = self._open_table(table_name) + table.add(list(rows)) + return table + + def _candidate_rows( + self, + table: Any, + *, + text_query: str, + query_vector: Sequence[float] | None, + vector_column: str, + where: str, + mode: str, + limit: int, + ) -> list[dict[str, Any]]: + mode = (mode or "hybrid").lower() + rows_by_key: dict[str, dict[str, Any]] = {} + + if mode in {"bm25", "hybrid"} and text_query.strip(): + for row in self._search_fts(table, text_query, where=where, limit=limit): + rows_by_key[_row_key(row)] = row + + if mode in {"vector", "hybrid"} and query_vector: + for row in self._search_vector( + table, + query_vector, + vector_column=vector_column, + where=where, + limit=limit, + ): + rows_by_key[_row_key(row)] = row + + if not rows_by_key: + for row in self._scan_rows(table, limit=limit): + rows_by_key[_row_key(row)] = row + + return list(rows_by_key.values()) + + def _search_fts( + self, + table: Any, + query: str, + *, + where: str, + limit: int, + ) -> list[dict[str, Any]]: + try: + search = table.search(query, query_type="fts") + if where: + search = search.where(where) + return list(search.limit(limit).to_list()) + except Exception as exc: + logger.warning("LanceDB FTS search failed; falling back if possible: %s", exc) + return [] + + def _search_vector( + self, + table: Any, + vector: Sequence[float], + *, + vector_column: str, + where: str, + limit: int, + ) -> list[dict[str, Any]]: + try: + search = table.search(list(vector), vector_column_name=vector_column) + if where: + search = search.where(where) + return list(search.limit(limit).to_list()) + except Exception as exc: + logger.warning( + "LanceDB vector search failed; falling back if possible: %s", exc + ) + return [] + + def _scan_rows(self, table: Any, *, limit: int) -> list[dict[str, Any]]: + try: + return list(table.to_arrow().to_pylist())[:limit] + except Exception as exc: + logger.warning("Could not scan LanceDB table rows: %s", exc) + return [] + + def _open_table(self, table_name: str) -> Any: + try: + return self.db.open_table(table_name) + except Exception as exc: + raise SearchBackendUnavailable( + f"LanceDB table is not available: {table_name}" + ) from exc + + def _table_names(self) -> list[str]: + try: + return list(self.db.table_names()) + except Exception as exc: + logger.warning("Could not list LanceDB tables: %s", exc) + return [] + + +def build_trial_record(doc: Mapping[str, Any]) -> dict[str, Any]: + row = dict(doc) + search_text = _flatten_text( + [ + row.get("condition"), + row.get("eligibility_criteria"), + row.get("brief_title"), + row.get("brief_summary"), + row.get("detailed_description"), + row.get("official_title"), + ] + ) + row["search_text"] = search_text + row["search_vector"] = _mean_vectors( + _clean_vector(row.get(field)) for field, _ in TRIAL_VECTOR_WEIGHTS + ) + return row + + +def build_criteria_record(doc: Mapping[str, Any]) -> dict[str, Any]: + row = dict(doc) + entity_text, synonym_text = _flatten_entities(row.get("entities")) + row["entity_text"] = entity_text + row["entity_synonyms_text"] = synonym_text + row["search_text"] = _flatten_text( + [row.get("criterion"), entity_text, synonym_text] + ) + return row + + +def _rank_trial_rows( + rows: Sequence[Mapping[str, Any]], + *, + primary_terms: Sequence[str], + other_terms: Sequence[str], + embeddings: Mapping[str, Sequence[float]], + age: int | None, + sex: str, + overall_status: str | None, + pre_selected_nct_ids: Sequence[str] | None, + size: int, + vector_score_threshold: float, + search_mode: str, +) -> list[SearchHit]: + mode = (search_mode or "hybrid").lower() + hits: list[SearchHit] = [] + for raw in rows: + row = build_trial_record(raw) + if not _trial_passes_filters( + row, + age=age, + sex=sex, + overall_status=overall_status, + pre_selected_nct_ids=pre_selected_nct_ids, + ): + continue + text_score = _weighted_text_score(row, primary_terms, TRIAL_TEXT_WEIGHTS) + if other_terms: + text_score = max( + text_score, + 0.75 * _weighted_text_score(row, other_terms, TRIAL_TEXT_WEIGHTS), + ) + vector_score = _trial_vector_score(row, primary_terms, other_terms, embeddings) + if mode in {"vector", "hybrid"} and vector_score < vector_score_threshold: + continue + score = _combine_scores(mode, text_score, vector_score) + if score <= 0: + continue + hits.append(SearchHit(source=row, score=score)) + hits.sort(key=lambda hit: hit.score, reverse=True) + return hits[:size] + + +def _rank_criteria_rows( + rows: Sequence[Mapping[str, Any]], + *, + query: str, + nct_ids: Sequence[str], + query_vector: Sequence[float] | None, + size: int, + search_mode: str, + use_entity_synonyms: bool, + vector_score_threshold: float, +) -> list[SearchHit]: + allowed = {nct_id for nct_id in nct_ids if nct_id} + mode = (search_mode or "hybrid").lower() + fields = ( + CRITERIA_TEXT_WEIGHTS + if use_entity_synonyms + else tuple(item for item in CRITERIA_TEXT_WEIGHTS if item[0] == "criterion") + ) + hits: list[SearchHit] = [] + for raw in rows: + row = build_criteria_record(raw) + if allowed and row.get("nct_id") not in allowed: + continue + text_score = _weighted_text_score(row, [query], fields) + vector_score = _vector_score(query_vector, _clean_vector(row.get("criterion_vector"))) + if mode in {"vector", "hybrid"} and vector_score < vector_score_threshold: + continue + score = _combine_scores(mode, text_score, vector_score) + if score <= 0: + continue + hits.append(SearchHit(source=row, score=score)) + hits.sort(key=lambda hit: hit.score, reverse=True) + return hits[:size] + + +def _combine_scores(mode: str, text_score: float, vector_score: float) -> float: + if mode == "bm25": + return text_score + if mode == "vector": + return vector_score + return 0.5 * text_score + 0.5 * vector_score + + +def _weighted_text_score( + row: Mapping[str, Any], + terms: Sequence[str], + fields: Sequence[tuple[str, float]], +) -> float: + clean_terms = [term for term in terms if term and term.strip()] + if not clean_terms: + return 0.0 + max_weight = sum(weight for _, weight in fields) or 1.0 + best = 0.0 + for term in clean_terms: + field_score = 0.0 + for field, weight in fields: + field_score += weight * _lexical_score(term, _flatten_text(row.get(field))) + best = max(best, field_score / max_weight) + return min(best, 1.0) + + +def _trial_vector_score( + row: Mapping[str, Any], + primary_terms: Sequence[str], + other_terms: Sequence[str], + embeddings: Mapping[str, Sequence[float]], +) -> float: + primary_vectors = [ + embeddings[term] for term in primary_terms if term in embeddings and embeddings[term] + ] + other_vectors = [ + embeddings[term] for term in other_terms if term in embeddings and embeddings[term] + ] + score = 0.0 + weight_total = 0.0 + for field, weight in TRIAL_VECTOR_WEIGHTS: + field_vector = _clean_vector(row.get(field)) + if not field_vector: + continue + score += weight * _max_vector_score(primary_vectors, field_vector) + weight_total += weight + if other_vectors: + other_field_scores: list[float] = [] + for field, _ in TRIAL_VECTOR_WEIGHTS: + field_vector = _clean_vector(row.get(field)) + if field_vector: + other_field_scores.append(_max_vector_score(other_vectors, field_vector)) + if other_field_scores: + score += 0.2 * (sum(other_field_scores) / len(other_field_scores)) + weight_total += 0.2 + if weight_total == 0: + return 0.0 + return min(score / weight_total, 1.0) + + +def _max_vector_score( + query_vectors: Sequence[Sequence[float]], + field_vector: Sequence[float], +) -> float: + if not query_vectors or not field_vector: + return 0.0 + return max(_vector_score(vector, field_vector) for vector in query_vectors) + + +def _vector_score( + left: Sequence[float] | None, + right: Sequence[float] | None, +) -> float: + if not left or not right: + return 0.0 + similarity = _cosine(left, right) + return max(0.0, min(1.0, (similarity + 1.0) / 2.0)) + + +def _cosine(left: Sequence[float], right: Sequence[float]) -> float: + size = min(len(left), len(right)) + if size == 0: + return 0.0 + dot = sum(float(left[i]) * float(right[i]) for i in range(size)) + left_norm = math.sqrt(sum(float(left[i]) ** 2 for i in range(size))) + right_norm = math.sqrt(sum(float(right[i]) ** 2 for i in range(size))) + if left_norm == 0 or right_norm == 0: + return 0.0 + return dot / (left_norm * right_norm) + + +def _lexical_score(query: str, text: str) -> float: + query_norm = _normalize_text(query) + text_norm = _normalize_text(text) + if not query_norm or not text_norm: + return 0.0 + if query_norm == text_norm: + return 1.0 + if query_norm in text_norm: + return 0.95 + query_tokens = set(query_norm.split()) + text_tokens = set(text_norm.split()) + if not query_tokens or not text_tokens: + return 0.0 + overlap = len(query_tokens & text_tokens) + if overlap == 0: + return 0.0 + coverage = overlap / len(query_tokens) + jaccard = overlap / len(query_tokens | text_tokens) + return min(1.0, 0.75 * coverage + 0.25 * jaccard) + + +def _trial_passes_filters( + row: Mapping[str, Any], + *, + age: int | None, + sex: str, + overall_status: str | None, + pre_selected_nct_ids: Sequence[str] | None, +) -> bool: + if pre_selected_nct_ids and row.get("nct_id") not in set(pre_selected_nct_ids): + return False + if overall_status and overall_status.casefold() != "all": + status = str(row.get("overall_status") or "") + if status.casefold() != overall_status.casefold(): + return False + if age is not None: + minimum = _as_float(row.get("minimum_age")) + maximum = _as_float(row.get("maximum_age")) + if minimum is not None and minimum > age: + return False + if maximum is not None and maximum < age: + return False + return _gender_matches(row.get("gender"), sex) + + +def _gender_matches(value: Any, sex: str) -> bool: + requested = (sex or "ALL").casefold() + raw = str(value or "All").casefold() + if requested in {"all", "both"}: + return True + if raw in {"", "all", "both"}: + return True + if requested == "male": + return raw in {"male", "m"} + if requested == "female": + return raw in {"female", "f"} + return True + + +def _flatten_entities(entities: Any) -> tuple[str, str]: + if not isinstance(entities, list): + return "", "" + texts: list[str] = [] + synonyms: list[str] = [] + for entity in entities: + if not isinstance(entity, Mapping): + continue + texts.append(_flatten_text([entity.get("text"), entity.get("entity")])) + synonyms.append(_flatten_text(entity.get("synonyms"))) + for candidate in entity.get("concept_candidates") or []: + if isinstance(candidate, Mapping): + synonyms.append(_flatten_text(candidate.get("concept_name"))) + return _flatten_text(texts), _flatten_text(synonyms) + + +def _flatten_text(value: Any) -> str: + if value is None: + return "" + if isinstance(value, str): + return " ".join(value.split()) + if isinstance(value, Mapping): + return " ".join(_flatten_text(item) for item in value.values()).strip() + if isinstance(value, Iterable) and not isinstance(value, (bytes, bytearray)): + return " ".join(_flatten_text(item) for item in value).strip() + return str(value) + + +def _normalize_text(value: str) -> str: + return " ".join(re.findall(r"[a-z0-9]+", value.casefold())) + + +def _clean_vector(value: Any) -> list[float]: + if value is None: + return [] + if hasattr(value, "tolist"): + value = value.tolist() + if not isinstance(value, Sequence) or isinstance(value, (str, bytes, bytearray)): + return [] + try: + return [float(item) for item in value] + except (TypeError, ValueError): + return [] + + +def _mean_vectors(vectors: Iterable[Sequence[float] | None]) -> list[float]: + cleaned = [_clean_vector(vector) for vector in vectors] + cleaned = [vector for vector in cleaned if vector] + if not cleaned: + return [] + width = min(len(vector) for vector in cleaned) + if width == 0: + return [] + return [ + sum(vector[index] for vector in cleaned) / len(cleaned) + for index in range(width) + ] + + +def _as_float(value: Any) -> float | None: + if value is None or value == "": + return None + try: + return float(value) + except (TypeError, ValueError): + return None + + +def _nct_where(nct_ids: Sequence[str] | None) -> str: + values = [str(item) for item in (nct_ids or []) if item] + if not values: + return "" + quoted = ", ".join(f"'{_sql_escape(value)}'" for value in values) + return f"nct_id IN ({quoted})" + + +def _sql_escape(value: str) -> str: + return value.replace("'", "''") + + +def _row_key(row: Mapping[str, Any]) -> str: + return str(row.get("criteria_id") or row.get("nct_id") or id(row)) + + +def _create_fts_index(table: Any, column: str) -> None: + try: + table.create_fts_index(column, replace=True) + except TypeError: + try: + table.create_fts_index(column) + except Exception as exc: + logger.warning("Could not create LanceDB FTS index on %s: %s", column, exc) + except Exception as exc: + logger.warning("Could not create LanceDB FTS index on %s: %s", column, exc) + + +def _create_vector_index(table: Any, column: str) -> None: + try: + table.create_index(vector_column_name=column, metric="cosine") + except TypeError: + try: + table.create_index(column) + except Exception as exc: + logger.warning("Could not create LanceDB vector index on %s: %s", column, exc) + except Exception as exc: + logger.warning("Could not create LanceDB vector index on %s: %s", column, exc) diff --git a/source/Matcher/services/elasticsearch_service.py b/source/Matcher/services/elasticsearch_service.py deleted file mode 100644 index 09cdcfbb..00000000 --- a/source/Matcher/services/elasticsearch_service.py +++ /dev/null @@ -1,95 +0,0 @@ -from __future__ import annotations - -import os -import shutil -import subprocess -import time -from pathlib import Path -from typing import Any, Dict - -from elasticsearch import Elasticsearch - -from Matcher.utils.logging_config import setup_logging - -logger = setup_logging(__name__) - - -def build_elasticsearch_client(config: Dict[str, Any]) -> Elasticsearch: - es_cfg = config["elasticsearch"] - paths = config.get("paths", {}) - kwargs: dict[str, Any] = { - "hosts": [es_cfg["host"]], - "basic_auth": (es_cfg["username"], es_cfg["password"]), - "request_timeout": es_cfg["request_timeout"], - "retry_on_timeout": es_cfg["retry_on_timeout"], - } - ca_certs = paths.get("docker_certs") - if ca_certs and Path(ca_certs).exists(): - kwargs["ca_certs"] = ca_certs - return Elasticsearch(**kwargs) - - -def ensure_elasticsearch(es_client: Any, config: Dict[str, Any]) -> bool: - if _ping(es_client): - return True - - es_cfg = config.get("elasticsearch", {}) - auto_start = bool(es_cfg.get("auto_start", False)) - if not auto_start: - logger.error( - "Elasticsearch is not reachable at %s and auto_start is disabled.", - es_cfg.get("host"), - ) - return False - - script_path = es_cfg.get("start_script", "elasticsearch/apptainer-run-es.sh") - timeout = int(es_cfg.get("start_timeout", 600)) - script = _resolve_repo_path(script_path) - - if not script.exists(): - logger.error("Elasticsearch start script not found: %s", script) - return False - - if shutil.which("apptainer") is None: - logger.error("Apptainer is not available in PATH.") - return False - - logger.info("Starting Elasticsearch via Apptainer script: %s", script) - try: - subprocess.run( - ["bash", str(script)], - cwd=str(script.parent), - check=True, - timeout=timeout, - env=os.environ.copy(), - ) - except subprocess.TimeoutExpired: - logger.error("Elasticsearch start timed out after %s seconds.", timeout) - return False - except subprocess.CalledProcessError as exc: - logger.error("Elasticsearch start failed: %s", exc) - return False - - return _wait_for_es(es_client, timeout=60) - - -def _ping(es_client: Any) -> bool: - try: - return bool(es_client.ping()) - except Exception: - return False - - -def _wait_for_es(es_client: Any, timeout: int = 60) -> bool: - deadline = time.time() + timeout - while time.time() < deadline: - if _ping(es_client): - return True - time.sleep(5) - logger.error("Elasticsearch did not become available within %s seconds.", timeout) - return False - - -def _resolve_repo_path(relative_path: str) -> Path: - root = Path(__file__).resolve().parents[3] - return (root / relative_path).resolve() diff --git a/source/Matcher/services/preflight.py b/source/Matcher/services/preflight.py index 57273448..2e52a53c 100644 --- a/source/Matcher/services/preflight.py +++ b/source/Matcher/services/preflight.py @@ -2,7 +2,7 @@ import importlib.util from pathlib import Path -from typing import Any, Dict, Iterable, List +from typing import Any, Dict, List import torch @@ -14,11 +14,11 @@ def run_preflight_checks( config: Dict[str, Any], *, - es_client: Any | None = None, + search_backend: Any | None = None, require_patient_inputs: bool = False, require_trials_json: bool = False, require_models: bool = False, - require_indices: bool = False, + require_search_tables: bool = False, ) -> List[str]: """Return blocking deployment/runtime issues discovered before heavy startup.""" issues: List[str] = [] @@ -38,15 +38,6 @@ def run_preflight_checks( ) _require_output_dir(issues, paths.get("output_dir")) - host = str(config.get("elasticsearch", {}).get("host", "")) - if host.startswith("https://"): - _require_path( - issues, - "paths.docker_certs", - paths.get("docker_certs"), - required=True, - ) - if require_models: entity_cfg = config.get("entity_extraction") if entity_cfg: @@ -98,21 +89,28 @@ def run_preflight_checks( if not torch.cuda.is_available(): issues.append("cot_backend=vllm requires a CUDA-capable runtime.") - if es_client is not None: - if not _ping(es_client): - issues.append( - f"Elasticsearch is not reachable at {config['elasticsearch']['host']}." - ) - elif require_indices: - missing = _missing_indices( - es_client, - [ - config["elasticsearch"]["index_trials"], - config["elasticsearch"]["index_trials_eligibility"], - ], - ) - if missing: - issues.append("Missing Elasticsearch indices: " + ", ".join(missing)) + search_cfg = config.get("search_backend", {}) + if search_cfg: + _require_path( + issues, + "search_backend.db_path", + search_cfg.get("db_path"), + required=require_search_tables, + ) + if require_search_tables: + if search_backend is None: + try: + from Matcher.search import LanceDBSearchBackend + + search_backend = LanceDBSearchBackend.from_config(config) + except Exception as exc: + issues.append(f"Search backend is not available: {exc}") + search_backend = None + if search_backend is not None: + if hasattr(search_backend, "health"): + issues.extend(search_backend.health(require_tables=True)) + else: + issues.append("Search backend does not expose a healthcheck.") for issue in issues: logger.error("Preflight: %s", issue) @@ -146,22 +144,3 @@ def _require_output_dir(issues: List[str], value: str | None) -> None: path.mkdir(parents=True, exist_ok=True) except OSError as exc: issues.append(f"paths.output_dir is not writable: {path} ({exc})") - - -def _ping(es_client: Any) -> bool: - try: - return bool(es_client.ping()) - except Exception: - return False - - -def _missing_indices(es_client: Any, names: Iterable[str]) -> list[str]: - missing: list[str] = [] - for name in names: - try: - if not es_client.indices.exists(index=name): - missing.append(name) - except Exception as exc: - logger.warning("Could not check Elasticsearch index %s: %s", name, exc) - missing.append(name) - return missing diff --git a/tests/test_config_pytest.py b/tests/test_config_pytest.py index 5d723e2a..36b08061 100644 --- a/tests/test_config_pytest.py +++ b/tests/test_config_pytest.py @@ -6,6 +6,6 @@ def test_load_config_from_repo(): config_path = Path(__file__).resolve().parents[1] / "source/Matcher/config/config.json" cfg = load_config(str(config_path)) - assert "elasticsearch" in cfg + assert cfg["search_backend"]["backend"] == "lancedb" assert "embedder" in cfg assert "paths" in cfg diff --git a/tests/test_deployment_readiness.py b/tests/test_deployment_readiness.py index 2ecb35da..052fc534 100644 --- a/tests/test_deployment_readiness.py +++ b/tests/test_deployment_readiness.py @@ -11,20 +11,22 @@ def test_default_config_resolution_from_repo_root(): assert path.as_posix().endswith("source/Matcher/config/config.json") -def test_config_env_overrides_and_standard_index_names(monkeypatch): - monkeypatch.setenv("TRIALMATCHAI_ES_HOST", "https://es.example.test:9200") - monkeypatch.setenv("TRIALMATCHAI_ES_PASSWORD", "secret-from-env") - monkeypatch.setenv("TRIALMATCHAI_INDEX_TRIALS_ELIGIBILITY", "trials_eligibility") +def test_config_env_overrides_and_search_tables(monkeypatch): + monkeypatch.setenv("TRIALMATCHAI_SEARCH_DB_PATH", "data/search-test") + monkeypatch.setenv("TRIALMATCHAI_SEARCH_TRIALS_TABLE", "trials-test") + monkeypatch.setenv("TRIALMATCHAI_SEARCH_CRITERIA_TABLE", "criteria-test") + monkeypatch.setenv("TRIALMATCHAI_SEARCH_MODE", "bm25") monkeypatch.setenv("TRIALMATCHAI_ENTITY_BACKEND", "regex") monkeypatch.setenv("TRIALMATCHAI_CONCEPT_DB_PATH", "data/concepts-test") monkeypatch.setenv("TRIALMATCHAI_LINK_ACCEPT", "0.9") cfg = load_config() - assert cfg["elasticsearch"]["host"] == "https://es.example.test:9200" - assert cfg["elasticsearch"]["password"] == "secret-from-env" - assert cfg["elasticsearch"]["index_trials"] == "clinical_trials" - assert cfg["elasticsearch"]["index_trials_eligibility"] == "trials_eligibility" + assert cfg["search_backend"]["backend"] == "lancedb" + assert cfg["search_backend"]["db_path"].endswith("data/search-test") + assert cfg["search_backend"]["trials_table"] == "trials-test" + assert cfg["search_backend"]["criteria_table"] == "criteria-test" + assert cfg["search"]["mode"] == "bm25" assert cfg["entity_extraction"]["backend"] == "regex" assert cfg["concept_linker"]["db_path"].endswith("data/concepts-test") assert cfg["concept_linker"]["accept_threshold"] == 0.9 diff --git a/tests/test_first_level_search_pytest.py b/tests/test_first_level_search_pytest.py index f03593c9..d6ed7454 100644 --- a/tests/test_first_level_search_pytest.py +++ b/tests/test_first_level_search_pytest.py @@ -1,18 +1,32 @@ from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch - - -class DummyES: - def search(self, index, body): - if body.get("track_total_hits") is False: - return {"hits": {"max_score": 2.0}} - return {"hits": {"hits": [{"_source": {"nct_id": "N1"}, "_score": 1.0}]}} +from Matcher.search import InMemorySearchBackend def test_search_trials_bm25_returns_hits(): + backend = InMemorySearchBackend( + trials=[ + { + "nct_id": "N1", + "condition": "lung cancer", + "brief_title": "Lung carcinoma treatment", + "eligibility_criteria": "Adults with lung cancer", + "minimum_age": 18, + "maximum_age": 80, + "gender": "All", + "overall_status": "Recruiting", + }, + { + "nct_id": "N2", + "condition": "diabetes mellitus", + "brief_title": "Diabetes lifestyle trial", + "eligibility_criteria": "Adults with diabetes", + "overall_status": "Recruiting", + }, + ] + ) search = ClinicalTrialSearch( - es_client=DummyES(), + search_backend=backend, embedder=None, - index_name="index", bio_med_ner=None, ) trials, scores = search.search_trials( @@ -24,4 +38,5 @@ def test_search_trials_bm25_returns_hits(): search_mode="bm25", ) assert len(trials) == 1 - assert scores == [1.0] + assert trials[0]["nct_id"] == "N1" + assert scores[0] > 0 diff --git a/tests/test_integration_es_pytest.py b/tests/test_integration_es_pytest.py deleted file mode 100644 index 97d72300..00000000 --- a/tests/test_integration_es_pytest.py +++ /dev/null @@ -1,23 +0,0 @@ -import os - -import pytest -from elasticsearch import Elasticsearch - -from Matcher.config.config_loader import load_config - - -@pytest.mark.integration -def test_elasticsearch_ping(): - if os.getenv("TRIALMATCHAI_RUN_INTEGRATION") != "1": - pytest.skip("Set TRIALMATCHAI_RUN_INTEGRATION=1 to enable integration tests.") - - cfg = load_config("Matcher/config/config.json") - es_cfg = cfg["elasticsearch"] - client = Elasticsearch( - hosts=[es_cfg["host"]], - ca_certs=cfg["paths"]["docker_certs"], - basic_auth=(es_cfg["username"], es_cfg["password"]), - request_timeout=es_cfg["request_timeout"], - retry_on_timeout=es_cfg["retry_on_timeout"], - ) - assert client.ping() diff --git a/tests/test_lancedb_search_backend.py b/tests/test_lancedb_search_backend.py new file mode 100644 index 00000000..bf111b37 --- /dev/null +++ b/tests/test_lancedb_search_backend.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import pytest + +from Matcher.search import LanceDBSearchBackend + + +pytest.importorskip("lancedb") + + +def test_lancedb_backend_indexes_and_searches_trials_and_criteria(tmp_path): + backend = LanceDBSearchBackend( + tmp_path / "search", + trials_table="trials", + criteria_table="criteria", + candidate_limit=25, + ) + + backend.index_trials( + [ + { + "nct_id": "N1", + "condition": "lung cancer", + "brief_title": "Targeted therapy for lung carcinoma", + "eligibility_criteria": "Adults with lung cancer", + "condition_vector": [1.0, 0.0], + "eligibility_criteria_vector": [1.0, 0.0], + "gender": "All", + "overall_status": "Recruiting", + }, + { + "nct_id": "N2", + "condition": "diabetes mellitus", + "brief_title": "Diabetes prevention", + "eligibility_criteria": "Adults with diabetes", + "condition_vector": [0.0, 1.0], + "eligibility_criteria_vector": [0.0, 1.0], + "gender": "All", + "overall_status": "Recruiting", + }, + ] + ) + backend.index_criteria( + [ + { + "criteria_id": "C1", + "nct_id": "N1", + "criterion": "Confirmed malignant neoplasm", + "criterion_vector": [1.0, 0.0], + "entities": [{"text": "malignant neoplasm", "synonyms": ["cancer"]}], + "eligibility_type": "Inclusion Criteria", + } + ] + ) + + issues = backend.health(require_tables=True) + trials, scores = backend.search_trials( + primary_terms=["lung cancer"], + embeddings={"lung cancer": [1.0, 0.0]}, + sex="ALL", + overall_status="Recruiting", + search_mode="hybrid", + vector_score_threshold=0.0, + ) + criteria_hits = backend.search_criteria( + query="cancer", + nct_ids=["N1"], + search_mode="bm25", + use_entity_synonyms=True, + ) + + assert issues == [] + assert trials[0]["nct_id"] == "N1" + assert scores[0] > 0 + assert criteria_hits[0]["_source"]["criteria_id"] == "C1" diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index 22270350..0d64b076 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -1,55 +1,33 @@ from __future__ import annotations -import json import importlib.util +import json from pathlib import Path +from Matcher.config.config_loader import load_config +from Matcher.search import InMemorySearchBackend from Matcher.services import preflight from Matcher.services.preflight import run_preflight_checks -ES_CONFIG_PATH = Path(__file__).resolve().parents[1] / "utils/Indexer/es_config.py" -spec = importlib.util.spec_from_file_location("indexer_es_config", ES_CONFIG_PATH) -assert spec and spec.loader -indexer_es_config = importlib.util.module_from_spec(spec) -spec.loader.exec_module(indexer_es_config) -load_config = indexer_es_config.load_config - - -class HealthyIndices: - def __init__(self, existing: set[str]): - self.existing = existing - - def exists(self, index: str) -> bool: - return index in self.existing - - -class FakeES: - def __init__(self, *, healthy: bool = True, existing: set[str] | None = None): - self.healthy = healthy - self.indices = HealthyIndices(existing or set()) - - def ping(self) -> bool: - return self.healthy - def _base_config(tmp_path): - cert = tmp_path / "ca.crt" - cert.write_text("cert") patients = tmp_path / "patients" patients.mkdir() trials = tmp_path / "trials" trials.mkdir() + search_db = tmp_path / "search" + search_db.mkdir() return { "paths": { "patients_dir": str(patients), "trials_json_folder": str(trials), "output_dir": str(tmp_path / "results"), - "docker_certs": str(cert), }, - "elasticsearch": { - "host": "https://localhost:9200", - "index_trials": "clinical_trials", - "index_trials_eligibility": "trials_eligibility", + "search_backend": { + "backend": "lancedb", + "db_path": str(search_db), + "trials_table": "trials", + "criteria_table": "criteria", }, "model": { "cot_adapter_path": str(tmp_path / "models" / "cot"), @@ -74,37 +52,51 @@ def _entity_config(tmp_path): return cfg -def test_preflight_passes_for_required_paths_and_indices(tmp_path): +class FakeSearchBackend: + def __init__(self, issues: list[str] | None = None): + self.issues = issues or [] + + def health(self, *, require_tables: bool = False): + return self.issues if require_tables else [] + + +def test_preflight_passes_for_required_paths_and_tables(tmp_path): cfg = _base_config(tmp_path) issues = run_preflight_checks( cfg, - es_client=FakeES( - existing={"clinical_trials", "trials_eligibility"}, - ), + search_backend=FakeSearchBackend(), require_patient_inputs=True, require_trials_json=True, - require_indices=True, + require_search_tables=True, ) assert issues == [] -def test_preflight_reports_missing_indices(tmp_path): +def test_preflight_reports_missing_search_tables(tmp_path): cfg = _base_config(tmp_path) issues = run_preflight_checks( cfg, - es_client=FakeES(existing={"clinical_trials"}), - require_indices=True, + search_backend=FakeSearchBackend(["Missing LanceDB tables: criteria"]), + require_search_tables=True, ) - assert issues == ["Missing Elasticsearch indices: trials_eligibility"] + assert issues == ["Missing LanceDB tables: criteria"] -def test_preflight_reports_unreachable_elasticsearch(tmp_path): +def test_preflight_reports_missing_search_db_path(tmp_path): cfg = _base_config(tmp_path) - issues = run_preflight_checks(cfg, es_client=FakeES(healthy=False)) + cfg["search_backend"]["db_path"] = str(tmp_path / "missing-search") - assert issues == ["Elasticsearch is not reachable at https://localhost:9200."] + issues = run_preflight_checks( + cfg, + search_backend=FakeSearchBackend(), + require_search_tables=True, + ) + + assert issues == [ + f"search_backend.db_path does not exist: {tmp_path / 'missing-search'}" + ] def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): @@ -135,28 +127,78 @@ def test_preflight_reports_missing_entity_extra(tmp_path, monkeypatch): ) in issues -def test_indexer_config_uses_env_overrides_and_resolves_certs(tmp_path, monkeypatch): +def test_main_config_resolves_search_paths(tmp_path, monkeypatch): config_path = tmp_path / "config.json" - cert = tmp_path / "certs" / "ca.crt" - cert.parent.mkdir() - cert.write_text("cert") + schema = tmp_path / "schema.yaml" + schema.write_text("version: 1\nentities: []\n") config_path.write_text( json.dumps( { - "elasticsearch": { - "hosts": ["https://localhost:9200"], - "ca_certs": "certs/ca.crt", - "username": "elastic", - "password": "CHANGE_ME", - } + "paths": { + "patients_dir": "patients", + "output_dir": "results", + "trials_json_folder": "trials", + }, + "search_backend": { + "backend": "lancedb", + "db_path": "search", + "trials_table": "trials", + "criteria_table": "criteria", + }, + "entity_extraction": { + "backend": "regex", + "schema_path": str(schema), + }, + "concept_linker": { + "enabled": False, + "db_path": "concepts", + }, + "model": { + "base_model": "m", + "quantization": {}, + "cot_adapter_path": "models/cot", + "reranker_model_path": "r", + "reranker_adapter_path": "models/reranker", + }, + "tokenizer": {}, + "global": {"device": "cpu"}, + "embedder": {}, + "cot": {}, + "LLM_reranker": {}, + "search": {}, + "rag": {}, + "vllm": {}, } ) ) - monkeypatch.setenv("TRIALMATCHAI_ES_HOST", "https://es.example.test:9200") - monkeypatch.setenv("TRIALMATCHAI_ES_PASSWORD", "from-env") + search_env = tmp_path / "search-env" + monkeypatch.setenv("TRIALMATCHAI_SEARCH_DB_PATH", str(search_env)) cfg = load_config(config_path) - assert cfg["elasticsearch"]["hosts"] == ["https://es.example.test:9200"] - assert cfg["elasticsearch"]["password"] == "from-env" - assert cfg["elasticsearch"]["ca_certs"] == str(cert.resolve()) + assert cfg["search_backend"]["db_path"] == str(search_env.resolve()) + + +def test_indexer_loads_prepared_criteria_docs(tmp_path): + indexer_path = Path(__file__).resolve().parents[1] / "utils/Indexer/index_criteria.py" + spec = importlib.util.spec_from_file_location("index_criteria", indexer_path) + assert spec and spec.loader + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + processed = tmp_path / "processed_criteria" + trial_dir = processed / "N1" + trial_dir.mkdir(parents=True) + (trial_dir / "C1.json").write_text( + json.dumps({"criteria_id": "C1", "nct_id": "N1", "criterion": "cancer"}) + ) + backend = InMemorySearchBackend() + criteria_indexer = module.CriteriaIndexer( + backend=backend, + processed_file=tmp_path / "processed_ids.txt", + ) + + docs, completed = criteria_indexer.load_docs(processed, recreate=True) + + assert docs[0]["criteria_id"] == "C1" + assert completed == {"N1"} diff --git a/tests/test_search_queries.py b/tests/test_search_queries.py deleted file mode 100644 index c53d185c..00000000 --- a/tests/test_search_queries.py +++ /dev/null @@ -1,59 +0,0 @@ -import unittest - -from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch - - -class DummyES: - def search(self, *args, **kwargs): - raise AssertionError("ES search should not be called in these tests.") - - -class TestFirstLevelQueryBuilding(unittest.TestCase): - def setUp(self) -> None: - self.search = ClinicalTrialSearch( - es_client=DummyES(), - embedder=None, - index_name="index", - bio_med_ner=None, - ) - - def test_create_query_bm25(self) -> None: - query = self.search.create_query( - synonyms=["lung cancer"], - embeddings={}, - age=45, - sex="ALL", - overall_status="Recruiting", - max_text_score=1.0, - vector_score_threshold=0.5, - pre_selected_nct_ids=None, - other_conditions=None, - search_mode="bm25", - ) - self.assertIn("bool", query) - self.assertIn("should", query["bool"]) - self.assertIn("filter", query["bool"]) - - def test_create_query_vector(self) -> None: - embeddings = {"lung cancer": [0.1, 0.2], "smoking": [0.3, 0.4]} - query = self.search.create_query( - synonyms=["lung cancer"], - embeddings=embeddings, - age=60, - sex="MALE", - overall_status=None, - max_text_score=1.0, - vector_score_threshold=0.2, - pre_selected_nct_ids=None, - other_conditions=["smoking"], - search_mode="vector", - ) - self.assertIn("script_score", query) - script = query["script_score"]["script"] - self.assertIn("params", script) - self.assertEqual(len(script["params"]["query_vectors"]), 1) - self.assertEqual(len(script["params"]["other_condition_vectors"]), 1) - - -if __name__ == "__main__": - unittest.main() diff --git a/tests/test_search_queries_pytest.py b/tests/test_search_queries_pytest.py index 2d376826..a19f7221 100644 --- a/tests/test_search_queries_pytest.py +++ b/tests/test_search_queries_pytest.py @@ -1,16 +1,11 @@ from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch +from Matcher.search import InMemorySearchBackend, build_criteria_record, build_trial_record -class DummyES: - def search(self, *args, **kwargs): - return {"hits": {"hits": [], "max_score": 1.0}} - - -def test_first_level_query_bm25(): +def test_first_level_query_describes_backend_search(): search = ClinicalTrialSearch( - es_client=DummyES(), + search_backend=InMemorySearchBackend(), embedder=None, - index_name="index", bio_med_ner=None, ) query = search.create_query( @@ -22,35 +17,45 @@ def test_first_level_query_bm25(): max_text_score=1.0, vector_score_threshold=0.5, pre_selected_nct_ids=None, - other_conditions=None, + other_conditions=["smoking"], search_mode="bm25", ) - assert "bool" in query - assert "should" in query["bool"] - assert "filter" in query["bool"] + assert query["primary_terms"] == ["lung cancer"] + assert query["other_terms"] == ["smoking"] + assert query["overall_status"] == "Recruiting" + assert query["search_mode"] == "bm25" -def test_first_level_query_vector(): - search = ClinicalTrialSearch( - es_client=DummyES(), - embedder=None, - index_name="index", - bio_med_ner=None, + +def test_build_trial_record_flattens_search_text_and_vector(): + record = build_trial_record( + { + "nct_id": "N1", + "condition": ["Lung cancer", "NSCLC"], + "brief_title": "Targeted therapy trial", + "condition_vector": [1.0, 0.0], + "brief_title_vector": [0.0, 1.0], + } ) - embeddings = {"lung cancer": [0.1, 0.2], "smoking": [0.3, 0.4]} - query = search.create_query( - synonyms=["lung cancer"], - embeddings=embeddings, - age=60, - sex="MALE", - overall_status=None, - max_text_score=1.0, - vector_score_threshold=0.2, - pre_selected_nct_ids=None, - other_conditions=["smoking"], - search_mode="vector", + + assert "Lung cancer" in record["search_text"] + assert record["search_vector"] == [0.5, 0.5] + + +def test_build_criteria_record_flattens_entity_synonyms(): + record = build_criteria_record( + { + "criteria_id": "C1", + "criterion": "Documented malignancy", + "entities": [ + { + "text": "malignancy", + "synonyms": ["cancer", "neoplasm"], + "concept_candidates": [{"concept_name": "Malignant neoplasm"}], + } + ], + } ) - assert "script_score" in query - params = query["script_score"]["script"]["params"] - assert len(params["query_vectors"]) == 1 - assert len(params["other_condition_vectors"]) == 1 + + assert "cancer" in record["entity_synonyms_text"] + assert "Malignant neoplasm" in record["search_text"] diff --git a/tests/test_second_level_search_pytest.py b/tests/test_second_level_search_pytest.py index 6c0717b0..3436b1c0 100644 --- a/tests/test_second_level_search_pytest.py +++ b/tests/test_second_level_search_pytest.py @@ -1,17 +1,12 @@ from Matcher.pipeline.trial_search.second_level_search import SecondStageRetriever - - -class DummyES: - def search(self, *args, **kwargs): - return {"hits": {"hits": []}} +from Matcher.search import InMemorySearchBackend def test_score_criteria_without_llm_weights(): retriever = SecondStageRetriever( - es_client=DummyES(), + search_backend=InMemorySearchBackend(), llm_reranker=None, embedder=None, - index_name="idx", inclusion_weight=1.0, exclusion_weight=0.25, ) @@ -26,10 +21,9 @@ def test_score_criteria_without_llm_weights(): def test_aggregate_to_trials_weighted(): retriever = SecondStageRetriever( - es_client=DummyES(), + search_backend=InMemorySearchBackend(), llm_reranker=None, embedder=None, - index_name="idx", ) criteria = [ {"llm_score": 0.6, "_source": {"nct_id": "N1"}}, @@ -38,3 +32,35 @@ def test_aggregate_to_trials_weighted(): ] trials = retriever.aggregate_to_trials(criteria, threshold=0.5, method="weighted") assert trials[0]["nct_id"] == "N2" + + +def test_retrieve_criteria_uses_entity_synonyms(): + backend = InMemorySearchBackend( + criteria=[ + { + "criteria_id": "C1", + "nct_id": "N1", + "criterion": "Documented malignant neoplasm", + "eligibility_type": "Inclusion Criteria", + "entities": [{"text": "malignant neoplasm", "synonyms": ["cancer"]}], + }, + { + "criteria_id": "C2", + "nct_id": "N2", + "criterion": "Documented diabetes mellitus", + "eligibility_type": "Inclusion Criteria", + "entities": [], + }, + ] + ) + retriever = SecondStageRetriever( + search_backend=backend, + llm_reranker=None, + embedder=None, + entity_annotator=object(), + search_mode="bm25", + ) + + hits = retriever.retrieve_criteria(["N1"], ["cancer"]) + + assert hits["cancer"][0]["_source"]["criteria_id"] == "C1" diff --git a/tests/test_settings.py b/tests/test_settings.py index 8108af96..a417277a 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -10,38 +10,43 @@ class TestConfigLoading(unittest.TestCase): def test_load_config_from_repo(self) -> None: config_path = Path(__file__).resolve().parents[1] / "source/Matcher/config/config.json" config = load_config(str(config_path)) - self.assertIn("elasticsearch", config) + self.assertIn("search_backend", config) self.assertIn("embedder", config) self.assertIn("paths", config) def test_env_overrides(self) -> None: raw = { - "elasticsearch": { - "host": "http://localhost:9200", - "username": "user", - "password": "pass", + "search_backend": { + "backend": "lancedb", + "db_path": "old-search", + "trials_table": "old-trials", + "criteria_table": "old-criteria", }, "embedder": {"model_name": "old"}, + "search": {"mode": "hybrid"}, "entity_extraction": {"backend": "gliner2"}, "concept_linker": {"db_path": "old"}, } - os.environ["TRIALMATCHAI_ES_HOST"] = "http://override:9200" + os.environ["TRIALMATCHAI_SEARCH_DB_PATH"] = "data/search-test" + os.environ["TRIALMATCHAI_SEARCH_TRIALS_TABLE"] = "trials-test" + os.environ["TRIALMATCHAI_SEARCH_MODE"] = "bm25" os.environ["TRIALMATCHAI_EMBEDDER_MODEL_NAME"] = "new-model" - os.environ["TRIALMATCHAI_ES_AUTO_START"] = "true" os.environ["TRIALMATCHAI_ENTITY_BACKEND"] = "regex" os.environ["TRIALMATCHAI_CONCEPT_DB_PATH"] = "concepts" try: updated = apply_env_overrides(raw) finally: - os.environ.pop("TRIALMATCHAI_ES_HOST", None) + os.environ.pop("TRIALMATCHAI_SEARCH_DB_PATH", None) + os.environ.pop("TRIALMATCHAI_SEARCH_TRIALS_TABLE", None) + os.environ.pop("TRIALMATCHAI_SEARCH_MODE", None) os.environ.pop("TRIALMATCHAI_EMBEDDER_MODEL_NAME", None) - os.environ.pop("TRIALMATCHAI_ES_AUTO_START", None) os.environ.pop("TRIALMATCHAI_ENTITY_BACKEND", None) os.environ.pop("TRIALMATCHAI_CONCEPT_DB_PATH", None) - self.assertEqual(updated["elasticsearch"]["host"], "http://override:9200") + self.assertEqual(updated["search_backend"]["db_path"], "data/search-test") + self.assertEqual(updated["search_backend"]["trials_table"], "trials-test") + self.assertEqual(updated["search"]["mode"], "bm25") self.assertEqual(updated["embedder"]["model_name"], "new-model") - self.assertTrue(updated["elasticsearch"]["auto_start"]) self.assertEqual(updated["entity_extraction"]["backend"], "regex") self.assertEqual(updated["concept_linker"]["db_path"], "concepts") diff --git a/utils/Indexer/config.json b/utils/Indexer/config.json deleted file mode 100644 index 4a421a57..00000000 --- a/utils/Indexer/config.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "elasticsearch": { - "hosts": ["https://localhost:9200"], - "ca_certs": "../../elasticsearch/certs/ca/ca.crt", - "username": "elastic", - "password": "CHANGE_ME", - "request_timeout": 300, - "retry_on_timeout": true, - "max_retries": 3 - } - } diff --git a/utils/Indexer/es_config.py b/utils/Indexer/es_config.py deleted file mode 100644 index b13a106b..00000000 --- a/utils/Indexer/es_config.py +++ /dev/null @@ -1,42 +0,0 @@ -from __future__ import annotations - -import json -import os -from pathlib import Path -from typing import Any - -from elasticsearch import Elasticsearch - - -def load_config(path: str | Path) -> dict[str, Any]: - config_path = Path(path) - cfg = json.loads(config_path.read_text()) - es_conf = cfg.setdefault("elasticsearch", {}) - if os.getenv("TRIALMATCHAI_ES_HOST"): - es_conf["hosts"] = [os.environ["TRIALMATCHAI_ES_HOST"]] - if os.getenv("TRIALMATCHAI_ES_USERNAME"): - es_conf["username"] = os.environ["TRIALMATCHAI_ES_USERNAME"] - if os.getenv("TRIALMATCHAI_ES_PASSWORD"): - es_conf["password"] = os.environ["TRIALMATCHAI_ES_PASSWORD"] - if os.getenv("TRIALMATCHAI_ES_CA_CERTS"): - es_conf["ca_certs"] = os.environ["TRIALMATCHAI_ES_CA_CERTS"] - if es_conf.get("ca_certs"): - ca_path = Path(es_conf["ca_certs"]).expanduser() - if not ca_path.is_absolute(): - es_conf["ca_certs"] = str((config_path.parent / ca_path).resolve()) - return cfg - - -def make_es_client(cfg: dict[str, Any]) -> Elasticsearch: - es_conf = cfg["elasticsearch"] - kwargs: dict[str, Any] = { - "hosts": es_conf["hosts"], - "basic_auth": (es_conf["username"], es_conf["password"]), - "verify_certs": True, - "request_timeout": es_conf.get("request_timeout", 60), - "max_retries": es_conf.get("max_retries", 3), - "retry_on_timeout": es_conf.get("retry_on_timeout", True), - } - if es_conf.get("ca_certs") and Path(es_conf["ca_certs"]).exists(): - kwargs["ca_certs"] = es_conf["ca_certs"] - return Elasticsearch(**kwargs) diff --git a/utils/Indexer/index_criteria.py b/utils/Indexer/index_criteria.py index 3db6750c..50ea66b1 100644 --- a/utils/Indexer/index_criteria.py +++ b/utils/Indexer/index_criteria.py @@ -1,17 +1,21 @@ #!/usr/bin/env python3 +from __future__ import annotations + import argparse import json import logging -from concurrent.futures import ThreadPoolExecutor, as_completed +import sys from pathlib import Path -from elasticsearch import Elasticsearch, NotFoundError -from elasticsearch.helpers import bulk -try: - from .es_config import load_config, make_es_client -except ImportError: # pragma: no cover - direct script execution - from es_config import load_config, make_es_client +ROOT = Path(__file__).resolve().parents[2] +SOURCE = ROOT / "source" +if str(SOURCE) not in sys.path: + sys.path.append(str(SOURCE)) + +from Matcher.config.config_loader import load_config # noqa: E402 +from Matcher.search import LanceDBSearchBackend # noqa: E402 + logging.basicConfig( format="%(asctime)s %(levelname)s %(name)s - %(message)s", @@ -21,243 +25,120 @@ class CriteriaIndexer: - def __init__(self, es: Elasticsearch, index_name: str, processed_file: Path): - self.es = es - self.index_name = index_name + def __init__( + self, + backend: LanceDBSearchBackend, + *, + processed_file: Path, + ) -> None: + self.backend = backend self.processed_file = processed_file self.processed_file.parent.mkdir(parents=True, exist_ok=True) self.processed_ids = self._load_processed_ids() def _load_processed_ids(self) -> set[str]: if self.processed_file.exists(): - return set(self.processed_file.read_text().splitlines()) + return set(self.processed_file.read_text(encoding="utf-8").splitlines()) return set() - def _save_processed_ids(self): - self.processed_file.write_text("\n".join(sorted(self.processed_ids)) + "\n") - - def _trial_indexed(self, nct_id: str) -> bool: - try: - res = self.es.count( - index=self.index_name, body={"query": {"term": {"nct_id": nct_id}}} - ) - return res.get("count", 0) > 0 - except NotFoundError: - return False - - def _detect_dim(self, processed_folder: Path) -> int: - # find first JSON under any trial subfolder - for trial_dir in processed_folder.iterdir(): - if not trial_dir.is_dir(): - continue - for f in trial_dir.glob("*.json"): - doc = json.loads(f.read_text()) - vec = doc.get("criterion_vector", []) - if isinstance(vec, list): - return len(vec) - raise RuntimeError("No criterion_vector found in any processed JSON.") - - def create_index(self, dims: int): - mapping = { - "settings": { - "analysis": { - "analyzer": { - "standard_lowercase": { - "type": "custom", - "tokenizer": "standard", - "filter": ["lowercase"], - } - } - } - }, - "mappings": { - "properties": { - "criteria_id": {"type": "keyword"}, - "criterion": {"type": "text", "analyzer": "standard_lowercase"}, - "criterion_vector": { - "type": "dense_vector", - "dims": dims, - "index": True, - "similarity": "cosine", - "index_options": { - "type": "hnsw", - "m": 16, - "ef_construction": 100, - }, - }, - "entities": { - "type": "nested", - "properties": { - "normalized_id": {"type": "keyword"}, - "synonyms": { - "type": "text", - "analyzer": "standard_lowercase", - }, - "entity": { - "type": "text", - "analyzer": "standard_lowercase", - }, - "class": {"type": "keyword"}, - "entity_group": {"type": "keyword"}, - "text": { - "type": "text", - "analyzer": "standard_lowercase", - }, - "score": {"type": "float"}, - "linker_score": {"type": "float"}, - "linker_status": {"type": "keyword"}, - "concept_candidates": { - "type": "nested", - "properties": { - "normalized_id": {"type": "keyword"}, - "vocabulary_id": {"type": "keyword"}, - "concept_code": {"type": "keyword"}, - "concept_name": { - "type": "text", - "analyzer": "standard_lowercase", - }, - "domain_id": {"type": "keyword"}, - "score": {"type": "float"}, - }, - }, - }, - }, - "nct_id": {"type": "keyword"}, - "eligibility_type": {"type": "keyword"}, - } - }, - } - self.es.indices.create( - index=self.index_name, body=mapping, timeout="60s", master_timeout="60s" + def _save_processed_ids(self) -> None: + self.processed_file.write_text( + "\n".join(sorted(self.processed_ids)) + "\n", + encoding="utf-8", ) - logger.info(f"Created index {self.index_name} with dims={dims}") - - def _index_trial(self, trial_dir: Path, batch_size: int) -> tuple[str, int]: - nct_id = trial_dir.name - - # skip if already done or in ES - if nct_id in self.processed_ids or self._trial_indexed(nct_id): - logger.info(f"Skipping {nct_id}: already indexed") - return nct_id, 0 - - # load docs - docs = [] - for f in trial_dir.glob("*.json"): - try: - docs.append(json.loads(f.read_text())) - except Exception as e: - logger.warning(f"{nct_id}: failed to load {f.name}: {e}") - - if not docs: - logger.info(f"No JSONs for {nct_id}; marking done") - return nct_id, 0 - # bulk‐index in sub‐batches - total = 0 - for i in range(0, len(docs), batch_size): - batch = docs[i : i + batch_size] - actions = [ - { - "_op_type": "index", - "_index": self.index_name, - "_id": doc["criteria_id"], - "_source": doc, - } - for doc in batch - ] - result = bulk( - client=self.es, - actions=actions, - raise_on_error=False, - chunk_size=batch_size, - ) - succ, fails = result if isinstance(result, tuple) else (result, []) - total += succ - logger.info( - f"{nct_id}: batch {i // batch_size + 1} → {succ} indexed, {len(fails) if isinstance(fails, list) else 0} failed" - ) - - return nct_id, total - - def index_all( + def load_docs( self, processed_folder: Path, - batch_size: int = 100, - max_workers: int = 4, - refresh: bool = True, - ): - trials = [d for d in processed_folder.iterdir() if d.is_dir()] - if not trials: - logger.info("No trial subfolders found.") - return - - # 1) determine vector dims & create index if missing - if not self.es.indices.exists(index=self.index_name): - dims = self._detect_dim(processed_folder) - self.create_index(dims) - else: - logger.info(f"Index {self.index_name} exists; skipping creation") - - # 2) parallel indexing across trials - total_indexed = 0 - newly_done = [] - with ThreadPoolExecutor(max_workers=max_workers) as executor: - future_to_nct = { - executor.submit(self._index_trial, td, batch_size): td.name - for td in trials - } - for future in as_completed(future_to_nct): - nct = future_to_nct[future] + *, + recreate: bool, + ) -> tuple[list[dict], set[str]]: + docs: list[dict] = [] + completed: set[str] = set() + trial_dirs = sorted(path for path in processed_folder.iterdir() if path.is_dir()) + for trial_dir in trial_dirs: + nct_id = trial_dir.name + if not recreate and nct_id in self.processed_ids: + logger.info("Skipping %s: already indexed", nct_id) + continue + trial_docs = [] + for path in sorted(trial_dir.glob("*.json")): try: - nct_id, count = future.result() - total_indexed += count - newly_done.append(nct_id) - except Exception as e: - logger.error(f"{nct}: unexpected error: {e}") - newly_done.append(nct) - - # 3) optionally refresh & persist processed IDs - if refresh: - self.es.indices.refresh(index=self.index_name) + trial_docs.append(json.loads(path.read_text(encoding="utf-8"))) + except Exception as exc: + logger.warning("%s: failed to load %s: %s", nct_id, path.name, exc) + if trial_docs: + docs.extend(trial_docs) + completed.add(nct_id) + return docs, completed - self.processed_ids.update(newly_done) + def index_all( + self, + processed_folder: Path, + *, + recreate: bool = True, + ) -> int: + if not processed_folder.exists(): + raise FileNotFoundError(f"Criteria folder not found: {processed_folder}") + docs, completed = self.load_docs(processed_folder, recreate=recreate) + if not docs: + logger.info("No prepared criteria JSON files found.") + return 0 + count = self.backend.index_criteria(docs, recreate=recreate) + self.processed_ids.update(completed) self._save_processed_ids() logger.info( - f"✅ Indexed {total_indexed} criteria across {len(newly_done)} trials (skipped: {len(self.processed_ids) - len(newly_done)})." + "Indexed %s criteria documents into %s/%s.", + count, + self.backend.db_path, + self.backend.criteria_table, ) + return count -def main(): +def main() -> int: parser = argparse.ArgumentParser( - description="Bulk‑index prepared eligibility criteria in parallel" - ) - parser.add_argument("--config", required=True, help="Path to config.json") - parser.add_argument( - "--processed-folder", required=True, help="Root folder of trial subfolders" + description="Create or update the LanceDB eligibility criteria search table." ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") parser.add_argument( - "--index-name", default="trials_eligibility", help="ES index name" + "--processed-folder", + required=True, + help="Root folder containing one prepared criteria subfolder per trial", ) + parser.add_argument("--db-path", default=None, help="Override search DB path") + parser.add_argument("--table", default=None, help="Override criteria table name") parser.add_argument( - "--batch-size", type=int, default=100, help="Docs per bulk request" + "--processed-file", + default="utils/Indexer/processed_ids.txt", + help="File used to track already appended trial IDs", ) parser.add_argument( - "--max-workers", type=int, default=4, help="Parallel trial threads" + "--recreate", + action=argparse.BooleanOptionalAction, + default=True, + help="Overwrite the target table before writing.", ) args = parser.parse_args() - cfg = load_config(args.config) - es = make_es_client(cfg) + config = load_config(args.config) + search_cfg = config["search_backend"] + if args.db_path: + search_cfg["db_path"] = str(Path(args.db_path).expanduser().resolve()) + if args.table: + search_cfg["criteria_table"] = args.table + backend = LanceDBSearchBackend.from_config(config) indexer = CriteriaIndexer( - es=es, index_name=args.index_name, processed_file=Path("processed_ids.txt") + backend=backend, + processed_file=(ROOT / args.processed_file).resolve(), ) - indexer.index_all( - processed_folder=Path(args.processed_folder), - batch_size=args.batch_size, - max_workers=args.max_workers, + count = indexer.index_all( + Path(args.processed_folder), + recreate=args.recreate, ) + return 0 if count else 1 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/utils/Indexer/index_trials.py b/utils/Indexer/index_trials.py index c0ee4803..25630b7f 100644 --- a/utils/Indexer/index_trials.py +++ b/utils/Indexer/index_trials.py @@ -1,151 +1,69 @@ #!/usr/bin/env python3 +from __future__ import annotations + import argparse import json -import os +import sys from pathlib import Path -from elasticsearch import Elasticsearch -from elasticsearch.helpers import bulk - -try: - from .es_config import load_config, make_es_client -except ImportError: # pragma: no cover - direct script execution - from es_config import load_config, make_es_client +ROOT = Path(__file__).resolve().parents[2] +SOURCE = ROOT / "source" +if str(SOURCE) not in sys.path: + sys.path.append(str(SOURCE)) -def detect_vector_dim(sample: dict) -> int: - for k, v in sample.items(): - if k.endswith("_vector") and isinstance(v, list): - return len(v) - raise ValueError("No vector field found in sample") +from Matcher.config.config_loader import load_config # noqa: E402 +from Matcher.search import LanceDBSearchBackend # noqa: E402 def load_processed(folder: Path) -> list[dict]: - docs = [] - for fn in os.listdir(folder): - if fn.endswith(".json"): - docs.append(json.loads((folder / fn).read_text())) + docs: list[dict] = [] + for path in sorted(folder.glob("*.json")): + docs.append(json.loads(path.read_text(encoding="utf-8"))) return docs -def create_index(es: Elasticsearch, name: str, dims: int): - body = { - "settings": { - "analysis": { - "analyzer": { - "standard_lowercase": { - "type": "custom", - "tokenizer": "standard", - "filter": ["lowercase"], - } - } - } - }, - "mappings": { - "properties": { - "nct_id": {"type": "keyword"}, - "brief_title": {"type": "text", "analyzer": "standard_lowercase"}, - "brief_title_vector": {"type": "dense_vector", "dims": dims}, - "brief_summary": {"type": "text", "analyzer": "standard_lowercase"}, - "brief_summary_vector": {"type": "dense_vector", "dims": dims}, - "condition": {"type": "text", "analyzer": "standard_lowercase"}, - "condition_vector": {"type": "dense_vector", "dims": dims}, - "overall_status": {"type": "keyword"}, - "start_date": {"type": "date", "format": "yyyy-MM-dd"}, - "completion_date": {"type": "date", "format": "yyyy-MM-dd"}, - "phase": {"type": "keyword"}, - "study_type": {"type": "keyword"}, - "intervention": { - "properties": { - "intervention_type": {"type": "keyword"}, - "intervention_name": {"type": "text"}, - } - }, - "gender": {"type": "keyword"}, - "minimum_age": {"type": "float"}, - "maximum_age": {"type": "float"}, - "location": { - "properties": { - "location_name": {"type": "text"}, - "location_address": {"type": "text"}, - } - }, - "reference": { - "type": "nested", - "properties": { - "citation": {"type": "text"}, - "PMID": {"type": "keyword"}, - }, - }, - "eligibility_criteria": { - "type": "text", - "analyzer": "standard_lowercase", - }, - "eligibility_criteria_vector": {"type": "dense_vector", "dims": dims}, - } - }, - } - es.indices.create(index=name, body=body) - print(f"Created index `{name}` with vector dims={dims}") - - -def main(): - parser = argparse.ArgumentParser(description="Bulk‑index processed trial JSONs") - parser.add_argument( - "--config", - required=True, - help="Path to JSON config file with Elasticsearch credentials", +def main() -> int: + parser = argparse.ArgumentParser( + description="Create or update the LanceDB trial search table." ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") parser.add_argument( - "--processed-folder", required=True, help="Folder of processed JSONs to index" - ) - parser.add_argument( - "--index-name", - default="clinical_trials", - help="Target Elasticsearch index name", + "--processed-folder", + required=True, + help="Folder of prepared trial JSON files", ) + parser.add_argument("--db-path", default=None, help="Override search DB path") + parser.add_argument("--table", default=None, help="Override trials table name") parser.add_argument( - "--batch-size", type=int, default=100, help="Number of docs per bulk request" + "--recreate", + action=argparse.BooleanOptionalAction, + default=True, + help="Overwrite the target table before writing.", ) args = parser.parse_args() - cfg = load_config(args.config) - es = make_es_client(cfg) + config = load_config(args.config) + search_cfg = config["search_backend"] + if args.db_path: + search_cfg["db_path"] = str(Path(args.db_path).expanduser().resolve()) + if args.table: + search_cfg["trials_table"] = args.table processed_path = Path(args.processed_folder) docs = load_processed(processed_path) if not docs: - print("❌ No JSONs found to index.") - return - - dims = detect_vector_dim(docs[0]) - - # <-- FIXED: use keyword arg `index=` - if not es.indices.exists(index=args.index_name): - create_index(es, args.index_name, dims) - else: - print(f"Index `{args.index_name}` already exists; skipping creation.") - - actions = [ - { - "_op_type": "index", - "_index": args.index_name, - "_id": doc["nct_id"], - "_source": doc, - } - for doc in docs - ] - - success, failures = bulk( - client=es, - actions=actions, - chunk_size=args.batch_size, - stats_only=True, - raise_on_error=False, + print(f"No prepared trial JSON files found in {processed_path}.") + return 1 + + backend = LanceDBSearchBackend.from_config(config) + count = backend.index_trials(docs, recreate=args.recreate) + print( + f"Indexed {count} trial documents into " + f"{backend.db_path}/{backend.trials_table}." ) - es.indices.refresh(index=args.index_name) - print(f"✅ Indexed {success} documents; {failures} failures.") + return 0 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/uv.lock b/uv.lock index ac3614e5..5756318d 100644 --- a/uv.lock +++ b/uv.lock @@ -731,31 +731,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/44/5a/f0b9ad6c0a9017e62d4735daaeb11ba3b6c009d69a26141b258cd37b5588/einops-0.8.0-py3-none-any.whl", hash = "sha256:9572fb63046264a862693b0a87088af3bdc8c068fde03de63453cbbde245465f", size = 43223, upload-time = "2024-04-28T04:07:49.718Z" }, ] -[[package]] -name = "elastic-transport" -version = "8.17.1" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "certifi", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - { name = "urllib3", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/6a/54/d498a766ac8fa475f931da85a154666cc81a70f8eb4a780bc8e4e934e9ac/elastic_transport-8.17.1.tar.gz", hash = "sha256:5edef32ac864dca8e2f0a613ef63491ee8d6b8cfb52881fa7313ba9290cac6d2", size = 73425, upload-time = "2025-03-13T07:28:30.776Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/cf/cd/b71d5bc74cde7fc6fd9b2ff9389890f45d9762cbbbf81dc5e51fd7588c4a/elastic_transport-8.17.1-py3-none-any.whl", hash = "sha256:192718f498f1d10c5e9aa8b9cf32aed405e469a7f0e9d6a8923431dbb2c59fb8", size = 64969, upload-time = "2025-03-13T07:28:29.031Z" }, -] - -[[package]] -name = "elasticsearch" -version = "8.17.2" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - { name = "elastic-transport", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, -] -sdist = { url = "https://files.pythonhosted.org/packages/5b/3d/f563e58f45d23565c0d0316a565638ce312f536b882a3281b8047fb4a58f/elasticsearch-8.17.2.tar.gz", hash = "sha256:ff7f1db8aeefd87ceba4edce3aa4070994582e6cf029d2e67b74e66d634509db", size = 602691, upload-time = "2025-03-04T12:14:27.382Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/c0/50/16306f4722ca2fcb64a5875bc1fa9b4d0bcb08c05967f60c23acd4cbb019/elasticsearch-8.17.2-py3-none-any.whl", hash = "sha256:2d058dcddd8f2686cd431a916cdf983f9fb7d211d902834f564ab7df05ba6478", size = 717971, upload-time = "2025-03-04T12:14:23.843Z" }, -] - [[package]] name = "email-validator" version = "2.3.0" @@ -3733,11 +3708,11 @@ dependencies = [ { name = "accelerate", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "bioregistry", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "einops", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - { name = "elasticsearch", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "faiss-cpu", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "gliner", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "huggingface-hub", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "joblib", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "lancedb", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "medspacy", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "medspacy-quickumls", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "nltk", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, @@ -3746,6 +3721,7 @@ dependencies = [ { name = "orjson", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "pandas", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "peft", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, + { name = "pyarrow", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "pydantic", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "pydantic-settings", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "pyfastner", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, @@ -3776,8 +3752,6 @@ dependencies = [ [package.optional-dependencies] entity = [ { name = "gliner2", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - { name = "lancedb", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - { name = "pyarrow", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, ] gpu = [ { name = "bitsandbytes", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, @@ -3811,7 +3785,6 @@ requires-dist = [ { name = "bitsandbytes", marker = "(sys_platform == 'linux' and extra == 'gpu') or (sys_platform == 'win32' and extra == 'gpu')", specifier = "==0.49.2" }, { name = "datasets", marker = "extra == 'training'", specifier = "==2.19.0" }, { name = "einops", specifier = "==0.8.0" }, - { name = "elasticsearch", specifier = "==8.17.2" }, { name = "faiss-cpu", specifier = "==1.9.0.post1" }, { name = "gliner", specifier = "==0.2.27" }, { name = "gliner2", marker = "extra == 'entity'", specifier = ">=1.3.1,<2" }, @@ -3819,7 +3792,7 @@ requires-dist = [ { name = "huggingface-hub", specifier = ">=1.5.0,<2" }, { name = "jinja2", marker = "extra == 'training'", specifier = "==3.1.6" }, { name = "joblib", specifier = "==1.4.2" }, - { name = "lancedb", marker = "extra == 'entity'", specifier = ">=0.25.0,<0.26" }, + { name = "lancedb", specifier = ">=0.25.0,<0.26" }, { name = "langchain", marker = "extra == 'llm'", specifier = "==1.3.10" }, { name = "langchain-community", marker = "extra == 'llm'", specifier = "==0.4.2" }, { name = "langchain-openai", marker = "extra == 'llm'", specifier = "==1.3.2" }, @@ -3831,7 +3804,7 @@ requires-dist = [ { name = "orjson", specifier = "==3.11.9" }, { name = "pandas", specifier = "==2.2.3" }, { name = "peft", specifier = "==0.15.2" }, - { name = "pyarrow", marker = "extra == 'entity'", specifier = ">=24.0.0,<25" }, + { name = "pyarrow", specifier = ">=24.0.0,<25" }, { name = "pydantic", specifier = ">=2.12.0,<3" }, { name = "pydantic-settings", specifier = ">=2.14.2,<3" }, { name = "pyfastner", specifier = "==1.0.10" }, From d51239dbfc31f36bcbad2b354271349b32bc1eef Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 21 Jun 2026 19:58:24 +0200 Subject: [PATCH 04/64] Professionalize package and add registry updater --- .dockerignore | 18 +- .env.example | 15 +- .gitignore | 35 +- Dockerfile | 5 +- README.md | 71 +- docker-compose.yml | 9 +- docs/architecture.md | 41 + docs/registry-updater.md | 89 ++ docs/release.md | 43 + pyproject.toml | 78 +- scripts/bootstrap_data.sh | 16 +- source/Matcher/cli/healthcheck.py | 50 - source/Matcher/models/embedding/__init__.py | 3 - source/Matcher/schemas/__init__.py | 3 - source/Matcher/utils/__init__.py | 0 source/Parser/__init__.py | 0 source/Parser/biomedner_engine.py | 104 -- source/Parser/convert.py | 416 ------ source/Parser/main.py | 192 --- source/Parser/normalizer.py | 28 - source/__init__.py | 0 src/Matcher/__init__.py | 13 + src/Matcher/config/__init__.py | 1 + src/Matcher/config/config_loader.py | 1 + src/Matcher/config/settings.py | 1 + src/Matcher/entities/__init__.py | 1 + src/Matcher/main.py | 1 + src/Matcher/pipeline/__init__.py | 1 + src/Matcher/pipeline/cot_reasoning.py | 1 + src/Matcher/pipeline/cot_reasoning_vllm.py | 1 + src/Matcher/pipeline/phenopacket_processor.py | 1 + src/Matcher/pipeline/trial_ranker.py | 1 + src/Matcher/pipeline/trial_search/__init__.py | 1 + .../trial_search/first_level_search.py | 1 + .../trial_search/second_level_search.py | 1 + src/Matcher/schemas/__init__.py | 1 + src/Matcher/search/__init__.py | 1 + src/Matcher/services/__init__.py | 1 + src/Matcher/utils/__init__.py | 1 + src/Matcher/utils/file_utils.py | 1 + src/Matcher/utils/json_utils.py | 1 + src/Matcher/utils/logging_config.py | 1 + src/Matcher/utils/retry.py | 1 + src/Matcher/utils/temporal_utils.py | 1 + src/Matcher/utils/timing.py | 1 + src/trialmatchai/__init__.py | 3 + src/trialmatchai/__main__.py | 4 + .../trialmatchai}/cli/__init__.py | 0 .../trialmatchai}/cli/bootstrap_data.py | 0 .../trialmatchai}/cli/build_concepts.py | 8 +- src/trialmatchai/cli/healthcheck.py | 101 ++ .../trialmatchai}/cli/index_data.py | 72 +- src/trialmatchai/cli/main.py | 39 + .../Matcher => src/trialmatchai}/cli/run.py | 2 +- src/trialmatchai/cli/update_registry.py | 180 +++ .../trialmatchai/config}/__init__.py | 0 .../trialmatchai}/config/config.json | 15 +- .../trialmatchai}/config/config_loader.py | 22 +- .../trialmatchai}/config/settings.py | 34 +- .../trialmatchai}/entities/__init__.py | 4 +- .../trialmatchai}/entities/annotator.py | 12 +- .../trialmatchai}/entities/builder.py | 2 +- .../trialmatchai}/entities/linker.py | 6 +- .../trialmatchai}/entities/recognizers.py | 4 +- .../trialmatchai}/entities/schemas.py | 4 +- .../trialmatchai}/entities/types.py | 0 .../entity_schemas/trialmatchai.yaml | 0 {source/Matcher => src/trialmatchai}/main.py | 36 +- .../trialmatchai/matching}/__init__.py | 0 .../matching/eligibility_reasoning.py | 6 +- .../matching/eligibility_reasoning_vllm.py | 6 +- .../matching}/phenopacket_processor.py | 10 +- .../matching/retrieval}/__init__.py | 0 .../matching/retrieval/criteria_retrieval.py | 10 +- .../matching/retrieval/trial_retrieval.py | 6 +- .../trialmatchai/matching}/trial_ranker.py | 4 +- .../trialmatchai/models}/__init__.py | 0 src/trialmatchai/models/embedding/__init__.py | 3 + .../models/embedding/query_embedder.py | 2 +- .../models/embedding/sentence_embedder.py | 2 +- .../models/embedding/text_embedder.py | 2 +- .../trialmatchai/models/llm}/__init__.py | 0 .../trialmatchai}/models/llm/llm_loader.py | 2 +- .../trialmatchai}/models/llm/llm_reranker.py | 2 +- .../trialmatchai}/models/llm/vllm_loader.py | 4 +- src/trialmatchai/preprocessing/__init__.py | 1 + .../preprocessing/regex/__init__.py | 1 + .../regex/exception_regex_patterns.json | 0 .../preprocessing}/regex/regex_patterns.json | 0 .../preprocessing}/regex/regex_variants.tsv | 0 src/trialmatchai/registry/__init__.py | 13 + .../registry/clinicaltrials_gov.py | 187 +++ src/trialmatchai/registry/defaults.py | 20 + src/trialmatchai/registry/manifest.py | 55 + src/trialmatchai/registry/normalization.py | 205 +++ src/trialmatchai/registry/preparation.py | 243 ++++ src/trialmatchai/registry/updater.py | 250 ++++ src/trialmatchai/schemas/__init__.py | 3 + .../trialmatchai}/schemas/phenopacket.py | 0 .../trialmatchai}/search/__init__.py | 2 +- .../trialmatchai}/search/lancedb_backend.py | 65 +- .../trialmatchai/services}/__init__.py | 0 .../trialmatchai}/services/preflight.py | 18 +- .../trialmatchai/utils}/__init__.py | 0 .../trialmatchai}/utils/evaluation.py | 6 +- .../trialmatchai}/utils/file_utils.py | 0 .../trialmatchai}/utils/json_utils.py | 0 .../trialmatchai}/utils/logging_config.py | 0 .../trialmatchai}/utils/retry.py | 0 .../trialmatchai}/utils/temporal_utils.py | 0 .../trialmatchai}/utils/timing.py | 0 tests/conftest.py | 2 +- tests/test_config_pytest.py | 4 +- tests/test_deployment_readiness.py | 12 +- tests/test_entities.py | 25 +- tests/test_file_utils_pytest.py | 2 +- tests/test_first_level_search_pytest.py | 4 +- tests/test_lancedb_search_backend.py | 2 +- tests/test_logging.py | 2 +- tests/test_logging_pytest.py | 2 +- tests/test_package_imports.py | 18 + tests/test_phenopacket_processor_pytest.py | 2 +- tests/test_preflight_and_indexer.py | 31 +- tests/test_registry_updater.py | 284 ++++ tests/test_retry_pytest.py | 2 +- tests/test_schemas.py | 2 +- tests/test_search_queries_pytest.py | 4 +- tests/test_second_level_search_pytest.py | 4 +- tests/test_settings.py | 13 +- tests/test_trial_ranker_pytest.py | 2 +- utils/DataLoader/__init__.py | 0 utils/DataLoader/downloader.py | 254 ---- utils/DataLoader/test/__init__.py | 0 utils/DataLoader/test/test_downloader.py | 179 --- utils/Indexer/__init__.py | 0 utils/Indexer/flatten.py | 41 - utils/Indexer/index_criteria.py | 144 -- utils/Indexer/index_trials.py | 69 - utils/Indexer/prepare_criteria.py | 249 ---- utils/Indexer/prepare_trials.py | 166 --- utils/Indexer/zipper.sh | 31 - utils/Preprocessor/__init__.py | 0 utils/Preprocessor/jsonify.py | 87 -- .../Preprocessor/preprocess_clinical_notes.py | 537 -------- utils/Preprocessor/preprocessing.py | 67 - utils/Preprocessor/preprocessing_utils.py | 649 ---------- utils/Preprocessor/test/__init__.py | 0 utils/Preprocessor/test/test_preprocessing.py | 336 ----- utils/Preprocessor/utils.py | 599 --------- .../finetuning/finetune_instruct/__init__.py | 1 - .../finetuning/finetune_instruct/arguments.py | 184 --- utils/finetuning/finetune_instruct/data.py | 233 ---- .../finetune_instruct/data_llama.py | 172 --- .../finetune_instruct/data_summary.py | 240 ---- .../finetuning/finetune_instruct/evaluate.py | 269 ---- .../finetune_instruct/evaluate_CoT.py | 160 --- .../finetune_instruct/evaluate_gemma2.py | 242 ---- .../finetune_instruct/evaluate_llama.py | 202 --- .../finetuning/finetune_instruct/finetune.sh | 25 - .../finetune_instruct/load_model.py | 64 - .../finetuning/finetune_instruct/modeling.py | 83 -- utils/finetuning/finetune_instruct/run.py | 161 --- .../finetune_instruct/split_train_test.py | 27 - utils/finetuning/finetune_instruct/trainer.py | 70 - utils/gpt/gpt-generate-summaries.py | 218 ---- utils/gpt/gpt_generate_ideal_candidates.py | 407 ------ utils/gpt/gpt_generate_reranking_data.py | 166 --- uv.lock | 1153 +---------------- 168 files changed, 2339 insertions(+), 8491 deletions(-) create mode 100644 docs/architecture.md create mode 100644 docs/registry-updater.md create mode 100644 docs/release.md delete mode 100644 source/Matcher/cli/healthcheck.py delete mode 100644 source/Matcher/models/embedding/__init__.py delete mode 100644 source/Matcher/schemas/__init__.py delete mode 100644 source/Matcher/utils/__init__.py delete mode 100644 source/Parser/__init__.py delete mode 100644 source/Parser/biomedner_engine.py delete mode 100644 source/Parser/convert.py delete mode 100644 source/Parser/main.py delete mode 100644 source/Parser/normalizer.py delete mode 100644 source/__init__.py create mode 100644 src/Matcher/__init__.py create mode 100644 src/Matcher/config/__init__.py create mode 100644 src/Matcher/config/config_loader.py create mode 100644 src/Matcher/config/settings.py create mode 100644 src/Matcher/entities/__init__.py create mode 100644 src/Matcher/main.py create mode 100644 src/Matcher/pipeline/__init__.py create mode 100644 src/Matcher/pipeline/cot_reasoning.py create mode 100644 src/Matcher/pipeline/cot_reasoning_vllm.py create mode 100644 src/Matcher/pipeline/phenopacket_processor.py create mode 100644 src/Matcher/pipeline/trial_ranker.py create mode 100644 src/Matcher/pipeline/trial_search/__init__.py create mode 100644 src/Matcher/pipeline/trial_search/first_level_search.py create mode 100644 src/Matcher/pipeline/trial_search/second_level_search.py create mode 100644 src/Matcher/schemas/__init__.py create mode 100644 src/Matcher/search/__init__.py create mode 100644 src/Matcher/services/__init__.py create mode 100644 src/Matcher/utils/__init__.py create mode 100644 src/Matcher/utils/file_utils.py create mode 100644 src/Matcher/utils/json_utils.py create mode 100644 src/Matcher/utils/logging_config.py create mode 100644 src/Matcher/utils/retry.py create mode 100644 src/Matcher/utils/temporal_utils.py create mode 100644 src/Matcher/utils/timing.py create mode 100644 src/trialmatchai/__init__.py create mode 100644 src/trialmatchai/__main__.py rename {source/Matcher => src/trialmatchai}/cli/__init__.py (100%) rename {source/Matcher => src/trialmatchai}/cli/bootstrap_data.py (100%) rename {source/Matcher => src/trialmatchai}/cli/build_concepts.py (93%) create mode 100644 src/trialmatchai/cli/healthcheck.py rename {source/Matcher => src/trialmatchai}/cli/index_data.py (53%) create mode 100644 src/trialmatchai/cli/main.py rename {source/Matcher => src/trialmatchai}/cli/run.py (88%) create mode 100644 src/trialmatchai/cli/update_registry.py rename {source/Matcher => src/trialmatchai/config}/__init__.py (100%) rename {source/Matcher => src/trialmatchai}/config/config.json (81%) rename {source/Matcher => src/trialmatchai}/config/config_loader.py (83%) rename {source/Matcher => src/trialmatchai}/config/settings.py (85%) rename {source/Matcher => src/trialmatchai}/entities/__init__.py (66%) rename {source/Matcher => src/trialmatchai}/entities/annotator.py (90%) rename {source/Matcher => src/trialmatchai}/entities/builder.py (99%) rename {source/Matcher => src/trialmatchai}/entities/linker.py (98%) rename {source/Matcher => src/trialmatchai}/entities/recognizers.py (98%) rename {source/Matcher => src/trialmatchai}/entities/schemas.py (96%) rename {source/Matcher => src/trialmatchai}/entities/types.py (100%) rename {source/Matcher => src/trialmatchai}/entity_schemas/trialmatchai.yaml (100%) rename {source/Matcher => src/trialmatchai}/main.py (91%) rename {source/Matcher/config => src/trialmatchai/matching}/__init__.py (100%) rename source/Matcher/pipeline/cot_reasoning.py => src/trialmatchai/matching/eligibility_reasoning.py (98%) rename source/Matcher/pipeline/cot_reasoning_vllm.py => src/trialmatchai/matching/eligibility_reasoning_vllm.py (99%) rename {source/Matcher/pipeline => src/trialmatchai/matching}/phenopacket_processor.py (98%) rename {source/Matcher/models => src/trialmatchai/matching/retrieval}/__init__.py (100%) rename source/Matcher/pipeline/trial_search/second_level_search.py => src/trialmatchai/matching/retrieval/criteria_retrieval.py (96%) rename source/Matcher/pipeline/trial_search/first_level_search.py => src/trialmatchai/matching/retrieval/trial_retrieval.py (97%) rename {source/Matcher/pipeline => src/trialmatchai/matching}/trial_ranker.py (95%) rename {source/Matcher/models/llm => src/trialmatchai/models}/__init__.py (100%) create mode 100644 src/trialmatchai/models/embedding/__init__.py rename {source/Matcher => src/trialmatchai}/models/embedding/query_embedder.py (87%) rename {source/Matcher => src/trialmatchai}/models/embedding/sentence_embedder.py (87%) rename {source/Matcher => src/trialmatchai}/models/embedding/text_embedder.py (98%) rename {source/Matcher/pipeline => src/trialmatchai/models/llm}/__init__.py (100%) rename {source/Matcher => src/trialmatchai}/models/llm/llm_loader.py (98%) rename {source/Matcher => src/trialmatchai}/models/llm/llm_reranker.py (99%) rename {source/Matcher => src/trialmatchai}/models/llm/vllm_loader.py (98%) create mode 100644 src/trialmatchai/preprocessing/__init__.py create mode 100644 src/trialmatchai/preprocessing/regex/__init__.py rename {source => src/trialmatchai/preprocessing}/regex/exception_regex_patterns.json (100%) rename {source => src/trialmatchai/preprocessing}/regex/regex_patterns.json (100%) rename {source => src/trialmatchai/preprocessing}/regex/regex_variants.tsv (100%) create mode 100644 src/trialmatchai/registry/__init__.py create mode 100644 src/trialmatchai/registry/clinicaltrials_gov.py create mode 100644 src/trialmatchai/registry/defaults.py create mode 100644 src/trialmatchai/registry/manifest.py create mode 100644 src/trialmatchai/registry/normalization.py create mode 100644 src/trialmatchai/registry/preparation.py create mode 100644 src/trialmatchai/registry/updater.py create mode 100644 src/trialmatchai/schemas/__init__.py rename {source/Matcher => src/trialmatchai}/schemas/phenopacket.py (100%) rename {source/Matcher => src/trialmatchai}/search/__init__.py (85%) rename {source/Matcher => src/trialmatchai}/search/lancedb_backend.py (91%) rename {source/Matcher/pipeline/trial_search => src/trialmatchai/services}/__init__.py (100%) rename {source/Matcher => src/trialmatchai}/services/preflight.py (89%) rename {source/Matcher/services => src/trialmatchai/utils}/__init__.py (100%) rename {source/Matcher => src/trialmatchai}/utils/evaluation.py (98%) rename {source/Matcher => src/trialmatchai}/utils/file_utils.py (100%) rename {source/Matcher => src/trialmatchai}/utils/json_utils.py (100%) rename {source/Matcher => src/trialmatchai}/utils/logging_config.py (100%) rename {source/Matcher => src/trialmatchai}/utils/retry.py (100%) rename {source/Matcher => src/trialmatchai}/utils/temporal_utils.py (100%) rename {source/Matcher => src/trialmatchai}/utils/timing.py (100%) create mode 100644 tests/test_package_imports.py create mode 100644 tests/test_registry_updater.py delete mode 100644 utils/DataLoader/__init__.py delete mode 100644 utils/DataLoader/downloader.py delete mode 100644 utils/DataLoader/test/__init__.py delete mode 100644 utils/DataLoader/test/test_downloader.py delete mode 100644 utils/Indexer/__init__.py delete mode 100644 utils/Indexer/flatten.py delete mode 100644 utils/Indexer/index_criteria.py delete mode 100644 utils/Indexer/index_trials.py delete mode 100644 utils/Indexer/prepare_criteria.py delete mode 100644 utils/Indexer/prepare_trials.py delete mode 100644 utils/Indexer/zipper.sh delete mode 100644 utils/Preprocessor/__init__.py delete mode 100644 utils/Preprocessor/jsonify.py delete mode 100644 utils/Preprocessor/preprocess_clinical_notes.py delete mode 100644 utils/Preprocessor/preprocessing.py delete mode 100644 utils/Preprocessor/preprocessing_utils.py delete mode 100644 utils/Preprocessor/test/__init__.py delete mode 100644 utils/Preprocessor/test/test_preprocessing.py delete mode 100644 utils/Preprocessor/utils.py delete mode 100644 utils/finetuning/finetune_instruct/__init__.py delete mode 100644 utils/finetuning/finetune_instruct/arguments.py delete mode 100644 utils/finetuning/finetune_instruct/data.py delete mode 100644 utils/finetuning/finetune_instruct/data_llama.py delete mode 100644 utils/finetuning/finetune_instruct/data_summary.py delete mode 100644 utils/finetuning/finetune_instruct/evaluate.py delete mode 100644 utils/finetuning/finetune_instruct/evaluate_CoT.py delete mode 100644 utils/finetuning/finetune_instruct/evaluate_gemma2.py delete mode 100644 utils/finetuning/finetune_instruct/evaluate_llama.py delete mode 100644 utils/finetuning/finetune_instruct/finetune.sh delete mode 100644 utils/finetuning/finetune_instruct/load_model.py delete mode 100644 utils/finetuning/finetune_instruct/modeling.py delete mode 100644 utils/finetuning/finetune_instruct/run.py delete mode 100644 utils/finetuning/finetune_instruct/split_train_test.py delete mode 100644 utils/finetuning/finetune_instruct/trainer.py delete mode 100644 utils/gpt/gpt-generate-summaries.py delete mode 100644 utils/gpt/gpt_generate_ideal_candidates.py delete mode 100644 utils/gpt/gpt_generate_reranking_data.py diff --git a/.dockerignore b/.dockerignore index 408dc51c..ac57fd4e 100644 --- a/.dockerignore +++ b/.dockerignore @@ -14,16 +14,8 @@ data/ results/ models/ logs/ -elasticsearch/certs/ -elasticsearch/config/ -elasticsearch/data/ -elasticsearch/data1/ -elasticsearch/logs/ -elasticsearch/sif/ -elasticsearch/tmp-config/ -source/Parser/input/ -source/Parser/output/ -source/Parser/logs/ -source/Parser/models/ -source/Parser/resources/ -utils/Indexer/*.log +build/ +dist/ +*.egg-info/ +source/ +utils/ diff --git a/.env.example b/.env.example index 2ee996fa..10dc8e14 100644 --- a/.env.example +++ b/.env.example @@ -21,11 +21,24 @@ TRIALMATCHAI_COT_BACKEND=vllm TRIALMATCHAI_ENTITY_BACKEND=gliner2 TRIALMATCHAI_ENTITY_MODEL_NAME=fastino/gliner2-base -TRIALMATCHAI_ENTITY_SCHEMA_PATH=source/Matcher/entity_schemas/trialmatchai.yaml +TRIALMATCHAI_ENTITY_SCHEMA_PATH=src/trialmatchai/entity_schemas/trialmatchai.yaml TRIALMATCHAI_ENTITY_TRUST_REMOTE_CODE=false TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts TRIALMATCHAI_CONCEPT_TABLE=concepts TRIALMATCHAI_LINK_ACCEPT=0.80 TRIALMATCHAI_LINK_REJECT=0.30 + +TRIALMATCHAI_REGISTRY_SOURCE=clinicaltrials.gov +TRIALMATCHAI_REGISTRY_API_BASE_URL=https://clinicaltrials.gov/api/v2/studies +TRIALMATCHAI_REGISTRY_KEYWORDS_FILE= +TRIALMATCHAI_REGISTRY_SINCE_DAYS=7 +TRIALMATCHAI_REGISTRY_MAX_STUDIES= +TRIALMATCHAI_REGISTRY_REQUEST_TIMEOUT=30 +TRIALMATCHAI_REGISTRY_RATE_LIMIT_PER_SECOND=2 +TRIALMATCHAI_REGISTRY_RAW_DIR=data/registry/raw +TRIALMATCHAI_REGISTRY_MANIFEST_PATH=data/registry/manifest.jsonl +TRIALMATCHAI_REGISTRY_REPORTS_DIR=data/registry/runs +TRIALMATCHAI_REGISTRY_FAILURE_THRESHOLD=0.25 + TRIALMATCHAI_LOG_LEVEL=INFO TRIALMATCHAI_LOG_JSON=1 diff --git a/.gitignore b/.gitignore index cc9e6955..6135ab2a 100644 --- a/.gitignore +++ b/.gitignore @@ -3,7 +3,6 @@ # ============================ data/ results/ -src/ ablation/ logs/ *.log @@ -11,43 +10,15 @@ logs/ .env.* !.env.example -# ============================ -# Finetuning ignore rules -# ============================ - -# Ignore ANY directory under finetuning/ starting with checkpoint -**/finetuning/**/checkpoint*/ - -# Ignore ANY directory under finetuning/ starting with finetuned -**/finetuning/**/finetuned*/ - -# Ignore ANY directory under finetuning/ ending with "data" -**/finetuning/**/*data/ - -# Ignore this explicit model folder -utils/finetuning/finetune_ner/RoBERTa-large-PM-M3-Voc/ -utils/finetuning/finetune_ner/output_eval -utils/finetuning/finetune_ner - -# ============================ -# Parser artifacts -# ============================ models/ -source/Parser/resources/ -source/Parser/models/ -source/Parser/input/ -source/Parser/output/ -source/Parser/logs/ -Parser/logs/ +source/ +Parser/ # ============================ # Generated indexing state # ============================ -utils/DataLoader/nct_ids.txt -utils/Indexer/nct_ids.txt -utils/Indexer/processed_ids.txt -utils/Indexer/*.log *.lance +*.lancedb # ============================ # Python cache diff --git a/Dockerfile b/Dockerfile index 03620340..8b9232e9 100644 --- a/Dockerfile +++ b/Dockerfile @@ -19,11 +19,10 @@ RUN apt-get update \ && pip install --no-cache-dir uv COPY pyproject.toml uv.lock README.md LICENSE ./ -COPY source ./source +COPY src ./src COPY scripts ./scripts -COPY utils ./utils -RUN uv sync --frozen --no-dev --extra gpu --extra entity +RUN uv sync --frozen --no-dev --extra llm --extra gpu --extra entity VOLUME ["/app/data", "/app/models", "/app/results"] diff --git a/README.md b/README.md index 9f0202e8..2cb567dd 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ The supported v1 deployment path is a single Python 3.11 GPU server or VM. Trial ## Security First -No real credentials, generated private keys, Parser outputs, datasets, models, local LanceDB data, or results should be committed. Copy the template and keep runtime values local: +No real credentials, generated private keys, datasets, models, local LanceDB data, run manifests, or results should be committed. Copy the template and keep runtime values local: ```bash cp .env.example .env @@ -32,24 +32,23 @@ Dependency auditing currently ignores `CVE-2025-3000` because vLLM 0.23 pins Tor ## Quickstart -Install deployment dependencies: +Install the package for local development and operational CLIs: ```bash -uv sync --extra gpu --extra entity +uv sync ``` -For local development, tests, healthchecks, or `TRIALMATCHAI_COT_BACKEND=default`: +Install deployment dependencies for model-backed indexing, entity extraction, and vLLM reasoning: ```bash -uv sync +uv sync --extra llm --extra gpu --extra entity ``` Optional tooling: ```bash -uv sync --extra llm # OpenAI/LangChain data-generation utilities -uv sync --extra entity # GLiNER2 entity extraction -uv sync --extra training # fine-tuning and evaluation utilities +uv sync --extra entity # GLiNER/GLiNER2 entity extraction +uv sync --extra llm # local embedding and LLM stack ``` Run a config and backend healthcheck: @@ -64,7 +63,8 @@ Provision data, models, concept KB, and search tables: ```bash uv run trialmatchai-bootstrap-data uv run trialmatchai-build-concepts --concept-csv data/omop/CONCEPT.csv --synonym-csv data/omop/CONCEPT_SYNONYM.csv -uv run trialmatchai-index +uv run trialmatchai-update-registry --since 2026-06-01 --max-studies 100 +uv run trialmatchai-index --prepare ``` Run the batch matcher: @@ -82,12 +82,13 @@ Docker is optional. The worker container uses mounted local folders and the same ```bash docker compose build trialmatchai-worker docker compose run --rm trialmatchai-worker trialmatchai-healthcheck +docker compose run --rm trialmatchai-worker trialmatchai-update-registry --max-studies 100 docker compose run --rm trialmatchai-worker trialmatchai-run ``` ## Configuration -Configuration defaults live in `source/Matcher/config/config.json`. Runtime overrides use `.env` or environment variables: +Configuration defaults live in `src/trialmatchai/config/config.json`. Runtime overrides use `.env` or environment variables: ```bash TRIALMATCHAI_PATIENTS_DIR=example @@ -103,11 +104,21 @@ TRIALMATCHAI_SEARCH_MODE=hybrid TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false TRIALMATCHAI_ENTITY_BACKEND=gliner2 -TRIALMATCHAI_ENTITY_SCHEMA_PATH=source/Matcher/entity_schemas/trialmatchai.yaml +TRIALMATCHAI_ENTITY_SCHEMA_PATH=src/trialmatchai/entity_schemas/trialmatchai.yaml TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts TRIALMATCHAI_CONCEPT_TABLE=concepts TRIALMATCHAI_LINK_ACCEPT=0.80 TRIALMATCHAI_LINK_REJECT=0.30 + +TRIALMATCHAI_REGISTRY_SOURCE=clinicaltrials.gov +TRIALMATCHAI_REGISTRY_KEYWORDS_FILE= +TRIALMATCHAI_REGISTRY_SINCE_DAYS=7 +TRIALMATCHAI_REGISTRY_MAX_STUDIES= +TRIALMATCHAI_REGISTRY_REQUEST_TIMEOUT=30 +TRIALMATCHAI_REGISTRY_RATE_LIMIT_PER_SECOND=2 +TRIALMATCHAI_REGISTRY_RAW_DIR=data/registry/raw +TRIALMATCHAI_REGISTRY_MANIFEST_PATH=data/registry/manifest.jsonl +TRIALMATCHAI_REGISTRY_REPORTS_DIR=data/registry/runs TRIALMATCHAI_LOG_JSON=1 ``` @@ -118,9 +129,45 @@ Use `TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=true` only when a selected model expli - `trialmatchai-healthcheck`: validate config, paths, and optionally LanceDB search tables. - `trialmatchai-bootstrap-data`: download and extract external data/model artifacts. - `trialmatchai-build-concepts`: build the LanceDB concept table used for entity normalization. -- `trialmatchai-index`: build the LanceDB trial and criteria search tables. +- `trialmatchai-update-registry`: fetch new/changed ClinicalTrials.gov studies, write normalized JSON, and upsert LanceDB. +- `trialmatchai-index`: build the LanceDB trial and criteria search tables from prepared rows or `--prepare` normalized JSON. - `trialmatchai-run`: run the batch matching pipeline. +The command group is also available as: + +```bash +uv run python -m trialmatchai healthcheck +uv run trialmatchai update-registry --dry-run --max-studies 25 +``` + +## Registry Updater + +The registry updater is designed for cron, systemd timers, and GitHub Actions. It uses ClinicalTrials.gov v2 `/api/v2/studies`, stores raw source JSON under `data/registry/raw`, writes normalized trial JSON under `data/trials_jsons`, appends idempotency records to `data/registry/manifest.jsonl`, and upserts only new or changed studies into LanceDB. + +Run with explicit keywords: + +```bash +uv run trialmatchai-update-registry \ + --keyword "lung cancer" \ + --keyword "EGFR" \ + --since 2026-06-01 \ + --max-studies 250 +``` + +Run with broad defaults: + +```bash +uv run trialmatchai-update-registry --max-studies 500 +``` + +Dry-run without writes or table changes: + +```bash +uv run trialmatchai-update-registry --dry-run --max-studies 25 +``` + +See `docs/registry-updater.md` for scheduler examples. + ## Tests and Checks ```bash diff --git a/docker-compose.yml b/docker-compose.yml index dbe07663..75830ffa 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -16,10 +16,15 @@ services: TRIALMATCHAI_SEARCH_CRITERIA_TABLE: criteria TRIALMATCHAI_SEARCH_MODE: hybrid TRIALMATCHAI_ENTITY_BACKEND: gliner2 - TRIALMATCHAI_ENTITY_SCHEMA_PATH: /app/source/Matcher/entity_schemas/trialmatchai.yaml + TRIALMATCHAI_ENTITY_SCHEMA_PATH: /app/src/trialmatchai/entity_schemas/trialmatchai.yaml TRIALMATCHAI_CONCEPT_DB_PATH: /app/data/concepts TRIALMATCHAI_CONCEPT_TABLE: concepts TRIALMATCHAI_LINK_ACCEPT: "0.80" TRIALMATCHAI_LINK_REJECT: "0.30" + TRIALMATCHAI_REGISTRY_SOURCE: clinicaltrials.gov + TRIALMATCHAI_REGISTRY_SINCE_DAYS: "7" + TRIALMATCHAI_REGISTRY_RAW_DIR: /app/data/registry/raw + TRIALMATCHAI_REGISTRY_MANIFEST_PATH: /app/data/registry/manifest.jsonl + TRIALMATCHAI_REGISTRY_REPORTS_DIR: /app/data/registry/runs TRIALMATCHAI_LOG_JSON: "1" - command: ["trialmatchai-healthcheck"] + command: ["trialmatchai-healthcheck", "--registry"] diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 00000000..707ee589 --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,41 @@ +# TrialMatchAI Architecture + +TrialMatchAI is an installable Python package exposed as `trialmatchai`. The supported runtime code lives under `src/trialmatchai`; `src/Matcher` is a temporary deprecated import shim for one release cycle. + +## Runtime Subsystems + +- `trialmatchai.config`: Pydantic-backed settings, path resolution, and `TRIALMATCHAI_` environment overrides. +- `trialmatchai.entities`: schema-driven biomedical entity recognition plus LanceDB-backed concept linking. +- `trialmatchai.registry`: ClinicalTrials.gov v2 client, record normalization, manifest idempotency, preparation, and update orchestration. +- `trialmatchai.search`: embedded LanceDB search tables for trials and eligibility criteria. +- `trialmatchai.matching`: first-stage retrieval, criteria retrieval, ranking, and eligibility reasoning. +- `trialmatchai.models`: local embedding and LLM loader utilities. Install with `--extra llm` and `--extra gpu` for production model runs. +- `trialmatchai.cli`: public command entry points. + +## Data Flow + +1. `trialmatchai-update-registry` fetches studies from ClinicalTrials.gov v2. +2. Raw source JSON is written to `data/registry/raw/.json`. +3. Normalized trial JSON is written to `data/trials_jsons/.json`. +4. Changed studies are embedded, criteria are optionally entity annotated, and LanceDB trial/criteria tables are upserted. +5. `trialmatchai-run` reads patient inputs, queries LanceDB, reranks candidate criteria/trials, and writes results. + +## Storage + +LanceDB is the only search database. It is embedded, file-backed, and stored under `data/search` by default. The concept linker uses a separate LanceDB database under `data/concepts`. + +The registry manifest is append-only JSONL at `data/registry/manifest.jsonl`. The latest record per `nct_id` determines idempotency. + +## Compatibility + +`Matcher` import shims are kept temporarily for downstream callers: + +```python +from Matcher.config.config_loader import load_config +``` + +New code must import: + +```python +from trialmatchai.config.config_loader import load_config +``` diff --git a/docs/registry-updater.md b/docs/registry-updater.md new file mode 100644 index 00000000..7846d450 --- /dev/null +++ b/docs/registry-updater.md @@ -0,0 +1,89 @@ +# Registry Updater + +`trialmatchai-update-registry` updates the local LanceDB-backed trial registry from ClinicalTrials.gov v2. It is command-based and safe to run from cron, systemd timers, or GitHub Actions. + +## Basic Usage + +```bash +uv run trialmatchai-update-registry --max-studies 500 +``` + +When no keyword is provided, TrialMatchAI uses broad default keyword queries covering oncology, cardiology, neurology, rare disease, immunology, infectious disease, metabolic disease, hematology, pediatrics, and precision medicine. + +Use explicit keywords: + +```bash +uv run trialmatchai-update-registry \ + --keyword "lung cancer" \ + --keyword "EGFR" \ + --since 2026-06-01 \ + --max-studies 250 +``` + +Use a keyword file: + +```bash +uv run trialmatchai-update-registry --keywords-file data/registry/keywords.txt +``` + +Dry-run: + +```bash +uv run trialmatchai-update-registry --dry-run --max-studies 25 +``` + +Dry-runs do not write raw JSON, normalized trial JSON, manifests, reports, or LanceDB tables unless an explicit `--report-path` is provided. + +## Idempotency + +Each fetched source record is hashed after canonical JSON serialization. If the latest manifest entry for an NCT ID has the same source hash, the updater skips preparation and indexing. + +Changed studies are written and then upserted: + +- Trial rows are replaced by `nct_id`. +- Criteria rows are replaced by `nct_id`. +- Failures are recorded per study and do not stop the run unless the failure rate exceeds `TRIALMATCHAI_REGISTRY_FAILURE_THRESHOLD`. + +## Cron + +Run daily at 02:30: + +```cron +30 2 * * * cd /opt/TrialMatchAI && /usr/local/bin/uv run trialmatchai-update-registry --max-studies 1000 >> logs/registry-update.log 2>&1 +``` + +## systemd + +Service: + +```ini +[Unit] +Description=TrialMatchAI registry update + +[Service] +Type=oneshot +WorkingDirectory=/opt/TrialMatchAI +ExecStart=/usr/local/bin/uv run trialmatchai-update-registry --max-studies 1000 +``` + +Timer: + +```ini +[Unit] +Description=Daily TrialMatchAI registry update + +[Timer] +OnCalendar=*-*-* 02:30:00 +Persistent=true + +[Install] +WantedBy=timers.target +``` + +## Healthcheck + +```bash +uv run trialmatchai-healthcheck --registry --require-tables +``` + +Use `--require-tables` after the first successful update or indexing run. diff --git a/docs/release.md b/docs/release.md new file mode 100644 index 00000000..26bcf207 --- /dev/null +++ b/docs/release.md @@ -0,0 +1,43 @@ +# Release Checklist + +TrialMatchAI uses a `src/` package layout and exposes the public package name `trialmatchai`. + +## Local Checks + +```bash +uv lock --check +uv run ruff check . +uv run pytest +uv build +uv run python scripts/scan_secrets.py +uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 +``` + +Smoke test console commands: + +```bash +uv run trialmatchai --help +uv run trialmatchai-healthcheck --help +uv run trialmatchai-index --help +uv run trialmatchai-build-concepts --help +uv run trialmatchai-update-registry --help +uv run trialmatchai-run --help +``` + +Wheel install smoke: + +```bash +uv build +python -m venv /tmp/trialmatchai-wheel-smoke +/tmp/trialmatchai-wheel-smoke/bin/pip install dist/trialmatchai-*.whl +/tmp/trialmatchai-wheel-smoke/bin/python -c "import trialmatchai; print(trialmatchai.__version__)" +``` + +## Container Checks + +```bash +docker compose config +docker build . +``` + +Docker is not required for local LanceDB operation; it is a packaging option for a worker environment. diff --git a/pyproject.toml b/pyproject.toml index c48615dc..b8212c2c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,59 +3,29 @@ requires = ["setuptools>=68", "wheel"] build-backend = "setuptools.build_meta" [project] -name = "TrialMatchAI" -version = "0.1.0" +name = "trialmatchai" +version = "0.2.0" description = "AI-driven clinical trial matching pipeline." readme = "README.md" requires-python = ">=3.11,<3.12" license = "MIT" license-files = ["LICENSE"] dependencies = [ - # Core ML / NLP - "torch==2.11.0", - "transformers==5.6.2", - "accelerate==1.8.1", - "tokenizers==0.22.1", - "safetensors>=0.6.2,<0.7", - "sentencepiece==0.2.1", - "faiss-cpu==1.9.0.post1", - "peft==0.15.2", - "einops==0.8.0", - "tiktoken==0.8.0", - # Classical stack & utilities "numpy>=2.0,<3", - "scipy==1.15.1", - "scikit-learn==1.6.1", "pandas==2.2.3", "python-dateutil==2.9.0.post0", "requests==2.34.2", "tenacity==9.0.0", - "joblib==1.4.2", "tqdm==4.67.1", "regex>=2025.10.22,<2027", - "orjson==3.11.9", "PyYAML==6.0.2", "python-dotenv==1.2.2", "rich==13.9.4", "lancedb>=0.25.0,<0.26", "pyarrow>=24.0.0,<25", - # spaCy / medspaCy (for your medical text work) - "spacy==3.7.5", - "medspacy==1.3.1", - "medspacy_quickumls==3.2", - "PyRuSH==1.0.9", - "PyFastNER==1.0.10", - "pysbd==0.3.4", - "quicksectx==0.4.0", - "gliner==0.2.27", - # App / infra - "SQLAlchemy==2.0.37", "pydantic>=2.12.0,<3", - "pydantic-settings>=2.14.2,<3", - "huggingface_hub>=1.5.0,<2", "bioregistry==0.13.62", "rapidfuzz==3.14.5", - "nltk==3.9.4", ] [project.optional-dependencies] @@ -64,27 +34,30 @@ gpu = [ "vllm==0.23.0; sys_platform == 'linux'", ] llm = [ - "openai>=2.26.0,<3", - "httpx==0.28.1", - "langchain==1.3.10", - "langchain-community==0.4.2", - "langchain-openai==1.3.2", + "torch==2.11.0", + "transformers==5.6.2", + "accelerate==1.8.1", + "tokenizers==0.22.1", + "safetensors>=0.6.2,<0.7", + "sentencepiece==0.2.1", + "peft==0.15.2", + "einops==0.8.0", ] entity = [ + "torch==2.11.0", + "transformers==5.6.2", + "gliner==0.2.27", "gliner2>=1.3.1,<2", ] -training = [ - "datasets==2.19.0", - "bert-score==0.3.13", - "jinja2==3.1.6", -] [project.scripts] -trialmatchai-healthcheck = "Matcher.cli.healthcheck:main" -trialmatchai-run = "Matcher.cli.run:main" -trialmatchai-bootstrap-data = "Matcher.cli.bootstrap_data:main" -trialmatchai-index = "Matcher.cli.index_data:main" -trialmatchai-build-concepts = "Matcher.cli.build_concepts:main" +trialmatchai = "trialmatchai.cli.main:main" +trialmatchai-healthcheck = "trialmatchai.cli.healthcheck:main" +trialmatchai-run = "trialmatchai.cli.run:main" +trialmatchai-bootstrap-data = "trialmatchai.cli.bootstrap_data:main" +trialmatchai-index = "trialmatchai.cli.index_data:main" +trialmatchai-build-concepts = "trialmatchai.cli.build_concepts:main" +trialmatchai-update-registry = "trialmatchai.cli.update_registry:main" [dependency-groups] dev = [ @@ -94,14 +67,19 @@ dev = [ ] [tool.setuptools] -package-dir = {"" = "source"} +package-dir = {"" = "src"} include-package-data = true [tool.setuptools.packages.find] -where = ["source"] +where = ["src"] [tool.setuptools.package-data] -Matcher = ["config/*.json", "entity_schemas/*.yaml"] +trialmatchai = [ + "config/*.json", + "entity_schemas/*.yaml", + "preprocessing/regex/*.json", + "preprocessing/regex/*.tsv", +] [tool.uv] environments = [ diff --git a/scripts/bootstrap_data.sh b/scripts/bootstrap_data.sh index ebf72565..e22e2369 100644 --- a/scripts/bootstrap_data.sh +++ b/scripts/bootstrap_data.sh @@ -3,14 +3,12 @@ set -euo pipefail IFS=$'\n\t' DATA_URL_1="https://zenodo.org/records/15516900/files/processed_trials.tar.gz?download=1" -RESOURCES_URL="https://zenodo.org/records/15516900/files/resources.tar.gz?download=1" MODELS_URL="https://zenodo.org/records/15516900/files/models.tar.gz?download=1" CRITERIA_ZIP_BASE_URL="https://zenodo.org/records/15516900/files" CHUNK_PREFIX="criteria_part" CHUNK_COUNT=6 ARCHIVE_1="processed_trials.tar.gz" -RESOURCES_ARCHIVE="resources.tar.gz" MODELS_ARCHIVE="models.tar.gz" GREEN='\033[0;32m' @@ -73,14 +71,6 @@ else fi verify_sha256 "$ARCHIVE_1" "${TRIALMATCHAI_PROCESSED_TRIALS_SHA256:-}" -if [ ! -f "$RESOURCES_ARCHIVE" ]; then - info "Downloading ${RESOURCES_ARCHIVE}..." - curl -fsSL "$RESOURCES_URL" -o "$RESOURCES_ARCHIVE" -else - info "${RESOURCES_ARCHIVE} already exists. Skipping download." -fi -verify_sha256 "$RESOURCES_ARCHIVE" "${TRIALMATCHAI_RESOURCES_SHA256:-}" - if [ ! -f "$MODELS_ARCHIVE" ]; then info "Downloading ${MODELS_ARCHIVE}..." curl -fsSL "$MODELS_URL" -o "$MODELS_ARCHIVE" @@ -122,16 +112,12 @@ fi cd "$ROOT_DIR" -info "Extracting resources into source/Parser..." -mkdir -p source/Parser -extract_tar_gz "$DATA_DIR/$RESOURCES_ARCHIVE" source/Parser - info "Extracting models into models/..." mkdir -p models extract_tar_gz "$DATA_DIR/$MODELS_ARCHIVE" models info "Cleaning up archives..." -rm -f "$DATA_DIR/$ARCHIVE_1" "$DATA_DIR/$RESOURCES_ARCHIVE" "$DATA_DIR/$MODELS_ARCHIVE" +rm -f "$DATA_DIR/$ARCHIVE_1" "$DATA_DIR/$MODELS_ARCHIVE" for i in $(seq 0 $((CHUNK_COUNT - 1))); do rm -f "$DATA_DIR/${CHUNK_PREFIX}_${i}.zip" done diff --git a/source/Matcher/cli/healthcheck.py b/source/Matcher/cli/healthcheck.py deleted file mode 100644 index 46edd2fb..00000000 --- a/source/Matcher/cli/healthcheck.py +++ /dev/null @@ -1,50 +0,0 @@ -from __future__ import annotations - -import argparse -import sys - -from Matcher.config.config_loader import load_config -from Matcher.search import LanceDBSearchBackend -from Matcher.services.preflight import run_preflight_checks -from Matcher.utils.logging_config import setup_logging - -logger = setup_logging(__name__) - - -def main() -> int: - parser = argparse.ArgumentParser(description="TrialMatchAI healthcheck") - parser.add_argument( - "--config", - default=None, - help="Path to config.json", - ) - parser.add_argument( - "--require-tables", - action="store_true", - help="Fail if configured LanceDB search tables are missing", - ) - args = parser.parse_args() - - config = load_config(args.config) - issues = 0 - search_backend = LanceDBSearchBackend.from_config(config) - - preflight_issues = run_preflight_checks( - config, - require_patient_inputs=False, - require_trials_json=False, - require_models=False, - ) - issues += len(preflight_issues) - - backend_issues = search_backend.health(require_tables=args.require_tables) - if backend_issues: - for issue in backend_issues: - logger.error("Search backend healthcheck failed: %s", issue) - issues += len(backend_issues) - else: - logger.info("LanceDB search backend reachable at %s.", search_backend.db_path) - - return 1 if issues else 0 -if __name__ == "__main__": - sys.exit(main()) diff --git a/source/Matcher/models/embedding/__init__.py b/source/Matcher/models/embedding/__init__.py deleted file mode 100644 index 06c33a33..00000000 --- a/source/Matcher/models/embedding/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from Matcher.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig - -__all__ = ["TextEmbedder", "TextEmbedderConfig"] diff --git a/source/Matcher/schemas/__init__.py b/source/Matcher/schemas/__init__.py deleted file mode 100644 index acbb6535..00000000 --- a/source/Matcher/schemas/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from Matcher.schemas.phenopacket import Keywords, Phenopacket - -__all__ = ["Keywords", "Phenopacket"] diff --git a/source/Matcher/utils/__init__.py b/source/Matcher/utils/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/source/Parser/__init__.py b/source/Parser/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/source/Parser/biomedner_engine.py b/source/Parser/biomedner_engine.py deleted file mode 100644 index 7bc00975..00000000 --- a/source/Parser/biomedner_engine.py +++ /dev/null @@ -1,104 +0,0 @@ -from __future__ import annotations - -from pathlib import Path -from typing import Any, Sequence - -from Matcher.entities import build_entity_annotator - - -class BioMedNER: - """Compatibility wrapper over the Python-native TrialMatchAI entity annotator.""" - - def __init__(self, *args: Any, config: dict[str, Any] | None = None, **kwargs: Any): - del args, kwargs - self._annotator = build_entity_annotator(config or _default_config()) - - @staticmethod - def load_dictionary_file(file_path: str | Path) -> list[str]: - with Path(file_path).open("r", encoding="utf-8") as handle: - return handle.read().splitlines() - - def annotate_text(self, text: str) -> list[dict[str, Any]]: - return self._annotator.annotate_text(text) - - def annotate_texts_in_parallel( - self, - texts: Sequence[str], - max_workers: int = 20, - retries: int = 1, - delay: float = 0, - ) -> list[list[dict[str, Any]]]: - return self._annotator.annotate_texts_in_parallel( - texts, - max_workers=max_workers, - retries=retries, - delay=delay, - ) - - -def _default_config() -> dict[str, Any]: - return { - "entity_extraction": { - "backend": "gliner2", - "model_name": "fastino/gliner2-base", - "fallback_model_name": "gliner-community/gliner_large-v2.5", - "schema_path": str(_repo_root() / "source/Matcher/entity_schemas/trialmatchai.yaml"), - "batch_size": 8, - "device": "auto", - "trust_remote_code": False, - }, - "concept_linker": { - "enabled": True, - "db_path": str(_repo_root() / "data/concepts"), - "table": "concepts", - "accept_threshold": 0.8, - "reject_threshold": 0.3, - "search_limit": 10, - }, - } - - -def _repo_root() -> Path: - start = Path(__file__).resolve() - for parent in start.parents: - if (parent / "pyproject.toml").exists(): - return parent - return Path.cwd().resolve() - - -def transform_results(data: dict[str, Any]) -> list[dict[str, Any]]: - """Best-effort adapter for callers that still pass PubTator-style JSON.""" - annotations = data.get("annotations", []) if isinstance(data, dict) else [] - results: list[dict[str, Any]] = [] - for annotation in annotations: - span = annotation.get("span", {}) - results.append( - { - "entity_group": annotation.get("obj", ""), - "score": annotation.get("prob", 0.0), - "text": annotation.get("mention", ""), - "start": span.get("begin", 0), - "end": span.get("end", 0), - "normalized_id": annotation.get("id", ["CUI-less"]), - "synonyms": [], - "concept_candidates": [], - "linker_score": None, - "linker_status": "not_linked", - } - ) - return results - - -def append_synonyms( - ner_results: list[dict[str, Any]], dict_paths: dict[str, str | Path] -) -> None: - del dict_paths - for entity in ner_results: - entity.setdefault("synonyms", []) - - -def get_synonyms_from_file( - file_path: str | Path, entity_ids: Sequence[str] -) -> list[str]: - del file_path, entity_ids - return [] diff --git a/source/Parser/convert.py b/source/Parser/convert.py deleted file mode 100644 index f0e036dd..00000000 --- a/source/Parser/convert.py +++ /dev/null @@ -1,416 +0,0 @@ -import copy -import json -from datetime import datetime, timezone -from operator import itemgetter - -# from download import query_pubtator2biocxml - - -def pubtator2dict_list(pubtator_file_path): - dict_list = list() - - title_pmid = "" - # abstract_pmid = '' - abstract_text = "" - doc_line_num = 0 - mutations = list() - - with open(pubtator_file_path, "r", encoding="utf-8") as f: - for line in f: - line = line.rstrip() - if len(line) == 0: - if len(mutations) > 0: - if len(mutations) > 1: - mutations = sorted(mutations, key=itemgetter("start")) - - mutations = get_bestplus_spans(mutations, abstract_text) - - doc_dict = { - "pmid": title_pmid, - "mutation_model": "tmVar 2.0", - "entities": {"mutation": copy.deepcopy(mutations)}, - } - doc_dict["abstract"] = abstract_text - - dict_list.append(doc_dict) - - doc_line_num = 0 - mutations.clear() - continue - - if doc_line_num == 0: - title_cols = line.split("|t|") - - if len(title_cols) != 2: - return '{"error": "wrong #title_cols %d"}' % len(title_cols) - - title_pmid = title_cols[0] - - # The 'title' variable is not used, so we skip assigning it. - elif doc_line_num == 1: - abstract_cols = line.split("|a|") - - if len(abstract_cols) != 2: - if len(abstract_cols) > 2: - abstract_text = "|a|".join(abstract_cols[1:]) - else: - return '{"error": "wrong #abstract_cols %d"}' % len( - abstract_cols - ) - else: - if "- No text -" == abstract_cols[1]: - # make tmvar2 results empty - abstract_text = "" - else: - abstract_text = abstract_cols[1] - elif doc_line_num > 1: - mutation_cols = line.split("\t") - - if len(mutation_cols) != 6: - return '{"error": "wrong #mutation_cols %d"}' % len(mutation_cols) - - mutations.append( - { - "start": int(mutation_cols[1]), - "end": int(mutation_cols[2]), - "mention": mutation_cols[3], - "mutationType": mutation_cols[4], - "normalizedName": mutation_cols[5], - } - ) - - doc_line_num += 1 - return dict_list - - -def preprocess(text): - text = text.replace("\r ", " ") - - text = text.replace("\u2028", " ") - text = text.replace("\u2029", " ") - - # HAIR SPACE - # https://www.fileformat.info/info/unicode/char/200a/index.htm - text = text.replace("\u200a", " ") - - # THIN SPACE - # https://www.fileformat.info/info/unicode/char/2009/index.htm - text = text.replace("\u2009", " ") - text = text.replace("\u2008", " ") - - # FOUR-PER-EM SPACE - # https://www.fileformat.info/info/unicode/char/2005/index.htm - text = text.replace("\u2005", " ") - text = text.replace("\u2004", " ") - text = text.replace("\u2003", " ") - - # EN SPACE - # https://www.fileformat.info/info/unicode/char/2002/index.htm - text = text.replace("\u2002", " ") - - # NO-BREAK SPACE - # https://www.fileformat.info/info/unicode/char/00a0/index.htm - text = text.replace("\u00a0", " ") - - # https://www.fileformat.info/info/unicode/char/f8ff/index.htm - text = text.replace("\uf8ff", " ") - - # https://www.fileformat.info/info/unicode/char/202f/index.htm - text = text.replace("\u202f", " ") - - text = text.replace("\ufeff", " ") - text = text.replace("\uf044", " ") - text = text.replace("\uf02d", " ") - text = text.replace("\uf0bb", " ") - - text = text.replace("\uf048", "Η") - text = text.replace("\uf0b0", "°") - - # MIDLINE HORIZONTAL ELLIPSIS: ⋯ - # https://www.fileformat.info/info/unicode/char/22ef/index.htm - # text = text.replace('\u22EF', '...') - - return text - - -def pubtator2pubannotation(pubtator): - dict_list = list() - - title_pmid = "" - abstract_pmid = "" - title = "" - abstract_text = "" - doc_line_num = 0 - entities = list() - - for line in pubtator.splitlines(): - if len(line) == 0: - if title_pmid != abstract_pmid: - return '{"error": "pmid disagreement"}' - - doc_dict = { - "text": title + " " + abstract_text, - "project": "biomedner", - "sourcedb": "PubMed", - "sourceid": title_pmid, - "annotations": copy.deepcopy(entities), - } - - dict_list.append(doc_dict) - - doc_line_num = 0 - entities.clear() - continue - - if doc_line_num == 0: - title_cols = line.split("|t|") - - if len(title_cols) != 2: - return '{"error": "wrong #title_cols=%d", "line": "%s"}' % ( - len(title_cols), - line, - ) - - title_pmid = title_cols[0] - - if "- No text -" == title_cols[1]: - # make tmvar2 results empty - title = "" - else: - title = title_cols[1] - elif doc_line_num == 1: - abstract_cols = line.split("|a|") - - if len(abstract_cols) != 2: - return '{"error": "wrong #abstract_cols %d"}' % len(abstract_cols) - - abstract_pmid = abstract_cols[0] - - if "- No text -" == abstract_cols[1] or "-NoAbstract-" == abstract_cols[1]: - # make tmvar2 results empty - abstract_text = "" - else: - abstract_text = abstract_cols[1] - elif doc_line_num > 1: - entity_cols = line.split("\t") - - if len(entity_cols) != 6: - return '{"error": "wrong #mutation_cols %d"}' % len(entity_cols) - - if entity_cols[4] in entity_cols: - entities.append( - { - "obj": entity_cols[4], - "id": entity_cols[5].split("|"), - "span": { - "begin": int(entity_cols[1]), - "end": int(entity_cols[2]), - }, - } - ) - - doc_line_num += 1 - return dict_list - - -def get_bestplus_spans(mutations, title_space_abstract): - adjusted_mutations = list() - - mention_count_dict = dict() - for m in mutations: - if "No text" in m["mention"]: - continue - - # (20220113) hotfix - if "text " in m["mention"]: - continue - - if m["mention"] in mention_count_dict: - mention_count_dict[m["mention"]] += 1 - else: - mention_count_dict[m["mention"]] = 1 - - count = mention_count_dict[m["mention"]] - - start = -1 - found = 0 - try: - while found < count: - start = title_space_abstract.index(m["mention"], start + 1) - assert start > -1 - found += 1 - except ValueError: - # hotfix for tmvar wrong mention - continue - - end = start + len(m["mention"]) - 1 # 2018.8.29 @chanho feedback - - assert m["mention"] == title_space_abstract[start : end + 1] - - adjusted_mutations.append( - { - "start": start, - "end": end, - "mention": m["mention"], - "mutationType": m["mutationType"], - "normalizedName": m["normalizedName"], - } - ) - - return adjusted_mutations - - -# Ref. -# http://pubannotation.org/docs/sourcedb/PubMed/sourceid/10022882/spans/606-710/annotations.json -# http://www.pubannotation.org/docs/annotation-format/ -def get_pub_annotation(biomedner_dict): - sourceid = biomedner_dict["pmid"] - - sourcedb = "" - text = biomedner_dict["abstract"] - - pa_dict = { - "project": "TrialMatchAI", - "sourcedb": sourcedb, - "sourceid": sourceid, - "text": text, - "annotations": biomedner2pub_annotation( - biomedner_dict["entities"], biomedner_dict, text - ), - "timestamp": datetime.now(tz=timezone.utc).strftime("%a %b %d %H:%M:%S %z %Y"), - } - - return pa_dict - - -def biomedner2pub_annotation(entity_dict, biomedner_dict, text): - entity_list = list() - for etype in entity_dict: - for entity_idx, entity in enumerate(entity_dict[etype]): - # TODO prevention in the previous step - if "id" not in entity: - entity["id"] = "CUI-less" - - assert "id" in entity, "{}, entity={}, entity_dict={}".format( - etype, entity, entity_dict - ) - assert "start" in entity and "end" in entity, ( - "{}, entity={}, entity_dict={}".format(etype, entity, entity_dict) - ) - - if "\t" in entity["id"]: - eid = entity["id"].split("\t") - else: - eid = [entity["id"]] - - entity_pa_dict = {} - # if 'mutation' == etype: - # assert 'mutationType' in entity \ - # and 'normalizedName' in entity, \ - # '{}, entity={}, entity_dict={}'.format( - # etype, entity, entity_dict) - - # entity_pa_dict['mutationType'] = entity['mutationType'] - # entity_pa_dict['normalizedName'] = entity['normalizedName'] - # entity['end'] += 1 # tmvar2 end span makes one character shift in mention - - entity_pa_dict.update( - { - "id": eid, - "span": {"begin": entity["start"], "end": entity["end"]}, - "obj": etype, - "mention": text[entity["start"] : entity["end"]], - "prob": biomedner_dict["prob"][etype][entity_idx][1] - if etype in biomedner_dict["prob"] - and biomedner_dict["prob"][etype][entity_idx][1] - else None, - "is_neural_normalized": entity["is_neural_normalized"] - if "is_neural_normalized" in entity - else False, - } - ) - - entity_list.append(entity_pa_dict) - - # sort by span begin - def get_item_key1(item): - return item["span"]["begin"] - - def get_item_key2(item): - return item["obj"] - - return sorted(sorted(entity_list, key=get_item_key2), key=get_item_key1) - - -def get_pubtator(biomedner_dict_list): - result = "" - for bd in biomedner_dict_list: - text = bd["title"] + " " + bd["abstract"] - - main = ( - bd["pmid"] - + "|t|" - + bd["title"] - + "\n" - + bd["pmid"] - + "|a|" - + bd["abstract"] - ) - - # sort by start - sorted_entities = list() - - for etype in bd["entities"]: - for entity in bd["entities"][etype]: - mention = text[entity["start"] : entity["end"]] - sorted_entities.append( - [ - entity["start"], - entity["end"], - mention, - etype, - "|".join(entity["id"].split("\t")), - ] - ) - - sorted_entities = sorted(sorted_entities, key=itemgetter(0)) - - entities = "" - for e in sorted_entities: - entities += "{}\t{}\t{}\t{}\t{}\t{}\n".format( - bd["pmid"], e[0], e[1], e[2], e[3], e[4] - ) - - result += main + "\n" + entities + "\n" - - return result - - -def tmtooljson2biomedner(tmtool_res): - tmtool_dicts = json.loads(tmtool_res) - - biomedner_dicts = list() - - for td in tmtool_dicts: - mutations = list() - for d in td["annotations"]: - mention = td["text"][d["span"]["begin"] : d["span"]["end"]] - d["span"]["end"] += 1 - mutations.append( - { - "start": d["span"]["start"], - "end": d["span"]["end"], - "mention": mention, - "normalizedName": d["obj"].replace("Mutation:", ""), - } - ) - - doc_dict = { - "pmid": td["sourceid"], - "text": td["text"], - "entities": {"mutation": mutations}, - } - - biomedner_dicts.append(doc_dict) - - return biomedner_dicts diff --git a/source/Parser/main.py b/source/Parser/main.py deleted file mode 100644 index bf9a87f0..00000000 --- a/source/Parser/main.py +++ /dev/null @@ -1,192 +0,0 @@ -from __future__ import annotations - -import argparse -import json -import multiprocessing -import os -import re -import sys -from pathlib import Path - -import pandas as pd - -SOURCE_DIR = Path(__file__).resolve().parents[1] -if str(SOURCE_DIR) not in sys.path: - sys.path.append(str(SOURCE_DIR)) - -from Matcher.config.config_loader import load_config # noqa: E402 -from Parser.biomedner_engine import BioMedNER # noqa: E402 - - -BASE_INPUT_FILEPATH = Path(__file__).resolve().parents[2] / "data/preprocessed_data" -BASE_OUTPUT_FILEPATH_CT = Path(__file__).resolve().parents[2] / "data/parsed_trec" - -entity_annotator: BioMedNER | None = None -data_source = "clinical trials" - - -def process_dataframe(df: pd.DataFrame, text_column: str) -> pd.DataFrame: - new_data = [] - for _, row in df.iterrows(): - row_copy = row.to_dict() - text = row_copy.pop(text_column) - if not isinstance(text, str): - text = str(text) - - word_count = len(text.split()) - sentence_end_count = text.count(".") + text.count("?") + text.count("!") - - if word_count > 512 and sentence_end_count > 1: - sentences = re.split(r"(?<=[.!?]) +", text) - for sentence in sentences: - new_row = row_copy.copy() - new_row[text_column] = sentence - new_data.append(new_row) - else: - row_copy[text_column] = text - new_data.append(row_copy) - - return pd.DataFrame(new_data) - - -def process_file(idx: str) -> None: - global entity_annotator - if entity_annotator is None: - raise RuntimeError("Entity annotator is not initialized.") - - output_filepath = BASE_OUTPUT_FILEPATH_CT / f"{idx}.json" - if output_filepath.exists(): - print(f"File {idx} already exists. Skipping...") - return - - df = _load_dataframe(idx) - if df is None or df.empty: - print(f"Dataframe {idx} is empty. Skipping.") - return - - print(f"Processing {idx}") - df = process_dataframe(df, "sentence") - sentences = df["sentence"].tolist() - if not sentences: - print(f"No sentences to process for {idx}. Skipping.") - return - - annotated_sentences = entity_annotator.annotate_texts_in_parallel( - sentences, - max_workers=5, - ) - df["entities"] = [entities for entities in annotated_sentences] - result_json = { - "nct_id": idx, - "criteria": [ - { - "criterion": row["sentence"], - "entities": row["entities"], - "type": row["criteria"], - } - for _, row in df.iterrows() - ], - } - output_filepath.parent.mkdir(parents=True, exist_ok=True) - output_filepath.write_text(json.dumps(result_json, indent=4)) - print(f"Saved {output_filepath}") - - -def process_files( - device_id: str, - ids_to_process: list[str], - config_path: str | None, -) -> None: - global entity_annotator - os.environ["CUDA_VISIBLE_DEVICES"] = device_id - config = load_config(config_path) if config_path else None - entity_annotator = BioMedNER(config=config) - for idx in ids_to_process: - process_file(idx) - - -def main() -> int: - parser = argparse.ArgumentParser( - description="Annotate preprocessed trial criteria with TrialMatchAI entities." - ) - parser.add_argument("--config", default=None, help="TrialMatchAI config path") - parser.add_argument("--processes", type=int, default=1) - parser.add_argument("--device-id", action="append", default=["0"]) - parser.add_argument( - "--ids-file", - default=None, - help="Optional file containing one NCT ID per line. Defaults to TREC ID files.", - ) - args = parser.parse_args() - - unique_ids = _load_ids(args.ids_file) - unprocessed_ids = [ - idx for idx in unique_ids if not (BASE_OUTPUT_FILEPATH_CT / f"{idx}.json").exists() - ] - if not unprocessed_ids: - print("No unprocessed IDs found.") - return 0 - - num_processes = max(1, args.processes) - process_device_ids = [ - args.device_id[i % len(args.device_id)] for i in range(num_processes) - ] - chunks = [[] for _ in range(num_processes)] - for i, idx in enumerate(unprocessed_ids): - chunks[i % num_processes].append(idx) - - processes = [] - for i, ids_chunk in enumerate(chunks): - if not ids_chunk: - continue - process = multiprocessing.Process( - target=process_files, - args=(process_device_ids[i], ids_chunk, args.config), - ) - processes.append(process) - process.start() - - for process in processes: - process.join() - if process.exitcode: - return process.exitcode - return 0 - - -def _load_dataframe(idx: str) -> pd.DataFrame | None: - if data_source == "clinical trials": - file_path = BASE_INPUT_FILEPATH / "clintra" / f"{idx}_preprocessed.tsv" - delimiter = "\t" - elif data_source == "patient": - file_path = BASE_INPUT_FILEPATH / "patient" / f"{idx}_preprocessed.csv" - delimiter = "," - else: - raise ValueError("data_source must be 'clinical trials' or 'patient'.") - - if not file_path.exists(): - print(f"File {file_path} does not exist. Skipping.") - return None - try: - return pd.read_csv(file_path, delimiter=delimiter) - except pd.errors.EmptyDataError: - print(f"No columns to parse from file {file_path}. Skipping.") - return None - - -def _load_ids(ids_file: str | None) -> list[str]: - if ids_file: - return [ - line.strip() - for line in Path(ids_file).read_text().splitlines() - if line.strip() - ] - trec_root = Path(__file__).resolve().parents[2] / "data/trec" - df_trec21 = pd.read_csv(trec_root / "Unique_NCT_IDs_from_2021_File.csv") - df_trec22 = pd.read_csv(trec_root / "Unique_NCT_IDs_from_2022_File.csv") - nct_ids21 = df_trec21["Unique NCT IDs"].unique().tolist() - nct_ids22 = df_trec22["Unique NCT IDs"].unique().tolist() - return sorted(set(nct_ids21 + nct_ids22)) - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/source/Parser/normalizer.py b/source/Parser/normalizer.py deleted file mode 100644 index 96d7e7a2..00000000 --- a/source/Parser/normalizer.py +++ /dev/null @@ -1,28 +0,0 @@ -from __future__ import annotations - -from typing import Any - -from Matcher.entities.linker import ConceptLinker - - -class Normalizer: - """Legacy placeholder for Parser callers. - - Normalization now happens in Matcher.entities.ConceptLinker. This class keeps old - imports from crashing while avoiding external normalizer daemons. - """ - - NO_ENTITY_ID = "CUI-less" - - def __init__(self, *args: Any, concept_linker: ConceptLinker | None = None, **kwargs: Any): - del args, kwargs - self.concept_linker = concept_linker - self.use_neural_normalizer = False - - def normalize(self, base_name: str, doc_dict_list: list[dict[str, Any]]): - del base_name - return doc_dict_list - - def neural_normalize(self, ent_type: str, tagged_docs: list[dict[str, Any]]): - del ent_type - return tagged_docs diff --git a/source/__init__.py b/source/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/Matcher/__init__.py b/src/Matcher/__init__.py new file mode 100644 index 00000000..cb9a15ed --- /dev/null +++ b/src/Matcher/__init__.py @@ -0,0 +1,13 @@ +from __future__ import annotations + +import warnings + +from trialmatchai import __version__ + +warnings.warn( + "The 'Matcher' namespace is deprecated. Import from 'trialmatchai' instead.", + DeprecationWarning, + stacklevel=2, +) + +__all__ = ["__version__"] diff --git a/src/Matcher/config/__init__.py b/src/Matcher/config/__init__.py new file mode 100644 index 00000000..eed74c0e --- /dev/null +++ b/src/Matcher/config/__init__.py @@ -0,0 +1 @@ +from trialmatchai.config import * # noqa: F401,F403 diff --git a/src/Matcher/config/config_loader.py b/src/Matcher/config/config_loader.py new file mode 100644 index 00000000..a3064396 --- /dev/null +++ b/src/Matcher/config/config_loader.py @@ -0,0 +1 @@ +from trialmatchai.config.config_loader import * # noqa: F401,F403 diff --git a/src/Matcher/config/settings.py b/src/Matcher/config/settings.py new file mode 100644 index 00000000..16e461fb --- /dev/null +++ b/src/Matcher/config/settings.py @@ -0,0 +1 @@ +from trialmatchai.config.settings import * # noqa: F401,F403 diff --git a/src/Matcher/entities/__init__.py b/src/Matcher/entities/__init__.py new file mode 100644 index 00000000..4fdc6d4b --- /dev/null +++ b/src/Matcher/entities/__init__.py @@ -0,0 +1 @@ +from trialmatchai.entities import * # noqa: F401,F403 diff --git a/src/Matcher/main.py b/src/Matcher/main.py new file mode 100644 index 00000000..26c493cd --- /dev/null +++ b/src/Matcher/main.py @@ -0,0 +1 @@ +from trialmatchai.main import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/__init__.py b/src/Matcher/pipeline/__init__.py new file mode 100644 index 00000000..e961a2e5 --- /dev/null +++ b/src/Matcher/pipeline/__init__.py @@ -0,0 +1 @@ +from trialmatchai.matching import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/cot_reasoning.py b/src/Matcher/pipeline/cot_reasoning.py new file mode 100644 index 00000000..484df347 --- /dev/null +++ b/src/Matcher/pipeline/cot_reasoning.py @@ -0,0 +1 @@ +from trialmatchai.matching.eligibility_reasoning import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/cot_reasoning_vllm.py b/src/Matcher/pipeline/cot_reasoning_vllm.py new file mode 100644 index 00000000..0943c3b0 --- /dev/null +++ b/src/Matcher/pipeline/cot_reasoning_vllm.py @@ -0,0 +1 @@ +from trialmatchai.matching.eligibility_reasoning_vllm import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/phenopacket_processor.py b/src/Matcher/pipeline/phenopacket_processor.py new file mode 100644 index 00000000..e8645cc2 --- /dev/null +++ b/src/Matcher/pipeline/phenopacket_processor.py @@ -0,0 +1 @@ +from trialmatchai.matching.phenopacket_processor import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_ranker.py b/src/Matcher/pipeline/trial_ranker.py new file mode 100644 index 00000000..21919c5c --- /dev/null +++ b/src/Matcher/pipeline/trial_ranker.py @@ -0,0 +1 @@ +from trialmatchai.matching.trial_ranker import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_search/__init__.py b/src/Matcher/pipeline/trial_search/__init__.py new file mode 100644 index 00000000..f04c1b19 --- /dev/null +++ b/src/Matcher/pipeline/trial_search/__init__.py @@ -0,0 +1 @@ +from trialmatchai.matching.retrieval import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_search/first_level_search.py b/src/Matcher/pipeline/trial_search/first_level_search.py new file mode 100644 index 00000000..fde78fc7 --- /dev/null +++ b/src/Matcher/pipeline/trial_search/first_level_search.py @@ -0,0 +1 @@ +from trialmatchai.matching.retrieval.trial_retrieval import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_search/second_level_search.py b/src/Matcher/pipeline/trial_search/second_level_search.py new file mode 100644 index 00000000..b286feb0 --- /dev/null +++ b/src/Matcher/pipeline/trial_search/second_level_search.py @@ -0,0 +1 @@ +from trialmatchai.matching.retrieval.criteria_retrieval import * # noqa: F401,F403 diff --git a/src/Matcher/schemas/__init__.py b/src/Matcher/schemas/__init__.py new file mode 100644 index 00000000..928cdeac --- /dev/null +++ b/src/Matcher/schemas/__init__.py @@ -0,0 +1 @@ +from trialmatchai.schemas import * # noqa: F401,F403 diff --git a/src/Matcher/search/__init__.py b/src/Matcher/search/__init__.py new file mode 100644 index 00000000..5b6cd37f --- /dev/null +++ b/src/Matcher/search/__init__.py @@ -0,0 +1 @@ +from trialmatchai.search import * # noqa: F401,F403 diff --git a/src/Matcher/services/__init__.py b/src/Matcher/services/__init__.py new file mode 100644 index 00000000..c704ca63 --- /dev/null +++ b/src/Matcher/services/__init__.py @@ -0,0 +1 @@ +from trialmatchai.services import * # noqa: F401,F403 diff --git a/src/Matcher/utils/__init__.py b/src/Matcher/utils/__init__.py new file mode 100644 index 00000000..5933e8a7 --- /dev/null +++ b/src/Matcher/utils/__init__.py @@ -0,0 +1 @@ +from trialmatchai.utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/file_utils.py b/src/Matcher/utils/file_utils.py new file mode 100644 index 00000000..9ae82244 --- /dev/null +++ b/src/Matcher/utils/file_utils.py @@ -0,0 +1 @@ +from trialmatchai.utils.file_utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/json_utils.py b/src/Matcher/utils/json_utils.py new file mode 100644 index 00000000..e0a84189 --- /dev/null +++ b/src/Matcher/utils/json_utils.py @@ -0,0 +1 @@ +from trialmatchai.utils.json_utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/logging_config.py b/src/Matcher/utils/logging_config.py new file mode 100644 index 00000000..4aa94f5c --- /dev/null +++ b/src/Matcher/utils/logging_config.py @@ -0,0 +1 @@ +from trialmatchai.utils.logging_config import * # noqa: F401,F403 diff --git a/src/Matcher/utils/retry.py b/src/Matcher/utils/retry.py new file mode 100644 index 00000000..bce0516f --- /dev/null +++ b/src/Matcher/utils/retry.py @@ -0,0 +1 @@ +from trialmatchai.utils.retry import * # noqa: F401,F403 diff --git a/src/Matcher/utils/temporal_utils.py b/src/Matcher/utils/temporal_utils.py new file mode 100644 index 00000000..4d2a72ca --- /dev/null +++ b/src/Matcher/utils/temporal_utils.py @@ -0,0 +1 @@ +from trialmatchai.utils.temporal_utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/timing.py b/src/Matcher/utils/timing.py new file mode 100644 index 00000000..682226c5 --- /dev/null +++ b/src/Matcher/utils/timing.py @@ -0,0 +1 @@ +from trialmatchai.utils.timing import * # noqa: F401,F403 diff --git a/src/trialmatchai/__init__.py b/src/trialmatchai/__init__.py new file mode 100644 index 00000000..a148edec --- /dev/null +++ b/src/trialmatchai/__init__.py @@ -0,0 +1,3 @@ +from __future__ import annotations + +__version__ = "0.2.0" diff --git a/src/trialmatchai/__main__.py b/src/trialmatchai/__main__.py new file mode 100644 index 00000000..86df07ae --- /dev/null +++ b/src/trialmatchai/__main__.py @@ -0,0 +1,4 @@ +from trialmatchai.cli.main import main + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/source/Matcher/cli/__init__.py b/src/trialmatchai/cli/__init__.py similarity index 100% rename from source/Matcher/cli/__init__.py rename to src/trialmatchai/cli/__init__.py diff --git a/source/Matcher/cli/bootstrap_data.py b/src/trialmatchai/cli/bootstrap_data.py similarity index 100% rename from source/Matcher/cli/bootstrap_data.py rename to src/trialmatchai/cli/bootstrap_data.py diff --git a/source/Matcher/cli/build_concepts.py b/src/trialmatchai/cli/build_concepts.py similarity index 93% rename from source/Matcher/cli/build_concepts.py rename to src/trialmatchai/cli/build_concepts.py index b8971a14..bee79aa5 100644 --- a/source/Matcher/cli/build_concepts.py +++ b/src/trialmatchai/cli/build_concepts.py @@ -4,16 +4,16 @@ import sys from pathlib import Path -from Matcher.config.config_loader import load_config -from Matcher.entities.builder import ( +from trialmatchai.config.config_loader import load_config +from trialmatchai.entities.builder import ( DEFAULT_OMOP_VOCABULARIES, build_legacy_dictionary_rows, build_omop_concept_rows, concept_texts_for_embedding, write_lancedb_table, ) -from Matcher.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig -from Matcher.utils.logging_config import setup_logging +from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) diff --git a/src/trialmatchai/cli/healthcheck.py b/src/trialmatchai/cli/healthcheck.py new file mode 100644 index 00000000..e584c615 --- /dev/null +++ b/src/trialmatchai/cli/healthcheck.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import argparse +import sys + +from trialmatchai.config.config_loader import load_config +from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.services.preflight import run_preflight_checks +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def main() -> int: + parser = argparse.ArgumentParser(description="TrialMatchAI healthcheck") + parser.add_argument( + "--config", + default=None, + help="Path to config.json", + ) + parser.add_argument( + "--require-tables", + action="store_true", + help="Fail if configured LanceDB search tables are missing", + ) + parser.add_argument( + "--registry", + action="store_true", + help="Check registry updater paths and manifest readability.", + ) + args = parser.parse_args() + + config = load_config(args.config) + issues = 0 + search_backend = LanceDBSearchBackend.from_config(config) + + preflight_issues = run_preflight_checks( + config, + require_patient_inputs=False, + require_trials_json=False, + require_models=False, + ) + issues += len(preflight_issues) + + backend_issues = search_backend.health(require_tables=args.require_tables) + if backend_issues: + for issue in backend_issues: + logger.error("Search backend healthcheck failed: %s", issue) + issues += len(backend_issues) + else: + logger.info("LanceDB search backend reachable at %s.", search_backend.db_path) + + if args.registry: + registry_issues = _check_registry(config) + for issue in registry_issues: + logger.error("Registry healthcheck failed: %s", issue) + issues += len(registry_issues) + + return 1 if issues else 0 + + +def _check_registry(config: dict) -> list[str]: + from pathlib import Path + + issues: list[str] = [] + registry_cfg = config.get("registry", {}) + paths_cfg = config.get("paths", {}) + for key in ("raw_dir", "reports_dir"): + value = registry_cfg.get(key) + if not value: + issues.append(f"registry.{key} is not configured.") + continue + try: + Path(value).mkdir(parents=True, exist_ok=True) + except OSError as exc: + issues.append(f"registry.{key} is not writable: {value} ({exc})") + + trials_folder = paths_cfg.get("trials_json_folder") + if not trials_folder: + issues.append("paths.trials_json_folder is not configured.") + else: + try: + Path(trials_folder).mkdir(parents=True, exist_ok=True) + except OSError as exc: + issues.append(f"paths.trials_json_folder is not writable: {trials_folder} ({exc})") + + manifest_path = registry_cfg.get("manifest_path") + if not manifest_path: + issues.append("registry.manifest_path is not configured.") + else: + manifest = Path(manifest_path) + try: + manifest.parent.mkdir(parents=True, exist_ok=True) + if manifest.exists(): + with manifest.open("r", encoding="utf-8"): + pass + except OSError as exc: + issues.append(f"registry.manifest_path is not readable: {manifest} ({exc})") + return issues +if __name__ == "__main__": + sys.exit(main()) diff --git a/source/Matcher/cli/index_data.py b/src/trialmatchai/cli/index_data.py similarity index 53% rename from source/Matcher/cli/index_data.py rename to src/trialmatchai/cli/index_data.py index c9c34efe..f36a0f02 100644 --- a/source/Matcher/cli/index_data.py +++ b/src/trialmatchai/cli/index_data.py @@ -4,18 +4,28 @@ import sys from pathlib import Path -from Matcher.config.config_loader import load_config -from Matcher.search import LanceDBSearchBackend -from Matcher.utils.logging_config import setup_logging +from trialmatchai.config.config_loader import load_config +from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) def main() -> int: parser = argparse.ArgumentParser( - description="Build TrialMatchAI LanceDB search tables from prepared JSON data." + description="Build TrialMatchAI LanceDB search tables." ) parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument( + "--prepare", + action="store_true", + help="Prepare embeddings/entities from normalized trial JSONs before indexing.", + ) + parser.add_argument( + "--trials-json-folder", + default=None, + help="Folder containing normalized trial JSON files. Defaults to config paths.trials_json_folder.", + ) parser.add_argument( "--processed-trials-folder", default="data/processed_trials", @@ -48,6 +58,16 @@ def main() -> int: backend = LanceDBSearchBackend.from_config(config) root = _repo_root() failures = 0 + if args.prepare: + _prepare_from_trials_jsons( + config=config, + trials_json_folder=_resolve_path( + args.trials_json_folder or config["paths"]["trials_json_folder"], + root, + ), + processed_trials_folder=_resolve_path(args.processed_trials_folder, root), + processed_criteria_folder=_resolve_path(args.processed_criteria_folder, root), + ) if not args.skip_trials: trials_folder = _resolve_path(args.processed_trials_folder, root) @@ -101,6 +121,50 @@ def _read_json(path: Path) -> dict: return json.loads(path.read_text(encoding="utf-8")) +def _prepare_from_trials_jsons( + *, + config: dict, + trials_json_folder: Path, + processed_trials_folder: Path, + processed_criteria_folder: Path, +) -> None: + from trialmatchai.entities import build_entity_annotator + from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig + from trialmatchai.registry.preparation import ( + prepare_criteria_documents, + prepare_trial_document, + write_prepared_criteria, + write_prepared_trial, + ) + + embedder_cfg = config.get("embedder", {}) + embedder = TextEmbedder( + TextEmbedderConfig( + model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), + revision=embedder_cfg.get("revision"), + trust_remote_code=embedder_cfg.get("trust_remote_code", False), + pooling=embedder_cfg.get("pooling", "mean"), + max_length=embedder_cfg.get("max_length", 512), + batch_size=embedder_cfg.get("batch_size", 32), + use_gpu=embedder_cfg.get("use_gpu", True), + use_fp16=embedder_cfg.get("use_fp16", False), + normalize=embedder_cfg.get("normalize", True), + ) + ) + entity_annotator = build_entity_annotator(config, embedder=embedder) + trial_docs = _load_flat_json_folder(trials_json_folder) + for doc in trial_docs: + trial_row = prepare_trial_document(doc, embedder) + criteria_rows = prepare_criteria_documents( + doc, + embedder, + entity_annotator=entity_annotator, + ) + write_prepared_trial(trial_row, processed_trials_folder) + write_prepared_criteria(criteria_rows, processed_criteria_folder) + logger.info("Prepared %s trial JSON files from %s.", len(trial_docs), trials_json_folder) + + def _resolve_path(value: str, root: Path) -> Path: path = Path(value).expanduser() if path.is_absolute(): diff --git a/src/trialmatchai/cli/main.py b/src/trialmatchai/cli/main.py new file mode 100644 index 00000000..efadc86e --- /dev/null +++ b/src/trialmatchai/cli/main.py @@ -0,0 +1,39 @@ +from __future__ import annotations + +import argparse +import sys + + +def main() -> int: + parser = argparse.ArgumentParser( + prog="trialmatchai", + description="TrialMatchAI command group.", + ) + subparsers = parser.add_subparsers(dest="command", required=True) + + subparsers.add_parser("healthcheck", help="Run deployment health checks") + subparsers.add_parser("index", help="Build LanceDB search tables") + subparsers.add_parser("build-concepts", help="Build LanceDB concept table") + subparsers.add_parser("update-registry", help="Fetch and upsert registry studies") + subparsers.add_parser("run", help="Run the matching pipeline") + + args, remainder = parser.parse_known_args() + if args.command == "healthcheck": + from trialmatchai.cli.healthcheck import main as command + elif args.command == "index": + from trialmatchai.cli.index_data import main as command + elif args.command == "build-concepts": + from trialmatchai.cli.build_concepts import main as command + elif args.command == "update-registry": + from trialmatchai.cli.update_registry import main as command + elif args.command == "run": + from trialmatchai.cli.run import main as command + else: # pragma: no cover - argparse enforces choices + parser.error(f"Unknown command: {args.command}") + + sys.argv = [f"trialmatchai {args.command}", *remainder] + return command() + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/source/Matcher/cli/run.py b/src/trialmatchai/cli/run.py similarity index 88% rename from source/Matcher/cli/run.py rename to src/trialmatchai/cli/run.py index 67f7b6b1..6adf147d 100644 --- a/source/Matcher/cli/run.py +++ b/src/trialmatchai/cli/run.py @@ -8,7 +8,7 @@ def main() -> int: parser = argparse.ArgumentParser(description="Run the TrialMatchAI batch pipeline") parser.add_argument("--config", default=None, help="Path to config.json") args = parser.parse_args() - from Matcher.main import main_pipeline + from trialmatchai.main import main_pipeline return main_pipeline(args.config) diff --git a/src/trialmatchai/cli/update_registry.py b/src/trialmatchai/cli/update_registry.py new file mode 100644 index 00000000..ff784a05 --- /dev/null +++ b/src/trialmatchai/cli/update_registry.py @@ -0,0 +1,180 @@ +from __future__ import annotations + +import argparse +import json +import sys +from datetime import date, timedelta +from pathlib import Path +from typing import Any + +from trialmatchai.config.config_loader import load_config +from trialmatchai.registry.clinicaltrials_gov import ClinicalTrialsGovClient +from trialmatchai.registry.defaults import DEFAULT_REGISTRY_STATUSES +from trialmatchai.registry.updater import ( + RegistryUpdateConfig, + RegistryUpdater, + normalize_keywords, +) +from trialmatchai.search import InMemorySearchBackend, LanceDBSearchBackend +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Fetch changed ClinicalTrials.gov studies and upsert LanceDB tables." + ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument( + "--keyword", + action="append", + default=[], + help="ClinicalTrials.gov keyword query. Repeat for multiple queries.", + ) + parser.add_argument( + "--keywords-file", + default=None, + help="Text file with one ClinicalTrials.gov keyword query per line.", + ) + parser.add_argument("--since", default=None, help="Only process updates since YYYY-MM-DD") + parser.add_argument("--max-studies", type=int, default=None, help="Maximum studies to process") + parser.add_argument( + "--status", + action="append", + default=[], + help="ClinicalTrials.gov overall status filter. Repeat for multiple statuses.", + ) + parser.add_argument("--dry-run", action="store_true", help="Fetch and plan without writing") + parser.add_argument( + "--reindex-all-changed", + action=argparse.BooleanOptionalAction, + default=True, + help="Recompute embeddings/entities and upsert changed studies.", + ) + parser.add_argument( + "--report-path", + default=None, + help="Optional exact path for a copy of the run report JSON.", + ) + args = parser.parse_args() + + config = load_config(args.config) + registry_cfg = config.get("registry", {}) + paths_cfg = config.get("paths", {}) + + since = _resolve_since(args.since, int(registry_cfg.get("since_days", 7))) + keywords = normalize_keywords( + [ + *args.keyword, + *_read_keywords_file( + args.keywords_file or registry_cfg.get("keywords_file") + ), + ] + ) + statuses = tuple(args.status or DEFAULT_REGISTRY_STATUSES) + max_studies = args.max_studies or registry_cfg.get("max_studies") + if max_studies is not None: + max_studies = int(max_studies) + + update_config = RegistryUpdateConfig( + raw_dir=Path(registry_cfg.get("raw_dir", "data/registry/raw")), + normalized_trials_dir=Path(paths_cfg.get("trials_json_folder", "data/trials_jsons")), + manifest_path=Path( + registry_cfg.get("manifest_path", "data/registry/manifest.jsonl") + ), + reports_dir=Path(registry_cfg.get("reports_dir", "data/registry/runs")), + keywords=keywords, + statuses=statuses, + since=since, + max_studies=max_studies, + dry_run=args.dry_run, + reindex_all_changed=args.reindex_all_changed, + failure_threshold=float(registry_cfg.get("failure_threshold", 0.25)), + ) + + client = ClinicalTrialsGovClient( + base_url=registry_cfg.get( + "api_base_url", + "https://clinicaltrials.gov/api/v2/studies", + ), + timeout=float(registry_cfg.get("request_timeout", 30.0)), + rate_limit_per_second=float(registry_cfg.get("rate_limit_per_second", 2.0)), + ) + backend = InMemorySearchBackend() if args.dry_run else LanceDBSearchBackend.from_config(config) + embedder = _NullEmbedder() if args.dry_run else _build_embedder(config) + entity_annotator = None if args.dry_run else _build_entity_annotator(config, embedder) + + updater = RegistryUpdater( + client=client, + backend=backend, + embedder=embedder, + entity_annotator=entity_annotator, + ) + report = updater.run(update_config) + + if args.report_path: + _write_report_copy(Path(args.report_path), report.to_dict()) + + logger.info("Registry update report: %s", json.dumps(report.to_dict(), sort_keys=True)) + return 1 if report.failure_rate > update_config.failure_threshold else 0 + + +class _NullEmbedder: + def embed_texts(self, texts): + raise RuntimeError("Dry-run updater must not request embeddings.") + + +def _build_embedder(config: dict[str, Any]): + from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig + + embedder_cfg = config.get("embedder", {}) + return TextEmbedder( + TextEmbedderConfig( + model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), + revision=embedder_cfg.get("revision"), + trust_remote_code=embedder_cfg.get("trust_remote_code", False), + pooling=embedder_cfg.get("pooling", "mean"), + max_length=embedder_cfg.get("max_length", 512), + batch_size=embedder_cfg.get("batch_size", 32), + use_gpu=embedder_cfg.get("use_gpu", True), + use_fp16=embedder_cfg.get("use_fp16", False), + normalize=embedder_cfg.get("normalize", True), + ) + ) + + +def _build_entity_annotator(config: dict[str, Any], embedder): + from trialmatchai.entities import build_entity_annotator + + return build_entity_annotator(config, embedder=embedder) + + +def _read_keywords_file(path: str | None) -> list[str]: + if not path: + return [] + keyword_path = Path(path) + if not keyword_path.exists(): + raise FileNotFoundError(f"Registry keywords file does not exist: {keyword_path}") + return [ + line.strip() + for line in keyword_path.read_text(encoding="utf-8").splitlines() + if line.strip() and not line.lstrip().startswith("#") + ] + + +def _resolve_since(value: str | None, since_days: int) -> date | None: + if value: + return date.fromisoformat(value) + if since_days <= 0: + return None + return date.today() - timedelta(days=since_days) + + +def _write_report_copy(path: Path, payload: dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/source/Matcher/__init__.py b/src/trialmatchai/config/__init__.py similarity index 100% rename from source/Matcher/__init__.py rename to src/trialmatchai/config/__init__.py diff --git a/source/Matcher/config/config.json b/src/trialmatchai/config/config.json similarity index 81% rename from source/Matcher/config/config.json rename to src/trialmatchai/config/config.json index d92fb5ea..afa3bf2e 100644 --- a/source/Matcher/config/config.json +++ b/src/trialmatchai/config/config.json @@ -4,7 +4,7 @@ "model_name": "fastino/gliner2-base", "fallback_model_name": "gliner-community/gliner_large-v2.5", "model_revision": null, - "schema_path": "source/Matcher/entity_schemas/trialmatchai.yaml", + "schema_path": "src/trialmatchai/entity_schemas/trialmatchai.yaml", "threshold": 0.8, "batch_size": 8, "device": "auto", @@ -52,6 +52,19 @@ "criteria_table": "criteria", "candidate_limit": 1000 }, + "registry": { + "source": "clinicaltrials.gov", + "api_base_url": "https://clinicaltrials.gov/api/v2/studies", + "keywords_file": null, + "since_days": 7, + "max_studies": null, + "request_timeout": 30, + "rate_limit_per_second": 2, + "raw_dir": "data/registry/raw", + "manifest_path": "data/registry/manifest.jsonl", + "reports_dir": "data/registry/runs", + "failure_threshold": 0.25 + }, "embedder": { "model_name": "BAAI/bge-m3", "revision": null, diff --git a/source/Matcher/config/config_loader.py b/src/trialmatchai/config/config_loader.py similarity index 83% rename from source/Matcher/config/config_loader.py rename to src/trialmatchai/config/config_loader.py index 92a8605e..464c5eb3 100644 --- a/source/Matcher/config/config_loader.py +++ b/src/trialmatchai/config/config_loader.py @@ -6,8 +6,8 @@ from pathlib import Path from typing import Any, Dict -from Matcher.config.settings import TrialMatchSettings, apply_env_overrides -from Matcher.utils.logging_config import setup_logging +from trialmatchai.config.settings import TrialMatchSettings, apply_env_overrides +from trialmatchai.utils.logging_config import setup_logging try: from dotenv import load_dotenv @@ -18,8 +18,12 @@ logger = setup_logging(__name__) -DEFAULT_CONFIG_RELATIVE_PATH = Path("source/Matcher/config/config.json") -LEGACY_CONFIG_RELATIVE_PATH = Path("Matcher/config/config.json") +DEFAULT_CONFIG_RELATIVE_PATH = Path("src/trialmatchai/config/config.json") +LEGACY_CONFIG_RELATIVE_PATHS = ( + Path("trialmatchai/config/config.json"), + Path("trialmatchai/config/config.json"), + Path("source/trialmatchai/config/config.json"), +) def load_config(config_path: str | os.PathLike[str] | None = None) -> Dict[str, Any]: @@ -53,10 +57,10 @@ def resolve_config_path( [ Path.cwd() / supplied, root / supplied, - root / "source" / supplied, + root / "src" / supplied, ] ) - if supplied == LEGACY_CONFIG_RELATIVE_PATH: + if supplied in LEGACY_CONFIG_RELATIVE_PATHS: candidates.append(root / DEFAULT_CONFIG_RELATIVE_PATH) else: candidates.extend( @@ -101,6 +105,12 @@ def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, _resolve_local_path(search_db_path, root) ) + registry_cfg = cfg.get("registry", {}) + for key in ("keywords_file", "raw_dir", "manifest_path", "reports_dir"): + value = registry_cfg.get(key) + if value: + registry_cfg[key] = str(_resolve_local_path(value, root)) + for key in ("cot_adapter_path", "reranker_adapter_path"): value = cfg.get("model", {}).get(key) if value: diff --git a/source/Matcher/config/settings.py b/src/trialmatchai/config/settings.py similarity index 85% rename from source/Matcher/config/settings.py rename to src/trialmatchai/config/settings.py index 40a98014..b60b400a 100644 --- a/source/Matcher/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -10,7 +10,7 @@ class EntityExtractionSettings(BaseModel): model_name: str = "fastino/gliner2-base" fallback_model_name: str = "gliner-community/gliner_large-v2.5" model_revision: str | None = None - schema_path: str = "source/Matcher/entity_schemas/trialmatchai.yaml" + schema_path: str = "src/trialmatchai/entity_schemas/trialmatchai.yaml" threshold: float = Field(0.8, ge=0.0, le=1.0) batch_size: int = Field(8, ge=1) device: str = "auto" @@ -75,6 +75,20 @@ class SearchBackendSettings(BaseModel): candidate_limit: int = Field(1000, ge=1) +class RegistrySettings(BaseModel): + source: Literal["clinicaltrials.gov"] = "clinicaltrials.gov" + api_base_url: str = "https://clinicaltrials.gov/api/v2/studies" + keywords_file: str | None = None + since_days: int = Field(7, ge=0) + max_studies: int | None = Field(default=None, ge=1) + request_timeout: float = Field(30.0, gt=0) + rate_limit_per_second: float = Field(2.0, gt=0) + raw_dir: str = "data/registry/raw" + manifest_path: str = "data/registry/manifest.jsonl" + reports_dir: str = "data/registry/runs" + failure_threshold: float = Field(0.25, ge=0.0, le=1.0) + + class EmbedderSettings(BaseModel): model_name: str = "BAAI/bge-m3" revision: str | None = None @@ -136,6 +150,7 @@ class TrialMatchSettings(BaseModel): tokenizer: TokenizerSettings global_: GlobalSettings = Field(alias="global") search_backend: SearchBackendSettings = Field(default_factory=SearchBackendSettings) + registry: RegistrySettings = Field(default_factory=RegistrySettings) embedder: EmbedderSettings cot: CotSettings LLM_reranker: LLMRerankerSettings @@ -204,6 +219,12 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "TRIALMATCHAI_ENTITY_DEVICE": ("entity_extraction", "device"), "TRIALMATCHAI_CONCEPT_DB_PATH": ("concept_linker", "db_path"), "TRIALMATCHAI_CONCEPT_TABLE": ("concept_linker", "table"), + "TRIALMATCHAI_REGISTRY_SOURCE": ("registry", "source"), + "TRIALMATCHAI_REGISTRY_API_BASE_URL": ("registry", "api_base_url"), + "TRIALMATCHAI_REGISTRY_KEYWORDS_FILE": ("registry", "keywords_file"), + "TRIALMATCHAI_REGISTRY_RAW_DIR": ("registry", "raw_dir"), + "TRIALMATCHAI_REGISTRY_MANIFEST_PATH": ("registry", "manifest_path"), + "TRIALMATCHAI_REGISTRY_REPORTS_DIR": ("registry", "reports_dir"), } for env_key, path in string_env_map.items(): value = os.getenv(env_key) @@ -237,6 +258,8 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "TRIALMATCHAI_EMBEDDER_BATCH_SIZE": ("embedder", "batch_size"), "TRIALMATCHAI_ENTITY_BATCH_SIZE": ("entity_extraction", "batch_size"), "TRIALMATCHAI_CONCEPT_SEARCH_LIMIT": ("concept_linker", "search_limit"), + "TRIALMATCHAI_REGISTRY_SINCE_DAYS": ("registry", "since_days"), + "TRIALMATCHAI_REGISTRY_MAX_STUDIES": ("registry", "max_studies"), "TRIALMATCHAI_SEARCH_MAX_TRIALS_FIRST_LEVEL": ( "search", "max_trials_first_level", @@ -261,6 +284,15 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "TRIALMATCHAI_ENTITY_THRESHOLD": ("entity_extraction", "threshold"), "TRIALMATCHAI_LINK_ACCEPT": ("concept_linker", "accept_threshold"), "TRIALMATCHAI_LINK_REJECT": ("concept_linker", "reject_threshold"), + "TRIALMATCHAI_REGISTRY_REQUEST_TIMEOUT": ("registry", "request_timeout"), + "TRIALMATCHAI_REGISTRY_RATE_LIMIT_PER_SECOND": ( + "registry", + "rate_limit_per_second", + ), + "TRIALMATCHAI_REGISTRY_FAILURE_THRESHOLD": ( + "registry", + "failure_threshold", + ), } for env_key, path in float_env_map.items(): value = os.getenv(env_key) diff --git a/source/Matcher/entities/__init__.py b/src/trialmatchai/entities/__init__.py similarity index 66% rename from source/Matcher/entities/__init__.py rename to src/trialmatchai/entities/__init__.py index f3f483a9..25109e55 100644 --- a/source/Matcher/entities/__init__.py +++ b/src/trialmatchai/entities/__init__.py @@ -1,9 +1,9 @@ -from Matcher.entities.annotator import ( +from trialmatchai.entities.annotator import ( CompatibilityEntityAnnotator, SchemaEntityAnnotator, build_entity_annotator, ) -from Matcher.entities.types import ConceptCandidate, EntityAnnotation, EntitySchema +from trialmatchai.entities.types import ConceptCandidate, EntityAnnotation, EntitySchema __all__ = [ "CompatibilityEntityAnnotator", diff --git a/source/Matcher/entities/annotator.py b/src/trialmatchai/entities/annotator.py similarity index 90% rename from source/Matcher/entities/annotator.py rename to src/trialmatchai/entities/annotator.py index 7fe23905..59649e7f 100644 --- a/source/Matcher/entities/annotator.py +++ b/src/trialmatchai/entities/annotator.py @@ -4,11 +4,11 @@ from pathlib import Path from typing import Any, Sequence -from Matcher.entities.linker import ConceptLinker, LanceDBConceptStore -from Matcher.entities.recognizers import EntityRecognizer, build_recognizer -from Matcher.entities.schemas import load_entity_schemas -from Matcher.entities.types import EntityAnnotation -from Matcher.utils.logging_config import setup_logging +from trialmatchai.entities.linker import ConceptLinker, LanceDBConceptStore +from trialmatchai.entities.recognizers import EntityRecognizer, build_recognizer +from trialmatchai.entities.schemas import load_entity_schemas +from trialmatchai.entities.types import EntityAnnotation +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -66,7 +66,7 @@ def annotate_texts_in_parallel( class CompatibilityEntityAnnotator(SchemaEntityAnnotator): - """Named compatibility shim for old call sites expecting BioMedNER shape.""" + """Compatibility annotator exposing the historical batch annotation shape.""" def build_entity_annotator( diff --git a/source/Matcher/entities/builder.py b/src/trialmatchai/entities/builder.py similarity index 99% rename from source/Matcher/entities/builder.py rename to src/trialmatchai/entities/builder.py index 5684e023..fa1819a8 100644 --- a/source/Matcher/entities/builder.py +++ b/src/trialmatchai/entities/builder.py @@ -4,7 +4,7 @@ from pathlib import Path from typing import Any, Iterable, Sequence -from Matcher.entities.types import dedupe_strings +from trialmatchai.entities.types import dedupe_strings DEFAULT_OMOP_VOCABULARIES = ( diff --git a/source/Matcher/entities/linker.py b/src/trialmatchai/entities/linker.py similarity index 98% rename from source/Matcher/entities/linker.py rename to src/trialmatchai/entities/linker.py index 6fd1605e..967f3c2e 100644 --- a/source/Matcher/entities/linker.py +++ b/src/trialmatchai/entities/linker.py @@ -4,15 +4,15 @@ from pathlib import Path from typing import Any, Protocol, Sequence -from Matcher.entities.schemas import schema_by_label -from Matcher.entities.types import ( +from trialmatchai.entities.schemas import schema_by_label +from trialmatchai.entities.types import ( NO_ENTITY_ID, ConceptCandidate, EntityAnnotation, EntitySchema, dedupe_strings, ) -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) diff --git a/source/Matcher/entities/recognizers.py b/src/trialmatchai/entities/recognizers.py similarity index 98% rename from source/Matcher/entities/recognizers.py rename to src/trialmatchai/entities/recognizers.py index 3e7e2b51..d5c05609 100644 --- a/source/Matcher/entities/recognizers.py +++ b/src/trialmatchai/entities/recognizers.py @@ -4,8 +4,8 @@ from dataclasses import replace from typing import Any, Protocol, Sequence -from Matcher.entities.schemas import schema_by_label -from Matcher.entities.types import EntityAnnotation, EntitySchema, NO_ENTITY_ID +from trialmatchai.entities.schemas import schema_by_label +from trialmatchai.entities.types import EntityAnnotation, EntitySchema, NO_ENTITY_ID class EntityRecognizer(Protocol): diff --git a/source/Matcher/entities/schemas.py b/src/trialmatchai/entities/schemas.py similarity index 96% rename from source/Matcher/entities/schemas.py rename to src/trialmatchai/entities/schemas.py index db3a182d..300c86d3 100644 --- a/source/Matcher/entities/schemas.py +++ b/src/trialmatchai/entities/schemas.py @@ -6,7 +6,7 @@ import yaml -from Matcher.entities.types import EntitySchema +from trialmatchai.entities.types import EntitySchema DEFAULT_SCHEMA_RESOURCE = "entity_schemas/trialmatchai.yaml" @@ -17,7 +17,7 @@ class EntitySchemaError(ValueError): def default_schema_path() -> Path: - return Path(str(resources.files("Matcher").joinpath(DEFAULT_SCHEMA_RESOURCE))) + return Path(str(resources.files("trialmatchai").joinpath(DEFAULT_SCHEMA_RESOURCE))) def load_entity_schemas(path: str | Path | None = None) -> list[EntitySchema]: diff --git a/source/Matcher/entities/types.py b/src/trialmatchai/entities/types.py similarity index 100% rename from source/Matcher/entities/types.py rename to src/trialmatchai/entities/types.py diff --git a/source/Matcher/entity_schemas/trialmatchai.yaml b/src/trialmatchai/entity_schemas/trialmatchai.yaml similarity index 100% rename from source/Matcher/entity_schemas/trialmatchai.yaml rename to src/trialmatchai/entity_schemas/trialmatchai.yaml diff --git a/source/Matcher/main.py b/src/trialmatchai/main.py similarity index 91% rename from source/Matcher/main.py rename to src/trialmatchai/main.py index 92b055ab..893673a0 100644 --- a/source/Matcher/main.py +++ b/src/trialmatchai/main.py @@ -5,34 +5,34 @@ import torch -from Matcher.config.config_loader import load_config -from Matcher.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig -from Matcher.models.llm.llm_loader import load_model_and_tokenizer -from Matcher.models.llm.llm_reranker import LLMReranker -from Matcher.models.llm.vllm_loader import load_vllm_engine -from Matcher.entities import build_entity_annotator -from Matcher.pipeline.cot_reasoning import BatchTrialProcessor -from Matcher.pipeline.cot_reasoning_vllm import BatchTrialProcessorVLLM -from Matcher.pipeline.phenopacket_processor import process_phenopacket -from Matcher.pipeline.trial_ranker import ( +from trialmatchai.config.config_loader import load_config +from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig +from trialmatchai.models.llm.llm_loader import load_model_and_tokenizer +from trialmatchai.models.llm.llm_reranker import LLMReranker +from trialmatchai.models.llm.vllm_loader import load_vllm_engine +from trialmatchai.entities import build_entity_annotator +from trialmatchai.matching.eligibility_reasoning import BatchTrialProcessor +from trialmatchai.matching.eligibility_reasoning_vllm import BatchTrialProcessorVLLM +from trialmatchai.matching.phenopacket_processor import process_phenopacket +from trialmatchai.matching.trial_ranker import ( load_trial_data, rank_trials, save_ranked_trials, ) -from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch -from Matcher.pipeline.trial_search.second_level_search import SecondStageRetriever -from Matcher.search import LanceDBSearchBackend -from Matcher.services.preflight import run_preflight_checks -from Matcher.utils.file_utils import ( +from trialmatchai.matching.retrieval.trial_retrieval import ClinicalTrialSearch +from trialmatchai.matching.retrieval.criteria_retrieval import SecondStageRetriever +from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.services.preflight import run_preflight_checks +from trialmatchai.utils.file_utils import ( create_directory, read_json_file, read_text_file, write_json_file, write_text_file, ) -from Matcher.schemas.phenopacket import Keywords, Phenopacket -from Matcher.utils.logging_config import reset_request_id, set_request_id, setup_logging -from Matcher.utils.timing import log_timing +from trialmatchai.schemas.phenopacket import Keywords, Phenopacket +from trialmatchai.utils.logging_config import reset_request_id, set_request_id, setup_logging +from trialmatchai.utils.timing import log_timing logger = setup_logging(__name__) diff --git a/source/Matcher/config/__init__.py b/src/trialmatchai/matching/__init__.py similarity index 100% rename from source/Matcher/config/__init__.py rename to src/trialmatchai/matching/__init__.py diff --git a/source/Matcher/pipeline/cot_reasoning.py b/src/trialmatchai/matching/eligibility_reasoning.py similarity index 98% rename from source/Matcher/pipeline/cot_reasoning.py rename to src/trialmatchai/matching/eligibility_reasoning.py index 61fbdb33..f4c6671e 100644 --- a/source/Matcher/pipeline/cot_reasoning.py +++ b/src/trialmatchai/matching/eligibility_reasoning.py @@ -4,9 +4,9 @@ from typing import Dict, List import torch -from Matcher.utils.file_utils import read_json_file, write_json_file, write_text_file -from Matcher.utils.json_utils import extract_json_object -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file +from trialmatchai.utils.json_utils import extract_json_object +from trialmatchai.utils.logging_config import setup_logging from tqdm import tqdm logger = setup_logging(__name__) diff --git a/source/Matcher/pipeline/cot_reasoning_vllm.py b/src/trialmatchai/matching/eligibility_reasoning_vllm.py similarity index 99% rename from source/Matcher/pipeline/cot_reasoning_vllm.py rename to src/trialmatchai/matching/eligibility_reasoning_vllm.py index 23f49049..f394e4bd 100644 --- a/source/Matcher/pipeline/cot_reasoning_vllm.py +++ b/src/trialmatchai/matching/eligibility_reasoning_vllm.py @@ -5,9 +5,9 @@ import time from typing import Dict, List, Optional -from Matcher.utils.file_utils import read_json_file, write_json_file, write_text_file -from Matcher.utils.json_utils import extract_json_object -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file +from trialmatchai.utils.json_utils import extract_json_object +from trialmatchai.utils.logging_config import setup_logging from tqdm import tqdm from vllm import LLM, SamplingParams diff --git a/source/Matcher/pipeline/phenopacket_processor.py b/src/trialmatchai/matching/phenopacket_processor.py similarity index 98% rename from source/Matcher/pipeline/phenopacket_processor.py rename to src/trialmatchai/matching/phenopacket_processor.py index e4bae585..6aef765b 100644 --- a/source/Matcher/pipeline/phenopacket_processor.py +++ b/src/trialmatchai/matching/phenopacket_processor.py @@ -2,11 +2,11 @@ from typing import Dict, List, Optional import torch -from Matcher.schemas.phenopacket import Phenopacket -from Matcher.utils.file_utils import read_json_file, write_json_file -from Matcher.utils.json_utils import extract_json_object -from Matcher.utils.logging_config import setup_logging -from Matcher.utils.temporal_utils import parse_iso_duration, parse_temporal +from trialmatchai.schemas.phenopacket import Phenopacket +from trialmatchai.utils.file_utils import read_json_file, write_json_file +from trialmatchai.utils.json_utils import extract_json_object +from trialmatchai.utils.logging_config import setup_logging +from trialmatchai.utils.temporal_utils import parse_iso_duration, parse_temporal from transformers import AutoModelForCausalLM, AutoTokenizer logger = setup_logging(__name__) diff --git a/source/Matcher/models/__init__.py b/src/trialmatchai/matching/retrieval/__init__.py similarity index 100% rename from source/Matcher/models/__init__.py rename to src/trialmatchai/matching/retrieval/__init__.py diff --git a/source/Matcher/pipeline/trial_search/second_level_search.py b/src/trialmatchai/matching/retrieval/criteria_retrieval.py similarity index 96% rename from source/Matcher/pipeline/trial_search/second_level_search.py rename to src/trialmatchai/matching/retrieval/criteria_retrieval.py index 2c06a607..fb5487f7 100644 --- a/source/Matcher/pipeline/trial_search/second_level_search.py +++ b/src/trialmatchai/matching/retrieval/criteria_retrieval.py @@ -3,11 +3,11 @@ from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Dict, List, Optional -from Matcher.models.embedding.text_embedder import TextEmbedder -from Matcher.models.llm.llm_reranker import LLMReranker -from Matcher.search.lancedb_backend import TrialSearchBackend -from Matcher.utils.file_utils import write_text_file -from Matcher.utils.logging_config import setup_logging +from trialmatchai.models.embedding.text_embedder import TextEmbedder +from trialmatchai.models.llm.llm_reranker import LLMReranker +from trialmatchai.search.lancedb_backend import TrialSearchBackend +from trialmatchai.utils.file_utils import write_text_file +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) diff --git a/source/Matcher/pipeline/trial_search/first_level_search.py b/src/trialmatchai/matching/retrieval/trial_retrieval.py similarity index 97% rename from source/Matcher/pipeline/trial_search/first_level_search.py rename to src/trialmatchai/matching/retrieval/trial_retrieval.py index 33e9be05..0d18c652 100644 --- a/source/Matcher/pipeline/trial_search/first_level_search.py +++ b/src/trialmatchai/matching/retrieval/trial_retrieval.py @@ -2,9 +2,9 @@ from typing import Dict, List, Optional, Tuple, Union from dateutil import parser as date_parser -from Matcher.models.embedding.text_embedder import TextEmbedder -from Matcher.search.lancedb_backend import TrialSearchBackend -from Matcher.utils.logging_config import setup_logging +from trialmatchai.models.embedding.text_embedder import TextEmbedder +from trialmatchai.search.lancedb_backend import TrialSearchBackend +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) diff --git a/source/Matcher/pipeline/trial_ranker.py b/src/trialmatchai/matching/trial_ranker.py similarity index 95% rename from source/Matcher/pipeline/trial_ranker.py rename to src/trialmatchai/matching/trial_ranker.py index 9de662c5..deb9c8d9 100644 --- a/source/Matcher/pipeline/trial_ranker.py +++ b/src/trialmatchai/matching/trial_ranker.py @@ -1,8 +1,8 @@ import os from typing import Dict, List -from Matcher.utils.file_utils import read_json_file, write_json_file -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.file_utils import read_json_file, write_json_file +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) diff --git a/source/Matcher/models/llm/__init__.py b/src/trialmatchai/models/__init__.py similarity index 100% rename from source/Matcher/models/llm/__init__.py rename to src/trialmatchai/models/__init__.py diff --git a/src/trialmatchai/models/embedding/__init__.py b/src/trialmatchai/models/embedding/__init__.py new file mode 100644 index 00000000..f91ae8bc --- /dev/null +++ b/src/trialmatchai/models/embedding/__init__.py @@ -0,0 +1,3 @@ +from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig + +__all__ = ["TextEmbedder", "TextEmbedderConfig"] diff --git a/source/Matcher/models/embedding/query_embedder.py b/src/trialmatchai/models/embedding/query_embedder.py similarity index 87% rename from source/Matcher/models/embedding/query_embedder.py rename to src/trialmatchai/models/embedding/query_embedder.py index 8290a366..66ca0bb6 100644 --- a/source/Matcher/models/embedding/query_embedder.py +++ b/src/trialmatchai/models/embedding/query_embedder.py @@ -1,4 +1,4 @@ -from Matcher.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig +from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig class QueryEmbedder(TextEmbedder): diff --git a/source/Matcher/models/embedding/sentence_embedder.py b/src/trialmatchai/models/embedding/sentence_embedder.py similarity index 87% rename from source/Matcher/models/embedding/sentence_embedder.py rename to src/trialmatchai/models/embedding/sentence_embedder.py index 69fd5684..2e81fe33 100644 --- a/source/Matcher/models/embedding/sentence_embedder.py +++ b/src/trialmatchai/models/embedding/sentence_embedder.py @@ -1,4 +1,4 @@ -from Matcher.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig +from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig class SecondLevelSentenceEmbedder(TextEmbedder): diff --git a/source/Matcher/models/embedding/text_embedder.py b/src/trialmatchai/models/embedding/text_embedder.py similarity index 98% rename from source/Matcher/models/embedding/text_embedder.py rename to src/trialmatchai/models/embedding/text_embedder.py index f263ae8a..acf21da4 100644 --- a/source/Matcher/models/embedding/text_embedder.py +++ b/src/trialmatchai/models/embedding/text_embedder.py @@ -7,7 +7,7 @@ import torch.nn.functional as F from transformers import AutoModel, AutoTokenizer -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) diff --git a/source/Matcher/pipeline/__init__.py b/src/trialmatchai/models/llm/__init__.py similarity index 100% rename from source/Matcher/pipeline/__init__.py rename to src/trialmatchai/models/llm/__init__.py diff --git a/source/Matcher/models/llm/llm_loader.py b/src/trialmatchai/models/llm/llm_loader.py similarity index 98% rename from source/Matcher/models/llm/llm_loader.py rename to src/trialmatchai/models/llm/llm_loader.py index f3b10422..97ddb38e 100644 --- a/source/Matcher/models/llm/llm_loader.py +++ b/src/trialmatchai/models/llm/llm_loader.py @@ -1,7 +1,7 @@ from typing import Tuple import torch -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.logging_config import setup_logging from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig diff --git a/source/Matcher/models/llm/llm_reranker.py b/src/trialmatchai/models/llm/llm_reranker.py similarity index 99% rename from source/Matcher/models/llm/llm_reranker.py rename to src/trialmatchai/models/llm/llm_reranker.py index df505611..2a60e6d1 100644 --- a/source/Matcher/models/llm/llm_reranker.py +++ b/src/trialmatchai/models/llm/llm_reranker.py @@ -6,7 +6,7 @@ import torch import torch.nn.functional as F -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.logging_config import setup_logging from peft import PeftModel from tqdm import tqdm from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig diff --git a/source/Matcher/models/llm/vllm_loader.py b/src/trialmatchai/models/llm/vllm_loader.py similarity index 98% rename from source/Matcher/models/llm/vllm_loader.py rename to src/trialmatchai/models/llm/vllm_loader.py index 0b2bdbcd..eb283637 100644 --- a/source/Matcher/models/llm/vllm_loader.py +++ b/src/trialmatchai/models/llm/vllm_loader.py @@ -1,10 +1,10 @@ -# Matcher/models/llm/vllm_loader.py +# trialmatchai/models/llm/vllm_loader.py from __future__ import annotations import os from typing import Optional, Tuple -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) diff --git a/src/trialmatchai/preprocessing/__init__.py b/src/trialmatchai/preprocessing/__init__.py new file mode 100644 index 00000000..9200581b --- /dev/null +++ b/src/trialmatchai/preprocessing/__init__.py @@ -0,0 +1 @@ +"""Preprocessing resources and helpers.""" diff --git a/src/trialmatchai/preprocessing/regex/__init__.py b/src/trialmatchai/preprocessing/regex/__init__.py new file mode 100644 index 00000000..32ce5226 --- /dev/null +++ b/src/trialmatchai/preprocessing/regex/__init__.py @@ -0,0 +1 @@ +"""Regex preprocessing resource package.""" diff --git a/source/regex/exception_regex_patterns.json b/src/trialmatchai/preprocessing/regex/exception_regex_patterns.json similarity index 100% rename from source/regex/exception_regex_patterns.json rename to src/trialmatchai/preprocessing/regex/exception_regex_patterns.json diff --git a/source/regex/regex_patterns.json b/src/trialmatchai/preprocessing/regex/regex_patterns.json similarity index 100% rename from source/regex/regex_patterns.json rename to src/trialmatchai/preprocessing/regex/regex_patterns.json diff --git a/source/regex/regex_variants.tsv b/src/trialmatchai/preprocessing/regex/regex_variants.tsv similarity index 100% rename from source/regex/regex_variants.tsv rename to src/trialmatchai/preprocessing/regex/regex_variants.tsv diff --git a/src/trialmatchai/registry/__init__.py b/src/trialmatchai/registry/__init__.py new file mode 100644 index 00000000..c15403f0 --- /dev/null +++ b/src/trialmatchai/registry/__init__.py @@ -0,0 +1,13 @@ +from trialmatchai.registry.clinicaltrials_gov import ClinicalTrialsGovClient +from trialmatchai.registry.defaults import DEFAULT_REGISTRY_KEYWORDS +from trialmatchai.registry.normalization import normalize_study +from trialmatchai.registry.updater import RegistryUpdateConfig, RegistryUpdateReport, RegistryUpdater + +__all__ = [ + "ClinicalTrialsGovClient", + "DEFAULT_REGISTRY_KEYWORDS", + "RegistryUpdateConfig", + "RegistryUpdateReport", + "RegistryUpdater", + "normalize_study", +] diff --git a/src/trialmatchai/registry/clinicaltrials_gov.py b/src/trialmatchai/registry/clinicaltrials_gov.py new file mode 100644 index 00000000..303cdba3 --- /dev/null +++ b/src/trialmatchai/registry/clinicaltrials_gov.py @@ -0,0 +1,187 @@ +from __future__ import annotations + +import time +from collections.abc import Iterator, Sequence +from dataclasses import dataclass, field +from datetime import date +from typing import Any + +import requests +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential_jitter, +) + +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +class RegistrySourceError(RuntimeError): + pass + + +class TransientRegistrySourceError(RegistrySourceError): + pass + + +@dataclass +class ClinicalTrialsGovClient: + base_url: str = "https://clinicaltrials.gov/api/v2/studies" + timeout: float = 30.0 + rate_limit_per_second: float = 2.0 + session: requests.Session = field(default_factory=requests.Session) + page_size: int = 100 + + def __post_init__(self) -> None: + if self.page_size < 1 or self.page_size > 1000: + raise ValueError("page_size must be between 1 and 1000.") + if self.rate_limit_per_second <= 0: + raise ValueError("rate_limit_per_second must be positive.") + self._last_request_at = 0.0 + + def iter_studies( + self, + *, + keyword: str, + statuses: Sequence[str] = (), + since: date | None = None, + max_studies: int | None = None, + ) -> Iterator[dict[str, Any]]: + """Yield studies for one ClinicalTrials.gov keyword query. + + The v2 API returns `studies` plus an optional `nextPageToken`. Date + filtering is intentionally post-fetch here because the upstream API has + changed filter names before; keeping it local makes the source client + easier to harden and test. + """ + yielded = 0 + page_token: str | None = None + status_filter = tuple(status for status in statuses if status) + while True: + payload = self._fetch_page( + keyword=keyword, + statuses=status_filter, + page_token=page_token, + ) + studies = payload.get("studies") or [] + if not isinstance(studies, list): + raise RegistrySourceError("ClinicalTrials.gov returned non-list studies.") + for study in studies: + if not isinstance(study, dict): + continue + if since is not None and not _study_updated_since(study, since): + continue + yield study + yielded += 1 + if max_studies is not None and yielded >= max_studies: + return + page_token = payload.get("nextPageToken") + if not page_token: + return + + def _fetch_page( + self, + *, + keyword: str, + statuses: Sequence[str], + page_token: str | None, + ) -> dict[str, Any]: + params: dict[str, Any] = { + "format": "json", + "pageSize": self.page_size, + "query.term": keyword, + } + if statuses: + params["filter.overallStatus"] = ",".join(statuses) + if page_token: + params["pageToken"] = page_token + return self._get_json(params) + + @retry( + retry=retry_if_exception_type( + (requests.Timeout, requests.ConnectionError, TransientRegistrySourceError) + ), + wait=wait_exponential_jitter(initial=0.5, max=8), + stop=stop_after_attempt(4), + reraise=True, + ) + def _get_json(self, params: dict[str, Any]) -> dict[str, Any]: + self._throttle() + try: + response = self.session.get( + self.base_url, + params=params, + timeout=self.timeout, + headers={"Accept": "application/json"}, + ) + except (requests.Timeout, requests.ConnectionError): + raise + except requests.RequestException as exc: + raise RegistrySourceError(f"ClinicalTrials.gov request failed: {exc}") from exc + + if response.status_code in {408, 425, 429, 500, 502, 503, 504}: + raise TransientRegistrySourceError( + f"ClinicalTrials.gov transient HTTP {response.status_code}" + ) + try: + response.raise_for_status() + except requests.HTTPError as exc: + raise RegistrySourceError( + f"ClinicalTrials.gov HTTP {response.status_code}: {response.text[:300]}" + ) from exc + payload = response.json() + if not isinstance(payload, dict): + raise RegistrySourceError("ClinicalTrials.gov returned non-object JSON.") + return payload + + def _throttle(self) -> None: + minimum_gap = 1.0 / self.rate_limit_per_second + now = time.monotonic() + elapsed = now - self._last_request_at + if elapsed < minimum_gap: + time.sleep(minimum_gap - elapsed) + self._last_request_at = time.monotonic() + + +def _study_updated_since(study: dict[str, Any], since: date) -> bool: + updated = _last_update_date(study) + return updated is None or updated >= since + + +def _last_update_date(study: dict[str, Any]) -> date | None: + protocol = study.get("protocolSection") if isinstance(study, dict) else None + if not isinstance(protocol, dict): + return None + status = protocol.get("statusModule") + if not isinstance(status, dict): + return None + for key in ( + "lastUpdatePostDateStruct", + "lastUpdateSubmitDateStruct", + "studyFirstPostDateStruct", + ): + value = status.get(key) + if isinstance(value, dict): + parsed = _parse_date(value.get("date")) + if parsed is not None: + return parsed + return None + + +def _parse_date(value: Any) -> date | None: + if not value: + return None + text = str(value) + try: + if len(text) == 4: + return date(int(text), 1, 1) + if len(text) == 7: + year, month = text.split("-", 1) + return date(int(year), int(month), 1) + return date.fromisoformat(text[:10]) + except ValueError: + logger.warning("Could not parse ClinicalTrials.gov date: %s", text) + return None diff --git a/src/trialmatchai/registry/defaults.py b/src/trialmatchai/registry/defaults.py new file mode 100644 index 00000000..6ee073f3 --- /dev/null +++ b/src/trialmatchai/registry/defaults.py @@ -0,0 +1,20 @@ +from __future__ import annotations + +DEFAULT_REGISTRY_KEYWORDS: tuple[str, ...] = ( + "cancer OR oncology OR neoplasm OR tumor", + "heart failure OR coronary artery disease OR atrial fibrillation", + "stroke OR Parkinson disease OR Alzheimer disease OR epilepsy", + "rare disease OR orphan disease", + "autoimmune disease OR immunotherapy OR inflammatory disease", + "COVID-19 OR HIV OR tuberculosis OR infectious disease", + "diabetes OR obesity OR metabolic syndrome", + "leukemia OR lymphoma OR anemia OR hematology", + "pediatric OR children OR adolescent", + "precision medicine OR genomic OR biomarker", +) + +DEFAULT_REGISTRY_STATUSES: tuple[str, ...] = ( + "RECRUITING", + "NOT_YET_RECRUITING", + "ACTIVE_NOT_RECRUITING", +) diff --git a/src/trialmatchai/registry/manifest.py b/src/trialmatchai/registry/manifest.py new file mode 100644 index 00000000..192f3eb0 --- /dev/null +++ b/src/trialmatchai/registry/manifest.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +import hashlib +import json +from dataclasses import asdict, dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + + +@dataclass(frozen=True) +class ManifestRecord: + nct_id: str + source_url: str + source_hash: str + fetched_at: str + last_update_posted: str | None + processing_status: str + error_summary: str | None = None + + +class RegistryManifest: + def __init__(self, path: str | Path) -> None: + self.path = Path(path) + + def load_latest(self) -> dict[str, ManifestRecord]: + latest: dict[str, ManifestRecord] = {} + if not self.path.exists(): + return latest + with self.path.open("r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + try: + raw = json.loads(line) + record = ManifestRecord(**raw) + except (TypeError, ValueError, json.JSONDecodeError): + continue + latest[record.nct_id] = record + return latest + + def append(self, record: ManifestRecord) -> None: + self.path.parent.mkdir(parents=True, exist_ok=True) + with self.path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(asdict(record), sort_keys=True) + "\n") + + +def source_hash(payload: dict[str, Any]) -> str: + encoded = json.dumps(payload, sort_keys=True, separators=(",", ":"), default=str) + return hashlib.sha256(encoded.encode("utf-8")).hexdigest() + + +def utc_now_iso() -> str: + return datetime.now(timezone.utc).replace(microsecond=0).isoformat() diff --git a/src/trialmatchai/registry/normalization.py b/src/trialmatchai/registry/normalization.py new file mode 100644 index 00000000..91d7ffd5 --- /dev/null +++ b/src/trialmatchai/registry/normalization.py @@ -0,0 +1,205 @@ +from __future__ import annotations + +import re +from typing import Any + + +def normalize_study(study: dict[str, Any]) -> dict[str, Any]: + """Normalize a ClinicalTrials.gov v2 study into TrialMatchAI trial JSON.""" + protocol = _mapping(study.get("protocolSection")) + identification = _mapping(protocol.get("identificationModule")) + status = _mapping(protocol.get("statusModule")) + description = _mapping(protocol.get("descriptionModule")) + conditions = _mapping(protocol.get("conditionsModule")) + design = _mapping(protocol.get("designModule")) + eligibility = _mapping(protocol.get("eligibilityModule")) + interventions = _mapping(protocol.get("armsInterventionsModule")) + contacts_locations = _mapping(protocol.get("contactsLocationsModule")) + references = _mapping(protocol.get("referencesModule")) + + nct_id = _text(identification.get("nctId")) + if not nct_id: + raise ValueError("ClinicalTrials.gov study is missing protocolSection.identificationModule.nctId") + + criteria_text = _multiline_text(eligibility.get("eligibilityCriteria")) + condition_values = _string_list(conditions.get("conditions")) + normalized = { + "nct_id": nct_id, + "source": "clinicaltrials.gov", + "source_url": f"https://clinicaltrials.gov/study/{nct_id}", + "brief_title": _text(identification.get("briefTitle")), + "official_title": _text(identification.get("officialTitle")), + "brief_summary": _text(description.get("briefSummary")), + "detailed_description": _text(description.get("detailedDescription")), + "condition": condition_values, + "eligibility_criteria": criteria_text, + "criteria": split_eligibility_criteria(criteria_text), + "overall_status": _text(status.get("overallStatus")), + "phase": _join(_string_list(design.get("phases"))), + "study_type": _text(design.get("studyType")), + "gender": _text(eligibility.get("sex")) or "All", + "minimum_age": _text(eligibility.get("minimumAge")), + "maximum_age": _text(eligibility.get("maximumAge")), + "start_date": _date_struct(status.get("startDateStruct")), + "completion_date": _date_struct(status.get("completionDateStruct")), + "last_update_posted": _date_struct(status.get("lastUpdatePostDateStruct")), + "intervention": _intervention_rows(interventions.get("interventions")), + "location": _location_rows(contacts_locations.get("locations")), + "reference": _reference_rows(references.get("references")), + } + return {key: value for key, value in normalized.items() if value not in (None, "", [])} + + +def split_eligibility_criteria(text: str) -> list[dict[str, str]]: + if not text or not text.strip(): + return [] + + current_type = "unknown" + entries: list[dict[str, str]] = [] + buffered: list[str] = [] + + def flush() -> None: + if not buffered: + return + criterion = " ".join(buffered).strip() + buffered.clear() + if _is_useful_criterion(criterion): + entries.append({"type": current_type, "criterion": criterion}) + + for raw_line in text.splitlines(): + line = raw_line.strip() + if not line: + flush() + continue + detected_type = _detect_criteria_header(line) + if detected_type: + flush() + current_type = detected_type + continue + cleaned = _clean_criterion_line(line) + if not cleaned: + flush() + continue + if _starts_new_criterion(line): + flush() + buffered.append(cleaned) + else: + buffered.append(cleaned) + + flush() + if entries: + return entries + + fallback = _clean_criterion_line(text) + return [{"type": "unknown", "criterion": fallback}] if fallback else [] + + +def _mapping(value: Any) -> dict[str, Any]: + return value if isinstance(value, dict) else {} + + +def _text(value: Any) -> str: + if value is None: + return "" + if isinstance(value, str): + return " ".join(value.split()) + return str(value) + + +def _multiline_text(value: Any) -> str: + if value is None: + return "" + if isinstance(value, str): + lines = [re.sub(r"[ \t]+", " ", line).strip() for line in value.splitlines()] + return "\n".join(lines).strip() + return _text(value) + + +def _string_list(value: Any) -> list[str]: + if isinstance(value, list): + return [_text(item) for item in value if _text(item)] + text = _text(value) + return [text] if text else [] + + +def _join(values: list[str]) -> str: + return ", ".join(values) + + +def _date_struct(value: Any) -> str: + if isinstance(value, dict): + return _text(value.get("date")) + return _text(value) + + +def _intervention_rows(value: Any) -> list[dict[str, str]]: + rows: list[dict[str, str]] = [] + for item in value if isinstance(value, list) else []: + if not isinstance(item, dict): + continue + row = { + "name": _text(item.get("name")), + "type": _text(item.get("type")), + "description": _text(item.get("description")), + } + rows.append({key: val for key, val in row.items() if val}) + return rows + + +def _location_rows(value: Any) -> list[dict[str, str]]: + rows: list[dict[str, str]] = [] + for item in value if isinstance(value, list) else []: + if not isinstance(item, dict): + continue + row = { + "facility": _text(item.get("facility")), + "city": _text(item.get("city")), + "state": _text(item.get("state")), + "country": _text(item.get("country")), + "status": _text(item.get("status")), + } + rows.append({key: val for key, val in row.items() if val}) + return rows + + +def _reference_rows(value: Any) -> list[dict[str, str]]: + rows: list[dict[str, str]] = [] + for item in value if isinstance(value, list) else []: + if not isinstance(item, dict): + continue + row = { + "pmid": _text(item.get("pmid")), + "type": _text(item.get("type")), + "citation": _text(item.get("citation")), + } + rows.append({key: val for key, val in row.items() if val}) + return rows + + +def _detect_criteria_header(line: str) -> str | None: + normalized = re.sub(r"[^a-z]+", " ", line.casefold()).strip() + if normalized in {"inclusion criteria", "inclusion"}: + return "inclusion" + if normalized in {"exclusion criteria", "exclusion"}: + return "exclusion" + return None + + +def _starts_new_criterion(line: str) -> bool: + return bool(re.match(r"^\s*(?:[-*•]|\d+[\).]|[a-zA-Z][\).])\s+", line)) + + +def _clean_criterion_line(line: str) -> str: + cleaned = re.sub(r"^\s*(?:[-*•]|\d+[\).]|[a-zA-Z][\).])\s+", "", line) + cleaned = re.sub(r"\s+", " ", cleaned).strip(" :-\t") + if _detect_criteria_header(cleaned): + return "" + return cleaned + + +def _is_useful_criterion(text: str) -> bool: + if len(text) < 3: + return False + if _detect_criteria_header(text): + return False + return True diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py new file mode 100644 index 00000000..48223716 --- /dev/null +++ b/src/trialmatchai/registry/preparation.py @@ -0,0 +1,243 @@ +from __future__ import annotations + +import hashlib +import json +import re +from collections.abc import Sequence +from pathlib import Path +from typing import Any, Protocol + +import dateutil.parser + + +class TextEmbeddingBackend(Protocol): + def embed_texts(self, texts: Sequence[str]) -> list[list[float]]: + ... + + +class EntityAnnotationBackend(Protocol): + def annotate_texts_in_parallel( + self, + texts: Sequence[str], + max_workers: int = 1, + retries: int = 1, + delay: float = 0, + ) -> list[list[dict[str, Any]]]: + ... + + +TRIAL_TEXT_FIELDS: tuple[tuple[str, str], ...] = ( + ("brief_title", "brief_title_vector"), + ("brief_summary", "brief_summary_vector"), + ("condition", "condition_vector"), + ("eligibility_criteria", "eligibility_criteria_vector"), +) + + +def prepare_trial_document( + doc: dict[str, Any], + embedder: TextEmbeddingBackend, +) -> dict[str, Any]: + out: dict[str, Any] = {"nct_id": doc["nct_id"]} + texts = [_preprocess_text(_flatten_text(doc.get(field))) for field, _ in TRIAL_TEXT_FIELDS] + vectors = _embed_texts(embedder, texts) + + for (field, vector_field), text, vector in zip(TRIAL_TEXT_FIELDS, texts, vectors): + out[field] = text + out[vector_field] = vector + + for simple in ( + "overall_status", + "phase", + "study_type", + "gender", + "source", + "source_url", + "last_update_posted", + ): + if doc.get(simple) not in (None, ""): + out[simple] = doc[simple] + + for date_field in ("start_date", "completion_date"): + iso = _to_iso_date(doc.get(date_field)) + if iso: + out[date_field] = iso + + for age_field in ("minimum_age", "maximum_age"): + years = _age_to_years(doc.get(age_field)) + if years is not None: + out[age_field] = years + + for nested in ("intervention", "location", "reference"): + if doc.get(nested): + out[nested] = doc[nested] + + return out + + +def prepare_criteria_documents( + doc: dict[str, Any], + embedder: TextEmbeddingBackend, + *, + entity_annotator: EntityAnnotationBackend | None = None, +) -> list[dict[str, Any]]: + nct_id = str(doc["nct_id"]) + entries: list[dict[str, Any]] = [] + texts: list[str] = [] + for criterion in doc.get("criteria") or []: + if not isinstance(criterion, dict): + continue + text = _preprocess_text( + _flatten_text(criterion.get("criterion") or criterion.get("sentence")) + ) + if not text: + continue + entries.append( + { + "nct_id": nct_id, + "criterion": text, + "entities": criterion.get("entities") or [], + "eligibility_type": criterion.get("type") or "unknown", + } + ) + texts.append(text) + + if not entries: + return [] + + _annotate_missing_entities(entries, texts, entity_annotator) + vectors = _embed_texts(embedder, texts) + rows: list[dict[str, Any]] = [] + for entry, vector in zip(entries, vectors): + criteria_id = compute_criteria_id(entry["nct_id"], entry["criterion"]) + rows.append( + { + "criteria_id": criteria_id, + "nct_id": entry["nct_id"], + "criterion": entry["criterion"], + "entities": _entities_for_index(entry.get("entities")), + "eligibility_type": entry["eligibility_type"], + "criterion_vector": vector, + } + ) + return rows + + +def write_prepared_trial(row: dict[str, Any], folder: str | Path) -> Path: + path = Path(folder) / f"{row['nct_id']}.json" + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(row, indent=2, sort_keys=True), encoding="utf-8") + return path + + +def write_prepared_criteria(rows: Sequence[dict[str, Any]], folder: str | Path) -> int: + if not rows: + return 0 + trial_folder = Path(folder) / str(rows[0]["nct_id"]) + trial_folder.mkdir(parents=True, exist_ok=True) + for row in rows: + path = trial_folder / f"{row['criteria_id']}.json" + path.write_text(json.dumps(row, indent=2, sort_keys=True), encoding="utf-8") + return len(rows) + + +def compute_criteria_id(nct_id: str, criterion: str) -> str: + return hashlib.sha256(f"{nct_id}:{criterion}".encode("utf-8")).hexdigest() + + +def _embed_texts(embedder: TextEmbeddingBackend, texts: Sequence[str]) -> list[list[float]]: + vectors_by_nonempty_text = iter( + embedder.embed_texts([text for text in texts if text.strip()]) + ) + vectors: list[list[float]] = [] + for text in texts: + if text.strip(): + vectors.append(list(next(vectors_by_nonempty_text))) + else: + vectors.append([]) + return vectors + + +def _annotate_missing_entities( + entries: list[dict[str, Any]], + texts: list[str], + entity_annotator: EntityAnnotationBackend | None, +) -> None: + if entity_annotator is None: + return + missing_indices = [ + index for index, entry in enumerate(entries) if not entry.get("entities") + ] + if not missing_indices: + return + missing_texts = [texts[index] for index in missing_indices] + annotations = entity_annotator.annotate_texts_in_parallel( + missing_texts, + max_workers=1, + ) + for index, entities in zip(missing_indices, annotations): + entries[index]["entities"] = entities + + +def _entities_for_index(entities: Any) -> list[dict[str, Any]]: + if not isinstance(entities, list): + return [] + indexed: list[dict[str, Any]] = [] + for entity in entities: + if not isinstance(entity, dict): + continue + normalized = dict(entity) + normalized.setdefault("entity", normalized.get("text", "")) + normalized.setdefault("class", normalized.get("entity_group", "")) + normalized.setdefault("normalized_id", ["CUI-less"]) + normalized.setdefault("synonyms", []) + normalized.setdefault("concept_candidates", []) + normalized.setdefault("linker_status", "not_linked") + indexed.append(normalized) + return indexed + + +def _preprocess_text(value: str) -> str: + return re.sub(r"\s+", " ", value).strip() + + +def _flatten_text(value: Any) -> str: + if value is None: + return "" + if isinstance(value, str): + return value + if isinstance(value, dict): + return " ".join(_flatten_text(item) for item in value.values()) + if isinstance(value, Sequence) and not isinstance(value, (bytes, bytearray)): + return " ".join(_flatten_text(item) for item in value) + return str(value) + + +def _to_iso_date(value: Any) -> str | None: + if not value: + return None + try: + return dateutil.parser.parse(str(value)).date().isoformat() + except (TypeError, ValueError, dateutil.parser.ParserError): + return None + + +def _age_to_years(value: Any) -> float | None: + if not value: + return None + match = re.search(r"([\d.]+)", str(value)) + if not match: + return None + amount = float(match.group(1)) + unit = str(value).casefold() + if "year" in unit: + years = amount + elif "month" in unit: + years = amount / 12 + elif "week" in unit: + years = amount / 52 + elif "day" in unit: + years = amount / 365 + else: + return None + return round(years, 2) diff --git a/src/trialmatchai/registry/updater.py b/src/trialmatchai/registry/updater.py new file mode 100644 index 00000000..611c6a94 --- /dev/null +++ b/src/trialmatchai/registry/updater.py @@ -0,0 +1,250 @@ +from __future__ import annotations + +import json +from collections.abc import Sequence +from dataclasses import asdict, dataclass, field +from datetime import date +from pathlib import Path +from typing import Any + +from trialmatchai.registry.clinicaltrials_gov import ClinicalTrialsGovClient +from trialmatchai.registry.defaults import DEFAULT_REGISTRY_KEYWORDS, DEFAULT_REGISTRY_STATUSES +from trialmatchai.registry.manifest import ( + ManifestRecord, + RegistryManifest, + source_hash, + utc_now_iso, +) +from trialmatchai.registry.normalization import normalize_study +from trialmatchai.registry.preparation import ( + EntityAnnotationBackend, + TextEmbeddingBackend, + prepare_criteria_documents, + prepare_trial_document, +) +from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +@dataclass(frozen=True) +class RegistryUpdateConfig: + raw_dir: Path + normalized_trials_dir: Path + manifest_path: Path + reports_dir: Path + keywords: tuple[str, ...] = DEFAULT_REGISTRY_KEYWORDS + statuses: tuple[str, ...] = DEFAULT_REGISTRY_STATUSES + since: date | None = None + max_studies: int | None = None + dry_run: bool = False + reindex_all_changed: bool = True + failure_threshold: float = 0.25 + + +@dataclass +class RegistryStudyFailure: + nct_id: str | None + error: str + + +@dataclass +class RegistryUpdateReport: + fetched: int = 0 + new: int = 0 + changed: int = 0 + unchanged: int = 0 + failed: int = 0 + duplicate: int = 0 + indexed: int = 0 + criteria_indexed: int = 0 + dry_run: bool = False + failures: list[RegistryStudyFailure] = field(default_factory=list) + + @property + def failure_rate(self) -> float: + denominator = max(1, self.fetched) + return self.failed / denominator + + def to_dict(self) -> dict[str, Any]: + data = asdict(self) + data["failure_rate"] = self.failure_rate + return data + + +class RegistryUpdater: + def __init__( + self, + *, + client: ClinicalTrialsGovClient, + backend: LanceDBSearchBackend, + embedder: TextEmbeddingBackend, + entity_annotator: EntityAnnotationBackend | None = None, + ) -> None: + self.client = client + self.backend = backend + self.embedder = embedder + self.entity_annotator = entity_annotator + + def run(self, config: RegistryUpdateConfig) -> RegistryUpdateReport: + report = RegistryUpdateReport(dry_run=config.dry_run) + manifest = RegistryManifest(config.manifest_path) + latest = manifest.load_latest() + seen: set[str] = set() + + for keyword in config.keywords: + remaining = _remaining(config.max_studies, report.fetched) + if remaining == 0: + break + logger.info("Fetching registry studies for keyword: %s", keyword) + try: + for study in self.client.iter_studies( + keyword=keyword, + statuses=config.statuses, + since=config.since, + max_studies=remaining, + ): + if ( + config.max_studies is not None + and report.fetched >= config.max_studies + ): + break + self._process_study( + study, + config=config, + manifest=manifest, + latest=latest, + seen=seen, + report=report, + ) + except Exception as exc: + logger.exception("Registry source fetch failed for keyword: %s", keyword) + report.failed += 1 + report.failures.append( + RegistryStudyFailure( + nct_id=None, + error=f"{keyword}: {exc}", + ) + ) + + if not config.dry_run: + self.write_run_report(config.reports_dir, report) + return report + + def _process_study( + self, + study: dict[str, Any], + *, + config: RegistryUpdateConfig, + manifest: RegistryManifest, + latest: dict[str, ManifestRecord], + seen: set[str], + report: RegistryUpdateReport, + ) -> None: + nct_id: str | None = None + try: + normalized = normalize_study(study) + nct_id = str(normalized["nct_id"]) + if nct_id in seen: + report.duplicate += 1 + return + seen.add(nct_id) + report.fetched += 1 + + digest = source_hash(study) + previous = latest.get(nct_id) + if previous and previous.source_hash == digest: + report.unchanged += 1 + return + + is_new = previous is None + if is_new: + report.new += 1 + else: + report.changed += 1 + + if config.dry_run: + return + + self._write_source_and_normalized(study, normalized, config=config) + if config.reindex_all_changed: + prepared_trial = prepare_trial_document(normalized, self.embedder) + prepared_criteria = prepare_criteria_documents( + normalized, + self.embedder, + entity_annotator=self.entity_annotator, + ) + report.indexed += self.backend.upsert_trials([prepared_trial]) + report.criteria_indexed += self.backend.replace_criteria_for_trials( + [nct_id], + prepared_criteria, + ) + + record = ManifestRecord( + nct_id=nct_id, + source_url=str(normalized.get("source_url", "")), + source_hash=digest, + fetched_at=utc_now_iso(), + last_update_posted=normalized.get("last_update_posted"), + processing_status="indexed" if config.reindex_all_changed else "fetched", + ) + manifest.append(record) + latest[nct_id] = record + except Exception as exc: + logger.exception("Registry update failed for study %s", nct_id or "") + report.failed += 1 + report.failures.append( + RegistryStudyFailure(nct_id=nct_id, error=str(exc)) + ) + if not config.dry_run and nct_id: + manifest.append( + ManifestRecord( + nct_id=nct_id, + source_url=f"https://clinicaltrials.gov/study/{nct_id}", + source_hash=source_hash(study), + fetched_at=utc_now_iso(), + last_update_posted=None, + processing_status="failed", + error_summary=str(exc), + ) + ) + + def _write_source_and_normalized( + self, + study: dict[str, Any], + normalized: dict[str, Any], + *, + config: RegistryUpdateConfig, + ) -> None: + nct_id = str(normalized["nct_id"]) + _write_json(config.raw_dir / f"{nct_id}.json", study) + _write_json(config.normalized_trials_dir / f"{nct_id}.json", normalized) + + @staticmethod + def write_run_report( + reports_dir: str | Path, + report: RegistryUpdateReport, + ) -> Path: + reports_path = Path(reports_dir) + reports_path.mkdir(parents=True, exist_ok=True) + path = reports_path / f"registry-update-{utc_now_iso().replace(':', '')}.json" + _write_json(path, report.to_dict()) + return path + + +def _remaining(max_studies: int | None, fetched: int) -> int | None: + if max_studies is None: + return None + return max(0, max_studies - fetched) + + +def _write_json(path: str | Path, payload: dict[str, Any]) -> None: + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") + + +def normalize_keywords(keywords: Sequence[str]) -> tuple[str, ...]: + values = tuple(dict.fromkeys(keyword.strip() for keyword in keywords if keyword.strip())) + return values or DEFAULT_REGISTRY_KEYWORDS diff --git a/src/trialmatchai/schemas/__init__.py b/src/trialmatchai/schemas/__init__.py new file mode 100644 index 00000000..680f9076 --- /dev/null +++ b/src/trialmatchai/schemas/__init__.py @@ -0,0 +1,3 @@ +from trialmatchai.schemas.phenopacket import Keywords, Phenopacket + +__all__ = ["Keywords", "Phenopacket"] diff --git a/source/Matcher/schemas/phenopacket.py b/src/trialmatchai/schemas/phenopacket.py similarity index 100% rename from source/Matcher/schemas/phenopacket.py rename to src/trialmatchai/schemas/phenopacket.py diff --git a/source/Matcher/search/__init__.py b/src/trialmatchai/search/__init__.py similarity index 85% rename from source/Matcher/search/__init__.py rename to src/trialmatchai/search/__init__.py index e086f34d..a6c2648c 100644 --- a/source/Matcher/search/__init__.py +++ b/src/trialmatchai/search/__init__.py @@ -1,4 +1,4 @@ -from Matcher.search.lancedb_backend import ( +from trialmatchai.search.lancedb_backend import ( InMemorySearchBackend, LanceDBSearchBackend, SearchBackendUnavailable, diff --git a/source/Matcher/search/lancedb_backend.py b/src/trialmatchai/search/lancedb_backend.py similarity index 91% rename from source/Matcher/search/lancedb_backend.py rename to src/trialmatchai/search/lancedb_backend.py index 0d4bda85..b47b2cd2 100644 --- a/source/Matcher/search/lancedb_backend.py +++ b/src/trialmatchai/search/lancedb_backend.py @@ -6,7 +6,7 @@ from pathlib import Path from typing import Any, Iterable, Mapping, Protocol, Sequence -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -91,6 +91,28 @@ def __init__( def health(self, *, require_tables: bool = False) -> list[str]: return [] + def upsert_trials(self, docs: Sequence[Mapping[str, Any]]) -> int: + rows = [build_trial_record(row) for row in docs] + updated_ids = {str(row.get("nct_id")) for row in rows if row.get("nct_id")} + self.trials = [ + row for row in self.trials if str(row.get("nct_id")) not in updated_ids + ] + self.trials.extend(rows) + return len(rows) + + def replace_criteria_for_trials( + self, + nct_ids: Sequence[str], + docs: Sequence[Mapping[str, Any]], + ) -> int: + updated_ids = {str(nct_id) for nct_id in nct_ids if nct_id} + self.criteria = [ + row for row in self.criteria if str(row.get("nct_id")) not in updated_ids + ] + rows = [build_criteria_record(row) for row in docs] + self.criteria.extend(rows) + return len(rows) + def search_trials( self, *, @@ -225,6 +247,36 @@ def index_criteria( _create_vector_index(table, "criterion_vector") return len(rows) + def upsert_trials(self, docs: Sequence[Mapping[str, Any]]) -> int: + rows = [build_trial_record(doc) for doc in docs] + if not rows: + return 0 + if self.table_exists(self.trials_table): + nct_ids = [str(row["nct_id"]) for row in rows if row.get("nct_id")] + if nct_ids: + self._delete_where(self.trials_table, _nct_where(nct_ids)) + table = self._write_rows(self.trials_table, rows, recreate=False) + _create_fts_index(table, "search_text") + _create_vector_index(table, "search_vector") + return len(rows) + + def replace_criteria_for_trials( + self, + nct_ids: Sequence[str], + docs: Sequence[Mapping[str, Any]], + ) -> int: + rows = [build_criteria_record(doc) for doc in docs] + if self.table_exists(self.criteria_table): + where = _nct_where(nct_ids) + if where: + self._delete_where(self.criteria_table, where) + if not rows: + return 0 + table = self._write_rows(self.criteria_table, rows, recreate=False) + _create_fts_index(table, "search_text") + _create_vector_index(table, "criterion_vector") + return len(rows) + def search_trials( self, *, @@ -321,6 +373,17 @@ def _write_rows( table.add(list(rows)) return table + def _delete_where(self, table_name: str, where: str) -> None: + if not where: + return + try: + table = self._open_table(table_name) + table.delete(where) + except Exception as exc: + raise SearchBackendUnavailable( + f"Could not delete existing rows from LanceDB table {table_name}: {exc}" + ) from exc + def _candidate_rows( self, table: Any, diff --git a/source/Matcher/pipeline/trial_search/__init__.py b/src/trialmatchai/services/__init__.py similarity index 100% rename from source/Matcher/pipeline/trial_search/__init__.py rename to src/trialmatchai/services/__init__.py diff --git a/source/Matcher/services/preflight.py b/src/trialmatchai/services/preflight.py similarity index 89% rename from source/Matcher/services/preflight.py rename to src/trialmatchai/services/preflight.py index 2e52a53c..c46c4178 100644 --- a/source/Matcher/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -4,9 +4,7 @@ from pathlib import Path from typing import Any, Dict, List -import torch - -from Matcher.utils.logging_config import setup_logging +from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -86,8 +84,16 @@ def run_preflight_checks( "cot_backend=vllm requires the GPU extra " "(`uv sync --extra gpu`) or the Docker worker image." ) - if not torch.cuda.is_available(): - issues.append("cot_backend=vllm requires a CUDA-capable runtime.") + try: + import torch + except Exception: + issues.append( + "cot_backend=vllm requires PyTorch " + "(`uv sync --extra llm --extra gpu`)." + ) + else: + if not torch.cuda.is_available(): + issues.append("cot_backend=vllm requires a CUDA-capable runtime.") search_cfg = config.get("search_backend", {}) if search_cfg: @@ -100,7 +106,7 @@ def run_preflight_checks( if require_search_tables: if search_backend is None: try: - from Matcher.search import LanceDBSearchBackend + from trialmatchai.search import LanceDBSearchBackend search_backend = LanceDBSearchBackend.from_config(config) except Exception as exc: diff --git a/source/Matcher/services/__init__.py b/src/trialmatchai/utils/__init__.py similarity index 100% rename from source/Matcher/services/__init__.py rename to src/trialmatchai/utils/__init__.py diff --git a/source/Matcher/utils/evaluation.py b/src/trialmatchai/utils/evaluation.py similarity index 98% rename from source/Matcher/utils/evaluation.py rename to src/trialmatchai/utils/evaluation.py index 99976dab..aa48cd7a 100644 --- a/source/Matcher/utils/evaluation.py +++ b/src/trialmatchai/utils/evaluation.py @@ -1,4 +1,4 @@ -# Matcher/utils/evaluation.py +# trialmatchai/utils/evaluation.py from __future__ import annotations import csv @@ -304,10 +304,10 @@ def evaluate_and_save_metrics( # Lazy import to avoid package cycles try: - from Matcher.utils.file_utils import read_json_file # type: ignore + from trialmatchai.utils.file_utils import read_json_file # type: ignore except Exception: # Fallback path if run standalone - from Matcher.utils.file_utils import read_json_file # type: ignore + from trialmatchai.utils.file_utils import read_json_file # type: ignore ranked = read_json_file(args.ranked) out_dir = args.out_dir or os.path.dirname(args.ranked) diff --git a/source/Matcher/utils/file_utils.py b/src/trialmatchai/utils/file_utils.py similarity index 100% rename from source/Matcher/utils/file_utils.py rename to src/trialmatchai/utils/file_utils.py diff --git a/source/Matcher/utils/json_utils.py b/src/trialmatchai/utils/json_utils.py similarity index 100% rename from source/Matcher/utils/json_utils.py rename to src/trialmatchai/utils/json_utils.py diff --git a/source/Matcher/utils/logging_config.py b/src/trialmatchai/utils/logging_config.py similarity index 100% rename from source/Matcher/utils/logging_config.py rename to src/trialmatchai/utils/logging_config.py diff --git a/source/Matcher/utils/retry.py b/src/trialmatchai/utils/retry.py similarity index 100% rename from source/Matcher/utils/retry.py rename to src/trialmatchai/utils/retry.py diff --git a/source/Matcher/utils/temporal_utils.py b/src/trialmatchai/utils/temporal_utils.py similarity index 100% rename from source/Matcher/utils/temporal_utils.py rename to src/trialmatchai/utils/temporal_utils.py diff --git a/source/Matcher/utils/timing.py b/src/trialmatchai/utils/timing.py similarity index 100% rename from source/Matcher/utils/timing.py rename to src/trialmatchai/utils/timing.py diff --git a/tests/conftest.py b/tests/conftest.py index b4c383e1..17d198bf 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,4 +2,4 @@ from pathlib import Path ROOT = Path(__file__).resolve().parents[1] -sys.path.append(str(ROOT / "source")) +sys.path.append(str(ROOT / "src")) diff --git a/tests/test_config_pytest.py b/tests/test_config_pytest.py index 36b08061..8e3cb05b 100644 --- a/tests/test_config_pytest.py +++ b/tests/test_config_pytest.py @@ -1,10 +1,10 @@ from pathlib import Path -from Matcher.config.config_loader import load_config +from trialmatchai.config.config_loader import load_config def test_load_config_from_repo(): - config_path = Path(__file__).resolve().parents[1] / "source/Matcher/config/config.json" + config_path = Path(__file__).resolve().parents[1] / "src/trialmatchai/config/config.json" cfg = load_config(str(config_path)) assert cfg["search_backend"]["backend"] == "lancedb" assert "embedder" in cfg diff --git a/tests/test_deployment_readiness.py b/tests/test_deployment_readiness.py index 052fc534..ba1a9328 100644 --- a/tests/test_deployment_readiness.py +++ b/tests/test_deployment_readiness.py @@ -1,14 +1,14 @@ from __future__ import annotations -from Matcher.config.config_loader import load_config, resolve_config_path -from Matcher.pipeline.cot_reasoning import BatchTrialProcessor -from Matcher.utils.json_utils import extract_json_object +from trialmatchai.config.config_loader import load_config, resolve_config_path +from trialmatchai.matching.eligibility_reasoning import BatchTrialProcessor +from trialmatchai.utils.json_utils import extract_json_object def test_default_config_resolution_from_repo_root(): path = resolve_config_path() assert path.name == "config.json" - assert path.as_posix().endswith("source/Matcher/config/config.json") + assert path.as_posix().endswith("src/trialmatchai/config/config.json") def test_config_env_overrides_and_search_tables(monkeypatch): @@ -19,6 +19,8 @@ def test_config_env_overrides_and_search_tables(monkeypatch): monkeypatch.setenv("TRIALMATCHAI_ENTITY_BACKEND", "regex") monkeypatch.setenv("TRIALMATCHAI_CONCEPT_DB_PATH", "data/concepts-test") monkeypatch.setenv("TRIALMATCHAI_LINK_ACCEPT", "0.9") + monkeypatch.setenv("TRIALMATCHAI_REGISTRY_SINCE_DAYS", "14") + monkeypatch.setenv("TRIALMATCHAI_REGISTRY_RAW_DIR", "data/registry/raw-test") cfg = load_config() @@ -30,6 +32,8 @@ def test_config_env_overrides_and_search_tables(monkeypatch): assert cfg["entity_extraction"]["backend"] == "regex" assert cfg["concept_linker"]["db_path"].endswith("data/concepts-test") assert cfg["concept_linker"]["accept_threshold"] == 0.9 + assert cfg["registry"]["since_days"] == 14 + assert cfg["registry"]["raw_dir"].endswith("data/registry/raw-test") def test_cot_prompt_does_not_inject_consent(): diff --git a/tests/test_entities.py b/tests/test_entities.py index fc5a3efd..8ee6ea5d 100644 --- a/tests/test_entities.py +++ b/tests/test_entities.py @@ -3,12 +3,12 @@ import csv from pathlib import Path -from Matcher.entities.annotator import CompatibilityEntityAnnotator -from Matcher.entities.builder import build_legacy_dictionary_rows, build_omop_concept_rows -from Matcher.entities.linker import ConceptLinker, InMemoryConceptStore -from Matcher.entities.recognizers import RegexSchemaRecognizer, resolve_overlaps -from Matcher.entities.schemas import load_entity_schemas -from Matcher.entities.types import ConceptCandidate, EntityAnnotation, NO_ENTITY_ID +from trialmatchai.entities.annotator import CompatibilityEntityAnnotator +from trialmatchai.entities.builder import build_legacy_dictionary_rows, build_omop_concept_rows +from trialmatchai.entities.linker import ConceptLinker, InMemoryConceptStore +from trialmatchai.entities.recognizers import RegexSchemaRecognizer, resolve_overlaps +from trialmatchai.entities.schemas import load_entity_schemas +from trialmatchai.entities.types import ConceptCandidate, EntityAnnotation, NO_ENTITY_ID ROOT = Path(__file__).resolve().parents[1] @@ -148,23 +148,20 @@ def test_concept_builders_import_omop_and_legacy_rows(tmp_path): def test_runtime_replacement_has_no_old_daemon_references(): - runtime_files = [ - "source/Matcher/config/config.json", - "source/Matcher/main.py", - "source/Parser/biomedner_engine.py", - "source/Parser/normalizer.py", - ] + assert not (ROOT / "source/Parser").exists() + runtime_files = sorted((ROOT / "src/trialmatchai").rglob("*.py")) forbidden = [ "18888", "18892", "18894", "18783", + "BioMedNER", "GNormPlus", "disease_normalizer_21.jar", "java -Xmx", "import socket", ] - for file_name in runtime_files: - content = (ROOT / file_name).read_text() + for path in runtime_files: + content = path.read_text() for term in forbidden: assert term not in content diff --git a/tests/test_file_utils_pytest.py b/tests/test_file_utils_pytest.py index bee17e6e..2dfbdfff 100644 --- a/tests/test_file_utils_pytest.py +++ b/tests/test_file_utils_pytest.py @@ -1,4 +1,4 @@ -from Matcher.utils.file_utils import read_json_file, read_text_file, write_json_file, write_text_file +from trialmatchai.utils.file_utils import read_json_file, read_text_file, write_json_file, write_text_file def test_write_and_read_json(tmp_path): diff --git a/tests/test_first_level_search_pytest.py b/tests/test_first_level_search_pytest.py index d6ed7454..8e92e7fd 100644 --- a/tests/test_first_level_search_pytest.py +++ b/tests/test_first_level_search_pytest.py @@ -1,5 +1,5 @@ -from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch -from Matcher.search import InMemorySearchBackend +from trialmatchai.matching.retrieval.trial_retrieval import ClinicalTrialSearch +from trialmatchai.search import InMemorySearchBackend def test_search_trials_bm25_returns_hits(): diff --git a/tests/test_lancedb_search_backend.py b/tests/test_lancedb_search_backend.py index bf111b37..012ed12e 100644 --- a/tests/test_lancedb_search_backend.py +++ b/tests/test_lancedb_search_backend.py @@ -2,7 +2,7 @@ import pytest -from Matcher.search import LanceDBSearchBackend +from trialmatchai.search import LanceDBSearchBackend pytest.importorskip("lancedb") diff --git a/tests/test_logging.py b/tests/test_logging.py index 8d82c083..b2314a4b 100644 --- a/tests/test_logging.py +++ b/tests/test_logging.py @@ -1,7 +1,7 @@ import logging import unittest -from Matcher.utils.logging_config import reset_request_id, set_request_id, setup_logging +from trialmatchai.utils.logging_config import reset_request_id, set_request_id, setup_logging class CaptureHandler(logging.Handler): diff --git a/tests/test_logging_pytest.py b/tests/test_logging_pytest.py index c7694f31..90320107 100644 --- a/tests/test_logging_pytest.py +++ b/tests/test_logging_pytest.py @@ -1,6 +1,6 @@ import logging -from Matcher.utils.logging_config import reset_request_id, set_request_id, setup_logging +from trialmatchai.utils.logging_config import reset_request_id, set_request_id, setup_logging class _CaptureHandler(logging.Handler): diff --git a/tests/test_package_imports.py b/tests/test_package_imports.py new file mode 100644 index 00000000..3c556292 --- /dev/null +++ b/tests/test_package_imports.py @@ -0,0 +1,18 @@ +from __future__ import annotations + +import warnings + +import trialmatchai + + +def test_trialmatchai_imports_with_version(): + assert trialmatchai.__version__ == "0.2.0" + + +def test_matcher_config_compatibility_shim(): + with warnings.catch_warnings(record=True) as captured: + warnings.simplefilter("always") + from Matcher.config.config_loader import resolve_config_path + + assert resolve_config_path().name == "config.json" + assert any("Matcher" in str(warning.message) for warning in captured) diff --git a/tests/test_phenopacket_processor_pytest.py b/tests/test_phenopacket_processor_pytest.py index 3621e492..6c42fb50 100644 --- a/tests/test_phenopacket_processor_pytest.py +++ b/tests/test_phenopacket_processor_pytest.py @@ -1,6 +1,6 @@ import json -from Matcher.pipeline.phenopacket_processor import PhenopacketProcessor +from trialmatchai.matching.phenopacket_processor import PhenopacketProcessor def test_phenopacket_processor_minimal(tmp_path): diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index 0d64b076..6bbc64d4 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -1,13 +1,13 @@ from __future__ import annotations -import importlib.util import json from pathlib import Path -from Matcher.config.config_loader import load_config -from Matcher.search import InMemorySearchBackend -from Matcher.services import preflight -from Matcher.services.preflight import run_preflight_checks +from trialmatchai.cli.index_data import _load_nested_json_folder +from trialmatchai.config.config_loader import load_config +from trialmatchai.search import InMemorySearchBackend +from trialmatchai.services import preflight +from trialmatchai.services.preflight import run_preflight_checks def _base_config(tmp_path): @@ -100,12 +100,14 @@ def test_preflight_reports_missing_search_db_path(tmp_path): def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): + import torch + cfg = _base_config(tmp_path) cfg["cot_backend"] = "vllm" Path(cfg["model"]["cot_adapter_path"]).mkdir(parents=True) Path(cfg["model"]["reranker_adapter_path"]).mkdir(parents=True) monkeypatch.setattr(preflight.importlib.util, "find_spec", lambda name: None) - monkeypatch.setattr(preflight.torch.cuda, "is_available", lambda: True) + monkeypatch.setattr(torch.cuda, "is_available", lambda: True) issues = run_preflight_checks(cfg, require_models=True) @@ -180,25 +182,16 @@ def test_main_config_resolves_search_paths(tmp_path, monkeypatch): def test_indexer_loads_prepared_criteria_docs(tmp_path): - indexer_path = Path(__file__).resolve().parents[1] / "utils/Indexer/index_criteria.py" - spec = importlib.util.spec_from_file_location("index_criteria", indexer_path) - assert spec and spec.loader - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - processed = tmp_path / "processed_criteria" trial_dir = processed / "N1" trial_dir.mkdir(parents=True) (trial_dir / "C1.json").write_text( json.dumps({"criteria_id": "C1", "nct_id": "N1", "criterion": "cancer"}) ) + docs = _load_nested_json_folder(processed) backend = InMemorySearchBackend() - criteria_indexer = module.CriteriaIndexer( - backend=backend, - processed_file=tmp_path / "processed_ids.txt", - ) - - docs, completed = criteria_indexer.load_docs(processed, recreate=True) + count = backend.replace_criteria_for_trials(["N1"], docs) assert docs[0]["criteria_id"] == "C1" - assert completed == {"N1"} + assert count == 1 + assert backend.criteria[0]["nct_id"] == "N1" diff --git a/tests/test_registry_updater.py b/tests/test_registry_updater.py new file mode 100644 index 00000000..4c4d0d05 --- /dev/null +++ b/tests/test_registry_updater.py @@ -0,0 +1,284 @@ +from __future__ import annotations + +import json +from datetime import date +from pathlib import Path +from typing import Any + +from trialmatchai.registry.clinicaltrials_gov import ClinicalTrialsGovClient +from trialmatchai.registry.defaults import DEFAULT_REGISTRY_KEYWORDS +from trialmatchai.registry.manifest import RegistryManifest, source_hash +from trialmatchai.registry.normalization import normalize_study, split_eligibility_criteria +from trialmatchai.registry.updater import RegistryUpdateConfig, RegistryUpdater, normalize_keywords +from trialmatchai.search import InMemorySearchBackend + + +def test_client_paginates_and_filters_since(): + session = _FakeSession( + [ + { + "studies": [ + _study("NCT00000001", last_update="2026-06-01"), + _study("NCT00000002", last_update="2026-01-01"), + ], + "nextPageToken": "page-2", + }, + {"studies": [_study("NCT00000003", last_update="2026-06-02")]}, + ] + ) + client = ClinicalTrialsGovClient( + session=session, + rate_limit_per_second=1000, + timeout=1, + ) + + studies = list( + client.iter_studies( + keyword="cancer", + statuses=("RECRUITING",), + since=date(2026, 6, 1), + ) + ) + + assert [study["protocolSection"]["identificationModule"]["nctId"] for study in studies] == [ + "NCT00000001", + "NCT00000003", + ] + assert session.calls[0]["params"]["query.term"] == "cancer" + assert session.calls[0]["params"]["filter.overallStatus"] == "RECRUITING" + assert session.calls[1]["params"]["pageToken"] == "page-2" + + +def test_normalize_study_maps_v2_modules_and_splits_criteria(): + normalized = normalize_study(_study("NCT00000004")) + + assert normalized["nct_id"] == "NCT00000004" + assert normalized["brief_title"] == "Trial for NCT00000004" + assert normalized["condition"] == ["Lung cancer"] + assert normalized["source_url"] == "https://clinicaltrials.gov/study/NCT00000004" + assert normalized["criteria"] == [ + {"type": "inclusion", "criterion": "Age 18 years or older"}, + {"type": "inclusion", "criterion": "Histologically confirmed lung cancer"}, + {"type": "exclusion", "criterion": "Prior investigational therapy"}, + ] + + +def test_split_eligibility_criteria_has_unknown_fallback(): + assert split_eligibility_criteria("Able to consent.") == [ + {"type": "unknown", "criterion": "Able to consent."} + ] + + +def test_manifest_keeps_latest_record_by_nct_id(tmp_path): + manifest = RegistryManifest(tmp_path / "manifest.jsonl") + study = _study("NCT00000005") + record = _manifest_record("NCT00000005", source_hash(study), status="indexed") + changed = _manifest_record("NCT00000005", "changed", status="indexed") + + manifest.append(record) + manifest.append(changed) + + latest = manifest.load_latest() + assert latest["NCT00000005"].source_hash == "changed" + + +def test_updater_writes_new_trial_and_upserts_tables(tmp_path): + study = _study("NCT00000006") + backend = InMemorySearchBackend( + criteria=[ + { + "criteria_id": "old", + "nct_id": "NCT00000006", + "criterion": "old criterion", + "criterion_vector": [0.0, 0.0], + } + ] + ) + updater = RegistryUpdater( + client=_FakeRegistryClient([study]), + backend=backend, + embedder=_FakeEmbedder(), + ) + + report = updater.run(_update_config(tmp_path)) + + assert report.fetched == 1 + assert report.new == 1 + assert report.indexed == 1 + assert report.criteria_indexed == 3 + assert (tmp_path / "raw/NCT00000006.json").exists() + assert (tmp_path / "trials/NCT00000006.json").exists() + assert len(backend.trials) == 1 + assert all(row["nct_id"] == "NCT00000006" for row in backend.criteria) + assert {row["criterion"] for row in backend.criteria} == { + "Age 18 years or older", + "Histologically confirmed lung cancer", + "Prior investigational therapy", + } + + +def test_updater_skips_unchanged_hashes(tmp_path): + study = _study("NCT00000007") + config = _update_config(tmp_path) + RegistryManifest(config.manifest_path).append( + _manifest_record("NCT00000007", source_hash(study), status="indexed") + ) + backend = InMemorySearchBackend() + updater = RegistryUpdater( + client=_FakeRegistryClient([study]), + backend=backend, + embedder=_FakeEmbedder(), + ) + + report = updater.run(config) + + assert report.unchanged == 1 + assert report.indexed == 0 + assert backend.trials == [] + + +def test_updater_dry_run_does_not_write_files_or_tables(tmp_path): + backend = InMemorySearchBackend() + updater = RegistryUpdater( + client=_FakeRegistryClient([_study("NCT00000008")]), + backend=backend, + embedder=_FakeEmbedder(), + ) + + report = updater.run(_update_config(tmp_path, dry_run=True)) + + assert report.new == 1 + assert not (tmp_path / "raw").exists() + assert not (tmp_path / "trials").exists() + assert not (tmp_path / "manifest.jsonl").exists() + assert backend.trials == [] + + +def test_normalize_keywords_uses_defaults_when_empty(): + assert normalize_keywords([]) == DEFAULT_REGISTRY_KEYWORDS + assert normalize_keywords([" cancer ", "cancer", "diabetes"]) == ( + "cancer", + "diabetes", + ) + + +class _FakeResponse: + def __init__(self, payload: dict[str, Any], status_code: int = 200): + self.payload = payload + self.status_code = status_code + self.text = json.dumps(payload) + + def json(self): + return self.payload + + def raise_for_status(self): + if self.status_code >= 400: + raise RuntimeError(self.text) + + +class _FakeSession: + def __init__(self, payloads: list[dict[str, Any]]): + self.payloads = payloads + self.calls: list[dict[str, Any]] = [] + + def get(self, url, *, params, timeout, headers): + self.calls.append( + { + "url": url, + "params": dict(params), + "timeout": timeout, + "headers": dict(headers), + } + ) + return _FakeResponse(self.payloads.pop(0)) + + +class _FakeRegistryClient: + def __init__(self, studies: list[dict[str, Any]]): + self.studies = studies + + def iter_studies(self, *, keyword, statuses, since, max_studies): + del keyword, statuses, since + for study in self.studies[:max_studies]: + yield study + + +class _FakeEmbedder: + def embed_texts(self, texts): + return [[float(len(text)), 1.0] for text in texts] + + +def _update_config(tmp_path: Path, *, dry_run: bool = False) -> RegistryUpdateConfig: + return RegistryUpdateConfig( + raw_dir=tmp_path / "raw", + normalized_trials_dir=tmp_path / "trials", + manifest_path=tmp_path / "manifest.jsonl", + reports_dir=tmp_path / "runs", + keywords=("cancer",), + statuses=("RECRUITING",), + since=None, + dry_run=dry_run, + ) + + +def _manifest_record(nct_id: str, digest: str, *, status: str): + from trialmatchai.registry.manifest import ManifestRecord, utc_now_iso + + return ManifestRecord( + nct_id=nct_id, + source_url=f"https://clinicaltrials.gov/study/{nct_id}", + source_hash=digest, + fetched_at=utc_now_iso(), + last_update_posted="2026-06-01", + processing_status=status, + ) + + +def _study(nct_id: str, *, last_update: str = "2026-06-01") -> dict[str, Any]: + return { + "protocolSection": { + "identificationModule": { + "nctId": nct_id, + "briefTitle": f"Trial for {nct_id}", + "officialTitle": f"Official trial for {nct_id}", + }, + "statusModule": { + "overallStatus": "RECRUITING", + "startDateStruct": {"date": "2026-01-01"}, + "completionDateStruct": {"date": "2027-01-01"}, + "lastUpdatePostDateStruct": {"date": last_update}, + }, + "descriptionModule": { + "briefSummary": "A test trial.", + "detailedDescription": "A detailed test trial.", + }, + "conditionsModule": {"conditions": ["Lung cancer"]}, + "designModule": { + "phases": ["PHASE2"], + "studyType": "INTERVENTIONAL", + }, + "eligibilityModule": { + "sex": "ALL", + "minimumAge": "18 Years", + "maximumAge": "80 Years", + "eligibilityCriteria": "\n".join( + [ + "Inclusion Criteria:", + "- Age 18 years or older", + "- Histologically confirmed lung cancer", + "Exclusion Criteria:", + "- Prior investigational therapy", + ] + ), + }, + "armsInterventionsModule": { + "interventions": [{"name": "Drug A", "type": "DRUG"}] + }, + "contactsLocationsModule": { + "locations": [{"facility": "Site A", "country": "United States"}] + }, + "referencesModule": { + "references": [{"pmid": "123", "citation": "Reference"}] + }, + } + } diff --git a/tests/test_retry_pytest.py b/tests/test_retry_pytest.py index 6cc900c6..58d93460 100644 --- a/tests/test_retry_pytest.py +++ b/tests/test_retry_pytest.py @@ -1,4 +1,4 @@ -from Matcher.utils.retry import with_retries +from trialmatchai.utils.retry import with_retries def test_with_retries_succeeds_after_failures(): diff --git a/tests/test_schemas.py b/tests/test_schemas.py index 73e6c5ce..9c02e994 100644 --- a/tests/test_schemas.py +++ b/tests/test_schemas.py @@ -1,6 +1,6 @@ import unittest -from Matcher.schemas.phenopacket import Keywords, Phenopacket +from trialmatchai.schemas.phenopacket import Keywords, Phenopacket class TestSchemas(unittest.TestCase): diff --git a/tests/test_search_queries_pytest.py b/tests/test_search_queries_pytest.py index a19f7221..aabeacae 100644 --- a/tests/test_search_queries_pytest.py +++ b/tests/test_search_queries_pytest.py @@ -1,5 +1,5 @@ -from Matcher.pipeline.trial_search.first_level_search import ClinicalTrialSearch -from Matcher.search import InMemorySearchBackend, build_criteria_record, build_trial_record +from trialmatchai.matching.retrieval.trial_retrieval import ClinicalTrialSearch +from trialmatchai.search import InMemorySearchBackend, build_criteria_record, build_trial_record def test_first_level_query_describes_backend_search(): diff --git a/tests/test_second_level_search_pytest.py b/tests/test_second_level_search_pytest.py index 3436b1c0..c133067c 100644 --- a/tests/test_second_level_search_pytest.py +++ b/tests/test_second_level_search_pytest.py @@ -1,5 +1,5 @@ -from Matcher.pipeline.trial_search.second_level_search import SecondStageRetriever -from Matcher.search import InMemorySearchBackend +from trialmatchai.matching.retrieval.criteria_retrieval import SecondStageRetriever +from trialmatchai.search import InMemorySearchBackend def test_score_criteria_without_llm_weights(): diff --git a/tests/test_settings.py b/tests/test_settings.py index a417277a..d65af376 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -2,13 +2,13 @@ import unittest from pathlib import Path -from Matcher.config.config_loader import load_config -from Matcher.config.settings import apply_env_overrides +from trialmatchai.config.config_loader import load_config +from trialmatchai.config.settings import apply_env_overrides class TestConfigLoading(unittest.TestCase): def test_load_config_from_repo(self) -> None: - config_path = Path(__file__).resolve().parents[1] / "source/Matcher/config/config.json" + config_path = Path(__file__).resolve().parents[1] / "src/trialmatchai/config/config.json" config = load_config(str(config_path)) self.assertIn("search_backend", config) self.assertIn("embedder", config) @@ -26,6 +26,7 @@ def test_env_overrides(self) -> None: "search": {"mode": "hybrid"}, "entity_extraction": {"backend": "gliner2"}, "concept_linker": {"db_path": "old"}, + "registry": {"since_days": 7, "raw_dir": "old-raw"}, } os.environ["TRIALMATCHAI_SEARCH_DB_PATH"] = "data/search-test" os.environ["TRIALMATCHAI_SEARCH_TRIALS_TABLE"] = "trials-test" @@ -33,6 +34,8 @@ def test_env_overrides(self) -> None: os.environ["TRIALMATCHAI_EMBEDDER_MODEL_NAME"] = "new-model" os.environ["TRIALMATCHAI_ENTITY_BACKEND"] = "regex" os.environ["TRIALMATCHAI_CONCEPT_DB_PATH"] = "concepts" + os.environ["TRIALMATCHAI_REGISTRY_SINCE_DAYS"] = "30" + os.environ["TRIALMATCHAI_REGISTRY_RAW_DIR"] = "registry/raw" try: updated = apply_env_overrides(raw) finally: @@ -42,6 +45,8 @@ def test_env_overrides(self) -> None: os.environ.pop("TRIALMATCHAI_EMBEDDER_MODEL_NAME", None) os.environ.pop("TRIALMATCHAI_ENTITY_BACKEND", None) os.environ.pop("TRIALMATCHAI_CONCEPT_DB_PATH", None) + os.environ.pop("TRIALMATCHAI_REGISTRY_SINCE_DAYS", None) + os.environ.pop("TRIALMATCHAI_REGISTRY_RAW_DIR", None) self.assertEqual(updated["search_backend"]["db_path"], "data/search-test") self.assertEqual(updated["search_backend"]["trials_table"], "trials-test") @@ -49,6 +54,8 @@ def test_env_overrides(self) -> None: self.assertEqual(updated["embedder"]["model_name"], "new-model") self.assertEqual(updated["entity_extraction"]["backend"], "regex") self.assertEqual(updated["concept_linker"]["db_path"], "concepts") + self.assertEqual(updated["registry"]["since_days"], 30) + self.assertEqual(updated["registry"]["raw_dir"], "registry/raw") if __name__ == "__main__": diff --git a/tests/test_trial_ranker_pytest.py b/tests/test_trial_ranker_pytest.py index 5bc32d0e..16265672 100644 --- a/tests/test_trial_ranker_pytest.py +++ b/tests/test_trial_ranker_pytest.py @@ -1,6 +1,6 @@ import json -from Matcher.pipeline.trial_ranker import ( +from trialmatchai.matching.trial_ranker import ( load_trial_data, rank_trials, save_ranked_trials, diff --git a/utils/DataLoader/__init__.py b/utils/DataLoader/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/utils/DataLoader/downloader.py b/utils/DataLoader/downloader.py deleted file mode 100644 index 79d0c55e..00000000 --- a/utils/DataLoader/downloader.py +++ /dev/null @@ -1,254 +0,0 @@ -import requests -import sys -import xml.etree.ElementTree as ET -import os -import time -import joblib -from tqdm.auto import tqdm -from tenacity import retry, wait_random_exponential, stop_after_attempt -import logging -from typing import List, Union - -# Configure logging for clear and timestamped output -logging.basicConfig( - level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s" -) - - -def normalize_whitespace(s: str) -> str: - """Normalize whitespace in a string.""" - return " ".join(s.split()) - - -@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(10)) -def get_cancer_trials_list(max_trials: int = 15000) -> List[str]: - """ - Retrieve a list of cancer-related clinical trial NCT IDs from ClinicalTrials.gov. - - Args: - max_trials (int): Maximum number of trial IDs to fetch. - - Returns: - List[str]: List of unique NCT IDs. - """ - base_url = "https://clinicaltrials.gov/api/query/full_studies" - trials_set = set() - page_size = 100 # Number of trials per page - current_rank = 1 - trials_fetched = 0 - - while trials_fetched < max_trials: - search_params = { - "expr": "((cancer) OR (neoplasm)) AND ((interventional) OR (treatment)) AND ((mutation) OR (variant))", - "min_rnk": current_rank, - "max_rnk": current_rank + page_size - 1, - "fmt": "json", - "fields": "NCTId", - } - response = requests.get(base_url, params=search_params) - if response.status_code == 200: - trials_data = response.json() - if "FullStudiesResponse" in trials_data: - studies = trials_data["FullStudiesResponse"].get("FullStudies", []) - if not studies: - break # No more studies found, exit the loop - for study in studies: - nct_id = ( - study.get("Study", {}) - .get("ProtocolSection", {}) - .get("IdentificationModule", {}) - .get("NCTId") - ) - if nct_id: - trials_set.add(nct_id) - trials_fetched += 1 - if trials_fetched >= max_trials: - break - current_rank += page_size - else: - logging.error("No trials found matching the criteria.") - break - else: - logging.error( - "Failed to retrieve data. Status code: %s", response.status_code - ) - break - - return list(trials_set) - - -def download_study_info( - nct_id: str, delay: float = 1.0, session: Union[requests.Session, None] = None -) -> bool: - """ - Download and update the XML information for a given clinical trial (NCT ID). - - If a local copy exists, compare selected fields with the online version. - If differences are found (or the file doesn't exist), update or create the local file. - - Args: - nct_id (str): The clinical trial NCT ID. - delay (float): Delay between requests in seconds. - session (requests.Session, optional): Requests session for connection pooling. - - Returns: - bool: True if the file was downloaded or updated successfully; False otherwise. - """ - local_file_path = os.path.join("..", "..", "data", "trials_xmls", f"{nct_id}.xml") - os.makedirs(os.path.dirname(local_file_path), exist_ok=True) - - session = session or requests.Session() - online_url = f"https://clinicaltrials.gov/ct2/show/{nct_id}?displayxml=true" - - try: - response = session.get(online_url) - except requests.exceptions.RequestException as e: - logging.error("Error fetching XML for trial %s: %s", nct_id, e) - time.sleep(delay) - return False - - if response.status_code != 200: - logging.error( - "Error: received status code %s for trial %s", response.status_code, nct_id - ) - time.sleep(delay) - return False - - try: - online_root = ET.fromstring(response.text) - except ET.ParseError as e: - logging.error("Error parsing online XML for trial %s: %s", nct_id, e) - time.sleep(delay) - return False - - # Check if a local version exists and compare key fields - if os.path.exists(local_file_path): - try: - with open(local_file_path, "r", encoding="utf-8") as f: - local_xml_content = f.read() - local_root = ET.fromstring(local_xml_content) - except (ET.ParseError, IOError) as e: - logging.error("Error reading/parsing local XML for trial %s: %s", nct_id, e) - os.remove(local_file_path) - local_root = None - else: - local_root = None - - fields_to_check = ["eligibility", "brief_title", "overall_status", "location"] - needs_update = False - - if local_root is not None: - for field in fields_to_check: - local_elem = local_root.find(f".//{field}") - online_elem = online_root.find(f".//{field}") - if local_elem is not None and online_elem is not None: - local_text = normalize_whitespace( - ET.tostring(local_elem, encoding="unicode").strip() - ) - online_text = normalize_whitespace( - ET.tostring(online_elem, encoding="unicode").strip() - ) - if local_text != online_text: - needs_update = True - break - else: - needs_update = True - break - - if local_root is None or needs_update: - try: - with open(local_file_path, "w", encoding="utf-8") as f: - f.write(ET.tostring(online_root, encoding="unicode")) - if local_root is None: - logging.info("Downloaded study information for %s", nct_id) - else: - logging.info("Updated study information for %s", nct_id) - except IOError as e: - logging.error("Error writing XML for trial %s: %s", nct_id, e) - time.sleep(delay) - return False - else: - logging.info("No changes in study information for %s", nct_id) - - time.sleep(delay) - return True - - -def parallel_downloader( - nct_ids: List[str], n_jobs: int = 10, delay: float = 1.0 -) -> List[bool]: - """ - Download and update clinical trial XMLs in parallel using joblib. - - Args: - nct_ids (List[str]): List of clinical trial NCT IDs. - n_jobs (int): Number of parallel jobs. - delay (float): Delay between requests. - - Returns: - List[bool]: List indicating the success status of each download/update. - """ - session = requests.Session() - - def download_wrapper(nct_id: str) -> bool: - return download_study_info(nct_id, delay=delay, session=session) - - results = joblib.Parallel(n_jobs=n_jobs)( - joblib.delayed(download_wrapper)(nct_id) - for nct_id in tqdm(nct_ids, desc="Downloading trials") - ) - return results - - -class Downloader: - """ - A class to manage the downloading and updating of clinical trial XML files. - - Attributes: - id_list (List[str]): List of clinical trial NCT IDs. - n_jobs (int): Number of parallel jobs. - delay (float): Delay between requests. - """ - - def __init__(self, id_list: List[str], n_jobs: int = 10, delay: float = 1.0): - self.id_list = id_list - self.n_jobs = n_jobs - self.delay = delay - - def download_and_update_trials(self) -> List[bool]: - """ - Download and update XML files for all trials in the id_list. - - Returns: - List[bool]: List of boolean statuses for each download/update. - """ - start_time = time.time() - results = parallel_downloader( - self.id_list, n_jobs=self.n_jobs, delay=self.delay - ) - elapsed_time = time.time() - start_time - logging.info("Elapsed time: %.2f seconds", elapsed_time) - return results - - -def main(): - """Main function to run the downloader.""" - id_file = "nct_ids.txt" - if not os.path.exists(id_file): - logging.error("ID file '%s' does not exist.", id_file) - sys.exit(1) - - with open(id_file, "r", encoding="utf-8") as file: - id_list = [line.strip() for line in file if line.strip()] - - if not id_list: - logging.error("No NCT IDs found in the file.") - sys.exit(1) - - n_jobs = 10 - downloader = Downloader(id_list, n_jobs=n_jobs, delay=1.0) - downloader.download_and_update_trials() - - -if __name__ == "__main__": - main() diff --git a/utils/DataLoader/test/__init__.py b/utils/DataLoader/test/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/utils/DataLoader/test/test_downloader.py b/utils/DataLoader/test/test_downloader.py deleted file mode 100644 index c7a849cb..00000000 --- a/utils/DataLoader/test/test_downloader.py +++ /dev/null @@ -1,179 +0,0 @@ -import unittest -from unittest.mock import patch, mock_open, MagicMock -import os - -from src.DataLoader.downloader import ( - normalize_whitespace, - get_cancer_trials_list, - download_study_info, - parallel_downloader, - Downloader, -) - - -class TestDownloaderFunctions(unittest.TestCase): - def test_normalize_whitespace(self): - input_str = " This is a test string \n with extra spaces. " - expected = "This is a test string with extra spaces." - self.assertEqual(normalize_whitespace(input_str), expected) - - @patch("src.DataLoader.downloader.requests.get") - def test_get_cancer_trials_list(self, mock_get): - # Set up a fake study entry - fake_study = { - "Study": { - "ProtocolSection": {"IdentificationModule": {"NCTId": "NCT12345678"}} - } - } - fake_json_response = {"FullStudiesResponse": {"FullStudies": [fake_study]}} - # Create a fake response object for the first call - fake_response = MagicMock() - fake_response.status_code = 200 - fake_response.json.return_value = fake_json_response - - # Second call returns an empty list to stop the loop. - fake_response_empty = MagicMock() - fake_response_empty.status_code = 200 - fake_response_empty.json.return_value = { - "FullStudiesResponse": {"FullStudies": []} - } - - mock_get.side_effect = [fake_response, fake_response_empty] - - result = get_cancer_trials_list(max_trials=1) - self.assertIn("NCT12345678", result) - - @patch("src.DataLoader.downloader.time.sleep", return_value=None) # avoid delay - @patch("src.DataLoader.downloader.requests.Session") - @patch("src.DataLoader.downloader.os.path.exists") - @patch("src.DataLoader.downloader.os.makedirs") - @patch("src.DataLoader.downloader.open", new_callable=mock_open, read_data="") - def test_download_study_info_no_local_file( - self, mock_file, mock_makedirs, mock_exists, mock_session_cls, mock_sleep - ): - # Simulate that the local file does not exist - mock_exists.return_value = False - - # Prepare a fake online XML - online_xml = """ - Test Trial - Test Eligibility - Recruiting - Test Location -""" - fake_response = MagicMock() - fake_response.status_code = 200 - fake_response.text = online_xml - - # Create a fake session instance - fake_session = MagicMock() - fake_session.get.return_value = fake_response - mock_session_cls.return_value = fake_session - - result = download_study_info("NCTTEST1", delay=0, session=fake_session) - self.assertTrue(result) - # Check that the file was opened for writing. - expected_path = os.path.join("..", "..", "data", "trials_xmls", "NCTTEST1.xml") - mock_file.assert_called_with(expected_path, "w", encoding="utf-8") - - @patch("src.DataLoader.downloader.time.sleep", return_value=None) - @patch("src.DataLoader.downloader.requests.Session") - @patch("src.DataLoader.downloader.os.path.exists") - @patch("src.DataLoader.downloader.os.makedirs") - def test_download_study_info_with_local_file_update_needed( - self, mock_makedirs, mock_exists, mock_session_cls, mock_sleep - ): - # Simulate that the local file exists. - mock_exists.return_value = True - - # Fake local XML (with an old title) and online XML (with an updated title) - local_xml = """ - Old Title - Test Eligibility - Recruiting - Test Location -""" - online_xml = """ - New Title - Test Eligibility - Recruiting - Test Location -""" - - fake_response = MagicMock() - fake_response.status_code = 200 - fake_response.text = online_xml - - fake_session = MagicMock() - fake_session.get.return_value = fake_response - mock_session_cls.return_value = fake_session - - # Patch open to simulate reading the local XML and then writing an updated version. - m = mock_open(read_data=local_xml) - with patch("src.DataLoader.downloader.open", m): - result = download_study_info("NCTTEST2", delay=0, session=fake_session) - self.assertTrue(result) - expected_path = os.path.join( - "..", "..", "data", "trials_xmls", "NCTTEST2.xml" - ) - # Check that open was called with write mode to update the file. - m.assert_called_with(expected_path, "w", encoding="utf-8") - - @patch("src.DataLoader.downloader.time.sleep", return_value=None) - @patch("src.DataLoader.downloader.requests.Session") - @patch("src.DataLoader.downloader.os.path.exists") - @patch("src.DataLoader.downloader.os.makedirs") - def test_download_study_info_with_local_file_no_update( - self, mock_makedirs, mock_exists, mock_session_cls, mock_sleep - ): - # Simulate that the local file exists. - mock_exists.return_value = True - - # Fake XML where both local and online versions are identical. - identical_xml = """ - Test Trial - Test Eligibility - Recruiting - Test Location -""" - - fake_response = MagicMock() - fake_response.status_code = 200 - fake_response.text = identical_xml - - fake_session = MagicMock() - fake_session.get.return_value = fake_response - mock_session_cls.return_value = fake_session - - m = mock_open(read_data=identical_xml) - with patch("src.DataLoader.downloader.open", m): - result = download_study_info("NCTTEST3", delay=0, session=fake_session) - self.assertTrue(result) - expected_path = os.path.join( - "..", "..", "data", "trials_xmls", "NCTTEST3.xml" - ) - # Check that open was called only for reading (the file was not updated). - m.assert_called_with(expected_path, "r", encoding="utf-8") - - @patch("src.DataLoader.downloader.download_study_info", return_value=True) - @patch( - "src.DataLoader.downloader.tqdm", lambda x, **kwargs: x - ) # bypass tqdm for testing - def test_parallel_downloader(self, mock_download): - trial_ids = ["NCT1", "NCT2", "NCT3"] - # Use n_jobs=1 to avoid issues with multiprocessing and patching. - results = parallel_downloader(trial_ids, n_jobs=1, delay=0) - self.assertEqual(results, [True, True, True]) - self.assertEqual(mock_download.call_count, 3) - - @patch("src.DataLoader.downloader.parallel_downloader", return_value=[True, True]) - def test_downloader_class(self, mock_parallel): - trial_ids = ["NCT1", "NCT2"] - downloader_obj = Downloader(trial_ids, n_jobs=2, delay=0) - results = downloader_obj.download_and_update_trials() - self.assertEqual(results, [True, True]) - mock_parallel.assert_called_with(trial_ids, n_jobs=2, delay=0) - - -if __name__ == "__main__": - unittest.main() diff --git a/utils/Indexer/__init__.py b/utils/Indexer/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/utils/Indexer/flatten.py b/utils/Indexer/flatten.py deleted file mode 100644 index 4c68d55e..00000000 --- a/utils/Indexer/flatten.py +++ /dev/null @@ -1,41 +0,0 @@ -import os -import shutil - - -def flatten_folder_structure( - root_dir, output_dir, separator="_", handle_duplicates=True -): - os.makedirs(output_dir, exist_ok=True) - filename_set = set() - - for subdir, dirs, files in os.walk(root_dir): - # Skip the root directory itself - if subdir == root_dir: - continue - - parent_folder = os.path.basename(subdir) - - for file in files: - old_path = os.path.join(subdir, file) - new_filename = f"{parent_folder}{separator}{file}" - new_path = os.path.join(output_dir, new_filename) - - if handle_duplicates: - base, ext = os.path.splitext(new_filename) - counter = 1 - while new_path in filename_set or os.path.exists(new_path): - new_filename = f"{base}_{counter}{ext}" - new_path = os.path.join(output_dir, new_filename) - counter += 1 - - filename_set.add(new_path) - shutil.copy2(old_path, new_path) - print(f"Copied: {old_path} → {new_path}") - - -# Example usage: -root_directory = "/home/mabdallah/scratch/TrialMatchAI/src/Indexer/processed_criteria" -output_directory = ( - "/home/mabdallah/scratch/TrialMatchAI/src/Indexer/processed_criteria_flattened" -) -flatten_folder_structure(root_directory, output_directory) diff --git a/utils/Indexer/index_criteria.py b/utils/Indexer/index_criteria.py deleted file mode 100644 index 50ea66b1..00000000 --- a/utils/Indexer/index_criteria.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import argparse -import json -import logging -import sys -from pathlib import Path - - -ROOT = Path(__file__).resolve().parents[2] -SOURCE = ROOT / "source" -if str(SOURCE) not in sys.path: - sys.path.append(str(SOURCE)) - -from Matcher.config.config_loader import load_config # noqa: E402 -from Matcher.search import LanceDBSearchBackend # noqa: E402 - - -logging.basicConfig( - format="%(asctime)s %(levelname)s %(name)s - %(message)s", - level=logging.INFO, -) -logger = logging.getLogger(__name__) - - -class CriteriaIndexer: - def __init__( - self, - backend: LanceDBSearchBackend, - *, - processed_file: Path, - ) -> None: - self.backend = backend - self.processed_file = processed_file - self.processed_file.parent.mkdir(parents=True, exist_ok=True) - self.processed_ids = self._load_processed_ids() - - def _load_processed_ids(self) -> set[str]: - if self.processed_file.exists(): - return set(self.processed_file.read_text(encoding="utf-8").splitlines()) - return set() - - def _save_processed_ids(self) -> None: - self.processed_file.write_text( - "\n".join(sorted(self.processed_ids)) + "\n", - encoding="utf-8", - ) - - def load_docs( - self, - processed_folder: Path, - *, - recreate: bool, - ) -> tuple[list[dict], set[str]]: - docs: list[dict] = [] - completed: set[str] = set() - trial_dirs = sorted(path for path in processed_folder.iterdir() if path.is_dir()) - for trial_dir in trial_dirs: - nct_id = trial_dir.name - if not recreate and nct_id in self.processed_ids: - logger.info("Skipping %s: already indexed", nct_id) - continue - trial_docs = [] - for path in sorted(trial_dir.glob("*.json")): - try: - trial_docs.append(json.loads(path.read_text(encoding="utf-8"))) - except Exception as exc: - logger.warning("%s: failed to load %s: %s", nct_id, path.name, exc) - if trial_docs: - docs.extend(trial_docs) - completed.add(nct_id) - return docs, completed - - def index_all( - self, - processed_folder: Path, - *, - recreate: bool = True, - ) -> int: - if not processed_folder.exists(): - raise FileNotFoundError(f"Criteria folder not found: {processed_folder}") - docs, completed = self.load_docs(processed_folder, recreate=recreate) - if not docs: - logger.info("No prepared criteria JSON files found.") - return 0 - count = self.backend.index_criteria(docs, recreate=recreate) - self.processed_ids.update(completed) - self._save_processed_ids() - logger.info( - "Indexed %s criteria documents into %s/%s.", - count, - self.backend.db_path, - self.backend.criteria_table, - ) - return count - - -def main() -> int: - parser = argparse.ArgumentParser( - description="Create or update the LanceDB eligibility criteria search table." - ) - parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") - parser.add_argument( - "--processed-folder", - required=True, - help="Root folder containing one prepared criteria subfolder per trial", - ) - parser.add_argument("--db-path", default=None, help="Override search DB path") - parser.add_argument("--table", default=None, help="Override criteria table name") - parser.add_argument( - "--processed-file", - default="utils/Indexer/processed_ids.txt", - help="File used to track already appended trial IDs", - ) - parser.add_argument( - "--recreate", - action=argparse.BooleanOptionalAction, - default=True, - help="Overwrite the target table before writing.", - ) - args = parser.parse_args() - - config = load_config(args.config) - search_cfg = config["search_backend"] - if args.db_path: - search_cfg["db_path"] = str(Path(args.db_path).expanduser().resolve()) - if args.table: - search_cfg["criteria_table"] = args.table - - backend = LanceDBSearchBackend.from_config(config) - indexer = CriteriaIndexer( - backend=backend, - processed_file=(ROOT / args.processed_file).resolve(), - ) - count = indexer.index_all( - Path(args.processed_folder), - recreate=args.recreate, - ) - return 0 if count else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/utils/Indexer/index_trials.py b/utils/Indexer/index_trials.py deleted file mode 100644 index 25630b7f..00000000 --- a/utils/Indexer/index_trials.py +++ /dev/null @@ -1,69 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import argparse -import json -import sys -from pathlib import Path - - -ROOT = Path(__file__).resolve().parents[2] -SOURCE = ROOT / "source" -if str(SOURCE) not in sys.path: - sys.path.append(str(SOURCE)) - -from Matcher.config.config_loader import load_config # noqa: E402 -from Matcher.search import LanceDBSearchBackend # noqa: E402 - - -def load_processed(folder: Path) -> list[dict]: - docs: list[dict] = [] - for path in sorted(folder.glob("*.json")): - docs.append(json.loads(path.read_text(encoding="utf-8"))) - return docs - - -def main() -> int: - parser = argparse.ArgumentParser( - description="Create or update the LanceDB trial search table." - ) - parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") - parser.add_argument( - "--processed-folder", - required=True, - help="Folder of prepared trial JSON files", - ) - parser.add_argument("--db-path", default=None, help="Override search DB path") - parser.add_argument("--table", default=None, help="Override trials table name") - parser.add_argument( - "--recreate", - action=argparse.BooleanOptionalAction, - default=True, - help="Overwrite the target table before writing.", - ) - args = parser.parse_args() - - config = load_config(args.config) - search_cfg = config["search_backend"] - if args.db_path: - search_cfg["db_path"] = str(Path(args.db_path).expanduser().resolve()) - if args.table: - search_cfg["trials_table"] = args.table - - processed_path = Path(args.processed_folder) - docs = load_processed(processed_path) - if not docs: - print(f"No prepared trial JSON files found in {processed_path}.") - return 1 - - backend = LanceDBSearchBackend.from_config(config) - count = backend.index_trials(docs, recreate=args.recreate) - print( - f"Indexed {count} trial documents into " - f"{backend.db_path}/{backend.trials_table}." - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/utils/Indexer/prepare_criteria.py b/utils/Indexer/prepare_criteria.py deleted file mode 100644 index 7c25de6f..00000000 --- a/utils/Indexer/prepare_criteria.py +++ /dev/null @@ -1,249 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import hashlib -import json -import logging -import sys -from pathlib import Path -from typing import Any - -import torch -import torch.nn.functional as F -from transformers import AutoModel, AutoTokenizer - -logging.basicConfig( - format="%(asctime)s %(levelname)s %(name)s - %(message)s", - level=logging.INFO, -) -logger = logging.getLogger(__name__) - - -ROOT = Path(__file__).resolve().parents[2] -SOURCE = ROOT / "source" -if str(SOURCE) not in sys.path: - sys.path.append(str(SOURCE)) - - -class SentenceEmbedder: - def __init__(self, model_name: str = "BAAI/bge-m3", use_gpu: bool = True): - self.device = torch.device( - "cuda" if use_gpu and torch.cuda.is_available() else "cpu" - ) - logger.info(f"Loading model {model_name} on device {self.device}") - self.tokenizer = AutoTokenizer.from_pretrained(model_name) - self.model = AutoModel.from_pretrained(model_name).to(self.device) - if self.device.type == "cuda": - self.model = self.model.half() - logger.info("Converted model to FP16") - - def mean_pool( - self, token_embeds: torch.Tensor, attention_mask: torch.Tensor - ) -> torch.Tensor: - mask = attention_mask.unsqueeze(-1).expand(token_embeds.size()).float() - summed = torch.sum(token_embeds * mask, dim=1) - counts = torch.clamp(mask.sum(dim=1), min=1e-9) - return summed / counts - - def embed(self, texts: list[str]) -> list[list[float]]: - """ - Batch‑embed a list of strings; returns a list of float vectors. - """ - enc = self.tokenizer( - texts, padding=True, truncation=True, return_tensors="pt" - ).to(self.device) - with torch.no_grad(): - outputs = self.model(**enc) - vecs = self.mean_pool(outputs.last_hidden_state, enc.attention_mask) - vecs = F.normalize(vecs, p=2, dim=1) - return vecs.cpu().tolist() - - -def compute_criteria_id(nct_id: str, criterion: str) -> str: - """ - Deterministically hash a trial‑criterion pair to a 64‑hex string. - """ - return hashlib.sha256(f"{nct_id}:{criterion}".encode("utf-8")).hexdigest() - - -def load_raw_trial(path: Path) -> dict: - return json.loads(path.read_text()) - - -def process_trial( - nct_id: str, - source_folder: Path, - processed_folder: Path, - embedder: SentenceEmbedder, - entity_annotator: Any | None = None, -) -> int: - raw_path = source_folder / f"{nct_id}.json" - if not raw_path.exists(): - logger.warning(f"Missing raw JSON for {nct_id}, skipping.") - return 0 - - data = load_raw_trial(raw_path) - criteria = data.get("criteria", []) - if not criteria: - logger.info(f"No criteria found for {nct_id}.") - return 0 - - # collect texts - entries = [] - texts = [] - for crit in criteria: - text = crit.get("criterion") or crit.get("sentence") - if not text: - continue - entries.append( - { - "nct_id": nct_id, - "criterion": text, - "entities": crit.get("entities", []), - "eligibility_type": crit.get("type"), - } - ) - texts.append(text) - - if not entries: - return 0 - - _annotate_missing_entities(entries, texts, entity_annotator) - - # embed all at once - vectors = embedder.embed(texts) - - # write out - trial_folder = processed_folder / nct_id - trial_folder.mkdir(parents=True, exist_ok=True) - - for entry, vec in zip(entries, vectors): - crit_id = compute_criteria_id(entry["nct_id"], entry["criterion"]) - out = { - "criteria_id": crit_id, - "nct_id": entry["nct_id"], - "criterion": entry["criterion"], - "entities": _entities_for_index(entry["entities"]), - "eligibility_type": entry["eligibility_type"], - "criterion_vector": vec, - } - (trial_folder / f"{crit_id}.json").write_text(json.dumps(out, indent=2)) - - logger.info(f"Processed {len(entries)} criteria for {nct_id}") - return len(entries) - - -def main(): - p = argparse.ArgumentParser( - description="Prepare & embed eligibility criteria per trial" - ) - p.add_argument( - "--ids-file", required=True, help="Path to nct_ids.txt (one NCT ID per line)" - ) - p.add_argument( - "--source-folder", - required=True, - help="Folder containing raw trial JSONs named .json", - ) - p.add_argument( - "--processed-folder", - default="processed_criteria", - help="Output root; will contain one subfolder per trial", - ) - p.add_argument( - "--model-name", default="BAAI/bge-m3", help="Sentence embedding model name" - ) - p.add_argument("--use-gpu", action="store_true", help="Enable GPU iff available") - p.add_argument( - "--annotate-entities", - action="store_true", - help="Annotate criteria without existing entities before writing processed JSON.", - ) - p.add_argument( - "--config", - default=None, - help="TrialMatchAI config path used when --annotate-entities is enabled.", - ) - args = p.parse_args() - - ids = [line.strip() for line in open(args.ids_file) if line.strip()] - source_folder = Path(args.source_folder) - processed_folder = Path(args.processed_folder) - processed_folder.mkdir(parents=True, exist_ok=True) - - embedder = SentenceEmbedder(model_name=args.model_name, use_gpu=args.use_gpu) - entity_annotator = _build_entity_annotator(args.config) if args.annotate_entities else None - - total = 0 - skipped = 0 - - for nct in ids: - trial_folder = processed_folder / nct - # Skip if already processed (i.e. folder exists and contains at least one .json) - if trial_folder.exists() and any(trial_folder.glob("*.json")): - logger.info(f"Skipping {nct}: already processed") - skipped += 1 - continue - - processed_count = process_trial( - nct, - source_folder, - processed_folder, - embedder, - entity_annotator=entity_annotator, - ) - total += processed_count - - logger.info( - f"✅ Finished embedding. Total criteria written: {total}. Trials skipped: {skipped}." - ) - - -def _build_entity_annotator(config_path: str | None): - from Matcher.config.config_loader import load_config - from Matcher.entities import build_entity_annotator - - config = load_config(config_path) - return build_entity_annotator(config) - - -def _annotate_missing_entities( - entries: list[dict[str, Any]], - texts: list[str], - entity_annotator: Any | None, -) -> None: - if entity_annotator is None: - return - missing_indices = [ - index for index, entry in enumerate(entries) if not entry.get("entities") - ] - if not missing_indices: - return - missing_texts = [texts[index] for index in missing_indices] - annotations = entity_annotator.annotate_texts_in_parallel( - missing_texts, - max_workers=1, - ) - for index, entities in zip(missing_indices, annotations): - entries[index]["entities"] = entities - - -def _entities_for_index(entities: Any) -> list[dict[str, Any]]: - if not isinstance(entities, list): - return [] - indexed: list[dict[str, Any]] = [] - for entity in entities: - if not isinstance(entity, dict): - continue - normalized = dict(entity) - normalized.setdefault("entity", normalized.get("text", "")) - normalized.setdefault("class", normalized.get("entity_group", "")) - normalized.setdefault("normalized_id", ["CUI-less"]) - normalized.setdefault("synonyms", []) - normalized.setdefault("concept_candidates", []) - normalized.setdefault("linker_status", "not_linked") - indexed.append(normalized) - return indexed - - -if __name__ == "__main__": - main() diff --git a/utils/Indexer/prepare_trials.py b/utils/Indexer/prepare_trials.py deleted file mode 100644 index d91c91da..00000000 --- a/utils/Indexer/prepare_trials.py +++ /dev/null @@ -1,166 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import json -import os -import re -import warnings - -import dateutil.parser -import torch -import torch.nn.functional as F -from transformers import AutoModel, AutoTokenizer - -warnings.filterwarnings( - "ignore", category=UserWarning, message="TypedStorage is deprecated" -) - - -class SentenceEmbedder: - def __init__(self, model_name: str = "BAAI/bge-m3"): - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - print(f"Embedding on device: {self.device}") - self.tokenizer = AutoTokenizer.from_pretrained(model_name) - self.model = AutoModel.from_pretrained(model_name).to(self.device) - - def mean_pooling(self, model_output, attention_mask): - tokens = model_output[0] - mask = attention_mask.unsqueeze(-1).expand(tokens.size()).float() - return torch.sum(tokens * mask, 1) / torch.clamp(mask.sum(1), min=1e-9) - - def get_embeddings(self, text: str): - if not text: - return None - enc = self.tokenizer(text, padding=True, truncation=True, return_tensors="pt") - enc = {k: v.to(self.device) for k, v in enc.items()} - with torch.no_grad(): - out = self.model(**enc) - emb = self.mean_pooling(out, enc["attention_mask"]) - emb = F.normalize(emb, p=2, dim=1) - return emb.squeeze().cpu().tolist() - - def preprocess_text(self, t: str) -> str: - return re.sub(r"\s+", " ", t).strip() if t else t - - def to_iso(self, s: str): - try: - return dateutil.parser.parse(s).date().isoformat() if s else None - except Exception: - return None - - def age_to_years(self, s: str): - if not s: - return None - m = re.search(r"([\d\.]+)", s) - if not m: - return None - v = float(m.group(1)) - u = s.lower() - if "year" in u: - y = v - elif "month" in u: - y = v / 12 - elif "week" in u: - y = v / 52 - elif "day" in u: - y = v / 365 - else: - return None - return round(y, 2) - - -def embed_and_prepare(doc: dict, embedder: SentenceEmbedder): - out = {"nct_id": doc["nct_id"]} - # text fields → clean + vector - for field, vec_name in [ - ("brief_title", "brief_title_vector"), - ("brief_summary", "brief_summary_vector"), - ("condition", "condition_vector"), - ("eligibility_criteria", "eligibility_criteria_vector"), - ]: - if field in doc: - txt = doc[field] - if isinstance(txt, list): - txt = " ".join( - [str(t) for t in txt if isinstance(t, str) and t.strip()] - ) - txt = embedder.preprocess_text(txt) - emb = embedder.get_embeddings(txt) or [0.0] * len( - embedder.get_embeddings("test") or [0.0] - ) - out[field] = txt - out[vec_name] = emb - - # passthroughs - for simple in ["overall_status", "phase", "study_type", "gender"]: - if simple in doc: - out[simple] = doc[simple] - - # dates - for d in ["start_date", "completion_date"]: - if d in doc: - iso = embedder.to_iso(doc[d]) - if iso: - out[d] = iso - - # ages - for a in ["minimum_age", "maximum_age"]: - if a in doc: - yrs = embedder.age_to_years(doc[a]) - if yrs is not None: - out[a] = yrs - - # nested passthroughs - for nest in ["intervention", "location", "reference"]: - if nest in doc: - out[nest] = doc[nest] - - return out - - -if __name__ == "__main__": - p = argparse.ArgumentParser(description="Prepare & embed clinical trial JSONs") - p.add_argument("--ids-file", required=True, help="One NCT ID per line") - p.add_argument("--source-folder", required=True, help="Raw JSONs dir") - p.add_argument( - "--processed-folder", - default="processed_docs", - help="Where to write embedded JSONs", - ) - p.add_argument( - "--model-name", default="BAAI/bge-m3", help="Sentence embedding model" - ) - args = p.parse_args() - - os.makedirs(args.processed_folder, exist_ok=True) - embedder = SentenceEmbedder(model_name=args.model_name) - - with open(args.ids_file) as f: - ids = [line.strip() for line in f if line.strip()] - - processed = 0 - skipped = 0 - - for nct in ids: - out_path = os.path.join(args.processed_folder, f"{nct}.json") - if os.path.exists(out_path): - print(f"🟡 Skipping {nct}: already processed") - skipped += 1 - continue - - in_path = os.path.join(args.source_folder, f"{nct}.json") - if not os.path.exists(in_path): - print(f"⚠️ Missing raw JSON for {nct}") - continue - - doc = json.load(open(in_path)) - doc["nct_id"] = nct - proc = embed_and_prepare(doc, embedder) - - with open(out_path, "w") as wf: - json.dump(proc, wf, indent=2) - processed += 1 - print(f"✅ Processed {nct}") - - print( - f"\nSummary: {processed} processed, {skipped} skipped, {len(ids) - processed - skipped} missing." - ) diff --git a/utils/Indexer/zipper.sh b/utils/Indexer/zipper.sh deleted file mode 100644 index e226ee02..00000000 --- a/utils/Indexer/zipper.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail -IFS=$'\n\t' - -SRC_DIR="processed_criteria" -CHUNKS=6 -CHUNK_PREFIX="criteria_part" - -# Create a working directory for chunks -mkdir -p zip_chunks - -# Step 1: List top-level folders and count them -folders=($(find "$SRC_DIR" -mindepth 1 -maxdepth 1 -type d)) -total=${#folders[@]} -per_chunk=$(( (total + CHUNKS - 1) / CHUNKS )) # Round up - -# Step 2: Divide folders into 6 chunks and zip each -info() { echo -e "\033[0;32m[INFO]\033[0m $*"; } - -info "Total subfolders: $total" -info "Creating $CHUNKS zip chunks, ~${per_chunk} folders each..." - -for ((i=0; i>", # Double greater than - "\xc2\xbc": "1/4", # one quarter - "\xc2\xbd": "1/2", # one half - "\xc2\xbe": "3/4", # three quarters - "\xca\xbf": "\x27", # c-single quote - "\xcc\xa8": "", # modifier - under curve - "\xcc\xb1": "", # modifier - under line - "\x95": " ", -} - - -def replace_chars(match): - char = match.group(0) - return chars[char] - - -def remove_special_chars_encoding(text): - pattern = "(" + "|".join(chars.keys()) + ")" - return re.sub(pattern, replace_chars, text) - - -def add_space_after_parenthesis(text): - # Use regular expression to find ")" followed by a letter and insert a space - result = re.sub(r"\)([a-zA-Z])", r") \1", text) - return result - - -def tokenize_clinical_note(id_list, source_directory=INPUT_FILEPATH): - data = [] - for _id in id_list: - with open(source_directory + "/" + "%s" % _id + "/" + "clinical_note.txt") as f: - text = f.read() - - phi_tags = re.findall("(\[\*\*.*?\*\*\])", text) - for i, tag in enumerate(phi_tags): - text = text.replace(tag, " __PHI_%d__ " % i) - text = text.replace(tag, "__PHI__") - - """ - Thoughts & Strategies - - If a newline happens in between matching parens, then ignore newline - - Identify prose v nonprose. Use nltk.sent_tokenize on prose - - not perfect, but it is pretty good with ignoring mid-sentence newlines - - - section header: "\n------ Protected Section ------\n" - - - If you can detect a bulleted list, then those are sentences - - sometimes the bullets are hyphens "-", sometimes numbers "1." - - - ecg reports are very short & seem to be exclusively prose - - echo reports are VERY structured. definitely useful to do a echo-specific one - - nursing notes have a linear structure. sections are easily identifiable - - nursing_other notes are typically very short (and parse-able) - - radiology reports have lots of "________________________" sections - - seems like they always have "MEDICAL CONDITION" and "FINAL REPORT" sections - - found list example where "1." is its own line and its info is own next line :/ - - could be rehab-specific for notes - - strong consistency format in social_work - """ - - # break into many segments - segments = mimic_sent_tokenize_rules(text) - - # run nltk tokenizer on these segments to split prose - sents = [] - for segment in segments: - s = nltk.sent_tokenize(segment) - sents += s - - # put the PHI back - for i in range(len(sents)): - tags = re.findall("(__PHI_(\d+)__)", sents[i]) - for tag, ind in tags: - sents[i] = sents[i].replace(tag, phi_tags[int(ind)]) - - most_recent_title = None - most_recent_inline_title = None - - for sentence in sents: - sentence = remove_full_stops_and_commas(sentence) - - if len(sentence) > 1: - is_sentence_title = is_title(sentence) - is_sentence_inline_title = is_inline_title(sentence) - - field = None - - if is_sentence_title: - most_recent_title = sentence - field = sentence[:-1] - sentence = "___" - elif is_sentence_inline_title: - most_recent_inline_title = sentence.split(":", 1)[0] - field = most_recent_inline_title - sentence = sentence.split(":", 1)[1] - else: - # Check for the most recent title or inline title encountered - if most_recent_title: - field = most_recent_title[:-1] - elif most_recent_inline_title: - field = most_recent_inline_title - - data.append( - ( - _id, - field, - sentence.replace("___", ""), - is_sentence_title, - is_sentence_inline_title, - ) - ) - else: - continue - - # Create a DataFrame with 'Sentence' and 'IsTitle' columns - df = pd.DataFrame( - data, columns=["id", "field", "sentence", "IsTitle", "IsInlineTitle"] - ) - df.to_csv(PREPROCESSED_OUTPUT_FILEPATH + "%s_preprocessed.csv" % id_list[0]) - return df - - -def mimic_sent_tokenize_rules(text): - # long sections are OBVIOUSLY different sentences - text = re.sub("---+", "\n\n-----\n\n", text) - text = re.sub("___+", "\n\n_____\n\n", text) - text = re.sub("\n\n+", "\n\n", text) - text = text.replace("___", "") - text = re.sub(r"(? 0: - new_segments.append(prefix.strip()) - - # add the header - new_segments.append(h) - - # remove the prefix from processing (very unlikely to be empty) - segment = rest.strip() - - # add the final piece (aka what comes after all headers are processed) - if len(segment) > 0: - new_segments.append(segment.strip()) - - # copy over the new list of segments (further segmented than original segments) - segments = list(new_segments) - new_segments = [] - - ### Low-hanging fruit: "_____" is a delimiter - for segment in segments: - subsections = segment.split("\n_____\n") - new_segments.append(subsections[0]) - for ss in subsections[1:]: - new_segments.append("_____") - new_segments.append(ss) - - segments = list(new_segments) - new_segments = [] - - ### Low-hanging fruit: "-----" is a delimiter - for segment in segments: - subsections = segment.split("\n-----\n") - new_segments.append(subsections[0]) - for ss in subsections[1:]: - new_segments.append("-----") - new_segments.append(ss) - - segments = list(new_segments) - new_segments = [] - - """ - for segment in segments: - print('------------START------------') - print(segment) - print('-------------END-------------') - print - exit() - """ - - ### Separate enumerated lists ### - for segment in segments: - if not re.search("\n\s*\d+\.", "\n" + segment): - new_segments.append(segment) - continue - - """ - print('------------START------------') - print(segment) - print('-------------END-------------') - print - """ - - # generalizes in case the list STARTS this section - segment = "\n" + segment - - # determine whether this segment contains a bulleted list (assumes i,i+1,...,n) - start = int(re.search("\n\s*(\d+)\.", "\n" + segment).groups()[0]) - n = start - while re.search("\n\s*%d." % n, segment): - n += 1 - n -= 1 - - # no bulleted list - if n < 1: - new_segments.append(segment) - continue - - """ - print('------------START------------') - print(segment) - print('-------------END-------------') - - print(start,n) - print - """ - - # break each list into its own line - # challenge: not clear how to tell when the list ends if more text happens next - for i in range(start, n + 1): - matching_text = re.search("(\n\s*\d+\.)", segment) - if matching_text: - matching_text = matching_text.groups()[0] - prefix = segment[: segment.index(matching_text)].strip() - segment = segment[segment.index(matching_text) :].strip() - else: - continue - - if len(prefix) > 0: - new_segments.append(prefix) - - if len(segment) > 0: - new_segments.append(segment) - - segments = list(new_segments) - new_segments = [] - - """ - TODO: Big Challenge - - There is so much variation in what makes a list. Intuitively, I can tell it's a - list because it shows repeated structure (often following a header) - - Examples of some lists (with numbers & symptoms changed around to noise) - - Past Medical History: - -- Hyperlipidemia - -- lactose intolerance - -- Hypertension - - - Physical Exam: - Vitals - T 82.2 BP 123/23 HR 73 R 21 75% on 2L NC - General - well appearing male, sitting up in chair in NAD - Neck - supple, JVP elevated to angle of jaw - CV - distant heart sounds, RRR, faint __PHI_43__ murmur at - - - Labs: - __PHI_10__ 12:00PM BLOOD WBC-8.8 RBC-8.88* Hgb-88.8* Hct-88.8* - MCV-88 MCH-88.8 MCHC-88.8 RDW-88.8* Plt Ct-888 - __PHI_14__ 04:54AM BLOOD WBC-8.8 RBC-8.88* Hgb-88.8* Hct-88.8* - MCV-88 MCH-88.8 MCHC-88.8 RDW-88.8* Plt Ct-888 - __PHI_23__ 03:33AM BLOOD WBC-8.8 RBC-8.88* Hgb-88.8* Hct-88.8* - MCV-88 MCH-88.8 MCHC-88.8 RDW-88.8* Plt Ct-888 - __PHI_109__ 03:06AM BLOOD WBC-8.8 RBC-8.88* Hgb-88.8* Hct-88.8* - MCV-88 MCH-88.8 MCHC-88.8 RDW-88.8* Plt Ct-888 - __PHI_1__ 05:09AM BLOOD WBC-8.8 RBC-8.88* Hgb-88.8* Hct-88.8* - MCV-88 MCH-88.8 MCHC-88.8 RDW-88.8* Plt Ct-888 - __PHI_26__ 04:53AM BLOOD WBC-8.8 RBC-8.88* Hgb-88.8* Hct-88.8* - MCV-88 MCH-88.8 MCHC-88.8 RDW-88.8* Plt Ct-888 - __PHI_301__ 05:30AM BLOOD WBC-8.8 RBC-8.88* Hgb-88.8* Hct-88.8* - MCV-88 MCH-88.8 MCHC-88.8 RDW-88.8* Plt Ct-888 - - - Medications on Admission: - Allopurinol 100 mg DAILY - Aspirin 250 mg DAILY - Atorvastatin 10 mg DAILY - Glimepiride 1 mg once a week. - Hexavitamin DAILY - Lasix 50mg M-W-F; 60mg T-Th-Sat-Sun - Metoprolol 12.5mg TID - Prilosec OTC 20 mg once a day - Verapamil 120 mg SR DAILY - """ - - ### Remove lines with inline titles from larger segments (clearly nonprose) - for segment in segments: - """ - With: __PHI_6__, MD __PHI_5__ - Building: De __PHI_45__ Building (__PHI_32__ Complex) __PHI_87__ - Campus: WEST - """ - - lines = segment.split("\n") - - buf = [] - for i in range(len(lines)): - if is_inline_title(lines[i]): - if len(buf) > 0: - new_segments.append("\n".join(buf)) - buf = [] - buf.append(lines[i]) - if len(buf) > 0: - new_segments.append("\n".join(buf)) - - segments = list(new_segments) - new_segments = [] - # Going to put one-liner answers with their sections - # (aka A A' B B' C D D' --> AA' BB' C DD' ) - N = len(segments) - for i in range(len(segments)): - # avoid segfaults - if i == 0: - new_segments.append(segments[i]) - continue - - if ( - segments[i].count("\n") == 0 - and is_title(segments[i - 1]) - and not is_title(segments[i]) - ): - if (i == N - 1) or is_title(segments[i + 1]): - new_segments = new_segments[:-1] - new_segments.append(segments[i - 1] + " " + segments[i]) - else: - new_segments.append(segments[i].replace("\n", " ")) - - segments = list(new_segments) - new_segments = [] - - """ - Should do some kind of regex to find "TEST: value" in segments? - - Indication: Source of embolism. - BP (mm Hg): 145/89 - HR (bpm): 80 - - Note: I made a temporary hack that fixes this particular problem. - We'll see how it shakes out - """ - - """ - Separate ALL CAPS lines (Warning... is there ever prose that can be all caps?) - """ - - """ - for segment in segments: - print('------------START------------') - print(segment) - print('-------------END-------------') - print - exit() - """ - - return segments - - -def strip(s): - return s.strip() - - -def is_inline_title(text): - m = re.search("^([a-zA-Z ]+:) ", text) - if not m: - return False - - return is_title(m.groups()[0]) - - -def remove_full_stops_and_commas(text): - text = text.replace(",", "") - if text.endswith("."): - return text[:-1] # Return text without the last character (full stop) - return text - - -def drop_leading_character(sentence, regex_patterns): - """ - Drop leading characters from a sentence based on regex patterns. - - This function takes a sentence and a list of regular expression (regex) patterns. It iterates over the regex patterns, and for each - pattern, it drops the leading character from the sentence if there is a match. The loop continues until no more matches are found - for any of the patterns. The resulting sentence is then stripped of leading and trailing whitespaces. - - Parameters: - sentence (str): The input sentence from which leading characters will be dropped. - regex_patterns (list): A list of regular expression patterns to match against the leading characters. - - Returns: - str: The sentence with leading characters dropped. - - Example: - sentence = "A. This is a sample sentence." - regex_patterns = [r"^[A-Z]\.", r"^\d+\."] - drop_leading_character(sentence, regex_patterns) - # Output: "This is a sample sentence." - """ - for pattern in regex_patterns: - while True: - match = re.match(pattern, sentence) - if match: - # Drop the leading character by substituting it with an empty string, - # but only replace the first occurrence - sentence = re.sub(pattern, "", sentence, count=1).strip() - else: - # If no more matches found, exit the loop - break - return sentence.strip() - - -stopwords = set(["of", "on", "or"]) - - -def is_title(text): - if not text.endswith(":"): - return False - text = text[:-1] - - # be a little loose here... can tighten if it causes errors - text = re.sub("(\([^\)]*?\))", "", text) - - # Are all non-stopwords capitalized? - for word in text.split(): - if word in stopwords: - continue - if not word[0].isupper(): - return False - - # I noticed this is a common issue (non-title aapears at beginning of line) - if text == "Disp": - return False - - # optionally: could assert that it is less than 6 tokens - - return True - - -def main(): - """ - # read text file from command line - if len(sys.argv) != 2: - print(>>sys.stderr, '\n\tusage: python %s \n' % sys.argv[0] - exit(1) - mimic_note_file = sys.argv[1] - - with open(mimic_note_file, 'r') as f: - text = f.read() - - # tokenize - sents = tokenize_clinical_note(text) - """ - - for mimic_note_file in sys.argv[1:]: - print(mimic_note_file) - - with open(mimic_note_file, "r") as f: - text = f.read() - - # tokenize - sents = tokenize_clinical_note(text) - - for sent in sents: - print("-" * 40) - print(sent) - print("=" * 40) - print("\n\n") - - -if __name__ == "__main__": - main() diff --git a/utils/Preprocessor/preprocessing.py b/utils/Preprocessor/preprocessing.py deleted file mode 100644 index 7bea2c1d..00000000 --- a/utils/Preprocessor/preprocessing.py +++ /dev/null @@ -1,67 +0,0 @@ -import joblib -from tqdm.auto import tqdm -from preprocessing_utils import eic_text_preprocessing -from preprocess_clinical_notes import tokenize_clinical_note -import pandas as pd -import os - -memory = joblib.Memory(".") - - -def ParallelExecutor(use_bar="tqdm", **joblib_args): - """Utility for tqdm progress bar in joblib.Parallel""" - all_bar_funcs = { - "tqdm": lambda args: lambda x: tqdm(x, **args), - "False": lambda args: iter, - "None": lambda args: iter, - } - - def aprun(bar=use_bar, **tq_args): - def tmp(op_iter): - if str(bar) in all_bar_funcs.keys(): - bar_func = all_bar_funcs[str(bar)](tq_args) - else: - raise ValueError("Value %s not supported as bar type" % bar) - # Pass n_jobs from joblib_args - return joblib.Parallel(n_jobs=joblib_args.get("n_jobs", 10))( - bar_func(op_iter) - ) - - return tmp - - return aprun - - -class Preprocessor: - def __init__(self, id_list, n_jobs): - self.id_list = id_list - self.n_jobs = n_jobs - - def preprocess_clinical_trials_text(self): - parallel_runner = ParallelExecutor(n_jobs=self.n_jobs)(total=len(self.id_list)) - X = parallel_runner( - joblib.delayed(eic_text_preprocessing)([_id]) for _id in self.id_list - ) - return pd.concat(X).reset_index(drop=True) - - def preprocess_patient_clinical_notes(self): - parallel_runner = ParallelExecutor(n_jobs=self.n_jobs)(total=len(self.id_list)) - X = parallel_runner( - joblib.delayed(tokenize_clinical_note)([_id]) for _id in self.id_list - ) - return pd.concat(X).reset_index(drop=True) - - -if __name__ == "__main__": - # Load the list of NCT IDs - folder_path = "../../data/trials_xmls" - file_names = [] - # List all files in the folder - for file in os.listdir(folder_path): - if os.path.isfile(os.path.join(folder_path, file)): - file_name, file_extension = os.path.splitext(file) - file_names.append(file_name) - nct_ids = file_names - n_jobs = 10 - preprocessor = Preprocessor(nct_ids, n_jobs) - preprocessor.preprocess_clinical_trials_text() diff --git a/utils/Preprocessor/preprocessing_utils.py b/utils/Preprocessor/preprocessing_utils.py deleted file mode 100644 index 7dc346cb..00000000 --- a/utils/Preprocessor/preprocessing_utils.py +++ /dev/null @@ -1,649 +0,0 @@ -""" -Description: This script contains functions for pre-processing clinical trials eligibility criteria texts. -The functions serve to split the raw unstructured text into clean and structured sentences to be processed by a more advanced downstream NLP analysis. -""" - -import os -import re -import json -import logging -import itertools -import pandas as pd -import xml.etree.ElementTree as ET - - -def load_regex_patterns(file_path): - """ - Load regular expression patterns from a JSON file. - - Parameters: - file_path (str): Path to the JSON file containing regex patterns. - - Returns: - dict: A dictionary with pattern names as keys and regex patterns as values. - """ - with open(file_path, "r") as file: - data = json.load(file) - patterns = {key: value["regex"] for key, value in data["patterns"].items()} - return patterns - - -def split_on_leading_markers(lines): - """ - Attempt to split lines on various common list markers: - - Bullets (•) - - Dashes (-) at the start of items - - Numeric or alphabetical lists (e.g., "1)", "a)") - Adjust the patterns to fit your data. - """ - new_lines = [] - for line in lines: - # First split on bullet points - bullet_parts = [p.strip() for p in re.split(r"•", line) if p.strip()] - temp_lines = [] - for part in bullet_parts: - # Split on leading dashes - # This will split lines like "- Something" or multiple dashes in a single line. - dash_parts = [ - dp.strip() for dp in re.split(r"(? next_line_indent: - return True - - return False - - -def split_on_carriage_returns(text): - """ - Split text into lines separated by double carriage returns. - - Parameters: - text (str): The input text. - - Returns: - list: A list of lines. - """ - lines = re.split(r"\n\n+", re.sub(r":\n", ":\n\n", text)) - lines = [line.strip() for line in lines if line.strip()] - return lines - - -def split_lines_on_semicolon(lines): - """ - Splits lines on semicolons not within braces. - - Parameters: - lines (list): A list of lines. - - Returns: - list: A list of split lines. - """ - split_lines = [] - for line in lines: - line = replace_parentheses_with_braces(line) - parts = [] - temp = "" - inside_braces = False - for char in line: - if char == "{": - inside_braces = True - elif char == "}": - inside_braces = False - elif char == ";" and not inside_braces: - parts.append(temp.strip()) - temp = "" - continue - temp += char - parts.append(temp.strip()) - split_lines.extend(parts) - return split_lines - - -def split_to_sentences(text, regex_patterns, exception_patterns): - """ - Split text into sentences based on specific criteria. - - Parameters: - text (str): The input text. - regex_patterns (list): A list of regex patterns for splitting. - exception_patterns (list): A list of regex patterns to ignore during splitting. - - Returns: - list: A list of sentences. - """ - lines = split_on_carriage_returns(text) - lines = split_on_leading_markers(lines) - lines = split_lines_on_semicolon(lines) - sentences = [] - - for line in lines: - line = re.sub(r"\n", " ", line) - line = re.sub(" +", " ", line) - split_line = split_by_leading_char_from_regex_patterns( - line, regex_patterns, exceptions_patterns=exception_patterns - ) - split_line = [s for s in split_line if len(s.split()) > 1] - sentences.extend(split_line) - - return sentences - - -def drop_leading_character(sentence, regex_patterns): - """ - Drop leading characters from a sentence based on regex patterns. - - Parameters: - sentence (str): The input sentence. - regex_patterns (list): A list of regex patterns. - - Returns: - str: The cleaned sentence. - """ - for pattern in regex_patterns: - while True: - match = re.match(pattern, sentence) - if match: - sentence = re.sub(pattern, "", sentence, count=1).strip() - else: - break - return sentence.strip() - - -def extract_criteria_sections_headers(lines): - """ - Extract criteria sub-section headers from a list of lines. - - Parameters: - lines (list): A list of sentences. - - Returns: - dict: A dictionary with headers as keys and line indices as values. - """ - criteria_sections = {} - # Define explicit patterns for different writing styles of group-specific criteria headers - patterns = [ - r"^(?:-?\s*)(?:Inclusion|INCLUSION|Exclusion|EXCLUSION|Eligibility|Selection)\s?(?:Criteria|Requirements?)?\s?(?:for|in)?\s?(?:Patients|Subjects|Population|Cohort|Group|Arm)?\s?(?:with|without|who|where|having)?\s?[\w\d\s-]*[:\-]?", - r"^(?:Key\s)?(?:Inclusion|INCLUSION|EXCLUSION|Exclusion|Eligibility|Selection)(?:\s(?:Criteria|Requirements))?(?:\s?[-+:]|\sfor)?(?:\s[\w\s+-]+)?(?:\([\w\s]+\))?\s?[-+:]?\s?[\w\s]+$", - r"^(?:Key\s)?(?:Inclusion|INCLUSION|EXCLUSION|Exclusion|Eligibility|Selection)(?:\s(?:Criteria|Requirements?))(?:\s(?:for|in))?(?:\s(?:Patients|Subjects|Population|Cohort|Group|Arm))?(?:\s(?:with|without|who|where|having))?\s?(?:\([\w\s]+\))?\s?[\w\s+-]*[:\-]?", - r"^(?:[\w\d\s-]+)\s*-\s*(?:Inclusion|INCLUSION|EXCLUSION|Exclusion|Eligibility|Selection)\s(?:Criteria|Requirements?)?$", - r"^(?:[\w\s]+?)\s(?:group|patients|population|arm|subjects|cohort)\s(?:inclusion|exclusion|eligibility|selection|criteria)(?:\s?:|-)?", - r"^\b(?:\w+\s\w+|\w+)?\s(?:Inclusion|INCLUSION|EXCLUSION|Exclusion|Eligibility|Selection)\s(?:Criteria|Requirements)\b", - ] - for i, line in enumerate(lines): - header_candidate = line.strip() - if ":" in header_candidate: - header_candidate = header_candidate.split(":")[0].strip() - if len(header_candidate.split()) <= 10: - if any( - re.search(pattern, header_candidate, re.IGNORECASE) - for pattern in patterns - ): - header = header_candidate.strip() - if header not in criteria_sections: - criteria_sections[header] = [i] - else: - criteria_sections[header].append(i) - return criteria_sections - - -################################################# -# NEW FUNCTION TO HANDLE INLINE HEADERS -################################################# - - -def fix_inline_headers(text): - """ - Ensure that recognized headers (e.g., Inclusion Criteria, Exclusion Criteria, etc.) - appear on their own line by inserting a newline right after the header phrase - if it's directly followed by non-whitespace text. - """ - # Adjust or add more patterns as needed - patterns = [ - r"(Inclusion\s*Criteria\s*:)\s*(?=\S)", - r"(Exclusion\s*Criteria\s*:)\s*(?=\S)", - # Add more if needed - # r"(Eligibility\s*Criteria\s*:)\s*(?=\S)", - ] - - fixed_text = text - for pat in patterns: - fixed_text = re.sub(pat, r"\1\n", fixed_text, flags=re.IGNORECASE) - return fixed_text - - -def extract_separate_inclusion_exclusion(text, regex_patterns, exception_patterns): - """ - Extract preprocessed inclusion and exclusion criteria from eligibility criteria text. - - Parameters: - text (str): The preprocessed eligibility criteria text. - regex_patterns (list): A list of regex patterns for splitting. - exception_patterns (list): A list of regex patterns to ignore during splitting. - - Returns: - dict: A dictionary containing Inclusion Criteria, Exclusion Criteria, and Original Eligibility Criteria. - """ - # First, fix the scenario where "Inclusion Criteria:" or "Exclusion Criteria:" - # is immediately followed by text on the same line - text = fix_inline_headers(text) - - criteria = { - "Inclusion Criteria": {}, - "Exclusion Criteria": {}, - "Original Eligibility Criteria": text, - } - - lines = split_to_sentences(text, regex_patterns, exception_patterns) - subsection_indices = extract_criteria_sections_headers(lines) - - # Fallback: If no sections are identified, treat the entire text as one block of inclusion criteria - if not subsection_indices: - criteria["Inclusion Criteria"]["General"] = lines - return criteria - - inclusion_pattern = r"(? 200: # Adjust the threshold as needed - sentences = split_on_full_stops(sentence) - for s in sentences: - if s: # Ensure the sentence is not empty - new_row = row.copy() - new_row["sentence"] = s - new_rows.append(new_row) - else: - new_rows.append(row) - return pd.DataFrame(new_rows) - - -def eic_text_preprocessing( - _ids, - regex_path="../../data/regex/regex_patterns.json", - exceptions_path="../../data/regex/exception_regex_patterns.json", - output_path="../../data/preprocessed_data/clintra/", -): - """ - Main preprocessing function for eligibility criteria text from a list of clinical trial IDs. - - Parameters: - _ids (list): A list of clinical trial IDs. - regex_path (str): Path to the regex patterns JSON file. - exceptions_path (str): Path to the exception regex patterns JSON file. - output_path (str): Directory path to save the preprocessed CSV file. - - Returns: - pandas.DataFrame or None: The preprocessed DataFrame or None if no data is processed. - """ - regex_patterns = list(load_regex_patterns(regex_path).values()) - exception_patterns = list(load_regex_patterns(exceptions_path).values()) - texts = [] - trial_ids = [] - - for nid in _ids: - print(f"Processing Trial ID: {nid}") - eic_text = extract_eligibility_criteria(nid) - if eic_text: - preprocessed_text = extract_separate_inclusion_exclusion( - eic_text, regex_patterns, exception_patterns - ) - texts.append(preprocessed_text) - trial_ids.append(nid) - else: - continue - - to_concat = [] - for index, item in enumerate(texts): - _id = trial_ids[index] - for criteria_key in ["Inclusion Criteria", "Exclusion Criteria"]: - criteria_dict = item.get(criteria_key, {}) - for sub_key, sub_value in criteria_dict.items(): - df = pd.DataFrame(sub_value, columns=["sentence"]) - df["criteria"] = criteria_key - df["sub_criteria"] = sub_key - df["id"] = _id - to_concat.append(df) - - if to_concat: - final_df = pd.concat(to_concat, ignore_index=True) - final_df["sentence"] = final_df["sentence"].apply( - drop_leading_character, regex_patterns=regex_patterns - ) - final_df["sentence"] = final_df["sentence"].apply( - replace_braces_with_parentheses - ) - final_df = split_large_sentences(final_df) - final_df.to_csv( - os.path.join(output_path, f"{_ids[0]}_preprocessed.tsv"), - index=False, - sep="\t", - ) - return final_df - else: - return None diff --git a/utils/Preprocessor/test/__init__.py b/utils/Preprocessor/test/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/utils/Preprocessor/test/test_preprocessing.py b/utils/Preprocessor/test/test_preprocessing.py deleted file mode 100644 index 8ed08c7a..00000000 --- a/utils/Preprocessor/test/test_preprocessing.py +++ /dev/null @@ -1,336 +0,0 @@ -""" -test_preprocessing.py - -This file contains unit tests for the preprocessing functions used for handling -clinical trial eligibility criteria texts. -""" - -import os -import json -import tempfile -import unittest -import pandas as pd -from unittest.mock import patch - - -from src.Preprocessor.preprocessing_utils import ( - load_regex_patterns, - split_on_leading_markers, - replace_parentheses_with_braces, - replace_braces_with_parentheses, - line_starts_with_capitalized_alphanumeric, - read_xml_file, - parse_xml_content, - extract_eligibility_criteria, - split_by_leading_char_from_regex_patterns, - split_on_carriage_returns, - split_lines_on_semicolon, - split_to_sentences, - drop_leading_character, - extract_criteria_sections_headers, - fix_inline_headers, - extract_separate_inclusion_exclusion, - split_on_full_stops, - split_large_sentences, - eic_text_preprocessing, -) - - -class TestLoadRegexPatterns(unittest.TestCase): - def setUp(self): - # Create a temporary JSON file with a patterns dictionary. - self.temp_file = tempfile.NamedTemporaryFile( - mode="w+", delete=False, suffix=".json" - ) - sample_data = { - "patterns": { - "bullet": {"regex": r"•"}, - "dash": {"regex": r"^-"}, - "number": {"regex": r"^\d+\)"}, - } - } - json.dump(sample_data, self.temp_file) - self.temp_file.close() - - def tearDown(self): - os.unlink(self.temp_file.name) - - def test_load_regex_patterns(self): - patterns = load_regex_patterns(self.temp_file.name) - expected = {"bullet": r"•", "dash": r"^-", "number": r"^\d+\)"} - self.assertEqual(patterns, expected) - - -class TestSplittingAndReplacingFunctions(unittest.TestCase): - def test_split_on_leading_markers(self): - # Test splitting a line with bullet, dash and asterisk markers. - input_lines = ["• First item - subitem * detail", "No marker here"] - result = split_on_leading_markers(input_lines) - # Check that more than one line is returned and that it contains expected fragments. - self.assertIsInstance(result, list) - self.assertGreater(len(result), 1) - self.assertIn("First item", result[0]) - self.assertIn("subitem", " ".join(result)) - - def test_replace_parentheses_with_braces(self): - input_text = "This is a test (example) with [brackets]." - expected = "This is a test {example} with {brackets}." - # Note: The implementation replaces both '(' and '[' with '{' and their closing pairs with '}' - result = replace_parentheses_with_braces(input_text) - self.assertEqual(result, expected) - - def test_replace_braces_with_parentheses(self): - input_text = "This is a test {example} with {brackets}." - expected = "This is a test (example) with (brackets)." - result = replace_braces_with_parentheses(input_text) - self.assertEqual(result, expected) - - def test_line_starts_with_capitalized_alphanumeric(self): - self.assertTrue(line_starts_with_capitalized_alphanumeric("Hello world")) - self.assertFalse(line_starts_with_capitalized_alphanumeric("hello world")) - self.assertFalse(line_starts_with_capitalized_alphanumeric(" ")) - self.assertTrue(line_starts_with_capitalized_alphanumeric("A1 is valid")) - - def test_split_by_leading_char_from_regex_patterns(self): - # Use a regex that matches any numeric marker in the text (without the start-of-string anchor). - line = "1) First sentence. 2) Second sentence." - patterns = [r"\b\d+\)"] - result = split_by_leading_char_from_regex_patterns(line, patterns) - # Expect two parts: one starting from "1)" and another starting from "2)" - self.assertGreaterEqual(len(result), 2, f"Result was {result}") - - -class TestXMLFunctions(unittest.TestCase): - def setUp(self): - # Create temporary XML content. - self.valid_xml = "Eligibility Content" - self.invalid_xml = ( - "Missing closing tags" - ) - # Create a temporary file for testing read_xml_file. - self.temp_xml_file = tempfile.NamedTemporaryFile( - mode="w+", delete=False, suffix=".xml" - ) - self.temp_xml_file.write(self.valid_xml) - self.temp_xml_file.close() - - def tearDown(self): - os.unlink(self.temp_xml_file.name) - - def test_read_xml_file_success(self): - content = read_xml_file(self.temp_xml_file.name) - self.assertIn("Eligibility Content", content) - - def test_read_xml_file_failure(self): - # Attempt to read a non-existent file. - content = read_xml_file("nonexistent_file.xml") - self.assertIsNone(content) - - def test_parse_xml_content_success(self): - root = parse_xml_content(self.valid_xml) - self.assertIsNotNone(root) - self.assertEqual(root.tag, "root") - - def test_parse_xml_content_failure(self): - root = parse_xml_content(self.invalid_xml) - self.assertIsNone(root) - - -class TestExtractEligibilityCriteria(unittest.TestCase): - def setUp(self): - # Create dummy XML content that includes the eligibility textblock. - self.dummy_xml = "Eligibility Content for Trial" - # Create a temporary file that will be used to simulate a trial XML file. - self.temp_xml_file = tempfile.NamedTemporaryFile( - mode="w+", delete=False, suffix=".xml" - ) - self.temp_xml_file.write(self.dummy_xml) - self.temp_xml_file.close() - - def tearDown(self): - os.unlink(self.temp_xml_file.name) - - def test_extract_eligibility_criteria_found(self): - trial_id = "dummy_trial" - # Patch os.path.exists and os.path.join using the correct module path. - with ( - patch( - "src.Preprocessor.preprocessing_utils.os.path.exists", return_value=True - ), - patch( - "src.Preprocessor.preprocessing_utils.os.path.join", - return_value=self.temp_xml_file.name, - ), - ): - text = extract_eligibility_criteria(trial_id) - self.assertEqual(text, "Eligibility Content for Trial") - - def test_extract_eligibility_criteria_not_found(self): - trial_id = "dummy_trial" - # Return False for file existence. - with patch( - "src.Preprocessor.preprocessing_utils.os.path.exists", return_value=False - ): - text = extract_eligibility_criteria(trial_id) - self.assertIsNone(text) - - -class TestOtherTextProcessingFunctions(unittest.TestCase): - def test_split_on_carriage_returns(self): - text = "Line one.\n\nLine two.\n\n\nLine three." - result = split_on_carriage_returns(text) - self.assertEqual(len(result), 3) - self.assertIn("Line two.", result) - - def test_split_lines_on_semicolon(self): - lines = ["Sentence one; Sentence two {ignore; this} end."] - result = split_lines_on_semicolon(lines) - # Expect the semicolon outside braces to split the line - # and preserve the entire text "ignore; this" inside braces. - self.assertIn("Sentence one", result[0]) - self.assertEqual(result[1], "Sentence two {ignore; this} end.") - - def test_split_to_sentences(self): - text = "Inclusion Criteria: Patients over 18. Exclusion Criteria: Non-eligible subjects." - regex_patterns = [r"Inclusion Criteria", r"Exclusion Criteria"] - exception_patterns = [] - result = split_to_sentences(text, regex_patterns, exception_patterns) - self.assertIsInstance(result, list) - self.assertGreaterEqual(len(result), 1) - - def test_drop_leading_character(self): - # Suppose our regex patterns remove leading numbers and punctuation. - patterns = [r"^\d+\.", r"^-"] - sentence = "1. This is a test sentence." - result = drop_leading_character(sentence, patterns) - self.assertNotEqual(result, sentence) - self.assertTrue(result.startswith("This")) - - def test_extract_criteria_sections_headers(self): - # Provide a list of simulated lines where some lines look like headers. - lines = [ - "Inclusion Criteria:", - "Patients must be over 18.", - "Exclusion Criteria:", - "Patients with comorbidities.", - ] - sections = extract_criteria_sections_headers(lines) - self.assertIsInstance(sections, dict) - # There should be keys that contain "Inclusion" and "Exclusion". - keys_joined = " ".join(sections.keys()).lower() - self.assertIn("inclusion", keys_joined) - self.assertIn("exclusion", keys_joined) - - def test_fix_inline_headers(self): - text = "Inclusion Criteria:Patients must be over 18. Exclusion Criteria:Patients with comorbidities." - fixed = fix_inline_headers(text) - # Check that there is a newline inserted after the colon for each header. - self.assertIn("Criteria:\n", fixed) - - def test_extract_separate_inclusion_exclusion(self): - # Create a dummy eligibility text with inline headers. - text = "Inclusion Criteria: Patients must be over 18. Exclusion Criteria: Patients with comorbidities." - regex_patterns = [r"Inclusion Criteria", r"Exclusion Criteria"] - exception_patterns = [] - result = extract_separate_inclusion_exclusion( - text, regex_patterns, exception_patterns - ) - self.assertIn("Inclusion Criteria", result) - self.assertIn("Exclusion Criteria", result) - # The text for each header should be non-empty dictionaries. - self.assertIsInstance(result["Inclusion Criteria"], dict) - self.assertIsInstance(result["Exclusion Criteria"], dict) - - def test_split_on_full_stops(self): - text = "This is a sentence. And here is another? Yes, indeed. Final sentence." - sentences = split_on_full_stops(text) - self.assertGreaterEqual(len(sentences), 3) - self.assertTrue(any("Final sentence" in s for s in sentences)) - - def test_split_large_sentences(self): - # Create a DataFrame with one long sentence. - df = pd.DataFrame( - { - "sentence": [ - "This is a very long sentence. It should be split. Here is another sentence." - ], - "criteria": ["Inclusion"], - "sub_criteria": ["General"], - "id": ["trial1"], - } - ) - new_df = split_large_sentences(df) - # We expect that if the threshold is 200 characters, a sentence shorter than that remains unchanged. - self.assertFalse(new_df.empty) - self.assertTrue(new_df["sentence"].str.len().min() > 0) - - -class TestEICTextPreprocessing(unittest.TestCase): - def setUp(self): - # Create temporary dummy regex patterns files. - self.temp_regex_file = tempfile.NamedTemporaryFile( - mode="w+", delete=False, suffix=".json" - ) - regex_data = { - "patterns": { - "pattern1": {"regex": r"\d+\)"}, - "pattern2": {"regex": r"[•*]"}, - } - } - json.dump(regex_data, self.temp_regex_file) - self.temp_regex_file.close() - - self.temp_exceptions_file = tempfile.NamedTemporaryFile( - mode="w+", delete=False, suffix=".json" - ) - exceptions_data = {"patterns": {"exception": {"regex": r"EXCEPTION"}}} - json.dump(exceptions_data, self.temp_exceptions_file) - self.temp_exceptions_file.close() - - # Create a dummy eligibility criteria XML content. - self.dummy_xml = "Inclusion Criteria: Patients over 18. Exclusion Criteria: Patients with comorbidities." - # Create a temporary XML file to be read by extract_eligibility_criteria. - self.temp_xml_file = tempfile.NamedTemporaryFile( - mode="w+", delete=False, suffix=".xml" - ) - self.temp_xml_file.write(self.dummy_xml) - self.temp_xml_file.close() - - # Create a temporary output directory. - self.temp_output_dir = tempfile.mkdtemp() - - def tearDown(self): - os.unlink(self.temp_regex_file.name) - os.unlink(self.temp_exceptions_file.name) - os.unlink(self.temp_xml_file.name) - # Remove output files if created. - for f in os.listdir(self.temp_output_dir): - os.unlink(os.path.join(self.temp_output_dir, f)) - os.rmdir(self.temp_output_dir) - - def dummy_extract_eligibility_criteria(self, trial_id): - # Instead of reading a file from disk, simply return the text inside our dummy XML. - return "Inclusion Criteria: Patients over 18. Exclusion Criteria: Patients with comorbidities." - - @patch("src.Preprocessor.preprocessing_utils.extract_eligibility_criteria") - def test_eic_text_preprocessing(self, mock_extract): - # Patch extract_eligibility_criteria to return a dummy string. - mock_extract.side_effect = self.dummy_extract_eligibility_criteria - _ids = ["trial123"] - df = eic_text_preprocessing( - _ids, - regex_path=self.temp_regex_file.name, - exceptions_path=self.temp_exceptions_file.name, - output_path=self.temp_output_dir, - ) - self.assertIsNotNone(df) - # Check that the DataFrame contains expected columns. - for col in ["sentence", "criteria", "sub_criteria", "id"]: - self.assertIn(col, df.columns) - # Check that an output file was written to the output directory. - files = os.listdir(self.temp_output_dir) - self.assertTrue(any(f.endswith("_preprocessed.tsv") for f in files)) - - -if __name__ == "__main__": - unittest.main() diff --git a/utils/Preprocessor/utils.py b/utils/Preprocessor/utils.py deleted file mode 100644 index 18aec18f..00000000 --- a/utils/Preprocessor/utils.py +++ /dev/null @@ -1,599 +0,0 @@ -import requests -import xml.etree.ElementTree as ET -import os -import time -import re - - -def normalize_whitespace(s): - return " ".join(s.split()) - - -def download_study_info(nct_id, runs=2): - local_file_path = f"../data/trials_xmls/{nct_id}.xml" - updated_cts = [] - for _ in range(runs): - if os.path.exists(local_file_path): - # Read the content of the existing local XML file - with open(local_file_path, "r") as f: - local_xml_content = f.read() - try: - local_root = ET.fromstring(local_xml_content) - except ET.ParseError as e: - print(f"Error parsing XML for trial {nct_id}: {e}") - os.remove(local_file_path) - continue - - # Download the online version of the XML - url = f"https://clinicaltrials.gov/ct2/show/{nct_id}?displayxml=true" - response = requests.get(url) - - if response.status_code == 200: - online_xml_content = response.text - # Parse the XML content - online_root = ET.fromstring(online_xml_content) - to_check = ["eligibility", "brief_title", "overall_status", "location"] - - local_version = [] - online_version = [] - - for s in to_check: - local_elem = local_root.find(".//%s" % s) - online_elem = online_root.find(".//%s" % s) - - # Check if the element exists in both versions - if local_elem is not None and online_elem is not None: - local_version.append(local_elem) - online_version.append(online_elem) - else: - continue - - is_updated = any( - [ - normalize_whitespace(ET.tostring(a, encoding="unicode").strip()) - != normalize_whitespace( - ET.tostring(b, encoding="unicode").strip() - ) - for a, b in zip(local_version, online_version) - ] - ) - - if is_updated: - updated_cts.append(nct_id) - # Update the local XML with the online version - with open(local_file_path, "w") as f: - f.write(ET.tostring(online_root, encoding="unicode")) - print(f"Updated eligibility criteria for {nct_id}") - else: - print(f"No changes in eligibility criteria for {nct_id}.") - else: - print(f"Error downloading study information for {nct_id}") - else: - downloaded = False - while not downloaded: - url = f"https://clinicaltrials.gov/ct2/show/{nct_id}?displayxml=true" - response = requests.get(url) - if response.status_code == 200: - root = ET.fromstring(response.text) - with open(local_file_path, "w") as f: - f.write(ET.tostring(root, encoding="unicode")) - downloaded = True - print(f"Study information downloaded for {nct_id}") - else: - print(f"Error downloading study information for {nct_id}") - - if not downloaded: - print(f"Download of {nct_id}.xml failed. Retrying in 2 seconds...") - time.sleep(2) - return updated_cts - - -def extract_study_info(nct_id): - """ - Extract various study information from a clinical trial text with the given NCT identifier. - - This function attempts to extract various study information for a clinical trial specified by its unique - NCT identifier (NCT ID). The function checks if a file named '{nct_id}_info.txt' already exists - in the 'trials_texts' directory. If the file exists, the function returns 0, indicating that the - extraction is not required, and the information is already available locally. - - If the file '{nct_id}_info.txt' does not exist, the function parses the XML file with the name '{nct_id}.xml' - located in the 'trials_texts' directory. The XML content is parsed using the `xml.etree.ElementTree` - module. The function then extracts various study information from the XML content and saves it in a text file - with the name '{nct_id}_info.txt' in the 'trials_texts' directory. - - The extracted study information includes: - - Long title - - Short title - - Cancer sites - - Start date - - End date - - Primary end date - - Overall status - - Study phase - - Study type - - Brief summary - - Detailed description - - Number of arms - - Arms information - - Eligibility criteria - - Gender - - Minimum age - - Maximum age - - Intervention details - - Location details - - Parameters: - nct_id (str): The unique identifier (NCT ID) of the clinical trial for which study information - needs to be extracted. - - Returns: - int: Returns 0 if the study information file already exists locally and doesn't require extraction. - Otherwise, the function doesn't return anything directly (implicit return). - Note: The extracted study information is saved in the 'trials_texts' directory. - - """ - if os.path.exists(f"../data/trials_xmls/{nct_id}_info.txt"): - return 0 - # print(f"{nct_id}_info.txt already exists. Skipping extraction.") - else: - tree = ET.parse(f"../data/trials_xmls/{nct_id}.xml") - root = tree.getroot() - with open(f"../data/trials_xmls/{nct_id}_info.txt", "w") as f: - # Extract Long title - official_title = root.find(".//official_title") - if official_title is not None: - title_text = official_title.text.strip() - f.write(f"Long Title:\n{title_text}\n\n") - - # Extract short title - brief_title = root.find(".//brief_title") - if brief_title is not None: - title_text = brief_title.text.strip() - f.write(f"Short Title:\n{title_text}\n\n") - - # Extract cancer sites - conditions = root.findall(".//condition") - if conditions is not None: - f.write("Cancer Site(s):\n") - for condition in conditions: - condition_text = condition.text.strip() - f.write(f"- {condition_text}\n") - f.write("\n") - - # Extract start date - start_date = root.find(".//start_date") - if start_date is not None: - start_date_text = start_date.text.strip() - f.write(f"Start Date:\n{start_date_text}\n\n") - - # Extract end date - end_date = root.find(".//completion_date") - if end_date is not None: - end_date_text = end_date.text.strip() - f.write(f"End Date:\n{end_date_text}\n\n") - - # Extract primary end date - primary_end_date = root.find(".//primary_completion_date") - if primary_end_date is not None: - primary_end_date_text = primary_end_date.text.strip() - f.write(f"Primary End Date:\n{primary_end_date_text}\n\n") - - # Extract overall status - overall_status = root.find(".//overall_status") - if overall_status is not None: - overall_status_text = overall_status.text.strip() - f.write(f"Overall Status:\n{overall_status_text}\n\n") - - # Extract study phase - study_phase = root.find(".//phase") - if study_phase is not None: - f.write(f"Study Phase: \n{study_phase.text.strip()}\n\n") - - # Extract study type - study_type = root.find(".//study_type") - if study_type is not None: - study_type_text = study_type.text.strip() - f.write(f"Study Type:\n{study_type_text}\n\n") - - # Extract brief summary - brief_summary = root.find(".//brief_summary") - if brief_summary is not None: - brief_summary_text = brief_summary.find(".//textblock").text.strip() - f.write(f"Brief Summary:\n{brief_summary_text}\n\n") - - # Extract detailed description - detailed_description = root.find(".//detailed_description") - if detailed_description is not None: - detailed_description_text = detailed_description.find( - ".//textblock" - ).text.strip() - f.write(f"Detailed Description:\n{detailed_description_text}\n\n") - - # Extract number of arms - number_of_arms = root.find(".//number_of_arms") - if number_of_arms is not None: - f.write(f"Number of Arms: {number_of_arms.text.strip()}\n\n") - - arms = root.findall(".//arm_group") - if arms is not None: - f.write("Arms:\n") - for arm in arms: - arm_group_label = arm.find(".//arm_group_label").text.strip() - arm_group_description = arm.find(".//arm_group_description") - if arm_group_description is not None: - arm_group_description_text = arm_group_description.text.strip() - f.write(f"- {arm_group_label}: {arm_group_description_text}\n") - else: - f.write(f"- {arm_group_label}\n") - f.write("\n") - - # Extract eligibility criteria - eligibility_criteria = root.find(".//eligibility/criteria") - if eligibility_criteria is not None: - eligibility_criteria_text = eligibility_criteria.find( - ".//textblock" - ).text.strip() - f.write(f"Eligibility Criteria:\n{eligibility_criteria_text}\n\n") - - # Extract gender - gender = root.find(".//gender") - if gender is not None: - gender_text = gender.text.strip() - f.write(f"Gender:\n{gender_text}\n\n") - - # Extract minimum age - min_age = root.find(".//eligibility/minimum_age") - if min_age is not None: - min_age_text = min_age.text.strip() - f.write(f"Minimum Age:\n{min_age_text}\n\n") - - # Extract maximum age - max_age = root.find(".//eligibility/maximum_age") - if max_age is not None: - max_age_text = max_age.text.strip() - f.write(f"Maximum Age:\n{max_age_text}\n\n") - - # Extract intervention - intervention = root.findall(".//intervention") - if intervention is not None: - f.write("Interventions:\n") - for i in intervention: - intervention_name = i.find(".//intervention_name").text.strip() - f.write(f"- {intervention_name}\n") - f.write("\n") - - # Extract locations - locations = root.findall(".//location") - if locations is not None: - f.write("Locations:\n") - for location in locations: - city = location.find(".//city") - country = location.find(".//country") - if city is not None and country is not None: - location_text = f"{city.text.strip()}, {country.text.strip()}" - f.write(f"- {location_text}\n") - f.write("\n") - - print(f"{nct_id} info extracted and saved to {nct_id}_info.txt") - - -def add_spaces_around_punctuation(text): - """ - Add spaces around punctuation - - Parameters - ---------- - text : str - The text to be preprocessed - - Returns - ------- - str - The preprocessed text - """ - text = re.sub(r"([.,!?()])", r" \1 ", text) - return text - - -def remove_special_characters(text): - """ - Remove special characters - - Parameters - ---------- - text : str - The text to be preprocessed - - Returns - ------- - str - The preprocessed text - """ - text = re.sub(r"[^a-zA-Z0-9]", " ", text) - return text - - -def remove_dashes_at_the_start_of_sentences(text): - """ - Remove dashes at the start of sentences - - Parameters - ---------- - text : str - The text to be preprocessed - - Returns - ------- - str - The preprocessed text - """ - text = re.sub(r"^- ", "", text) - return text - - -def post_process_entities(entities): - """ - Merge consecutive entities and post-process the results. - - This function takes a list of entities generated from a named entity recognition (NER) model's output - and performs post-processing to merge consecutive entities of the same type. The input entities list - contains dictionaries representing each detected entity with the following keys: - - "entity" (str): The entity type represented as a prefixed tag (e.g., "B-ORG", "I-LOC"). - - "score" (float): The confidence score assigned to the entity by the NER model. - - "word" (str): The text of the entity in the input text. - - "start" (int): The starting index of the entity in the input text. - - "end" (int): The ending index (exclusive) of the entity in the input text. - - The function iterates through the entities and merges consecutive entities with the same type into a single - entity. It also handles entities that span multiple words, indicated by the presence of "I-" prefixes. - The merged entity is represented by a dictionary containing the merged information: - - "entity" (str): The entity type without the prefix (e.g., "ORG", "LOC"). - - "score" (float): The maximum confidence score among the merged entities. - - "word" (str): The combined text of the merged entities. - - "start" (int): The starting index of the first entity in the merged sequence. - - "end" (int): The ending index (exclusive) of the last entity in the merged sequence. - - Parameters: - entities (list): A list of dictionaries representing detected entities. - - Returns: - list: A list of dictionaries representing merged entities after post-processing. - Each dictionary contains the keys "entity", "score", "word", "start", and "end" - representing the entity type, confidence score, text, start index, and end index respectively. - """ - merged_entities = [] - current_entity = None - - for entity in entities: - if entity["entity"].startswith("B-"): - if current_entity is not None: - merged_entities.append(current_entity) - current_entity = { - "entity": entity["entity"][2:], - "score": entity["score"], - "word": entity["word"].replace("##", " "), - "start": entity["start"], - "end": entity["end"], - } - elif entity["entity"].startswith("I-"): - if (current_entity is not None) and entity["word"].startswith("##"): - current_entity["word"] += entity["word"].replace("##", "") - current_entity["end"] = entity["end"] - current_entity["score"] = max(current_entity["score"], entity["score"]) - else: - current_entity["word"] += " " + entity["word"].lstrip() - current_entity["end"] = entity["end"] - current_entity["score"] = max(current_entity["score"], entity["score"]) - else: - if current_entity is not None: - merged_entities.append(current_entity) - current_entity = None - - if current_entity is not None: - merged_entities.append(current_entity) - - return merged_entities - - -def get_dictionaries_with_values(list_of_dicts, key, values): - """ - Filter a list of dictionaries based on the presence of specific values in a specified key. - - This function takes a list of dictionaries and filters them based on the presence of specific values in a specified key. - The function checks each dictionary in the input list and includes only those dictionaries where any of the given values - are present in the specified key. The filtering is performed using list comprehensions. - - Parameters: - list_of_dicts (list): A list of dictionaries to be filtered. - key (str): The key in the dictionaries where the filtering is applied. - values (list): A list of values. The function will filter dictionaries where any of these values are present in the specified key. - - Returns: - list: A list of dictionaries that meet the filtering criteria. - - Example: - list_of_dicts = [ - {"name": "Alice", "age": 30}, - {"name": "Bob", "age": 25}, - {"name": "Charlie", "age": 35}, - {"name": "David", "age": 30}, - ] - - get_dictionaries_with_values(list_of_dicts, "age", [30, 35]) - # Output: [ - # {"name": "Alice", "age": 30}, - # {"name": "Charlie", "age": 35}, - # {"name": "David", "age": 30} - # ] - """ - return [d for d in list_of_dicts if any(val in d.get(key, []) for val in values)] - - -def resolve_ner_overlaps(ner1_results, ner2_results): - """ - Resolve overlaps between entities detected by two named entity recognition (NER) models. - - This function takes the results of two NER models (ner1_results and ner2_results) and resolves overlaps - between the entities detected by these models. An overlap occurs when the span of an entity detected by one - model partially or fully overlaps with the span of an entity detected by the other model. - - The function iterates through the entities detected by the first NER model (ner1_results). For each entity, - it checks if it overlaps with any entity from the second model (ner2_results). If there are no overlaps, - the entity from the first model is added to the resolved results. - - After processing the entities from the first model, the function then adds entities from the second model - that do not overlap with any entities from the first model. - - Parameters: - ner1_results (list): A list of dictionaries representing entities detected by the first NER model. - ner2_results (list): A list of dictionaries representing entities detected by the second NER model. - - Returns: - list: A list of dictionaries representing the resolved entities with overlaps removed. - - Example: - ner1_results = [ - {"start": 5, "end": 10, "entity_group": "PERSON"}, - {"start": 20, "end": 25, "entity_group": "LOCATION"} - ] - - ner2_results = [ - {"start": 8, "end": 15, "entity_group": "PERSON"}, - {"start": 18, "end": 30, "entity_group": "ORGANIZATION"} - ] - - resolve_ner_overlaps(ner1_results, ner2_results) - # Output: [ - # {"start": 5, "end": 10, "entity_group": "PERSON"}, - # {"start": 18, "end": 30, "entity_group": "ORGANIZATION"}, - # {"start": 20, "end": 25, "entity_group": "LOCATION"} - # ] - """ - resolved_results = [] - # Iterate over the entities detected by the first NER model - for entity1 in ner1_results: - entity1_start = entity1["start"] - entity1_end = entity1["end"] - - # Check if the entity from the first model overlaps with any entity from the second model - overlaps = False - for entity2 in ner2_results: - entity2_start = entity2["start"] - entity2_end = entity2["end"] - - if entity1_start < entity2_end and entity1_end > entity2_start: - overlaps = True - break - - # If there were no overlaps, add the entity from the first model to the resolved results - if not overlaps: - resolved_results.append(entity1) - - # Add entities from the second model that don't overlap with any entities from the first model - for entity2 in ner2_results: - entity2_start = entity2["start"] - entity2_end = entity2["end"] - - overlaps = False - for entity1 in resolved_results: - entity1_start = entity1["start"] - entity1_end = entity1["end"] - - if entity2_start < entity1_end and entity2_end > entity1_start: - overlaps = True - break - - if not overlaps: - resolved_results.append(entity2) - - return resolved_results - - -def extract_eligibility_criteria(trial_id): - """ - Extract the eligibility criteria text for a clinical trial with the given trial ID. - - This function attempts to locate and extract the eligibility criteria text for a clinical trial - specified by its trial ID. The function reads an XML file named '{trial_id}.xml' which is expected - to contain information for the clinical trial. It searches for the eligibility criteria textblock within - the XML and extracts the corresponding text. - - Parameters: - trial_id (str): The unique identifier of the clinical trial. - - Returns: - str or None: The extracted eligibility criteria text for the specified trial if found, - otherwise None. - """ - xml_file_path = f"../data/trials_xmls/{trial_id}.xml" - - if os.path.exists(xml_file_path): - with open(xml_file_path, "r") as xml_file: - xml_content = xml_file.read() - try: - tree = ET.ElementTree(ET.fromstring(xml_content)) - root = tree.getroot() - except ET.ParseError as e: - print(f"Error parsing XML for trial {trial_id}: {e}") - return None - # Find the Eligibility Criteria TextBlock section within the XML - eligibility_criteria_textblock = root.find(".//eligibility/criteria/textblock") - - if eligibility_criteria_textblock is not None: - # Extract the text from the Eligibility Criteria TextBlock section - eligibility_criteria_text = eligibility_criteria_textblock.text - return eligibility_criteria_text.strip() - - # If the trial ID is not found or the eligibility criteria textblock is missing, return None - return None - - -def replace_parentheses_with_braces(text): - """ - Replace parentheses with curly braces in the given text. - - This function takes a text as input and replaces all occurrences of opening parentheses '(' - with an opening curly brace '{', and closing parentheses ')' with a closing curly brace '}'. - The function maintains a stack to ensure proper matching of parentheses. If a closing parenthesis - is encountered without a corresponding opening parenthesis in the stack, it is left unchanged. - - Parameters: - text (str): The input text containing parentheses that need to be replaced. - - Returns: - str: The modified text with parentheses replaced by curly braces. - """ - stack = [] - result = "" - for char in text: - if char == "(" or char == "[": - stack.append(char) - result += "{" - elif char == ")" or char == "]": - if stack: - stack.pop() - result += "}" - else: - result += char - else: - result += char - return result - - -def line_starts_with_capitalized_alphanumeric(line): - """ - Check if the given line starts with a capitalized alphanumeric word. - - Parameters: - line (str): The input string representing a line. - - Returns: - bool: True if the line starts with a capitalized alphanumeric word, False otherwise. - """ - words = line.split() - if len(words) > 0: - first_word = words[0] - if first_word[0].isalpha() and first_word[0].isupper(): - return True - return False diff --git a/utils/finetuning/finetune_instruct/__init__.py b/utils/finetuning/finetune_instruct/__init__.py deleted file mode 100644 index d3f5a12f..00000000 --- a/utils/finetuning/finetune_instruct/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/utils/finetuning/finetune_instruct/arguments.py b/utils/finetuning/finetune_instruct/arguments.py deleted file mode 100644 index 6fdc440e..00000000 --- a/utils/finetuning/finetune_instruct/arguments.py +++ /dev/null @@ -1,184 +0,0 @@ -import os -from dataclasses import dataclass, field -from typing import Optional, List - -from transformers import TrainingArguments - -# Set desired GPU indices if needed -os.environ["CUDA_VISIBLE_DEVICES"] = "0,2,3,4,5,6,7" - - -def default_list() -> List[str]: - return ["q_proj", "v_proj", "o_proj", "down_proj", "up_proj", "gate_proj"] - - -@dataclass -class ModelArguments: - """ - Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. - """ - - model_name_or_path: str = field( - metadata={ - "help": "Path to pretrained model or model identifier from huggingface.co/models" - } - ) - peft_model_path: str = field(default="") - config_name: Optional[str] = field( - default=None, - metadata={ - "help": "Pretrained config name or path if not the same as model_name" - }, - ) - tokenizer_name: Optional[str] = field( - default=None, - metadata={ - "help": "Pretrained tokenizer name or path if not the same as model_name" - }, - ) - use_lora: bool = field( - default=True, - metadata={ - "help": "If passed, will use LORA (low-rank parameter-efficient training) to train the model." - }, - ) - lora_rank: int = field( - default=64, metadata={"help": "The rank dimension for LoRA."} - ) - lora_alpha: float = field( - default=16, metadata={"help": "The scaling factor (alpha) for LoRA."} - ) - lora_dropout: float = field( - default=0.05, metadata={"help": "The dropout rate for LoRA layers."} - ) - target_modules: List[str] = field( - default_factory=default_list, - metadata={"help": "List of modules to apply LoRA to."}, - ) - save_merged_lora_model: bool = field( - default=False, - metadata={ - "help": "If True, merges the LoRA parameters into the base model before saving." - }, - ) - use_flash_attn: bool = field( - default=True, - metadata={ - "help": "If True, use flash attention during training (if supported)." - }, - ) - use_slow_tokenizer: bool = field( - default=False, - metadata={ - "help": "If True, use a slow (Python-based) tokenizer instead of a fast (C++/Rust) one." - }, - ) - low_cpu_mem_usage: bool = field( - default=False, - metadata={ - "help": "If True, create the model as an empty shell and then load weights, reducing RAM usage." - }, - ) - cache_dir: str = field( - default="tmp", - metadata={ - "help": "Path to the directory where models and tokenizers are cached." - }, - ) - token: str = field( - default=None, metadata={"help": "HuggingFace hub token for private models."} - ) - from_peft: str = field( - default=None, - metadata={"help": "Path to a PEFT checkpoint from which to load a model."}, - ) - lora_extra_parameters: str = field( - default=None, metadata={"help": "Additional modules to save when using LoRA."} - ) - - -@dataclass -class DataArguments: - train_data: str = field( - default="toy_finetune_data.jsonl", - metadata={"help": "Path to the training data file (in JSONL format)."}, - ) - - query_max_len: int = field( - default=32, - metadata={ - "help": "Max length of the input sequence for the instruction/input portion." - }, - ) - passage_max_len: int = field( - default=128, - metadata={ - "help": "Max length of the entire sequence (instruction + input + output)." - }, - ) - - max_example_num_per_dataset: int = field( - default=10, - metadata={"help": "Maximum number of examples to load from the dataset."}, - ) - - cache_path: str = field( - default="./data_dir", - metadata={"help": "Directory for caching processed datasets."}, - ) - - load_from_disk: bool = field( - default=False, - metadata={ - "help": "If True, load a previously saved dataset from disk instead of processing from scratch." - }, - ) - - load_disk_path: str = field( - default=None, - metadata={ - "help": "Path to the saved dataset on disk if load_from_disk is True." - }, - ) - - save_to_disk: bool = field( - default=False, metadata={"help": "If True, save the processed dataset to disk."} - ) - - save_disk_path: str = field( - default=None, - metadata={ - "help": "Path to save the processed dataset if save_to_disk is True." - }, - ) - - num_shards: int = field( - default=0, - metadata={"help": "Number of shards to split the dataset into when saving."}, - ) - - save_max_shard_size: str = field( - default="50GB", - metadata={"help": "Maximum size of each shard when saving the dataset."}, - ) - - exit_after_save: bool = field( - default=False, - metadata={"help": "If True, exit the program after saving the dataset."}, - ) - - def __post_init__(self): - if not os.path.exists(self.train_data): - raise FileNotFoundError( - f"Cannot find file: {self.train_data}. Please provide a valid path." - ) - - -@dataclass -class SFTTrainingArguments(TrainingArguments): - """ - Training arguments specifically for supervised fine-tuning a causal language model. - """ - - # Additional arguments can be added if needed. - pass diff --git a/utils/finetuning/finetune_instruct/data.py b/utils/finetuning/finetune_instruct/data.py deleted file mode 100644 index fa2e6039..00000000 --- a/utils/finetuning/finetune_instruct/data.py +++ /dev/null @@ -1,233 +0,0 @@ -import random -from dataclasses import dataclass -from typing import Any, Dict, List, Optional - -import torch -import datasets -from torch.utils.data import Dataset -from transformers import ( - DataCollatorForSeq2Seq, - PreTrainedTokenizer, - PreTrainedTokenizerBase, -) -from jinja2 import Template - -# Replace this import with your own arguments class or define it below -from arguments import DataArguments - -instruction_template = r""" -{%- set ns = namespace(found=false) -%} -{%- for message in messages -%} - {%- if message['role'] == 'system' -%} - {%- set ns.found = true -%} - {%- endif -%} -{%- endfor -%} - -{% for message in messages %} -{% if loop.first and message['role'] == 'system' %} - {% set role = 'user' %} -{% elif message['role'] == 'assistant' %} - {% set role = 'model' %} -{% else %} - {% set role = message['role'] %} -{% endif %} -{{ role }} -{{ message['content'].rstrip() }} -{% endfor %} -""" - - -def apply_chat_template(messages, add_generation_prompt=True): - """ - Renders a list of messages (dicts with 'role' and 'content') into - a single text prompt for causal language models, using Jinja. - """ - t = Template(instruction_template) - rendered = t.render(messages=messages) - if add_generation_prompt: - # Optionally append a small trigger or instruction for the model to continue - rendered += "\nuser\n" - return rendered - - -class TrainDataset(Dataset): - """ - A PyTorch Dataset for instruction-tuning on a causal language model. - """ - - def __init__(self, args: DataArguments, tokenizer: PreTrainedTokenizer): - """ - :param args: Custom DataArguments containing dataset paths, cache paths, etc. - :param tokenizer: A pretrained tokenizer (GPT-2, GPT-NeoX, or similar). - """ - # Load the dataset - self.dataset = datasets.load_dataset( - "json", data_files=args.train_data, split="train", cache_dir=args.cache_path - ) - - # Shuffle the dataset using random - self.dataset = self.dataset.shuffle(seed=42) - - # Randomly sample specified number of examples if needed - if len(self.dataset) > args.max_example_num_per_dataset: - sampled_indices = random.sample( - range(len(self.dataset)), args.max_example_num_per_dataset - ) - self.dataset = self.dataset.select(sampled_indices) - - # Basic setup - self.tokenizer = tokenizer - self.args = args - self.total_len = len(self.dataset) - - # Set the tokenizer's pad token - self.tokenizer.pad_token = self.tokenizer.eos_token - - # Generate the prompt format - messages = [ - {"role": "system", "content": "{instruction}"}, - { - "role": "user", - "content": ("{input}\n"), - }, - ] - self.prompt_format = self.tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - - print("Prompt format:", self.prompt_format) - - # Set max_length for prompt+output - self.max_length = self.args.query_max_len + self.args.passage_max_len - - def __len__(self): - return self.total_len - - def __getitem__(self, index: int) -> Dict[str, List[int]]: - """ - For each example: - 1. Read instruction, input, and output from the dataset. - 2. Build the prompt using the stored template. - 3. Concatenate prompt + output_text as the full input. - 4. Tokenize everything. - 5. Mask out the prompt portion in labels with -100. - """ - example = self.dataset[index] - instruction = example["instruction"] - input_text = example.get("input", "") - output_text = example["output"] - - # Build the prompt from our custom format - prompt = self.prompt_format.format( - instruction=instruction.strip(), input=input_text.strip() - ) - - # Concatenate prompt + output in one sequence for causal LM - full_input = prompt + output_text.strip() - - # Tokenize the entire sequence - tokenized = self.tokenizer( - full_input, - max_length=self.max_length, - truncation=True, - return_tensors=None, # return raw python lists - add_special_tokens=True, - ) - input_ids = tokenized["input_ids"] - attention_mask = tokenized["attention_mask"] - - # Tokenize just the prompt to find boundary - prompt_tokenized = self.tokenizer( - prompt, - max_length=self.max_length, - truncation=True, - return_tensors=None, - add_special_tokens=True, - ) - prompt_len = len(prompt_tokenized["input_ids"]) - - # Create labels array and mask out prompt tokens with -100 - labels = [-100] * len(input_ids) - if prompt_len < len(input_ids): - labels[prompt_len:] = input_ids[prompt_len:] - - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "labels": labels, - } - - -@dataclass -class DataCollatorForFinetuning(DataCollatorForSeq2Seq): - """ - Collator that pads input_ids, attention_mask, and labels for a - causal instruction-tuning scenario. - - In many seq2seq settings (T5/BART), you would rely on DataCollatorForSeq2Seq - to handle encoder/decoder inputs. However, here we can still inherit from it - for convenience if we only need padding on a single sequence + labels. - - If using a GPT-style model, consider using DataCollatorForLanguageModeling - from Hugging Face Transformers. But this approach will still work - if you only need to handle padding. - """ - - query_max_len: int = 32 - passage_max_len: int = 128 - label_pad_token_id: int = -100 - tokenizer: PreTrainedTokenizerBase = None - padding: bool = True - pad_to_multiple_of: Optional[int] = None - return_tensors: str = "pt" - - def __post_init__(self): - if self.tokenizer is None: - raise ValueError("Tokenizer must be provided to the DataCollator.") - - def __call__( - self, features: List[Dict[str, Any]], return_tensors: Optional[str] = None - ) -> Dict[str, Any]: - """ - Pads inputs and labels separately, then returns a batch of tensors. - """ - # 1. We use return_tensors from self if not explicitly provided - return_tensors = return_tensors or self.return_tensors - - # 2. Extract and remove labels from features - labels = [f.pop("labels") for f in features] - - # 3. Pad labels (treated as input_ids) to the longest label length - label_features = [{"input_ids": label} for label in labels] - padded_labels = self.tokenizer.pad( - label_features, - padding="longest", - max_length=None, # Let tokenizer do max length for labels - pad_to_multiple_of=self.pad_to_multiple_of, - return_tensors=return_tensors, - ) - - # Convert padded label IDs to a torch.Tensor of type long - labels_tensor = padded_labels["input_ids"] - if not isinstance(labels_tensor, torch.Tensor): - labels_tensor = torch.tensor(labels_tensor, dtype=torch.long) - - # Replace pad_token_id with -100 so they don't affect the loss - labels_tensor[labels_tensor == self.tokenizer.pad_token_id] = ( - self.label_pad_token_id - ) - - # 4. Pad the input features themselves (input_ids, attention_mask) - max_length = self.query_max_len + self.passage_max_len - padded_features = self.tokenizer.pad( - features, - padding=self.padding, - max_length=max_length, - pad_to_multiple_of=self.pad_to_multiple_of, - return_tensors=return_tensors, - ) - - # 5. Add the labels back in - padded_features["labels"] = labels_tensor - - return padded_features diff --git a/utils/finetuning/finetune_instruct/data_llama.py b/utils/finetuning/finetune_instruct/data_llama.py deleted file mode 100644 index c8ac64cc..00000000 --- a/utils/finetuning/finetune_instruct/data_llama.py +++ /dev/null @@ -1,172 +0,0 @@ -import sys -import os -import random -from typing import List, Any, Dict, Optional -from dataclasses import dataclass - -import datasets -from torch.utils.data import Dataset -from transformers import ( - PreTrainedTokenizer, - PreTrainedTokenizerBase, - DataCollatorForSeq2Seq, -) - - -class TrainDataset(Dataset): - def __init__(self, args, tokenizer: PreTrainedTokenizer): - # Load dataset (assuming args.train_data points to a directory or a JSON file) - if os.path.isdir(args.train_data): - train_datasets = [] - for file in os.listdir(args.train_data): - try: - temp_dataset = datasets.load_dataset( - "json", - data_files=os.path.join(args.train_data, file), - split="train", - cache_dir=args.cache_path, - ) - except Exception as e: - print(e, file) - sys.exit() - if len(temp_dataset) > args.max_example_num_per_dataset: - temp_dataset = temp_dataset.select( - random.sample( - range(len(temp_dataset)), args.max_example_num_per_dataset - ) - ) - train_datasets.append(temp_dataset) - - self.dataset = datasets.concatenate_datasets(train_datasets) - else: - self.dataset = datasets.load_dataset( - "json", - data_files=args.train_data, - split="train", - cache_dir=args.cache_path, - ) - - self.tokenizer = tokenizer - self.args = args - self.total_len = len(self.dataset) - - # Define prompt format - messages = [ - { - "role": "system", - "content": "You are an expert trained on healthcare and biomedical domain!{instruction}", - }, - {"role": "user", "content": "{input}\n"}, - ] - self.prompt_format = self.tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - - # Maximum length for the model input - self.max_length = self.args.query_max_len + self.args.passage_max_len - - def __len__(self): - return self.total_len - - def __getitem__(self, index): - example = self.dataset[index] - instruction = example["instruction"] - input_text = example.get("input", "") - output_text = example["output"] - - # Create the prompt - prompt = self.prompt_format.format( - instruction=instruction.strip(), input=input_text.strip() - ) - - # Tokenize the prompt and output together - full_input = prompt + output_text - tokenized = self.tokenizer( - full_input, - max_length=self.max_length, - truncation=True, - return_tensors=None, - add_special_tokens=True, - ) - - input_ids = tokenized["input_ids"] - attention_mask = tokenized["attention_mask"] - - # Determine where output starts - prompt_tokenized = self.tokenizer( - prompt, - max_length=self.max_length, - truncation=True, - return_tensors=None, - add_special_tokens=True, - ) - prompt_len = len(prompt_tokenized["input_ids"]) - - # Create labels - labels = [-100] * len(input_ids) - if prompt_len < len(input_ids): - labels[prompt_len:] = input_ids[prompt_len:] - - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "labels": labels, - } - - -@dataclass -class DataCollatorForFinetuning(DataCollatorForSeq2Seq): - """ - Collator that pads input_ids, attention_masks, and labels for LLaMA fine-tuning. - """ - - query_max_len: int = 32 - passage_max_len: int = 128 - label_pad_token_id: int = -100 - tokenizer: PreTrainedTokenizerBase = None - padding: bool = True - pad_to_multiple_of: Optional[int] = None - return_tensors: str = "pt" - - def __post_init__(self): - if self.tokenizer is None: - raise ValueError("Tokenizer must be provided to the DataCollator.") - if self.tokenizer.pad_token_id != 128009: - raise ValueError( - "The tokenizer pad_token_id must be set to 128009 for LLaMA." - ) - - def __call__( - self, features: List[Dict[str, Any]], return_tensors: Optional[str] = None - ) -> Dict[str, Any]: - return_tensors = return_tensors or self.return_tensors - - # Separate labels - labels = [feature.pop("labels") for feature in features] - - # Pad labels - label_features = [{"input_ids": label} for label in labels] - padded_labels = self.tokenizer.pad( - label_features, - padding="longest", - return_tensors=return_tensors, - pad_to_multiple_of=self.pad_to_multiple_of, - ) - labels_tensor = padded_labels["input_ids"] - labels_tensor[labels_tensor == self.tokenizer.pad_token_id] = ( - self.label_pad_token_id - ) - - # Pad input_ids and attention_mask - max_length = self.query_max_len + self.passage_max_len - padded_features = self.tokenizer.pad( - features, - padding=self.padding, - max_length=max_length, - pad_to_multiple_of=self.pad_to_multiple_of, - return_tensors=return_tensors, - ) - - # Add labels to the padded features - padded_features["labels"] = labels_tensor - return padded_features diff --git a/utils/finetuning/finetune_instruct/data_summary.py b/utils/finetuning/finetune_instruct/data_summary.py deleted file mode 100644 index c7f5cf7b..00000000 --- a/utils/finetuning/finetune_instruct/data_summary.py +++ /dev/null @@ -1,240 +0,0 @@ -import os -import os.path -import random -import sys -from dataclasses import dataclass - -import datasets -import numpy as np -import torch -from arguments import DataArguments -from torch.utils.data import Dataset -from transformers import DataCollatorForSeq2Seq, PreTrainedTokenizer - - -class TrainDataset(Dataset): - def __init__(self, args: DataArguments, tokenizer: PreTrainedTokenizer): - # Load dataset - if os.path.isdir(args.train_data): - train_datasets = [] - for file in os.listdir(args.train_data): - try: - temp_dataset = datasets.load_dataset( - "json", - data_files=os.path.join(args.train_data, file), - split="train", - cache_dir=args.cache_path, - ) - except Exception as e: - print(e) - print(file) - sys.exit() - if len(temp_dataset) > args.max_example_num_per_dataset: - temp_dataset = temp_dataset.select( - random.sample( - list(range(len(temp_dataset))), - args.max_example_num_per_dataset, - ) - ) - train_datasets.append(temp_dataset) - - self.dataset = datasets.concatenate_datasets(train_datasets) - else: - self.dataset = datasets.load_dataset( - "json", - data_files=args.train_data, - split="train", - cache_dir=args.cache_path, - ) - - self.tokenizer = tokenizer - self.args = args - self.total_len = len(self.dataset) - self.max_length = self.args.query_max_len + self.args.passage_max_len - - def __len__(self): - return self.total_len - - def apply_chat_template(self, chat_messages): - """Formats chat messages into a prompt for the model.""" - return ( - "\n".join( - f"{msg['role'].capitalize()}: {msg['content']}" for msg in chat_messages - ) - + "\nRespond with only the JSON object conforming to the schema." - ) - - def __getitem__(self, index): - example = self.dataset[index] - - # Extract fields from the dataset - inclusion_criteria = example.get("inclusion_criteria", "").strip() - exclusion_criteria = example.get("exclusion_criteria", "").strip() - classification = example.get("classification", "").strip() - reasoning = example.get("reasoning", "").strip() - - # Universal instruction as in the system chat role - system_message = ( - "You are a medical assistant tasked with evaluating a patient's eligibility for a clinical trial given the provided eligibility criteria and patient description." - "Your assessment must be detailed, accurate, and strictly based on the provided patient profile and eligibility criteria." - "\n\n" - "### Key Guidelines\n" - "1. **Critical Information**:\n" - " - Essential data includes age, sex, primary diagnosis (e.g., cancer type and stage), and treatment history.\n" - " - If any critical information is missing from the patient profile, classify the case as **Excluded**, explaining which data is missing and why it affects the decision.\n\n" - "2. **Classification Rules**:\n" - " - **Included**: The patient meets all inclusion criteria and violates no exclusion criteria.\n" - " - **Excluded**: The patient violates any exclusion criterion or fails to meet one or more inclusion criteria.\n" - " - **Undetermined**: When critical information is missing, preventing a definitive classification.\n\n" - "3. **Evaluation Process**:\n" - " - Compare each eligibility criterion with the patient's profile.\n" - " - Evaluate strictly based on the provided data—do not infer or assume missing details.\n" - " - Clearly outline your reasoning, step by step, ensuring it is logically structured and references specific criteria.\n\n" - "4. **Reasoning Template**:\n" - " - Always begin with a summary of the patient's key information relevant to the trial.\n" - " - Evaluate inclusion criteria one-by-one:\n" - " - Specify whether each criterion is met or not, with evidence from the profile.\n" - " - Evaluate exclusion criteria one-by-one:\n" - " - Specify whether each criterion is violated or not, with evidence from the profile.\n" - " - Conclude with your classification decision, summarizing how inclusion and exclusion criteria were addressed.\n\n" - "### Response Format\n" - "Your response must be a JSON object with the following structure:\n" - "{\n" - ' "classification": "string",\n' - ' "reasoning": "string"\n' - "}\n\n" - "### Example Reasoning Structure\n" - "Here is an example reasoning structure:\n\n" - "1. **Summary**:\n" - " The patient is a 65-year-old male with stage IV NSCLC who has completed two prior lines of chemotherapy.\n" - "2. **Inclusion Criteria Evaluation**:\n" - " - Criterion: Patient must have stage IV NSCLC. **Met**: Patient profile confirms stage IV NSCLC.\n" - " - Criterion: Must have measurable disease. **Not Evaluated**: Patient profile does not specify this information.\n" - "3. **Exclusion Criteria Evaluation**:\n" - " - Criterion: Prior treatment with immunotherapy. **Violated**: Patient profile confirms prior treatment with immunotherapy.\n" - "4. **Conclusion**:\n" - " Based on the criteria evaluation, the patient is classified as **Excluded** due to violation of the exclusion criterion regarding prior immunotherapy treatment.\n\n" - "### Additional Notes\n" - "- Be concise and precise in your reasoning.\n" - "- Avoid making assumptions about missing or unspecified information.\n" - "- If multiple factors lead to a classification, clearly state their contributions." - ) - - # Construct the user message with inclusion and exclusion criteria - user_message = ( - f"**Clinical Trial Eligibility Criteria:**\n\n" - f"**Inclusion Criteria:**\n{inclusion_criteria}\n\n" - f"**Exclusion Criteria:**\n{exclusion_criteria}\n\n" - "Evaluate the compatibility of the patient's profile with the eligibility criteria. Base your classification and reasoning only on the information provided." - ) - - # Build the chat-based prompt - chat = [ - {"role": "system", "content": system_message}, - {"role": "user", "content": user_message}, - ] - prompt = self.apply_chat_template(chat) - - # Construct the full input with the expected output appended - full_input = f'{prompt}\n\n{{\n "classification": "{classification}",\n "reasoning": "{reasoning}"\n}}' - - # Tokenize the full input - tokenized = self.tokenizer( - full_input, - max_length=self.max_length, - truncation=True, - return_tensors=None, - add_special_tokens=True, - ) - - input_ids = tokenized["input_ids"] - attention_mask = tokenized["attention_mask"] - - # Tokenize just the prompt to find where the completion starts - prompt_tokenized = self.tokenizer( - prompt, - max_length=self.max_length, - truncation=True, - return_tensors=None, - add_special_tokens=True, - ) - prompt_len = len(prompt_tokenized["input_ids"]) - - # Create labels, masking the prompt portion - labels = [-100] * len(input_ids) - if prompt_len < len(input_ids): - labels[prompt_len:] = input_ids[prompt_len:] - - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "labels": labels, - } - - -@dataclass -class DataCollatorForFinetuning(DataCollatorForSeq2Seq): - query_max_len: int = 32 # Maximum length for the prompt - passage_max_len: int = 128 # Maximum length for reasoning and classification - - def __call__(self, features, return_tensors="pt"): - """ - Custom data collator for fine-tuning a seq2seq model with chat-style prompts and structured output. - - Args: - features (list): A list of feature dictionaries containing `input_ids`, `attention_mask`, and `labels`. - return_tensors (str): The format for returned tensors (default is 'pt'). - """ - if return_tensors is None: - return_tensors = self.return_tensors - - # Extract labels and calculate the maximum label length - labels = [f["labels"] for f in features] - max_label_length = max(len(label) for label in labels) - - # Apply padding to labels - if self.pad_to_multiple_of is not None: - max_label_length = ( - (max_label_length + self.pad_to_multiple_of - 1) - // self.pad_to_multiple_of - * self.pad_to_multiple_of - ) - - # Determine padding side (left or right) - padding_side = self.tokenizer.padding_side - for feature in features: - remainder = [self.label_pad_token_id] * ( - max_label_length - len(feature["labels"]) - ) - if isinstance(feature["labels"], list): - feature["labels"] = ( - feature["labels"] + remainder - if padding_side == "right" - else remainder + feature["labels"] - ) - elif padding_side == "right": - feature["labels"] = np.concatenate( - [feature["labels"], remainder] - ).astype(np.int64) - else: - feature["labels"] = np.concatenate( - [remainder, feature["labels"]] - ).astype(np.int64) - - # Pad input_ids and attention_mask, ensuring proper alignment with labels - collated = self.tokenizer.pad( - features, - padding=self.padding, - max_length=self.query_max_len + self.passage_max_len, - return_tensors=return_tensors, - pad_to_multiple_of=self.pad_to_multiple_of, - ) - - # Apply masking to prevent training on prompt tokens - if "labels" in collated: - collated["labels"] = collated["labels"].clone().detach().to(torch.long) - collated["labels"][ - collated["labels"] == self.tokenizer.pad_token_id - ] = -100 # Mask padding tokens for labels - - return collated diff --git a/utils/finetuning/finetune_instruct/evaluate.py b/utils/finetuning/finetune_instruct/evaluate.py deleted file mode 100644 index 0adafc66..00000000 --- a/utils/finetuning/finetune_instruct/evaluate.py +++ /dev/null @@ -1,269 +0,0 @@ -import json -import os -import random -import re -import unicodedata -from multiprocessing import get_context - -import torch -from sklearn.metrics import accuracy_score, precision_recall_fscore_support -from tqdm import tqdm -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig - -# Set CUDA devices -os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5,6,7" - - -class EvaluationScript: - def __init__( - self, model_path, adapter_path=None, devices=None, torch_dtype=torch.float16 - ): - self.model_path = model_path - self.adapter_path = adapter_path - self.torch_dtype = torch_dtype - self.devices = devices or list(range(torch.cuda.device_count())) - - # Initialize tokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - model_path, trust_remote_code=True - ) - - # Initialize token IDs for responses - self._initialize_token_ids() - - def _initialize_token_ids(self): - responses = ["Match", "Mismatch", "Neutral"] - token_ids = [ - self.tokenizer(response, add_special_tokens=False)["input_ids"] - for response in responses - ] - self.match_token_id, self.mismatch_token_id, self.neutral_token_id = [ - ids[0] for ids in token_ids - ] - - def load_model(self, device): - print(f"Loading model on device cuda:{device}...") - quant_config = BitsAndBytesConfig(load_in_8bit=True) - model = AutoModelForCausalLM.from_pretrained( - self.model_path, - torch_dtype=self.torch_dtype, - quantization_config=quant_config, - device_map=f"cuda:{device}", - trust_remote_code=True, - ) - model.eval() - return model - - def preprocess_text(self, text): - match_a = re.search( - r"Statement A:\s*(.+?)(?=\nStatement B:|$)", text, re.DOTALL - ) - match_b = re.search(r"Statement B:\s*(.+)", text, re.DOTALL) - - statement_a = ( - unicodedata.normalize("NFKD", match_a.group(1).strip()) if match_a else "" - ) - statement_b = ( - unicodedata.normalize("NFKD", match_b.group(1).strip()) if match_b else "" - ) - - # remove unecessary punctuations - statement_a = re.sub(r"[^\w\s]", "", statement_a) - statement_b = re.sub(r"[^\w\s]", "", statement_b) - - combined_text = f"Statement A: {statement_a}\nStatement B: {statement_b}" - - return combined_text - - def create_messages(self, input_text): - messages = [ - { - "role": "system", - "content": ( - "You are a knowledgeable AI medical assistant. Your task is to evaluate the semantic consistency " - "and logical alignment between Statement A (patient description) and Statement B (eligibility criterion). " - "Apply the following rules strictly:\n" - "- Reply 'Match' if Statement A aligns with and satisfies Statement B.\n" - "- Reply 'Mismatch' if Statement A contradicts or is incompatible with Statement B.\n" - "- Reply 'Neutral' if Statement A and Statement B are unrelated, loosely related, or ambiguously connected " - "without clear alignment or contradiction.\n\n" - "Provide your response using only 'Match,' 'Mismatch,' or 'Neutral'." - ), - }, - # Few-shot example 1 - Match - { - "role": "user", - "content": ( - "Statement A: The patient has a confirmed diagnosis of Stage IV non-small cell lung cancer (NSCLC) " - "with an EGFR exon 19 deletion mutation.\n" - "Statement B: NSCLC cases associated with EGFR exon 19 mutations." - ), - }, - { - "role": "assistant", - "content": "Match", - }, - # Few-shot example 2 - Mismatch - { - "role": "user", - "content": ( - "Statement A: The patient has a history of allergic reactions to penicillin.\n" - "Statement B: No known severe hypersensitivity reactions to study drugs, especially to penicillin." - ), - }, - { - "role": "assistant", - "content": "Mismatch", - }, - # Few-shot example 3 - Neutral - { - "role": "user", - "content": ( - "Statement A: The patient is a 65-year-old male with mild osteoarthritis managed with physical therapy.\n" - "Statement B: Patients with respiratory disorders currently attending rehabilition and physical therapy." - ), - }, - { - "role": "assistant", - "content": "Neutral", - }, - # Input example to evaluate - { - "role": "user", - "content": input_text, - }, - ] - return messages - - def process_on_device(self, device, input_queue, output_queue): - model = self.load_model(device) - - while True: - input_data = input_queue.get() - if input_data is None: - break - - input_text = self.preprocess_text(input_data["input"]) - messages = self.create_messages(input_text) - prompt = self.tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - - inputs = self.tokenizer(prompt, return_tensors="pt").to(f"cuda:{device}") - - with torch.no_grad(): - outputs = model(**inputs, use_cache=False) - - last_token_logits = outputs.logits[0, -1, :] - probabilities = torch.softmax(last_token_logits, dim=-1) - top_prob, top_token_id = torch.topk(probabilities, 1) - prob = top_prob.item() - - if top_token_id == self.match_token_id: - answer = "Match" - elif top_token_id == self.mismatch_token_id: - answer = "Mismatch" - elif top_token_id == self.neutral_token_id: - answer = "Neutral" - else: - answer = "Unknown" - - result = { - "input": input_data["input"], - "ground_truth": input_data["output"], - "prediction": answer, - "llm_score": prob, - } - output_queue.put(result) - - def generate_predictions(self, dataset): - ctx = get_context("spawn") - input_queue = ctx.Queue() - output_queue = ctx.Queue() - - workers = [ - ctx.Process( - target=self.process_on_device, args=(device, input_queue, output_queue) - ) - for device in self.devices - ] - - for worker in workers: - worker.start() - - for entry in dataset: - input_queue.put(entry) - - for _ in self.devices: - input_queue.put(None) - - all_predictions = [] - for _ in tqdm(range(len(dataset)), desc="Processing inputs"): - all_predictions.append(output_queue.get()) - - for worker in workers: - worker.join() - - return all_predictions - - def evaluate(self, dataset, save_path="incorrect_predictions.jsonl"): - predictions = self.generate_predictions(dataset) - - incorrect_examples = [ - { - "input": entry["input"], - "ground_truth": entry["output"], - "prediction": pred["prediction"], - "llm_score": pred["llm_score"], - } - for entry, pred in zip(dataset, predictions) - if pred["prediction"] != entry["output"] - ] - - with open(save_path, "w", encoding="utf-8") as f: - for example in incorrect_examples: - json.dump(example, f) - f.write("\n") - - references = [entry["output"] for entry in dataset] - predictions_only = [pred["prediction"] for pred in predictions] - - accuracy = accuracy_score(references, predictions_only) - precision, recall, f1, _ = precision_recall_fscore_support( - references, predictions_only, average="weighted" - ) - - print(f"Incorrect examples saved to {save_path}") - - return { - "accuracy": accuracy, - "precision": precision, - "recall": recall, - "f1": f1, - } - - -if __name__ == "__main__": - model_path = "tiiuae/Falcon3-10B-Instruct" - adapter_path = None - devices = [0, 1] - - evaluator = EvaluationScript( - model_path=model_path, - adapter_path=adapter_path, - devices=devices, - torch_dtype=torch.float16, - ) - dataset_path = "finetuning_data/cleaned_filtered_test_data.jsonl" - with open(dataset_path, "r", encoding="utf-8") as f: - dataset = [json.loads(line) for line in f] - dataset = random.sample(dataset, 100) - # dataset = [ - # {"input": "Statement A: no history of liver diseases or kidney disease is present, and serum creatinine levels are maintained below 1.0 mg/dl.\nStatement B: participants must not have a history of liver diseases (such as hepatitis, biliary atresia, or cirrhosis) or kidney disease, defined by a serum creatinine level exceeding 1.0 mg/dl.", "output": "Match"} - - # ] - results = evaluator.evaluate(dataset) - - print("Evaluation Metrics:") - for metric, value in results.items(): - print(f"{metric.capitalize()}: {value:.4f}") diff --git a/utils/finetuning/finetune_instruct/evaluate_CoT.py b/utils/finetuning/finetune_instruct/evaluate_CoT.py deleted file mode 100644 index 7ce64d24..00000000 --- a/utils/finetuning/finetune_instruct/evaluate_CoT.py +++ /dev/null @@ -1,160 +0,0 @@ -import torch -import json -from bert_score import score -from transformers import ( - AutoModelForCausalLM, - AutoTokenizer, - BitsAndBytesConfig, - pipeline, -) -from peft import PeftModel -import random - - -def load_model(device, model_path, adapter_path): - """ - Loads the model on the specified device using 4-bit quantization. - """ - print(f"Loading model on device cuda:{device}...") - tokenizer = AutoTokenizer.from_pretrained(model_path) - tokenizer.pad_token = tokenizer.eos_token - quant_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=torch.float16, - ) - base_model = AutoModelForCausalLM.from_pretrained( - model_path, - torch_dtype=torch.float16, - device_map=f"cuda:{device}", - attn_implementation="flash_attention_2", - trust_remote_code=True, - quantization_config=quant_config, - ) - - pipe = pipeline( - "text-generation", - model=base_model, - tokenizer=tokenizer, - max_new_tokens=1024, - do_sample=False, - repetition_penalty=1.05, - ) - - # Load the fine-tuned adapter model - pipe.model = PeftModel.from_pretrained(base_model, adapter_path) - - return pipe, tokenizer - - -def generate_output(pipe, tokenizer, instruction, input_text): - """ - Generates model output using the instruction as system prompt and input as user prompt. - This version instructs the model to include its internal chain-of-thought exactly once, - followed by the final answer. - """ - # Instruct the model to reveal its chain-of-thought once. - system_msg = ( - instruction + "\nPlease include your internal chain-of-thought exactly once, " - "followed by the final answer. Do not repeat the chain-of-thought." - ) - messages = [ - {"role": "system", "content": system_msg}, - {"role": "user", "content": input_text}, - ] - prompt = tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - generated = pipe(prompt)[0]["generated_text"].strip() - - # Optional post-processing: If the chain-of-thought is repeated, keep only the first occurrence. - # This snippet assumes the model labels its reasoning with "Chain-of-thought:" and final answer with "Final Answer:". - if generated.count("Chain-of-thought:") > 1: - # Split on the label and reconstruct output using only the first instance. - parts = generated.split("Chain-of-thought:") - first_cot = ( - parts[1].split("Final Answer:")[0] - if "Final Answer:" in parts[1] - else parts[1] - ) - final_answer = "" - if "Final Answer:" in generated: - final_answer = "Final Answer:" + generated.split("Final Answer:")[-1] - # Rebuild the output with a single chain-of-thought section. - generated = parts[0] + "Chain-of-thought:" + first_cot + "\n" + final_answer - generated = generated.strip() - - return generated - - -def compute_bertscore( - model_outputs, - reference_outputs, - lang="en", - model_type="allenai/longformer-base-4096", -): - """ - Computes BERTScore for evaluating model-generated outputs against reference texts. - """ - assert len(model_outputs) == len(reference_outputs), ( - "Mismatch in number of model and reference outputs" - ) - - print("Computing BERTScore...") - precision, recall, f1 = score( - model_outputs, - reference_outputs, - lang=lang, - model_type=model_type, - device="cuda" if torch.cuda.is_available() else "cpu", - ) - - return { - "precision": precision.mean().item(), - "recall": recall.mean().item(), - "f1": f1.mean().item(), - } - - -def main(): - device = 0 - model_path = "microsoft/phi-4" - adapter_path = "finetuned_phi_reasoning/" - pipe, tokenizer = load_model(device, model_path, adapter_path) - - file_path = "finetuning_data/medical_o1_reasoning_test.jsonl" - model_outputs = [] - reference_outputs = [] - - with open(file_path, "r") as f: - lines = f.readlines() - - # Randomly select 15 test cases - selected_lines = random.sample(lines, 500) - - for idx, line in enumerate(selected_lines, 1): - print(f"Processing randomly selected line {idx}") - data = json.loads(line) - generated_text = generate_output( - pipe, tokenizer, data["instruction"], data["input"] - ) - print("Generated Output:\n", generated_text, "\n") - model_outputs.append(generated_text) - reference_outputs.append(data["output"]) - - results = compute_bertscore(model_outputs, reference_outputs) - - print("\nBERTScore Results:") - print(f"Precision: {results['precision']:.4f}") - print(f"Recall: {results['recall']:.4f}") - print(f"F1 Score: {results['f1']:.4f}") - - with open("bertscore_results.txt", "w") as f: - f.write(f"Precision: {results['precision']:.4f}\n") - f.write(f"Recall: {results['recall']:.4f}\n") - f.write(f"F1 Score: {results['f1']:.4f}\n") - - -if __name__ == "__main__": - main() diff --git a/utils/finetuning/finetune_instruct/evaluate_gemma2.py b/utils/finetuning/finetune_instruct/evaluate_gemma2.py deleted file mode 100644 index a38227b0..00000000 --- a/utils/finetuning/finetune_instruct/evaluate_gemma2.py +++ /dev/null @@ -1,242 +0,0 @@ -import json -import os -import re -import unicodedata -from multiprocessing import get_context - -import torch -from peft import PeftModel -from sklearn.metrics import accuracy_score, precision_recall_fscore_support -from tqdm import tqdm -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig - -# Set CUDA devices -os.environ["CUDA_VISIBLE_DEVICES"] = "1" - - -class EvaluationScript: - def __init__( - self, model_path, adapter_path=None, devices=None, torch_dtype=torch.float16 - ): - self.model_path = model_path - self.adapter_path = adapter_path - self.torch_dtype = torch_dtype - self.devices = devices or list(range(torch.cuda.device_count())) - - # Initialize tokenizer - self.tokenizer = AutoTokenizer.from_pretrained( - model_path, trust_remote_code=True - ) - - # Initialize token IDs for responses "Yes" and "No" - self._initialize_token_ids() - - def _initialize_token_ids(self): - responses = ["Yes", "No"] - token_ids = [ - self.tokenizer(response, add_special_tokens=False)["input_ids"] - for response in responses - ] - self.yes_token_id, self.no_token_id = [ids[0] for ids in token_ids] - - def load_model(self, device): - """ - Loads the model on the specified device using 4-bit quantization. - If an adapter path is provided, it loads the adapter using PEFT. - """ - print(f"Loading model on device cuda:{device}...") - quant_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=torch.float16, - ) - model = AutoModelForCausalLM.from_pretrained( - self.model_path, - torch_dtype=self.torch_dtype, - device_map=f"cuda:{device}", - attn_implementation="flash_attention_2", - quantization_config=quant_config, - trust_remote_code=True, - ) - if self.adapter_path is not None: - model = PeftModel.from_pretrained(model, self.adapter_path) - model.eval() - return model - - def preprocess_text(self, text): - # Extract Statement A and Statement B using regex. - match_a = re.search( - r"Statement A:\s*(.+?)(?=\nStatement B:|$)", text, re.DOTALL - ) - match_b = re.search(r"Statement B:\s*(.+)", text, re.DOTALL) - - statement_a = ( - unicodedata.normalize("NFKD", match_a.group(1).strip()) if match_a else "" - ) - statement_b = ( - unicodedata.normalize("NFKD", match_b.group(1).strip()) if match_b else "" - ) - - # Optionally, remove unnecessary punctuations - statement_a = re.sub(r"[^\w\s]", "", statement_a) - statement_b = re.sub(r"[^\w\s]", "", statement_b) - - combined_text = f"Statement A: {statement_a}\nStatement B: {statement_b}" - return combined_text - - def create_messages(self, input_text): - # Create a prompt with only the system instruction and the input example. - messages = [ - { - "role": "user", - "content": ( - "You are a clinical assistant tasked with determining whether the patient information (Statement A) " - "is related to the " - "trial eligibility criterion (Statement B). Respond with 'Yes' if Statement A contains sufficient " - "information to make this evaluation, or 'No' if it does not." - ), - }, - {"role": "assistant", "content": " "}, - { - "role": "user", - "content": input_text, - }, - ] - return messages - - def process_on_device(self, device, input_queue, output_queue): - model = self.load_model(device) - - while True: - input_data = input_queue.get() - if input_data is None: - break - - # Preprocess the input text (extract and clean Statement A and B) - input_text = self.preprocess_text(input_data["input"]) - messages = self.create_messages(input_text) - - # Use the model's chat template if available to format the prompt. - prompt = self.tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - inputs = self.tokenizer(prompt, return_tensors="pt").to(f"cuda:{device}") - - with torch.no_grad(): - outputs = model(**inputs, use_cache=False) - - # Get the logits of the last token and determine the top prediction. - last_token_logits = outputs.logits[0, -1, :] - probabilities = torch.softmax(last_token_logits, dim=-1) - top_prob, top_token_id = torch.topk(probabilities, 1) - prob = top_prob.item() - - if top_token_id == self.yes_token_id: - answer = "Yes" - elif top_token_id == self.no_token_id: - answer = "No" - else: - answer = "Unknown" - - result = { - "input": input_data["input"], - "ground_truth": input_data["output"], - "prediction": answer, - "llm_score": prob, - } - output_queue.put(result) - - def generate_predictions(self, dataset): - ctx = get_context("spawn") - input_queue = ctx.Queue() - output_queue = ctx.Queue() - - workers = [ - ctx.Process( - target=self.process_on_device, args=(device, input_queue, output_queue) - ) - for device in self.devices - ] - - for worker in workers: - worker.start() - - for entry in dataset: - input_queue.put(entry) - - # Signal workers to exit - for _ in self.devices: - input_queue.put(None) - - all_predictions = [] - for _ in tqdm(range(len(dataset)), desc="Processing inputs"): - all_predictions.append(output_queue.get()) - - for worker in workers: - worker.join() - - return all_predictions - - def evaluate(self, dataset, save_path="incorrect_predictions.jsonl"): - predictions = self.generate_predictions(dataset) - - incorrect_examples = [ - { - "input": entry["input"], - "ground_truth": entry["output"], - "prediction": pred["prediction"], - "llm_score": pred["llm_score"], - } - for entry, pred in zip(dataset, predictions) - if pred["prediction"] != entry["output"] - ] - with open(save_path, "w", encoding="utf-8") as f: - for example in incorrect_examples: - json.dump(example, f) - f.write("\n") - - references = [entry["output"] for entry in dataset] - predictions_only = [pred["prediction"] for pred in predictions] - - accuracy = accuracy_score(references, predictions_only) - precision, recall, f1, _ = precision_recall_fscore_support( - references, predictions_only, average="weighted", zero_division=0 - ) - - print(f"Incorrect examples saved to {save_path}") - - return { - "accuracy": accuracy, - "precision": precision, - "recall": recall, - "f1": f1, - } - - -if __name__ == "__main__": - # Use the model from the specified path. - model_path = "google/gemma-2-2b-it" - # Set adapter_path to a valid path if you want to load a PEFT adapter; otherwise, keep it as None. - adapter_path = "finetuned_gemma2/" - devices = [0] # Adjust the devices as needed. - - evaluator = EvaluationScript( - model_path=model_path, - adapter_path=adapter_path, - devices=devices, - torch_dtype=torch.float16, - ) - - # Path to the transformed test dataset. - dataset_path = "finetuning_data/transformed_test_data.jsonl" - with open(dataset_path, "r", encoding="utf-8") as f: - dataset = [json.loads(line) for line in f] - # Optionally, sample a subset for evaluation. - # dataset = random.sample(dataset, 100) - - results = evaluator.evaluate(dataset) - - print("Evaluation Metrics:") - for metric, value in results.items(): - print(f"{metric.capitalize()}: {value:.4f}") diff --git a/utils/finetuning/finetune_instruct/evaluate_llama.py b/utils/finetuning/finetune_instruct/evaluate_llama.py deleted file mode 100644 index 45a40bb5..00000000 --- a/utils/finetuning/finetune_instruct/evaluate_llama.py +++ /dev/null @@ -1,202 +0,0 @@ -import json - -import torch -import transformers -from sklearn.metrics import accuracy_score, precision_recall_fscore_support - - -class LlamaMatchMismatchEvaluator: - """ - Evaluates a Llama-based instruction model on a classification task: - ('Match', 'Mismatch', or 'Neutral'). - - Each data entry is expected to contain: - { - "instruction": , - "input": , - "output": - } - """ - - def __init__( - self, - model_id: str = "ContactDoctor/Bio-Medical-Llama-3-8B", - torch_dtype: torch.dtype = torch.bfloat16, - device_map: str = "auto", - ): - """ - Initialize the evaluator with the specified Llama-based model. - - :param model_id: Hugging Face model ID (must be Llama-based). - :param torch_dtype: Floating-point precision (bfloat16 recommended on GPUs like A100). - :param device_map: Device mapping setting for large models, e.g. "auto". - """ - self.model_id = model_id - self.torch_dtype = torch_dtype - self.device_map = device_map - - # Create a text-generation pipeline for Llama - self.pipe = transformers.pipeline( - task="text-generation", - model=self.model_id, - model_kwargs={"torch_dtype": self.torch_dtype}, - device_map=self.device_map, - trust_remote_code=True, - ) - - # Grab tokenizer from the pipeline (useful for custom logic) - self.tokenizer = self.pipe.tokenizer - - # Define a system prompt for the conversation: - # (Customizable to remind the model of its domain) - self.system_prompt = ( - "You are a clinical trials expert specializing in matching patients " - "to eligibility criteria. You must respond with exactly one of: " - "'Match', 'Mismatch', or 'Neutral'." - ) - - def _build_prompt(self, data_entry): - """ - Builds a single prompt (string) that includes both system and user instructions. - Uses apply_chat_template to emulate a conversation. - - :param data_entry: A dict with keys: "instruction", "input". - :return: A single text prompt ready for the pipeline. - """ - # Format role-based messages - messages = [ - {"role": "system", "content": self.system_prompt}, - { - "role": "user", - "content": f"{data_entry['instruction']}\n\n" - f"{data_entry['input']}\n\n" - "Respond with 'Match', 'Mismatch', or 'Neutral' only.", - }, - ] - - # Llama's apply_chat_template for multi-turn chat - prompt = self.tokenizer.apply_chat_template( - [messages], # pass a nested list of conversation turns - tokenize=False, - add_generation_prompt=True, - ) - return prompt - - def _extract_class_label(self, generated_text: str) -> str: - """ - Attempts to extract the predicted label from the model's output text. - Returns 'Match', 'Mismatch', or 'Neutral' if found; otherwise 'Unknown'. - - :param generated_text: The raw string output from the pipeline. - :return: One of "Match", "Mismatch", "Neutral", or "Unknown". - """ - # Simple case-insensitive substring check. You can refine parsing if needed. - for label in ["Match", "Mismatch", "Neutral"]: - if label.lower() in generated_text.lower(): - return label - return "Unknown" - - def generate_predictions( - self, - dataset: list, - max_new_tokens: int = 64, - temperature: float = 0.0, - top_p: float = 1.0, - ) -> list: - """ - Generates label predictions for each dataset entry using the pipeline. - - :param dataset: List of examples, each with "instruction", "input". - :param max_new_tokens: Maximum tokens to generate for each inference call. - :param temperature: Temperature for sampling. 0.0 = deterministic. - :param top_p: Nucleus sampling parameter (1.0 = no nucleus sampling). - :return: List of predicted labels (strings). - """ - predictions = [] - for data_entry in dataset: - # Build the prompt from system + user messages - prompt = self._build_prompt(data_entry) - - # Define custom end-of-sequence tokens if needed. - # Some Llama-based models use custom tokens or just rely on eos_token_id. - eos_token_ids = [ - self.tokenizer.eos_token_id, - # If your model uses <|end_of_turn|> or similar, convert to ID: - # self.tokenizer.convert_tokens_to_ids("<|end_of_turn|>") - ] - - # Run inference. We get a list of generated sequences; pick the first. - outputs = self.pipe( - prompt, - max_new_tokens=max_new_tokens, - eos_token_id=eos_token_ids, - do_sample=(temperature > 0.0), - temperature=temperature, - top_p=top_p, - ) - # The pipeline returns a list of dicts. We extract the full text from the first. - raw_generated = outputs[0]["generated_text"] - - # Slice out only the newly generated portion if desired. - # The prompt is the first part; everything after that is the response. - response_text = raw_generated[len(prompt) :] - - # Extract class label from the response - predicted_label = self._extract_class_label(response_text) - predictions.append(predicted_label) - - return predictions - - def evaluate(self, dataset: list) -> dict: - """ - Generates predictions and computes the classification metrics. - - :param dataset: List of examples, each with "output" as the ground truth label. - :return: Dictionary with accuracy, precision, recall, and f1 (weighted). - """ - # Ground truth labels - references = [entry["output"] for entry in dataset] - # Predictions - predictions = self.generate_predictions(dataset) - - # Compute metrics - accuracy = accuracy_score(references, predictions) - precision, recall, f1, _ = precision_recall_fscore_support( - references, - predictions, - average="weighted", - labels=["Match", "Mismatch", "Neutral"], - ) - - return { - "accuracy": accuracy, - "precision": precision, - "recall": recall, - "f1": f1, - } - - -if __name__ == "__main__": - # Example usage: - - # 1. Load a dataset from JSONL or any other source. - # Each line should have keys: 'instruction', 'input', 'output' - # where 'output' is one of "Match", "Mismatch", "Neutral". - dataset_path = "cleaned_filtered_test_data.jsonl" - with open(dataset_path, "r", encoding="utf-8") as f: - dataset = [json.loads(line) for line in f] - - # 2. Initialize the evaluator - evaluator = LlamaMatchMismatchEvaluator( - model_id="ContactDoctor/Bio-Medical-Llama-3-8B", - torch_dtype=torch.bfloat16, # or torch.float16 - device_map="auto", - ) - - # 3. Run evaluation - results = evaluator.evaluate(dataset) - - # 4. Print results - print("Evaluation Metrics:") - for metric, value in results.items(): - print(f"{metric.capitalize()}: {value:.4f}") diff --git a/utils/finetuning/finetune_instruct/finetune.sh b/utils/finetuning/finetune_instruct/finetune.sh deleted file mode 100644 index 4415ce8f..00000000 --- a/utils/finetuning/finetune_instruct/finetune.sh +++ /dev/null @@ -1,25 +0,0 @@ -nohup torchrun --nproc_per_node 1 ./run.py \ - --output_dir ./finetuned_phi_reasoning \ - --model_name_or_path microsoft/phi-4 \ - --train_data ./finetuning_data/medical_o1_reasoning_train.jsonl\ - --learning_rate 5e-5 \ - --num_train_epochs 2 \ - --per_device_train_batch_size 3 \ - --gradient_accumulation_steps 16 \ - --dataloader_drop_last True \ - --query_max_len 1024 \ - --passage_max_len 1024 \ - --logging_steps 10 \ - --save_steps 1000 \ - --save_total_limit 5 \ - --ddp_find_unused_parameters False \ - --warmup_ratio 0.1 \ - --use_lora True \ - --lora_rank 32 \ - --lora_alpha 64 \ - --lora_dropout 0.1 \ - --use_flash_attn True \ - --max_example_num_per_dataset 26000 \ - --cache_dir scratch/huggingface_cache/hub \ - --target_modules q_proj k_proj v_proj o_proj gate_proj up_proj down_proj \ - --bf16 > ./finetune_log.log 2>&1 & disown \ No newline at end of file diff --git a/utils/finetuning/finetune_instruct/load_model.py b/utils/finetuning/finetune_instruct/load_model.py deleted file mode 100644 index c3aeb3d8..00000000 --- a/utils/finetuning/finetune_instruct/load_model.py +++ /dev/null @@ -1,64 +0,0 @@ -import torch -from peft import LoraConfig, PeftModel, TaskType, get_peft_model -from transformers import AutoModelForCausalLM, BitsAndBytesConfig - -# from local_gemma import LocalGemma2ForCausalLM - - -def get_model(model_args, training_args): - # model = LocalGemma2ForCausalLM.from_pretrained(model_args.model_name_or_path, - # preset="auto", - # attn_implementation='eager', - # cache_dir=model_args.cache_dir, - # torch_dtype=torch.float16 if training_args.fp16 else torch.bfloat16, - # token=model_args.token, - # from_tf=bool(".ckpt" in model_args.model_name_or_path), - # trust_remote_code=True, - # use_flash_attention_2=True if model_args.use_flash_attn else False) - quantization_config = BitsAndBytesConfig( - load_in_4bit=True, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype="float16", - ) - - model = AutoModelForCausalLM.from_pretrained( - model_args.model_name_or_path, - torch_dtype=torch.float16 if training_args.fp16 else torch.bfloat16, - token=model_args.token, - cache_dir=model_args.cache_dir, - from_tf=bool(".ckpt" in model_args.model_name_or_path), - trust_remote_code=True, - attn_implementation="flash_attention_2", - quantization_config=quantization_config, - ) - - model = model.to("cuda") - - if torch.cuda.device_count() > 1: # If more than 1 GPU - model.is_parallelizable = True - model.model_parallel = True - - model.config.use_cache = False - - if model_args.from_peft is not None: - model = PeftModel.from_pretrained( - model, model_args.from_peft, is_trainable=True - ) - model.print_trainable_parameters() - else: - if model_args.use_lora: - peft_config = LoraConfig( - task_type=TaskType.CAUSAL_LM, - inference_mode=False, - r=model_args.lora_rank, - target_modules=model_args.target_modules, - lora_alpha=model_args.lora_alpha, - lora_dropout=model_args.lora_dropout, - modules_to_save=model_args.lora_extra_parameters, - ) - model = get_peft_model(model, peft_config) - model.print_trainable_parameters() - - print(model) - return model diff --git a/utils/finetuning/finetune_instruct/modeling.py b/utils/finetuning/finetune_instruct/modeling.py deleted file mode 100644 index d8bc5bcf..00000000 --- a/utils/finetuning/finetune_instruct/modeling.py +++ /dev/null @@ -1,83 +0,0 @@ -import logging -from dataclasses import dataclass -from typing import Optional - -import torch -from torch import Tensor, nn -from transformers import AutoTokenizer -from transformers.modeling_outputs import ModelOutput - -logger = logging.getLogger(__name__) - - -@dataclass -class LMOutput(ModelOutput): - loss: Optional[Tensor] = None - logits: Optional[Tensor] = None - - -class LanguageModelFinetuner(nn.Module): - def __init__( - self, - model: nn.Module, - tokenizer: AutoTokenizer = None, - train_batch_size: int = 4, - enable_gradient_checkpointing: bool = False, - ): - super().__init__() - self.model = model - self.tokenizer = tokenizer - self.train_batch_size = train_batch_size - - if self.model.config.pad_token_id is None and self.tokenizer is not None: - self.model.config.pad_token_id = self.tokenizer.pad_token_id - self.config = self.model.config - - if enable_gradient_checkpointing and hasattr( - self.model, "gradient_checkpointing_enable" - ): - self.model.gradient_checkpointing_enable() - - def gradient_checkpointing_enable(self, **kwargs): - if hasattr(self.model, "gradient_checkpointing_enable"): - self.model.gradient_checkpointing_enable(**kwargs) - - def enable_input_require_grads(self, **kwargs): - if hasattr(self.model, "enable_input_require_grads"): - self.model.enable_input_require_grads(**kwargs) - - def forward( - self, - input_ids: torch.Tensor = None, - attention_mask: torch.Tensor = None, - labels: torch.Tensor = None, - ) -> LMOutput: - device = next( - self.model.parameters() - ).device # Move inputs to the model's device - input_ids = input_ids.to(device) - attention_mask = attention_mask.to(device) - if labels is not None: - labels = labels.to(device) - - outputs = self.model( - input_ids=input_ids, attention_mask=attention_mask, labels=labels - ) - - return LMOutput( - loss=outputs.loss if hasattr(outputs, "loss") else None, - logits=outputs.logits if hasattr(outputs, "logits") else None, - ) - - def save(self, output_dir: str): - # Save the model (with weights) to output_dir - state_dict = self.model.state_dict() - state_dict = type(state_dict)( - {k: v.clone().cpu() for k, v in state_dict.items()} - ) - self.model.save_pretrained(output_dir, state_dict=state_dict) - - def save_pretrained(self, **kwargs): - if self.tokenizer is not None: - self.tokenizer.save_pretrained(**kwargs) - return self.model.save_pretrained(**kwargs) diff --git a/utils/finetuning/finetune_instruct/run.py b/utils/finetuning/finetune_instruct/run.py deleted file mode 100644 index 9aa3045a..00000000 --- a/utils/finetuning/finetune_instruct/run.py +++ /dev/null @@ -1,161 +0,0 @@ -import logging -import os -from pathlib import Path - -import torch -import torch.distributed as dist - -from transformers import AutoConfig, AutoTokenizer, HfArgumentParser, set_seed - -from arguments import ( - ModelArguments, - DataArguments, - SFTTrainingArguments as TrainingArguments, -) -from data import TrainDataset, DataCollatorForFinetuning -from modeling import LanguageModelFinetuner -from trainer import SFTTrainer -from load_model import get_model - - -logger = logging.getLogger(__name__) - -# Initialize the distributed environment if needed -dist.init_process_group(backend="nccl") - -# Get the rank of the current process -rank = dist.get_rank() - -# Map the rank to a specific GPU -device_id = rank # This assumes rank maps to GPU ID -torch.cuda.set_device(device_id) - -print( - f"Rank {rank} using device {device_id} on {torch.cuda.get_device_name(device_id)}" -) - - -def main(): - parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) - model_args, data_args, training_args = parser.parse_args_into_dataclasses() - model_args: ModelArguments - data_args: DataArguments - training_args: TrainingArguments - - if ( - os.path.exists(training_args.output_dir) - and os.listdir(training_args.output_dir) - and training_args.do_train - and not training_args.overwrite_output_dir - ): - raise ValueError( - f"Output directory ({training_args.output_dir}) already exists and is not empty. " - f"Use --overwrite_output_dir to overcome." - ) - - # Setup logging - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, - ) - logger.warning( - "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", - training_args.local_rank, - training_args.device, - training_args.n_gpu, - bool(training_args.local_rank != -1), - training_args.fp16, - ) - logger.info("Training/evaluation parameters %s", training_args) - logger.info("Model parameters %s", model_args) - logger.info("Data parameters %s", data_args) - - # Set seed - set_seed(training_args.seed) - - base_model = get_model(model_args, training_args) - - # Load tokenizer - tokenizer = AutoTokenizer.from_pretrained( - model_args.tokenizer_name - if model_args.tokenizer_name - else model_args.model_name_or_path, - cache_dir=model_args.cache_dir, - use_fast=not model_args.use_slow_tokenizer, - trust_remote_code=True, - token=model_args.token, - ) - - # Ensure pad_token_id is defined - if tokenizer.pad_token_id is None: - if tokenizer.unk_token_id is not None: - tokenizer.pad_token_id = tokenizer.unk_token_id - else: - # As a fallback if the tokenizer doesn't have unk_token_id, set pad_token_id to a known token - # If using a special tokenizer, make sure to adapt accordingly. - tokenizer.pad_token_id = tokenizer.eos_token_id - - config = AutoConfig.from_pretrained( - model_args.config_name - if model_args.config_name - else model_args.model_name_or_path, - cache_dir=model_args.cache_dir, - trust_remote_code=True, - ) - logger.info("Config: %s", config) - - model = LanguageModelFinetuner( - model=base_model, - tokenizer=tokenizer, - train_batch_size=training_args.per_device_train_batch_size, - ) - - if training_args.gradient_checkpointing: - model.enable_input_require_grads() - - # Load the training dataset - train_dataset = TrainDataset(args=data_args, tokenizer=tokenizer) - - # Setup data collator - data_collator = DataCollatorForFinetuning( - tokenizer=tokenizer, - query_max_len=data_args.query_max_len, - passage_max_len=data_args.passage_max_len, - pad_to_multiple_of=8, - return_tensors="pt", - padding=True, - ) - - trainer = SFTTrainer( - model=model, - args=training_args, - train_dataset=train_dataset, - data_collator=data_collator, - tokenizer=tokenizer, - ) - trainer.use_lora = model_args.use_lora - - Path(training_args.output_dir).mkdir(parents=True, exist_ok=True) - - # Training - trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint) - trainer.save_model() - - # If not using LoRA, you can save a final checkpoint if desired - if not model_args.use_lora and trainer.deepspeed is not None: - checkpoint_dir = os.path.join(training_args.output_dir, "checkpoint-final") - trainer.deepspeed.save_checkpoint(checkpoint_dir) - - # If world process zero, save tokenizer - if trainer.is_world_process_zero(): - tokenizer.save_pretrained(training_args.output_dir) - - -if __name__ == "__main__": - try: - main() - finally: - # Ensure all processes finalize - dist.barrier() # Synchronize all processes - dist.destroy_process_group() # Clean up diff --git a/utils/finetuning/finetune_instruct/split_train_test.py b/utils/finetuning/finetune_instruct/split_train_test.py deleted file mode 100644 index b2f0bfec..00000000 --- a/utils/finetuning/finetune_instruct/split_train_test.py +++ /dev/null @@ -1,27 +0,0 @@ -import json - -from sklearn.model_selection import train_test_split - -# File paths -input_file = "medical_o1_reasoning.jsonl" -train_file = "medical_o1_reasoning_train.jsonl" -test_file = "medical_o1_reasoning_test.jsonl" - -# Load data -with open(input_file, "r") as file: - data = [json.loads(line) for line in file] - -# Split data -train_data, test_data = train_test_split(data, test_size=0.1, random_state=42) - -# Save to JSONL -with open(train_file, "w") as file: - for item in train_data: - file.write(json.dumps(item) + "\n") - -with open(test_file, "w") as file: - for item in test_data: - file.write(json.dumps(item) + "\n") - -print(f"Train set size: {len(train_data)}") -print(f"Test set size: {len(test_data)}") diff --git a/utils/finetuning/finetune_instruct/trainer.py b/utils/finetuning/finetune_instruct/trainer.py deleted file mode 100644 index c7da4ea7..00000000 --- a/utils/finetuning/finetune_instruct/trainer.py +++ /dev/null @@ -1,70 +0,0 @@ -import logging -import os -from typing import Optional - -import torch -from peft import get_peft_model_state_dict -from transformers.integrations import is_deepspeed_zero3_enabled -from transformers.trainer import Trainer - -logger = logging.getLogger(__name__) - - -class SFTTrainer(Trainer): - use_lora: bool - - def _save(self, output_dir: Optional[str] = None, state_dict=None): - # Custom saving logic depending on whether we're using LoRA or not - if not self.use_lora: - # If not using LoRA, just use the default save implementation - super()._save(output_dir, state_dict) - return - - # Using LoRA - output_dir = output_dir if output_dir is not None else self.args.output_dir - os.makedirs(output_dir, exist_ok=True) - logger.info("Saving model checkpoint to %s", output_dir) - - # Ensure model has the `save` method implemented - if not hasattr(self.model, "save"): - raise NotImplementedError( - f"MODEL {self.model.__class__.__name__} does not support save interface" - ) - else: - self.model.save(output_dir) - - # Save training arguments - torch.save(self.args, os.path.join(output_dir, "training_args.bin")) - - # If using DeepSpeed ZeRO-3, save LoRA adapters separately - if is_deepspeed_zero3_enabled(): - if state_dict is None: - state_dict = self.model.state_dict() - prefix = "model." - assert all(k.startswith(prefix) for k in state_dict.keys()), list( - state_dict.keys() - ) - state_dict = {k[len(prefix) :]: v for k, v in state_dict.items()} - lora_state_dict = get_peft_model_state_dict(self.model.model, state_dict) - if self.args.process_index <= 0: - torch.save( - lora_state_dict, os.path.join(output_dir, "adapter_model.bin") - ) - logger.info(f"Saved LoRA adapter model at {output_dir}") - - def compute_loss( - self, model, inputs, return_outputs=False, num_items_in_batch=None - ): - """ - How the loss is computed by Trainer. - For causal language modeling tasks, the model returns the loss directly if labels are provided. - """ - outputs = model(**inputs) - loss = outputs.loss - - # Optionally use num_items_in_batch if needed for custom logic - if num_items_in_batch is not None: - # Example: Adjust loss based on batch size if required - pass - - return (loss, outputs) if return_outputs else loss diff --git a/utils/gpt/gpt-generate-summaries.py b/utils/gpt/gpt-generate-summaries.py deleted file mode 100644 index dbf5c3ac..00000000 --- a/utils/gpt/gpt-generate-summaries.py +++ /dev/null @@ -1,218 +0,0 @@ -import json -import os -from typing import List, Dict, Optional -from langchain_community.chat_models import ChatOpenAI -from langchain.schema import HumanMessage -from pydantic import BaseModel, Field -import re - -# Set OpenAI API key -os.environ["OPENAI_API_KEY"] = "" - - -# Define the schema for structured output -class PatientStory(BaseModel): - condition: Optional[str] = Field( - default=None, description="The main condition of the patient." - ) - synonyms: Optional[List[str]] = Field( - default=None, description="Synonyms or related terms for the main condition." - ) - age: Optional[str] = Field(default=None, description="The age of the patient.") - gender: Optional[str] = Field( - default=None, description="The gender of the patient." - ) - meaningful_sentences: List[str] = Field( - description="A list of factual, meaningful sentences describing the patient's conditions and entities." - ) - - -# Initialize OpenAI LLM -llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.5, top_p=0.9) - - -# Function to extract age and gender from the raw description -def extract_age_and_gender(description: str) -> Dict[str, Optional[str]]: - """ - Extracts age and gender information from the patient description. - """ - age = None - gender = None - - # Regex patterns for extracting age and gender - age_pattern = r"(\b\d{1,3}\b)-?(year-old|yr-old|years old)" - gender_pattern = r"\b(male|female|man|woman|boy|girl|gentleman|lady)\b" - - age_match = re.search(age_pattern, description, re.IGNORECASE) - gender_match = re.search(gender_pattern, description, re.IGNORECASE) - - if age_match: - age = age_match.group(1) - if gender_match: - gender = gender_match.group(0).lower() - - # Normalize gender - if gender in {"man", "male", "boy", "gentleman"}: - gender = "male" - elif gender in {"woman", "female", "girl", "lady"}: - gender = "female" - - return {"age": age, "gender": gender} - - -# Function to create meaningful sentences for entities -def generate_sentences(description: str, entities: List[str]) -> List[str]: - """ - Generates meaningful sentences for each entity in the description. - """ - prompt = f""" - You are a highly skilled medical assistant tasked with rewriting patient descriptions into factual, descriptive narratives. Each entity provided must be described in its own meaningful sentence. The sentences should strictly describe the patient and avoid making inferences, assumptions, or suggestions. - - Patient Description: - {description} - - Entities: - {json.dumps(entities)} - - Guidelines for writing the sentences: - - Each sentence must describe one entity factually, based on the information provided in the description or the entities list. - - Avoid making inferences, or suggesting potential outcomes, or suggesting improvements (e.g., no phrases like 'critical for improving' or 'should focus on'). - - Maintain a formal, clinical tone suitable for medical documentation. - - Ensure statements look as they are written by a medical professional in an official medical record. - - Ensure that the narrative remains coherent and logical when all sentences are read together. As if they are part of a single patient summary. - - Ensure to adhere to the description when describing the patient without adding any new information not found in the description. - - Provide the output as a JSON object with the key 'meaningful_sentences' containing the list of sentences. - """ - response = llm.invoke( - [HumanMessage(content=prompt)] - ) # Use invoke for LangChain models - try: - # Clean Markdown formatting if present - structured_output = ( - response.content.strip().strip("```json").strip("```").strip() - ) - return PatientStory.parse_raw(structured_output).meaningful_sentences - except Exception as e: - print(f"Error parsing response: {e}") - return [f"Error generating sentences for description: {description}"] - - -# Function to prompt the model to extract the patient's age and gender -def prompt_extract_age_and_gender(description: str) -> Dict[str, Optional[str]]: - """ - Uses the model to extract age and gender information from the patient description. - """ - prompt = f""" - You are a highly skilled medical assistant tasked with identifying specific information from patient descriptions. - - Patient Description: - {description} - - Extract the following information: - - Age of the patient: Explicitly mention the age if it is present in the description. - - Gender of the patient: Extract gender if it is stated in the description. - - Normalize the Age to an integer number and Gender to either Male or Female - - If not mentioned at all in the description, provide 'None' for both age and gender. - - Provide the output as a JSON object with the keys 'age' and 'gender'. - """ - response = llm.invoke([HumanMessage(content=prompt)]) - try: - # Parse the JSON response from the model - structured_output = ( - response.content.strip().strip("```json").strip("```").strip() - ) - extracted_info = json.loads(structured_output) - return { - "age": extracted_info.get("age"), - "gender": extracted_info.get("gender"), - } - except Exception as e: - print(f"Error extracting age and gender: {e}") - return {"age": None, "gender": None} - - -def prompt_extract_main_condition(description: str) -> Dict[str, Optional[str]]: - """ - Uses the model to extract the main condition from the patient description. - """ - prompt = f""" - You are a highly skilled medical assistant tasked with identifying the main condition/disease from patient descriptions. - - Patient Description: - {description} - - Extract the main condition mentioned in the description. - - The main condition should be a specific medical condition or disease that the patient suffers from. - - If multiple conditions are mentioned, extract the most prominent or relevant one that describes the patient. - - Ensure the extracted condition is factual and directly related to the patient's health. - - If no condition is mentioned, provide 'None' as the output. - - Provide a list of 10 well known aliases or synonyms or related terms if the condition can be described in multiple ways. - - Provide the output as a JSON object with the keys 'condition' and 'synonyms'. - """ - response = llm.invoke([HumanMessage(content=prompt)]) - try: - # Parse the JSON response from the model - structured_output = ( - response.content.strip().strip("```json").strip("```").strip() - ) - extracted_info = json.loads(structured_output) - return { - "condition": extracted_info.get("condition"), - "synonyms": extracted_info.get("synonyms"), - } - except Exception as e: - print(f"Error extracting main condition: {e}") - return {"condition": None, "synonyms": None} - - -# Update the process_file function to use the new age and gender prompt -def process_file_with_prompt(input_file: str, output_file: str): - """ - Reads patient data from the input file, prompts the model to extract age and gender, - generates expanded sentences for each patient's entities, and writes the output to the output file. - """ - with open(input_file, "r") as file: - data = json.load(file) - - results = {} - for patient_id, patient_data in data.items(): - print(f"Processing patient {patient_id}...") - raw_description = patient_data.get("raw", "") - conditions = patient_data.get("gpt-4-turbo", {}).get("conditions", []) - - # Prompt the model to extract age and gender - age_gender_info = prompt_extract_age_and_gender(raw_description) - - # Generate meaningful sentences - expanded_sentences = generate_sentences(raw_description, conditions) - - # Get the main condition - main_condition_info = prompt_extract_main_condition(raw_description) - - # Save the processed result - results[patient_id] = { - "raw_description": raw_description, - "age": age_gender_info.get("age"), - "gender": age_gender_info.get("gender"), - "main_condition": main_condition_info.get("condition"), - "synonyms": main_condition_info.get("synonyms"), - "conditions": conditions, - "expanded_sentences": expanded_sentences, - } - - # Write the results to the output file - with open(output_file, "w") as file: - json.dump(results, file, indent=2) - print(f"Processing complete. Results saved to {output_file}") - - -# Main script -if __name__ == "__main__": - input_file = ( - "../../data/id2queries21.json" # Replace with the path to your input JSON file - ) - output_file = "processed_patients21.json" # Path to save the processed output - process_file_with_prompt(input_file, output_file) diff --git a/utils/gpt/gpt_generate_ideal_candidates.py b/utils/gpt/gpt_generate_ideal_candidates.py deleted file mode 100644 index 2e31f511..00000000 --- a/utils/gpt/gpt_generate_ideal_candidates.py +++ /dev/null @@ -1,407 +0,0 @@ -import os -import json -import random -import re -import ast -from typing import List, Dict, Tuple -from dateutil.parser import parse -from langchain_openai import ChatOpenAI -from langchain.schema import HumanMessage - -# Set your API key securely in production. -os.environ["OPENAI_API_KEY"] = "" - -# Initialize the LLM -llm = ChatOpenAI(model="gpt-4o-mini", max_retries=3) - -# Minimum word count for eligibility criteria to be considered "rich" -MIN_ELIGIBILITY_WORD_COUNT = 512 - - -def safe_parse_list(response_str: str) -> List: - """ - Attempts to parse a Python list from a string. - Extracts the content between the first "[" and the last "]". - First tries ast.literal_eval; if that fails, replaces single quotes with double quotes and tries json.loads. - Returns an empty list if parsing is unsuccessful. - """ - start = response_str.find("[") - end = response_str.rfind("]") - if start == -1 or end == -1: - print("No list brackets found in the response.") - return [] - list_str = response_str[start : end + 1] - - try: - result = ast.literal_eval(list_str) - if isinstance(result, list): - return result - except Exception as e: - print(f"literal_eval failed on extracted string: {e}") - - try: - json_str = list_str.replace("'", '"') - result = json.loads(json_str) - if isinstance(result, list): - return result - except Exception as e: - print(f"json.loads fallback failed: {e}") - - return [] - - -def safe_parse_dict(response_str: str) -> Dict: - """ - Attempts to parse a Python dict from a string. - Extracts the substring between the first "{" and the last "}". - First tries ast.literal_eval; if that fails, replaces single quotes with double quotes and tries json.loads. - Returns an empty dict if unsuccessful. - """ - start = response_str.find("{") - end = response_str.rfind("}") - if start == -1 or end == -1: - print("No dictionary found in the response.") - return {} - dict_str = response_str[start : end + 1] - - try: - result = ast.literal_eval(dict_str) - if isinstance(result, dict): - return result - except Exception as e: - print(f"literal_eval failed on extracted string: {e}") - - try: - json_str = dict_str.replace("'", '"') - result = json.loads(json_str) - if isinstance(result, dict): - return result - except Exception as e: - print(f"json.loads fallback failed: {e}") - - return {} - - -def generate_synonyms(condition: str) -> List[str]: - """ - Generates 10 well-known synonyms or alternative names for the given condition. - The model is instructed to provide the output as a Python list. - """ - prompt = f""" - You are a medical expert. Generate 10 well-known synonyms or alternative names for the following condition: - Condition: {condition} - Provide the output as a Python list of strings. - """ - response = llm.invoke([HumanMessage(content=prompt)]) - print("Synonyms response:", response.content) - synonyms = safe_parse_list(response.content) - if isinstance(synonyms, list): - synonyms = [str(s) for s in synonyms if isinstance(s, (str, int, float))] - else: - synonyms = [] - print(f"Error parsing synonyms for condition '{condition}'.") - return synonyms - - -def generate_summary( - details: List[str], conditions: List[str] = None, age_spec: str = None -) -> str: - """ - Generates a one-paragraph patient note based on the provided details and conditions. - The note should naturally incorporate the necessary patient information without explicitly referencing - eligibility criteria, trial requirements, or any commentary on meeting specific conditions. - If an age_spec is provided, include this information in the note. The note must end with a separate - line exactly in the format: - "Age: , Gender: " - """ - combined_details = " ".join(details) - - condition_info = "" - if conditions: - condition_info = "diagnosed with one of : " + ", ".join(conditions) + "." - - age_sentence = f" The patient should be {age_spec}." if age_spec else "" - - prompt = f""" -You are a seasoned medical expert. Based on the clinical trial information provided below, generate a detailed, professional one-paragraph patient note describing an ideal candidate {condition_info} for the trial. Ensure that the candidate's medical history and current condition strictly satisfy every single inclusion criterion without exception while clearly and explicitly not violating any exclusion criteria. - -Age and gender requirements for the trial: {age_sentence} - -Eligibility Details: {combined_details} - -Please integrate the above information naturally into the note as if describing the patient's history and presentation, without any explicit reference to suitability of the patient with the provided eligibility criteria, trial requirements, or statements like "meets the criterion" or "satisfies the requirements of the trial" etc... Your note should simply convey the patient's condition and background in a realistic manner similar to an EHR or admission note. -Go over the criteria one-by-one. They should all be covered in the patient's note without exception. - -At the end of the note, include a new line exactly in the following format: -"Age: , Gender: " -""" - response = llm.invoke([HumanMessage(content=prompt)]) - print("Summary response:", response.content) - return response.content.strip() - - -def extract_age_gender_from_summary(summary: str) -> Tuple[int, str]: - """ - Extracts the age and gender from the summary using a regular expression. - Expects the summary to end with a line in the format: - "Age: , Gender: " - """ - pattern = r"Age:\s*(\d+)\s*,\s*Gender:\s*([A-Za-z]+)" - match = re.search(pattern, summary) - if match: - age = int(match.group(1)) - gender = match.group(2).lower() - if gender not in ["male", "female"]: - gender = "male" - return age, gender - else: - print("Could not extract age and gender from summary. Using default values.") - return 50, "male" - - -def split_into_sentences(text: str) -> List[str]: - """ - Splits the provided text into sentences using punctuation as delimiters. - """ - sentences = re.split(r"(?<=[.!?]) +", text) - return [s.strip() for s in sentences if s.strip()] - - -def generate_conditions(raw_description: str) -> List[str]: - """ - Extracts all relevant medical conditions solely based on the patient’s raw description. - The model is instructed to provide the output as a Python list of strings. - """ - prompt = f""" - You are a medical expert. Based solely on the following patient note, extract all relevant medical conditions mentioned. - Provide the output as a Python list of strings. - Patient Note: {raw_description} - """ - response = llm.invoke([HumanMessage(content=prompt)]) - print("Conditions response:", response.content) - conditions = safe_parse_list(response.content) - if isinstance(conditions, list): - conditions = [str(c) for c in conditions if isinstance(c, (str, int, float))] - else: - conditions = [] - print("Error parsing conditions.") - return conditions - - -def extract_main_condition_from_summary(summary: str) -> str: - """ - Extracts the primary condition for which the patient is being treated from the patient note. - The model is instructed to return a concise, single phrase. - """ - prompt = f""" - You are a medical expert. Based solely on the following patient note, identify the primary condition for which the patient is being treated. Provide only a concise, single phrase. - Patient Note: {summary} - """ - response = llm.invoke([HumanMessage(content=prompt)]) - main_condition = response.content.strip() - print("Extracted main condition:", main_condition) - return main_condition - - -def extract_age_gender_from_trial(trial_data: Dict) -> str: - """ - Extracts the age and gender requirements from the trial's JSON data using the fields: - "minumum_age", "maximum_age", and "gender". - Constructs and returns an age specification string. - """ - try: - min_age_raw = trial_data.get("minumum_age") - max_age_raw = trial_data.get("maximum_age") - trial_gender = trial_data.get("gender", "male") - if min_age_raw is not None and max_age_raw is not None: - if isinstance(min_age_raw, str): - min_age = int("".join(filter(str.isdigit, min_age_raw))) - else: - min_age = int(min_age_raw) - if isinstance(max_age_raw, str): - max_age = int("".join(filter(str.isdigit, max_age_raw))) - else: - max_age = int(max_age_raw) - age_spec = f"aged between {min_age} and {max_age} years old; gender must be {trial_gender}" - else: - age_spec = f"gender must be {trial_gender}" - except Exception as e: - print(f"Error constructing age specification: {e}") - age_spec = f"gender must be {trial_gender}" - return age_spec - - -def generate_patient_profile( - eligibility_criteria: str, ground_nctid: str, trial_data: Dict -) -> Dict: - """ - Generates a patient profile for a trial. - - Steps: - 1. Construct an age specification string from the trial's JSON. - 2. Extract condition(s) from the trial data. - 3. Generate a comprehensive raw patient note (summary) based on the trial's eligibility criteria and conditions. - The note will be generated using the eligibility criteria, conditions, and will include a final line with age and gender. - 4. Extract the age and gender from the generated note. - 5. Extract the main condition from the note. - 6. Generate synonyms for the extracted main condition. - 7. Extract additional conditions solely from the raw note. - - Returns a JSON object with the following keys: - - raw_description (the patient note) - - age - - gender - - main_condition - - synonyms - - conditions - - split_raw_description (the note split into sentences) - - ground_nctid - """ - # 1. Build an age specification string from trial_data. - age_spec = extract_age_gender_from_trial(trial_data) - - # 2. Extract condition(s) from the trial data. - condition_field = trial_data.get("condition", "") - if isinstance(condition_field, list): - conditions_input = condition_field # use the list directly - elif isinstance(condition_field, str) and condition_field.strip(): - conditions_input = [condition_field] - else: - conditions_input = [] - - # 3. Generate the raw patient note using eligibility criteria, conditions, and age specifications. - raw_description = generate_summary( - [eligibility_criteria], conditions=conditions_input, age_spec=age_spec - ) - - # 4. Extract age and gender from the generated note. - age, gender = extract_age_gender_from_summary(raw_description) - - # 5. Extract the main condition from the raw description. - main_condition = extract_main_condition_from_summary(raw_description) - - # 6. Generate synonyms for the main condition. - synonyms = generate_synonyms(main_condition) - - # 7. Extract additional conditions solely from the raw note. - conditions_from_note = generate_conditions(raw_description) - if main_condition not in conditions_from_note: - conditions = [main_condition] + conditions_from_note - else: - conditions = conditions_from_note - - return { - "raw_description": raw_description, - "age": age, - "gender": gender, - "main_condition": main_condition, - "synonyms": synonyms, - "conditions": conditions, - "split_raw_description": split_into_sentences(raw_description), - "ground_nctid": ground_nctid, - } - - -def process_trials(input_folder: str, output_file: str): - """ - Processes trial JSON files in the given folder: - 1. Searches for files with names starting with "NCT" and ending with ".json". - 2. For each file, checks that the trial's "start_date" is after 2015, that the "condition" - (when lowercased) contains cancer-related terms (e.g., "cancer", "tumor", "malignancy", "neoplasm", "carcinoma"), - that the eligibility criteria are sufficiently long and rich, and that the trial's overall_status is "Recruiting". - 3. Randomly samples 100 trials that meet these conditions. - 4. For each trial, uses its "eligibility_criteria" to generate a patient profile that perfectly fits the trial. - The profile is built by generating a raw patient note (aware of age/gender specs and conditions) and then extracting the main condition, - synonyms, and additional conditions from that note. - 5. Saves the 100 patient profiles to a single JSON file. - """ - all_files = [ - f - for f in os.listdir(input_folder) - if f.startswith("NCT") and f.endswith(".json") - ] - matching_trials = [] - cancer_terms = ["cancer", "tumor", "malignancy", "neoplasm", "carcinoma"] - - for filename in all_files: - file_path = os.path.join(input_folder, filename) - try: - with open(file_path, "r") as f: - trial_data = json.load(f) - except Exception as e: - print(f"Failed to load {filename}: {e}") - continue - - # Check if the start_date is after 2015. - start_date_str = trial_data.get("start_date") - if not start_date_str or not isinstance(start_date_str, str): - print( - f"Skipping trial {filename} because start_date is missing or not a valid string." - ) - continue - - try: - parsed_date = parse(start_date_str) - year = parsed_date.year - except Exception as e: - print(f"Error parsing start_date in {filename}: {e}") - year = 0 - - # Check if the eligibility criteria exist and are long and rich. - eligibility_criteria = trial_data.get("eligibility_criteria", "") - if ( - not eligibility_criteria - or len(eligibility_criteria.split()) < MIN_ELIGIBILITY_WORD_COUNT - ): - print( - f"Skipping trial {filename} due to insufficient eligibility criteria (less than {MIN_ELIGIBILITY_WORD_COUNT} words)." - ) - continue - - # Handle the condition field, which can be a list or a string. - condition_field = trial_data.get("condition", "") - if isinstance(condition_field, list): - condition_str = " ".join(condition_field) - else: - condition_str = condition_field - condition_lower = condition_str.lower() - - # Additional condition: trial must have overall_status "Recruiting" - overall_status = trial_data.get("overall_status", "").strip().lower() - - if ( - year > 2015 - and any(term in condition_lower for term in cancer_terms) - and overall_status == "recruiting" - ): - matching_trials.append((filename, trial_data)) - - if len(matching_trials) < 100: - print( - f"Warning: Only {len(matching_trials)} matching trials found. Proceeding with available trials." - ) - sample_trials = matching_trials - else: - sample_trials = random.sample(matching_trials, 100) - - results = {} - for filename, trial_data in sample_trials: - ground_nctid = filename.replace(".json", "") - eligibility_criteria = trial_data.get("eligibility_criteria", "") - print(f"Processing trial {ground_nctid}...") - patient_profile = generate_patient_profile( - eligibility_criteria, ground_nctid, trial_data - ) - results[ground_nctid] = patient_profile - - with open(output_file, "w") as out_file: - json.dump(results, out_file, indent=2) - - print(f"Processing complete. Results saved to {output_file}") - - -# Set your input folder and output file paths accordingly. -input_folder = "../../data/trials_jsons" # Folder containing NCT*.json files -output_file = "perfect_patient_profiles.json" - -process_trials(input_folder, output_file) diff --git a/utils/gpt/gpt_generate_reranking_data.py b/utils/gpt/gpt_generate_reranking_data.py deleted file mode 100644 index af07882a..00000000 --- a/utils/gpt/gpt_generate_reranking_data.py +++ /dev/null @@ -1,166 +0,0 @@ -import json -import os -import random -from typing import Dict, List, Optional - -from langchain.schema import HumanMessage -from langchain_community.chat_models import ChatOpenAI -from pydantic import BaseModel - -os.environ["OPENAI_API_KEY"] = "" - -INPUT_MEDNLI_FILE = "mednli_train.jsonl" -OUTPUT_AUGMENTED_FILE = "mednli_yesno_aug.jsonl" - -N_VARIANTS_PER_SEED = 3 -MAX_SEED_EXAMPLES = 500 - -MODEL_NAME = "gpt-4o-mini" -TEMPERATURE = 0.7 - -INSTRUCTION_TEXT = ( - "You are a clinical assistant tasked with determining whether the patient " - "information (Statement A) provides enough details to evaluate whether the " - "patient satisfies or violates the clinical trial eligibility criterion " - "(Statement B). Respond with 'Yes' if Statement A contains sufficient " - "information to make this evaluation, or 'No' if it does not." -) - - -class MedNLISeed(BaseModel): - sentence1: str - sentence2: str - gold_label: str - - -class YesNoExample(BaseModel): - instruction: str - sentence1: str - sentence2: str - gold_label: str - - -llm = ChatOpenAI( - model=MODEL_NAME, - temperature=TEMPERATURE, - top_p=0.9, -) - - -def load_mednli_jsonl(path: str) -> List[MedNLISeed]: - examples: List[MedNLISeed] = [] - with open(path, "r") as f: - for line in f: - raw = json.loads(line.strip()) - examples.append( - MedNLISeed( - sentence1=raw["sentence1"], - sentence2=raw["sentence2"], - gold_label=raw["gold_label"], - ) - ) - return examples - - -def write_jsonl(path: str, data: List[Dict]): - with open(path, "a", encoding="utf-8") as f: - for obj in data: - f.write(json.dumps(obj, ensure_ascii=False) + "\n") - - -def generate_yesno_variants( - seed_example: MedNLISeed, n_variants: int -) -> List[YesNoExample]: - prompt = f""" -You are generating training data for a clinical trial matching system. - -We want examples following this pattern: - -- Statement A: patient information. -- Statement B: clinical trial eligibility criterion. -- A fixed instruction string. -- A label "Yes" or "No". - -Semantics: -- "Yes": Statement A contains enough information to determine if the patient satisfies or violates Statement B. -- "No": Statement A does not contain enough information. - -MedNLI mapping: -- entailment/contradiction → Yes -- neutral → No - -Seed example: -{{ - "sentence1": {json.dumps(seed_example.sentence1)}, - "sentence2": {json.dumps(seed_example.sentence2)}, - "gold_label": {json.dumps(seed_example.gold_label)} -}} - -Generate {n_variants} new examples. Each example must have: -- "instruction": {json.dumps(INSTRUCTION_TEXT)} -- "sentence1": Statement A -- "sentence2": Statement B -- "gold_label": "Yes" or "No" - -Return only valid JSON: a list of objects with exactly these keys. -""".strip() - - response = llm.invoke([HumanMessage(content=prompt)]) - raw = response.content.strip() - if raw.startswith("```"): - raw = raw.strip("`").replace("json", "", 1).strip() - - try: - parsed = json.loads(raw) - out = [] - for item in parsed: - try: - ex = YesNoExample(**item) - if ex.gold_label in {"Yes", "No"}: - ex.instruction = INSTRUCTION_TEXT - out.append(ex) - except Exception: - pass - return out - except Exception: - return [] - - -def augment_mednli_yesno( - input_path: str, - output_path: str, - n_variants_per_seed: int = 3, - max_seed_examples: Optional[int] = None, -): - all_seeds = load_mednli_jsonl(input_path) - - if max_seed_examples is not None and max_seed_examples < len(all_seeds): - seed_examples = random.sample(all_seeds, max_seed_examples) - else: - seed_examples = all_seeds - - open(output_path, "w").close() - - for seed in seed_examples: - generated = generate_yesno_variants(seed, n_variants_per_seed) - if not generated: - continue - to_write = [ - { - "instruction": ex.instruction, - "sentence1": ex.sentence1, - "sentence2": ex.sentence2, - "gold_label": ex.gold_label, - } - for ex in generated - ] - write_jsonl(output_path, to_write) - - -if __name__ == "__main__": - augment_mednli_yesno( - input_path=INPUT_MEDNLI_FILE, - output_path=OUTPUT_AUGMENTED_FILE, - n_variants_per_seed=N_VARIANTS_PER_SEED, - max_seed_examples=MAX_SEED_EXAMPLES, - ) diff --git a/uv.lock b/uv.lock index 5756318d..932f1250 100644 --- a/uv.lock +++ b/uv.lock @@ -43,20 +43,17 @@ name = "aiohttp" version = "3.14.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "aiohappyeyeballs", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - { name = "aiosignal", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - 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name: Dependency audit # vLLM 0.23 pins torch 2.11.0; CVE-2025-3000 has no fixed version listed. run: uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 - - - name: Validate Compose - run: docker compose config - - - name: Build Docker image - run: docker build . diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 8b9232e9..00000000 --- a/Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -FROM --platform=linux/amd64 python:3.11-slim AS runtime - -ENV PYTHONDONTWRITEBYTECODE=1 \ - PYTHONUNBUFFERED=1 \ - UV_SYSTEM_PYTHON=1 \ - PATH="/app/.venv/bin:${PATH}" - -WORKDIR /app - -RUN apt-get update \ - && apt-get install -y --no-install-recommends \ - bash \ - build-essential \ - ca-certificates \ - curl \ - git \ - unzip \ - && rm -rf /var/lib/apt/lists/* \ - && pip install --no-cache-dir uv - -COPY pyproject.toml uv.lock README.md LICENSE ./ -COPY src ./src -COPY scripts ./scripts - -RUN uv sync --frozen --no-dev --extra llm --extra gpu --extra entity - -VOLUME ["/app/data", "/app/models", "/app/results"] - -CMD ["trialmatchai-healthcheck"] diff --git a/README.md b/README.md index 2cb567dd..e95cfce2 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ This software is for research and informational use only. It is not medical advi ## Deployment Target -The supported v1 deployment path is a single Python 3.11 GPU server or VM. Trial and criteria search use embedded LanceDB tables under `data/search`, so no separate search service, container, socket, TLS certificate, or service credential is required. Docker remains optional for packaging the worker. +The supported v1 deployment path is a single Python 3.11 GPU server or VM. Trial and criteria search use embedded LanceDB tables under `data/search`, so no separate search service, container, socket, TLS certificate, or service credential is required. ## Requirements @@ -75,17 +75,6 @@ uv run trialmatchai-run Results are written under `results/`. -## Docker Worker - -Docker is optional. The worker container uses mounted local folders and the same embedded LanceDB tables: - -```bash -docker compose build trialmatchai-worker -docker compose run --rm trialmatchai-worker trialmatchai-healthcheck -docker compose run --rm trialmatchai-worker trialmatchai-update-registry --max-studies 100 -docker compose run --rm trialmatchai-worker trialmatchai-run -``` - ## Configuration Configuration defaults live in `src/trialmatchai/config/config.json`. Runtime overrides use `.env` or environment variables: @@ -175,8 +164,6 @@ uv run ruff check . uv run pytest uv run python scripts/scan_secrets.py uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 -docker compose config -docker build . ``` ## Support diff --git a/docker-compose.yml b/docker-compose.yml deleted file mode 100644 index 75830ffa..00000000 --- a/docker-compose.yml +++ /dev/null @@ -1,30 +0,0 @@ -services: - trialmatchai-worker: - build: . - volumes: - - ./data:/app/data - - ./models:/app/models - - ./results:/app/results - - ./example:/app/example:ro - environment: - TRIALMATCHAI_PATIENTS_DIR: /app/example - TRIALMATCHAI_OUTPUT_DIR: /app/results - TRIALMATCHAI_TRIALS_JSON_FOLDER: /app/data/trials_jsons - TRIALMATCHAI_SEARCH_BACKEND: lancedb - TRIALMATCHAI_SEARCH_DB_PATH: /app/data/search - TRIALMATCHAI_SEARCH_TRIALS_TABLE: trials - TRIALMATCHAI_SEARCH_CRITERIA_TABLE: criteria - TRIALMATCHAI_SEARCH_MODE: hybrid - TRIALMATCHAI_ENTITY_BACKEND: gliner2 - TRIALMATCHAI_ENTITY_SCHEMA_PATH: /app/src/trialmatchai/entity_schemas/trialmatchai.yaml - TRIALMATCHAI_CONCEPT_DB_PATH: /app/data/concepts - TRIALMATCHAI_CONCEPT_TABLE: concepts - TRIALMATCHAI_LINK_ACCEPT: "0.80" - TRIALMATCHAI_LINK_REJECT: "0.30" - TRIALMATCHAI_REGISTRY_SOURCE: clinicaltrials.gov - TRIALMATCHAI_REGISTRY_SINCE_DAYS: "7" - TRIALMATCHAI_REGISTRY_RAW_DIR: /app/data/registry/raw - TRIALMATCHAI_REGISTRY_MANIFEST_PATH: /app/data/registry/manifest.jsonl - TRIALMATCHAI_REGISTRY_REPORTS_DIR: /app/data/registry/runs - TRIALMATCHAI_LOG_JSON: "1" - command: ["trialmatchai-healthcheck", "--registry"] diff --git a/docs/HLD.md b/docs/HLD.md index b3bd33cb..bf87f43c 100644 --- a/docs/HLD.md +++ b/docs/HLD.md @@ -147,7 +147,7 @@ Default deployment requires only the Python worker process and local mounted dir - `models/` - `results/` -Docker is optional and packages only the worker. The application does not auto-start external services. +The application runs as a Python process and does not auto-start external services. ## 7. Runtime Configuration diff --git a/docs/release.md b/docs/release.md index 26bcf207..873558ee 100644 --- a/docs/release.md +++ b/docs/release.md @@ -32,12 +32,3 @@ python -m venv /tmp/trialmatchai-wheel-smoke /tmp/trialmatchai-wheel-smoke/bin/pip install dist/trialmatchai-*.whl /tmp/trialmatchai-wheel-smoke/bin/python -c "import trialmatchai; print(trialmatchai.__version__)" ``` - -## Container Checks - -```bash -docker compose config -docker build . -``` - -Docker is not required for local LanceDB operation; it is a packaging option for a worker environment. diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index c46c4178..05c641fd 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -82,7 +82,7 @@ def run_preflight_checks( if importlib.util.find_spec("vllm") is None: issues.append( "cot_backend=vllm requires the GPU extra " - "(`uv sync --extra gpu`) or the Docker worker image." + "(`uv sync --extra llm --extra gpu`)." ) try: import torch diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index 6bbc64d4..30bd1c3f 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -113,7 +113,7 @@ def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): assert issues == [ "cot_backend=vllm requires the GPU extra " - "(`uv sync --extra gpu`) or the Docker worker image." + "(`uv sync --extra llm --extra gpu`)." ] From 40b2a0975b6c5ce8748c1ba146ae0296823f01c3 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 21 Jun 2026 20:14:22 +0200 Subject: [PATCH 06/64] Clean up package workflows and bootstrap CLI --- .env.example | 9 ++ Makefile | 43 +++-- README.md | 3 + docs/HLD.md | 177 --------------------- requirements.txt | 64 -------- scripts/bootstrap_data.sh | 125 --------------- scripts/index_data.sh | 18 --- setup.sh | 54 ------- src/trialmatchai/cli/bootstrap_data.py | 212 +++++++++++++++++++++++-- src/trialmatchai/cli/main.py | 3 + tests/test_bootstrap_data.py | 112 +++++++++++++ tests/test_package_imports.py | 13 ++ 12 files changed, 365 insertions(+), 468 deletions(-) delete mode 100644 docs/HLD.md delete mode 100644 requirements.txt delete mode 100644 scripts/bootstrap_data.sh delete mode 100644 scripts/index_data.sh delete mode 100644 setup.sh create mode 100644 tests/test_bootstrap_data.py diff --git a/.env.example b/.env.example index 10dc8e14..01b25ca0 100644 --- a/.env.example +++ b/.env.example @@ -40,5 +40,14 @@ TRIALMATCHAI_REGISTRY_MANIFEST_PATH=data/registry/manifest.jsonl TRIALMATCHAI_REGISTRY_REPORTS_DIR=data/registry/runs TRIALMATCHAI_REGISTRY_FAILURE_THRESHOLD=0.25 +TRIALMATCHAI_PROCESSED_TRIALS_SHA256= +TRIALMATCHAI_MODELS_SHA256= +TRIALMATCHAI_CRITERIA_PART_0_SHA256= +TRIALMATCHAI_CRITERIA_PART_1_SHA256= +TRIALMATCHAI_CRITERIA_PART_2_SHA256= +TRIALMATCHAI_CRITERIA_PART_3_SHA256= +TRIALMATCHAI_CRITERIA_PART_4_SHA256= +TRIALMATCHAI_CRITERIA_PART_5_SHA256= + TRIALMATCHAI_LOG_LEVEL=INFO TRIALMATCHAI_LOG_JSON=1 diff --git a/Makefile b/Makefile index e26a94f2..abcb0b47 100644 --- a/Makefile +++ b/Makefile @@ -1,37 +1,46 @@ -.PHONY: venv sync sync-gpu test lock lint audit healthcheck bootstrap index run setup - -venv: - uv venv +.PHONY: audit bootstrap build clean healthcheck index lint lock release-check sync sync-model test update-registry sync: uv sync -sync-gpu: - uv sync --extra gpu - -test: - uv run pytest +sync-model: + uv sync --extra llm --extra gpu --extra entity lock: - uv lock + uv lock --check lint: - uv run python -m ruff check . + uv run ruff check . + +test: + uv run pytest audit: uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 +build: + uv build + healthcheck: - uv run trialmatchai-healthcheck + uv run trialmatchai-healthcheck --registry bootstrap: uv run trialmatchai-bootstrap-data +update-registry: + uv run trialmatchai-update-registry + index: - uv run trialmatchai-index + uv run trialmatchai-index --prepare -run: - uv run trialmatchai-run +release-check: + uv lock --check + uv run ruff check . + uv run pytest + uv build + uv run python scripts/scan_secrets.py + uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 -setup: - bash setup.sh +clean: + rm -rf build dist src/*.egg-info .pytest_cache .ruff_cache + find . -name '__pycache__' -type d -prune -exec rm -rf {} + diff --git a/README.md b/README.md index e95cfce2..4593dcd7 100644 --- a/README.md +++ b/README.md @@ -30,6 +30,8 @@ cp .env.example .env Dependency auditing currently ignores `CVE-2025-3000` because vLLM 0.23 pins Torch 2.11.0 and the advisory has no fixed Torch version listed. Revisit that exception whenever upgrading vLLM or Torch. +Artifact bootstrap supports optional SHA-256 verification through `TRIALMATCHAI_PROCESSED_TRIALS_SHA256`, `TRIALMATCHAI_MODELS_SHA256`, and `TRIALMATCHAI_CRITERIA_PART__SHA256`. + ## Quickstart Install the package for local development and operational CLIs: @@ -126,6 +128,7 @@ The command group is also available as: ```bash uv run python -m trialmatchai healthcheck +uv run trialmatchai bootstrap-data --skip-models uv run trialmatchai update-registry --dry-run --max-studies 25 ``` diff --git a/docs/HLD.md b/docs/HLD.md deleted file mode 100644 index bf87f43c..00000000 --- a/docs/HLD.md +++ /dev/null @@ -1,177 +0,0 @@ -# High Level Design - TrialMatchAI - -## 1. Overview - -TrialMatchAI is a batch clinical trial matching system. Given a GA4GH Phenopacket, it retrieves candidate trials from embedded LanceDB tables, reranks eligibility criteria with an LLM, performs criterion-level reasoning, and writes ranked recommendations. - -### Goals - -- Match de-identified research patient profiles to relevant clinical trials. -- Keep deployment local-first and service-light for a single GPU server. -- Provide criterion-level explainability. -- Use ontology-grounded entity extraction and normalization. - -### Non-Goals - -- Real-time clinical web application deployment. -- Direct EHR integration. -- Use with identifiable patient data without additional controls. -- Trial outcome prediction. - -## 2. Architecture - -``` -ClinicalTrials.gov JSONs - | - v -prepare_trials.py / prepare_criteria.py - | - v -Schema entity annotation + LanceDB concept linking - | - v -BGE-M3 embeddings - | - v -data/search LanceDB tables - | - v -Phenopacket -> keywords -> Stage 1 retrieval -> Stage 2 criteria rerank - | - v -Stage 3 eligibility reasoning -> ranked_trials.json -``` - -The search layer is embedded in the Python worker. There is no separate database service to start, secure, or monitor for the default v1 deployment. - -## 3. Persistent Stores - -### `data/search` Trial Table - -One row per trial. - -| Field | Notes | -|---|---| -| `nct_id` | Primary trial identifier | -| `brief_title`, `brief_summary`, `condition`, `eligibility_criteria` | Source text fields | -| `*_vector` | Prepared BGE-M3 vectors from the existing preparation pipeline | -| `search_text` | Flattened text used for local full-text candidate generation | -| `search_vector` | Averaged vector used for vector candidate generation | -| `minimum_age`, `maximum_age`, `gender`, `overall_status` | Runtime filters | - -### `data/search` Criteria Table - -One row per eligibility criterion. - -| Field | Notes | -|---|---| -| `criteria_id` | Stable criterion identifier | -| `nct_id` | Parent trial identifier | -| `criterion` | Criterion text | -| `criterion_vector` | BGE-M3 criterion vector | -| `entities` | Schema annotations and concept-link candidates | -| `entity_text`, `entity_synonyms_text` | Flattened entity search fields | -| `search_text` | Combined criterion/entity text | - -### `data/concepts` Concept Table - -The concept table is built from OMOP `CONCEPT.csv`, `CONCEPT_SYNONYM.csv`, and optional legacy dictionaries. It is used only for entity normalization and synonym expansion. - -## 4. Retrieval - -### Stage 1 - Trial Retrieval - -`ClinicalTrialSearch` calls the configured search backend with: - -- Primary disease/condition terms. -- Synonyms from accepted concept links. -- Other patient conditions. -- Age, sex, status, and optional NCT filters. -- Query embeddings when running in `vector` or `hybrid` mode. - -The LanceDB backend generates candidates with local full-text/vector search and applies Python-side weighted scoring: - -``` -hybrid_score = 0.5 * text_score + 0.5 * vector_score -``` - -Text scoring weights trial fields by clinical relevance: - -- `condition`: 6.0 -- `eligibility_criteria`: 4.0 -- `brief_title`: 3.0 -- `brief_summary`: 2.0 -- `detailed_description`: 1.5 -- `official_title`: 1.0 - -Vector scoring combines condition, title, summary, eligibility, and other-condition similarities. - -### Stage 2 - Criteria Retrieval - -`SecondStageRetriever` searches the criteria table within the Stage 1 NCT subset. It searches criterion text and, when the entity annotator is enabled, flattened entity synonyms. - -The retriever returns ES-like hit dictionaries internally (`_source`, `_score`) to keep reranking and aggregation code stable while the backend is LanceDB-native. - -### Stage 3 - Eligibility Reasoning - -The top trials are passed to `BatchTrialProcessor` or `BatchTrialProcessorVLLM`. The model evaluates inclusion and exclusion criteria and writes structured JSON outputs. The final ranker computes a normalized eligibility score from those outputs. - -## 5. Entity Extraction - -The BioNER path is Python-native: - -- Schema-driven recognizer interface. -- GLiNER2-style backend as the target recognizer. -- GLiNER fallback and regex test backend behind the same interface. -- LanceDB concept linking with confidence bands. -- No runtime Java daemons, socket IPC, Sieve, or GNormPlus dependency. - -Entity output preserves the fields downstream search expects: - -- `entity_group` -- `text` -- `start` -- `end` -- `score` -- `normalized_id` -- `synonyms` -- `concept_candidates` -- `linker_score` -- `linker_status` - -## 6. Deployment - -Default deployment requires only the Python worker process and local mounted directories: - -- `data/` -- `models/` -- `results/` - -The application runs as a Python process and does not auto-start external services. - -## 7. Runtime Configuration - -Important settings: - -| Env var | Default | Purpose | -|---|---|---| -| `TRIALMATCHAI_SEARCH_DB_PATH` | `data/search` | LanceDB search table directory | -| `TRIALMATCHAI_SEARCH_TRIALS_TABLE` | `trials` | Trial table | -| `TRIALMATCHAI_SEARCH_CRITERIA_TABLE` | `criteria` | Criteria table | -| `TRIALMATCHAI_SEARCH_MODE` | `hybrid` | `bm25`, `vector`, or `hybrid` | -| `TRIALMATCHAI_CONCEPT_DB_PATH` | `data/concepts` | Concept linker table directory | -| `TRIALMATCHAI_ENTITY_BACKEND` | `gliner2` | Entity recognition backend | -| `TRIALMATCHAI_COT_BACKEND` | `vllm` | Reasoning backend | - -## 8. Operational Checks - -Use: - -```bash -trialmatchai-healthcheck -trialmatchai-healthcheck --require-tables -trialmatchai-index -trialmatchai-run -``` - -Preflight checks validate local paths, optional model artifacts, entity dependencies, GPU/vLLM readiness, and LanceDB table availability before expensive model startup. diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index ac39b6cc..00000000 --- a/requirements.txt +++ /dev/null @@ -1,64 +0,0 @@ -# Core ML / NLP -torch==2.11.0 -transformers==5.6.2 -accelerate==1.8.1 -tokenizers==0.22.1 -safetensors>=0.6.2,<0.7 -sentencepiece==0.2.1 -faiss-cpu==1.9.0.post1 -peft==0.15.2 -einops==0.8.0 -tiktoken==0.8.0 - -# GPU inference extra: install with `uv sync --extra gpu`. -bitsandbytes==0.49.2; sys_platform == "linux" or sys_platform == "win32" -vllm==0.23.0; sys_platform == "linux" - -# Classical stack & utilities -numpy>=2.0,<3 -scipy==1.15.1 -scikit-learn==1.6.1 -pandas==2.2.3 -python-dateutil==2.9.0.post0 -requests==2.34.2 -tenacity==9.0.0 -joblib==1.4.2 -tqdm==4.67.1 -regex>=2025.10.22,<2027 -orjson==3.11.9 -PyYAML==6.0.2 -python-dotenv==1.2.2 -rich==13.9.4 -lancedb>=0.25.0,<0.26 -pyarrow>=24.0.0,<25 - -# spaCy / medspaCy (for your medical text work) -spacy==3.7.5 -medspacy==1.3.1 -medspacy_quickumls==3.2 -PyRuSH==1.0.9 -PyFastNER==1.0.10 -pysbd==0.3.4 -quicksectx==0.4.0 -gliner==0.2.27 - -# App / infra -SQLAlchemy==2.0.37 -pydantic>=2.12.0,<3 -pydantic-settings>=2.10.1,<3 - -# LLM tooling extra: install with `uv sync --extra llm`. -openai>=2.26.0,<3 -httpx==0.28.1 -langchain==1.3.10 -langchain-community==0.4.2 -langchain-openai==1.3.2 -huggingface_hub>=1.5.0,<2 -bioregistry==0.13.62 -rapidfuzz==3.14.5 - -# Training/evaluation extra: install with `uv sync --extra training`. -datasets==2.19.0 -bert-score==0.3.13 -jinja2==3.1.6 -nltk==3.9.4 diff --git a/scripts/bootstrap_data.sh b/scripts/bootstrap_data.sh deleted file mode 100644 index e22e2369..00000000 --- a/scripts/bootstrap_data.sh +++ /dev/null @@ -1,125 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail -IFS=$'\n\t' - -DATA_URL_1="https://zenodo.org/records/15516900/files/processed_trials.tar.gz?download=1" -MODELS_URL="https://zenodo.org/records/15516900/files/models.tar.gz?download=1" -CRITERIA_ZIP_BASE_URL="https://zenodo.org/records/15516900/files" -CHUNK_PREFIX="criteria_part" -CHUNK_COUNT=6 - -ARCHIVE_1="processed_trials.tar.gz" -MODELS_ARCHIVE="models.tar.gz" - -GREEN='\033[0;32m' -NC='\033[0m' -info() { echo -e "${GREEN}[INFO]${NC} $*"; } -warn() { echo -e "[WARN] $*" >&2; } -error() { echo -e "[ERROR] $*" >&2; exit 1; } - -verify_sha256() { - local file="$1" - local expected="$2" - if [ -z "$expected" ]; then - warn "No SHA-256 checksum configured for $file; skipping verification." - return 0 - fi - local actual - actual="$(sha256sum "$file" 2>/dev/null | awk '{print $1}' || shasum -a 256 "$file" | awk '{print $1}')" - if [ "$actual" != "$expected" ]; then - error "Checksum mismatch for $file: expected $expected, got $actual" - fi -} - -assert_safe_archive_paths() { - local archive="$1" - shift - local list_cmd=("$@") - local unsafe - unsafe="$("${list_cmd[@]}" "$archive" | awk '($0 ~ /^\// || $0 ~ /(^|\/)\.\.($|\/)/) {print; exit}')" - if [ -n "$unsafe" ]; then - error "Archive $archive contains unsafe path: $unsafe" - fi -} - -extract_tar_gz() { - local archive="$1" - local target="$2" - assert_safe_archive_paths "$archive" tar -tzf - tar -xzf "$archive" -C "$target" -} - -extract_zip() { - local archive="$1" - local target="$2" - assert_safe_archive_paths "$archive" unzip -Z1 - unzip -q "$archive" -d "$target" -} - -ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" -DATA_DIR="${ROOT_DIR}/data" - -info "Preparing data directory..." -mkdir -p "$DATA_DIR" -cd "$DATA_DIR" - -if [ ! -f "$ARCHIVE_1" ]; then - info "Downloading ${ARCHIVE_1}..." - curl -fsSL "$DATA_URL_1" -o "$ARCHIVE_1" -else - info "${ARCHIVE_1} already exists. Skipping download." -fi -verify_sha256 "$ARCHIVE_1" "${TRIALMATCHAI_PROCESSED_TRIALS_SHA256:-}" - -if [ ! -f "$MODELS_ARCHIVE" ]; then - info "Downloading ${MODELS_ARCHIVE}..." - curl -fsSL "$MODELS_URL" -o "$MODELS_ARCHIVE" -else - info "${MODELS_ARCHIVE} already exists. Skipping download." -fi -verify_sha256 "$MODELS_ARCHIVE" "${TRIALMATCHAI_MODELS_SHA256:-}" - -if [ ! -d "processed_criteria" ]; then - info "Downloading and extracting processed_criteria chunks..." - mkdir -p processed_criteria - - for i in $(seq 0 $((CHUNK_COUNT - 1))); do - chunk_zip="${CHUNK_PREFIX}_${i}.zip" - chunk_url="${CRITERIA_ZIP_BASE_URL}/${chunk_zip}?download=1" - - if [ ! -f "$chunk_zip" ]; then - info "Downloading $chunk_zip..." - curl -fsSL "$chunk_url" -o "$chunk_zip" - else - info "$chunk_zip already exists. Skipping download." - fi - - checksum_var="TRIALMATCHAI_CRITERIA_PART_${i}_SHA256" - verify_sha256 "$chunk_zip" "${!checksum_var:-}" - info "Extracting $chunk_zip into processed_criteria..." - extract_zip "$chunk_zip" processed_criteria - done -else - info "processed_criteria already exists. Skipping extraction." -fi - -if [ ! -d "processed_trials" ]; then - info "Extracting $ARCHIVE_1..." - extract_tar_gz "$ARCHIVE_1" "$DATA_DIR" -else - info "processed_trials already exists. Skipping extraction of $ARCHIVE_1." -fi - -cd "$ROOT_DIR" - -info "Extracting models into models/..." -mkdir -p models -extract_tar_gz "$DATA_DIR/$MODELS_ARCHIVE" models - -info "Cleaning up archives..." -rm -f "$DATA_DIR/$ARCHIVE_1" "$DATA_DIR/$MODELS_ARCHIVE" -for i in $(seq 0 $((CHUNK_COUNT - 1))); do - rm -f "$DATA_DIR/${CHUNK_PREFIX}_${i}.zip" -done - -info "Data bootstrap complete." diff --git a/scripts/index_data.sh b/scripts/index_data.sh deleted file mode 100644 index 732bb433..00000000 --- a/scripts/index_data.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail -IFS=$'\n\t' - -GREEN='\033[0;32m' -NC='\033[0m' -info() { echo -e "${GREEN}[INFO]${NC} $*"; } -error() { echo -e "[ERROR] $*" >&2; exit 1; } - -ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" - -cd "$ROOT_DIR" -info "Building LanceDB search tables ..." -uv run trialmatchai-index \ - --processed-trials-folder data/processed_trials \ - --processed-criteria-folder data/processed_criteria \ - --recreate -info "Indexing complete." diff --git a/setup.sh b/setup.sh deleted file mode 100644 index 1d17395a..00000000 --- a/setup.sh +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail -IFS=$'\n\t' - -#=== COLORS ===# -GREEN='\033[0;32m' -NC='\033[0m' # No Color - -#=== HELPERS ===# -info() { echo -e "${GREEN}[INFO]${NC} $*"; } -error() { echo -e "[ERROR] $*" >&2; exit 1; } - -#=== MAIN SCRIPT ===# -info "Starting TrialMatchAI setup..." - -# 0) Check for available GPUs -info "Checking for available GPUs..." - -if command -v nvidia-smi &> /dev/null; then - if nvidia-smi &> /dev/null; then - info "NVIDIA GPUs detected:" - nvidia-smi --query-gpu=index,name,memory.total --format=csv - else - info "nvidia-smi found, but no NVIDIA GPU detected or driver not loaded." - fi -else - info "No NVIDIA GPUs detected." -fi - -# 1) Install Python dependencies -if command -v uv &> /dev/null; then - info "Installing Python dependencies with uv..." - uv sync - RUNNER=(uv run) -else - if ! command -v pip &> /dev/null; then - error "pip not found. Please install Python and pip first." - fi - info "Installing Python requirements with pip..." - pip install --upgrade pip - pip install -r requirements.txt - pip install -e . - RUNNER=() -fi - -# 2) Prepare data and models -info "Bootstrapping data and models..." -"${RUNNER[@]}" trialmatchai-bootstrap-data - -# 3) Build local LanceDB search tables -info "Indexing trial data..." -"${RUNNER[@]}" trialmatchai-index - -info "TrialMatchAI setup is complete." diff --git a/src/trialmatchai/cli/bootstrap_data.py b/src/trialmatchai/cli/bootstrap_data.py index 69d9944a..ea1a5002 100644 --- a/src/trialmatchai/cli/bootstrap_data.py +++ b/src/trialmatchai/cli/bootstrap_data.py @@ -1,35 +1,221 @@ from __future__ import annotations import argparse -import subprocess +import hashlib +import os import sys +import tarfile +import zipfile +from collections.abc import Sequence from pathlib import Path +import requests -def main() -> int: +DATA_URL = "https://zenodo.org/records/15516900/files/processed_trials.tar.gz?download=1" +MODELS_URL = "https://zenodo.org/records/15516900/files/models.tar.gz?download=1" +CRITERIA_ZIP_BASE_URL = "https://zenodo.org/records/15516900/files" +CHUNK_PREFIX = "criteria_part" +CHUNK_COUNT = 6 +PROCESSED_TRIALS_ARCHIVE = "processed_trials.tar.gz" +MODELS_ARCHIVE = "models.tar.gz" + + +def main(argv: Sequence[str] | None = None) -> int: parser = argparse.ArgumentParser( description="Download and prepare TrialMatchAI data and model artifacts" ) parser.add_argument( - "--script", - default="scripts/bootstrap_data.sh", - help="Bootstrap script path relative to the repository root", + "--root", + type=Path, + default=None, + help="Runtime root for data/ and models/; defaults to repository root or current directory", + ) + parser.add_argument( + "--data-url", + default=DATA_URL, + help="processed_trials.tar.gz URL", + ) + parser.add_argument( + "--models-url", + default=MODELS_URL, + help="models.tar.gz URL", ) - args = parser.parse_args() + parser.add_argument( + "--criteria-base-url", + default=CRITERIA_ZIP_BASE_URL, + help="Base URL containing criteria_part_.zip chunks", + ) + parser.add_argument( + "--criteria-chunks", + type=int, + default=CHUNK_COUNT, + help="Number of criteria zip chunks to download", + ) + parser.add_argument( + "--skip-models", + action="store_true", + help="Do not download or extract model artifacts", + ) + parser.add_argument( + "--force", + action="store_true", + help="Re-extract archives even when target directories already exist", + ) + args = parser.parse_args(argv) + + root = (args.root or _runtime_root()).resolve() + bootstrap_data( + root=root, + data_url=args.data_url, + models_url=args.models_url, + criteria_base_url=args.criteria_base_url, + criteria_chunks=args.criteria_chunks, + skip_models=args.skip_models, + force=args.force, + ) + return 0 + + +def bootstrap_data( + *, + root: Path, + data_url: str = DATA_URL, + models_url: str = MODELS_URL, + criteria_base_url: str = CRITERIA_ZIP_BASE_URL, + criteria_chunks: int = CHUNK_COUNT, + skip_models: bool = False, + force: bool = False, +) -> None: + data_dir = root / "data" + models_dir = root / "models" + data_dir.mkdir(parents=True, exist_ok=True) + + criteria_dir = data_dir / "processed_criteria" + if force or not _has_entries(criteria_dir): + criteria_dir.mkdir(parents=True, exist_ok=True) + for index in range(criteria_chunks): + chunk_name = f"{CHUNK_PREFIX}_{index}.zip" + chunk_path = data_dir / chunk_name + _download_if_missing( + f"{criteria_base_url.rstrip('/')}/{chunk_name}?download=1", + chunk_path, + ) + _verify_sha256( + chunk_path, + os.getenv(f"TRIALMATCHAI_CRITERIA_PART_{index}_SHA256"), + ) + _safe_extract_zip(chunk_path, criteria_dir) + + processed_trials_dir = data_dir / "processed_trials" + if force or not _has_entries(processed_trials_dir): + processed_archive = data_dir / PROCESSED_TRIALS_ARCHIVE + _download_if_missing(data_url, processed_archive) + _verify_sha256( + processed_archive, os.getenv("TRIALMATCHAI_PROCESSED_TRIALS_SHA256") + ) + _safe_extract_tar_gz(processed_archive, data_dir) + + if not skip_models: + models_dir.mkdir(parents=True, exist_ok=True) + if force or not _has_entries(models_dir): + models_archive = data_dir / MODELS_ARCHIVE + _download_if_missing(models_url, models_archive) + _verify_sha256(models_archive, os.getenv("TRIALMATCHAI_MODELS_SHA256")) + _safe_extract_tar_gz(models_archive, models_dir) - root = _repo_root() - script = (root / args.script).resolve() - if not script.exists(): - raise FileNotFoundError(f"Bootstrap script not found: {script}") - return subprocess.run(["bash", str(script)], cwd=str(root), check=False).returncode + _cleanup_archives(data_dir, criteria_chunks) -def _repo_root() -> Path: +def _download_if_missing(url: str, destination: Path) -> None: + if destination.exists(): + _info(f"{destination.name} already exists; skipping download.") + return + + _info(f"Downloading {destination.name}...") + destination.parent.mkdir(parents=True, exist_ok=True) + response = requests.get(url, stream=True, timeout=120) + response.raise_for_status() + with destination.open("wb") as file: + for chunk in response.iter_content(chunk_size=1024 * 1024): + if chunk: + file.write(chunk) + + +def _verify_sha256(path: Path, expected: str | None) -> None: + if not expected: + _warn(f"No SHA-256 checksum configured for {path.name}; skipping verification.") + return + + digest = hashlib.sha256() + with path.open("rb") as file: + for chunk in iter(lambda: file.read(1024 * 1024), b""): + digest.update(chunk) + actual = digest.hexdigest() + if actual != expected: + raise ValueError( + f"Checksum mismatch for {path}: expected {expected}, got {actual}" + ) + + +def _safe_extract_tar_gz(archive: Path, target: Path) -> None: + target.mkdir(parents=True, exist_ok=True) + with tarfile.open(archive, "r:gz") as tar: + for member in tar.getmembers(): + _validated_target_path(target, member.name) + if member.issym() or member.islnk() or member.isdev(): + raise ValueError(f"Archive contains an unsafe member: {member.name}") + tar.extractall(target) + + +def _safe_extract_zip(archive: Path, target: Path) -> None: + target.mkdir(parents=True, exist_ok=True) + with zipfile.ZipFile(archive) as zip_file: + for member in zip_file.namelist(): + _validated_target_path(target, member) + zip_file.extractall(target) + + +def _validated_target_path(target: Path, member_name: str) -> Path: + if not member_name: + raise ValueError("Archive contains an empty path") + member_path = Path(member_name) + if member_path.is_absolute(): + raise ValueError(f"Archive contains an absolute path: {member_name}") + + resolved_target = target.resolve() + resolved_member = (resolved_target / member_path).resolve() + try: + resolved_member.relative_to(resolved_target) + except ValueError as exc: + raise ValueError(f"Archive contains an unsafe path: {member_name}") from exc + return resolved_member + + +def _cleanup_archives(data_dir: Path, criteria_chunks: int) -> None: + for path in [data_dir / PROCESSED_TRIALS_ARCHIVE, data_dir / MODELS_ARCHIVE]: + path.unlink(missing_ok=True) + for index in range(criteria_chunks): + (data_dir / f"{CHUNK_PREFIX}_{index}.zip").unlink(missing_ok=True) + + +def _has_entries(path: Path) -> bool: + return path.exists() and any(path.iterdir()) + + +def _runtime_root() -> Path: start = Path(__file__).resolve() for parent in start.parents: if (parent / "pyproject.toml").exists(): return parent - return Path.cwd().resolve() + return Path.cwd() + + +def _info(message: str) -> None: + print(f"[INFO] {message}") + + +def _warn(message: str) -> None: + print(f"[WARN] {message}", file=sys.stderr) if __name__ == "__main__": diff --git a/src/trialmatchai/cli/main.py b/src/trialmatchai/cli/main.py index efadc86e..ce6d48c5 100644 --- a/src/trialmatchai/cli/main.py +++ b/src/trialmatchai/cli/main.py @@ -12,6 +12,7 @@ def main() -> int: subparsers = parser.add_subparsers(dest="command", required=True) subparsers.add_parser("healthcheck", help="Run deployment health checks") + subparsers.add_parser("bootstrap-data", help="Download data and model artifacts") subparsers.add_parser("index", help="Build LanceDB search tables") subparsers.add_parser("build-concepts", help="Build LanceDB concept table") subparsers.add_parser("update-registry", help="Fetch and upsert registry studies") @@ -20,6 +21,8 @@ def main() -> int: args, remainder = parser.parse_known_args() if args.command == "healthcheck": from trialmatchai.cli.healthcheck import main as command + elif args.command == "bootstrap-data": + from trialmatchai.cli.bootstrap_data import main as command elif args.command == "index": from trialmatchai.cli.index_data import main as command elif args.command == "build-concepts": diff --git a/tests/test_bootstrap_data.py b/tests/test_bootstrap_data.py new file mode 100644 index 00000000..1e09499c --- /dev/null +++ b/tests/test_bootstrap_data.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +import hashlib +import io +import tarfile +import zipfile +from pathlib import Path + +import pytest + +from trialmatchai.cli.bootstrap_data import ( + MODELS_ARCHIVE, + PROCESSED_TRIALS_ARCHIVE, + bootstrap_data, + _safe_extract_tar_gz, + _verify_sha256, +) + + +def test_bootstrap_data_uses_existing_archives_and_removes_them(tmp_path, monkeypatch): + data_dir = tmp_path / "data" + data_dir.mkdir() + + processed_archive = data_dir / PROCESSED_TRIALS_ARCHIVE + models_archive = data_dir / MODELS_ARCHIVE + criteria_archive = data_dir / "criteria_part_0.zip" + + _write_tar_gz( + processed_archive, + {"processed_trials/NCT000001.json": b'{"nct_id": "NCT000001"}'}, + ) + _write_tar_gz(models_archive, {"demo-model/config.json": b"{}"}) + _write_zip(criteria_archive, {"criterion.txt": "Age >= 18"}) + + monkeypatch.setenv( + "TRIALMATCHAI_PROCESSED_TRIALS_SHA256", _sha256(processed_archive) + ) + monkeypatch.setenv("TRIALMATCHAI_MODELS_SHA256", _sha256(models_archive)) + monkeypatch.setenv("TRIALMATCHAI_CRITERIA_PART_0_SHA256", _sha256(criteria_archive)) + + bootstrap_data( + root=tmp_path, + data_url="https://example.invalid/processed_trials.tar.gz", + models_url="https://example.invalid/models.tar.gz", + criteria_base_url="https://example.invalid", + criteria_chunks=1, + ) + + assert (tmp_path / "data/processed_trials/NCT000001.json").exists() + assert (tmp_path / "data/processed_criteria/criterion.txt").exists() + assert (tmp_path / "models/demo-model/config.json").exists() + assert not processed_archive.exists() + assert not models_archive.exists() + assert not criteria_archive.exists() + + bootstrap_data( + root=tmp_path, + data_url="https://example.invalid/missing-processed_trials.tar.gz", + models_url="https://example.invalid/missing-models.tar.gz", + criteria_base_url="https://example.invalid/missing", + criteria_chunks=1, + ) + + +def test_verify_sha256_rejects_mismatches(tmp_path): + path = tmp_path / "artifact.txt" + path.write_text("contents") + + with pytest.raises(ValueError, match="Checksum mismatch"): + _verify_sha256(path, "0" * 64) + + +def test_tar_extraction_rejects_path_traversal(tmp_path): + archive = tmp_path / "unsafe.tar.gz" + with tarfile.open(archive, "w:gz") as tar: + data = b"bad" + info = tarfile.TarInfo("../escape.txt") + info.size = len(data) + tar.addfile(info, io.BytesIO(data)) + + with pytest.raises(ValueError, match="unsafe path"): + _safe_extract_tar_gz(archive, tmp_path / "target") + + +def test_tar_extraction_rejects_links(tmp_path): + archive = tmp_path / "unsafe-link.tar.gz" + with tarfile.open(archive, "w:gz") as tar: + info = tarfile.TarInfo("link") + info.type = tarfile.SYMTYPE + info.linkname = "/etc/passwd" + tar.addfile(info) + + with pytest.raises(ValueError, match="unsafe member"): + _safe_extract_tar_gz(archive, tmp_path / "target") + + +def _write_tar_gz(path: Path, files: dict[str, bytes]) -> None: + with tarfile.open(path, "w:gz") as tar: + for name, data in files.items(): + info = tarfile.TarInfo(name) + info.size = len(data) + tar.addfile(info, io.BytesIO(data)) + + +def _write_zip(path: Path, files: dict[str, str]) -> None: + with zipfile.ZipFile(path, "w") as zip_file: + for name, data in files.items(): + zip_file.writestr(name, data) + + +def _sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() diff --git a/tests/test_package_imports.py b/tests/test_package_imports.py index 3c556292..56a14e37 100644 --- a/tests/test_package_imports.py +++ b/tests/test_package_imports.py @@ -2,6 +2,8 @@ import warnings +import pytest + import trialmatchai @@ -16,3 +18,14 @@ def test_matcher_config_compatibility_shim(): assert resolve_config_path().name == "config.json" assert any("Matcher" in str(warning.message) for warning in captured) + + +def test_command_group_includes_bootstrap_data(monkeypatch, capsys): + from trialmatchai.cli.main import main + + monkeypatch.setattr("sys.argv", ["trialmatchai", "--help"]) + with pytest.raises(SystemExit) as exc: + main() + + assert exc.value.code == 0 + assert "bootstrap-data" in capsys.readouterr().out From 3f737b258e0d4bc004b42ccd6fdea369778e26ea Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 21 Jun 2026 21:08:10 +0200 Subject: [PATCH 07/64] Add patient interoperability profiles --- .env.example | 6 + README.md | 18 + docs/interoperability.md | 105 +++++ pyproject.toml | 1 + src/trialmatchai/cli/import_patient.py | 100 +++++ src/trialmatchai/cli/main.py | 35 +- src/trialmatchai/config/config.json | 8 + src/trialmatchai/config/config_loader.py | 6 + src/trialmatchai/config/settings.py | 19 + src/trialmatchai/interop/__init__.py | 25 ++ src/trialmatchai/interop/detect.py | 57 +++ .../interop/exporters/__init__.py | 9 + src/trialmatchai/interop/exporters/fhir.py | 83 ++++ .../interop/exporters/matching_summary.py | 28 ++ .../interop/exporters/phenopacket.py | 87 ++++ .../interop/importers/__init__.py | 35 ++ src/trialmatchai/interop/importers/fhir.py | 380 ++++++++++++++++++ src/trialmatchai/interop/importers/omop.py | 322 +++++++++++++++ .../interop/importers/phenopacket.py | 299 ++++++++++++++ src/trialmatchai/interop/importers/text.py | 116 ++++++ src/trialmatchai/interop/models.py | 165 ++++++++ src/trialmatchai/interop/narrative.py | 101 +++++ src/trialmatchai/interop/utils.py | 207 ++++++++++ src/trialmatchai/main.py | 114 ++++-- .../matching/eligibility_reasoning_vllm.py | 16 +- src/trialmatchai/services/preflight.py | 30 +- tests/test_import_patient_cli.py | 36 ++ tests/test_package_imports.py | 4 +- tests/test_patient_interop.py | 220 ++++++++++ tests/test_patient_runtime_loading.py | 75 ++++ tests/test_settings.py | 7 + 31 files changed, 2658 insertions(+), 56 deletions(-) create mode 100644 docs/interoperability.md create mode 100644 src/trialmatchai/cli/import_patient.py create mode 100644 src/trialmatchai/interop/__init__.py create mode 100644 src/trialmatchai/interop/detect.py create mode 100644 src/trialmatchai/interop/exporters/__init__.py create mode 100644 src/trialmatchai/interop/exporters/fhir.py create mode 100644 src/trialmatchai/interop/exporters/matching_summary.py create mode 100644 src/trialmatchai/interop/exporters/phenopacket.py create mode 100644 src/trialmatchai/interop/importers/__init__.py create mode 100644 src/trialmatchai/interop/importers/fhir.py create mode 100644 src/trialmatchai/interop/importers/omop.py create mode 100644 src/trialmatchai/interop/importers/phenopacket.py create mode 100644 src/trialmatchai/interop/importers/text.py create mode 100644 src/trialmatchai/interop/models.py create mode 100644 src/trialmatchai/interop/narrative.py create mode 100644 src/trialmatchai/interop/utils.py create mode 100644 tests/test_import_patient_cli.py create mode 100644 tests/test_patient_interop.py create mode 100644 tests/test_patient_runtime_loading.py diff --git a/.env.example b/.env.example index 01b25ca0..9f28e37e 100644 --- a/.env.example +++ b/.env.example @@ -4,6 +4,12 @@ TRIALMATCHAI_PATIENTS_DIR=example TRIALMATCHAI_OUTPUT_DIR=results TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons +TRIALMATCHAI_PATIENT_RAW_DIR=data/patients/raw +TRIALMATCHAI_PATIENT_PROFILE_DIR=data/patients/profiles +TRIALMATCHAI_PATIENT_SUMMARY_DIR=data/patients/summaries +TRIALMATCHAI_PATIENT_INPUT_FORMAT=auto +TRIALMATCHAI_PATIENT_STRICT_VALIDATION=false +TRIALMATCHAI_PATIENT_COPY_RAW=true TRIALMATCHAI_SEARCH_BACKEND=lancedb TRIALMATCHAI_SEARCH_DB_PATH=data/search diff --git a/README.md b/README.md index 4593dcd7..7b495acd 100644 --- a/README.md +++ b/README.md @@ -69,6 +69,15 @@ uv run trialmatchai-update-registry --since 2026-06-01 --max-studies 100 uv run trialmatchai-index --prepare ``` +Import patient data into canonical profiles before matching: + +```bash +uv run trialmatchai-import-patient --input data/patients/raw/patient-1.txt --format text +uv run trialmatchai-import-patient --input data/patients/raw/patient-1.fhir.json +uv run trialmatchai-import-patient --input data/patients/raw/patient-1.phenopacket.json +uv run trialmatchai-import-patient --input data/patients/omop_extract --format omop +``` + Run the batch matcher: ```bash @@ -85,6 +94,11 @@ Configuration defaults live in `src/trialmatchai/config/config.json`. Runtime ov TRIALMATCHAI_PATIENTS_DIR=example TRIALMATCHAI_OUTPUT_DIR=results TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons +TRIALMATCHAI_PATIENT_RAW_DIR=data/patients/raw +TRIALMATCHAI_PATIENT_PROFILE_DIR=data/patients/profiles +TRIALMATCHAI_PATIENT_SUMMARY_DIR=data/patients/summaries +TRIALMATCHAI_PATIENT_INPUT_FORMAT=auto +TRIALMATCHAI_PATIENT_STRICT_VALIDATION=false TRIALMATCHAI_SEARCH_BACKEND=lancedb TRIALMATCHAI_SEARCH_DB_PATH=data/search @@ -122,6 +136,7 @@ Use `TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=true` only when a selected model expli - `trialmatchai-build-concepts`: build the LanceDB concept table used for entity normalization. - `trialmatchai-update-registry`: fetch new/changed ClinicalTrials.gov studies, write normalized JSON, and upsert LanceDB. - `trialmatchai-index`: build the LanceDB trial and criteria search tables from prepared rows or `--prepare` normalized JSON. +- `trialmatchai-import-patient`: import text, Phenopacket, FHIR, or OMOP patient data into canonical profiles. - `trialmatchai-run`: run the batch matching pipeline. The command group is also available as: @@ -129,9 +144,12 @@ The command group is also available as: ```bash uv run python -m trialmatchai healthcheck uv run trialmatchai bootstrap-data --skip-models +uv run trialmatchai import-patient --input data/patients/raw/patient-1.txt --format text uv run trialmatchai update-registry --dry-run --max-studies 25 ``` +Patient interoperability details are documented in `docs/interoperability.md`. + ## Registry Updater The registry updater is designed for cron, systemd timers, and GitHub Actions. It uses ClinicalTrials.gov v2 `/api/v2/studies`, stores raw source JSON under `data/registry/raw`, writes normalized trial JSON under `data/trials_jsons`, appends idempotency records to `data/registry/manifest.jsonl`, and upserts only new or changed studies into LanceDB. diff --git a/docs/interoperability.md b/docs/interoperability.md new file mode 100644 index 00000000..af75690a --- /dev/null +++ b/docs/interoperability.md @@ -0,0 +1,105 @@ +# Patient Interoperability + +TrialMatchAI uses a canonical `PatientProfile` for patient data. Source-specific +importers preserve raw evidence and provenance, then exporters render the profile +for matching, LLM context, and optional standards output. + +## Supported v1 Inputs + +- Free text notes: `.txt` and `.md` files. +- GA4GH Phenopacket JSON. +- HL7 FHIR R4 Bundle JSON, individual FHIR resource JSON, and NDJSON. +- OMOP CDM extract folders with CSV or Parquet tables. + +The first import milestone intentionally does not implement C-CDA, HL7 v2, +DICOMweb, or CDISC adapters. Those formats should be added as importers on top of +the same `PatientProfile` model, not as new runtime data models. + +## Canonical Profile + +Each imported patient is written to `data/patients/profiles/.json`. +The profile contains: + +- demographics +- conditions +- phenotypes +- observations, labs, and vitals +- medications +- procedures +- diagnostic reports +- genomic findings +- cancer profile fields +- family history +- notes +- source documents +- provenance and unsupported source elements + +Every clinical fact carries source format, source path, source resource or table, +original code, normalized code candidates, evidence text and offsets when +available, confidence, negation, temporality, and mapping status. + +## Narrative Generation + +Narrative rendering is deliberately separate from import. Importers create +structured facts. `trialmatchai.interop.narrative` turns those facts into +deterministic sentences for retrieval and LLM context. This keeps source parsing, +normalization, and prompt context generation independently testable. + +The matching exporter writes `data/patients/summaries/.json` with the +current-compatible fields: + +- `main_conditions` +- `other_conditions` +- `expanded_sentences` +- `age` +- `gender` +- `provenance` + +`trialmatchai-run` consumes canonical profiles first. If no profiles exist, it +temporarily imports legacy Phenopacket JSON files from `paths.patients_dir` and +writes profiles and summaries before matching. + +## Commands + +Import a free text note: + +```bash +uv run trialmatchai-import-patient \ + --input data/patients/raw/patient-1.txt \ + --format text +``` + +Import a Phenopacket: + +```bash +uv run trialmatchai import-patient \ + --input data/patients/raw/patient-1.phenopacket.json +``` + +Import a FHIR Bundle: + +```bash +uv run trialmatchai-import-patient \ + --input data/patients/raw/patient-1.fhir.json +``` + +Import an OMOP extract folder: + +```bash +uv run trialmatchai-import-patient \ + --input data/patients/omop_extract \ + --format omop +``` + +Then run matching: + +```bash +uv run trialmatchai-run +``` + +## Conversion Limits + +FHIR and Phenopacket exports are best-effort views of `PatientProfile`. +Unsupported or lossy fields remain in the profile and are reported instead of +being silently dropped. OMOP support is file-based for v1; live database +connectors are out of scope. diff --git a/pyproject.toml b/pyproject.toml index b8212c2c..e83f1479 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,6 +58,7 @@ trialmatchai-bootstrap-data = "trialmatchai.cli.bootstrap_data:main" trialmatchai-index = "trialmatchai.cli.index_data:main" trialmatchai-build-concepts = "trialmatchai.cli.build_concepts:main" trialmatchai-update-registry = "trialmatchai.cli.update_registry:main" +trialmatchai-import-patient = "trialmatchai.cli.import_patient:main" [dependency-groups] dev = [ diff --git a/src/trialmatchai/cli/import_patient.py b/src/trialmatchai/cli/import_patient.py new file mode 100644 index 00000000..3c642174 --- /dev/null +++ b/src/trialmatchai/cli/import_patient.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any + +from trialmatchai.config.config_loader import load_config +from trialmatchai.interop.exporters import profile_to_matching_summary +from trialmatchai.interop.importers import import_patient_path +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Import patient data into canonical TrialMatchAI profiles." + ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument( + "--input", + required=True, + help="Patient input file or OMOP extract directory.", + ) + parser.add_argument( + "--format", + default="auto", + choices=["auto", "text", "phenopacket", "fhir", "fhir-ndjson", "omop"], + help="Input format. Defaults to auto-detection.", + ) + parser.add_argument( + "--output-dir", + default=None, + help="Profile output directory. Defaults to config patient_inputs.profile_dir.", + ) + parser.add_argument( + "--summary-dir", + default=None, + help="Matching summary output directory. Defaults to config patient_inputs.summary_dir.", + ) + parser.add_argument( + "--strict", + action="store_true", + help="Fail on invalid or unsupported source records instead of degrading.", + ) + parser.add_argument( + "--no-entities", + action="store_true", + help="Skip model-backed entity annotation for free-text inputs.", + ) + args = parser.parse_args() + + config = load_config(args.config) + patient_cfg = config.get("patient_inputs", {}) + output_dir = Path(args.output_dir or patient_cfg.get("profile_dir", "data/patients/profiles")) + summary_dir = Path(args.summary_dir or patient_cfg.get("summary_dir", "data/patients/summaries")) + output_dir.mkdir(parents=True, exist_ok=True) + summary_dir.mkdir(parents=True, exist_ok=True) + + entity_annotator = None if args.no_entities else _try_build_entity_annotator(config) + profiles = import_patient_path( + args.input, + input_format=args.format, + entity_annotator=entity_annotator, + strict=args.strict or bool(patient_cfg.get("strict_validation", False)), + ) + if not profiles: + logger.error("No patient profiles were imported from %s.", args.input) + return 1 + + for profile in profiles: + profile_path = output_dir / f"{profile.patient_id}.json" + summary_path = summary_dir / f"{profile.patient_id}.json" + _write_json(profile.model_dump(mode="json", exclude_none=True), profile_path) + _write_json(profile_to_matching_summary(profile), summary_path) + logger.info("Imported patient profile %s -> %s", profile.patient_id, profile_path) + return 0 + + +def _try_build_entity_annotator(config: dict[str, Any]): + try: + from trialmatchai.entities import build_entity_annotator + + return build_entity_annotator(config) + except Exception as exc: + logger.warning("Entity annotation unavailable; importing without entities: %s", exc) + return None + + +def _write_json(payload: dict, path: Path) -> None: + path.write_text( + json.dumps(payload, indent=2, sort_keys=True, ensure_ascii=False), + encoding="utf-8", + ) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/trialmatchai/cli/main.py b/src/trialmatchai/cli/main.py index ce6d48c5..f5393719 100644 --- a/src/trialmatchai/cli/main.py +++ b/src/trialmatchai/cli/main.py @@ -11,12 +11,33 @@ def main() -> int: ) subparsers = parser.add_subparsers(dest="command", required=True) - subparsers.add_parser("healthcheck", help="Run deployment health checks") - subparsers.add_parser("bootstrap-data", help="Download data and model artifacts") - subparsers.add_parser("index", help="Build LanceDB search tables") - subparsers.add_parser("build-concepts", help="Build LanceDB concept table") - subparsers.add_parser("update-registry", help="Fetch and upsert registry studies") - subparsers.add_parser("run", help="Run the matching pipeline") + subparsers.add_parser( + "healthcheck", + help="Run deployment health checks", + add_help=False, + ) + subparsers.add_parser( + "bootstrap-data", + help="Download data and model artifacts", + add_help=False, + ) + subparsers.add_parser("index", help="Build LanceDB search tables", add_help=False) + subparsers.add_parser( + "build-concepts", + help="Build LanceDB concept table", + add_help=False, + ) + subparsers.add_parser( + "update-registry", + help="Fetch and upsert registry studies", + add_help=False, + ) + subparsers.add_parser( + "import-patient", + help="Import patient data profiles", + add_help=False, + ) + subparsers.add_parser("run", help="Run the matching pipeline", add_help=False) args, remainder = parser.parse_known_args() if args.command == "healthcheck": @@ -29,6 +50,8 @@ def main() -> int: from trialmatchai.cli.build_concepts import main as command elif args.command == "update-registry": from trialmatchai.cli.update_registry import main as command + elif args.command == "import-patient": + from trialmatchai.cli.import_patient import main as command elif args.command == "run": from trialmatchai.cli.run import main as command else: # pragma: no cover - argparse enforces choices diff --git a/src/trialmatchai/config/config.json b/src/trialmatchai/config/config.json index afa3bf2e..187bfc37 100644 --- a/src/trialmatchai/config/config.json +++ b/src/trialmatchai/config/config.json @@ -23,6 +23,14 @@ "output_dir": "results", "trials_json_folder": "data/trials_jsons" }, + "patient_inputs": { + "raw_dir": "data/patients/raw", + "profile_dir": "data/patients/profiles", + "summary_dir": "data/patients/summaries", + "default_format": "auto", + "strict_validation": false, + "copy_raw": true + }, "model": { "base_model": "microsoft/phi-4", "base_model_revision": null, diff --git a/src/trialmatchai/config/config_loader.py b/src/trialmatchai/config/config_loader.py index 464c5eb3..5c5c9539 100644 --- a/src/trialmatchai/config/config_loader.py +++ b/src/trialmatchai/config/config_loader.py @@ -87,6 +87,12 @@ def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, if value: cfg["paths"][key] = str(_resolve_local_path(value, root)) + patient_inputs = cfg.get("patient_inputs", {}) + for key in ("raw_dir", "profile_dir", "summary_dir"): + value = patient_inputs.get(key) + if value: + patient_inputs[key] = str(_resolve_local_path(value, root)) + schema_path = cfg.get("entity_extraction", {}).get("schema_path") if schema_path: cfg["entity_extraction"]["schema_path"] = str( diff --git a/src/trialmatchai/config/settings.py b/src/trialmatchai/config/settings.py index b60b400a..2ba011e2 100644 --- a/src/trialmatchai/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -40,6 +40,15 @@ class PathsSettings(BaseModel): trials_json_folder: str +class PatientInputSettings(BaseModel): + raw_dir: str = "data/patients/raw" + profile_dir: str = "data/patients/profiles" + summary_dir: str = "data/patients/summaries" + default_format: Literal["auto", "text", "phenopacket", "fhir", "fhir-ndjson", "omop"] = "auto" + strict_validation: bool = False + copy_raw: bool = True + + class ModelQuantizationSettings(BaseModel): load_in_4bit: bool = True bnb_4bit_use_double_quant: bool = True @@ -150,6 +159,7 @@ class TrialMatchSettings(BaseModel): tokenizer: TokenizerSettings global_: GlobalSettings = Field(alias="global") search_backend: SearchBackendSettings = Field(default_factory=SearchBackendSettings) + patient_inputs: PatientInputSettings = Field(default_factory=PatientInputSettings) registry: RegistrySettings = Field(default_factory=RegistrySettings) embedder: EmbedderSettings cot: CotSettings @@ -179,6 +189,10 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "TRIALMATCHAI_PATIENTS_DIR": ("paths", "patients_dir"), "TRIALMATCHAI_OUTPUT_DIR": ("paths", "output_dir"), "TRIALMATCHAI_TRIALS_JSON_FOLDER": ("paths", "trials_json_folder"), + "TRIALMATCHAI_PATIENT_RAW_DIR": ("patient_inputs", "raw_dir"), + "TRIALMATCHAI_PATIENT_PROFILE_DIR": ("patient_inputs", "profile_dir"), + "TRIALMATCHAI_PATIENT_SUMMARY_DIR": ("patient_inputs", "summary_dir"), + "TRIALMATCHAI_PATIENT_INPUT_FORMAT": ("patient_inputs", "default_format"), "TRIALMATCHAI_SEARCH_BACKEND": ("search_backend", "backend"), "TRIALMATCHAI_SEARCH_DB_PATH": ("search_backend", "db_path"), "TRIALMATCHAI_SEARCH_TRIALS_TABLE": ("search_backend", "trials_table"), @@ -244,6 +258,11 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "trust_remote_code", ), "TRIALMATCHAI_CONCEPT_LINKER_ENABLED": ("concept_linker", "enabled"), + "TRIALMATCHAI_PATIENT_STRICT_VALIDATION": ( + "patient_inputs", + "strict_validation", + ), + "TRIALMATCHAI_PATIENT_COPY_RAW": ("patient_inputs", "copy_raw"), } for env_key, path in bool_env_map.items(): value = os.getenv(env_key) diff --git a/src/trialmatchai/interop/__init__.py b/src/trialmatchai/interop/__init__.py new file mode 100644 index 00000000..a7379cbe --- /dev/null +++ b/src/trialmatchai/interop/__init__.py @@ -0,0 +1,25 @@ +from trialmatchai.interop.detect import detect_patient_input_format +from trialmatchai.interop.importers import import_patient_path +from trialmatchai.interop.models import ( + ClinicalFact, + Demographics, + EvidenceSpan, + NormalizedCode, + PatientNote, + PatientProfile, + Provenance, + SourceDocument, +) + +__all__ = [ + "ClinicalFact", + "Demographics", + "EvidenceSpan", + "NormalizedCode", + "PatientNote", + "PatientProfile", + "Provenance", + "SourceDocument", + "detect_patient_input_format", + "import_patient_path", +] diff --git a/src/trialmatchai/interop/detect.py b/src/trialmatchai/interop/detect.py new file mode 100644 index 00000000..55302067 --- /dev/null +++ b/src/trialmatchai/interop/detect.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Literal + + +PatientInputFormat = Literal["text", "phenopacket", "fhir", "fhir-ndjson", "omop"] + +OMOP_TABLE_NAMES = { + "person", + "condition_occurrence", + "measurement", + "drug_exposure", + "procedure_occurrence", + "observation", + "note", + "note_nlp", +} + + +def detect_patient_input_format(path: str | Path) -> PatientInputFormat: + candidate = Path(path) + if candidate.is_dir(): + table_stems = { + item.stem.casefold() + for item in candidate.iterdir() + if item.suffix.casefold() in {".csv", ".parquet"} + } + if table_stems & OMOP_TABLE_NAMES: + return "omop" + raise ValueError(f"Could not detect patient input format for directory: {path}") + + suffix = candidate.suffix.casefold() + if suffix in {".txt", ".md"}: + return "text" + if suffix == ".ndjson": + return "fhir-ndjson" + if suffix not in {".json", ".jsonl"}: + raise ValueError(f"Unsupported patient input file extension: {suffix}") + + with candidate.open("r", encoding="utf-8") as handle: + data = json.load(handle) + if isinstance(data, dict): + if data.get("resourceType") == "Bundle": + return "fhir" + if data.get("resourceType"): + return "fhir" + if "metaData" in data and ("subject" in data or "phenotypicFeatures" in data): + return "phenopacket" + if {"id", "subject"} <= set(data): + return "phenopacket" + if isinstance(data, list) and any( + isinstance(item, dict) and item.get("resourceType") for item in data + ): + return "fhir" + raise ValueError(f"Could not detect patient input format for file: {path}") diff --git a/src/trialmatchai/interop/exporters/__init__.py b/src/trialmatchai/interop/exporters/__init__.py new file mode 100644 index 00000000..4d808e7f --- /dev/null +++ b/src/trialmatchai/interop/exporters/__init__.py @@ -0,0 +1,9 @@ +from trialmatchai.interop.exporters.fhir import profile_to_fhir_bundle +from trialmatchai.interop.exporters.matching_summary import profile_to_matching_summary +from trialmatchai.interop.exporters.phenopacket import profile_to_phenopacket + +__all__ = [ + "profile_to_fhir_bundle", + "profile_to_matching_summary", + "profile_to_phenopacket", +] diff --git a/src/trialmatchai/interop/exporters/fhir.py b/src/trialmatchai/interop/exporters/fhir.py new file mode 100644 index 00000000..5ea7f2d3 --- /dev/null +++ b/src/trialmatchai/interop/exporters/fhir.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +from trialmatchai.interop.models import ClinicalFact, PatientProfile + + +def profile_to_fhir_bundle(profile: PatientProfile) -> dict: + patient_ref = f"Patient/{profile.patient_id}" + entries = [ + { + "resource": { + "resourceType": "Patient", + "id": profile.patient_id, + "gender": _fhir_gender(profile.demographics.sex), + **( + {"birthDate": profile.demographics.birth_date.isoformat()} + if profile.demographics.birth_date + else {} + ), + } + } + ] + entries.extend( + _fact_entry("Condition", fact, patient_ref) for fact in profile.conditions + ) + entries.extend( + _fact_entry("Observation", fact, patient_ref) + for fact in [*profile.phenotypes, *profile.observations] + ) + entries.extend( + _fact_entry("MedicationStatement", fact, patient_ref) + for fact in profile.medications + ) + entries.extend( + _fact_entry("Procedure", fact, patient_ref) for fact in profile.procedures + ) + entries.extend( + _fact_entry("DiagnosticReport", fact, patient_ref) + for fact in profile.diagnostic_reports + ) + return { + "resourceType": "Bundle", + "type": "collection", + "entry": entries, + } + + +def _fact_entry(resource_type: str, fact: ClinicalFact, patient_ref: str) -> dict: + return { + "resource": { + "resourceType": resource_type, + "id": fact.fact_id, + "subject": {"reference": patient_ref}, + "code": _codeable(fact), + "note": [{"text": fact.description or fact.evidence_text}] + if fact.description or fact.evidence_text + else [], + } + } + + +def _codeable(fact: ClinicalFact) -> dict: + code = fact.normalized_codes[0] if fact.normalized_codes else fact.original_code + if code is None: + return {"text": fact.label} + return { + "text": code.label or fact.label, + "coding": [ + { + "system": code.system or code.vocabulary, + "code": code.code, + "display": code.label or fact.label, + } + ], + } + + +def _fhir_gender(value: str | None) -> str | None: + if not value: + return None + normalized = value.casefold() + if normalized in {"male", "female", "other", "unknown"}: + return normalized + return "unknown" diff --git a/src/trialmatchai/interop/exporters/matching_summary.py b/src/trialmatchai/interop/exporters/matching_summary.py new file mode 100644 index 00000000..ac934a1b --- /dev/null +++ b/src/trialmatchai/interop/exporters/matching_summary.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from trialmatchai.interop.models import PatientProfile +from trialmatchai.interop.narrative import render_patient_narrative, render_search_terms + + +def profile_to_matching_summary(profile: PatientProfile) -> dict: + main_conditions, other_conditions = render_search_terms(profile) + age = ( + int(profile.demographics.age_years) + if profile.demographics.age_years is not None + else "all" + ) + gender = profile.demographics.sex or profile.demographics.gender or "all" + expanded_sentences = render_patient_narrative(profile, style="rag") + return { + "patient_id": profile.patient_id, + "main_conditions": main_conditions, + "other_conditions": other_conditions, + "expanded_sentences": expanded_sentences, + "age": age, + "gender": gender, + "split_raw_description": expanded_sentences, + "provenance": [ + provenance.model_dump(mode="json", exclude_none=True) + for provenance in profile.provenance + ], + } diff --git a/src/trialmatchai/interop/exporters/phenopacket.py b/src/trialmatchai/interop/exporters/phenopacket.py new file mode 100644 index 00000000..100dfd28 --- /dev/null +++ b/src/trialmatchai/interop/exporters/phenopacket.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +from trialmatchai.interop.models import ClinicalFact, PatientProfile + + +def profile_to_phenopacket(profile: PatientProfile) -> dict: + unsupported: list[dict] = [] + packet = { + "id": profile.patient_id, + "subject": { + "id": profile.patient_id, + **({"sex": profile.demographics.sex.upper()} if profile.demographics.sex else {}), + **( + {"dateOfBirth": profile.demographics.birth_date.isoformat()} + if profile.demographics.birth_date + else {} + ), + }, + "phenotypicFeatures": [_phenotype_fact(fact) for fact in profile.phenotypes], + "diseases": [_disease_fact(fact) for fact in profile.conditions], + "measurements": [_measurement_fact(fact) for fact in profile.observations], + "medicalActions": [ + *[_treatment_fact(fact) for fact in profile.medications], + *[_procedure_fact(fact) for fact in profile.procedures], + ], + "metaData": { + "createdBy": "TrialMatchAI", + "resources": [], + }, + } + for fact in [ + *profile.diagnostic_reports, + *profile.genomic_findings, + *profile.cancer_profile, + *profile.family_history, + ]: + unsupported.append( + { + "fact_id": fact.fact_id, + "category": fact.category, + "label": fact.label, + "reason": "No lossless Phenopacket v1 exporter mapping implemented.", + } + ) + packet["trialmatchaiConversionReport"] = { + "lossy": bool(unsupported), + "unsupported": unsupported, + } + return packet + + +def _ontology_class(fact: ClinicalFact) -> dict: + code = fact.normalized_codes[0] if fact.normalized_codes else fact.original_code + if code is None: + return {"id": fact.label, "label": fact.label} + return {"id": f"{code.vocabulary}:{code.code}", "label": code.label or fact.label} + + +def _phenotype_fact(fact: ClinicalFact) -> dict: + return { + "type": _ontology_class(fact), + "excluded": fact.negated, + **({"description": fact.description} if fact.description else {}), + } + + +def _disease_fact(fact: ClinicalFact) -> dict: + return { + "term": _ontology_class(fact), + **({"excluded": fact.negated} if fact.negated else {}), + **({"description": fact.description} if fact.description else {}), + } + + +def _measurement_fact(fact: ClinicalFact) -> dict: + return { + "assay": _ontology_class(fact), + "value": {"value": fact.description or fact.label}, + } + + +def _treatment_fact(fact: ClinicalFact) -> dict: + return {"treatment": {"agent": _ontology_class(fact)}} + + +def _procedure_fact(fact: ClinicalFact) -> dict: + return {"procedure": {"code": _ontology_class(fact)}} diff --git a/src/trialmatchai/interop/importers/__init__.py b/src/trialmatchai/interop/importers/__init__.py new file mode 100644 index 00000000..2b79cfa6 --- /dev/null +++ b/src/trialmatchai/interop/importers/__init__.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from trialmatchai.interop.detect import detect_patient_input_format +from trialmatchai.interop.importers.fhir import import_fhir +from trialmatchai.interop.importers.omop import import_omop_extract +from trialmatchai.interop.importers.phenopacket import import_phenopacket +from trialmatchai.interop.importers.text import import_text_note +from trialmatchai.interop.models import PatientProfile + + +def import_patient_path( + path: str | Path, + *, + input_format: str = "auto", + entity_annotator: Any | None = None, + strict: bool = False, +) -> list[PatientProfile]: + resolved_format = ( + detect_patient_input_format(path) if input_format == "auto" else input_format + ) + if resolved_format == "text": + return [import_text_note(path, entity_annotator=entity_annotator)] + if resolved_format == "phenopacket": + return [import_phenopacket(path, strict=strict)] + if resolved_format in {"fhir", "fhir-ndjson"}: + return [import_fhir(path, input_format=resolved_format, strict=strict)] + if resolved_format == "omop": + return import_omop_extract(path, strict=strict) + raise ValueError(f"Unsupported patient input format: {resolved_format}") + + +__all__ = ["import_patient_path"] diff --git a/src/trialmatchai/interop/importers/fhir.py b/src/trialmatchai/interop/importers/fhir.py new file mode 100644 index 00000000..8c15c9a7 --- /dev/null +++ b/src/trialmatchai/interop/importers/fhir.py @@ -0,0 +1,380 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Mapping + +from trialmatchai.interop.models import Demographics, PatientProfile, Provenance, SourceDocument +from trialmatchai.interop.utils import ( + age_years_from_birth_date, + clean_text, + code_from_fhir_codeable, + label_from_fhir_codeable, + make_fact, + normalize_gender, + parse_date, + safe_patient_id, + source_path_string, +) + + +def import_fhir( + path: str | Path, + *, + input_format: str = "fhir", + strict: bool = False, +) -> PatientProfile: + source_path = Path(path) + resources = ( + _load_ndjson(source_path) + if input_format == "fhir-ndjson" + else _load_json_resources(source_path) + ) + patient = next((res for res in resources if res.get("resourceType") == "Patient"), {}) + patient_id = safe_patient_id(patient.get("id"), source_path.stem) + provenance = Provenance( + source_format=input_format, + source_id=patient_id, + source_path=source_path_string(source_path), + ) + birth_date = parse_date(patient.get("birthDate")) + profile = PatientProfile( + patient_id=patient_id, + demographics=Demographics( + sex=normalize_gender(patient.get("gender")), + gender=normalize_gender(patient.get("gender")), + birth_date=birth_date, + age_years=age_years_from_birth_date(birth_date), + ), + provenance=[provenance], + ) + for resource in resources: + try: + _add_resource(profile, resource, provenance) + except Exception: + if strict: + raise + profile.unsupported.append( + { + "resourceType": resource.get("resourceType"), + "id": resource.get("id"), + "reason": "resource mapping failed", + } + ) + return profile + + +def _load_json_resources(path: Path) -> list[dict[str, Any]]: + data = json.loads(path.read_text(encoding="utf-8")) + if isinstance(data, list): + return [item for item in data if isinstance(item, dict)] + if not isinstance(data, dict): + return [] + if data.get("resourceType") == "Bundle": + return [ + entry["resource"] + for entry in data.get("entry") or [] + if isinstance(entry, dict) and isinstance(entry.get("resource"), dict) + ] + return [data] + + +def _load_ndjson(path: Path) -> list[dict[str, Any]]: + resources = [] + with path.open("r", encoding="utf-8") as handle: + for line in handle: + if line.strip(): + resources.append(json.loads(line)) + return resources + + +def _add_resource( + profile: PatientProfile, + resource: Mapping[str, Any], + base_provenance: Provenance, +) -> None: + resource_type = resource.get("resourceType") + if resource_type in {None, "Patient"}: + return + provenance = base_provenance.model_copy( + update={ + "source_resource": f"{resource_type}/{resource.get('id', 'unknown')}", + } + ) + if resource_type == "Condition": + _add_condition(profile, resource, provenance) + elif resource_type == "Observation": + _add_observation(profile, resource, provenance) + elif resource_type in { + "MedicationRequest", + "MedicationStatement", + "MedicationAdministration", + }: + _add_medication(profile, resource, provenance) + elif resource_type == "Procedure": + _add_procedure(profile, resource, provenance) + elif resource_type == "DiagnosticReport": + _add_diagnostic_report(profile, resource, provenance) + elif resource_type == "DocumentReference": + _add_document_reference(profile, resource, provenance) + elif resource_type == "AllergyIntolerance": + _add_allergy(profile, resource, provenance) + elif resource_type == "FamilyMemberHistory": + _add_family_history(profile, resource, provenance) + elif resource_type in {"MolecularSequence", "GenomicStudy"}: + _add_genomic(profile, resource, provenance) + elif resource_type == "Specimen": + _add_specimen(profile, resource, provenance) + else: + profile.unsupported.append( + { + "resourceType": resource_type, + "id": resource.get("id"), + "reason": "FHIR resource type not mapped in v1 importer", + } + ) + + +def _add_condition( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + code = code_from_fhir_codeable(resource.get("code") or {}) + label = label_from_fhir_codeable(resource.get("code") or {}) + if not label: + return + profile.conditions.append( + make_fact( + category="condition", + label=label, + original_code=code, + provenance=provenance, + description=clean_text(resource.get("note")) or None, + temporality=clean_text(resource.get("onsetDateTime")) or None, + negated=_is_negated(resource), + ) + ) + + +def _add_observation( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + code = code_from_fhir_codeable(resource.get("code") or {}) + label = label_from_fhir_codeable(resource.get("code") or {}) + if not label: + return + value = _observation_value(resource) + category = "genomic_finding" if _is_genomic_observation(resource) else "observation" + profile.add_fact( + make_fact( + category=category, + label=label, + original_code=code, + provenance=provenance, + description=value, + temporality=clean_text(resource.get("effectiveDateTime")) or None, + ) + ) + + +def _add_medication( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + medication = ( + resource.get("medicationCodeableConcept") + or resource.get("medication") + or resource.get("contained") + or {} + ) + code = code_from_fhir_codeable(medication) if isinstance(medication, Mapping) else None + label = ( + label_from_fhir_codeable(medication) + if isinstance(medication, Mapping) + else clean_text(medication) + ) + if not label: + return + profile.medications.append( + make_fact( + category="medication", + label=label, + original_code=code, + provenance=provenance, + description=clean_text(resource.get("dosageInstruction")) or None, + ) + ) + + +def _add_procedure( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + code = code_from_fhir_codeable(resource.get("code") or {}) + label = label_from_fhir_codeable(resource.get("code") or {}) + if label: + profile.procedures.append( + make_fact( + category="procedure", + label=label, + original_code=code, + provenance=provenance, + temporality=clean_text(resource.get("performedDateTime")) or None, + ) + ) + + +def _add_diagnostic_report( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + code = code_from_fhir_codeable(resource.get("code") or {}) + label = label_from_fhir_codeable(resource.get("code") or {}) or clean_text( + resource.get("conclusion") + ) + if label: + profile.diagnostic_reports.append( + make_fact( + category="diagnostic_report", + label=label, + original_code=code, + provenance=provenance, + description=clean_text(resource.get("conclusion")) or None, + ) + ) + + +def _add_document_reference( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + profile.source_documents.append( + SourceDocument( + document_id=clean_text(resource.get("id")) or provenance.source_resource or "document", + title=clean_text(resource.get("description") or resource.get("docStatus")) or None, + document_type=label_from_fhir_codeable(resource.get("type") or {}) or None, + url=_document_url(resource), + provenance=provenance, + ) + ) + + +def _add_allergy( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + code = code_from_fhir_codeable(resource.get("code") or {}) + label = label_from_fhir_codeable(resource.get("code") or {}) + if label: + profile.conditions.append( + make_fact( + category="condition", + label=f"Allergy: {label}", + original_code=code, + provenance=provenance, + extra={"clinical_status": resource.get("clinicalStatus")}, + ) + ) + + +def _add_family_history( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + relationship = label_from_fhir_codeable(resource.get("relationship") or {}) + conditions = resource.get("condition") or [] + label = relationship or clean_text(resource.get("id")) + if conditions: + labels = [ + label_from_fhir_codeable(condition.get("code") or {}) + for condition in conditions + if isinstance(condition, Mapping) + ] + label = f"{relationship}: {', '.join(item for item in labels if item)}" + if label: + profile.family_history.append( + make_fact( + category="family_history", + label=label, + provenance=provenance, + extra={"relationship": relationship, "conditions": conditions}, + ) + ) + + +def _add_genomic( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + label = clean_text(resource.get("id") or resource.get("type") or "Genomic finding") + profile.genomic_findings.append( + make_fact( + category="genomic_finding", + label=label, + provenance=provenance, + extra=dict(resource), + ) + ) + + +def _add_specimen( + profile: PatientProfile, + resource: Mapping[str, Any], + provenance: Provenance, +) -> None: + label = label_from_fhir_codeable(resource.get("type") or {}) or clean_text( + resource.get("id") + ) + if label: + profile.diagnostic_reports.append( + make_fact( + category="diagnostic_report", + label=f"Specimen: {label}", + provenance=provenance, + extra=dict(resource), + ) + ) + + +def _observation_value(resource: Mapping[str, Any]) -> str | None: + if resource.get("valueQuantity"): + value = resource["valueQuantity"] + return clean_text( + f"{value.get('value', '')} {value.get('unit') or value.get('code') or ''}" + ) + if resource.get("valueCodeableConcept"): + return label_from_fhir_codeable(resource.get("valueCodeableConcept") or {}) + for key in ("valueString", "valueBoolean", "valueInteger", "valueDateTime"): + if key in resource: + return clean_text(resource.get(key)) + return None + + +def _is_negated(resource: Mapping[str, Any]) -> bool: + verification = resource.get("verificationStatus") or {} + text = label_from_fhir_codeable(verification).casefold() + return "refuted" in text or "entered-in-error" in text + + +def _is_genomic_observation(resource: Mapping[str, Any]) -> bool: + categories = resource.get("category") or [] + text = clean_text(categories).casefold() + return "genetic" in text or "genomic" in text + + +def _document_url(resource: Mapping[str, Any]) -> str | None: + for content in resource.get("content") or []: + attachment = (content or {}).get("attachment") or {} + if attachment.get("url"): + return clean_text(attachment.get("url")) + return None diff --git a/src/trialmatchai/interop/importers/omop.py b/src/trialmatchai/interop/importers/omop.py new file mode 100644 index 00000000..c8a45214 --- /dev/null +++ b/src/trialmatchai/interop/importers/omop.py @@ -0,0 +1,322 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import pandas as pd + +from trialmatchai.interop.models import Demographics, NormalizedCode, PatientNote, PatientProfile, Provenance +from trialmatchai.interop.utils import ( + age_years_from_birth_date, + clean_text, + make_fact, + normalize_gender, + parse_date, + safe_patient_id, + source_path_string, +) + + +def import_omop_extract( + path: str | Path, + *, + strict: bool = False, +) -> list[PatientProfile]: + root = Path(path) + tables = _load_tables(root) + concepts = _concept_lookup(tables.get("concept")) + person = tables.get("person") + if person is None or person.empty: + if strict: + raise ValueError(f"OMOP extract is missing PERSON table: {root}") + return [] + + profiles: list[PatientProfile] = [] + for _, row in person.iterrows(): + patient_id = safe_patient_id(row.get("person_id"), "omop-patient") + provenance = Provenance( + source_format="omop", + source_id=patient_id, + source_path=source_path_string(root), + source_table="PERSON", + ) + birth_date = _person_birth_date(row) + profile = PatientProfile( + patient_id=patient_id, + demographics=Demographics( + sex=_concept_label(row.get("gender_concept_id"), concepts) + or normalize_gender(row.get("gender_source_value")), + gender=normalize_gender(row.get("gender_source_value")), + birth_date=birth_date, + age_years=age_years_from_birth_date(birth_date), + ), + provenance=[provenance], + ) + _add_condition_rows(profile, tables.get("condition_occurrence"), concepts, root) + _add_measurement_rows(profile, tables.get("measurement"), concepts, root) + _add_drug_rows(profile, tables.get("drug_exposure"), concepts, root) + _add_procedure_rows(profile, tables.get("procedure_occurrence"), concepts, root) + _add_observation_rows(profile, tables.get("observation"), concepts, root) + _add_note_rows(profile, tables.get("note"), root) + _add_note_nlp_rows(profile, tables.get("note_nlp"), concepts, root) + profiles.append(profile) + return profiles + + +def _load_tables(root: Path) -> dict[str, pd.DataFrame]: + tables: dict[str, pd.DataFrame] = {} + for file_path in root.iterdir(): + if file_path.suffix.casefold() not in {".csv", ".parquet"}: + continue + name = file_path.stem.casefold() + if file_path.suffix.casefold() == ".csv": + table = pd.read_csv(file_path) + else: + table = pd.read_parquet(file_path) + table.columns = [str(column).casefold() for column in table.columns] + tables[name] = table + return tables + + +def _concept_lookup(table: pd.DataFrame | None) -> dict[Any, dict[str, Any]]: + if table is None: + return {} + return { + row.get("concept_id"): row.to_dict() + for _, row in table.iterrows() + if row.get("concept_id") is not None + } + + +def _person_birth_date(row) -> Any: + if row.get("birth_datetime"): + return parse_date(row.get("birth_datetime")) + year = row.get("year_of_birth") + if pd.isna(year): + return None + month = int(row.get("month_of_birth") or 1) + day = int(row.get("day_of_birth") or 1) + return parse_date(f"{int(year):04d}-{month:02d}-{day:02d}") + + +def _add_condition_rows( + profile: PatientProfile, + table: pd.DataFrame | None, + concepts: dict[Any, dict[str, Any]], + root: Path, +) -> None: + for row in _rows_for_patient(table, profile.patient_id, "person_id"): + code = _omop_code(row.get("condition_concept_id"), concepts) + label = _concept_label(row.get("condition_concept_id"), concepts) or clean_text( + row.get("condition_source_value") + ) + if label: + profile.conditions.append( + make_fact( + category="condition", + label=label, + original_code=code, + provenance=_row_provenance(root, profile.patient_id, "CONDITION_OCCURRENCE"), + temporality=clean_text(row.get("condition_start_date")) or None, + ) + ) + + +def _add_measurement_rows( + profile: PatientProfile, + table: pd.DataFrame | None, + concepts: dict[Any, dict[str, Any]], + root: Path, +) -> None: + for row in _rows_for_patient(table, profile.patient_id, "person_id"): + code = _omop_code(row.get("measurement_concept_id"), concepts) + label = _concept_label(row.get("measurement_concept_id"), concepts) or clean_text( + row.get("measurement_source_value") + ) + value = row.get("value_as_number") + if pd.isna(value): + value = _concept_label(row.get("value_as_concept_id"), concepts) + unit = _concept_label(row.get("unit_concept_id"), concepts) + if label: + profile.observations.append( + make_fact( + category="observation", + label=label, + original_code=code, + provenance=_row_provenance(root, profile.patient_id, "MEASUREMENT"), + description=clean_text(f"{value or ''} {unit or ''}") or None, + temporality=clean_text(row.get("measurement_date")) or None, + ) + ) + + +def _add_drug_rows( + profile: PatientProfile, + table: pd.DataFrame | None, + concepts: dict[Any, dict[str, Any]], + root: Path, +) -> None: + for row in _rows_for_patient(table, profile.patient_id, "person_id"): + code = _omop_code(row.get("drug_concept_id"), concepts) + label = _concept_label(row.get("drug_concept_id"), concepts) or clean_text( + row.get("drug_source_value") + ) + if label: + profile.medications.append( + make_fact( + category="medication", + label=label, + original_code=code, + provenance=_row_provenance(root, profile.patient_id, "DRUG_EXPOSURE"), + temporality=clean_text(row.get("drug_exposure_start_date")) or None, + ) + ) + + +def _add_procedure_rows( + profile: PatientProfile, + table: pd.DataFrame | None, + concepts: dict[Any, dict[str, Any]], + root: Path, +) -> None: + for row in _rows_for_patient(table, profile.patient_id, "person_id"): + code = _omop_code(row.get("procedure_concept_id"), concepts) + label = _concept_label(row.get("procedure_concept_id"), concepts) or clean_text( + row.get("procedure_source_value") + ) + if label: + profile.procedures.append( + make_fact( + category="procedure", + label=label, + original_code=code, + provenance=_row_provenance(root, profile.patient_id, "PROCEDURE_OCCURRENCE"), + temporality=clean_text(row.get("procedure_date")) or None, + ) + ) + + +def _add_observation_rows( + profile: PatientProfile, + table: pd.DataFrame | None, + concepts: dict[Any, dict[str, Any]], + root: Path, +) -> None: + for row in _rows_for_patient(table, profile.patient_id, "person_id"): + code = _omop_code(row.get("observation_concept_id"), concepts) + label = _concept_label(row.get("observation_concept_id"), concepts) or clean_text( + row.get("observation_source_value") + ) + if label: + profile.observations.append( + make_fact( + category="observation", + label=label, + original_code=code, + provenance=_row_provenance(root, profile.patient_id, "OBSERVATION"), + temporality=clean_text(row.get("observation_date")) or None, + ) + ) + + +def _add_note_rows( + profile: PatientProfile, + table: pd.DataFrame | None, + root: Path, +) -> None: + for row in _rows_for_patient(table, profile.patient_id, "person_id"): + text = clean_text(row.get("note_text")) + if not text: + continue + provenance = _row_provenance(root, profile.patient_id, "NOTE") + profile.notes.append( + PatientNote( + note_id=clean_text(row.get("note_id")) or f"{profile.patient_id}-note", + text=text, + note_type=clean_text(row.get("note_type_concept_id")) or "omop-note", + provenance=provenance, + ) + ) + + +def _add_note_nlp_rows( + profile: PatientProfile, + table: pd.DataFrame | None, + concepts: dict[Any, dict[str, Any]], + root: Path, +) -> None: + for row in _rows_for_patient(table, profile.patient_id, "person_id"): + code = _omop_code(row.get("note_nlp_concept_id"), concepts) + label = _concept_label(row.get("note_nlp_concept_id"), concepts) or clean_text( + row.get("lexical_variant") or row.get("snippet") + ) + if not label: + continue + concept = concepts.get(row.get("note_nlp_concept_id"), {}) + domain = clean_text(concept.get("domain_id")).casefold() + category = "condition" if domain == "condition" else "observation" + profile.add_fact( + make_fact( + category=category, + label=label, + original_code=code, + provenance=_row_provenance(root, profile.patient_id, "NOTE_NLP"), + evidence_text=clean_text(row.get("snippet")) or None, + evidence_start=_int_or_none(row.get("offset")), + negated=str(row.get("term_exists")).casefold() == "false", + ) + ) + + +def _rows_for_patient( + table: pd.DataFrame | None, + patient_id: str, + column: str, +) -> list[dict[str, Any]]: + if table is None or table.empty or column not in table.columns: + return [] + patient_rows = table[table[column].astype(str) == str(patient_id)] + return [row.to_dict() for _, row in patient_rows.iterrows()] + + +def _row_provenance(root: Path, patient_id: str, table_name: str) -> Provenance: + return Provenance( + source_format="omop", + source_id=patient_id, + source_path=source_path_string(root), + source_table=table_name, + ) + + +def _omop_code(concept_id: Any, concepts: dict[Any, dict[str, Any]]) -> NormalizedCode | None: + if concept_id is None or pd.isna(concept_id): + return None + concept = concepts.get(concept_id) + if not concept: + return NormalizedCode( + vocabulary="OMOP", + code=str(int(concept_id)) if isinstance(concept_id, float) else str(concept_id), + mapping_status="unmapped", + ) + return NormalizedCode( + vocabulary=clean_text(concept.get("vocabulary_id")) or "OMOP", + code=clean_text(concept.get("concept_code")) or str(concept_id), + label=clean_text(concept.get("concept_name")) or None, + mapping_status="exact", + ) + + +def _concept_label(concept_id: Any, concepts: dict[Any, dict[str, Any]]) -> str | None: + if concept_id is None or pd.isna(concept_id): + return None + concept = concepts.get(concept_id) + return clean_text((concept or {}).get("concept_name")) or None + + +def _int_or_none(value: Any) -> int | None: + if value is None or pd.isna(value): + return None + try: + return int(value) + except (TypeError, ValueError): + return None diff --git a/src/trialmatchai/interop/importers/phenopacket.py b/src/trialmatchai/interop/importers/phenopacket.py new file mode 100644 index 00000000..59e52b73 --- /dev/null +++ b/src/trialmatchai/interop/importers/phenopacket.py @@ -0,0 +1,299 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Mapping + +from trialmatchai.interop.models import Demographics, PatientProfile, Provenance, SourceDocument +from trialmatchai.interop.utils import ( + clean_text, + code_from_ontology_class, + make_fact, + normalize_gender, + parse_date, + parse_iso8601_age_years, + safe_patient_id, + source_path_string, +) +from trialmatchai.schemas.phenopacket import Phenopacket + + +def import_phenopacket( + path: str | Path, + *, + strict: bool = False, +) -> PatientProfile: + packet_path = Path(path) + data = json.loads(packet_path.read_text(encoding="utf-8")) + try: + Phenopacket.model_validate(data) + except Exception: + if strict: + raise + patient_id = safe_patient_id(data.get("id"), packet_path.stem) + provenance = Provenance( + source_format="phenopacket", + source_id=patient_id, + source_path=source_path_string(packet_path), + ) + profile = PatientProfile( + patient_id=patient_id, + demographics=_demographics(data.get("subject") or {}), + provenance=[provenance], + ) + _add_phenotypes(profile, data, provenance) + _add_diseases(profile, data, provenance) + _add_biosamples(profile, data, provenance) + _add_measurements(profile, data, provenance) + _add_actions(profile, data, provenance) + _add_interpretations(profile, data, provenance) + _add_family(profile, data, provenance) + _add_files(profile, data, provenance) + return profile + + +def _demographics(subject: Mapping[str, Any]) -> Demographics: + age_years = None + encounter = subject.get("timeAtLastEncounter") or {} + if isinstance(encounter, Mapping): + age = encounter.get("age") or {} + if isinstance(age, Mapping): + age_years = parse_iso8601_age_years(age.get("iso8601duration")) + birth_date = parse_date(subject.get("dateOfBirth")) + return Demographics( + sex=normalize_gender(subject.get("sex")), + gender=normalize_gender(subject.get("gender")), + birth_date=birth_date, + age_years=age_years, + species=clean_text((subject.get("taxonomy") or {}).get("label")), + description=clean_text(subject.get("description")) or None, + ) + + +def _add_phenotypes( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + for index, item in enumerate(data.get("phenotypicFeatures") or []): + feature = item.get("type") or {} + code = code_from_ontology_class(feature) + label = clean_text(feature.get("label") or feature.get("id")) + if not label: + continue + profile.phenotypes.append( + make_fact( + category="phenotype", + label=label, + original_code=code, + provenance=provenance.model_copy( + update={"source_resource": f"phenotypicFeatures[{index}]"} + ), + description=clean_text(item.get("description")) or None, + negated=bool(item.get("excluded", False)), + temporality=clean_text(item.get("onset")) or None, + extra={"severity": item.get("severity"), "modifiers": item.get("modifiers")}, + ) + ) + + +def _add_diseases( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + for index, item in enumerate(data.get("diseases") or []): + term = item.get("term") or {} + code = code_from_ontology_class(term) + label = clean_text(term.get("label") or term.get("id")) + if not label: + continue + category = "cancer" if item.get("tnmFinding") or item.get("diseaseStage") else "condition" + profile.add_fact( + make_fact( + category=category, + label=label, + original_code=code, + provenance=provenance.model_copy( + update={"source_resource": f"diseases[{index}]"} + ), + description=clean_text(item.get("description")) or None, + temporality=clean_text(item.get("onset")) or None, + extra={ + "disease_stage": item.get("diseaseStage") or [], + "tnm_finding": item.get("tnmFinding") or [], + }, + ) + ) + + +def _add_biosamples( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + for index, sample in enumerate(data.get("biosamples") or []): + label = clean_text( + (sample.get("histologicalDiagnosis") or {}).get("label") + or (sample.get("sampledTissue") or {}).get("label") + or (sample.get("sampleType") or {}).get("label") + or sample.get("id") + ) + if not label: + continue + profile.diagnostic_reports.append( + make_fact( + category="diagnostic_report", + label=f"Biosample: {label}", + provenance=provenance.model_copy( + update={"source_resource": f"biosamples[{index}]"} + ), + description=clean_text(sample.get("description")) or None, + extra=sample, + ) + ) + + +def _add_measurements( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + for index, item in enumerate(data.get("measurements") or []): + assay = item.get("assay") or {} + code = code_from_ontology_class(assay) + label = clean_text(assay.get("label") or assay.get("id")) + if not label: + continue + value = item.get("value") or {} + profile.observations.append( + make_fact( + category="observation", + label=label, + original_code=code, + provenance=provenance.model_copy( + update={"source_resource": f"measurements[{index}]"} + ), + description=clean_text(value or item.get("description")) or None, + extra={"value": value}, + ) + ) + + +def _add_actions( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + for index, action in enumerate(data.get("medicalActions") or []): + if action.get("treatment"): + tx = action["treatment"] + agent = tx.get("agent") or {} + code = code_from_ontology_class(agent) + label = clean_text(agent.get("label") or agent.get("id") or tx.get("description")) + if label: + profile.medications.append( + make_fact( + category="medication", + label=label, + original_code=code, + provenance=provenance.model_copy( + update={"source_resource": f"medicalActions[{index}].treatment"} + ), + description=clean_text(tx.get("description")) or None, + extra=tx, + ) + ) + if action.get("procedure"): + proc = action["procedure"] + codeable = proc.get("code") or {} + code = code_from_ontology_class(codeable) + label = clean_text(codeable.get("label") or codeable.get("id") or proc.get("description")) + if label: + profile.procedures.append( + make_fact( + category="procedure", + label=label, + original_code=code, + provenance=provenance.model_copy( + update={"source_resource": f"medicalActions[{index}].procedure"} + ), + description=clean_text(proc.get("description")) or None, + temporality=clean_text(proc.get("performed")) or None, + extra=proc, + ) + ) + + +def _add_interpretations( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + for index, item in enumerate(data.get("interpretations") or []): + diagnosis = item.get("diagnosis") or {} + for gi_index, interpretation in enumerate(diagnosis.get("genomicInterpretations") or []): + variant = (interpretation.get("variantInterpretation") or {}).get( + "variationDescriptor" + ) or {} + gene = (variant.get("geneContext") or {}).get("symbol") + label = clean_text(" ".join(part for part in [gene, variant.get("label")] if part)) + if label: + profile.genomic_findings.append( + make_fact( + category="genomic_finding", + label=label, + provenance=provenance.model_copy( + update={ + "source_resource": ( + f"interpretations[{index}].diagnosis." + f"genomicInterpretations[{gi_index}]" + ) + } + ), + description=clean_text(diagnosis.get("description")) or None, + extra=variant, + ) + ) + + +def _add_family( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + family = data.get("family") or {} + for index, relative in enumerate(family.get("relatives") or []): + label = clean_text(relative.get("description") or relative.get("id")) + if not label: + continue + profile.family_history.append( + make_fact( + category="family_history", + label=label, + provenance=provenance.model_copy( + update={"source_resource": f"family.relatives[{index}]"} + ), + extra=relative, + ) + ) + + +def _add_files( + profile: PatientProfile, + data: Mapping[str, Any], + provenance: Provenance, +) -> None: + for index, file_ref in enumerate(data.get("files") or []): + uri = clean_text(file_ref.get("uri") or file_ref.get("path")) + profile.source_documents.append( + SourceDocument( + document_id=clean_text(file_ref.get("individualToFileIdentifiers")) or f"file-{index}", + title=clean_text(file_ref.get("description")) or None, + document_type=clean_text(file_ref.get("fileAttributes")) or None, + url=uri or None, + provenance=provenance.model_copy( + update={"source_resource": f"files[{index}]"} + ), + ) + ) diff --git a/src/trialmatchai/interop/importers/text.py b/src/trialmatchai/interop/importers/text.py new file mode 100644 index 00000000..db1d93b0 --- /dev/null +++ b/src/trialmatchai/interop/importers/text.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from trialmatchai.interop.models import Demographics, PatientNote, PatientProfile, Provenance +from trialmatchai.interop.utils import make_fact, safe_patient_id, source_path_string + + +ENTITY_GROUP_TO_CATEGORY = { + "disease": "condition", + "condition": "condition", + "drug": "medication", + "medication": "medication", + "procedure": "procedure", + "diagnostic_test": "observation", + "laboratory_test": "observation", + "radiology": "diagnostic_report", + "sign_symptom": "phenotype", + "gene": "genomic_finding", + "cell_type": "phenotype", + "species": "phenotype", +} + + +def import_text_note( + path: str | Path, + *, + entity_annotator: Any | None = None, +) -> PatientProfile: + note_path = Path(path) + text = note_path.read_text(encoding="utf-8") + patient_id = safe_patient_id(note_path.stem, note_path.stem) + provenance = Provenance( + source_format="text", + source_id=patient_id, + source_path=source_path_string(note_path), + source_field="note_text", + ) + entities = _annotate(text, entity_annotator) + profile = PatientProfile( + patient_id=patient_id, + demographics=Demographics(), + notes=[ + PatientNote( + note_id=f"{patient_id}-note", + text=text, + entities=entities, + provenance=provenance, + ) + ], + provenance=[provenance], + ) + for entity in entities: + fact = _entity_to_fact(entity, provenance) + if fact is not None: + profile.add_fact(fact) + return profile + + +def _annotate(text: str, entity_annotator: Any | None) -> list[dict]: + if entity_annotator is None: + return [] + if hasattr(entity_annotator, "annotate_texts_in_parallel"): + result = entity_annotator.annotate_texts_in_parallel([text], max_workers=1) + return list(result[0]) if result else [] + if hasattr(entity_annotator, "annotate_texts"): + result = entity_annotator.annotate_texts([text]) + annotations = result[0] if result else [] + return [ + annotation.to_dict() if hasattr(annotation, "to_dict") else dict(annotation) + for annotation in annotations + ] + return [] + + +def _entity_to_fact(entity: dict, provenance: Provenance): + if entity.get("error_code"): + return None + group = str(entity.get("entity_group") or entity.get("class") or "").casefold() + category = ENTITY_GROUP_TO_CATEGORY.get(group, "observation") + text = str(entity.get("text") or entity.get("entity") or "").strip() + if not text: + return None + normalized_codes = [] + for normalized_id in entity.get("normalized_id") or []: + if normalized_id == "CUI-less" or ":" not in normalized_id: + continue + vocabulary, code = normalized_id.split(":", 1) + normalized_codes.append( + { + "vocabulary": vocabulary, + "code": code, + "label": text, + "confidence": entity.get("linker_score") or entity.get("score"), + "mapping_status": "normalized", + } + ) + from trialmatchai.interop.models import NormalizedCode + + return make_fact( + category=category, + label=text, + provenance=provenance, + normalized_codes=[NormalizedCode.model_validate(code) for code in normalized_codes], + evidence_text=text, + evidence_start=entity.get("start"), + evidence_end=entity.get("end"), + confidence=entity.get("score"), + extra={ + "entity_group": entity.get("entity_group"), + "synonyms": entity.get("synonyms") or [], + "concept_candidates": entity.get("concept_candidates") or [], + "linker_status": entity.get("linker_status"), + }, + ) diff --git a/src/trialmatchai/interop/models.py b/src/trialmatchai/interop/models.py new file mode 100644 index 00000000..34e41699 --- /dev/null +++ b/src/trialmatchai/interop/models.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +from datetime import date +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field, field_validator + + +MappingStatus = Literal[ + "exact", + "normalized", + "broader", + "narrower", + "inferred", + "unsupported", + "unmapped", +] + + +class EvidenceSpan(BaseModel): + text: str | None = None + start: int | None = Field(default=None, ge=0) + end: int | None = Field(default=None, ge=0) + + @field_validator("end") + @classmethod + def validate_end(cls, value: int | None, info): + start = info.data.get("start") + if value is not None and start is not None and value < start: + raise ValueError("evidence span end must be >= start") + return value + + +class Provenance(BaseModel): + source_format: str + source_id: str | None = None + source_path: str | None = None + source_resource: str | None = None + source_table: str | None = None + source_field: str | None = None + raw_text_span: EvidenceSpan | None = None + + model_config = ConfigDict(extra="allow") + + +class NormalizedCode(BaseModel): + vocabulary: str + code: str + label: str | None = None + system: str | None = None + confidence: float | None = Field(default=None, ge=0.0, le=1.0) + mapping_status: MappingStatus = "unmapped" + + model_config = ConfigDict(extra="allow") + + +class Demographics(BaseModel): + sex: str | None = None + gender: str | None = None + birth_date: date | None = None + age_years: float | None = Field(default=None, ge=0) + species: str | None = None + description: str | None = None + + model_config = ConfigDict(extra="allow") + + +class ClinicalFact(BaseModel): + fact_id: str + category: str + label: str + description: str | None = None + original_code: NormalizedCode | None = None + normalized_codes: list[NormalizedCode] = Field(default_factory=list) + vocabulary: str | None = None + evidence_text: str | None = None + evidence_start: int | None = Field(default=None, ge=0) + evidence_end: int | None = Field(default=None, ge=0) + confidence: float | None = Field(default=None, ge=0.0, le=1.0) + negated: bool = False + temporality: str | None = None + mapping_status: MappingStatus = "unmapped" + provenance: Provenance + extra: dict[str, Any] = Field(default_factory=dict) + + model_config = ConfigDict(extra="allow") + + @field_validator("evidence_end") + @classmethod + def validate_evidence_end(cls, value: int | None, info): + start = info.data.get("evidence_start") + if value is not None and start is not None and value < start: + raise ValueError("evidence_end must be >= evidence_start") + return value + + +class PatientNote(BaseModel): + note_id: str + text: str + note_type: str = "clinical-note" + entities: list[dict[str, Any]] = Field(default_factory=list) + provenance: Provenance + + model_config = ConfigDict(extra="allow") + + +class SourceDocument(BaseModel): + document_id: str + title: str | None = None + document_type: str | None = None + url: str | None = None + text: str | None = None + provenance: Provenance + + model_config = ConfigDict(extra="allow") + + +class PatientProfile(BaseModel): + patient_id: str + demographics: Demographics = Field(default_factory=Demographics) + conditions: list[ClinicalFact] = Field(default_factory=list) + phenotypes: list[ClinicalFact] = Field(default_factory=list) + observations: list[ClinicalFact] = Field(default_factory=list) + medications: list[ClinicalFact] = Field(default_factory=list) + procedures: list[ClinicalFact] = Field(default_factory=list) + diagnostic_reports: list[ClinicalFact] = Field(default_factory=list) + genomic_findings: list[ClinicalFact] = Field(default_factory=list) + cancer_profile: list[ClinicalFact] = Field(default_factory=list) + family_history: list[ClinicalFact] = Field(default_factory=list) + notes: list[PatientNote] = Field(default_factory=list) + source_documents: list[SourceDocument] = Field(default_factory=list) + provenance: list[Provenance] = Field(default_factory=list) + unsupported: list[dict[str, Any]] = Field(default_factory=list) + + model_config = ConfigDict(extra="allow") + + def all_facts(self) -> list[ClinicalFact]: + return [ + *self.conditions, + *self.phenotypes, + *self.observations, + *self.medications, + *self.procedures, + *self.diagnostic_reports, + *self.genomic_findings, + *self.cancer_profile, + *self.family_history, + ] + + def add_fact(self, fact: ClinicalFact) -> None: + bucket = { + "condition": self.conditions, + "phenotype": self.phenotypes, + "observation": self.observations, + "medication": self.medications, + "procedure": self.procedures, + "diagnostic_report": self.diagnostic_reports, + "genomic_finding": self.genomic_findings, + "cancer": self.cancer_profile, + "family_history": self.family_history, + }.get(fact.category) + if bucket is None: + self.observations.append(fact) + else: + bucket.append(fact) diff --git a/src/trialmatchai/interop/narrative.py b/src/trialmatchai/interop/narrative.py new file mode 100644 index 00000000..5fffee41 --- /dev/null +++ b/src/trialmatchai/interop/narrative.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +from trialmatchai.interop.models import ClinicalFact, PatientProfile + + +def render_patient_narrative( + profile: PatientProfile, + *, + style: str = "rag", +) -> list[str]: + """Render structured profile facts into deterministic LLM-ready sentences.""" + lines: list[str] = [] + demographics = profile.demographics + demo_bits = [] + if demographics.age_years is not None: + demo_bits.append(f"age {demographics.age_years:g} years") + if demographics.sex: + demo_bits.append(f"sex {demographics.sex}") + if demographics.gender and demographics.gender != demographics.sex: + demo_bits.append(f"gender {demographics.gender}") + if demographics.species: + demo_bits.append(f"species {demographics.species}") + if demographics.description: + demo_bits.append(demographics.description) + if demo_bits: + lines.append("Patient demographics: " + "; ".join(demo_bits) + ".") + + lines.extend(_render_fact_group("Diagnoses", profile.conditions)) + lines.extend(_render_fact_group("Phenotypes", profile.phenotypes)) + lines.extend(_render_fact_group("Observations", profile.observations)) + lines.extend(_render_fact_group("Medications", profile.medications)) + lines.extend(_render_fact_group("Procedures", profile.procedures)) + lines.extend(_render_fact_group("Diagnostic reports", profile.diagnostic_reports)) + lines.extend(_render_fact_group("Genomic findings", profile.genomic_findings)) + lines.extend(_render_fact_group("Cancer profile", profile.cancer_profile)) + lines.extend(_render_fact_group("Family history", profile.family_history)) + + if style == "audit": + for note in profile.notes: + lines.append(f"Source note {note.note_id}: {note.text}") + elif profile.notes: + note_text = " ".join(note.text for note in profile.notes[:3]) + if note_text: + lines.append(f"Clinical note context: {note_text[:2000]}") + + return lines or ["No structured patient facts were available."] + + +def render_search_terms(profile: PatientProfile) -> tuple[list[str], list[str]]: + main_conditions = _dedupe( + fact.label for fact in profile.conditions if not fact.negated + ) + if not main_conditions: + main_conditions = _dedupe( + fact.label for fact in profile.phenotypes if not fact.negated + ) + other_terms = _dedupe( + [ + *[fact.label for fact in profile.phenotypes if not fact.negated], + *[fact.label for fact in profile.observations if not fact.negated], + *[fact.label for fact in profile.medications if not fact.negated], + *[fact.label for fact in profile.procedures if not fact.negated], + *[fact.label for fact in profile.genomic_findings if not fact.negated], + *[fact.label for fact in profile.cancer_profile if not fact.negated], + *[fact.label for fact in profile.family_history if not fact.negated], + ] + ) + return main_conditions, [term for term in other_terms if term not in main_conditions] + + +def _render_fact_group(label: str, facts: list[ClinicalFact]) -> list[str]: + if not facts: + return [] + rendered = [] + for fact in facts: + status = "absent" if fact.negated else "present" + pieces = [fact.label, status] + if fact.description: + pieces.append(fact.description) + if fact.temporality: + pieces.append(f"timing {fact.temporality}") + code = fact.normalized_codes[0] if fact.normalized_codes else None + if code and code.code: + pieces.append(f"code {code.vocabulary}:{code.code}") + rendered.append(f"{label}: " + "; ".join(pieces) + ".") + return rendered + + +def _dedupe(values) -> list[str]: + seen: set[str] = set() + output: list[str] = [] + for value in values: + cleaned = str(value).strip() + if not cleaned: + continue + key = cleaned.casefold() + if key in seen: + continue + seen.add(key) + output.append(cleaned) + return output diff --git a/src/trialmatchai/interop/utils.py b/src/trialmatchai/interop/utils.py new file mode 100644 index 00000000..ab4d67a7 --- /dev/null +++ b/src/trialmatchai/interop/utils.py @@ -0,0 +1,207 @@ +from __future__ import annotations + +import hashlib +import re +from datetime import date, datetime +from pathlib import Path +from typing import Any, Iterable, Mapping + +from trialmatchai.interop.models import ClinicalFact, NormalizedCode, Provenance + + +FHIR_SYSTEM_TO_VOCABULARY = { + "http://snomed.info/sct": "SNOMED", + "http://loinc.org": "LOINC", + "http://www.nlm.nih.gov/research/umls/rxnorm": "RxNorm", + "http://hl7.org/fhir/sid/icd-10-cm": "ICD10CM", + "http://hl7.org/fhir/sid/icd-10": "ICD10", + "https://hpo.jax.org/app/browse/term": "HP", + "http://purl.obolibrary.org/obo/hp.owl": "HP", + "http://purl.obolibrary.org/obo/mondo.owl": "MONDO", +} + + +def stable_id(*parts: Any) -> str: + payload = "|".join(str(part) for part in parts if part not in (None, "")) + return hashlib.sha256(payload.encode("utf-8")).hexdigest()[:16] + + +def clean_text(value: Any) -> str: + if value is None: + return "" + if isinstance(value, str): + return re.sub(r"\s+", " ", value).strip() + if isinstance(value, Mapping): + return clean_text(" ".join(clean_text(item) for item in value.values())) + if isinstance(value, Iterable) and not isinstance(value, (bytes, bytearray)): + return clean_text(" ".join(clean_text(item) for item in value)) + return clean_text(str(value)) + + +def normalize_gender(value: Any) -> str | None: + if value is None: + return None + text = str(value).strip().casefold() + if text in {"female", "f", "woman", "women"}: + return "Female" + if text in {"male", "m", "man", "men"}: + return "Male" + if text in {"all", "any", "unknown", "other", "undifferentiated"}: + return text.title() + return str(value).strip() or None + + +def parse_date(value: Any) -> date | None: + if not value: + return None + try: + return datetime.fromisoformat(str(value).replace("Z", "+00:00")).date() + except ValueError: + return None + + +def age_years_from_birth_date(birth_date: date | None) -> float | None: + if birth_date is None: + return None + today = date.today() + years = today.year - birth_date.year + if (today.month, today.day) < (birth_date.month, birth_date.day): + years -= 1 + return float(years) if years >= 0 else None + + +def parse_iso8601_age_years(value: Any) -> float | None: + if not value: + return None + text = str(value) + match = re.fullmatch(r"P(?:(\d+(?:\.\d+)?)Y)?(?:(\d+(?:\.\d+)?)M)?", text) + if not match: + return None + years = float(match.group(1) or 0) + months = float(match.group(2) or 0) + return round(years + months / 12, 2) + + +def code_from_ontology_class(value: Mapping[str, Any] | None) -> NormalizedCode | None: + if not value: + return None + identifier = str(value.get("id") or value.get("code") or "").strip() + label = str(value.get("label") or value.get("display") or "").strip() or None + if not identifier and not label: + return None + vocabulary = "local" + code = identifier or label or "" + if ":" in identifier: + vocabulary, code = identifier.split(":", 1) + elif "/" in identifier: + code = identifier.rstrip("/").split("/")[-1] + return NormalizedCode( + vocabulary=vocabulary, + code=code, + label=label, + system=value.get("system"), + mapping_status="exact" if identifier else "unmapped", + ) + + +def code_from_fhir_codeable(value: Mapping[str, Any] | None) -> NormalizedCode | None: + if not value: + return None + text = clean_text(value.get("text")) + codings = value.get("coding") or [] + if isinstance(codings, list) and codings: + coding = codings[0] or {} + system = str(coding.get("system") or "") + vocabulary = FHIR_SYSTEM_TO_VOCABULARY.get(system, system.rsplit("/", 1)[-1]) + code = str(coding.get("code") or "").strip() + label = clean_text(coding.get("display")) or text or None + if code: + return NormalizedCode( + vocabulary=vocabulary or "FHIR", + code=code, + label=label, + system=system or None, + mapping_status="exact", + ) + if text: + return NormalizedCode( + vocabulary="FHIR", + code=text, + label=text, + mapping_status="unmapped", + ) + return None + + +def label_from_fhir_codeable(value: Mapping[str, Any] | None) -> str: + if not value: + return "" + text = clean_text(value.get("text")) + if text: + return text + codings = value.get("coding") or [] + if isinstance(codings, list): + for coding in codings: + label = clean_text((coding or {}).get("display")) + if label: + return label + code = clean_text((coding or {}).get("code")) + if code: + return code + return "" + + +def make_fact( + *, + category: str, + label: str, + provenance: Provenance, + description: str | None = None, + original_code: NormalizedCode | None = None, + normalized_codes: list[NormalizedCode] | None = None, + evidence_text: str | None = None, + evidence_start: int | None = None, + evidence_end: int | None = None, + confidence: float | None = None, + negated: bool = False, + temporality: str | None = None, + extra: dict[str, Any] | None = None, +) -> ClinicalFact: + cleaned_label = clean_text(label) or "Unknown" + codes = normalized_codes or ([] if original_code is None else [original_code]) + return ClinicalFact( + fact_id=stable_id( + category, + cleaned_label, + provenance.source_format, + provenance.source_path, + provenance.source_resource, + provenance.source_table, + evidence_start, + evidence_end, + ), + category=category, + label=cleaned_label, + description=clean_text(description) or None, + original_code=original_code, + normalized_codes=codes, + vocabulary=(codes[0].vocabulary if codes else None), + evidence_text=clean_text(evidence_text) or None, + evidence_start=evidence_start, + evidence_end=evidence_end, + confidence=confidence, + negated=negated, + temporality=temporality, + mapping_status=codes[0].mapping_status if codes else "unmapped", + provenance=provenance, + extra=extra or {}, + ) + + +def source_path_string(path: str | Path | None) -> str | None: + return str(Path(path).resolve()) if path else None + + +def safe_patient_id(value: Any, fallback: str) -> str: + candidate = clean_text(value) or fallback + return re.sub(r"[^A-Za-z0-9_.-]+", "-", candidate).strip("-") or fallback diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index 893673a0..6ab13041 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -9,11 +9,8 @@ from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig from trialmatchai.models.llm.llm_loader import load_model_and_tokenizer from trialmatchai.models.llm.llm_reranker import LLMReranker -from trialmatchai.models.llm.vllm_loader import load_vllm_engine from trialmatchai.entities import build_entity_annotator from trialmatchai.matching.eligibility_reasoning import BatchTrialProcessor -from trialmatchai.matching.eligibility_reasoning_vllm import BatchTrialProcessorVLLM -from trialmatchai.matching.phenopacket_processor import process_phenopacket from trialmatchai.matching.trial_ranker import ( load_trial_data, rank_trials, @@ -23,6 +20,9 @@ from trialmatchai.matching.retrieval.criteria_retrieval import SecondStageRetriever from trialmatchai.search import LanceDBSearchBackend from trialmatchai.services.preflight import run_preflight_checks +from trialmatchai.interop.exporters import profile_to_matching_summary +from trialmatchai.interop.importers import import_patient_path +from trialmatchai.interop.models import PatientProfile from trialmatchai.utils.file_utils import ( create_directory, read_json_file, @@ -30,7 +30,7 @@ write_json_file, write_text_file, ) -from trialmatchai.schemas.phenopacket import Keywords, Phenopacket +from trialmatchai.schemas.phenopacket import Keywords from trialmatchai.utils.logging_config import reset_request_id, set_request_id, setup_logging from trialmatchai.utils.timing import log_timing @@ -168,6 +168,10 @@ def run_rag_processing( if use_vllm: logger.info("Using vLLM backend for CoT reasoning") + from trialmatchai.matching.eligibility_reasoning_vllm import ( + BatchTrialProcessorVLLM, + ) + from trialmatchai.models.llm.vllm_loader import load_vllm_engine # Load vLLM configuration vllm_cfg = config.get("vllm", {}) @@ -240,6 +244,11 @@ def main_pipeline(config_path: str | None = None) -> int: if index_issues: return 1 + patient_inputs = _load_patient_inputs(config) + if not patient_inputs: + logger.error("No patient profiles were available for matching.") + return 1 + if torch.cuda.is_available(): torch.backends.cudnn.benchmark = True if hasattr(torch.backends.cuda, "enable_flash_sdp"): @@ -301,39 +310,21 @@ def main_pipeline(config_path: str | None = None) -> int: search_mode=config["search"].get("mode", "hybrid"), ) - # Process phenopackets - patient_folder = Path(paths["patients_dir"]) - if not patient_folder.exists(): - logger.error("Patients folder not found: %s", patient_folder) - return 1 - phenopacket_files = sorted( - [p for p in patient_folder.iterdir() if p.suffix == ".json"] - ) - if not phenopacket_files: - logger.warning("No patient files found in %s", patient_folder) - return 1 - - for phenopacket_path in phenopacket_files: - patient_id = phenopacket_path.stem + for profile, summary in patient_inputs: + patient_id = profile.patient_id token = set_request_id(patient_id) output_folder = Path(paths["output_dir"]) / patient_id create_directory(str(output_folder)) - input_file = str(phenopacket_path) - output_file = str(output_folder / "keywords.json") - try: - with log_timing(logger, "Phenopacket processing"): - with torch.no_grad(): - process_phenopacket( - input_file, output_file, model=model, tokenizer=tokenizer - ) - - keywords = Keywords.model_validate(read_json_file(output_file)).model_dump() - patient_info = Phenopacket.model_validate( - read_json_file(input_file) - ).model_dump() - patient_info["split_raw_description"] = keywords.get( + write_json_file(summary, str(output_folder / "keywords.json")) + write_json_file( + profile.model_dump(mode="json", exclude_none=True), + str(output_folder / "patient_profile.json"), + ) + keywords = Keywords.model_validate(summary).model_dump() + patient_info = dict(summary) + patient_info["split_raw_description"] = summary.get( "expanded_sentences", [] ) @@ -402,5 +393,64 @@ def main_pipeline(config_path: str | None = None) -> int: return 0 +def _load_patient_inputs(config: Dict) -> list[tuple[PatientProfile, Dict]]: + patient_cfg = config.get("patient_inputs", {}) + profile_dir = Path(patient_cfg.get("profile_dir", "data/patients/profiles")) + summary_dir = Path(patient_cfg.get("summary_dir", "data/patients/summaries")) + profile_files = sorted(profile_dir.glob("*.json")) if profile_dir.exists() else [] + if profile_files: + loaded: list[tuple[PatientProfile, Dict]] = [] + for profile_file in profile_files: + try: + profile = PatientProfile.model_validate(read_json_file(str(profile_file))) + summary_path = summary_dir / profile_file.name + if summary_path.exists(): + summary = read_json_file(str(summary_path)) + else: + summary = profile_to_matching_summary(profile) + loaded.append((profile, summary)) + except Exception: + logger.exception("Failed to load patient profile: %s", profile_file) + return loaded + + patient_folder = Path(config.get("paths", {}).get("patients_dir", "")) + if not patient_folder.exists(): + logger.error("Patient profile directory and legacy patients_dir are missing.") + return [] + + legacy_files = sorted(path for path in patient_folder.glob("*.json") if path.is_file()) + if not legacy_files: + logger.warning("No patient profile or legacy phenopacket files found.") + return [] + + logger.warning( + "No canonical patient profiles found in %s. Importing legacy Phenopacket " + "JSON files from %s for this run.", + profile_dir, + patient_folder, + ) + profile_dir.mkdir(parents=True, exist_ok=True) + summary_dir.mkdir(parents=True, exist_ok=True) + imported: list[tuple[PatientProfile, Dict]] = [] + for legacy_file in legacy_files: + try: + profiles = import_patient_path( + legacy_file, + input_format="phenopacket", + strict=bool(patient_cfg.get("strict_validation", False)), + ) + for profile in profiles: + summary = profile_to_matching_summary(profile) + write_json_file( + profile.model_dump(mode="json", exclude_none=True), + str(profile_dir / f"{profile.patient_id}.json"), + ) + write_json_file(summary, str(summary_dir / f"{profile.patient_id}.json")) + imported.append((profile, summary)) + except Exception: + logger.exception("Failed to import legacy patient file: %s", legacy_file) + return imported + + if __name__ == "__main__": raise SystemExit(main_pipeline()) diff --git a/src/trialmatchai/matching/eligibility_reasoning_vllm.py b/src/trialmatchai/matching/eligibility_reasoning_vllm.py index f394e4bd..88d05626 100644 --- a/src/trialmatchai/matching/eligibility_reasoning_vllm.py +++ b/src/trialmatchai/matching/eligibility_reasoning_vllm.py @@ -3,28 +3,20 @@ import json import os import time -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file from trialmatchai.utils.json_utils import extract_json_object from trialmatchai.utils.logging_config import setup_logging from tqdm import tqdm -from vllm import LLM, SamplingParams - -try: - # Present in vLLM when LoRA is enabled - from vllm.lora.request import LoRARequest # type: ignore -except Exception: # pragma: no cover - LoRARequest = None # type: ignore - logger = setup_logging(__name__) class BatchTrialProcessorVLLM: def __init__( self, - llm: LLM, + llm: Any, tokenizer=None, batch_size: int = 16, use_cot: bool = True, @@ -33,7 +25,7 @@ def __init__( top_p: float = 1.0, seed: Optional[int] = 1234, length_bucket: bool = True, - lora_request: Optional[LoRARequest] = None, # type: ignore + lora_request: Optional[Any] = None, ): """ vLLM-backed trial processor for CoT eligibility evaluation. @@ -54,6 +46,8 @@ def __init__( # Validate LoRA request during initialization self.lora_request = self._init_validate_lora_request(lora_request) + from vllm import SamplingParams # type: ignore + self.sampling_params = SamplingParams( max_tokens=self.max_new_tokens, temperature=self.temperature, diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index 05c641fd..28318131 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -22,12 +22,15 @@ def run_preflight_checks( issues: List[str] = [] paths = config.get("paths", {}) - _require_path( - issues, - "paths.patients_dir", - paths.get("patients_dir"), - required=require_patient_inputs, - ) + if require_patient_inputs: + _require_patient_inputs(issues, config) + else: + _require_path( + issues, + "paths.patients_dir", + paths.get("patients_dir"), + required=False, + ) _require_path( issues, "paths.trials_json_folder", @@ -150,3 +153,18 @@ def _require_output_dir(issues: List[str], value: str | None) -> None: path.mkdir(parents=True, exist_ok=True) except OSError as exc: issues.append(f"paths.output_dir is not writable: {path} ({exc})") + + +def _require_patient_inputs(issues: List[str], config: Dict[str, Any]) -> None: + paths = config.get("paths", {}) + patient_cfg = config.get("patient_inputs", {}) + profile_dir = Path(patient_cfg.get("profile_dir", "")) + legacy_dir = Path(paths.get("patients_dir", "")) + has_profiles = profile_dir.exists() + has_legacy = legacy_dir.exists() + if has_profiles or has_legacy: + return + if profile_dir: + issues.append(f"patient_inputs.profile_dir does not exist: {profile_dir}") + if legacy_dir: + issues.append(f"paths.patients_dir does not exist: {legacy_dir}") diff --git a/tests/test_import_patient_cli.py b/tests/test_import_patient_cli.py new file mode 100644 index 00000000..5a8d0266 --- /dev/null +++ b/tests/test_import_patient_cli.py @@ -0,0 +1,36 @@ +from __future__ import annotations + +import json + +from trialmatchai.cli.import_patient import main + + +def test_import_patient_cli_writes_profile_and_summary(tmp_path, monkeypatch): + note = tmp_path / "patient.txt" + note.write_text("Patient has breast cancer.", encoding="utf-8") + profile_dir = tmp_path / "profiles" + summary_dir = tmp_path / "summaries" + + monkeypatch.setattr( + "sys.argv", + [ + "trialmatchai-import-patient", + "--input", + str(note), + "--format", + "text", + "--output-dir", + str(profile_dir), + "--summary-dir", + str(summary_dir), + "--no-entities", + ], + ) + + assert main() == 0 + + profile = json.loads((profile_dir / "patient.json").read_text(encoding="utf-8")) + summary = json.loads((summary_dir / "patient.json").read_text(encoding="utf-8")) + assert profile["patient_id"] == "patient" + assert summary["patient_id"] == "patient" + assert summary["expanded_sentences"] diff --git a/tests/test_package_imports.py b/tests/test_package_imports.py index 56a14e37..6d13a5dd 100644 --- a/tests/test_package_imports.py +++ b/tests/test_package_imports.py @@ -28,4 +28,6 @@ def test_command_group_includes_bootstrap_data(monkeypatch, capsys): main() assert exc.value.code == 0 - assert "bootstrap-data" in capsys.readouterr().out + output = capsys.readouterr().out + assert "bootstrap-data" in output + assert "import-patient" in output diff --git a/tests/test_patient_interop.py b/tests/test_patient_interop.py new file mode 100644 index 00000000..d517854f --- /dev/null +++ b/tests/test_patient_interop.py @@ -0,0 +1,220 @@ +from __future__ import annotations + +import json + +import pandas as pd + +from trialmatchai.interop import detect_patient_input_format, import_patient_path +from trialmatchai.interop.exporters import ( + profile_to_fhir_bundle, + profile_to_matching_summary, + profile_to_phenopacket, +) +from trialmatchai.interop.models import PatientProfile +from trialmatchai.interop.narrative import render_patient_narrative + + +class FakeAnnotator: + def annotate_texts_in_parallel(self, texts, max_workers=1, retries=1, delay=0): + del max_workers, retries, delay + text = texts[0] + start = text.index("melanoma") + return [ + [ + { + "entity_group": "disease", + "text": "melanoma", + "start": start, + "end": start + len("melanoma"), + "score": 0.97, + "normalized_id": ["SNOMED:372244006"], + "synonyms": ["malignant melanoma"], + "linker_score": 0.91, + "linker_status": "linked", + } + ] + ] + + +def test_patient_profile_schema_and_summary(): + schema = PatientProfile.model_json_schema() + assert "patient_id" in schema["properties"] + + profile = PatientProfile(patient_id="p1") + summary = profile_to_matching_summary(profile) + assert summary["patient_id"] == "p1" + assert summary["age"] == "all" + assert summary["expanded_sentences"] + + +def test_text_importer_preserves_offsets_and_entities(tmp_path): + note = tmp_path / "patient-note.txt" + note.write_text("Patient has metastatic melanoma.", encoding="utf-8") + + profile = import_patient_path( + note, + input_format="text", + entity_annotator=FakeAnnotator(), + )[0] + + assert detect_patient_input_format(note) == "text" + assert profile.conditions[0].label == "melanoma" + assert profile.conditions[0].evidence_start == 23 + assert profile.conditions[0].normalized_codes[0].vocabulary == "SNOMED" + assert profile.notes[0].entities[0]["linker_status"] == "linked" + + +def test_phenopacket_importer_maps_core_sections(tmp_path): + packet = { + "id": "patient-phen", + "metaData": {}, + "subject": { + "sex": "FEMALE", + "timeAtLastEncounter": {"age": {"iso8601duration": "P42Y"}}, + }, + "phenotypicFeatures": [ + {"type": {"id": "HP:0001250", "label": "Seizure"}} + ], + "diseases": [ + {"term": {"id": "MONDO:0007254", "label": "breast cancer"}} + ], + "measurements": [ + {"assay": {"id": "LOINC:718-7", "label": "Hemoglobin"}} + ], + "medicalActions": [ + {"treatment": {"agent": {"id": "RxNorm:123", "label": "trastuzumab"}}} + ], + } + path = tmp_path / "patient.json" + path.write_text(json.dumps(packet), encoding="utf-8") + + profile = import_patient_path(path)[0] + + assert detect_patient_input_format(path) == "phenopacket" + assert profile.demographics.sex == "Female" + assert profile.demographics.age_years == 42 + assert profile.conditions[0].label == "breast cancer" + assert profile.phenotypes[0].label == "Seizure" + assert profile.medications[0].label == "trastuzumab" + + +def test_fhir_bundle_importer_and_exporter(tmp_path): + bundle = { + "resourceType": "Bundle", + "type": "collection", + "entry": [ + {"resource": {"resourceType": "Patient", "id": "fhir-p1", "gender": "female"}}, + { + "resource": { + "resourceType": "Condition", + "id": "c1", + "code": { + "coding": [ + { + "system": "http://snomed.info/sct", + "code": "254637007", + "display": "Non-small cell lung cancer", + } + ] + }, + } + }, + { + "resource": { + "resourceType": "Observation", + "id": "o1", + "code": {"coding": [{"system": "http://loinc.org", "code": "718-7"}]}, + "valueQuantity": {"value": 12.5, "unit": "g/dL"}, + } + }, + ], + } + path = tmp_path / "bundle.json" + path.write_text(json.dumps(bundle), encoding="utf-8") + + profile = import_patient_path(path)[0] + exported = profile_to_fhir_bundle(profile) + + assert detect_patient_input_format(path) == "fhir" + assert profile.patient_id == "fhir-p1" + assert profile.conditions[0].normalized_codes[0].vocabulary == "SNOMED" + assert profile.observations[0].description == "12.5 g/dL" + assert exported["resourceType"] == "Bundle" + + +def test_fhir_ndjson_detection_and_import(tmp_path): + path = tmp_path / "patient.ndjson" + path.write_text( + "\n".join( + [ + json.dumps({"resourceType": "Patient", "id": "ndjson-p1"}), + json.dumps( + { + "resourceType": "Procedure", + "id": "p1", + "code": {"text": "bone marrow biopsy"}, + } + ), + ] + ), + encoding="utf-8", + ) + + profile = import_patient_path(path)[0] + + assert detect_patient_input_format(path) == "fhir-ndjson" + assert profile.procedures[0].label == "bone marrow biopsy" + + +def test_omop_importer_from_csv_extract(tmp_path): + omop = tmp_path / "omop" + omop.mkdir() + pd.DataFrame( + [{"person_id": 1, "gender_source_value": "F", "year_of_birth": 1980}] + ).to_csv(omop / "PERSON.csv", index=False) + pd.DataFrame( + [ + { + "person_id": 1, + "condition_concept_id": 10, + "condition_start_date": "2026-01-01", + } + ] + ).to_csv(omop / "CONDITION_OCCURRENCE.csv", index=False) + pd.DataFrame( + [ + { + "concept_id": 10, + "vocabulary_id": "SNOMED", + "concept_code": "44054006", + "concept_name": "Diabetes mellitus", + "domain_id": "Condition", + } + ] + ).to_csv(omop / "CONCEPT.csv", index=False) + + profiles = import_patient_path(omop) + + assert detect_patient_input_format(omop) == "omop" + assert len(profiles) == 1 + assert profiles[0].conditions[0].label == "Diabetes mellitus" + assert profiles[0].conditions[0].normalized_codes[0].code == "44054006" + + +def test_narrative_and_phenopacket_export_are_deterministic(tmp_path): + packet = { + "id": "patient-export", + "metaData": {}, + "subject": {}, + "diseases": [{"term": {"id": "MONDO:0004992", "label": "cancer"}}], + } + path = tmp_path / "patient.json" + path.write_text(json.dumps(packet), encoding="utf-8") + profile = import_patient_path(path)[0] + + narrative = render_patient_narrative(profile) + packet_out = profile_to_phenopacket(profile) + + assert narrative[0].startswith("Diagnoses:") + assert packet_out["id"] == "patient-export" + assert packet_out["diseases"][0]["term"]["label"] == "cancer" diff --git a/tests/test_patient_runtime_loading.py b/tests/test_patient_runtime_loading.py new file mode 100644 index 00000000..815d8ba7 --- /dev/null +++ b/tests/test_patient_runtime_loading.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import json + +from trialmatchai.main import _load_patient_inputs + + +def test_runtime_loads_profiles_from_configured_directory(tmp_path): + profiles = tmp_path / "profiles" + profiles.mkdir() + summaries = tmp_path / "summaries" + summaries.mkdir() + (profiles / "p1.json").write_text( + json.dumps({"patient_id": "p1", "demographics": {}}), + encoding="utf-8", + ) + (summaries / "p1.json").write_text( + json.dumps( + { + "patient_id": "p1", + "main_conditions": ["melanoma"], + "other_conditions": [], + "expanded_sentences": ["Patient has melanoma."], + "age": "all", + "gender": "all", + } + ), + encoding="utf-8", + ) + + loaded = _load_patient_inputs( + { + "patient_inputs": { + "profile_dir": str(profiles), + "summary_dir": str(summaries), + }, + "paths": {"patients_dir": str(tmp_path / "legacy")}, + } + ) + + assert loaded[0][0].patient_id == "p1" + assert loaded[0][1]["main_conditions"] == ["melanoma"] + + +def test_runtime_imports_legacy_phenopackets_when_profiles_missing(tmp_path): + legacy = tmp_path / "legacy" + legacy.mkdir() + profiles = tmp_path / "profiles" + summaries = tmp_path / "summaries" + (legacy / "patient.json").write_text( + json.dumps( + { + "id": "legacy-p1", + "metaData": {}, + "subject": {}, + "diseases": [{"term": {"label": "sarcoma"}}], + } + ), + encoding="utf-8", + ) + + loaded = _load_patient_inputs( + { + "patient_inputs": { + "profile_dir": str(profiles), + "summary_dir": str(summaries), + }, + "paths": {"patients_dir": str(legacy)}, + } + ) + + assert loaded[0][0].patient_id == "legacy-p1" + assert loaded[0][1]["main_conditions"] == ["sarcoma"] + assert (profiles / "legacy-p1.json").exists() + assert (summaries / "legacy-p1.json").exists() diff --git a/tests/test_settings.py b/tests/test_settings.py index d65af376..c8f13d3b 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -26,6 +26,7 @@ def test_env_overrides(self) -> None: "search": {"mode": "hybrid"}, "entity_extraction": {"backend": "gliner2"}, "concept_linker": {"db_path": "old"}, + "patient_inputs": {"profile_dir": "old-profiles"}, "registry": {"since_days": 7, "raw_dir": "old-raw"}, } os.environ["TRIALMATCHAI_SEARCH_DB_PATH"] = "data/search-test" @@ -34,6 +35,8 @@ def test_env_overrides(self) -> None: os.environ["TRIALMATCHAI_EMBEDDER_MODEL_NAME"] = "new-model" os.environ["TRIALMATCHAI_ENTITY_BACKEND"] = "regex" os.environ["TRIALMATCHAI_CONCEPT_DB_PATH"] = "concepts" + os.environ["TRIALMATCHAI_PATIENT_PROFILE_DIR"] = "patients/profiles" + os.environ["TRIALMATCHAI_PATIENT_STRICT_VALIDATION"] = "true" os.environ["TRIALMATCHAI_REGISTRY_SINCE_DAYS"] = "30" os.environ["TRIALMATCHAI_REGISTRY_RAW_DIR"] = "registry/raw" try: @@ -45,6 +48,8 @@ def test_env_overrides(self) -> None: os.environ.pop("TRIALMATCHAI_EMBEDDER_MODEL_NAME", None) os.environ.pop("TRIALMATCHAI_ENTITY_BACKEND", None) os.environ.pop("TRIALMATCHAI_CONCEPT_DB_PATH", None) + os.environ.pop("TRIALMATCHAI_PATIENT_PROFILE_DIR", None) + os.environ.pop("TRIALMATCHAI_PATIENT_STRICT_VALIDATION", None) os.environ.pop("TRIALMATCHAI_REGISTRY_SINCE_DAYS", None) os.environ.pop("TRIALMATCHAI_REGISTRY_RAW_DIR", None) @@ -54,6 +59,8 @@ def test_env_overrides(self) -> None: self.assertEqual(updated["embedder"]["model_name"], "new-model") self.assertEqual(updated["entity_extraction"]["backend"], "regex") self.assertEqual(updated["concept_linker"]["db_path"], "concepts") + self.assertEqual(updated["patient_inputs"]["profile_dir"], "patients/profiles") + self.assertTrue(updated["patient_inputs"]["strict_validation"]) self.assertEqual(updated["registry"]["since_days"], 30) self.assertEqual(updated["registry"]["raw_dir"], "registry/raw") From c4b753e9aaca02f3fc05f4dc8db94f8d68eae57c Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 21 Jun 2026 21:21:47 +0200 Subject: [PATCH 08/64] Fix lean CI optional ML imports --- src/trialmatchai/main.py | 26 ++++++++++++---- .../matching/eligibility_reasoning.py | 29 +++++++++++++----- .../matching/phenopacket_processor.py | 29 +++++++++++++++--- .../matching/retrieval/criteria_retrieval.py | 10 +++++-- .../matching/retrieval/trial_retrieval.py | 8 +++-- .../models/embedding/text_embedder.py | 30 +++++++++++++------ src/trialmatchai/services/preflight.py | 24 ++++++++------- tests/test_preflight_and_indexer.py | 3 -- 8 files changed, 115 insertions(+), 44 deletions(-) diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index 6ab13041..791bcc04 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -1,14 +1,9 @@ from __future__ import annotations from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import torch +from typing import TYPE_CHECKING, Dict, List, Optional, Tuple from trialmatchai.config.config_loader import load_config -from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig -from trialmatchai.models.llm.llm_loader import load_model_and_tokenizer -from trialmatchai.models.llm.llm_reranker import LLMReranker from trialmatchai.entities import build_entity_annotator from trialmatchai.matching.eligibility_reasoning import BatchTrialProcessor from trialmatchai.matching.trial_ranker import ( @@ -34,6 +29,9 @@ from trialmatchai.utils.logging_config import reset_request_id, set_request_id, setup_logging from trialmatchai.utils.timing import log_timing +if TYPE_CHECKING: + from trialmatchai.models.embedding.text_embedder import TextEmbedder + logger = setup_logging(__name__) @@ -249,6 +247,22 @@ def main_pipeline(config_path: str | None = None) -> int: logger.error("No patient profiles were available for matching.") return 1 + try: + import torch + except ImportError: + logger.error( + "PyTorch is required to run matching. Install the ML extras with " + "`uv sync --extra llm --extra entity`." + ) + return 1 + + from trialmatchai.models.embedding.text_embedder import ( + TextEmbedder, + TextEmbedderConfig, + ) + from trialmatchai.models.llm.llm_loader import load_model_and_tokenizer + from trialmatchai.models.llm.llm_reranker import LLMReranker + if torch.cuda.is_available(): torch.backends.cudnn.benchmark = True if hasattr(torch.backends.cuda, "enable_flash_sdp"): diff --git a/src/trialmatchai/matching/eligibility_reasoning.py b/src/trialmatchai/matching/eligibility_reasoning.py index f4c6671e..bc15d646 100644 --- a/src/trialmatchai/matching/eligibility_reasoning.py +++ b/src/trialmatchai/matching/eligibility_reasoning.py @@ -3,7 +3,6 @@ import time from typing import Dict, List -import torch from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file from trialmatchai.utils.json_utils import extract_json_object from trialmatchai.utils.logging_config import setup_logging @@ -11,6 +10,20 @@ logger = setup_logging(__name__) +try: + import torch +except ImportError: # pragma: no cover - exercised by lean package imports + torch = None # type: ignore[assignment] + + +def _require_torch(): + if torch is None: + raise RuntimeError( + "BatchTrialProcessor requires PyTorch. Install the ML extras with " + "`uv sync --extra llm` or `pip install 'trialmatchai[llm]'`." + ) + return torch + class BatchTrialProcessor: def __init__( @@ -30,6 +43,7 @@ def __init__( - length bucketing (sort by prompt token length) to reduce padding waste - telemetry for tokens/sec and stage timings """ + torch_module = _require_torch() self.device = device self.device_str = f"cuda:{device}" self.batch_size = batch_size @@ -42,8 +56,8 @@ def __init__( self.model.eval() try: # Allow TF32 on Ampere+ (gives a free speedup for matmuls with minimal accuracy loss) - torch.backends.cuda.matmul.allow_tf32 = True - torch.set_float32_matmul_precision("high") + torch_module.backends.cuda.matmul.allow_tf32 = True + torch_module.set_float32_matmul_precision("high") except Exception: pass @@ -198,6 +212,7 @@ def _process_batch(self, batch: List[Dict], output_folder: str): """ Expects a list of dicts with keys: nct_id, prompt """ + torch_module = _require_torch() try: t0 = time.time() # Tokenize once; pad to the longest in this batch only @@ -213,13 +228,13 @@ def _process_batch(self, batch: List[Dict], output_folder: str): # Autocast to model dtype if it's half/bfloat16 for extra speed model_dtype = next(self.model.parameters()).dtype - use_autocast = model_dtype in (torch.float16, torch.bfloat16) + use_autocast = model_dtype in (torch_module.float16, torch_module.bfloat16) - with torch.inference_mode(): + with torch_module.inference_mode(): ctx = ( - torch.autocast(device_type="cuda", dtype=model_dtype) + torch_module.autocast(device_type="cuda", dtype=model_dtype) if use_autocast - else torch.cuda.amp.autocast(enabled=False) + else torch_module.cuda.amp.autocast(enabled=False) ) with ctx: outputs = self.model.generate( diff --git a/src/trialmatchai/matching/phenopacket_processor.py b/src/trialmatchai/matching/phenopacket_processor.py index 6aef765b..aeff21be 100644 --- a/src/trialmatchai/matching/phenopacket_processor.py +++ b/src/trialmatchai/matching/phenopacket_processor.py @@ -1,16 +1,35 @@ import json from typing import Dict, List, Optional -import torch from trialmatchai.schemas.phenopacket import Phenopacket from trialmatchai.utils.file_utils import read_json_file, write_json_file from trialmatchai.utils.json_utils import extract_json_object from trialmatchai.utils.logging_config import setup_logging from trialmatchai.utils.temporal_utils import parse_iso_duration, parse_temporal -from transformers import AutoModelForCausalLM, AutoTokenizer logger = setup_logging(__name__) +try: + import torch +except ImportError: # pragma: no cover - exercised by lean package imports + torch = None # type: ignore[assignment] + + +def _require_llm_dependencies(): + if torch is None: + raise RuntimeError( + "ClinicalSummarizer requires PyTorch. Install the ML extras with " + "`uv sync --extra llm` or `pip install 'trialmatchai[llm]'`." + ) + try: + from transformers import AutoModelForCausalLM, AutoTokenizer + except ImportError as exc: + raise RuntimeError( + "ClinicalSummarizer requires Transformers. Install the ML extras with " + "`uv sync --extra llm` or `pip install 'trialmatchai[llm]'`." + ) from exc + return torch, AutoModelForCausalLM, AutoTokenizer + class PhenopacketProcessor: def __init__(self, file_path: str): @@ -246,10 +265,11 @@ def __init__(self, model=None, tokenizer=None, model_name: Optional[str] = None) self.model = model self.tokenizer = tokenizer elif model_name is not None: + torch_module, AutoModelForCausalLM, AutoTokenizer = _require_llm_dependencies() self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained( model_name, - torch_dtype=torch.float16, + torch_dtype=torch_module.float16, device_map="auto", ) else: @@ -260,6 +280,7 @@ def __init__(self, model=None, tokenizer=None, model_name: Optional[str] = None) self.model.eval() def generate_summary(self, sentences: List[str]) -> Dict: + torch_module = _require_llm_dependencies()[0] SYSTEM_PROMPT = """ You are a specialized medical assistant designed for precise and accurate clinical trial matching. Analyze the patient's medical description carefully and extract clinically relevant information for trial eligibility assessment. @@ -303,7 +324,7 @@ def generate_summary(self, sentences: List[str]) -> Dict: return_tensors="pt", ).to(self.model.device) - with torch.no_grad(): + with torch_module.no_grad(): output_ids = self.model.generate( prompt, max_new_tokens=2048, diff --git a/src/trialmatchai/matching/retrieval/criteria_retrieval.py b/src/trialmatchai/matching/retrieval/criteria_retrieval.py index fb5487f7..ece91552 100644 --- a/src/trialmatchai/matching/retrieval/criteria_retrieval.py +++ b/src/trialmatchai/matching/retrieval/criteria_retrieval.py @@ -1,14 +1,18 @@ +from __future__ import annotations + import math from collections import defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Dict, List, Optional +from typing import TYPE_CHECKING, Dict, List, Optional -from trialmatchai.models.embedding.text_embedder import TextEmbedder -from trialmatchai.models.llm.llm_reranker import LLMReranker from trialmatchai.search.lancedb_backend import TrialSearchBackend from trialmatchai.utils.file_utils import write_text_file from trialmatchai.utils.logging_config import setup_logging +if TYPE_CHECKING: + from trialmatchai.models.embedding.text_embedder import TextEmbedder + from trialmatchai.models.llm.llm_reranker import LLMReranker + logger = setup_logging(__name__) diff --git a/src/trialmatchai/matching/retrieval/trial_retrieval.py b/src/trialmatchai/matching/retrieval/trial_retrieval.py index 0d18c652..c1e02463 100644 --- a/src/trialmatchai/matching/retrieval/trial_retrieval.py +++ b/src/trialmatchai/matching/retrieval/trial_retrieval.py @@ -1,11 +1,15 @@ +from __future__ import annotations + from datetime import datetime -from typing import Dict, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union from dateutil import parser as date_parser -from trialmatchai.models.embedding.text_embedder import TextEmbedder from trialmatchai.search.lancedb_backend import TrialSearchBackend from trialmatchai.utils.logging_config import setup_logging +if TYPE_CHECKING: + from trialmatchai.models.embedding.text_embedder import TextEmbedder + logger = setup_logging(__name__) diff --git a/src/trialmatchai/models/embedding/text_embedder.py b/src/trialmatchai/models/embedding/text_embedder.py index acf21da4..da5c9929 100644 --- a/src/trialmatchai/models/embedding/text_embedder.py +++ b/src/trialmatchai/models/embedding/text_embedder.py @@ -3,10 +3,6 @@ from dataclasses import dataclass from typing import Iterable, List, Literal, Sequence, Any -import torch -import torch.nn.functional as F -from transformers import AutoModel, AutoTokenizer - from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -30,6 +26,9 @@ class TextEmbedderConfig: class TextEmbedder: def __init__(self, config: TextEmbedderConfig): + torch, F, AutoModel, AutoTokenizer = _load_embedding_dependencies() + self._torch = torch + self._functional = F self.config = config self.device = torch.device( "cuda" if config.use_gpu and torch.cuda.is_available() else "cpu" @@ -71,21 +70,21 @@ def embed_texts(self, texts: Sequence[str]) -> List[List[float]]: return_tensors="pt", max_length=self.config.max_length, ).to(self.device) - with torch.inference_mode(): + with self._torch.inference_mode(): outputs = self.model(**enc) pooled = self._pool(outputs, enc["attention_mask"]) if self.config.normalize: - pooled = F.normalize(pooled, p=2, dim=1) + pooled = self._functional.normalize(pooled, p=2, dim=1) vectors.extend(pooled.cpu().tolist()) return vectors - def _pool(self, outputs: Any, attention_mask: torch.Tensor) -> torch.Tensor: + def _pool(self, outputs: Any, attention_mask: Any) -> Any: if self.config.pooling == "cls": return outputs.last_hidden_state[:, 0, :] token_embeddings = outputs.last_hidden_state mask = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() - summed = torch.sum(token_embeddings * mask, dim=1) - counts = torch.clamp(mask.sum(dim=1), min=1e-9) + summed = self._torch.sum(token_embeddings * mask, dim=1) + counts = self._torch.clamp(mask.sum(dim=1), min=1e-9) return summed / counts @@ -94,3 +93,16 @@ def _batched(items: Sequence[str], batch_size: int) -> Iterable[Sequence[str]]: raise ValueError("batch_size must be positive.") for i in range(0, len(items), batch_size): yield items[i : i + batch_size] + + +def _load_embedding_dependencies(): + try: + import torch + import torch.nn.functional as F + from transformers import AutoModel, AutoTokenizer + except ImportError as exc: + raise RuntimeError( + "Text embedding requires the ML dependencies. Install them with " + "`uv sync --extra llm` or `pip install 'trialmatchai[llm]'`." + ) from exc + return torch, F, AutoModel, AutoTokenizer diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index 28318131..e0ff0122 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -82,21 +82,25 @@ def run_preflight_checks( required=True, ) if config.get("cot_backend") == "vllm": - if importlib.util.find_spec("vllm") is None: + vllm_available = importlib.util.find_spec("vllm") is not None + if not vllm_available: issues.append( "cot_backend=vllm requires the GPU extra " "(`uv sync --extra llm --extra gpu`)." ) - try: - import torch - except Exception: - issues.append( - "cot_backend=vllm requires PyTorch " - "(`uv sync --extra llm --extra gpu`)." - ) else: - if not torch.cuda.is_available(): - issues.append("cot_backend=vllm requires a CUDA-capable runtime.") + try: + import torch + except Exception: + issues.append( + "cot_backend=vllm requires PyTorch " + "(`uv sync --extra llm --extra gpu`)." + ) + else: + if not torch.cuda.is_available(): + issues.append( + "cot_backend=vllm requires a CUDA-capable runtime." + ) search_cfg = config.get("search_backend", {}) if search_cfg: diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index 30bd1c3f..2d9375ab 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -100,14 +100,11 @@ def test_preflight_reports_missing_search_db_path(tmp_path): def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): - import torch - cfg = _base_config(tmp_path) cfg["cot_backend"] = "vllm" Path(cfg["model"]["cot_adapter_path"]).mkdir(parents=True) Path(cfg["model"]["reranker_adapter_path"]).mkdir(parents=True) monkeypatch.setattr(preflight.importlib.util, "find_spec", lambda name: None) - monkeypatch.setattr(torch.cuda, "is_available", lambda: True) issues = run_preflight_checks(cfg, require_models=True) From 463bfe021cea210169ba4f643c2edfb295d6d07a Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 21 Jun 2026 22:18:11 +0200 Subject: [PATCH 09/64] Harden package release and remove legacy shims --- .env.example | 3 +- .github/workflows/ci.yml | 28 ++++ .gitignore | 6 +- README.md | 5 +- docs/architecture.md | 14 +- docs/interoperability.md | 8 +- src/Matcher/__init__.py | 13 -- src/Matcher/config/__init__.py | 1 - src/Matcher/config/config_loader.py | 1 - src/Matcher/config/settings.py | 1 - src/Matcher/entities/__init__.py | 1 - src/Matcher/main.py | 1 - src/Matcher/pipeline/__init__.py | 1 - src/Matcher/pipeline/cot_reasoning.py | 1 - src/Matcher/pipeline/cot_reasoning_vllm.py | 1 - src/Matcher/pipeline/phenopacket_processor.py | 1 - src/Matcher/pipeline/trial_ranker.py | 1 - src/Matcher/pipeline/trial_search/__init__.py | 1 - .../trial_search/first_level_search.py | 1 - .../trial_search/second_level_search.py | 1 - src/Matcher/schemas/__init__.py | 1 - src/Matcher/search/__init__.py | 1 - src/Matcher/services/__init__.py | 1 - src/Matcher/utils/__init__.py | 1 - src/Matcher/utils/file_utils.py | 1 - src/Matcher/utils/json_utils.py | 1 - src/Matcher/utils/logging_config.py | 1 - src/Matcher/utils/retry.py | 1 - src/Matcher/utils/temporal_utils.py | 1 - src/Matcher/utils/timing.py | 1 - src/trialmatchai/cli/bootstrap_data.py | 8 +- src/trialmatchai/cli/build_concepts.py | 16 +-- src/trialmatchai/cli/healthcheck.py | 7 +- src/trialmatchai/config/config.json | 3 +- src/trialmatchai/config/config_loader.py | 51 ++++++- src/trialmatchai/config/settings.py | 4 +- src/trialmatchai/entities/builder.py | 18 ++- src/trialmatchai/entities/schemas.py | 11 +- src/trialmatchai/interop/detect.py | 4 +- .../interop/importers/__init__.py | 2 +- src/trialmatchai/interop/importers/fhir.py | 129 +++++++++++++++--- src/trialmatchai/interop/narrative.py | 14 ++ src/trialmatchai/main.py | 64 +++------ src/trialmatchai/models/llm/llm_loader.py | 35 +++-- src/trialmatchai/models/llm/llm_reranker.py | 59 +++++--- src/trialmatchai/models/llm/vllm_loader.py | 5 +- src/trialmatchai/services/preflight.py | 29 ++-- tests/test_bootstrap_data.py | 12 ++ tests/test_config_pytest.py | 17 +++ tests/test_entities.py | 60 ++++++-- tests/test_import_patient_cli.py | 1 + tests/test_package_imports.py | 24 +++- tests/test_patient_interop.py | 64 +++++++++ tests/test_patient_runtime_loading.py | 22 +-- tests/test_preflight_and_indexer.py | 14 +- 55 files changed, 527 insertions(+), 246 deletions(-) delete mode 100644 src/Matcher/__init__.py delete mode 100644 src/Matcher/config/__init__.py delete mode 100644 src/Matcher/config/config_loader.py delete mode 100644 src/Matcher/config/settings.py delete mode 100644 src/Matcher/entities/__init__.py delete mode 100644 src/Matcher/main.py delete mode 100644 src/Matcher/pipeline/__init__.py delete mode 100644 src/Matcher/pipeline/cot_reasoning.py delete mode 100644 src/Matcher/pipeline/cot_reasoning_vllm.py delete mode 100644 src/Matcher/pipeline/phenopacket_processor.py delete mode 100644 src/Matcher/pipeline/trial_ranker.py delete mode 100644 src/Matcher/pipeline/trial_search/__init__.py delete mode 100644 src/Matcher/pipeline/trial_search/first_level_search.py delete mode 100644 src/Matcher/pipeline/trial_search/second_level_search.py delete mode 100644 src/Matcher/schemas/__init__.py delete mode 100644 src/Matcher/search/__init__.py delete mode 100644 src/Matcher/services/__init__.py delete mode 100644 src/Matcher/utils/__init__.py delete mode 100644 src/Matcher/utils/file_utils.py delete mode 100644 src/Matcher/utils/json_utils.py delete mode 100644 src/Matcher/utils/logging_config.py delete mode 100644 src/Matcher/utils/retry.py delete mode 100644 src/Matcher/utils/temporal_utils.py delete mode 100644 src/Matcher/utils/timing.py diff --git a/.env.example b/.env.example index 9f28e37e..68a22c5a 100644 --- a/.env.example +++ b/.env.example @@ -1,7 +1,6 @@ # Copy this file to .env for local TrialMatchAI runtime configuration. # Do not commit .env. -TRIALMATCHAI_PATIENTS_DIR=example TRIALMATCHAI_OUTPUT_DIR=results TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons TRIALMATCHAI_PATIENT_RAW_DIR=data/patients/raw @@ -27,7 +26,7 @@ TRIALMATCHAI_COT_BACKEND=vllm TRIALMATCHAI_ENTITY_BACKEND=gliner2 TRIALMATCHAI_ENTITY_MODEL_NAME=fastino/gliner2-base -TRIALMATCHAI_ENTITY_SCHEMA_PATH=src/trialmatchai/entity_schemas/trialmatchai.yaml +TRIALMATCHAI_ENTITY_SCHEMA_PATH=entity_schemas/trialmatchai.yaml TRIALMATCHAI_ENTITY_TRUST_REMOTE_CODE=false TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts TRIALMATCHAI_CONCEPT_TABLE=concepts diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 450d7bc9..910a6d44 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -35,6 +35,34 @@ jobs: - name: Run tests run: uv run pytest -v + - name: Build package + run: uv build + + - name: Installed package smoke + run: | + WHEEL="$(ls dist/trialmatchai-*.whl | head -n 1)" + mkdir -p /tmp/trialmatchai-installed-smoke + cd /tmp/trialmatchai-installed-smoke + uv run --python 3.11 --isolated --with "$WHEEL" python - <<'PY' + from pathlib import Path + from trialmatchai.config.config_loader import load_config + + cfg = load_config() + assert Path(cfg["entity_extraction"]["schema_path"]).exists() + assert cfg["paths"]["output_dir"].endswith("results") + PY + + - name: CLI help smoke + run: | + WHEEL="$(ls dist/trialmatchai-*.whl | head -n 1)" + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai --help + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-healthcheck --help + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-index --help + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-build-concepts --help + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-update-registry --help + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-import-patient --help + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-run --help + - name: Secret scan run: uv run python scripts/scan_secrets.py diff --git a/.gitignore b/.gitignore index 6135ab2a..1efa6385 100644 --- a/.gitignore +++ b/.gitignore @@ -10,9 +10,9 @@ logs/ .env.* !.env.example -models/ -source/ -Parser/ +/models/ +/source/ +/Parser/ # ============================ # Generated indexing state diff --git a/README.md b/README.md index 7b495acd..1f1ac5cd 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ The supported v1 deployment path is a single Python 3.11 GPU server or VM. Trial - `uv` recommended, or `pip` with editable install - NVIDIA GPU with enough VRAM for the selected LLM backend - 100 GB+ disk space for datasets, models, LanceDB tables, and results -- A LanceDB concept table built from OMOP/legacy dictionaries for entity normalization +- A LanceDB concept table built from OMOP and curated concept dictionaries for entity normalization ## Security First @@ -91,7 +91,6 @@ Results are written under `results/`. Configuration defaults live in `src/trialmatchai/config/config.json`. Runtime overrides use `.env` or environment variables: ```bash -TRIALMATCHAI_PATIENTS_DIR=example TRIALMATCHAI_OUTPUT_DIR=results TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons TRIALMATCHAI_PATIENT_RAW_DIR=data/patients/raw @@ -109,7 +108,7 @@ TRIALMATCHAI_SEARCH_MODE=hybrid TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false TRIALMATCHAI_ENTITY_BACKEND=gliner2 -TRIALMATCHAI_ENTITY_SCHEMA_PATH=src/trialmatchai/entity_schemas/trialmatchai.yaml +TRIALMATCHAI_ENTITY_SCHEMA_PATH=entity_schemas/trialmatchai.yaml TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts TRIALMATCHAI_CONCEPT_TABLE=concepts TRIALMATCHAI_LINK_ACCEPT=0.80 diff --git a/docs/architecture.md b/docs/architecture.md index 707ee589..685e6374 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -1,6 +1,6 @@ # TrialMatchAI Architecture -TrialMatchAI is an installable Python package exposed as `trialmatchai`. The supported runtime code lives under `src/trialmatchai`; `src/Matcher` is a temporary deprecated import shim for one release cycle. +TrialMatchAI is an installable Python package exposed as `trialmatchai`. The supported runtime code lives under `src/trialmatchai`. ## Runtime Subsystems @@ -26,16 +26,12 @@ LanceDB is the only search database. It is embedded, file-backed, and stored und The registry manifest is append-only JSONL at `data/registry/manifest.jsonl`. The latest record per `nct_id` determines idempotency. -## Compatibility +## Public API -`Matcher` import shims are kept temporarily for downstream callers: - -```python -from Matcher.config.config_loader import load_config -``` - -New code must import: +New code imports from the canonical package namespace: ```python from trialmatchai.config.config_loader import load_config ``` + +The old `Matcher` namespace is not shipped. diff --git a/docs/interoperability.md b/docs/interoperability.md index af75690a..6f6e9b3c 100644 --- a/docs/interoperability.md +++ b/docs/interoperability.md @@ -46,7 +46,7 @@ deterministic sentences for retrieval and LLM context. This keeps source parsing normalization, and prompt context generation independently testable. The matching exporter writes `data/patients/summaries/.json` with the -current-compatible fields: +runtime summary fields: - `main_conditions` - `other_conditions` @@ -55,9 +55,9 @@ current-compatible fields: - `gender` - `provenance` -`trialmatchai-run` consumes canonical profiles first. If no profiles exist, it -temporarily imports legacy Phenopacket JSON files from `paths.patients_dir` and -writes profiles and summaries before matching. +`trialmatchai-run` consumes canonical profiles from `patient_inputs.profile_dir`. +Import source files explicitly with `trialmatchai-import-patient` before running +matching. ## Commands diff --git a/src/Matcher/__init__.py b/src/Matcher/__init__.py deleted file mode 100644 index cb9a15ed..00000000 --- a/src/Matcher/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from __future__ import annotations - -import warnings - -from trialmatchai import __version__ - -warnings.warn( - "The 'Matcher' namespace is deprecated. Import from 'trialmatchai' instead.", - DeprecationWarning, - stacklevel=2, -) - -__all__ = ["__version__"] diff --git a/src/Matcher/config/__init__.py b/src/Matcher/config/__init__.py deleted file mode 100644 index eed74c0e..00000000 --- a/src/Matcher/config/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.config import * # noqa: F401,F403 diff --git a/src/Matcher/config/config_loader.py b/src/Matcher/config/config_loader.py deleted file mode 100644 index a3064396..00000000 --- a/src/Matcher/config/config_loader.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.config.config_loader import * # noqa: F401,F403 diff --git a/src/Matcher/config/settings.py b/src/Matcher/config/settings.py deleted file mode 100644 index 16e461fb..00000000 --- a/src/Matcher/config/settings.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.config.settings import * # noqa: F401,F403 diff --git a/src/Matcher/entities/__init__.py b/src/Matcher/entities/__init__.py deleted file mode 100644 index 4fdc6d4b..00000000 --- a/src/Matcher/entities/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.entities import * # noqa: F401,F403 diff --git a/src/Matcher/main.py b/src/Matcher/main.py deleted file mode 100644 index 26c493cd..00000000 --- a/src/Matcher/main.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.main import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/__init__.py b/src/Matcher/pipeline/__init__.py deleted file mode 100644 index e961a2e5..00000000 --- a/src/Matcher/pipeline/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/cot_reasoning.py b/src/Matcher/pipeline/cot_reasoning.py deleted file mode 100644 index 484df347..00000000 --- a/src/Matcher/pipeline/cot_reasoning.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching.eligibility_reasoning import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/cot_reasoning_vllm.py b/src/Matcher/pipeline/cot_reasoning_vllm.py deleted file mode 100644 index 0943c3b0..00000000 --- a/src/Matcher/pipeline/cot_reasoning_vllm.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching.eligibility_reasoning_vllm import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/phenopacket_processor.py b/src/Matcher/pipeline/phenopacket_processor.py deleted file mode 100644 index e8645cc2..00000000 --- a/src/Matcher/pipeline/phenopacket_processor.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching.phenopacket_processor import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_ranker.py b/src/Matcher/pipeline/trial_ranker.py deleted file mode 100644 index 21919c5c..00000000 --- a/src/Matcher/pipeline/trial_ranker.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching.trial_ranker import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_search/__init__.py b/src/Matcher/pipeline/trial_search/__init__.py deleted file mode 100644 index f04c1b19..00000000 --- a/src/Matcher/pipeline/trial_search/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching.retrieval import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_search/first_level_search.py b/src/Matcher/pipeline/trial_search/first_level_search.py deleted file mode 100644 index fde78fc7..00000000 --- a/src/Matcher/pipeline/trial_search/first_level_search.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching.retrieval.trial_retrieval import * # noqa: F401,F403 diff --git a/src/Matcher/pipeline/trial_search/second_level_search.py b/src/Matcher/pipeline/trial_search/second_level_search.py deleted file mode 100644 index b286feb0..00000000 --- a/src/Matcher/pipeline/trial_search/second_level_search.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.matching.retrieval.criteria_retrieval import * # noqa: F401,F403 diff --git a/src/Matcher/schemas/__init__.py b/src/Matcher/schemas/__init__.py deleted file mode 100644 index 928cdeac..00000000 --- a/src/Matcher/schemas/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.schemas import * # noqa: F401,F403 diff --git a/src/Matcher/search/__init__.py b/src/Matcher/search/__init__.py deleted file mode 100644 index 5b6cd37f..00000000 --- a/src/Matcher/search/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.search import * # noqa: F401,F403 diff --git a/src/Matcher/services/__init__.py b/src/Matcher/services/__init__.py deleted file mode 100644 index c704ca63..00000000 --- a/src/Matcher/services/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.services import * # noqa: F401,F403 diff --git a/src/Matcher/utils/__init__.py b/src/Matcher/utils/__init__.py deleted file mode 100644 index 5933e8a7..00000000 --- a/src/Matcher/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/file_utils.py b/src/Matcher/utils/file_utils.py deleted file mode 100644 index 9ae82244..00000000 --- a/src/Matcher/utils/file_utils.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.utils.file_utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/json_utils.py b/src/Matcher/utils/json_utils.py deleted file mode 100644 index e0a84189..00000000 --- a/src/Matcher/utils/json_utils.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.utils.json_utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/logging_config.py b/src/Matcher/utils/logging_config.py deleted file mode 100644 index 4aa94f5c..00000000 --- a/src/Matcher/utils/logging_config.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.utils.logging_config import * # noqa: F401,F403 diff --git a/src/Matcher/utils/retry.py b/src/Matcher/utils/retry.py deleted file mode 100644 index bce0516f..00000000 --- a/src/Matcher/utils/retry.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.utils.retry import * # noqa: F401,F403 diff --git a/src/Matcher/utils/temporal_utils.py b/src/Matcher/utils/temporal_utils.py deleted file mode 100644 index 4d2a72ca..00000000 --- a/src/Matcher/utils/temporal_utils.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.utils.temporal_utils import * # noqa: F401,F403 diff --git a/src/Matcher/utils/timing.py b/src/Matcher/utils/timing.py deleted file mode 100644 index 682226c5..00000000 --- a/src/Matcher/utils/timing.py +++ /dev/null @@ -1 +0,0 @@ -from trialmatchai.utils.timing import * # noqa: F401,F403 diff --git a/src/trialmatchai/cli/bootstrap_data.py b/src/trialmatchai/cli/bootstrap_data.py index ea1a5002..bdf2eb6f 100644 --- a/src/trialmatchai/cli/bootstrap_data.py +++ b/src/trialmatchai/cli/bootstrap_data.py @@ -3,6 +3,7 @@ import argparse import hashlib import os +import stat import sys import tarfile import zipfile @@ -170,8 +171,11 @@ def _safe_extract_tar_gz(archive: Path, target: Path) -> None: def _safe_extract_zip(archive: Path, target: Path) -> None: target.mkdir(parents=True, exist_ok=True) with zipfile.ZipFile(archive) as zip_file: - for member in zip_file.namelist(): - _validated_target_path(target, member) + for member in zip_file.infolist(): + _validated_target_path(target, member.filename) + mode = member.external_attr >> 16 + if stat.S_ISLNK(mode): + raise ValueError(f"Archive contains an unsafe member: {member.filename}") zip_file.extractall(target) diff --git a/src/trialmatchai/cli/build_concepts.py b/src/trialmatchai/cli/build_concepts.py index bee79aa5..1ca94966 100644 --- a/src/trialmatchai/cli/build_concepts.py +++ b/src/trialmatchai/cli/build_concepts.py @@ -7,7 +7,7 @@ from trialmatchai.config.config_loader import load_config from trialmatchai.entities.builder import ( DEFAULT_OMOP_VOCABULARIES, - build_legacy_dictionary_rows, + build_dictionary_rows, build_omop_concept_rows, concept_texts_for_embedding, write_lancedb_table, @@ -30,11 +30,11 @@ def main() -> int: help="OMOP CONCEPT_SYNONYM.csv path", ) parser.add_argument( - "--legacy-dictionary", + "--dictionary", action="append", default=[], metavar="VOCAB:DOMAIN:PATH", - help="Import a legacy dictionary file, e.g. EntrezGene:Gene:/path/dict_Gene.txt", + help="Import a concept dictionary file, e.g. EntrezGene:Gene:/path/dict_Gene.txt", ) parser.add_argument( "--vocabulary", @@ -69,10 +69,10 @@ def main() -> int: args.synonym_csv, vocabularies=vocabularies, ) - for spec in args.legacy_dictionary: - vocab, domain, path = _parse_legacy_spec(spec) + for spec in args.dictionary: + vocab, domain, path = _parse_dictionary_spec(spec) rows.extend( - build_legacy_dictionary_rows( + build_dictionary_rows( path, vocabulary_id=vocab, domain_id=domain, @@ -108,11 +108,11 @@ def main() -> int: return 0 -def _parse_legacy_spec(spec: str) -> tuple[str, str, str]: +def _parse_dictionary_spec(spec: str) -> tuple[str, str, str]: parts = spec.split(":", 2) if len(parts) != 3: raise ValueError( - "--legacy-dictionary must use VOCAB:DOMAIN:PATH, " + "--dictionary must use VOCAB:DOMAIN:PATH, " f"received: {spec}" ) return parts[0], parts[1], parts[2] diff --git a/src/trialmatchai/cli/healthcheck.py b/src/trialmatchai/cli/healthcheck.py index e584c615..8aabf8f1 100644 --- a/src/trialmatchai/cli/healthcheck.py +++ b/src/trialmatchai/cli/healthcheck.py @@ -28,6 +28,11 @@ def main() -> int: action="store_true", help="Check registry updater paths and manifest readability.", ) + parser.add_argument( + "--models", + action="store_true", + help="Require configured model artifacts and optional model dependencies.", + ) args = parser.parse_args() config = load_config(args.config) @@ -38,7 +43,7 @@ def main() -> int: config, require_patient_inputs=False, require_trials_json=False, - require_models=False, + require_models=args.models, ) issues += len(preflight_issues) diff --git a/src/trialmatchai/config/config.json b/src/trialmatchai/config/config.json index 187bfc37..f137a097 100644 --- a/src/trialmatchai/config/config.json +++ b/src/trialmatchai/config/config.json @@ -4,7 +4,7 @@ "model_name": "fastino/gliner2-base", "fallback_model_name": "gliner-community/gliner_large-v2.5", "model_revision": null, - "schema_path": "src/trialmatchai/entity_schemas/trialmatchai.yaml", + "schema_path": "entity_schemas/trialmatchai.yaml", "threshold": 0.8, "batch_size": 8, "device": "auto", @@ -19,7 +19,6 @@ "search_limit": 10 }, "paths": { - "patients_dir": "example", "output_dir": "results", "trials_json_folder": "data/trials_jsons" }, diff --git a/src/trialmatchai/config/config_loader.py b/src/trialmatchai/config/config_loader.py index 5c5c9539..1a1472bb 100644 --- a/src/trialmatchai/config/config_loader.py +++ b/src/trialmatchai/config/config_loader.py @@ -2,6 +2,7 @@ import json import os +from importlib import resources from copy import deepcopy from pathlib import Path from typing import Any, Dict @@ -19,10 +20,8 @@ DEFAULT_CONFIG_RELATIVE_PATH = Path("src/trialmatchai/config/config.json") -LEGACY_CONFIG_RELATIVE_PATHS = ( +CONFIG_RELATIVE_PATHS = ( Path("trialmatchai/config/config.json"), - Path("trialmatchai/config/config.json"), - Path("source/trialmatchai/config/config.json"), ) @@ -45,7 +44,7 @@ def load_config(config_path: str | os.PathLike[str] | None = None) -> Dict[str, def resolve_config_path( config_path: str | os.PathLike[str] | None = None, ) -> Path: - """Resolve explicit, repo-root, legacy, and packaged config paths.""" + """Resolve explicit, repo-root, and packaged config paths.""" root = _repo_root() candidates: list[Path] = [] if config_path: @@ -60,7 +59,7 @@ def resolve_config_path( root / "src" / supplied, ] ) - if supplied in LEGACY_CONFIG_RELATIVE_PATHS: + if supplied in CONFIG_RELATIVE_PATHS: candidates.append(root / DEFAULT_CONFIG_RELATIVE_PATH) else: candidates.extend( @@ -82,7 +81,7 @@ def resolve_config_path( def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, Any]: """Normalize known local paths while leaving remote model IDs untouched.""" root = _repo_root(config_path) - for key in ("patients_dir", "output_dir", "trials_json_folder"): + for key in ("output_dir", "trials_json_folder"): value = cfg.get("paths", {}).get(key) if value: cfg["paths"][key] = str(_resolve_local_path(value, root)) @@ -96,7 +95,7 @@ def normalize_config_paths(cfg: Dict[str, Any], config_path: Path) -> Dict[str, schema_path = cfg.get("entity_extraction", {}).get("schema_path") if schema_path: cfg["entity_extraction"]["schema_path"] = str( - _resolve_local_path(schema_path, root) + _resolve_trialmatchai_resource_or_local_path(schema_path, root) ) concept_db_path = cfg.get("concept_linker", {}).get("db_path") @@ -132,6 +131,44 @@ def _resolve_local_path(value: str, root: Path) -> Path: return (root / path).resolve() +def _resolve_trialmatchai_resource_or_local_path(value: str, root: Path) -> Path: + path = Path(value).expanduser() + if path.is_absolute(): + return path.resolve() + + local_path = (root / path).resolve() + if local_path.exists(): + return local_path + + resource_path = _trialmatchai_resource_path(path) + if resource_path is not None: + return resource_path + + return local_path + + +def _trialmatchai_resource_path(path: Path) -> Path | None: + parts = path.parts + if parts[:2] == ("src", "trialmatchai"): + relative = Path(*parts[2:]) + elif parts and parts[0] == "trialmatchai": + relative = Path(*parts[1:]) + else: + relative = path + + if not relative.parts or relative.parts[0] not in { + "config", + "entity_schemas", + "preprocessing", + }: + return None + + resource = resources.files("trialmatchai").joinpath(*relative.parts) + if not resource.exists(): + return None + return Path(str(resource)).resolve() + + def _repo_root(anchor: Path | None = None) -> Path: start = (anchor or Path(__file__).resolve()).resolve() if start.is_file(): diff --git a/src/trialmatchai/config/settings.py b/src/trialmatchai/config/settings.py index 2ba011e2..6dd638b0 100644 --- a/src/trialmatchai/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -10,7 +10,7 @@ class EntityExtractionSettings(BaseModel): model_name: str = "fastino/gliner2-base" fallback_model_name: str = "gliner-community/gliner_large-v2.5" model_revision: str | None = None - schema_path: str = "src/trialmatchai/entity_schemas/trialmatchai.yaml" + schema_path: str = "entity_schemas/trialmatchai.yaml" threshold: float = Field(0.8, ge=0.0, le=1.0) batch_size: int = Field(8, ge=1) device: str = "auto" @@ -35,7 +35,6 @@ def validate_reject_threshold(cls, value: float, info): class PathsSettings(BaseModel): - patients_dir: str output_dir: str trials_json_folder: str @@ -186,7 +185,6 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: import os string_env_map: dict[str, Tuple[str, ...]] = { - "TRIALMATCHAI_PATIENTS_DIR": ("paths", "patients_dir"), "TRIALMATCHAI_OUTPUT_DIR": ("paths", "output_dir"), "TRIALMATCHAI_TRIALS_JSON_FOLDER": ("paths", "trials_json_folder"), "TRIALMATCHAI_PATIENT_RAW_DIR": ("patient_inputs", "raw_dir"), diff --git a/src/trialmatchai/entities/builder.py b/src/trialmatchai/entities/builder.py index fa1819a8..4ef792ca 100644 --- a/src/trialmatchai/entities/builder.py +++ b/src/trialmatchai/entities/builder.py @@ -53,12 +53,12 @@ def build_omop_concept_rows( return rows -def build_legacy_dictionary_rows( +def build_dictionary_rows( dictionary_path: str | Path, *, vocabulary_id: str, domain_id: str = "", - concept_class_id: str = "LegacyDictionary", + concept_class_id: str = "Dictionary", ) -> list[dict[str, Any]]: rows: list[dict[str, Any]] = [] with Path(dictionary_path).open("r", encoding="utf-8") as handle: @@ -113,8 +113,11 @@ def write_lancedb_table( row["embedding"] = list(embedding) db = lancedb.connect(str(db_path)) - mode = "overwrite" if recreate else "create" - table = db.create_table(table_name, data=payload, mode=mode) + if recreate or not _lancedb_table_exists(db, table_name): + table = db.create_table(table_name, data=payload, mode="overwrite") + else: + table = db.open_table(table_name) + table.add(payload) try: table.create_fts_index("fts_text", replace=True) except Exception: @@ -132,6 +135,13 @@ def concept_texts_for_embedding(rows: Sequence[dict[str, Any]]) -> list[str]: return texts +def _lancedb_table_exists(db: Any, table_name: str) -> bool: + try: + return table_name in set(db.table_names()) + except Exception: + return False + + def _read_omop_synonyms(path: str | Path | None) -> dict[str, list[str]]: if path is None: return {} diff --git a/src/trialmatchai/entities/schemas.py b/src/trialmatchai/entities/schemas.py index 300c86d3..d683d270 100644 --- a/src/trialmatchai/entities/schemas.py +++ b/src/trialmatchai/entities/schemas.py @@ -21,8 +21,15 @@ def default_schema_path() -> Path: def load_entity_schemas(path: str | Path | None = None) -> list[EntitySchema]: - schema_path = Path(path).expanduser() if path else default_schema_path() - with schema_path.open("r", encoding="utf-8") as handle: + if path: + handle_source = Path(path).expanduser() + handle = handle_source.open("r", encoding="utf-8") + else: + handle = resources.files("trialmatchai").joinpath(DEFAULT_SCHEMA_RESOURCE).open( + "r", + encoding="utf-8", + ) + with handle: raw = yaml.safe_load(handle) or {} return parse_entity_schemas(raw) diff --git a/src/trialmatchai/interop/detect.py b/src/trialmatchai/interop/detect.py index 55302067..90e17436 100644 --- a/src/trialmatchai/interop/detect.py +++ b/src/trialmatchai/interop/detect.py @@ -34,9 +34,9 @@ def detect_patient_input_format(path: str | Path) -> PatientInputFormat: suffix = candidate.suffix.casefold() if suffix in {".txt", ".md"}: return "text" - if suffix == ".ndjson": + if suffix in {".ndjson", ".jsonl"}: return "fhir-ndjson" - if suffix not in {".json", ".jsonl"}: + if suffix != ".json": raise ValueError(f"Unsupported patient input file extension: {suffix}") with candidate.open("r", encoding="utf-8") as handle: diff --git a/src/trialmatchai/interop/importers/__init__.py b/src/trialmatchai/interop/importers/__init__.py index 2b79cfa6..3e59c3e9 100644 --- a/src/trialmatchai/interop/importers/__init__.py +++ b/src/trialmatchai/interop/importers/__init__.py @@ -26,7 +26,7 @@ def import_patient_path( if resolved_format == "phenopacket": return [import_phenopacket(path, strict=strict)] if resolved_format in {"fhir", "fhir-ndjson"}: - return [import_fhir(path, input_format=resolved_format, strict=strict)] + return import_fhir(path, input_format=resolved_format, strict=strict) if resolved_format == "omop": return import_omop_extract(path, strict=strict) raise ValueError(f"Unsupported patient input format: {resolved_format}") diff --git a/src/trialmatchai/interop/importers/fhir.py b/src/trialmatchai/interop/importers/fhir.py index 8c15c9a7..e4648faa 100644 --- a/src/trialmatchai/interop/importers/fhir.py +++ b/src/trialmatchai/interop/importers/fhir.py @@ -23,14 +23,64 @@ def import_fhir( *, input_format: str = "fhir", strict: bool = False, -) -> PatientProfile: +) -> list[PatientProfile]: source_path = Path(path) resources = ( _load_ndjson(source_path) if input_format == "fhir-ndjson" else _load_json_resources(source_path) ) - patient = next((res for res in resources if res.get("resourceType") == "Patient"), {}) + patients = [res for res in resources if res.get("resourceType") == "Patient"] + if not patients: + patients = [{}] + + profiles = [ + _profile_from_patient(patient, source_path=source_path, input_format=input_format) + for patient in patients + ] + profiles_by_reference = _profiles_by_reference(patients, profiles) + + for resource in resources: + if resource.get("resourceType") == "Patient": + continue + profile = _profile_for_resource(resource, profiles, profiles_by_reference) + if profile is None: + message = "FHIR resource has no resolvable patient reference" + if strict: + raise ValueError( + f"{message}: {resource.get('resourceType')}/{resource.get('id')}" + ) + for candidate in profiles: + candidate.unsupported.append( + { + "resourceType": resource.get("resourceType"), + "id": resource.get("id"), + "reason": message, + } + ) + continue + try: + base_provenance = profile.provenance[0] + _add_resource(profile, resource, base_provenance) + except Exception: + if strict: + raise + profile.unsupported.append( + { + "resourceType": resource.get("resourceType"), + "id": resource.get("id"), + "reason": "resource mapping failed", + } + ) + return profiles + + +def _profile_from_patient( + patient: Mapping[str, Any], + *, + source_path: Path, + input_format: str, +) -> PatientProfile: patient_id = safe_patient_id(patient.get("id"), source_path.stem) provenance = Provenance( source_format=input_format, @@ -38,7 +88,7 @@ def import_fhir( source_path=source_path_string(source_path), ) birth_date = parse_date(patient.get("birthDate")) - profile = PatientProfile( + return PatientProfile( patient_id=patient_id, demographics=Demographics( sex=normalize_gender(patient.get("gender")), @@ -48,20 +98,6 @@ def import_fhir( ), provenance=[provenance], ) - for resource in resources: - try: - _add_resource(profile, resource, provenance) - except Exception: - if strict: - raise - profile.unsupported.append( - { - "resourceType": resource.get("resourceType"), - "id": resource.get("id"), - "reason": "resource mapping failed", - } - ) - return profile def _load_json_resources(path: Path) -> list[dict[str, Any]]: @@ -71,11 +107,15 @@ def _load_json_resources(path: Path) -> list[dict[str, Any]]: if not isinstance(data, dict): return [] if data.get("resourceType") == "Bundle": - return [ - entry["resource"] - for entry in data.get("entry") or [] - if isinstance(entry, dict) and isinstance(entry.get("resource"), dict) - ] + resources = [] + for entry in data.get("entry") or []: + if not isinstance(entry, dict) or not isinstance(entry.get("resource"), dict): + continue + resource = dict(entry["resource"]) + if entry.get("fullUrl"): + resource["_bundle_full_url"] = entry["fullUrl"] + resources.append(resource) + return resources return [data] @@ -88,6 +128,51 @@ def _load_ndjson(path: Path) -> list[dict[str, Any]]: return resources +def _profiles_by_reference( + patients: list[Mapping[str, Any]], + profiles: list[PatientProfile], +) -> dict[str, PatientProfile]: + mapping: dict[str, PatientProfile] = {} + for patient, profile in zip(patients, profiles): + patient_id = str(patient.get("id") or "").strip() + if patient_id: + mapping[patient_id] = profile + mapping[f"Patient/{patient_id}"] = profile + full_url = str(patient.get("_bundle_full_url") or "").strip() + if full_url: + mapping[full_url] = profile + return mapping + + +def _profile_for_resource( + resource: Mapping[str, Any], + profiles: list[PatientProfile], + profiles_by_reference: Mapping[str, PatientProfile], +) -> PatientProfile | None: + reference = _patient_reference(resource) + if reference: + return profiles_by_reference.get(reference) + if len(profiles) == 1: + return profiles[0] + return None + + +def _patient_reference(resource: Mapping[str, Any]) -> str | None: + for key in ("subject", "patient", "beneficiary"): + reference = _reference_value(resource.get(key)) + if reference: + return reference + return None + + +def _reference_value(value: Any) -> str | None: + if isinstance(value, Mapping): + reference = value.get("reference") + if isinstance(reference, str) and reference.strip(): + return reference.strip() + return None + + def _add_resource( profile: PatientProfile, resource: Mapping[str, Any], diff --git a/src/trialmatchai/interop/narrative.py b/src/trialmatchai/interop/narrative.py index 5fffee41..388e68ed 100644 --- a/src/trialmatchai/interop/narrative.py +++ b/src/trialmatchai/interop/narrative.py @@ -1,5 +1,7 @@ from __future__ import annotations +import re + from trialmatchai.interop.models import ClinicalFact, PatientProfile @@ -54,6 +56,10 @@ def render_search_terms(profile: PatientProfile) -> tuple[list[str], list[str]]: main_conditions = _dedupe( fact.label for fact in profile.phenotypes if not fact.negated ) + if not main_conditions: + main_conditions = _dedupe( + _note_search_term(note.text) for note in profile.notes if note.text + )[:1] other_terms = _dedupe( [ *[fact.label for fact in profile.phenotypes if not fact.negated], @@ -99,3 +105,11 @@ def _dedupe(values) -> list[str]: seen.add(key) output.append(cleaned) return output + + +def _note_search_term(text: str, *, max_chars: int = 500) -> str: + cleaned = re.sub(r"\s+", " ", text).strip() + if len(cleaned) <= max_chars: + return cleaned + truncated = cleaned[:max_chars].rsplit(" ", 1)[0].strip() + return truncated or cleaned[:max_chars].strip() diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index 791bcc04..b2d88f02 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -16,7 +16,6 @@ from trialmatchai.search import LanceDBSearchBackend from trialmatchai.services.preflight import run_preflight_checks from trialmatchai.interop.exporters import profile_to_matching_summary -from trialmatchai.interop.importers import import_patient_path from trialmatchai.interop.models import PatientProfile from trialmatchai.utils.file_utils import ( create_directory, @@ -412,58 +411,27 @@ def _load_patient_inputs(config: Dict) -> list[tuple[PatientProfile, Dict]]: profile_dir = Path(patient_cfg.get("profile_dir", "data/patients/profiles")) summary_dir = Path(patient_cfg.get("summary_dir", "data/patients/summaries")) profile_files = sorted(profile_dir.glob("*.json")) if profile_dir.exists() else [] - if profile_files: - loaded: list[tuple[PatientProfile, Dict]] = [] - for profile_file in profile_files: - try: - profile = PatientProfile.model_validate(read_json_file(str(profile_file))) - summary_path = summary_dir / profile_file.name - if summary_path.exists(): - summary = read_json_file(str(summary_path)) - else: - summary = profile_to_matching_summary(profile) - loaded.append((profile, summary)) - except Exception: - logger.exception("Failed to load patient profile: %s", profile_file) - return loaded - - patient_folder = Path(config.get("paths", {}).get("patients_dir", "")) - if not patient_folder.exists(): - logger.error("Patient profile directory and legacy patients_dir are missing.") - return [] - - legacy_files = sorted(path for path in patient_folder.glob("*.json") if path.is_file()) - if not legacy_files: - logger.warning("No patient profile or legacy phenopacket files found.") + if not profile_files: + logger.error( + "No canonical patient profiles found in %s. Run " + "`trialmatchai-import-patient` first.", + profile_dir, + ) return [] - logger.warning( - "No canonical patient profiles found in %s. Importing legacy Phenopacket " - "JSON files from %s for this run.", - profile_dir, - patient_folder, - ) - profile_dir.mkdir(parents=True, exist_ok=True) - summary_dir.mkdir(parents=True, exist_ok=True) - imported: list[tuple[PatientProfile, Dict]] = [] - for legacy_file in legacy_files: + loaded: list[tuple[PatientProfile, Dict]] = [] + for profile_file in profile_files: try: - profiles = import_patient_path( - legacy_file, - input_format="phenopacket", - strict=bool(patient_cfg.get("strict_validation", False)), - ) - for profile in profiles: + profile = PatientProfile.model_validate(read_json_file(str(profile_file))) + summary_path = summary_dir / profile_file.name + if summary_path.exists(): + summary = read_json_file(str(summary_path)) + else: summary = profile_to_matching_summary(profile) - write_json_file( - profile.model_dump(mode="json", exclude_none=True), - str(profile_dir / f"{profile.patient_id}.json"), - ) - write_json_file(summary, str(summary_dir / f"{profile.patient_id}.json")) - imported.append((profile, summary)) + loaded.append((profile, summary)) except Exception: - logger.exception("Failed to import legacy patient file: %s", legacy_file) - return imported + logger.exception("Failed to load patient profile: %s", profile_file) + return loaded if __name__ == "__main__": diff --git a/src/trialmatchai/models/llm/llm_loader.py b/src/trialmatchai/models/llm/llm_loader.py index 97ddb38e..1aea7f33 100644 --- a/src/trialmatchai/models/llm/llm_loader.py +++ b/src/trialmatchai/models/llm/llm_loader.py @@ -1,17 +1,15 @@ -from typing import Tuple +from typing import Any, Tuple -import torch from trialmatchai.utils.logging_config import setup_logging -from peft import PeftModel -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig logger = setup_logging(__name__) -def load_model_and_tokenizer( - model_config: dict, device: int -) -> Tuple[AutoModelForCausalLM, AutoTokenizer]: +def load_model_and_tokenizer(model_config: dict, device: int) -> Tuple[Any, Any]: """Load a model and tokenizer with safe device handling and optional 4-bit.""" + torch, peft_model, auto_model, auto_tokenizer, bits_and_bytes_config = ( + _load_llm_dependencies() + ) use_cuda = torch.cuda.is_available() device_str = "cuda" if use_cuda else "cpu" quant_config = None @@ -55,7 +53,7 @@ def load_model_and_tokenizer( except Exception: logger.info("flash-attn not available; using SDPA.") - quant_config = BitsAndBytesConfig( + quant_config = bits_and_bytes_config( load_in_4bit=bool(model_config["quantization"]["load_in_4bit"]), bnb_4bit_use_double_quant=bool( model_config["quantization"]["bnb_4bit_use_double_quant"] @@ -71,11 +69,11 @@ def load_model_and_tokenizer( "CUDA not available; loading model on CPU without 4-bit quantization." ) device_str = "cpu" - quant_config = BitsAndBytesConfig(load_in_4bit=False) + quant_config = bits_and_bytes_config(load_in_4bit=False) trust_remote_code = bool(model_config.get("trust_remote_code", False)) revision = model_config.get("base_model_revision") - tokenizer = AutoTokenizer.from_pretrained( + tokenizer = auto_tokenizer.from_pretrained( model_config["base_model"], revision=revision, use_fast=True, @@ -91,7 +89,7 @@ def load_model_and_tokenizer( "Using FlashAttention-2; keeping padding_side='left' for decoder-only models." ) - model = AutoModelForCausalLM.from_pretrained( + model = auto_model.from_pretrained( model_config["base_model"], revision=revision, trust_remote_code=trust_remote_code, @@ -107,7 +105,7 @@ def load_model_and_tokenizer( except Exception: pass - model = PeftModel.from_pretrained( + model = peft_model.from_pretrained( model, model_config["cot_adapter_path"], device_map=device_str ) @@ -125,3 +123,16 @@ def load_model_and_tokenizer( logger.warning("Model is not an instance of torch.nn.Module; skipping eval.") logger.info(f"Model loaded on {device_str}.") return model, tokenizer # type: ignore[return-value] + + +def _load_llm_dependencies() -> tuple[Any, Any, Any, Any, Any]: + try: + import torch + from peft import PeftModel + from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig + except Exception as exc: # pragma: no cover - exercised in lean installs + raise RuntimeError( + "LLM model loading requires the optional `llm` dependencies " + "(`uv sync --extra llm`)." + ) from exc + return torch, PeftModel, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig diff --git a/src/trialmatchai/models/llm/llm_reranker.py b/src/trialmatchai/models/llm/llm_reranker.py index 2a60e6d1..1eb08771 100644 --- a/src/trialmatchai/models/llm/llm_reranker.py +++ b/src/trialmatchai/models/llm/llm_reranker.py @@ -2,14 +2,10 @@ import threading import unicodedata from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional -import torch -import torch.nn.functional as F from trialmatchai.utils.logging_config import setup_logging -from peft import PeftModel from tqdm import tqdm -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig logger = setup_logging(__name__) @@ -20,20 +16,28 @@ def __init__( model_path: str, adapter_path: Optional[str] = None, device: int = 0, - torch_dtype=torch.float16, + torch_dtype: Any | None = None, batch_size: int = 8, revision: Optional[str] = None, trust_remote_code: bool = False, ): + ( + self._torch, + self._torch_functional, + self._peft_model, + self._auto_model, + self._auto_tokenizer, + self._bits_and_bytes_config, + ) = _load_llm_dependencies() self.model_path = model_path self.adapter_path = adapter_path - self.torch_dtype = torch_dtype + self.torch_dtype = torch_dtype or self._torch.float16 self.batch_size = batch_size self.revision = revision self.trust_remote_code = trust_remote_code # Resolve device string - if torch.cuda.is_available(): - cuda_count = torch.cuda.device_count() + if self._torch.cuda.is_available(): + cuda_count = self._torch.cuda.device_count() idx = int(device) if isinstance(device, int) else 0 if idx < 0 or idx >= cuda_count: logger.warning( @@ -43,14 +47,14 @@ def __init__( self.device_str = f"cuda:{idx}" # Ensure Accelerate/HF loaders use the selected GPU when device_map='auto' try: - torch.cuda.set_device(idx) + self._torch.cuda.set_device(idx) except Exception as e: logger.warning(f"Could not set CUDA device to {idx}: {e}") else: logger.warning("LLMReranker: CUDA not available; using CPU.") self.device_str = "cpu" - self.tokenizer = AutoTokenizer.from_pretrained( + self.tokenizer = self._auto_tokenizer.from_pretrained( self.model_path, revision=self.revision, trust_remote_code=self.trust_remote_code, @@ -72,7 +76,7 @@ def _initialize_token_ids(self): def load_model(self): use_cuda = self.device_str.startswith("cuda") quant_config = ( - BitsAndBytesConfig( + self._bits_and_bytes_config( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", @@ -81,17 +85,17 @@ def load_model(self): if use_cuda else None ) - model = AutoModelForCausalLM.from_pretrained( + model = self._auto_model.from_pretrained( self.model_path, revision=self.revision, - torch_dtype=self.torch_dtype if use_cuda else torch.float32, + torch_dtype=self.torch_dtype if use_cuda else self._torch.float32, quantization_config=quant_config, device_map="auto" if use_cuda else None, attn_implementation="flash_attention_2" if use_cuda else None, trust_remote_code=self.trust_remote_code, ) if self.adapter_path: - model = PeftModel.from_pretrained(model, self.adapter_path) + model = self._peft_model.from_pretrained(model, self.adapter_path) model.eval() return model @@ -129,10 +133,10 @@ def process_batch(self, batch: List[tuple]) -> List[Dict]: inputs = self.tokenizer(batch_prompts, return_tensors="pt", padding=True) inputs = {k: v.to(self.device_str) for k, v in inputs.items()} with self.model_lock: - with torch.no_grad(): + with self._torch.no_grad(): outputs = self.model(**inputs) logits = outputs.logits[:, -1, :] - probabilities = F.softmax(logits, dim=-1) + probabilities = self._torch_functional.softmax(logits, dim=-1) applicable_probs = probabilities[:, self.applicable_token_id].tolist() return [ {"llm_score": prob, "answer": "Yes" if prob > 0.5 else "No"} @@ -152,3 +156,24 @@ def rank_pairs(self, patient_trial_pairs: List[tuple]) -> List[Dict]: ): results.extend(future.result()) return results + + +def _load_llm_dependencies() -> tuple[Any, Any, Any, Any, Any, Any]: + try: + import torch + import torch.nn.functional as torch_functional + from peft import PeftModel + from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig + except Exception as exc: # pragma: no cover - exercised in lean installs + raise RuntimeError( + "LLM reranking requires the optional `llm` dependencies " + "(`uv sync --extra llm`)." + ) from exc + return ( + torch, + torch_functional, + PeftModel, + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + ) diff --git a/src/trialmatchai/models/llm/vllm_loader.py b/src/trialmatchai/models/llm/vllm_loader.py index eb283637..7166729d 100644 --- a/src/trialmatchai/models/llm/vllm_loader.py +++ b/src/trialmatchai/models/llm/vllm_loader.py @@ -203,14 +203,13 @@ def load_vllm_engine( logger.debug(f"[vLLM] Second LoRA signature failed: {e2}") try: - # Older versions might have different parameter names - # Some versions might use just name and path + # Some vLLM releases use just name and path. lora_request = LoRARequest(name, adapter_path) # type: ignore # Manually set the ID if the object allows it if hasattr(lora_request, "lora_int_id"): setattr(lora_request, "lora_int_id", adapter_id) logger.info( - "[vLLM] Created LoRA request (legacy): name=%s path=%s", + "[vLLM] Created LoRA request with fallback signature: name=%s path=%s", name, adapter_path, ) diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index e0ff0122..d1eb03e8 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -25,10 +25,11 @@ def run_preflight_checks( if require_patient_inputs: _require_patient_inputs(issues, config) else: + patient_cfg = config.get("patient_inputs", {}) _require_path( issues, - "paths.patients_dir", - paths.get("patients_dir"), + "patient_inputs.profile_dir", + patient_cfg.get("profile_dir"), required=False, ) _require_path( @@ -39,15 +40,17 @@ def run_preflight_checks( ) _require_output_dir(issues, paths.get("output_dir")) + entity_cfg = config.get("entity_extraction") + if entity_cfg: + _require_path( + issues, + "entity_extraction.schema_path", + entity_cfg.get("schema_path"), + required=True, + ) + if require_models: - entity_cfg = config.get("entity_extraction") if entity_cfg: - _require_path( - issues, - "entity_extraction.schema_path", - entity_cfg.get("schema_path"), - required=True, - ) backend = entity_cfg.get("backend", "gliner2") if backend == "gliner2" and importlib.util.find_spec("gliner2") is None: issues.append( @@ -160,15 +163,9 @@ def _require_output_dir(issues: List[str], value: str | None) -> None: def _require_patient_inputs(issues: List[str], config: Dict[str, Any]) -> None: - paths = config.get("paths", {}) patient_cfg = config.get("patient_inputs", {}) profile_dir = Path(patient_cfg.get("profile_dir", "")) - legacy_dir = Path(paths.get("patients_dir", "")) - has_profiles = profile_dir.exists() - has_legacy = legacy_dir.exists() - if has_profiles or has_legacy: + if profile_dir.exists(): return if profile_dir: issues.append(f"patient_inputs.profile_dir does not exist: {profile_dir}") - if legacy_dir: - issues.append(f"paths.patients_dir does not exist: {legacy_dir}") diff --git a/tests/test_bootstrap_data.py b/tests/test_bootstrap_data.py index 1e09499c..f4653f25 100644 --- a/tests/test_bootstrap_data.py +++ b/tests/test_bootstrap_data.py @@ -13,6 +13,7 @@ PROCESSED_TRIALS_ARCHIVE, bootstrap_data, _safe_extract_tar_gz, + _safe_extract_zip, _verify_sha256, ) @@ -94,6 +95,17 @@ def test_tar_extraction_rejects_links(tmp_path): _safe_extract_tar_gz(archive, tmp_path / "target") +def test_zip_extraction_rejects_symlinks(tmp_path): + archive = tmp_path / "unsafe-link.zip" + with zipfile.ZipFile(archive, "w") as zip_file: + info = zipfile.ZipInfo("link") + info.external_attr = 0o120777 << 16 + zip_file.writestr(info, "/etc/passwd") + + with pytest.raises(ValueError, match="unsafe member"): + _safe_extract_zip(archive, tmp_path / "target") + + def _write_tar_gz(path: Path, files: dict[str, bytes]) -> None: with tarfile.open(path, "w:gz") as tar: for name, data in files.items(): diff --git a/tests/test_config_pytest.py b/tests/test_config_pytest.py index 8e3cb05b..bcf97a47 100644 --- a/tests/test_config_pytest.py +++ b/tests/test_config_pytest.py @@ -1,6 +1,7 @@ from pathlib import Path from trialmatchai.config.config_loader import load_config +from trialmatchai.entities.schemas import default_schema_path def test_load_config_from_repo(): @@ -9,3 +10,19 @@ def test_load_config_from_repo(): assert cfg["search_backend"]["backend"] == "lancedb" assert "embedder" in cfg assert "paths" in cfg + + +def test_packaged_schema_path_resolves_outside_repo(tmp_path, monkeypatch): + source_config = ( + Path(__file__).resolve().parents[1] / "src/trialmatchai/config/config.json" + ) + installed_config = tmp_path / "site-packages/trialmatchai/config/config.json" + installed_config.parent.mkdir(parents=True) + installed_config.write_text(source_config.read_text(encoding="utf-8"), encoding="utf-8") + monkeypatch.chdir(tmp_path) + + cfg = load_config(installed_config) + + assert cfg["entity_extraction"]["schema_path"] == str(default_schema_path().resolve()) + assert Path(cfg["entity_extraction"]["schema_path"]).exists() + assert cfg["paths"]["output_dir"] == str((tmp_path / "results").resolve()) diff --git a/tests/test_entities.py b/tests/test_entities.py index 8ee6ea5d..d2f7b80f 100644 --- a/tests/test_entities.py +++ b/tests/test_entities.py @@ -4,7 +4,11 @@ from pathlib import Path from trialmatchai.entities.annotator import CompatibilityEntityAnnotator -from trialmatchai.entities.builder import build_legacy_dictionary_rows, build_omop_concept_rows +from trialmatchai.entities.builder import ( + build_dictionary_rows, + build_omop_concept_rows, + write_lancedb_table, +) from trialmatchai.entities.linker import ConceptLinker, InMemoryConceptStore from trialmatchai.entities.recognizers import RegexSchemaRecognizer, resolve_overlaps from trialmatchai.entities.schemas import load_entity_schemas @@ -95,7 +99,7 @@ def test_concept_linker_accepts_rejects_and_marks_ambiguous(): assert rejected.linker_status == "rejected" -def test_concept_builders_import_omop_and_legacy_rows(tmp_path): +def test_concept_builders_import_omop_and_dictionary_rows(tmp_path): concept_csv = tmp_path / "CONCEPT.csv" synonym_csv = tmp_path / "CONCEPT_SYNONYM.csv" with concept_csv.open("w", newline="") as handle: @@ -132,23 +136,63 @@ def test_concept_builders_import_omop_and_legacy_rows(tmp_path): writer.writerow({"concept_id": "1", "concept_synonym_name": "Hgb"}) rows = build_omop_concept_rows(concept_csv, synonym_csv, vocabularies=("LOINC",)) - legacy = tmp_path / "dict_Gene.txt" - legacy.write_text("EntrezGene:1956||EGFR|ERBB1\n") + dictionary = tmp_path / "dict_Gene.txt" + dictionary.write_text("EntrezGene:1956||EGFR|ERBB1\n") - legacy_rows = build_legacy_dictionary_rows( - legacy, + dictionary_rows = build_dictionary_rows( + dictionary, vocabulary_id="EntrezGene", domain_id="Gene", ) assert rows[0]["concept_code"] == "718-7" assert rows[0]["synonyms"] == ["Hgb"] - assert legacy_rows[0]["concept_code"] == "1956" - assert legacy_rows[0]["synonyms"] == ["EGFR", "ERBB1"] + assert dictionary_rows[0]["concept_code"] == "1956" + assert dictionary_rows[0]["synonyms"] == ["EGFR", "ERBB1"] + + +def test_concept_table_no_recreate_appends_rows(tmp_path): + db_path = tmp_path / "concepts" + first = [ + { + "concept_id": "SNOMED:1", + "vocabulary_id": "SNOMED", + "concept_code": "1", + "concept_name": "Melanoma", + "domain_id": "Condition", + "concept_class_id": "Clinical Finding", + "standard_concept": "S", + "synonyms": ["malignant melanoma"], + "fts_text": "Melanoma malignant melanoma", + } + ] + second = [ + { + "concept_id": "SNOMED:2", + "vocabulary_id": "SNOMED", + "concept_code": "2", + "concept_name": "Sarcoma", + "domain_id": "Condition", + "concept_class_id": "Clinical Finding", + "standard_concept": "S", + "synonyms": ["soft tissue sarcoma"], + "fts_text": "Sarcoma soft tissue sarcoma", + } + ] + + write_lancedb_table(first, db_path=db_path, table_name="concepts", recreate=True) + write_lancedb_table(second, db_path=db_path, table_name="concepts", recreate=False) + + import lancedb + + table = lancedb.connect(str(db_path)).open_table("concepts") + codes = {row["concept_code"] for row in table.to_arrow().to_pylist()} + assert codes == {"1", "2"} def test_runtime_replacement_has_no_old_daemon_references(): assert not (ROOT / "source/Parser").exists() + assert not (ROOT / "src/Matcher").exists() runtime_files = sorted((ROOT / "src/trialmatchai").rglob("*.py")) forbidden = [ "18888", diff --git a/tests/test_import_patient_cli.py b/tests/test_import_patient_cli.py index 5a8d0266..dfa8e429 100644 --- a/tests/test_import_patient_cli.py +++ b/tests/test_import_patient_cli.py @@ -33,4 +33,5 @@ def test_import_patient_cli_writes_profile_and_summary(tmp_path, monkeypatch): summary = json.loads((summary_dir / "patient.json").read_text(encoding="utf-8")) assert profile["patient_id"] == "patient" assert summary["patient_id"] == "patient" + assert summary["main_conditions"] == ["Patient has breast cancer."] assert summary["expanded_sentences"] diff --git a/tests/test_package_imports.py b/tests/test_package_imports.py index 6d13a5dd..5853ea03 100644 --- a/tests/test_package_imports.py +++ b/tests/test_package_imports.py @@ -1,6 +1,7 @@ from __future__ import annotations -import warnings +import importlib.util +import pkgutil import pytest @@ -11,13 +12,22 @@ def test_trialmatchai_imports_with_version(): assert trialmatchai.__version__ == "0.2.0" -def test_matcher_config_compatibility_shim(): - with warnings.catch_warnings(record=True) as captured: - warnings.simplefilter("always") - from Matcher.config.config_loader import resolve_config_path +def test_matcher_namespace_is_removed(): + assert importlib.util.find_spec("Matcher") is None - assert resolve_config_path().name == "config.json" - assert any("Matcher" in str(warning.message) for warning in captured) + +def test_trialmatchai_modules_import_with_core_dependencies(): + failures = [] + for module in pkgutil.walk_packages( + trialmatchai.__path__, + prefix=f"{trialmatchai.__name__}.", + ): + try: + __import__(module.name) + except Exception as exc: # pragma: no cover - assertion path + failures.append(f"{module.name}: {type(exc).__name__}: {exc}") + + assert failures == [] def test_command_group_includes_bootstrap_data(monkeypatch, capsys): diff --git a/tests/test_patient_interop.py b/tests/test_patient_interop.py index d517854f..78089d1b 100644 --- a/tests/test_patient_interop.py +++ b/tests/test_patient_interop.py @@ -142,6 +142,45 @@ def test_fhir_bundle_importer_and_exporter(tmp_path): assert exported["resourceType"] == "Bundle" +def test_fhir_bundle_groups_resources_by_patient_reference(tmp_path): + bundle = { + "resourceType": "Bundle", + "type": "collection", + "entry": [ + { + "fullUrl": "urn:uuid:patient-1", + "resource": {"resourceType": "Patient", "id": "p1"}, + }, + {"resource": {"resourceType": "Patient", "id": "p2"}}, + { + "resource": { + "resourceType": "Condition", + "id": "c1", + "subject": {"reference": "urn:uuid:patient-1"}, + "code": {"text": "melanoma"}, + } + }, + { + "resource": { + "resourceType": "Condition", + "id": "c2", + "subject": {"reference": "Patient/p2"}, + "code": {"text": "sarcoma"}, + } + }, + ], + } + path = tmp_path / "bundle.json" + path.write_text(json.dumps(bundle), encoding="utf-8") + + profiles = import_patient_path(path) + + by_id = {profile.patient_id: profile for profile in profiles} + assert sorted(by_id) == ["p1", "p2"] + assert by_id["p1"].conditions[0].label == "melanoma" + assert by_id["p2"].conditions[0].label == "sarcoma" + + def test_fhir_ndjson_detection_and_import(tmp_path): path = tmp_path / "patient.ndjson" path.write_text( @@ -166,6 +205,31 @@ def test_fhir_ndjson_detection_and_import(tmp_path): assert profile.procedures[0].label == "bone marrow biopsy" +def test_fhir_jsonl_detection_and_import(tmp_path): + path = tmp_path / "patient.jsonl" + path.write_text( + "\n".join( + [ + json.dumps({"resourceType": "Patient", "id": "jsonl-p1"}), + json.dumps( + { + "resourceType": "Condition", + "id": "c1", + "subject": {"reference": "Patient/jsonl-p1"}, + "code": {"text": "glioma"}, + } + ), + ] + ), + encoding="utf-8", + ) + + profile = import_patient_path(path)[0] + + assert detect_patient_input_format(path) == "fhir-ndjson" + assert profile.conditions[0].label == "glioma" + + def test_omop_importer_from_csv_extract(tmp_path): omop = tmp_path / "omop" omop.mkdir() diff --git a/tests/test_patient_runtime_loading.py b/tests/test_patient_runtime_loading.py index 815d8ba7..349984f8 100644 --- a/tests/test_patient_runtime_loading.py +++ b/tests/test_patient_runtime_loading.py @@ -34,7 +34,6 @@ def test_runtime_loads_profiles_from_configured_directory(tmp_path): "profile_dir": str(profiles), "summary_dir": str(summaries), }, - "paths": {"patients_dir": str(tmp_path / "legacy")}, } ) @@ -42,22 +41,9 @@ def test_runtime_loads_profiles_from_configured_directory(tmp_path): assert loaded[0][1]["main_conditions"] == ["melanoma"] -def test_runtime_imports_legacy_phenopackets_when_profiles_missing(tmp_path): - legacy = tmp_path / "legacy" - legacy.mkdir() +def test_runtime_requires_canonical_profiles(tmp_path): profiles = tmp_path / "profiles" summaries = tmp_path / "summaries" - (legacy / "patient.json").write_text( - json.dumps( - { - "id": "legacy-p1", - "metaData": {}, - "subject": {}, - "diseases": [{"term": {"label": "sarcoma"}}], - } - ), - encoding="utf-8", - ) loaded = _load_patient_inputs( { @@ -65,11 +51,7 @@ def test_runtime_imports_legacy_phenopackets_when_profiles_missing(tmp_path): "profile_dir": str(profiles), "summary_dir": str(summaries), }, - "paths": {"patients_dir": str(legacy)}, } ) - assert loaded[0][0].patient_id == "legacy-p1" - assert loaded[0][1]["main_conditions"] == ["sarcoma"] - assert (profiles / "legacy-p1.json").exists() - assert (summaries / "legacy-p1.json").exists() + assert loaded == [] diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index 2d9375ab..e1799ef0 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -11,18 +11,21 @@ def _base_config(tmp_path): - patients = tmp_path / "patients" - patients.mkdir() + profiles = tmp_path / "profiles" + profiles.mkdir() trials = tmp_path / "trials" trials.mkdir() search_db = tmp_path / "search" search_db.mkdir() return { "paths": { - "patients_dir": str(patients), "trials_json_folder": str(trials), "output_dir": str(tmp_path / "results"), }, + "patient_inputs": { + "profile_dir": str(profiles), + "summary_dir": str(tmp_path / "summaries"), + }, "search_backend": { "backend": "lancedb", "db_path": str(search_db), @@ -134,10 +137,13 @@ def test_main_config_resolves_search_paths(tmp_path, monkeypatch): json.dumps( { "paths": { - "patients_dir": "patients", "output_dir": "results", "trials_json_folder": "trials", }, + "patient_inputs": { + "profile_dir": "patients/profiles", + "summary_dir": "patients/summaries", + }, "search_backend": { "backend": "lancedb", "db_path": "search", From 6aaebc208ab72f07d1d5fc98d19f0c37e428b9a9 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 09:35:20 +0200 Subject: [PATCH 10/64] PR0: CI ML-extras smoke job + scoring characterization tests Safety net before the audit-driven refactor (see REFACTOR_PLAN.md). - CI: add ml-extras-smoke job that `uv sync --extra entity`, imports the heavy libs (torch/transformers/gliner/gliner2) and the six local-model modules the default job never exercises, so a broken import or API drift is caught instead of shipping silently. - CI: keep the installed-smoke wheel path absolute ("$PWD"/dist) so $WHEEL survives the cd into /tmp. - tests: characterization tests pinning the current (buggy) score_trial behavior, plus a strict xfail encoding the PR1 contract (a Violated exclusion must hard-disqualify). Co-Authored-By: Claude Opus 4.8 --- .github/workflows/ci.yml | 50 ++++++++- REFACTOR_PLAN.md | 106 ++++++++++++++++++++ tests/test_trial_ranker_scoring_contract.py | 61 +++++++++++ 3 files changed, 215 insertions(+), 2 deletions(-) create mode 100644 REFACTOR_PLAN.md create mode 100644 tests/test_trial_ranker_scoring_contract.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 910a6d44..2e59511d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,7 +2,7 @@ name: CI on: push: - branches: [main, dev, deployment-readiness-audit] + branches: [main, dev, deployment-readiness-audit, refactor/audit-fixes] pull_request: branches: [main, dev] workflow_dispatch: @@ -40,7 +40,7 @@ jobs: - name: Installed package smoke run: | - WHEEL="$(ls dist/trialmatchai-*.whl | head -n 1)" + WHEEL="$(ls "$PWD"/dist/trialmatchai-*.whl | head -n 1)" mkdir -p /tmp/trialmatchai-installed-smoke cd /tmp/trialmatchai-installed-smoke uv run --python 3.11 --isolated --with "$WHEEL" python - <<'PY' @@ -69,3 +69,49 @@ jobs: - name: Dependency audit # vLLM 0.23 pins torch 2.11.0; CVE-2025-3000 has no fixed version listed. run: uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 + + ml-extras-smoke: + # Installs the optional ML extras (CPU-only) and imports the local-model + # surface that the default job never touches, so a broken import or wrong + # transformers/gliner API call is caught instead of shipping silently. + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install with entity extra (CPU) + run: uv sync --extra entity + + - name: Import ML surface + run: | + uv run python - <<'PY' + import importlib + + # The entity extra's heavy libs must import cleanly in the resolved + # environment (catches a bad transformers API or a torch pin conflict). + for lib in ("torch", "transformers", "gliner", "gliner2"): + importlib.import_module(lib) + print(f"imported {lib}") + + # The package's local-model modules must import with extras present. + modules = [ + "trialmatchai.entities.recognizers", + "trialmatchai.models.embedding.text_embedder", + "trialmatchai.models.llm.llm_loader", + "trialmatchai.models.llm.llm_reranker", + "trialmatchai.matching.eligibility_reasoning", + "trialmatchai.matching.eligibility_reasoning_vllm", + ] + for name in modules: + importlib.import_module(name) + print(f"imported {name}") + PY diff --git a/REFACTOR_PLAN.md b/REFACTOR_PLAN.md new file mode 100644 index 00000000..65a053f2 --- /dev/null +++ b/REFACTOR_PLAN.md @@ -0,0 +1,106 @@ +# TrialMatchAI Refactor Plan + +Derived from the comprehensive audit (170 findings → 151 confirmed, 8 uncertain, 11 refuted). +Each phase below is an independently shippable PR. Work proceeds on branch `refactor/audit-fixes` +(or per-PR branches off it). + +## Principles +1. **Safety net before surgery** — CI must exercise the ML surface and characterization tests + must lock current behavior before any behavior-changing PR merges. +2. **One PR = one theme**, independently reviewable and revertable. +3. **Behavior-preserving by default**; behavior-changing items are flagged and validated. +4. **Delete before dedup** — remove dead code before refactoring duplicates. +5. **Re-grep every deletion at edit time**, not just from the audit. + +## Resolved decisions +- **Phenopacket pipeline:** `interop/importers/phenopacket.py` + `interop/narrative.py` are canonical. + Delete `matching/phenopacket_processor.py` wholesale (only caller is its own test — verified). +- **`utils/evaluation.py`:** delete (orphaned, no entry point, no test). +- **Delivery:** this plan is tracked here; execution starts with PR0. + +## PR sequence + +| PR | Theme | Risk | Behavior change | Depends on | +|----|-------|------|-----------------|-----------| +| 0 | Safety net: CI ML-extras job + characterization tests + CI wheel-path fix | low | no | — | +| 1 | 🔴 Eligibility scoring contract (C1): exclusion = hard disqualifier | med | **yes** | 0 | +| 2 | 🔴 `models/llm/_common.py` + reranker hardening (C2 + padding/attn/dtype/device) | med | **yes (correctness)** | 0 | +| 3 | Dead-code sweep #1: whole modules (evaluation, embedders, regex tree, phenopacket) | low | no | 0 | +| 4 | Dead-code sweep #2: symbols/params/config/shims | low | no | 0 | +| 5 | OMOP importer: float/sanitized id join + groupby perf | med | **yes (recovers dropped records)** | 0 | +| 6 | Retrieval/indexing: restore fields, NCT sidecar filter, fallback WHERE, create_query | med | **yes (re-index required)** | 0 | +| 7 | Deduplication: BaseTrialProcessor, build_embedder, flatten_text, get_synonyms | med | no | 1,3,4 | +| 8 | Performance: lazy HF model under vLLM, precompute lancedb records | low | no | 2,5 | +| 9 | Hygiene: logging, broad excepts, packaging/deps, 8 uncertain findings | low | no | all | + +Critical path: `0 → {1,2,5,6} → {3,4} → 7 → 8 → 9`. + +## Per-PR detail + +### PR0 — Safety net +- CI: add a Linux job that runs `uv sync --extra entity` and smoke-imports the ML modules + (`entities.recognizers`, `models.embedding.text_embedder`, `models.llm.llm_loader`, + `models.llm.llm_reranker`, `matching.eligibility_reasoning_vllm`). +- CI: fix the installed-smoke wheel path (`ls "$PWD"/dist/...`) so `$WHEEL` survives the `cd`. +- Tests: characterization test for `score_trial` (current behavior) + `xfail` tests encoding the + desired post-PR1 contract (a Violated exclusion hard-disqualifies). PR1 flips xfail→pass. +- Note: OMOP (PR5) and indexing (PR6) characterization tests are added at the start of those PRs, + immediately before their changes. + +### PR1 — Eligibility scoring contract (C1) +- `trial_ranker.score_trial`: score inclusion and exclusion separately; **any `Violated` exclusion + is a hard disqualifier** (ranks below all eligible trials). Remove impossible labels + (`Not Violated`/`Violated` from inclusion, `Met` from exclusion) from both eligibility_reasoning + prompt calls. Document the scoring contract. +- Update `test_score_trial_basic` (currently encodes the buggy averaging) and flip PR0's xfail tests. +- **Behavior change:** ranking order — validate against a labeled trial set. + +### PR2 — LLM `_common.py` + reranker hardening +- New `models/llm/_common.py`: `load_llm_dependencies`, `resolve_cuda_device`, + `build_4bit_quant_config`, `select_attn_impl` (flash-attn probe → `sdpa`). +- Rewrite `llm_reranker.py` on it: device pinning (C2), `padding_side='left'` + pad token, + sdpa fallback, dtype-from-config, `device:int` coercion, honest concurrency. +- Refactor `llm_loader.py` to reuse the helpers. + +### PR3 — Dead-code sweep #1 (whole modules) +Delete (re-grep first): `utils/evaluation.py`, `models/embedding/query_embedder.py`, +`models/embedding/sentence_embedder.py`, `preprocessing/regex/` tree (+ `pyproject` package-data + +`config_loader` whitelist), `matching/phenopacket_processor.py` (+ its test). Resolves the +phenopacket ontology bug, dead summarizer, always-true guards, `truncate` typo, and duplicate +pipeline by deletion. + +### PR4 — Dead-code sweep #2 (symbols/params/config) +Remove `recognizers.with_schema_threshold`, `types.to_index_entity`, `retry.with_retries`, +`interop` `EvidenceSpan`/`Provenance.raw_text_span`/`all_facts`, `narrative` `style='audit'`, +`CompatibilityEntityAnnotator`, `annotator` `retries`/`delay`, dead settings (`cot`, +`LLM_reranker`, `TokenizerSettings`, `entity_extraction.threshold`, `max_text_score`, +`rerank_criteria.queries`). + +### PR5 — OMOP importer +Join on raw `person_id`; normalize `'1.0'→'1'`; group child tables by `person_id` once (kills N+1); +replace `iterrows` with `itertuples`/indexed dict. Test: null `person_id` in a child table no longer +drops records. + +### PR6 — Retrieval/indexing correctness +Restore `detailed_description` + `official_title` in `prepare_trial_document`; filter +`load_trial_data` to `NCT*` (or trials subdir); thread `where` into `_scan_rows` fallback; resolve +`create_query` dead keys + age semantics. **Requires LanceDB trial index rebuild after merge.** + +### PR7 — Deduplication +`BaseTrialProcessor` with abstract `_generate_batch` (HF + vLLM override only that); `build_embedder(cfg)` +replacing 4 copy-pasted blocks; one canonical `flatten_text`/`clean_text`; shared `get_synonyms`. + +### PR8 — Performance +Lazy-load HF CoT model only when `cot_backend != 'vllm'`; precompute +`build_trial_record`/`build_criteria_record` instead of per-row at query time. + +### PR9 — Hygiene & uncertain triage +Centralize logging (no import-time `basicConfig`); replace broad bare-`except` in `import_patient` and +pass the embedder so semantic linking isn't silently disabled; drop 5 unused deps, dedupe +`torch`/`transformers` pins via shared base extra, gitignore `egg-info`/`__pycache__`/`dist`; +investigate & resolve the 8 uncertain findings (LanceDB status/age/sex push-down, vLLM sampling under +greedy, unused `query_vector`). + +## Behavior-change register (validate, don't just unit-test) +PRs **1, 5, 6** change pipeline outputs. Diff before/after on a fixed patient+trial set: +ranking order (PR1), patient record counts (PR5), retrieval recall after re-index (PR6). diff --git a/tests/test_trial_ranker_scoring_contract.py b/tests/test_trial_ranker_scoring_contract.py new file mode 100644 index 00000000..f37c9b58 --- /dev/null +++ b/tests/test_trial_ranker_scoring_contract.py @@ -0,0 +1,61 @@ +"""Characterization + contract tests for trial_ranker.score_trial. + +PR0 (safety net): these lock the CURRENT behavior of the scorer and pin the +DESIRED behavior of the eligibility-scoring contract as an xfail. PR1 fixes +score_trial so that a Violated exclusion becomes a hard disqualifier; at that +point the xfail below flips to pass and the characterization test is updated. + +See REFACTOR_PLAN.md (PR1) and audit finding C1. +""" + +import pytest + +from trialmatchai.matching.trial_ranker import rank_trials, score_trial + +# A trial the patient is clearly ineligible for: every inclusion is Met, but an +# exclusion criterion is Violated. +TRIAL_VIOLATED_EXCLUSION = { + "TrialID": "VIOLATED", + "Inclusion_Criteria_Evaluation": [ + {"Classification": "Met"}, + {"Classification": "Met"}, + ], + "Exclusion_Criteria_Evaluation": [ + {"Classification": "Not Violated"}, + {"Classification": "Violated"}, + ], +} + +# A trial the patient partially matches and violates no exclusions. +TRIAL_PARTIAL_ELIGIBLE = { + "TrialID": "PARTIAL", + "Inclusion_Criteria_Evaluation": [ + {"Classification": "Met"}, + {"Classification": "Not Met"}, + ], + "Exclusion_Criteria_Evaluation": [], +} + + +def test_characterization_violated_exclusion_only_partially_penalized(): + """CURRENT behavior: a Violated exclusion is averaged away rather than + disqualifying. This documents the C1 bug and will be updated in PR1.""" + # inclusion_ratio = (2 - 0) / 2 = 1.0 + # exclusion_ratio = (1 - 1) / 2 = 0.0 (Not Violated positive, Violated negative) + # score = (1.0 + 0.0) / 2 = 0.5 + assert score_trial(TRIAL_VIOLATED_EXCLUSION) == 0.5 + + +def test_characterization_violated_exclusion_outranks_eligible_trial(): + """CURRENT (buggy) ranking: the trial with a Violated exclusion (0.5) ranks + ABOVE the violation-free partial match (0.0).""" + ranked = rank_trials([TRIAL_PARTIAL_ELIGIBLE, TRIAL_VIOLATED_EXCLUSION]) + assert ranked[0]["TrialID"] == "VIOLATED" + + +@pytest.mark.xfail(reason="PR1: a Violated exclusion must hard-disqualify", strict=True) +def test_contract_violated_exclusion_ranks_below_eligible(): + """DESIRED contract (PR1): any trial with a Violated exclusion ranks strictly + below any trial that violates no exclusions.""" + ranked = rank_trials([TRIAL_VIOLATED_EXCLUSION, TRIAL_PARTIAL_ELIGIBLE]) + assert ranked[-1]["TrialID"] == "VIOLATED" From 611ffa666296011902b655b9c9ae86f3c86410cc Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 09:37:57 +0200 Subject: [PATCH 11/64] =?UTF-8?q?PR1:=20eligibility=20scoring=20=E2=80=94?= =?UTF-8?q?=20a=20violated=20exclusion=20hard-disqualifies=20(C1)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Previously score_trial averaged the inclusion ratio with the exclusion ratio, so a trial whose exclusion criteria were Violated could still outrank an eligible trial (audit finding C1). The classification lists also counted impossible labels (inclusion can never be "Not Violated", exclusion can never be "Met"). score_trial now: - returns DISQUALIFIED_SCORE (-1.0) if any exclusion is "Violated", so it ranks strictly below every eligible trial; - otherwise scores eligible trials in [0, 1] by the fraction of decided inclusion criteria (Met or Not Met) that are Met. The prompts already constrain the label sets correctly, so only the scorer changed; the duplicated prompt text is addressed in PR7. Contract tests (disqualification, met-fraction, ranking order) replace the prior xfail. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/matching/trial_ranker.py | 68 ++++++++--------- tests/test_trial_ranker_pytest.py | 5 +- tests/test_trial_ranker_scoring_contract.py | 81 +++++++++++++-------- 3 files changed, 84 insertions(+), 70 deletions(-) diff --git a/src/trialmatchai/matching/trial_ranker.py b/src/trialmatchai/matching/trial_ranker.py index deb9c8d9..1c1fceae 100644 --- a/src/trialmatchai/matching/trial_ranker.py +++ b/src/trialmatchai/matching/trial_ranker.py @@ -22,46 +22,38 @@ def load_trial_data(json_folder: str) -> List[Dict]: return trial_data +# Eligibility scoring contract (see REFACTOR_PLAN.md PR1, audit finding C1). +# +# The eligibility model classifies each inclusion criterion as one of +# {Met, Not Met, Unclear, Irrelevant} and each exclusion criterion as one of +# {Violated, Not Violated, Unclear, Irrelevant}. A single Violated exclusion +# makes the patient ineligible, so it HARD-DISQUALIFIES the trial rather than +# being averaged against the inclusion score (the previous behavior, which let a +# violated trial outrank an eligible one). Eligible trials are ranked in [0, 1] +# by the fraction of decided inclusion criteria (Met or Not Met) that are Met. +DISQUALIFIED_SCORE = -1.0 + +_DECIDED_INCLUSION = {"Met", "Not Met"} + + def score_trial(trial: Dict) -> float: - def calculate_ratio( - criteria_list, positive_classifications, negative_classifications - ): - criteria_to_exclude = ["Irrelevant", "Unclear"] - criteria_list = [ - c - for c in criteria_list - if c.get("Classification") not in criteria_to_exclude - ] - total_criteria = len(criteria_list) - if total_criteria == 0: - return 0.0 - positive_count = sum( - 1 - for c in criteria_list - if c.get("Classification") in positive_classifications - ) - negative_count = sum( - 1 - for c in criteria_list - if c.get("Classification") in negative_classifications - ) - penalty_factor_negative = 1.0 - reward_factor_positive = 1.0 - score = ( - reward_factor_positive * positive_count - - penalty_factor_negative * negative_count - ) / total_criteria - return score + inclusion = [ + c.get("Classification") for c in trial.get("Inclusion_Criteria_Evaluation", []) + ] + exclusion = [ + c.get("Classification") for c in trial.get("Exclusion_Criteria_Evaluation", []) + ] + + # Any violated exclusion is a hard disqualifier: rank below all eligible trials. + if "Violated" in exclusion: + return DISQUALIFIED_SCORE - inclusion_criteria = trial.get("Inclusion_Criteria_Evaluation", []) - exclusion_criteria = trial.get("Exclusion_Criteria_Evaluation", []) - inclusion_ratio = calculate_ratio( - inclusion_criteria, ["Met", "Not Violated"], ["Violated", "Not Met"] - ) - exclusion_ratio = calculate_ratio( - exclusion_criteria, ["Not Violated", "Met"], ["Violated"] - ) - return (inclusion_ratio + exclusion_ratio) / 2 + # Eligible: score by the fraction of decided inclusion criteria that are Met. + decided = [c for c in inclusion if c in _DECIDED_INCLUSION] + if not decided: + return 0.0 + met = sum(1 for c in decided if c == "Met") + return met / len(decided) def rank_trials(trial_data: List[Dict]) -> List[Dict]: diff --git a/tests/test_trial_ranker_pytest.py b/tests/test_trial_ranker_pytest.py index 16265672..aba7fc42 100644 --- a/tests/test_trial_ranker_pytest.py +++ b/tests/test_trial_ranker_pytest.py @@ -9,19 +9,18 @@ def test_score_trial_basic(): + # A violated exclusion hard-disqualifies the trial regardless of inclusions. trial = { "Inclusion_Criteria_Evaluation": [ {"Classification": "Met"}, {"Classification": "Met"}, - {"Classification": "Violated"}, ], "Exclusion_Criteria_Evaluation": [ {"Classification": "Not Violated"}, {"Classification": "Violated"}, ], } - score = score_trial(trial) - assert score == ((2 - 1) / 3 + (1 - 1) / 2) / 2 + assert score_trial(trial) == -1.0 def test_rank_trials_orders_by_score(): diff --git a/tests/test_trial_ranker_scoring_contract.py b/tests/test_trial_ranker_scoring_contract.py index f37c9b58..ab25c7ed 100644 --- a/tests/test_trial_ranker_scoring_contract.py +++ b/tests/test_trial_ranker_scoring_contract.py @@ -1,19 +1,18 @@ -"""Characterization + contract tests for trial_ranker.score_trial. +"""Contract tests for trial_ranker.score_trial (audit finding C1, PR1). -PR0 (safety net): these lock the CURRENT behavior of the scorer and pin the -DESIRED behavior of the eligibility-scoring contract as an xfail. PR1 fixes -score_trial so that a Violated exclusion becomes a hard disqualifier; at that -point the xfail below flips to pass and the characterization test is updated. - -See REFACTOR_PLAN.md (PR1) and audit finding C1. +A Violated exclusion hard-disqualifies a trial: it must rank strictly below every +trial that violates no exclusion. Eligible trials are scored in [0, 1] by the +fraction of decided inclusion criteria (Met or Not Met) that are Met. +See REFACTOR_PLAN.md (PR1). """ -import pytest - -from trialmatchai.matching.trial_ranker import rank_trials, score_trial +from trialmatchai.matching.trial_ranker import ( + DISQUALIFIED_SCORE, + rank_trials, + score_trial, +) -# A trial the patient is clearly ineligible for: every inclusion is Met, but an -# exclusion criterion is Violated. +# Ineligible: every inclusion is Met, but an exclusion criterion is Violated. TRIAL_VIOLATED_EXCLUSION = { "TrialID": "VIOLATED", "Inclusion_Criteria_Evaluation": [ @@ -26,7 +25,7 @@ ], } -# A trial the patient partially matches and violates no exclusions. +# Eligible partial match: half the decided inclusions are Met, no violations. TRIAL_PARTIAL_ELIGIBLE = { "TrialID": "PARTIAL", "Inclusion_Criteria_Evaluation": [ @@ -36,26 +35,50 @@ "Exclusion_Criteria_Evaluation": [], } +# Eligible but a poor match: all inclusions Not Met, still violates nothing. +TRIAL_ALL_NOT_MET = { + "TrialID": "NOT_MET", + "Inclusion_Criteria_Evaluation": [ + {"Classification": "Not Met"}, + {"Classification": "Not Met"}, + ], + "Exclusion_Criteria_Evaluation": [{"Classification": "Not Violated"}], +} -def test_characterization_violated_exclusion_only_partially_penalized(): - """CURRENT behavior: a Violated exclusion is averaged away rather than - disqualifying. This documents the C1 bug and will be updated in PR1.""" - # inclusion_ratio = (2 - 0) / 2 = 1.0 - # exclusion_ratio = (1 - 1) / 2 = 0.0 (Not Violated positive, Violated negative) - # score = (1.0 + 0.0) / 2 = 0.5 - assert score_trial(TRIAL_VIOLATED_EXCLUSION) == 0.5 +def test_violated_exclusion_is_disqualified(): + assert score_trial(TRIAL_VIOLATED_EXCLUSION) == DISQUALIFIED_SCORE -def test_characterization_violated_exclusion_outranks_eligible_trial(): - """CURRENT (buggy) ranking: the trial with a Violated exclusion (0.5) ranks - ABOVE the violation-free partial match (0.0).""" - ranked = rank_trials([TRIAL_PARTIAL_ELIGIBLE, TRIAL_VIOLATED_EXCLUSION]) - assert ranked[0]["TrialID"] == "VIOLATED" +def test_eligible_scored_by_met_fraction(): + assert score_trial(TRIAL_PARTIAL_ELIGIBLE) == 0.5 + assert score_trial(TRIAL_ALL_NOT_MET) == 0.0 + assert score_trial( + {"Inclusion_Criteria_Evaluation": [{"Classification": "Met"}]} + ) == 1.0 -@pytest.mark.xfail(reason="PR1: a Violated exclusion must hard-disqualify", strict=True) -def test_contract_violated_exclusion_ranks_below_eligible(): - """DESIRED contract (PR1): any trial with a Violated exclusion ranks strictly - below any trial that violates no exclusions.""" + +def test_unclear_and_irrelevant_inclusions_are_ignored(): + # Only Met/Not Met count toward the fraction; Unclear/Irrelevant are excluded. + trial = { + "Inclusion_Criteria_Evaluation": [ + {"Classification": "Met"}, + {"Classification": "Unclear"}, + {"Classification": "Irrelevant"}, + ], + "Exclusion_Criteria_Evaluation": [], + } + assert score_trial(trial) == 1.0 + + +def test_violated_exclusion_ranks_below_eligible(): ranked = rank_trials([TRIAL_VIOLATED_EXCLUSION, TRIAL_PARTIAL_ELIGIBLE]) + assert ranked[0]["TrialID"] == "PARTIAL" + assert ranked[-1]["TrialID"] == "VIOLATED" + + +def test_disqualified_ranks_below_even_a_zero_score_eligible_trial(): + # An all-Not-Met eligible trial (0.0) still outranks a disqualified one (-1.0). + ranked = rank_trials([TRIAL_VIOLATED_EXCLUSION, TRIAL_ALL_NOT_MET]) + assert ranked[0]["TrialID"] == "NOT_MET" assert ranked[-1]["TrialID"] == "VIOLATED" From 008b0800866af46123525d1a53923be04dcd1eed Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 09:45:31 +0200 Subject: [PATCH 12/64] PR2: shared models/llm/_common.py + reranker hardening (C2 + bugs) The reranker re-derived device/dtype/attention setup independently of llm_loader and got several wrong. Extract the shared logic into models/llm/_common.py and rebuild both loaders on it. Reranker fixes (audit C2 + highs/mediums): - device_map pinned to the resolved device instead of "auto", so the model's first layer lives where inputs are moved (fixes multi-GPU "tensors on different devices" crash); - left padding + pad token via configure_decoder_tokenizer, so logits[:, -1, :] reads the last real token, not a PAD position; - FlashAttention-2 -> SDPA fallback instead of hardcoding flash_attn; - compute dtype defaults to bf16/fp16 by capability instead of fp16; - device accepts int or "auto" (no silent coercion of non-ints to GPU 0); - dropped the ThreadPoolExecutor + model_lock that serialized anyway. llm_loader now reuses the same helpers. Stub-based unit tests cover the tokenizer/device/dtype/attention logic without the llm extra. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/models/llm/_common.py | 162 ++++++++++++++++++++ src/trialmatchai/models/llm/llm_loader.py | 105 ++++--------- src/trialmatchai/models/llm/llm_reranker.py | 115 ++++++-------- tests/test_llm_common.py | 104 +++++++++++++ 4 files changed, 337 insertions(+), 149 deletions(-) create mode 100644 src/trialmatchai/models/llm/_common.py create mode 100644 tests/test_llm_common.py diff --git a/src/trialmatchai/models/llm/_common.py b/src/trialmatchai/models/llm/_common.py new file mode 100644 index 00000000..3f92abbb --- /dev/null +++ b/src/trialmatchai/models/llm/_common.py @@ -0,0 +1,162 @@ +"""Shared helpers for the optional LLM model loaders (llm_loader, llm_reranker). + +The heavy dependencies (torch/transformers/peft) are imported lazily by +``load_llm_dependencies`` so this module imports cleanly without the ``llm`` +extra installed. The torch-dependent helpers take ``torch`` as an argument and +are otherwise pure, which keeps them unit-testable with a stub. +""" + +from __future__ import annotations + +from typing import Any, NamedTuple, Optional + +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +class LLMDeps(NamedTuple): + torch: Any + torch_functional: Any + peft_model: Any + auto_model: Any + auto_tokenizer: Any + bnb_config: Any + + +def load_llm_dependencies() -> LLMDeps: + """Import the optional LLM stack, raising a clear error when it is missing.""" + try: + import torch + import torch.nn.functional as torch_functional + from peft import PeftModel + from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + ) + except Exception as exc: # pragma: no cover - exercised in lean installs + raise RuntimeError( + "LLM model loading requires the optional `llm` dependencies " + "(`uv sync --extra llm`)." + ) from exc + return LLMDeps( + torch, + torch_functional, + PeftModel, + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + ) + + +def resolve_cuda_device( + torch: Any, device: Any, *, label: str = "LLM" +) -> tuple[str, Optional[int]]: + """Resolve a requested device to ``(device_str, cuda_index)``. + + Validates the requested GPU index (accepting ``int`` or ``"auto"``), selects + it via ``set_device``, and falls back to GPU 0 with a warning on anything + invalid. Returns ``("cpu", None)`` when CUDA is unavailable. + """ + if not torch.cuda.is_available(): + logger.warning("%s: CUDA not available; using CPU.", label) + return "cpu", None + + cuda_count = torch.cuda.device_count() + if device == "auto" or device is None: + idx = 0 + elif isinstance(device, bool): # bool is an int subclass; reject it explicitly + logger.warning("%s: invalid device %r; using 0.", label, device) + idx = 0 + elif isinstance(device, int): + idx = device + else: + try: + idx = int(device) + except (TypeError, ValueError): + logger.warning("%s: non-numeric device %r; using 0.", label, device) + idx = 0 + + if idx < 0 or idx >= cuda_count: + logger.warning( + "%s: requested CUDA device %r invalid; using 0 (num_gpus=%d).", + label, + device, + cuda_count, + ) + idx = 0 + + try: + torch.cuda.set_device(idx) + except Exception as e: + logger.warning( + "%s: torch.cuda.set_device(%d) failed: %s; using 0.", label, idx, e + ) + idx = 0 + torch.cuda.set_device(idx) + return f"cuda:{idx}", idx + + +def select_compute_dtype(torch: Any, use_cuda: bool) -> Any: + """bfloat16 when supported, else float16 on GPU, else float32 on CPU.""" + if use_cuda and torch.cuda.is_bf16_supported(): + return torch.bfloat16 + if use_cuda: + return torch.float16 + return torch.float32 + + +def select_attn_impl(torch: Any, cuda_index: Optional[int]) -> Optional[str]: + """Prefer FlashAttention-2 when installed and supported, else SDPA. + + Returns ``None`` on CPU (let transformers pick its default). + """ + if cuda_index is None: + return None + attn_impl = "sdpa" + try: + import flash_attn # noqa: F401 + + major, minor = torch.cuda.get_device_capability(cuda_index) + if (major * 10 + minor) >= 75: + attn_impl = "flash_attention_2" + logger.info("Using FlashAttention-2.") + else: + logger.info("FlashAttention-2 unsupported on this GPU; using SDPA.") + except Exception: + logger.info("flash-attn not available; using SDPA.") + return attn_impl + + +def build_4bit_quant_config( + bnb_config: Any, + *, + load_in_4bit: bool, + double_quant: bool = True, + quant_type: str = "nf4", + compute_dtype: Any = None, +) -> Any: + """Build a BitsAndBytesConfig; a no-op config when ``load_in_4bit`` is False.""" + if not load_in_4bit: + return bnb_config(load_in_4bit=False) + return bnb_config( + load_in_4bit=True, + bnb_4bit_use_double_quant=double_quant, + bnb_4bit_quant_type=quant_type, + bnb_4bit_compute_dtype=compute_dtype, + ) + + +def configure_decoder_tokenizer(tokenizer: Any) -> Any: + """Left-pad/-truncate a decoder-only tokenizer and ensure a pad token exists. + + Decoder-only next-token prediction reads ``logits[:, -1, :]``. With right + padding, that position is a PAD token for the shorter rows in a batch, + producing wrong probabilities — so left padding is required for correctness. + """ + tokenizer.padding_side = "left" + tokenizer.truncation_side = "left" + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + return tokenizer diff --git a/src/trialmatchai/models/llm/llm_loader.py b/src/trialmatchai/models/llm/llm_loader.py index 1aea7f33..0930a0e4 100644 --- a/src/trialmatchai/models/llm/llm_loader.py +++ b/src/trialmatchai/models/llm/llm_loader.py @@ -1,5 +1,13 @@ from typing import Any, Tuple +from trialmatchai.models.llm._common import ( + build_4bit_quant_config, + configure_decoder_tokenizer, + load_llm_dependencies, + resolve_cuda_device, + select_attn_impl, + select_compute_dtype, +) from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -7,93 +15,49 @@ def load_model_and_tokenizer(model_config: dict, device: int) -> Tuple[Any, Any]: """Load a model and tokenizer with safe device handling and optional 4-bit.""" - torch, peft_model, auto_model, auto_tokenizer, bits_and_bytes_config = ( - _load_llm_dependencies() + deps = load_llm_dependencies() + torch = deps.torch + + device_str, cuda_index = resolve_cuda_device( + torch, device, label="load_model_and_tokenizer" ) - use_cuda = torch.cuda.is_available() - device_str = "cuda" if use_cuda else "cpu" - quant_config = None - attn_impl = None - # Select best dtype - compute_dtype = torch.float32 - if use_cuda and torch.cuda.is_bf16_supported(): - compute_dtype = torch.bfloat16 - elif use_cuda: - compute_dtype = torch.float16 + use_cuda = cuda_index is not None + compute_dtype = select_compute_dtype(torch, use_cuda) + attn_impl = select_attn_impl(torch, cuda_index) if use_cuda: - cuda_count = torch.cuda.device_count() - idx = int(device) if isinstance(device, int) else 0 - if idx < 0 or idx >= cuda_count: - logger.warning( - f"Requested CUDA device {device} invalid; using 0 (num_gpus={cuda_count})." - ) - idx = 0 - try: - torch.cuda.set_device(idx) - except Exception as e: - logger.warning( - f"torch.cuda.set_device({idx}) failed: {e}. Falling back to 0." - ) - idx = 0 - torch.cuda.set_device(idx) - device_str = f"cuda:{idx}" - - # Prefer FlashAttention-2 if available, else SDPA - attn_impl = "sdpa" - try: - import flash_attn # noqa: F401 - - major, minor = torch.cuda.get_device_capability(idx) - if (major * 10 + minor) >= 75: - attn_impl = "flash_attention_2" - logger.info("Using FlashAttention-2.") - else: - logger.info("FlashAttention-2 unsupported on this GPU; using SDPA.") - except Exception: - logger.info("flash-attn not available; using SDPA.") - - quant_config = bits_and_bytes_config( + quant_config = build_4bit_quant_config( + deps.bnb_config, load_in_4bit=bool(model_config["quantization"]["load_in_4bit"]), - bnb_4bit_use_double_quant=bool( + double_quant=bool( model_config["quantization"]["bnb_4bit_use_double_quant"] ), - bnb_4bit_quant_type=str( - model_config["quantization"]["bnb_4bit_quant_type"] - ), - bnb_4bit_compute_dtype=compute_dtype, + quant_type=str(model_config["quantization"]["bnb_4bit_quant_type"]), + compute_dtype=compute_dtype, ) logger.info(f"Loading model on {device_str} with 4-bit quantization.") else: logger.warning( "CUDA not available; loading model on CPU without 4-bit quantization." ) - device_str = "cpu" - quant_config = bits_and_bytes_config(load_in_4bit=False) + quant_config = build_4bit_quant_config(deps.bnb_config, load_in_4bit=False) trust_remote_code = bool(model_config.get("trust_remote_code", False)) revision = model_config.get("base_model_revision") - tokenizer = auto_tokenizer.from_pretrained( + tokenizer = deps.auto_tokenizer.from_pretrained( model_config["base_model"], revision=revision, use_fast=True, - padding_side="left", trust_remote_code=trust_remote_code, ) - # Always left-pad decoder-only models; keep most recent tokens if truncation occurs. - tokenizer.padding_side = "left" - tokenizer.truncation_side = "left" - tokenizer.pad_token = tokenizer.eos_token - if attn_impl == "flash_attention_2": - logger.info( - "Using FlashAttention-2; keeping padding_side='left' for decoder-only models." - ) + # Always left-pad decoder-only models; keep most recent tokens on truncation. + configure_decoder_tokenizer(tokenizer) - model = auto_model.from_pretrained( + model = deps.auto_model.from_pretrained( model_config["base_model"], revision=revision, trust_remote_code=trust_remote_code, - torch_dtype=compute_dtype if use_cuda else torch.float32, + torch_dtype=compute_dtype, device_map=device_str, attn_implementation=attn_impl, quantization_config=quant_config, @@ -105,7 +69,7 @@ def load_model_and_tokenizer(model_config: dict, device: int) -> Tuple[Any, Any] except Exception: pass - model = peft_model.from_pretrained( + model = deps.peft_model.from_pretrained( model, model_config["cot_adapter_path"], device_map=device_str ) @@ -123,16 +87,3 @@ def load_model_and_tokenizer(model_config: dict, device: int) -> Tuple[Any, Any] logger.warning("Model is not an instance of torch.nn.Module; skipping eval.") logger.info(f"Model loaded on {device_str}.") return model, tokenizer # type: ignore[return-value] - - -def _load_llm_dependencies() -> tuple[Any, Any, Any, Any, Any]: - try: - import torch - from peft import PeftModel - from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig - except Exception as exc: # pragma: no cover - exercised in lean installs - raise RuntimeError( - "LLM model loading requires the optional `llm` dependencies " - "(`uv sync --extra llm`)." - ) from exc - return torch, PeftModel, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig diff --git a/src/trialmatchai/models/llm/llm_reranker.py b/src/trialmatchai/models/llm/llm_reranker.py index 1eb08771..18e1d636 100644 --- a/src/trialmatchai/models/llm/llm_reranker.py +++ b/src/trialmatchai/models/llm/llm_reranker.py @@ -1,9 +1,15 @@ import re -import threading import unicodedata -from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Any, Dict, List, Optional +from trialmatchai.models.llm._common import ( + build_4bit_quant_config, + configure_decoder_tokenizer, + load_llm_dependencies, + resolve_cuda_device, + select_attn_impl, + select_compute_dtype, +) from trialmatchai.utils.logging_config import setup_logging from tqdm import tqdm @@ -15,53 +21,39 @@ def __init__( self, model_path: str, adapter_path: Optional[str] = None, - device: int = 0, + device: Any = 0, torch_dtype: Any | None = None, batch_size: int = 8, revision: Optional[str] = None, trust_remote_code: bool = False, ): - ( - self._torch, - self._torch_functional, - self._peft_model, - self._auto_model, - self._auto_tokenizer, - self._bits_and_bytes_config, - ) = _load_llm_dependencies() + self._deps = load_llm_dependencies() + self._torch = self._deps.torch + self._torch_functional = self._deps.torch_functional self.model_path = model_path self.adapter_path = adapter_path - self.torch_dtype = torch_dtype or self._torch.float16 self.batch_size = batch_size self.revision = revision self.trust_remote_code = trust_remote_code - # Resolve device string - if self._torch.cuda.is_available(): - cuda_count = self._torch.cuda.device_count() - idx = int(device) if isinstance(device, int) else 0 - if idx < 0 or idx >= cuda_count: - logger.warning( - f"LLMReranker: requested CUDA device {device} invalid; using 0 (num_gpus={cuda_count})." - ) - idx = 0 - self.device_str = f"cuda:{idx}" - # Ensure Accelerate/HF loaders use the selected GPU when device_map='auto' - try: - self._torch.cuda.set_device(idx) - except Exception as e: - logger.warning(f"Could not set CUDA device to {idx}: {e}") - else: - logger.warning("LLMReranker: CUDA not available; using CPU.") - self.device_str = "cpu" - self.tokenizer = self._auto_tokenizer.from_pretrained( + # Validate/select the GPU once (handles int or "auto"); pins the device so + # device_map below is consistent with where inputs are moved. + self.device_str, self._cuda_index = resolve_cuda_device( + self._torch, device, label="LLMReranker" + ) + use_cuda = self._cuda_index is not None + self.torch_dtype = torch_dtype or select_compute_dtype(self._torch, use_cuda) + + self.tokenizer = self._deps.auto_tokenizer.from_pretrained( self.model_path, revision=self.revision, trust_remote_code=self.trust_remote_code, ) + # Left padding is required: process_batch reads logits[:, -1, :], which + # must be the last real token, not a right-pad position. + configure_decoder_tokenizer(self.tokenizer) self._initialize_token_ids() self.model = self.load_model() - self.model_lock = threading.Lock() def _initialize_token_ids(self): responses = ["Yes", "No"] @@ -74,28 +66,31 @@ def _initialize_token_ids(self): ] def load_model(self): - use_cuda = self.device_str.startswith("cuda") + use_cuda = self._cuda_index is not None quant_config = ( - self._bits_and_bytes_config( + build_4bit_quant_config( + self._deps.bnb_config, load_in_4bit=True, - bnb_4bit_use_double_quant=True, - bnb_4bit_quant_type="nf4", - bnb_4bit_compute_dtype=self.torch_dtype, + double_quant=True, + quant_type="nf4", + compute_dtype=self.torch_dtype, ) if use_cuda else None ) - model = self._auto_model.from_pretrained( + model = self._deps.auto_model.from_pretrained( self.model_path, revision=self.revision, torch_dtype=self.torch_dtype if use_cuda else self._torch.float32, quantization_config=quant_config, - device_map="auto" if use_cuda else None, - attn_implementation="flash_attention_2" if use_cuda else None, + # Pin to the selected GPU (not "auto"): inputs are moved to + # self.device_str, so the model's first layer must live there too. + device_map=self.device_str if use_cuda else None, + attn_implementation=select_attn_impl(self._torch, self._cuda_index), trust_remote_code=self.trust_remote_code, ) if self.adapter_path: - model = self._peft_model.from_pretrained(model, self.adapter_path) + model = self._deps.peft_model.from_pretrained(model, self.adapter_path) model.eval() return model @@ -132,9 +127,8 @@ def process_batch(self, batch: List[tuple]) -> List[Dict]: batch_prompts.append(prompt) inputs = self.tokenizer(batch_prompts, return_tensors="pt", padding=True) inputs = {k: v.to(self.device_str) for k, v in inputs.items()} - with self.model_lock: - with self._torch.no_grad(): - outputs = self.model(**inputs) + with self._torch.no_grad(): + outputs = self.model(**inputs) logits = outputs.logits[:, -1, :] probabilities = self._torch_functional.softmax(logits, dim=-1) applicable_probs = probabilities[:, self.applicable_token_id].tolist() @@ -144,36 +138,13 @@ def process_batch(self, batch: List[tuple]) -> List[Dict]: ] def rank_pairs(self, patient_trial_pairs: List[tuple]) -> List[Dict]: + # Inference on a single device is serial regardless; iterate batches + # directly rather than behind a thread pool + lock that serialized anyway. batches = [ patient_trial_pairs[i : i + self.batch_size] for i in range(0, len(patient_trial_pairs), self.batch_size) ] - results = [] - with ThreadPoolExecutor(max_workers=4) as executor: - futures = [executor.submit(self.process_batch, batch) for batch in batches] - for future in tqdm( - as_completed(futures), total=len(futures), desc="Processing batches" - ): - results.extend(future.result()) + results: List[Dict] = [] + for batch in tqdm(batches, desc="Reranking batches"): + results.extend(self.process_batch(batch)) return results - - -def _load_llm_dependencies() -> tuple[Any, Any, Any, Any, Any, Any]: - try: - import torch - import torch.nn.functional as torch_functional - from peft import PeftModel - from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig - except Exception as exc: # pragma: no cover - exercised in lean installs - raise RuntimeError( - "LLM reranking requires the optional `llm` dependencies " - "(`uv sync --extra llm`)." - ) from exc - return ( - torch, - torch_functional, - PeftModel, - AutoModelForCausalLM, - AutoTokenizer, - BitsAndBytesConfig, - ) diff --git a/tests/test_llm_common.py b/tests/test_llm_common.py new file mode 100644 index 00000000..d0fd6704 --- /dev/null +++ b/tests/test_llm_common.py @@ -0,0 +1,104 @@ +"""Unit tests for the shared LLM helpers (models/llm/_common.py). + +These lock the correctness fixes from PR2 — left padding, device resolution, +dtype selection — using lightweight stubs so they run without the `llm` extra. +""" + +from trialmatchai.models.llm._common import ( + configure_decoder_tokenizer, + resolve_cuda_device, + select_attn_impl, + select_compute_dtype, +) + + +class FakeTokenizer: + def __init__(self, pad_token=None, eos_token=""): + self.pad_token = pad_token + self.eos_token = eos_token + self.padding_side = "right" + self.truncation_side = "right" + + +class FakeCuda: + def __init__(self, available=True, count=2, bf16=False): + self._available = available + self._count = count + self._bf16 = bf16 + self.selected = None + + def is_available(self): + return self._available + + def device_count(self): + return self._count + + def is_bf16_supported(self): + return self._bf16 + + def set_device(self, idx): + self.selected = idx + + +class FakeTorch: + def __init__(self, **kw): + self.cuda = FakeCuda(**kw) + self.bfloat16 = "bfloat16" + self.float16 = "float16" + self.float32 = "float32" + + +def test_configure_decoder_tokenizer_sets_left_padding_and_pad_token(): + tok = FakeTokenizer(pad_token=None, eos_token="") + configure_decoder_tokenizer(tok) + assert tok.padding_side == "left" + assert tok.truncation_side == "left" + assert tok.pad_token == "" # filled from eos when missing + + +def test_configure_decoder_tokenizer_keeps_existing_pad_token(): + tok = FakeTokenizer(pad_token="", eos_token="") + configure_decoder_tokenizer(tok) + assert tok.pad_token == "" + + +def test_resolve_cuda_device_cpu_when_unavailable(): + torch = FakeTorch(available=False) + assert resolve_cuda_device(torch, 0) == ("cpu", None) + + +def test_resolve_cuda_device_auto_selects_zero(): + torch = FakeTorch(available=True, count=2) + assert resolve_cuda_device(torch, "auto") == ("cuda:0", 0) + assert torch.cuda.selected == 0 + + +def test_resolve_cuda_device_valid_index(): + torch = FakeTorch(available=True, count=2) + assert resolve_cuda_device(torch, 1) == ("cuda:1", 1) + assert torch.cuda.selected == 1 + + +def test_resolve_cuda_device_invalid_index_falls_back_to_zero(): + torch = FakeTorch(available=True, count=2) + assert resolve_cuda_device(torch, 5) == ("cuda:0", 0) + + +def test_resolve_cuda_device_non_numeric_string_falls_back(): + torch = FakeTorch(available=True, count=2) + assert resolve_cuda_device(torch, "gpu0") == ("cuda:0", 0) + + +def test_select_compute_dtype(): + assert select_compute_dtype(FakeTorch(available=True, bf16=True), True) == "bfloat16" + assert select_compute_dtype(FakeTorch(available=True, bf16=False), True) == "float16" + assert select_compute_dtype(FakeTorch(available=False), False) == "float32" + + +def test_select_attn_impl_cpu_is_none(): + assert select_attn_impl(FakeTorch(available=False), None) is None + + +def test_select_attn_impl_gpu_without_flash_attn_is_sdpa(): + # flash_attn is not installed in the base test env, so this falls back. + assert select_attn_impl(FakeTorch(available=True), 0) == "sdpa" From 0f0447058976494266ff67ea524ee3165a9fd52d Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 09:51:13 +0200 Subject: [PATCH 13/64] PR3: remove dead whole modules (~900 lines) Re-grepped each target to confirm zero live references (only auto-generated egg-info/SOURCES.txt and the now-removed test/whitelist referenced them): - utils/evaluation.py: orphaned TREC eval, no caller, no entry point. - models/embedding/query_embedder.py, sentence_embedder.py: vestigial TextEmbedder subclasses nothing constructs. - preprocessing/regex/ tree: regex resource files with no Python consumer (leftovers from the deleted src/Matcher); also drop the package-data globs in pyproject and the "preprocessing" entry in the config_loader resource whitelist. - matching/phenopacket_processor.py (+ its test): superseded by the canonical interop/importers/phenopacket.py + interop/narrative.py path. This deletion also resolves the dead ClinicalSummarizer, the ontology label bug, the always-true temporal guards, the truncate typo, and the duplicate phenopacket pipeline. Co-Authored-By: Claude Opus 4.8 --- pyproject.toml | 2 - src/trialmatchai/config/config_loader.py | 1 - .../matching/phenopacket_processor.py | 395 ------------------ .../models/embedding/query_embedder.py | 24 -- .../models/embedding/sentence_embedder.py | 24 -- src/trialmatchai/preprocessing/__init__.py | 1 - .../preprocessing/regex/__init__.py | 1 - .../regex/exception_regex_patterns.json | 105 ----- .../preprocessing/regex/regex_patterns.json | 193 --------- .../preprocessing/regex/regex_variants.tsv | 52 --- src/trialmatchai/utils/evaluation.py | 322 -------------- tests/test_phenopacket_processor_pytest.py | 13 - 12 files changed, 1133 deletions(-) delete mode 100644 src/trialmatchai/matching/phenopacket_processor.py delete mode 100644 src/trialmatchai/models/embedding/query_embedder.py delete mode 100644 src/trialmatchai/models/embedding/sentence_embedder.py delete mode 100644 src/trialmatchai/preprocessing/__init__.py delete mode 100644 src/trialmatchai/preprocessing/regex/__init__.py delete mode 100644 src/trialmatchai/preprocessing/regex/exception_regex_patterns.json delete mode 100644 src/trialmatchai/preprocessing/regex/regex_patterns.json delete mode 100644 src/trialmatchai/preprocessing/regex/regex_variants.tsv delete mode 100644 src/trialmatchai/utils/evaluation.py delete mode 100644 tests/test_phenopacket_processor_pytest.py diff --git a/pyproject.toml b/pyproject.toml index e83f1479..bfbe518e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -78,8 +78,6 @@ where = ["src"] trialmatchai = [ "config/*.json", "entity_schemas/*.yaml", - "preprocessing/regex/*.json", - "preprocessing/regex/*.tsv", ] [tool.uv] diff --git a/src/trialmatchai/config/config_loader.py b/src/trialmatchai/config/config_loader.py index 1a1472bb..af9ca99e 100644 --- a/src/trialmatchai/config/config_loader.py +++ b/src/trialmatchai/config/config_loader.py @@ -159,7 +159,6 @@ def _trialmatchai_resource_path(path: Path) -> Path | None: if not relative.parts or relative.parts[0] not in { "config", "entity_schemas", - "preprocessing", }: return None diff --git a/src/trialmatchai/matching/phenopacket_processor.py b/src/trialmatchai/matching/phenopacket_processor.py deleted file mode 100644 index aeff21be..00000000 --- a/src/trialmatchai/matching/phenopacket_processor.py +++ /dev/null @@ -1,395 +0,0 @@ -import json -from typing import Dict, List, Optional - -from trialmatchai.schemas.phenopacket import Phenopacket -from trialmatchai.utils.file_utils import read_json_file, write_json_file -from trialmatchai.utils.json_utils import extract_json_object -from trialmatchai.utils.logging_config import setup_logging -from trialmatchai.utils.temporal_utils import parse_iso_duration, parse_temporal - -logger = setup_logging(__name__) - -try: - import torch -except ImportError: # pragma: no cover - exercised by lean package imports - torch = None # type: ignore[assignment] - - -def _require_llm_dependencies(): - if torch is None: - raise RuntimeError( - "ClinicalSummarizer requires PyTorch. Install the ML extras with " - "`uv sync --extra llm` or `pip install 'trialmatchai[llm]'`." - ) - try: - from transformers import AutoModelForCausalLM, AutoTokenizer - except ImportError as exc: - raise RuntimeError( - "ClinicalSummarizer requires Transformers. Install the ML extras with " - "`uv sync --extra llm` or `pip install 'trialmatchai[llm]'`." - ) from exc - return torch, AutoModelForCausalLM, AutoTokenizer - - -class PhenopacketProcessor: - def __init__(self, file_path: str): - self.phenopacket = self._load_and_validate(file_path) - self.medical_sentences: List[str] = [] - self.ontology_cache: Dict[str, str] = {} - - def _load_and_validate(self, file_path: str) -> Dict: - data = read_json_file(file_path) - try: - Phenopacket.model_validate(data) - logger.info("Phenopacket loaded and validated successfully.") - except Exception as exc: - raise ValueError(f"Invalid Phenopacket: {exc}") from exc - return data - - def _add_medical_sentence(self, category: str, content: str): - self.medical_sentences.append(f"{category.upper()}: {content}") - - def _get_ontology_label(self, term_id: str) -> str: - if term_id in self.ontology_cache: - return self.ontology_cache[term_id] - for resource in self.phenopacket.get("metaData", {}).get("resources", []): - if term_id.startswith(resource.get("namespacePrefix", "")): - label = term_id.split("/")[-1].replace("_", " ") - self.ontology_cache[term_id] = label - return label - return term_id - - def generate_medical_narrative(self) -> List[str]: - try: - self._extract_subject() - self._extract_phenotypic_features() - self._extract_diseases() - self._extract_biosamples() - self._extract_interpretations() - self._extract_medical_actions() - self._extract_measurements() - self._extract_family() - logger.info("Medical narrative generation completed successfully.") - return self.medical_sentences - except Exception as e: - logger.error(f"Narrative generation failed: {e}") - raise - - def _extract_subject(self): - subject = self.phenopacket.get("subject", {}) - demographics = [ - f"Sex: {subject.get('sex', 'Unknown')}", - f"DOB: {subject.get('dateOfBirth', 'Unknown')}", - ] - if "timeAtLastEncounter" in subject: - encounter_time = parse_temporal(subject.get("timeAtLastEncounter")) - demographics.append(f"Last Encounter: {encounter_time}") - if "taxonomy" in subject: - demographics.append( - f"Species: {subject['taxonomy'].get('label', 'Unknown')}" - ) - if subject.get("description"): - demographics.append(f"Description: {subject.get('description')}") - self._add_medical_sentence("DEMOGRAPHICS", "; ".join(demographics)) - - def _extract_phenotypic_features(self): - for pf in self.phenopacket.get("phenotypicFeatures", []): - feature = pf.get("type", {}) - label = self._get_ontology_label(feature.get("label", "Unknown")) - details = ["Absent" if pf.get("excluded", False) else "Present"] - if "severity" in pf: - severity = self._get_ontology_label( - pf["severity"].get("label", "Unknown") - ) - details.append(f"Severity: {severity}") - temporal = parse_temporal(pf.get("onset")) - if temporal: - details.append(f"Onset: {temporal}") - if "modifiers" in pf: - mods = [ - self._get_ontology_label(m.get("label", "Unknown")) - for m in pf["modifiers"] - ] - details.append(f"Modifiers: {', '.join(mods)}") - descs = [] - if pf.get("description"): - descs.append(pf.get("description")) - if feature.get("description"): - descs.append(feature.get("description")) - if descs: - details.append("Description: " + " ".join(descs)) - self._add_medical_sentence("PHENOTYPE", f"{label}: {'; '.join(details)}") - - def _extract_diseases(self): - for disease in self.phenopacket.get("diseases", []): - term = disease.get("term", {}) - label = self._get_ontology_label(term.get("label", "Unknown")) - details = [] - if "diseaseStage" in disease: - stages = [ - self._get_ontology_label(s.get("label", "Unknown")) - for s in disease["diseaseStage"] - ] - details.append(f"Stage: {', '.join(stages)}") - if "tnmFinding" in disease: - tnm = [ - self._get_ontology_label(t.get("label", "Unknown")) - for t in disease["tnmFinding"] - ] - details.append(f"TNM: {', '.join(tnm)}") - onset = parse_temporal(disease.get("onset")) - if onset: - details.append(f"Onset: {onset}") - if disease.get("description"): - details.append(f"Description: {disease.get('description')}") - self._add_medical_sentence("DIAGNOSIS", f"{label}: {'; '.join(details)}") - - def _extract_biosamples(self): - for sample in self.phenopacket.get("biosamples", []): - details = [ - f"Type: {self._get_ontology_label(sample.get('sampleType', {}).get('label', 'Unknown'))}", - f"Tissue: {self._get_ontology_label(sample.get('sampledTissue', {}).get('label', 'Unknown'))}", - f"Collection: {parse_temporal(sample.get('timeOfCollection'))}", - ] - if "histologicalDiagnosis" in sample: - hd = sample["histologicalDiagnosis"] - details.append( - f"Histology: {self._get_ontology_label(hd.get('label', 'Unknown'))}" - ) - if sample.get("description"): - details.append(f"Description: {sample.get('description')}") - self._add_medical_sentence("BIOSAMPLE", "; ".join(details)) - - def _extract_measurements(self): - for meas in self.phenopacket.get("measurements", []): - assay = self._get_ontology_label(meas.get("assay", {}).get("id", "Unknown")) - value_info = meas.get("value", {}) - unit_label = value_info.get("unit", {}).get("label", "") - value = f"{value_info.get('value', 'Unknown')} {unit_label}".strip() - content = f"{assay}: {value}" - if meas.get("description"): - content += f"; Description: {meas.get('description')}" - self._add_medical_sentence("MEASUREMENT", content) - - def _extract_medical_actions(self): - for action in self.phenopacket.get("medicalActions", []): - if "treatment" in action: - tx = action["treatment"] - details = [ - f"Agent: {self._get_ontology_label(tx.get('agent', {}).get('label', 'Unknown'))}", - f"Route: {tx.get('routeOfAdministration', {}).get('label', 'Unknown')}", - ] - if "doseIntervals" in tx and tx["doseIntervals"]: - dose_info = tx["doseIntervals"][0].get("quantity", {}) - dose_unit = dose_info.get("unit", {}).get("label", "") - details.append( - f"Dose: {dose_info.get('value', 'Unknown')} {dose_unit}".strip() - ) - if tx.get("description"): - details.append(f"Description: {tx.get('description')}") - self._add_medical_sentence("TREATMENT", "; ".join(details)) - if "procedure" in action: - proc = action["procedure"] - performed = proc.get("performed", "unknown date") - proc_details = ( - f"{proc.get('code', {}).get('label', 'Procedure')} on {performed}" - ) - if proc.get("description"): - proc_details += f"; Description: {proc.get('description')}" - self._add_medical_sentence("PROCEDURE", proc_details) - - def _extract_interpretations(self): - for interpret in self.phenopacket.get("interpretations", []): - if "diagnosis" not in interpret: - continue - dx = interpret["diagnosis"] - details = [ - f"Status: {dx.get('diagnosisStatus', {}).get('label', 'unknown')}" - ] - if dx.get("description"): - details.append(f"Description: {dx.get('description')}") - for gi in dx.get("genomicInterpretations", []): - if "variantInterpretation" in gi: - var = gi["variantInterpretation"].get("variationDescriptor", {}) - gene = var.get("geneContext", {}).get("symbol", "Unknown gene") - details.append(f"{gene} {var.get('label', 'variant')}") - if interpret.get("description"): - details.append(f"Note: {interpret.get('description')}") - self._add_medical_sentence("INTERPRETATION", "; ".join(details)) - - def _extract_family(self): - family = self.phenopacket.get("family") - if not family: - return - relatives = family.get("relatives", []) - for relative in relatives: - rel_id = relative.get("id", "Unknown") - sex = relative.get("sex", "Unknown") - vital_status = relative.get("vitalStatus", {}).get("status", "Unknown") - age_at_death = parse_iso_duration( - relative.get("vitalStatus", {}) - .get("ageAtDeath", {}) - .get("iso8601duration", "") - ) - rel_phens = [] - for pf in relative.get("phenotypicFeatures", []): - label = self._get_ontology_label( - pf.get("type", {}).get("label", "Unknown") - ) - onset = parse_temporal(pf.get("onset")) - rel_phens.append(f"{label} (onset: {onset})") - description = relative.get("description", "") - summary = f"Relative {rel_id} ({sex}) - Vital status: {vital_status}" - if age_at_death: - summary += f", Age at death: {age_at_death}" - if rel_phens: - summary += f"; Phenotypes: {', '.join(rel_phens)}" - if description: - summary += f"; Description: {description}" - self._add_medical_sentence("FAMILY_HISTORY", summary) - pedigree = family.get("pedigree", {}) - if pedigree.get("persons"): - summary = ( - f"Pedigree defined with {len(pedigree['persons'])} individuals." - ) - self._add_medical_sentence("FAMILY_PEDIGREE", summary) - - -class ClinicalSummarizer: - def __init__(self, model=None, tokenizer=None, model_name: Optional[str] = None): - if model is not None: - if tokenizer is None: - raise ValueError( - "A tokenizer must be provided if a model instance is given." - ) - self.model = model - self.tokenizer = tokenizer - elif model_name is not None: - torch_module, AutoModelForCausalLM, AutoTokenizer = _require_llm_dependencies() - self.tokenizer = AutoTokenizer.from_pretrained(model_name) - self.model = AutoModelForCausalLM.from_pretrained( - model_name, - torch_dtype=torch_module.float16, - device_map="auto", - ) - else: - raise ValueError( - "Must provide either a model instance with its tokenizer or a model_name." - ) - - self.model.eval() - - def generate_summary(self, sentences: List[str]) -> Dict: - torch_module = _require_llm_dependencies()[0] - SYSTEM_PROMPT = """ - You are a specialized medical assistant designed for precise and accurate clinical trial matching. - Analyze the patient's medical description carefully and extract clinically relevant information for trial eligibility assessment. - - 1. **Primary Condition**: - - Determine the primary medical conditions based on explicit patient information and overall clinical context. - - List up to 10 medically recognized synonyms, aliases, or closely related medical terms for the primary conditions. - - Include the identified primary conditions and their associated synonyms or related terms within the "main_conditions" list. - - 2. **Secondary Clinical Factors**: - - Provide up to 50 clinically significant additional factors, including comorbidities, concurrent medical conditions, molecular or genetic biomarkers, prior therapies, relevant medical history, and clinically notable patient characteristics explicitly mentioned in the patient description. - - Provide these factors in the "other_conditions" list. - - 3. **Expanded Clinical Descriptions**: - - Based solely on the original patient-provided data, generate semantically accurate and medically sound statements resembling real-life medical notes. - - **Crucial**: Expanded descriptions must strictly reflect explicit patient-reported information without introducing new or inferred medical details. - - Output: - Return a JSON object in the exact following structure without any additional commentary: - - { - "main_conditions": ["PrimaryCondition", "Synonym1", "Synonym2", "..."], - "other_conditions": ["AdditionalCondition1", "AdditionalCondition2", "..."], - "expanded_sentences": [ - "Expanded note for sentence 1...", - "Expanded note for sentence 2...", - "..." - ] - } - """ - messages = [ - {"role": "system", "content": SYSTEM_PROMPT.strip()}, - {"role": "user", "content": " ".join(sentences)}, - ] - - try: - prompt = self.tokenizer.apply_chat_template( - messages, - truncate=False, - add_generation_prompt=True, - return_tensors="pt", - ).to(self.model.device) - - with torch_module.no_grad(): - output_ids = self.model.generate( - prompt, - max_new_tokens=2048, - do_sample=False, - return_dict_in_generate=False, - pad_token_id=self.tokenizer.eos_token_id, - ) - - # Only decode the newly generated tokens - generated_text = self.tokenizer.decode( - output_ids[0][prompt.shape[-1] :], skip_special_tokens=True - ) - return self._extract_llm_output(generated_text) - - except Exception as e: - logger.error(f"LLM generation failed: {e}") - return { - "main_conditions": [], - "other_conditions": [], - "expanded_sentences": [], - "error": str(e), - } - - def _extract_llm_output(self, generated_text: str) -> Dict: - try: - result = extract_json_object(generated_text) - expected_keys = [ - "main_conditions", - "other_conditions", - "expanded_sentences", - ] - if not all(key in result for key in expected_keys): - raise ValueError("Missing required JSON keys in LLM output") - return result - except (json.JSONDecodeError, ValueError) as e: - logger.error(f"JSON extraction failed: {e}") - return { - "error": f"Invalid JSON format: {str(e)}", - "main_conditions": [], - "other_conditions": [], - "expanded_sentences": [], - } - - -def process_phenopacket( - input_file: str, - output_file: str, - model=None, - tokenizer=None, - model_name: str = "microsoft/phi-2", -) -> bool: - try: - processor = PhenopacketProcessor(input_file) - narrative = processor.generate_medical_narrative() - summarizer = ( - ClinicalSummarizer(model=model, tokenizer=tokenizer) - if model and tokenizer - else ClinicalSummarizer(model_name=model_name) - ) - summary = summarizer.generate_summary(narrative) - write_json_file(summary, output_file) - logger.info( - f"Successfully processed {input_file} and saved results to {output_file}" - ) - return True - except Exception as e: - logger.error(f"Processing failed: {e}") - return False diff --git a/src/trialmatchai/models/embedding/query_embedder.py b/src/trialmatchai/models/embedding/query_embedder.py deleted file mode 100644 index 66ca0bb6..00000000 --- a/src/trialmatchai/models/embedding/query_embedder.py +++ /dev/null @@ -1,24 +0,0 @@ -from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig - - -class QueryEmbedder(TextEmbedder): - def __init__( - self, - model_name: str = "ncbi/MedCPT-Query-Encoder", - max_length: int = 512, - use_gpu: bool = True, - use_fp16: bool = False, - batch_size: int = 32, - normalize: bool = True, - ): - super().__init__( - TextEmbedderConfig( - model_name=model_name, - pooling="cls", - max_length=max_length, - batch_size=batch_size, - use_gpu=use_gpu, - use_fp16=use_fp16, - normalize=normalize, - ) - ) diff --git a/src/trialmatchai/models/embedding/sentence_embedder.py b/src/trialmatchai/models/embedding/sentence_embedder.py deleted file mode 100644 index 2e81fe33..00000000 --- a/src/trialmatchai/models/embedding/sentence_embedder.py +++ /dev/null @@ -1,24 +0,0 @@ -from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig - - -class SecondLevelSentenceEmbedder(TextEmbedder): - def __init__( - self, - model_name: str = "BAAI/bge-m3", - use_gpu: bool = True, - use_fp16: bool = False, - max_length: int = 512, - batch_size: int = 32, - normalize: bool = True, - ): - super().__init__( - TextEmbedderConfig( - model_name=model_name, - pooling="mean", - max_length=max_length, - batch_size=batch_size, - use_gpu=use_gpu, - use_fp16=use_fp16, - normalize=normalize, - ) - ) diff --git a/src/trialmatchai/preprocessing/__init__.py b/src/trialmatchai/preprocessing/__init__.py deleted file mode 100644 index 9200581b..00000000 --- a/src/trialmatchai/preprocessing/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Preprocessing resources and helpers.""" diff --git a/src/trialmatchai/preprocessing/regex/__init__.py b/src/trialmatchai/preprocessing/regex/__init__.py deleted file mode 100644 index 32ce5226..00000000 --- a/src/trialmatchai/preprocessing/regex/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Regex preprocessing resource package.""" diff --git a/src/trialmatchai/preprocessing/regex/exception_regex_patterns.json b/src/trialmatchai/preprocessing/regex/exception_regex_patterns.json deleted file mode 100644 index bd2fe774..00000000 --- a/src/trialmatchai/preprocessing/regex/exception_regex_patterns.json +++ /dev/null @@ -1,105 +0,0 @@ -{ - "patterns": { - "pattern1": { - "regex": "^\\d+\\s$", - "comment": "Matches digits followed by a space." - }, - "pattern2": { - "regex": "^\\d+(\\.|\\-)\\d+$", - "comment": "Matches digits separated by a dot (.) or hyphen (-)." - }, - "pattern3": { - "regex": "^\\d+(\\.\\d+)?\\s?[×x]\\s?\\d+(\\.\\d+)?/?[A-Za-z/]+?$", - "comment": "Matches expressions like '3x 2.5mm' or '5.75x2.5mm/s'." - }, - "pattern4": { - "regex": "^\\d+/[A-Za-z]$", - "comment": "Matches fraction expressions like '2/A' or '5/m'." - }, - "pattern5": { - "regex": "^\\d+(\\.\\d+)?%$", - "comment": "Matches percentage values like '25%', '3.5%', etc." - }, - "pattern6": { - "regex": "^\\d+(\\.\\d+)?\\s*[°°°CFC]$", - "comment": "Matches temperature values like '25°C', '3.5°F', etc." - }, - "pattern7": { - "regex": "^\\d+[\\/\\^]\\d+[\\/\\^]\\w$", - "comment": "Matches expressions like '2/3/m' or '5^2/n'." - }, - "pattern8": { - "regex": "\\b\\d+\\b(?![.\\-+*/\\\\()\\[\\]{}])", - "comment": "Matches standalone whole numbers that are not part of larger expressions." - }, - "pattern9": { - "regex": "\\b\\d+-\\w+\\b", - "comment": "Matches expressions like '12-abc' or '5-xyz'." - }, - "pattern10": { - "regex": "\\b\\d+\\/\\d+\\b", - "comment": "Matches fraction expressions like '2/3' or '5/8'." - }, - "pattern11": { - "regex": "\\b\\d+[A-Za-z]+\\b", - "comment": "Matches expressions like '25kg' or '10m'." - }, - "pattern12": { - "regex": "e.g.", - "comment": "Matches 'e.g.' (for example)." - }, - "pattern13": { - "regex": "i.e.", - "comment": "Matches 'i.e.' (that is)." - }, - "pattern14": { - "regex": "\\b\\d+\\.\\d+[A-Za-z]\\b", - "comment": "Matches expressions like '3.14pi' or '2.75x'." - }, - "pattern15": { - "regex": "\\b\\d+\\/\\w+\\b", - "comment": "Matches patterns like '≥ 10/μL', '< 9/cl', '+ 12/hgmm', etc." - }, - "pattern16": { - "regex": "\\b\\d+\\.\\d+[)]?", - "comment": "Matches patterns like '2.0)', '3.0)', '15.0)', '16.1-', '2.1)', '9.6/'." - }, - - "pattern17": { - "regex": "(?(A|C|T|G)))|((p\\.)?[A-Z][1-9][0-9]{0,3}([A-Z]|((_|-)[A-Z]?[1-9][0-9]{0,3}(delins|del|dup|ins)*[A-Z]{0,}))?)|(rs[0-9]{3,})|((((p\\.)?((NON|non|Non)(-| )?)?((Ala|Asx|Cys|Asp|Glu|Phe|Gly|His|Ile|Lys|Leu|Met|Asn|Pro|Gln|Arg|Ser|Thr|Sec|Val|Trp|Xaa|Tyr|Ter|Glx)|[A-Z])[1-9][0-9]{1,3}(del|dup|ins|=|(Ala|Asx|Cys|Asp|Glu|Phe|Gly|His|Ile|Lys|Leu|Met|Ter|Asn|Pro|Gln|Arg|Ser|Thr|Sec|Val|Trp|Xaa|Tyr|Glx)|[A-Z]){0,})(fs(\\*[0-9]{1,})?)?)))((-|/)[A-Z]([0-9]{1,3}([A-Z])?)?)?( |/|$)) -chromosome arm chromosome arm ((chromosome ([0-9]{1,2}|X|Y)(q|p)?([1-9]{1,2})?(\\.[0-9]{1,})?)|((chromosome)?( ([1-9]{1}|(1[0-9]{1})|(2([0-2]){1})|X|Y)(q|p)([1-9]{1}[0-9]{0,1})? ))) -aberration aberration ((((cytogenetic(al)?)|(chromosom(e|al)|genetic) )?(abnormalit(ies|y)|alteration(s)?|variant(s)?|aberrant|aberration(s)?))( of)?) -demethylation demethylation demethylat(ed|es)|demethylation|hypomethylation|decreased methylation -expression expression (((E|e)xpression( (S|s)tatus)?( of)?)|(((mRNA|RNA|(G|g)ene)? (E|e)xpress(ed|es|ion|ing))( status)?( of)?)|(m?RNA levels( of)?)|((((G|g)ene )?(E|e)xpression) level(s)?( of)?)) -frameshift frameshift (frame(-| )?shift(s)?( ((alteration|variant|mutation)(s)?))?( of )?) -inframe deletion inframe deletion (in(-)?frame (deletion|insertion)(s)?( of)?) -fusion fusion ((((G|g)ene )?(F|f)usion(s)?( (G|g)ene)?)|(NTRK( (G|g)ene)?(( |-)((T|t)ranslocation(s)?|(F|f)usion(s)?)))) -exon exon (((E|e)xon)(s)?)( |-)[0-9]{1,}(( |-|( (or|and) ))[0-9]{1,})* -gain of function gain of function (((gain(s)?(-| )of(-| )function)( (alteration|mutation|variant)s?)( of)?)|(( activating(( (gene|point))? (alteration|mutation|variant)s?)?)( of )?)) -cna cna ((gene )?copy (number|aberration|alteration)( of )?) -amplification amplification (- amplified|amplification|((gene )?amplifi(ed|cation)( of )?)|((chromosom(e|al) )?amplification(s)?( of )?)|((gene )?copy( number)? (gain(ed|s)?( of )?))|(increase(d|s)?)|(extra copy( of )?)|cn gain) -deletion deletion -deleted|- deleted| del([0-9]{1,})? |gene deleted|gene-deleted|gene deletion( of )?|((chromosom(e|al) )?deletion(s)?( of )?)|(copy los(t|s|ses)(of )?)|([0-9]{1,}(p|q)(-| )del(etion))|copy number decrease(d)?|copy decrease|deleted|((homozygous|heterozygous)(ly)?( (deletion|eleted|loss|lost))( of)?)|cn loss -germline amplification germline amplification germline amplification|germ-line amplification -germline deletion germline deletion (inherited )?germ(-)?line deletion|(delet(ed|ion)|loss|lost) in germline -germline loh germline loh (germ(-)?line (loh|(loss(-| )of(-| )heterozygosity))) -germline mutation germline mutation ((germ(-)?line( or somatic)?( mutation(s)?( (in |of |(status (of )?))?)))|((inherited )?germ(-)?line (variant(s)?|snv|alteration(s)?|mutation(s)?))) -inactivation inactivation inactivat(ed|ion)|((gene|mutation(al)?) (inactivat(ion|ed)))|(inactivating (deletion(s)?|insertion(s)?|mutation(s)?)) -indel indel indel|indels|insertion or deletion -insertion insertion insertion(s)? -inhibitor inhibitor inhibitor(s)?|inhibition|inhibited|inhibit(s)? -loss loss (loss( of)?( (both|either))?) -knockdown knockdown knockdown -loss of function loss of function ((loss(-| )of(-| )function( (mutation|variant|substitution)(s)?))|(deactivating( point)? (alteration|variant|substitution|mutation)(s)?)) -loss of heterozygosity loss of heterozygosity loss of heterozygosity|loss-of-heterozygosity|LOH -methylation methylation ((un|non-)?methylat(ed|es))|methylation|hypermethylation|increased methylation -mutation mutation (((missense|nonsense|(nonsynonymous( coding)?)|(protein(-| )coding)|point|coding|actionable|hotspot|acquired|de(-| )novo|driver|molecular(ly)?|(somatic( coding)?))( ((mutation|(alter(ed|ation))|variant|snv|(nucleotide )?substitution)(s)?)))|((mutation|variant|alteration)(s)? (of|in|within) the)|(gene (substitution(s)?|mutated|alteration(s)?|mutation(s)?))|( (-)?mutated)|(-mut )|(((gene|known) )?mutation(s)?((-| )(positive|negative|in |testing|((wild(-| )?type or muta(nt|ed))|(status( of )?)|of ))?))|((pathway )?muta(ted|nt)( or ((wt|(wild(-| )?type))))?)) -phosphorylation phosphorylation ((un|non-)?phosphorylat(ed|ion|es)) -promoter demethylation promoter demethylation ((promoter (de|hypo|non|un)(-)?methylat(ed|ion))|((de|hypo|un|non)(-)?methylat(ed|ion)( of( the)?)? promoter)) -promoter methylation promoter methylation ((promoter (hyper)?methylat(ed|ion))|((hyper)?methylation of( the)? promoter)) -promoter mutation promoter mutation (promoter (alteration|variant|mutation)s?) -protein expression protein expression ((((p|P)rotein )(E|e)xpression( of )?)|((P|p)rotein (L|l)evel(s)?( of )?)|((I|i)mmunohistochemi(cal|stry)( ((S|s)taining|(S|s)tatus|(E|e)xpression|(A|a)ssay))?( of)?)|(IHC( |-)(((P|p)ositive)|((N|n)egative)))|(IHC( )?[0-9]\\+)) -protein overexpression protein overexpression protein over(-)?express(ed|ion|ing|es) -protein underexpression protein underexpression protein under(-)?express(ed|ion|ing|es) -overexpression overexpression ((((mrna|rna) )?(non-)?over(-| )?express(ed|ion|ing|es)( of )?)|(transcriptional up(-)?regulation)|(elevated level(s)? of )|(up(-)?regulat(es|ion|ed))|((increased|increase in |up(-)?regulated|prominent(ly)?|high(ly|er)?) express(ed|ion))) -underexpression underexpression ((((mrna|rna) )?under(-| )?express(ed|ion|es|ing)( of )?)|(ihc loss)|silenced|(transcriptional down(-)?regulation( of )?)|(down(-)?regulat(es|ion|ed)( of )?)|(loss of expression( of )?)|((decreased|low(er)?|no|zero|down(-)?regulated) expression)|not expressed|(no expresssion( of )?)) -single nucleotide polymorphism single nucleotide polymorphism single nucleotide polymorphism|snp -splice variant splice variant ((splice ((mutation|variant)(s)?))|(splice site (alteration|mutation|substitution|variant)(s)?)) -structural variant structural variant (structural variant(s)?|((cytogenetic|gene|chromosom(e|al) )?rearrangement(s)?)|(large( |-)?scale (gene )?rearrangement(s))) -synonymous mutation synonymous mutation ((synonymous|silent)( coding)?( (mutation(s)?|substitution(s)?))) -switch of function switch of function switch of function -translocation translocation (gene )?translocat(ion|ing)(s)? -truncation truncation (truncat(ed|ing|ion(s)?)( mutation(s)?)?)|truncated form -wildtype wildtype ((N|n)on-(A|a)mplified)|((N|n)onamplified)|((N|n)on(-| )?(D|d)eleted)|((W|w)ild(-| )?(T|t)ype)|((U|u)nmutated)|((N|n)on(-)?(M|m)utated)|( WT )|-(WT|wt)|(WILD( |-)?TYPE) -codon codon ((amino acid position [0-9]{1,})|( codon(s)? [0-9]{1,}((/[0-9]{1,})|(( or | and | et | |-)[0-9]{1,}))*)) -codon mutation codon mutation (mutation(s)? (in|of) codon(s)? [0-9]{1,}((/[0-9]{1,})|(( or | and | et | |-)[0-9]{1,}))*) -exon mutation exon mutation ((((met|c(-)?met) )?(exon(s)? [0-9]{1,} skipping))|(metex14)|(exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})* ((splice|skipping) )?(substitution|snp|variant|mutation|alteration)(s)?)|((splice )?(mutation|variant|substitution|snp|alteration)(s)?( (detected|occur(red)?))? (in|of)( [A-Z0-9]{3,8})? exon(s)? [0-9]{1,2}(( (or|and|to|et) |-| |/)[0-9]{1,2})*)) -exon deletion exon deletion ((ex[1-9][0-9]{0,}del)|(exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})* del(etion(s)?)?)|(deletion(s)?( (detected|occur(red)?|found))? (in|of)( [A-Z0-9]{3,8})? exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})*)|(exon del-[0-9]{1,2})|(exon [0-9]{1,}-del)) -exon insertion exon insertion ((ex[1-9][0-9]{0,}ins)|(exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})* insertion(s)?)|(insertion(s)?( (detected|occur(red)?|found))? (in|of) exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})*)|(exon ins-[0-9]{1,2})|(exon [0-9]{1,2}-ins)) -deficiency deficiency deficienc(y|ies)|deficancy -rearrangement rearrangement re(arrangement(s)?|arranged|arranging)|gen(e|etic|omic)? rearrangement(s)?|chromosomal rearrangement(s)? -mutation mutation mutation(s)?|mutated|mutant|mutate|mutating|mutational|mutate|mutating|mutate|mutating diff --git a/src/trialmatchai/utils/evaluation.py b/src/trialmatchai/utils/evaluation.py deleted file mode 100644 index aa48cd7a..00000000 --- a/src/trialmatchai/utils/evaluation.py +++ /dev/null @@ -1,322 +0,0 @@ -# trialmatchai/utils/evaluation.py -from __future__ import annotations - -import csv -import json -import math -import os -import re -from typing import Any, Dict, Iterable, List, Tuple, Union - -# Strict NCT id pattern (case-insensitive on input; stored uppercase) -_NCT_RE = re.compile(r"^NCT\d{8}$", re.IGNORECASE) - - -def load_ground_truth(trec_csv_path: str) -> Dict[str, Dict[str, int]]: - """ - Load TREC ground-truth (query-id, corpus-id, score) from .tsv or .csv. - Returns: { query_id: { NCT########: int(score) } } - - Notes: - - Score semantics (TREC 2021 Clinical Trials): - 2 = eligible (relevant), 1 = excluded (ineligible), 0 = not relevant - - We uppercase trial ids for consistency. - - Header row is optional; delimiter auto-detected between tab/comma. - """ - gt: Dict[str, Dict[str, int]] = {} - if not os.path.exists(trec_csv_path): - raise FileNotFoundError(f"TREC ground truth file not found: {trec_csv_path}") - - with open(trec_csv_path, "r", encoding="utf-8") as fh: - sample = fh.read(4096) - fh.seek(0) - first_line = sample.splitlines()[0] if sample else "" - delimiter = "\t" if ("\t" in first_line) else "," - reader = csv.reader(fh, delimiter=delimiter) - - # Detect header - try: - first = next(reader) - except StopIteration: - return gt - - def _is_header(row: List[str]) -> bool: - if not row: - return False - low = [c.strip().lower() for c in row[:3]] - joined = "|".join(low) - return ( - ("query-id" in joined or "qid" in joined) - and ("corpus-id" in joined or "docid" in joined or "doc-id" in joined) - and ("score" in joined or "label" in joined or "relevance" in joined) - ) - - if not _is_header(first): - fh.seek(0) - reader = csv.reader(fh, delimiter=delimiter) - - for row in reader: - if not row or len(row) < 3: - continue - qid = row[0].strip() - nid = row[1].strip().upper() - # Only store proper NCT ids - if not _NCT_RE.match(nid): - continue - try: - score = int(float(row[2])) - except Exception: - continue - gt.setdefault(qid, {})[nid] = score - return gt - - -def _dcg(rels: Iterable[int], gain_scheme: str = "linear") -> float: - """ - DCG with log2 discount. Two gain schemes: - - 'linear': gain = rel (default; typical for TREC ndcg) - - 'exp2' : gain = 2^rel - 1 - """ - dcg = 0.0 - for i, rel in enumerate(rels, start=1): - if gain_scheme == "exp2": - gain = (2**rel) - 1.0 - else: - gain = float(rel) - dcg += gain / math.log2(i + 1) - return dcg - - -def _idcg(ground_rels: List[int], k: int, gain_scheme: str = "linear") -> float: - ideal = sorted(ground_rels, reverse=True)[:k] - return _dcg(ideal, gain_scheme=gain_scheme) - - -def ndcg_at_k( - pred_ids: List[str], - ground_truth: Dict[str, int], - k: int, - gain_scheme: str = "linear", -) -> float: - """ - nDCG@K using graded labels (2 eligible, 1 excluded, 0 not relevant). - Assumes pred_ids already filtered to labelled trials (see evaluate_ranking). - """ - if k <= 0: - return 0.0 - pred_rels = [int(ground_truth.get(nid, 0)) for nid in pred_ids[:k]] - dcg = _dcg(pred_rels, gain_scheme=gain_scheme) - ideal_rels = list(ground_truth.values()) - if not ideal_rels: - return 0.0 - idcg = _idcg(ideal_rels, k, gain_scheme=gain_scheme) - return dcg / idcg if idcg > 0 else 0.0 - - -def precision_at_k( - pred_ids: List[str], - ground_truth: Dict[str, int], - k: int, - eligible_threshold: int = 2, -) -> float: - """ - Precision@K for TREC CT: - - Count ONLY 'eligible' trials as relevant (label >= eligible_threshold, default 2). - - Unlabelled trials are assumed removed by the caller (we don't count them). - - Denominator is the number of labelled predictions among the first K after filtering. - """ - if k <= 0: - return 0.0 - topk = pred_ids[:k] - if not topk: - return 0.0 - rels = [ - 1 if int(ground_truth.get(nid, 0)) >= eligible_threshold else 0 for nid in topk - ] - return sum(rels) / float(len(topk)) - - -def _get_case_insensitive(d: Dict[str, Any], *keys: str) -> Any: - """Fetch any matching key (case-insensitive) from dict.""" - lower_map = {k.lower(): k for k in d.keys()} - for k in keys: - real = lower_map.get(k.lower()) - if real is not None: - return d[real] - return None - - -def _extract_top_list(container: Any) -> Any: - """ - If 'container' is a dict that wraps the ranked list, extract the list. - Accepts keys: RankedTrials, ranked_trials, ranked, trials (any case). - Otherwise return container as is. - """ - if isinstance(container, dict): - for k in ("RankedTrials", "ranked_trials", "ranked", "trials"): - v = _get_case_insensitive(container, k) - if isinstance(v, list): - return v - return container - - -def _normalize_ranked_input(ranked: Any) -> List[str]: - """ - Accept common ranked formats and return a clean list of NCT IDs (uppercase), preserving order. - - list[str] - - list[dict] with id keys: TrialID, trial_id, nct_id, id, nctId (any case) - - list[tuple/list] like (id, score) - - dict with top-level list under RankedTrials / ranked_trials / ranked / trials - Filters out non-NCT-shaped IDs and normalizes to uppercase. - """ - ranked = _extract_top_list(ranked) - - ids: List[str] = [] - - if not ranked: - return ids - - # list[str] - if isinstance(ranked, list) and ranked and isinstance(ranked[0], str): - ids = [s for s in ranked] - - # list[tuple/list] - elif isinstance(ranked, list) and ranked and isinstance(ranked[0], (list, tuple)): - ids = [str(item[0]) for item in ranked if item] - - # list[dict] - elif isinstance(ranked, list) and ranked and isinstance(ranked[0], dict): - for r in ranked: - nid = _get_case_insensitive( - r, "TrialID", "trial_id", "nct_id", "id", "nctId" - ) - if nid is None: - continue - ids.append(str(nid)) - - # fallback - else: - try: - ids = [str(x) for x in ranked] # type: ignore - except Exception: - ids = [] - - # Normalize + keep only proper NCT IDs - cleaned: List[str] = [] - for x in ids: - x_u = x.strip().upper() - if _NCT_RE.match(x_u): - cleaned.append(x_u) - return cleaned - - -def evaluate_ranking( - predicted_ranked: Any, - ground_truth_for_query: Dict[str, int], - ks: Tuple[int, ...] = (5, 10, 20), - gain_scheme: str = "linear", -) -> Dict[str, float]: - """ - Compute metrics for a single patient/query: - - nDCG@K with graded labels (0/1/2) - - Precision@K with 'eligible-only' relevance (label >= 2) - - IMPORTANT: Unlabelled trials are REMOVED before computing metrics, per your requirement. - """ - pred_ids_all = _normalize_ranked_input(predicted_ranked) - - # Keep only labelled trials (ignore unjudged) - pred_ids = ( - [pid for pid in pred_ids_all if pid in ground_truth_for_query] - if ground_truth_for_query - else [] - ) - - results: Dict[str, float] = {} - for k in ks: - results[f"ndcg@{k}"] = ndcg_at_k( - pred_ids, ground_truth_for_query, k, gain_scheme=gain_scheme - ) - results[f"precision@{k}"] = precision_at_k( - pred_ids, ground_truth_for_query, k, eligible_threshold=2 - ) - return results - - -def evaluate_and_save_metrics( - ranked_trials: Any, - patient_qid: str, - ground_truth_source: Union[str, Dict[str, Dict[str, int]]], - output_folder: str, - ks: Tuple[int, ...] = (5, 10, 20), - gain_scheme: str = "linear", -) -> Dict[str, float]: - """ - Evaluate 'ranked_trials' for a patient using either: - - ground_truth_source: str path to TREC .tsv/.csv, OR - - a preloaded dict { qid: { nct_id: score } } - - Saves JSON to {output_folder}/evaluation_metrics.json - """ - # Load ground truth if a file path is provided - if isinstance(ground_truth_source, str): - ground_truth_map = load_ground_truth(ground_truth_source) - else: - ground_truth_map = ground_truth_source - - gt = ground_truth_map.get(patient_qid) - if gt is None: - metrics: Dict[str, float] = {"error": "no_ground_truth_for_patient"} # type: ignore[assignment] - else: - metrics = evaluate_ranking(ranked_trials, gt, ks=ks, gain_scheme=gain_scheme) # type: ignore[assignment] - metrics["patient_id"] = patient_qid # type: ignore[index] - - os.makedirs(output_folder, exist_ok=True) - out_path = os.path.join(output_folder, "evaluation_metrics.json") - with open(out_path, "w", encoding="utf-8") as fh: - json.dump(metrics, fh, indent=2) - return metrics - - -if __name__ == "__main__": - import argparse - - ap = argparse.ArgumentParser("Evaluate ranked trials against TREC ground truth") - ap.add_argument("--ranked", required=True, help="Path to ranked_trials.json") - ap.add_argument( - "--ground-truth", - required=True, - help="Path to TREC TSV/CSV (query-id, corpus-id, score)", - ) - ap.add_argument( - "--patient", required=True, help="Query-id in the ground truth to evaluate" - ) - ap.add_argument( - "--out-dir", default=None, help="Output dir (default: folder of ranked)" - ) - ap.add_argument( - "--gain", - default="linear", - choices=["linear", "exp2"], - help="DCG gain scheme (default: linear)", - ) - args = ap.parse_args() - - # Lazy import to avoid package cycles - try: - from trialmatchai.utils.file_utils import read_json_file # type: ignore - except Exception: - # Fallback path if run standalone - from trialmatchai.utils.file_utils import read_json_file # type: ignore - - ranked = read_json_file(args.ranked) - out_dir = args.out_dir or os.path.dirname(args.ranked) - res = evaluate_and_save_metrics( - ranked_trials=ranked, - patient_qid=args.patient, - ground_truth_source=args.ground_truth, - output_folder=out_dir, - ks=(5, 10, 20), - gain_scheme=args.gain, - ) - print(json.dumps(res, indent=2)) diff --git a/tests/test_phenopacket_processor_pytest.py b/tests/test_phenopacket_processor_pytest.py deleted file mode 100644 index 6c42fb50..00000000 --- a/tests/test_phenopacket_processor_pytest.py +++ /dev/null @@ -1,13 +0,0 @@ -import json - -from trialmatchai.matching.phenopacket_processor import PhenopacketProcessor - - -def test_phenopacket_processor_minimal(tmp_path): - data = {"id": "patient-1", "metaData": {}, "subject": {}} - path = tmp_path / "patient.json" - path.write_text(json.dumps(data)) - - processor = PhenopacketProcessor(str(path)) - narrative = processor.generate_medical_narrative() - assert narrative From a33091d07d070f34f26b934d4ac761b40fcc82ab Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:00:58 +0200 Subject: [PATCH 14/64] PR4: remove dead symbols, params, and a legacy shim MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Verified-safe in-file dead code (re-grepped each before removal): - recognizers.with_schema_threshold (+ now-unused dataclasses.replace import) - types.EntityAnnotation.to_index_entity - utils/retry.py (with_retries) + its test — only the test used it; production retries go through tenacity - interop EvidenceSpan model, Provenance.raw_text_span field, and the PatientProfile.all_facts helper — none populated, read, or referenced (Provenance keeps extra="allow", so dropping the field is safe) - narrative.render_patient_narrative: dead style="audit" branch and the unused style parameter (only caller passed "rag") - annotator.annotate_texts_in_parallel: dead retries/delay params (accepted then immediately del'd; no caller supplies them) - criteria_retrieval.rerank_criteria: unused `queries` parameter (body keys off criterion["query"]); updated the call site Legacy shim removed: - CompatibilityEntityAnnotator was an empty SchemaEntityAnnotator subclass; build_entity_annotator now returns SchemaEntityAnnotator directly, and the export/test were repointed. Deferred: max_text_score -> PR6 (folded into the create_query rework); dead config settings (cot/LLM_reranker/TokenizerSettings/entity_extraction.threshold) -> PR9, since they are entangled with config.json + the env-override map and carry config-validation risk that does not belong in a deletion PR. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/entities/__init__.py | 2 - src/trialmatchai/entities/annotator.py | 11 +----- src/trialmatchai/entities/recognizers.py | 11 ------ src/trialmatchai/entities/types.py | 6 --- src/trialmatchai/interop/__init__.py | 2 - .../interop/exporters/matching_summary.py | 2 +- src/trialmatchai/interop/models.py | 28 -------------- src/trialmatchai/interop/narrative.py | 11 +----- .../matching/retrieval/criteria_retrieval.py | 4 +- src/trialmatchai/utils/retry.py | 37 ------------------- tests/test_entities.py | 4 +- tests/test_retry_pytest.py | 13 ------- 12 files changed, 9 insertions(+), 122 deletions(-) delete mode 100644 src/trialmatchai/utils/retry.py delete mode 100644 tests/test_retry_pytest.py diff --git a/src/trialmatchai/entities/__init__.py b/src/trialmatchai/entities/__init__.py index 25109e55..03525712 100644 --- a/src/trialmatchai/entities/__init__.py +++ b/src/trialmatchai/entities/__init__.py @@ -1,12 +1,10 @@ from trialmatchai.entities.annotator import ( - CompatibilityEntityAnnotator, SchemaEntityAnnotator, build_entity_annotator, ) from trialmatchai.entities.types import ConceptCandidate, EntityAnnotation, EntitySchema __all__ = [ - "CompatibilityEntityAnnotator", "ConceptCandidate", "EntityAnnotation", "EntitySchema", diff --git a/src/trialmatchai/entities/annotator.py b/src/trialmatchai/entities/annotator.py index 59649e7f..ea3b69b1 100644 --- a/src/trialmatchai/entities/annotator.py +++ b/src/trialmatchai/entities/annotator.py @@ -38,10 +38,7 @@ def annotate_texts_in_parallel( self, texts: Sequence[str], max_workers: int = 20, - retries: int = 1, - delay: float = 0, ) -> list[list[dict[str, Any]]]: - del retries, delay if max_workers <= 1 or len(texts) <= 1: return [ [annotation.to_dict() for annotation in annotations] @@ -65,15 +62,11 @@ def annotate_texts_in_parallel( return results -class CompatibilityEntityAnnotator(SchemaEntityAnnotator): - """Compatibility annotator exposing the historical batch annotation shape.""" - - def build_entity_annotator( config: dict[str, Any], *, embedder: Any | None = None, -) -> CompatibilityEntityAnnotator: +) -> SchemaEntityAnnotator: extraction_cfg = dict(config.get("entity_extraction") or {}) linker_cfg = dict(config.get("concept_linker") or {}) schema_path = extraction_cfg.get("schema_path") @@ -91,7 +84,7 @@ def build_entity_annotator( search_limit=int(linker_cfg.get("search_limit", 10)), ) - return CompatibilityEntityAnnotator(recognizer, schemas, linker=linker) + return SchemaEntityAnnotator(recognizer, schemas, linker=linker) def _build_concept_store( diff --git a/src/trialmatchai/entities/recognizers.py b/src/trialmatchai/entities/recognizers.py index d5c05609..e064a8b0 100644 --- a/src/trialmatchai/entities/recognizers.py +++ b/src/trialmatchai/entities/recognizers.py @@ -1,7 +1,6 @@ from __future__ import annotations import re -from dataclasses import replace from typing import Any, Protocol, Sequence from trialmatchai.entities.schemas import schema_by_label @@ -266,13 +265,3 @@ def _as_int(value: Any) -> int | None: return int(value) except (TypeError, ValueError): return None - - -def with_schema_threshold( - annotation: EntityAnnotation, - schemas: Sequence[EntitySchema], -) -> EntityAnnotation | None: - schema = next((item for item in schemas if item.id == annotation.schema_id), None) - if schema is not None and annotation.score < schema.threshold: - return None - return replace(annotation) diff --git a/src/trialmatchai/entities/types.py b/src/trialmatchai/entities/types.py index 970647ab..72a6e87c 100644 --- a/src/trialmatchai/entities/types.py +++ b/src/trialmatchai/entities/types.py @@ -94,12 +94,6 @@ def to_dict(self) -> dict[str, Any]: "linker_status": self.linker_status, } - def to_index_entity(self) -> dict[str, Any]: - data = self.to_dict() - data["entity"] = self.text - data["class"] = self.entity_group - return data - def normalize_concept_id(vocabulary_id: str, concept_code: str) -> str: vocab = vocabulary_id.strip() diff --git a/src/trialmatchai/interop/__init__.py b/src/trialmatchai/interop/__init__.py index a7379cbe..1919662c 100644 --- a/src/trialmatchai/interop/__init__.py +++ b/src/trialmatchai/interop/__init__.py @@ -3,7 +3,6 @@ from trialmatchai.interop.models import ( ClinicalFact, Demographics, - EvidenceSpan, NormalizedCode, PatientNote, PatientProfile, @@ -14,7 +13,6 @@ __all__ = [ "ClinicalFact", "Demographics", - "EvidenceSpan", "NormalizedCode", "PatientNote", "PatientProfile", diff --git a/src/trialmatchai/interop/exporters/matching_summary.py b/src/trialmatchai/interop/exporters/matching_summary.py index ac934a1b..4d8f91fc 100644 --- a/src/trialmatchai/interop/exporters/matching_summary.py +++ b/src/trialmatchai/interop/exporters/matching_summary.py @@ -12,7 +12,7 @@ def profile_to_matching_summary(profile: PatientProfile) -> dict: else "all" ) gender = profile.demographics.sex or profile.demographics.gender or "all" - expanded_sentences = render_patient_narrative(profile, style="rag") + expanded_sentences = render_patient_narrative(profile) return { "patient_id": profile.patient_id, "main_conditions": main_conditions, diff --git a/src/trialmatchai/interop/models.py b/src/trialmatchai/interop/models.py index 34e41699..7e8e567b 100644 --- a/src/trialmatchai/interop/models.py +++ b/src/trialmatchai/interop/models.py @@ -17,20 +17,6 @@ ] -class EvidenceSpan(BaseModel): - text: str | None = None - start: int | None = Field(default=None, ge=0) - end: int | None = Field(default=None, ge=0) - - @field_validator("end") - @classmethod - def validate_end(cls, value: int | None, info): - start = info.data.get("start") - if value is not None and start is not None and value < start: - raise ValueError("evidence span end must be >= start") - return value - - class Provenance(BaseModel): source_format: str source_id: str | None = None @@ -38,7 +24,6 @@ class Provenance(BaseModel): source_resource: str | None = None source_table: str | None = None source_field: str | None = None - raw_text_span: EvidenceSpan | None = None model_config = ConfigDict(extra="allow") @@ -134,19 +119,6 @@ class PatientProfile(BaseModel): model_config = ConfigDict(extra="allow") - def all_facts(self) -> list[ClinicalFact]: - return [ - *self.conditions, - *self.phenotypes, - *self.observations, - *self.medications, - *self.procedures, - *self.diagnostic_reports, - *self.genomic_findings, - *self.cancer_profile, - *self.family_history, - ] - def add_fact(self, fact: ClinicalFact) -> None: bucket = { "condition": self.conditions, diff --git a/src/trialmatchai/interop/narrative.py b/src/trialmatchai/interop/narrative.py index 388e68ed..cf762ed1 100644 --- a/src/trialmatchai/interop/narrative.py +++ b/src/trialmatchai/interop/narrative.py @@ -5,11 +5,7 @@ from trialmatchai.interop.models import ClinicalFact, PatientProfile -def render_patient_narrative( - profile: PatientProfile, - *, - style: str = "rag", -) -> list[str]: +def render_patient_narrative(profile: PatientProfile) -> list[str]: """Render structured profile facts into deterministic LLM-ready sentences.""" lines: list[str] = [] demographics = profile.demographics @@ -37,10 +33,7 @@ def render_patient_narrative( lines.extend(_render_fact_group("Cancer profile", profile.cancer_profile)) lines.extend(_render_fact_group("Family history", profile.family_history)) - if style == "audit": - for note in profile.notes: - lines.append(f"Source note {note.note_id}: {note.text}") - elif profile.notes: + if profile.notes: note_text = " ".join(note.text for note in profile.notes[:3]) if note_text: lines.append(f"Clinical note context: {note_text[:2000]}") diff --git a/src/trialmatchai/matching/retrieval/criteria_retrieval.py b/src/trialmatchai/matching/retrieval/criteria_retrieval.py index ece91552..356e701f 100644 --- a/src/trialmatchai/matching/retrieval/criteria_retrieval.py +++ b/src/trialmatchai/matching/retrieval/criteria_retrieval.py @@ -110,7 +110,7 @@ def execute_query(query): query_to_hits[query] = hits return query_to_hits - def rerank_criteria(self, queries: List[str], criteria: List[Dict]) -> List[Dict]: + def rerank_criteria(self, criteria: List[Dict]) -> List[Dict]: if self.llm_reranker is None: logger.warning("LLM reranker not available, using search scores only") return self.score_criteria_without_llm(criteria) @@ -210,7 +210,7 @@ def retrieve_and_rank( # Check if reranker is available before trying to use it if use_reranker and self.llm_reranker is not None: - ranked_criteria = self.rerank_criteria(queries, all_criteria) + ranked_criteria = self.rerank_criteria(all_criteria) else: if use_reranker and self.llm_reranker is None: logger.info( diff --git a/src/trialmatchai/utils/retry.py b/src/trialmatchai/utils/retry.py deleted file mode 100644 index c4429eef..00000000 --- a/src/trialmatchai/utils/retry.py +++ /dev/null @@ -1,37 +0,0 @@ -from __future__ import annotations - -import random -import time -from typing import Callable, Optional, Tuple, TypeVar - -T = TypeVar("T") - - -def with_retries( - fn: Callable[[], T], - *, - retries: int = 3, - base_delay: float = 0.5, - max_delay: float = 5.0, - jitter: float = 0.1, - exceptions: Tuple[type[BaseException], ...] = (Exception,), - logger: Optional[object] = None, - action: str = "operation", -) -> T: - last_exc: Optional[BaseException] = None - for attempt in range(1, retries + 1): - try: - return fn() - except exceptions as exc: # type: ignore[misc] - last_exc = exc - if logger is not None: - logger.warning( - "%s failed (attempt %s/%s): %s", action, attempt, retries, exc - ) - if attempt < retries: - delay = min(max_delay, base_delay * (2 ** (attempt - 1))) - delay += random.uniform(0, jitter) - time.sleep(delay) - if last_exc is not None: - raise last_exc - raise RuntimeError(f"{action} failed after {retries} attempts") diff --git a/tests/test_entities.py b/tests/test_entities.py index d2f7b80f..36b63ec1 100644 --- a/tests/test_entities.py +++ b/tests/test_entities.py @@ -3,7 +3,7 @@ import csv from pathlib import Path -from trialmatchai.entities.annotator import CompatibilityEntityAnnotator +from trialmatchai.entities.annotator import SchemaEntityAnnotator from trialmatchai.entities.builder import ( build_dictionary_rows, build_omop_concept_rows, @@ -29,7 +29,7 @@ def test_default_schema_validates_vocab_routing(): def test_regex_backend_returns_current_output_shape(): schemas = [schema for schema in load_entity_schemas() if schema.id == "disease"] - annotator = CompatibilityEntityAnnotator(RegexSchemaRecognizer(), schemas) + annotator = SchemaEntityAnnotator(RegexSchemaRecognizer(), schemas) result = annotator.annotate_texts_in_parallel(["metastatic cancer"], max_workers=1) diff --git a/tests/test_retry_pytest.py b/tests/test_retry_pytest.py deleted file mode 100644 index 58d93460..00000000 --- a/tests/test_retry_pytest.py +++ /dev/null @@ -1,13 +0,0 @@ -from trialmatchai.utils.retry import with_retries - - -def test_with_retries_succeeds_after_failures(): - calls = {"count": 0} - - def flaky(): - calls["count"] += 1 - if calls["count"] < 3: - raise RuntimeError("boom") - return "ok" - - assert with_retries(flaky, retries=3, base_delay=0.0, max_delay=0.0) == "ok" From ac0ded4eca2ce292f27578e533abf97ff682d718 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:07:39 +0200 Subject: [PATCH 15/64] PR5: fix OMOP child-table join (silent data loss) + remove N+1 The join compared each child table's raw person_id against the patient's *sanitized* profile id via astype(str). Two real failures: 1. a null person_id in a child table promotes the column to float64, so person_id 1 serialized as "1.0" and "1.0" != "1" dropped every condition/measurement/drug/procedure/observation/note for that patient; 2. a person_id needing sanitization ("pat 01" -> "pat-01") never matched the raw child value. Now join on the raw person_id via _normalize_join_id (handles float promotion and string/int mismatch) and pre-group each child table once with _group_by_person, so each patient is an O(1) lookup instead of a full astype scan per table (removes the O(P*R) N+1). _concept_lookup also switches off iterrows. Profile ids are still sanitized for display/provenance. Two regression tests (null-person_id float promotion; sanitized id) fail on the old code and pass now. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/interop/importers/omop.py | 115 ++++++++++++++------- tests/test_patient_interop.py | 63 +++++++++++ 2 files changed, 143 insertions(+), 35 deletions(-) diff --git a/src/trialmatchai/interop/importers/omop.py b/src/trialmatchai/interop/importers/omop.py index c8a45214..a026b64d 100644 --- a/src/trialmatchai/interop/importers/omop.py +++ b/src/trialmatchai/interop/importers/omop.py @@ -31,9 +31,27 @@ def import_omop_extract( raise ValueError(f"OMOP extract is missing PERSON table: {root}") return [] + # Group each child table by normalized person_id ONCE, so each patient is an + # O(1) lookup instead of a full-table scan (no N+1), and the join is robust + # to float promotion from null person_id and to id sanitization. + grouped = { + name: _group_by_person(tables.get(name)) + for name in ( + "condition_occurrence", + "measurement", + "drug_exposure", + "procedure_occurrence", + "observation", + "note", + "note_nlp", + ) + } + profiles: list[PatientProfile] = [] - for _, row in person.iterrows(): - patient_id = safe_patient_id(row.get("person_id"), "omop-patient") + for row in person.to_dict("records"): + raw_person_id = row.get("person_id") + person_key = _normalize_join_id(raw_person_id) + patient_id = safe_patient_id(raw_person_id, "omop-patient") provenance = Provenance( source_format="omop", source_id=patient_id, @@ -52,13 +70,13 @@ def import_omop_extract( ), provenance=[provenance], ) - _add_condition_rows(profile, tables.get("condition_occurrence"), concepts, root) - _add_measurement_rows(profile, tables.get("measurement"), concepts, root) - _add_drug_rows(profile, tables.get("drug_exposure"), concepts, root) - _add_procedure_rows(profile, tables.get("procedure_occurrence"), concepts, root) - _add_observation_rows(profile, tables.get("observation"), concepts, root) - _add_note_rows(profile, tables.get("note"), root) - _add_note_nlp_rows(profile, tables.get("note_nlp"), concepts, root) + _add_condition_rows(profile, grouped["condition_occurrence"].get(person_key, []), concepts, root) + _add_measurement_rows(profile, grouped["measurement"].get(person_key, []), concepts, root) + _add_drug_rows(profile, grouped["drug_exposure"].get(person_key, []), concepts, root) + _add_procedure_rows(profile, grouped["procedure_occurrence"].get(person_key, []), concepts, root) + _add_observation_rows(profile, grouped["observation"].get(person_key, []), concepts, root) + _add_note_rows(profile, grouped["note"].get(person_key, []), root) + _add_note_nlp_rows(profile, grouped["note_nlp"].get(person_key, []), concepts, root) profiles.append(profile) return profiles @@ -82,9 +100,9 @@ def _concept_lookup(table: pd.DataFrame | None) -> dict[Any, dict[str, Any]]: if table is None: return {} return { - row.get("concept_id"): row.to_dict() - for _, row in table.iterrows() - if row.get("concept_id") is not None + record.get("concept_id"): record + for record in table.to_dict("records") + if record.get("concept_id") is not None } @@ -101,11 +119,11 @@ def _person_birth_date(row) -> Any: def _add_condition_rows( profile: PatientProfile, - table: pd.DataFrame | None, + rows: list[dict[str, Any]], concepts: dict[Any, dict[str, Any]], root: Path, ) -> None: - for row in _rows_for_patient(table, profile.patient_id, "person_id"): + for row in rows: code = _omop_code(row.get("condition_concept_id"), concepts) label = _concept_label(row.get("condition_concept_id"), concepts) or clean_text( row.get("condition_source_value") @@ -124,11 +142,11 @@ def _add_condition_rows( def _add_measurement_rows( profile: PatientProfile, - table: pd.DataFrame | None, + rows: list[dict[str, Any]], concepts: dict[Any, dict[str, Any]], root: Path, ) -> None: - for row in _rows_for_patient(table, profile.patient_id, "person_id"): + for row in rows: code = _omop_code(row.get("measurement_concept_id"), concepts) label = _concept_label(row.get("measurement_concept_id"), concepts) or clean_text( row.get("measurement_source_value") @@ -152,11 +170,11 @@ def _add_measurement_rows( def _add_drug_rows( profile: PatientProfile, - table: pd.DataFrame | None, + rows: list[dict[str, Any]], concepts: dict[Any, dict[str, Any]], root: Path, ) -> None: - for row in _rows_for_patient(table, profile.patient_id, "person_id"): + for row in rows: code = _omop_code(row.get("drug_concept_id"), concepts) label = _concept_label(row.get("drug_concept_id"), concepts) or clean_text( row.get("drug_source_value") @@ -175,11 +193,11 @@ def _add_drug_rows( def _add_procedure_rows( profile: PatientProfile, - table: pd.DataFrame | None, + rows: list[dict[str, Any]], concepts: dict[Any, dict[str, Any]], root: Path, ) -> None: - for row in _rows_for_patient(table, profile.patient_id, "person_id"): + for row in rows: code = _omop_code(row.get("procedure_concept_id"), concepts) label = _concept_label(row.get("procedure_concept_id"), concepts) or clean_text( row.get("procedure_source_value") @@ -198,11 +216,11 @@ def _add_procedure_rows( def _add_observation_rows( profile: PatientProfile, - table: pd.DataFrame | None, + rows: list[dict[str, Any]], concepts: dict[Any, dict[str, Any]], root: Path, ) -> None: - for row in _rows_for_patient(table, profile.patient_id, "person_id"): + for row in rows: code = _omop_code(row.get("observation_concept_id"), concepts) label = _concept_label(row.get("observation_concept_id"), concepts) or clean_text( row.get("observation_source_value") @@ -221,10 +239,10 @@ def _add_observation_rows( def _add_note_rows( profile: PatientProfile, - table: pd.DataFrame | None, + rows: list[dict[str, Any]], root: Path, ) -> None: - for row in _rows_for_patient(table, profile.patient_id, "person_id"): + for row in rows: text = clean_text(row.get("note_text")) if not text: continue @@ -241,11 +259,11 @@ def _add_note_rows( def _add_note_nlp_rows( profile: PatientProfile, - table: pd.DataFrame | None, + rows: list[dict[str, Any]], concepts: dict[Any, dict[str, Any]], root: Path, ) -> None: - for row in _rows_for_patient(table, profile.patient_id, "person_id"): + for row in rows: code = _omop_code(row.get("note_nlp_concept_id"), concepts) label = _concept_label(row.get("note_nlp_concept_id"), concepts) or clean_text( row.get("lexical_variant") or row.get("snippet") @@ -268,15 +286,42 @@ def _add_note_nlp_rows( ) -def _rows_for_patient( - table: pd.DataFrame | None, - patient_id: str, - column: str, -) -> list[dict[str, Any]]: - if table is None or table.empty or column not in table.columns: - return [] - patient_rows = table[table[column].astype(str) == str(patient_id)] - return [row.to_dict() for _, row in patient_rows.iterrows()] +def _normalize_join_id(value: Any) -> str: + """Canonicalize a person_id for joining across tables. + + Robust to float promotion (a null in the column makes person_id 1 -> 1.0) + and to the raw-vs-string mismatch, so PERSON and child tables join on the + same key regardless of dtype. + """ + if value is None: + return "" + if isinstance(value, float): + if pd.isna(value): + return "" + if value.is_integer(): + return str(int(value)) + return str(value) + try: + if pd.isna(value): + return "" + except (TypeError, ValueError): + pass + text = str(value).strip() + if text.endswith(".0") and text[:-2].isdigit(): + return text[:-2] + return text + + +def _group_by_person(table: pd.DataFrame | None) -> dict[str, list[dict[str, Any]]]: + if table is None or table.empty or "person_id" not in table.columns: + return {} + grouped: dict[str, list[dict[str, Any]]] = {} + for record in table.to_dict("records"): + key = _normalize_join_id(record.get("person_id")) + if not key: + continue + grouped.setdefault(key, []).append(record) + return grouped def _row_provenance(root: Path, patient_id: str, table_name: str) -> Provenance: diff --git a/tests/test_patient_interop.py b/tests/test_patient_interop.py index 78089d1b..81efba93 100644 --- a/tests/test_patient_interop.py +++ b/tests/test_patient_interop.py @@ -265,6 +265,69 @@ def test_omop_importer_from_csv_extract(tmp_path): assert profiles[0].conditions[0].normalized_codes[0].code == "44054006" +def test_omop_join_survives_null_person_id_float_promotion(tmp_path): + # A NULL person_id in a child table promotes the whole column to float64, so + # person_id 1 serializes as "1.0". The join must still match the PERSON row + # (previously every child record was silently dropped). + omop = tmp_path / "omop" + omop.mkdir() + pd.DataFrame( + [{"person_id": 1, "gender_source_value": "F", "year_of_birth": 1980}] + ).to_csv(omop / "PERSON.csv", index=False) + pd.DataFrame( + [ + {"person_id": 1, "condition_concept_id": 10}, + {"person_id": None, "condition_concept_id": 11}, + ] + ).to_csv(omop / "CONDITION_OCCURRENCE.csv", index=False) + pd.DataFrame( + [ + { + "concept_id": 10, + "vocabulary_id": "SNOMED", + "concept_code": "44054006", + "concept_name": "Diabetes mellitus", + "domain_id": "Condition", + } + ] + ).to_csv(omop / "CONCEPT.csv", index=False) + + profiles = import_patient_path(omop) + + assert len(profiles) == 1 + assert [c.label for c in profiles[0].conditions] == ["Diabetes mellitus"] + + +def test_omop_join_uses_raw_person_id_not_sanitized_profile_id(tmp_path): + # The profile id is sanitized ("pat 01" -> "pat-01"), but child-table joins + # must key off the raw person_id, not the sanitized profile id. + omop = tmp_path / "omop" + omop.mkdir() + pd.DataFrame( + [{"person_id": "pat 01", "gender_source_value": "M", "year_of_birth": 1970}] + ).to_csv(omop / "PERSON.csv", index=False) + pd.DataFrame( + [{"person_id": "pat 01", "condition_concept_id": 10}] + ).to_csv(omop / "CONDITION_OCCURRENCE.csv", index=False) + pd.DataFrame( + [ + { + "concept_id": 10, + "vocabulary_id": "SNOMED", + "concept_code": "44054006", + "concept_name": "Diabetes mellitus", + "domain_id": "Condition", + } + ] + ).to_csv(omop / "CONCEPT.csv", index=False) + + profiles = import_patient_path(omop) + + assert len(profiles) == 1 + assert profiles[0].patient_id == "pat-01" + assert [c.label for c in profiles[0].conditions] == ["Diabetes mellitus"] + + def test_narrative_and_phenopacket_export_are_deterministic(tmp_path): packet = { "id": "patient-export", From a68d108e52ffd83deb9a99dde00bd84119008b67 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:14:09 +0200 Subject: [PATCH 16/64] PR6: retrieval/indexing correctness (4 fixes) 1. prepare_trial_document now writes detailed_description and official_title. The backend weights both in TRIAL_TEXT_WEIGHTS (1.5 / 1.0) but they were never indexed, so the weights were dead and trials were under-indexed. (Requires rebuilding the LanceDB trial index to take effect.) 2. trial_ranker.load_trial_data only loads NCT-named files. It previously globbed every *.json in the output folder, scoring run sidecars (keywords/patient_profile/first_level_scores/rag_output) as bogus 0.0 trials. 3. _scan_rows fallback now applies the nct_id WHERE filter (table.search().where(...)). When FTS and vector both returned nothing it scanned an unfiltered head slice that could exclude the requested trials. Verified table.search().where() against lancedb 0.25.3. 4. create_query drops the 7 keys the backend never reads (age/sex/ overall_status/pre_selected_nct_ids/vector_score_threshold/max_text_score/ search_mode) and the misleading age=0-vs-None contract; search_trials already passes those filters to the backend directly. Removes the deferred max_text_score param. Regression tests: scan-fallback nct filter, NCT-only loading (+ sidecars skipped), minimal create_query contract, and prepared-doc field preservation. Co-Authored-By: Claude Opus 4.8 --- .../matching/retrieval/trial_retrieval.py | 30 ++------------ src/trialmatchai/matching/trial_ranker.py | 5 ++- src/trialmatchai/registry/preparation.py | 6 +++ src/trialmatchai/search/lancedb_backend.py | 10 ++++- tests/test_lancedb_search_backend.py | 39 +++++++++++++++++++ tests/test_registry_updater.py | 25 ++++++++++++ tests/test_search_queries_pytest.py | 12 ++---- tests/test_trial_ranker_pytest.py | 11 ++++-- 8 files changed, 97 insertions(+), 41 deletions(-) diff --git a/src/trialmatchai/matching/retrieval/trial_retrieval.py b/src/trialmatchai/matching/retrieval/trial_retrieval.py index c1e02463..2d93a556 100644 --- a/src/trialmatchai/matching/retrieval/trial_retrieval.py +++ b/src/trialmatchai/matching/retrieval/trial_retrieval.py @@ -78,15 +78,11 @@ def create_query( self, synonyms: List[str], embeddings: Dict[str, List[float]], - age: int, - sex: str, - overall_status: Optional[str], - max_text_score: float, - vector_score_threshold: float = 0.5, - pre_selected_nct_ids: Optional[List[str]] = None, other_conditions: Optional[List[str]] = None, - search_mode: str = "hybrid", ) -> Dict: + # Assemble the term lists (with a conservative cap) and embeddings the + # backend consumes. Filters like age/sex/status/nct_ids are passed to the + # backend directly by search_trials, not threaded through this dict. max_conditions_per_query = 800 # Conservative limit all_conditions = synonyms + (other_conditions or []) if len(all_conditions) > max_conditions_per_query: @@ -107,13 +103,6 @@ def create_query( "primary_terms": synonyms, "other_terms": other_conditions or [], "embeddings": embeddings, - "age": age, - "sex": sex, - "overall_status": overall_status, - "pre_selected_nct_ids": pre_selected_nct_ids or [], - "search_mode": search_mode, - "vector_score_threshold": vector_score_threshold, - "max_text_score": max_text_score, } def search_trials( @@ -155,18 +144,7 @@ def search_trials( ) mode = "bm25" - query = self.create_query( - primary_synonyms, - embeddings, - age if age is not None else 0, - sex, - overall_status, - 1.0, - vector_score_threshold, - pre_selected_nct_ids, - other_conditions, - search_mode=mode, - ) + query = self.create_query(primary_synonyms, embeddings, other_conditions) try: trials, scores = self.search_backend.search_trials( primary_terms=query["primary_terms"], diff --git a/src/trialmatchai/matching/trial_ranker.py b/src/trialmatchai/matching/trial_ranker.py index 1c1fceae..cd9dcf24 100644 --- a/src/trialmatchai/matching/trial_ranker.py +++ b/src/trialmatchai/matching/trial_ranker.py @@ -10,7 +10,10 @@ def load_trial_data(json_folder: str) -> List[Dict]: trial_data = [] for file_name in os.listdir(json_folder): - if file_name.endswith(".json"): + # Only NCT-named files are trials; skip run sidecars written to the same + # folder (keywords.json, patient_profile.json, first_level_scores.json, + # rag_output.json), which would otherwise be scored as bogus trials. + if file_name.endswith(".json") and file_name.upper().startswith("NCT"): file_path = os.path.join(json_folder, file_name) trial_id = os.path.splitext(file_name)[0] try: diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py index 48223716..620a6c20 100644 --- a/src/trialmatchai/registry/preparation.py +++ b/src/trialmatchai/registry/preparation.py @@ -46,6 +46,12 @@ def prepare_trial_document( out[field] = text out[vector_field] = vector + # Text-only fields the backend scores on (TRIAL_TEXT_WEIGHTS) but does not embed. + for text_field in ("detailed_description", "official_title"): + value = _preprocess_text(_flatten_text(doc.get(text_field))) + if value: + out[text_field] = value + for simple in ( "overall_status", "phase", diff --git a/src/trialmatchai/search/lancedb_backend.py b/src/trialmatchai/search/lancedb_backend.py index b47b2cd2..575a0309 100644 --- a/src/trialmatchai/search/lancedb_backend.py +++ b/src/trialmatchai/search/lancedb_backend.py @@ -413,7 +413,7 @@ def _candidate_rows( rows_by_key[_row_key(row)] = row if not rows_by_key: - for row in self._scan_rows(table, limit=limit): + for row in self._scan_rows(table, where=where, limit=limit): rows_by_key[_row_key(row)] = row return list(rows_by_key.values()) @@ -455,8 +455,14 @@ def _search_vector( ) return [] - def _scan_rows(self, table: Any, *, limit: int) -> list[dict[str, Any]]: + def _scan_rows( + self, table: Any, *, where: str, limit: int + ) -> list[dict[str, Any]]: try: + # Honor the nct_id filter on the fallback path; an unfiltered head + # slice could return rows that exclude the requested trials entirely. + if where: + return list(table.search().where(where).limit(limit).to_list()) return list(table.to_arrow().to_pylist())[:limit] except Exception as exc: logger.warning("Could not scan LanceDB table rows: %s", exc) diff --git a/tests/test_lancedb_search_backend.py b/tests/test_lancedb_search_backend.py index 012ed12e..5b94a4a6 100644 --- a/tests/test_lancedb_search_backend.py +++ b/tests/test_lancedb_search_backend.py @@ -3,6 +3,7 @@ import pytest from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.search.lancedb_backend import _nct_where pytest.importorskip("lancedb") @@ -73,3 +74,41 @@ def test_lancedb_backend_indexes_and_searches_trials_and_criteria(tmp_path): assert trials[0]["nct_id"] == "N1" assert scores[0] > 0 assert criteria_hits[0]["_source"]["criteria_id"] == "C1" + + +def test_scan_rows_fallback_applies_nct_filter(tmp_path): + # The fallback scan (used when FTS and vector both return nothing) must honor + # the nct_id filter; otherwise it returns arbitrary rows that may exclude the + # requested trials entirely. + backend = LanceDBSearchBackend( + tmp_path / "search", + trials_table="trials", + criteria_table="criteria", + candidate_limit=25, + ) + backend.index_criteria( + [ + { + "criteria_id": "C1", + "nct_id": "N1", + "criterion": "alpha", + "criterion_vector": [1.0, 0.0], + "entities": [], + "eligibility_type": "Inclusion Criteria", + }, + { + "criteria_id": "C2", + "nct_id": "N2", + "criterion": "beta", + "criterion_vector": [0.0, 1.0], + "entities": [], + "eligibility_type": "Inclusion Criteria", + }, + ] + ) + table = backend._open_table("criteria") + + rows = backend._scan_rows(table, where=_nct_where(["N1"]), limit=25) + + assert rows + assert {row["nct_id"] for row in rows} == {"N1"} diff --git a/tests/test_registry_updater.py b/tests/test_registry_updater.py index 4c4d0d05..061428de 100644 --- a/tests/test_registry_updater.py +++ b/tests/test_registry_updater.py @@ -49,6 +49,31 @@ def test_client_paginates_and_filters_since(): assert session.calls[1]["params"]["pageToken"] == "page-2" +def test_prepare_trial_document_preserves_detailed_description_and_official_title(): + # These two fields carry BM25 weight in the backend (TRIAL_TEXT_WEIGHTS) but + # were previously dropped before indexing, silently losing retrieval signal. + from trialmatchai.registry.preparation import prepare_trial_document + + class _StubEmbedder: + def embed_texts(self, texts): + return [[0.0, 0.0] for _ in texts] + + prepared = prepare_trial_document( + { + "nct_id": "NCT00000009", + "brief_title": "Brief", + "condition": ["Lung cancer"], + "eligibility_criteria": "Adults", + "detailed_description": "A longer detailed description of the study.", + "official_title": "The Official Long Title of the Study", + }, + _StubEmbedder(), + ) + + assert "description" in prepared["detailed_description"].lower() + assert "official" in prepared["official_title"].lower() + + def test_normalize_study_maps_v2_modules_and_splits_criteria(): normalized = normalize_study(_study("NCT00000004")) diff --git a/tests/test_search_queries_pytest.py b/tests/test_search_queries_pytest.py index aabeacae..8ee62a04 100644 --- a/tests/test_search_queries_pytest.py +++ b/tests/test_search_queries_pytest.py @@ -11,20 +11,14 @@ def test_first_level_query_describes_backend_search(): query = search.create_query( synonyms=["lung cancer"], embeddings={}, - age=45, - sex="ALL", - overall_status="Recruiting", - max_text_score=1.0, - vector_score_threshold=0.5, - pre_selected_nct_ids=None, other_conditions=["smoking"], - search_mode="bm25", ) assert query["primary_terms"] == ["lung cancer"] assert query["other_terms"] == ["smoking"] - assert query["overall_status"] == "Recruiting" - assert query["search_mode"] == "bm25" + assert query["embeddings"] == {} + # Filters are passed to the backend directly, not via the query dict. + assert set(query) == {"primary_terms", "other_terms", "embeddings"} def test_build_trial_record_flattens_search_text_and_vector(): diff --git a/tests/test_trial_ranker_pytest.py b/tests/test_trial_ranker_pytest.py index aba7fc42..45e279d5 100644 --- a/tests/test_trial_ranker_pytest.py +++ b/tests/test_trial_ranker_pytest.py @@ -39,12 +39,12 @@ def test_rank_trials_orders_by_score(): def test_load_and_save_ranked_trials(tmp_path): trial_folder = tmp_path / "trials" trial_folder.mkdir() - (trial_folder / "T1.json").write_text( + (trial_folder / "NCT0001.json").write_text( json.dumps( {"Inclusion_Criteria_Evaluation": [], "Exclusion_Criteria_Evaluation": []} ) ) - (trial_folder / "T2.json").write_text( + (trial_folder / "NCT0002.json").write_text( json.dumps( { "Inclusion_Criteria_Evaluation": [{"Classification": "Met"}], @@ -52,12 +52,17 @@ def test_load_and_save_ranked_trials(tmp_path): } ) ) + # Run sidecars in the same folder must NOT be loaded as trials. + (trial_folder / "keywords.json").write_text(json.dumps({"keywords": ["x"]})) + (trial_folder / "patient_profile.json").write_text(json.dumps({"id": "p1"})) trials = load_trial_data(str(trial_folder)) + assert {t["TrialID"] for t in trials} == {"NCT0001", "NCT0002"} + ranked = rank_trials(trials) out_file = tmp_path / "ranked.json" save_ranked_trials(ranked, str(out_file)) saved = json.loads(out_file.read_text()) assert "RankedTrials" in saved - assert saved["RankedTrials"][0]["TrialID"] == "T2" + assert saved["RankedTrials"][0]["TrialID"] == "NCT0002" From d0fc7667fd9a4124dc79b01af77fc3981812ed85 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:23:18 +0200 Subject: [PATCH 17/64] PR7a: deduplicate embedder/synonyms/flatten_text helpers Behavior-preserving consolidation of copy-pasted logic: - models/embedding.build_embedder(config): single embedder factory replacing the identical TextEmbedder(TextEmbedderConfig(...)) block in main.py and the index/build-concepts/update-registry CLIs. - matching/retrieval/synonyms.disease_synonyms(): one disease-synonym extractor that ClinicalTrialSearch and SecondStageRetriever now delegate to. - utils/text.flatten_text(): single whitespace-normalizing flatten used by both registry.preparation and search.lancedb_backend (the two _flatten_text copies). Behavior-preserving for preparation since _preprocess_text re-normalizes. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/cli/build_concepts.py | 17 ++-------- src/trialmatchai/cli/index_data.py | 17 ++-------- src/trialmatchai/cli/update_registry.py | 19 ++--------- src/trialmatchai/main.py | 20 ++--------- .../matching/retrieval/criteria_retrieval.py | 17 ++-------- .../matching/retrieval/synonyms.py | 33 +++++++++++++++++++ .../matching/retrieval/trial_retrieval.py | 20 ++--------- src/trialmatchai/models/embedding/__init__.py | 8 +++-- .../models/embedding/text_embedder.py | 22 +++++++++++++ src/trialmatchai/registry/preparation.py | 20 +++-------- src/trialmatchai/search/lancedb_backend.py | 27 +++++---------- src/trialmatchai/utils/text.py | 21 ++++++++++++ 12 files changed, 108 insertions(+), 133 deletions(-) create mode 100644 src/trialmatchai/matching/retrieval/synonyms.py create mode 100644 src/trialmatchai/utils/text.py diff --git a/src/trialmatchai/cli/build_concepts.py b/src/trialmatchai/cli/build_concepts.py index 1ca94966..6937a34b 100644 --- a/src/trialmatchai/cli/build_concepts.py +++ b/src/trialmatchai/cli/build_concepts.py @@ -12,7 +12,7 @@ concept_texts_for_embedding, write_lancedb_table, ) -from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig +from trialmatchai.models.embedding import build_embedder from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -59,7 +59,6 @@ def main() -> int: config = load_config(args.config) linker_cfg = config.get("concept_linker", {}) - embedder_cfg = config.get("embedder", {}) db_path = args.db_path or linker_cfg.get("db_path") or "data/concepts" table_name = args.table or linker_cfg.get("table") or "concepts" @@ -81,19 +80,7 @@ def main() -> int: embeddings = None if not args.skip_embeddings: - embedder = TextEmbedder( - TextEmbedderConfig( - model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), - revision=embedder_cfg.get("revision"), - trust_remote_code=embedder_cfg.get("trust_remote_code", False), - pooling=embedder_cfg.get("pooling", "mean"), - max_length=embedder_cfg.get("max_length", 512), - batch_size=embedder_cfg.get("batch_size", 32), - use_gpu=embedder_cfg.get("use_gpu", True), - use_fp16=embedder_cfg.get("use_fp16", False), - normalize=embedder_cfg.get("normalize", True), - ) - ) + embedder = build_embedder(config) embeddings = embedder.embed_texts(concept_texts_for_embedding(rows)) Path(db_path).mkdir(parents=True, exist_ok=True) diff --git a/src/trialmatchai/cli/index_data.py b/src/trialmatchai/cli/index_data.py index f36a0f02..0cf2c7fd 100644 --- a/src/trialmatchai/cli/index_data.py +++ b/src/trialmatchai/cli/index_data.py @@ -129,7 +129,7 @@ def _prepare_from_trials_jsons( processed_criteria_folder: Path, ) -> None: from trialmatchai.entities import build_entity_annotator - from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig + from trialmatchai.models.embedding import build_embedder from trialmatchai.registry.preparation import ( prepare_criteria_documents, prepare_trial_document, @@ -137,20 +137,7 @@ def _prepare_from_trials_jsons( write_prepared_trial, ) - embedder_cfg = config.get("embedder", {}) - embedder = TextEmbedder( - TextEmbedderConfig( - model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), - revision=embedder_cfg.get("revision"), - trust_remote_code=embedder_cfg.get("trust_remote_code", False), - pooling=embedder_cfg.get("pooling", "mean"), - max_length=embedder_cfg.get("max_length", 512), - batch_size=embedder_cfg.get("batch_size", 32), - use_gpu=embedder_cfg.get("use_gpu", True), - use_fp16=embedder_cfg.get("use_fp16", False), - normalize=embedder_cfg.get("normalize", True), - ) - ) + embedder = build_embedder(config) entity_annotator = build_entity_annotator(config, embedder=embedder) trial_docs = _load_flat_json_folder(trials_json_folder) for doc in trial_docs: diff --git a/src/trialmatchai/cli/update_registry.py b/src/trialmatchai/cli/update_registry.py index ff784a05..b1bf5971 100644 --- a/src/trialmatchai/cli/update_registry.py +++ b/src/trialmatchai/cli/update_registry.py @@ -126,22 +126,9 @@ def embed_texts(self, texts): def _build_embedder(config: dict[str, Any]): - from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig - - embedder_cfg = config.get("embedder", {}) - return TextEmbedder( - TextEmbedderConfig( - model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), - revision=embedder_cfg.get("revision"), - trust_remote_code=embedder_cfg.get("trust_remote_code", False), - pooling=embedder_cfg.get("pooling", "mean"), - max_length=embedder_cfg.get("max_length", 512), - batch_size=embedder_cfg.get("batch_size", 32), - use_gpu=embedder_cfg.get("use_gpu", True), - use_fp16=embedder_cfg.get("use_fp16", False), - normalize=embedder_cfg.get("normalize", True), - ) - ) + from trialmatchai.models.embedding import build_embedder + + return build_embedder(config) def _build_entity_annotator(config: dict[str, Any], embedder): diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index b2d88f02..a157f315 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -255,10 +255,7 @@ def main_pipeline(config_path: str | None = None) -> int: ) return 1 - from trialmatchai.models.embedding.text_embedder import ( - TextEmbedder, - TextEmbedderConfig, - ) + from trialmatchai.models.embedding import build_embedder from trialmatchai.models.llm.llm_loader import load_model_and_tokenizer from trialmatchai.models.llm.llm_reranker import LLMReranker @@ -286,20 +283,7 @@ def main_pipeline(config_path: str | None = None) -> int: model = model.half() # type: ignore # Initialize components - embedder_cfg = config.get("embedder", {}) - embedder = TextEmbedder( - TextEmbedderConfig( - model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), - revision=embedder_cfg.get("revision"), - trust_remote_code=embedder_cfg.get("trust_remote_code", False), - pooling=embedder_cfg.get("pooling", "mean"), - max_length=embedder_cfg.get("max_length", 512), - batch_size=embedder_cfg.get("batch_size", 32), - use_gpu=embedder_cfg.get("use_gpu", True), - use_fp16=embedder_cfg.get("use_fp16", False), - normalize=embedder_cfg.get("normalize", True), - ) - ) + embedder = build_embedder(config) entity_annotator = build_entity_annotator(config, embedder=embedder) with warnings.catch_warnings(): diff --git a/src/trialmatchai/matching/retrieval/criteria_retrieval.py b/src/trialmatchai/matching/retrieval/criteria_retrieval.py index 356e701f..0439117a 100644 --- a/src/trialmatchai/matching/retrieval/criteria_retrieval.py +++ b/src/trialmatchai/matching/retrieval/criteria_retrieval.py @@ -5,6 +5,7 @@ from concurrent.futures import ThreadPoolExecutor, as_completed from typing import TYPE_CHECKING, Dict, List, Optional +from trialmatchai.matching.retrieval.synonyms import disease_synonyms from trialmatchai.search.lancedb_backend import TrialSearchBackend from trialmatchai.utils.file_utils import write_text_file from trialmatchai.utils.logging_config import setup_logging @@ -39,21 +40,7 @@ def __init__( self.search_mode = search_mode.lower() if search_mode else "hybrid" def get_synonyms(self, condition: str) -> List[str]: - if self.entity_annotator is None: - logger.warning("Entity annotator not initialized; cannot extract synonyms.") - return [] - raw_result = self.entity_annotator.annotate_texts_in_parallel( - [condition], max_workers=1 - ) - ner_results = raw_result - if ner_results and ner_results[0]: - synonyms = set() - for entity in ner_results[0]: - if entity.get("entity_group", "").lower() == "disease": - synonyms.update(entity.get("synonyms", [])) - return list(synonyms) - logger.warning(f"No annotations found for condition: {condition}") - return [] + return disease_synonyms(self.entity_annotator, condition) def retrieve_criteria( self, nct_ids: List[str], queries: List[str] diff --git a/src/trialmatchai/matching/retrieval/synonyms.py b/src/trialmatchai/matching/retrieval/synonyms.py new file mode 100644 index 00000000..7b8ad381 --- /dev/null +++ b/src/trialmatchai/matching/retrieval/synonyms.py @@ -0,0 +1,33 @@ +"""Shared disease-synonym extraction used by the first- and second-stage retrievers.""" + +from __future__ import annotations + +from typing import Any, List + +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def disease_synonyms(entity_annotator: Any, condition: str) -> List[str]: + """Return linked disease synonyms for a condition via the entity annotator. + + Returns an empty list when the annotator is unavailable or extraction fails. + """ + if entity_annotator is None: + logger.info("Entity annotator disabled; skipping synonyms extraction.") + return [] + try: + raw_result = entity_annotator.annotate_texts_in_parallel( + [condition], max_workers=1 + ) + if raw_result and raw_result[0]: + synonyms: set[str] = set() + for entity in raw_result[0]: + if entity.get("entity_group", "").lower() == "disease": + synonyms.update(entity.get("synonyms", [])) + return list(synonyms) + logger.warning("No annotations found for condition: %s", condition) + except Exception as exc: + logger.error("Entity synonym extraction failed for '%s': %s", condition, exc) + return [] diff --git a/src/trialmatchai/matching/retrieval/trial_retrieval.py b/src/trialmatchai/matching/retrieval/trial_retrieval.py index 2d93a556..62eafc90 100644 --- a/src/trialmatchai/matching/retrieval/trial_retrieval.py +++ b/src/trialmatchai/matching/retrieval/trial_retrieval.py @@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union from dateutil import parser as date_parser +from trialmatchai.matching.retrieval.synonyms import disease_synonyms from trialmatchai.search.lancedb_backend import TrialSearchBackend from trialmatchai.utils.logging_config import setup_logging @@ -26,24 +27,7 @@ def __init__( self.entity_annotator = entity_annotator or bio_med_ner def get_synonyms(self, condition: str) -> List[str]: - if not self.entity_annotator: - logger.info("Entity annotator disabled; skipping synonyms extraction.") - return [] - try: - raw_result = self.entity_annotator.annotate_texts_in_parallel( - [condition], max_workers=1 - ) - ner_results = raw_result - if ner_results and ner_results[0]: - synonyms = set() - for entity in ner_results[0]: - if entity.get("entity_group", "").lower() == "disease": - synonyms.update(entity.get("synonyms", [])) - return list(synonyms) - logger.warning(f"No annotations found for condition: {condition}") - except Exception as e: - logger.error(f"Entity synonym extraction failed for '{condition}': {e}") - return [] + return disease_synonyms(self.entity_annotator, condition) def parse_age_input(self, age_input: Union[int, str]) -> Optional[int]: if isinstance(age_input, int): diff --git a/src/trialmatchai/models/embedding/__init__.py b/src/trialmatchai/models/embedding/__init__.py index f91ae8bc..85fc9fdf 100644 --- a/src/trialmatchai/models/embedding/__init__.py +++ b/src/trialmatchai/models/embedding/__init__.py @@ -1,3 +1,7 @@ -from trialmatchai.models.embedding.text_embedder import TextEmbedder, TextEmbedderConfig +from trialmatchai.models.embedding.text_embedder import ( + TextEmbedder, + TextEmbedderConfig, + build_embedder, +) -__all__ = ["TextEmbedder", "TextEmbedderConfig"] +__all__ = ["TextEmbedder", "TextEmbedderConfig", "build_embedder"] diff --git a/src/trialmatchai/models/embedding/text_embedder.py b/src/trialmatchai/models/embedding/text_embedder.py index da5c9929..b86eadfa 100644 --- a/src/trialmatchai/models/embedding/text_embedder.py +++ b/src/trialmatchai/models/embedding/text_embedder.py @@ -95,6 +95,28 @@ def _batched(items: Sequence[str], batch_size: int) -> Iterable[Sequence[str]]: yield items[i : i + batch_size] +def build_embedder(config: dict) -> TextEmbedder: + """Construct a TextEmbedder from the ``embedder`` section of a config dict. + + Single source for the embedder wiring previously copy-pasted across main.py + and the index/build-concepts/update-registry CLIs. + """ + embedder_cfg = config.get("embedder", {}) or {} + return TextEmbedder( + TextEmbedderConfig( + model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), + revision=embedder_cfg.get("revision"), + trust_remote_code=embedder_cfg.get("trust_remote_code", False), + pooling=embedder_cfg.get("pooling", "mean"), + max_length=embedder_cfg.get("max_length", 512), + batch_size=embedder_cfg.get("batch_size", 32), + use_gpu=embedder_cfg.get("use_gpu", True), + use_fp16=embedder_cfg.get("use_fp16", False), + normalize=embedder_cfg.get("normalize", True), + ) + ) + + def _load_embedding_dependencies(): try: import torch diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py index 620a6c20..eba4c15e 100644 --- a/src/trialmatchai/registry/preparation.py +++ b/src/trialmatchai/registry/preparation.py @@ -9,6 +9,8 @@ import dateutil.parser +from trialmatchai.utils.text import flatten_text + class TextEmbeddingBackend(Protocol): def embed_texts(self, texts: Sequence[str]) -> list[list[float]]: @@ -39,7 +41,7 @@ def prepare_trial_document( embedder: TextEmbeddingBackend, ) -> dict[str, Any]: out: dict[str, Any] = {"nct_id": doc["nct_id"]} - texts = [_preprocess_text(_flatten_text(doc.get(field))) for field, _ in TRIAL_TEXT_FIELDS] + texts = [_preprocess_text(flatten_text(doc.get(field))) for field, _ in TRIAL_TEXT_FIELDS] vectors = _embed_texts(embedder, texts) for (field, vector_field), text, vector in zip(TRIAL_TEXT_FIELDS, texts, vectors): @@ -48,7 +50,7 @@ def prepare_trial_document( # Text-only fields the backend scores on (TRIAL_TEXT_WEIGHTS) but does not embed. for text_field in ("detailed_description", "official_title"): - value = _preprocess_text(_flatten_text(doc.get(text_field))) + value = _preprocess_text(flatten_text(doc.get(text_field))) if value: out[text_field] = value @@ -94,7 +96,7 @@ def prepare_criteria_documents( if not isinstance(criterion, dict): continue text = _preprocess_text( - _flatten_text(criterion.get("criterion") or criterion.get("sentence")) + flatten_text(criterion.get("criterion") or criterion.get("sentence")) ) if not text: continue @@ -207,18 +209,6 @@ def _preprocess_text(value: str) -> str: return re.sub(r"\s+", " ", value).strip() -def _flatten_text(value: Any) -> str: - if value is None: - return "" - if isinstance(value, str): - return value - if isinstance(value, dict): - return " ".join(_flatten_text(item) for item in value.values()) - if isinstance(value, Sequence) and not isinstance(value, (bytes, bytearray)): - return " ".join(_flatten_text(item) for item in value) - return str(value) - - def _to_iso_date(value: Any) -> str | None: if not value: return None diff --git a/src/trialmatchai/search/lancedb_backend.py b/src/trialmatchai/search/lancedb_backend.py index 575a0309..ef46ed70 100644 --- a/src/trialmatchai/search/lancedb_backend.py +++ b/src/trialmatchai/search/lancedb_backend.py @@ -7,6 +7,7 @@ from typing import Any, Iterable, Mapping, Protocol, Sequence from trialmatchai.utils.logging_config import setup_logging +from trialmatchai.utils.text import flatten_text logger = setup_logging(__name__) @@ -486,7 +487,7 @@ def _table_names(self) -> list[str]: def build_trial_record(doc: Mapping[str, Any]) -> dict[str, Any]: row = dict(doc) - search_text = _flatten_text( + search_text = flatten_text( [ row.get("condition"), row.get("eligibility_criteria"), @@ -508,7 +509,7 @@ def build_criteria_record(doc: Mapping[str, Any]) -> dict[str, Any]: entity_text, synonym_text = _flatten_entities(row.get("entities")) row["entity_text"] = entity_text row["entity_synonyms_text"] = synonym_text - row["search_text"] = _flatten_text( + row["search_text"] = flatten_text( [row.get("criterion"), entity_text, synonym_text] ) return row @@ -613,7 +614,7 @@ def _weighted_text_score( for term in clean_terms: field_score = 0.0 for field, weight in fields: - field_score += weight * _lexical_score(term, _flatten_text(row.get(field))) + field_score += weight * _lexical_score(term, flatten_text(row.get(field))) best = max(best, field_score / max_weight) return min(best, 1.0) @@ -750,24 +751,12 @@ def _flatten_entities(entities: Any) -> tuple[str, str]: for entity in entities: if not isinstance(entity, Mapping): continue - texts.append(_flatten_text([entity.get("text"), entity.get("entity")])) - synonyms.append(_flatten_text(entity.get("synonyms"))) + texts.append(flatten_text([entity.get("text"), entity.get("entity")])) + synonyms.append(flatten_text(entity.get("synonyms"))) for candidate in entity.get("concept_candidates") or []: if isinstance(candidate, Mapping): - synonyms.append(_flatten_text(candidate.get("concept_name"))) - return _flatten_text(texts), _flatten_text(synonyms) - - -def _flatten_text(value: Any) -> str: - if value is None: - return "" - if isinstance(value, str): - return " ".join(value.split()) - if isinstance(value, Mapping): - return " ".join(_flatten_text(item) for item in value.values()).strip() - if isinstance(value, Iterable) and not isinstance(value, (bytes, bytearray)): - return " ".join(_flatten_text(item) for item in value).strip() - return str(value) + synonyms.append(flatten_text(candidate.get("concept_name"))) + return flatten_text(texts), flatten_text(synonyms) def _normalize_text(value: str) -> str: diff --git a/src/trialmatchai/utils/text.py b/src/trialmatchai/utils/text.py new file mode 100644 index 00000000..f4f2ed09 --- /dev/null +++ b/src/trialmatchai/utils/text.py @@ -0,0 +1,21 @@ +"""Shared text helpers.""" + +from __future__ import annotations + +from collections.abc import Iterable, Mapping +from typing import Any + + +def flatten_text(value: Any) -> str: + """Flatten a possibly-nested value (str/mapping/sequence) into whitespace- + normalized text. Used for both trial indexing and backend search-text building. + """ + if value is None: + return "" + if isinstance(value, str): + return " ".join(value.split()) + if isinstance(value, Mapping): + return " ".join(flatten_text(item) for item in value.values()).strip() + if isinstance(value, Iterable) and not isinstance(value, (bytes, bytearray)): + return " ".join(flatten_text(item) for item in value).strip() + return str(value) From b481b9df26f74057f7b49ee95094d093f38a52a7 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:26:51 +0200 Subject: [PATCH 18/64] PR7b: extract BaseTrialProcessor to dedupe the eligibility processors MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The HF (eligibility_reasoning) and vLLM (eligibility_reasoning_vllm) CoT processors duplicated the ~90-line prompt template, _load_trial_data, _save_outputs, and the worklist/length-bucketing orchestration verbatim — the largest duplication in the repo and a drift risk for the scoring contract. New matching/eligibility_base.BaseTrialProcessor holds the shared prompt, trial I/O, output persistence, and process_trials skeleton (parameterized by the _token_length and _progress_desc hooks). Each backend now subclasses it and implements only __init__ and _process_batch (plus vLLM's LoRA/token-length helpers). Prompt output is byte-identical; behavior preserved. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/matching/eligibility_base.py | 215 ++++++++++++++++ .../matching/eligibility_reasoning.py | 193 +-------------- .../matching/eligibility_reasoning_vllm.py | 229 +----------------- 3 files changed, 227 insertions(+), 410 deletions(-) create mode 100644 src/trialmatchai/matching/eligibility_base.py diff --git a/src/trialmatchai/matching/eligibility_base.py b/src/trialmatchai/matching/eligibility_base.py new file mode 100644 index 00000000..7ca14557 --- /dev/null +++ b/src/trialmatchai/matching/eligibility_base.py @@ -0,0 +1,215 @@ +"""Shared base for the CoT eligibility processors (HF and vLLM backends). + +Holds the prompt, trial I/O, output persistence, and the worklist/bucketing +orchestration. Backends subclass this and implement only ``_process_batch`` (and +optionally override ``_token_length`` / ``_progress_desc``). +""" + +from __future__ import annotations + +import json +import os +from typing import Dict, List + +from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file +from trialmatchai.utils.json_utils import extract_json_object +from trialmatchai.utils.logging_config import setup_logging +from tqdm import tqdm + +logger = setup_logging(__name__) + + +class BaseTrialProcessor: + # Subclasses set these in __init__. + tokenizer = None + batch_size: int = 4 + use_cot: bool = True + + # ---------------------- I/O helpers ---------------------- + + def _load_trial_data(self, nct_id: str, json_folder: str) -> str: + try: + path = f"{json_folder}/{nct_id}.json" + trial_data = read_json_file(path) + return trial_data.get("eligibility_criteria", "") + except Exception as e: + logger.error(f"Error loading {nct_id}: {str(e)}") + return "" + + # ---------------------- Prompting ---------------------- + + def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: + criteria_text_formatted = ( + f"Eligibility Criteria:\n{criteria_text}" + if criteria_text + else "No eligibility criteria provided." + ) + + if self.use_cot: + system_msg = ( + "You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning. " + "Answer the following question. Before answering, create a concise chain of thoughts reasoning to ensure a logical and accurate response.\n" + ) + chat = [ + {"role": "system", "content": system_msg}, + { + "role": "user", + "content": ( + "Assess the given patient's eligibility for a clinical trial by evaluating each and every criterion individually.\n\n" + "### INCLUSION CRITERIA ASSESSMENT\n" + "For each inclusion criterion, classify it as one of:\n" + "- **Met:** The patient's data explicitly and unequivocally satisfies the criterion.\n" + "- **Not Met:** The patient's data explicitly and unequivocally contradicts or fails to satisfy the criterion.\n" + "- **Unclear:** Insufficient or missing patient data to verify.\n" + "- **Irrelevant:** The criterion does not apply to the patient's context.\n\n" + "### EXCLUSION CRITERIA ASSESSMENT\n" + "For each exclusion criterion, classify it as one of:\n" + "- **Violated:** The patient's data explicitly and unequivocally violates the criterion.\n" + "- **Not Violated:** The patient's data confirms compliance with the criterion.\n" + "- **Unclear:** Insufficient or missing patient data to verify.\n" + "- **Irrelevant:** The criterion does not apply to the patient's context.\n\n" + "### IMPORTANT INSTRUCTIONS\n" + "- Ensure all criteria are assessed one-by-one.\n" + "- Use **only** the provided patient data; **do not infer, assume, or extrapolate beyond the given information.**\n" + "- Justifications must be strictly based on direct evidence from the patient profile.\n" + "### RESPONSE FORMAT (STRICTLY FOLLOW)\n" + "{\n" + ' "Inclusion_Criteria_Evaluation": [\n' + ' {"Criterion": "Exact inclusion criterion text", "Classification": "Met | Not Met | Unclear | Irrelevant", "Justification": "Clear, evidence-based rationale using ONLY provided data"}\n' + " ],\n" + ' "Exclusion_Criteria_Evaluation": [\n' + ' {"Criterion": "Exact exclusion criterion text", "Classification": "Violated | Not Violated | Unclear | Irrelevant", "Justification": "Clear, evidence-based rationale using ONLY provided data"}\n' + " ],\n" + ' "Recap": "Concise summary of key qualifying/disqualifying factors",\n' + ' "Final Decision": "Eligible | Likely Eligible (leaning toward inclusion) | Likely Ineligible (leaning toward exclusion) | Ineligible"\n' + "}\n\n" + "### INPUT\n" + "---Start of Clinical Trial Criteria---\n" + f"{criteria_text_formatted}\n" + "---End of Clinical Trial Criteria---\n\n" + "----\n" + "---Start of Patient Description---\n" + f"{patient_profile}\n" + "---End of Patient Description---\n" + "## IMPORTANT REMINDER:\n" + "NEVER make assumptions, inferences, or extrapolations beyond the explicitly stated patient information." + ), + }, + ] + else: + chat = [ + { + "role": "system", + "content": ( + "You are a clinical assistant tasked with assessing the eligibility of a patient for a clinical trial. " + "Output only a JSON object evaluating trial eligibility for the patient based only on the provided criteria and patient profile.\n" + ), + }, + { + "role": "user", + "content": ( + "For each criterion, classify:\n" + '- If Inclusion Criterion: "Met" | "Not Met" | "Unclear" | "Irrelevant"\n' + '- If Exclusion Criterion: "Violated" | "Not Violated" | "Unclear" | "Irrelevant"\n\n' + "Provide a justification for each classification based strictly on the provided data. " + "Output this JSON schema:\n" + "{\n" + ' "Inclusion_Criteria_Evaluation": [ {"Criterion": "...", "Classification": "...", "Justification": "..."} ],\n' + ' "Exclusion_Criteria_Evaluation": [ {"Criterion": "...", "Classification": "...", "Justification": "..."} ],\n' + ' "Final Decision": "Eligible | Likely Eligible | Likely Ineligible | Ineligible"\n' + "}\n\n" + "---Start of Clinical Trial Criteria---\n" + f"{criteria_text_formatted}\n" + "---End of Clinical Trial Criteria---\n\n" + "---Start of Patient Description---\n" + f"{patient_profile}\n" + "---End of Patient Description---\n" + ), + }, + ] + + if self.tokenizer is not None and hasattr(self.tokenizer, "apply_chat_template"): + return self.tokenizer.apply_chat_template( + chat, tokenize=False, add_generation_prompt=True + ) + # Fallback: simple concatenation + system_part = f"{chat[0]['content']}\n\n" + user_part = f"{chat[1]['content']}\n\n" + return system_part + user_part + "Answer: " + + # ---------------------- Persistence ---------------------- + + def _save_outputs(self, nct_id: str, response: str, output_folder: str): + try: + os.makedirs(output_folder, exist_ok=True) + txt_path = f"{output_folder}/{nct_id}.txt" + write_text_file([response], txt_path) + try: + json_data = extract_json_object(response) + write_json_file(json_data, f"{output_folder}/{nct_id}.json") + logger.info(f"Processed {nct_id} successfully") + except (json.JSONDecodeError, ValueError) as e: + logger.error(f"Invalid JSON response for {nct_id}: {str(e)}") + write_json_file( + {"error": "invalid_json_response", "raw_output": response}, + f"{output_folder}/{nct_id}.json", + ) + except Exception as e: + logger.error(f"Failed to save {nct_id}: {str(e)}") + + # ---------------------- Hooks for subclasses ---------------------- + + def _token_length(self, prompt: str, nct_id: str = "") -> int: + """Token count used for length bucketing; char-based fallback on error.""" + try: + return len(self.tokenizer(prompt, add_special_tokens=False)["input_ids"]) + except Exception: + return max(1, len(prompt) // 4) + + def _progress_desc(self) -> str: + return "Processing Trials" + + def _process_batch(self, batch: List[Dict], output_folder: str): # pragma: no cover + raise NotImplementedError + + # ---------------------- Public API ---------------------- + + def process_trials( + self, + nct_ids: List[str], + json_folder: str, + output_folder: str, + patient_profile: List[str], + ): + """Build a worklist (skipping already-processed trials), length-bucket to + minimize padding, then process in batches.""" + patient_text = " ".join( + str(line).strip() for line in patient_profile if str(line).strip() + ) + + items: List[Dict] = [] + for nct_id in nct_ids: + if os.path.exists(f"{output_folder}/{nct_id}.json"): + logger.info(f"Skipping existing: {nct_id}") + continue + criteria_text = self._load_trial_data(nct_id, json_folder) + prompt = self._format_prompt(criteria_text, patient_text) + items.append( + { + "nct_id": nct_id, + "prompt": prompt, + "tok_len": self._token_length(prompt, nct_id), + } + ) + + if not items: + logger.info("No work to do.") + return + + if getattr(self, "length_bucket", True): + items.sort(key=lambda x: x["tok_len"]) + + for i in tqdm( + range(0, len(items), self.batch_size), desc=self._progress_desc() + ): + self._process_batch(items[i : i + self.batch_size], output_folder) diff --git a/src/trialmatchai/matching/eligibility_reasoning.py b/src/trialmatchai/matching/eligibility_reasoning.py index bc15d646..4df07283 100644 --- a/src/trialmatchai/matching/eligibility_reasoning.py +++ b/src/trialmatchai/matching/eligibility_reasoning.py @@ -1,12 +1,8 @@ -import json -import os import time from typing import Dict, List -from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file -from trialmatchai.utils.json_utils import extract_json_object +from trialmatchai.matching.eligibility_base import BaseTrialProcessor from trialmatchai.utils.logging_config import setup_logging -from tqdm import tqdm logger = setup_logging(__name__) @@ -25,7 +21,7 @@ def _require_torch(): return torch -class BatchTrialProcessor: +class BatchTrialProcessor(BaseTrialProcessor): def __init__( self, model, @@ -94,117 +90,8 @@ def __init__( except Exception: pass - # ---------------------- I/O helpers ---------------------- - - def _load_trial_data(self, nct_id: str, json_folder: str) -> str: - try: - path = f"{json_folder}/{nct_id}.json" - trial_data = read_json_file(path) - return trial_data.get("eligibility_criteria", "") - except Exception as e: - logger.error(f"Error loading {nct_id}: {str(e)}") - return "" - - # ---------------------- Prompting ---------------------- - - def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: - criteria_text_formatted = ( - f"Eligibility Criteria:\n{criteria_text}" - if criteria_text - else "No eligibility criteria provided." - ) - - if self.use_cot: - system_msg = ( - "You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning. " - "Answer the following question. Before answering, create a concise chain of thoughts reasoning to ensure a logical and accurate response.\n" - ) - chat = [ - {"role": "system", "content": system_msg}, - { - "role": "user", - "content": ( - "Assess the given patient's eligibility for a clinical trial by evaluating each and every criterion individually.\n\n" - "### INCLUSION CRITERIA ASSESSMENT\n" - "For each inclusion criterion, classify it as one of:\n" - "- **Met:** The patient's data explicitly and unequivocally satisfies the criterion.\n" - "- **Not Met:** The patient's data explicitly and unequivocally contradicts or fails to satisfy the criterion.\n" - "- **Unclear:** Insufficient or missing patient data to verify.\n" - "- **Irrelevant:** The criterion does not apply to the patient's context.\n\n" - "### EXCLUSION CRITERIA ASSESSMENT\n" - "For each exclusion criterion, classify it as one of:\n" - "- **Violated:** The patient's data explicitly and unequivocally violates the criterion.\n" - "- **Not Violated:** The patient's data confirms compliance with the criterion.\n" - "- **Unclear:** Insufficient or missing patient data to verify.\n" - "- **Irrelevant:** The criterion does not apply to the patient's context.\n\n" - "### IMPORTANT INSTRUCTIONS\n" - "- Ensure all criteria are assessed one-by-one.\n" - "- Use **only** the provided patient data; **do not infer, assume, or extrapolate beyond the given information.**\n" - "- Justifications must be strictly based on direct evidence from the patient profile.\n" - "### RESPONSE FORMAT (STRICTLY FOLLOW)\n" - "{\n" - ' "Inclusion_Criteria_Evaluation": [\n' - ' {"Criterion": "Exact inclusion criterion text", "Classification": "Met | Not Met | Unclear | Irrelevant", "Justification": "Clear, evidence-based rationale using ONLY provided data"}\n' - " ],\n" - ' "Exclusion_Criteria_Evaluation": [\n' - ' {"Criterion": "Exact exclusion criterion text", "Classification": "Violated | Not Violated | Unclear | Irrelevant", "Justification": "Clear, evidence-based rationale using ONLY provided data"}\n' - " ],\n" - ' "Recap": "Concise summary of key qualifying/disqualifying factors",\n' - ' "Final Decision": "Eligible | Likely Eligible (leaning toward inclusion) | Likely Ineligible (leaning toward exclusion) | Ineligible"\n' - "}\n\n" - "### INPUT\n" - "---Start of Clinical Trial Criteria---\n" - f"{criteria_text_formatted}\n" - "---End of Clinical Trial Criteria---\n\n" - "----\n" - "---Start of Patient Description---\n" - f"{patient_profile}\n" - "---End of Patient Description---\n" - "## IMPORTANT REMINDER:\n" - "NEVER make assumptions, inferences, or extrapolations beyond the explicitly stated patient information." - ), - }, - ] - else: - chat = [ - { - "role": "system", - "content": ( - "You are a clinical assistant tasked with assessing the eligibility of a patient for a clinical trial. " - "Output only a JSON object evaluating trial eligibility for the patient based only on the provided criteria and patient profile.\n" - ), - }, - { - "role": "user", - "content": ( - "For each criterion, classify:\n" - '- If Inclusion Criterion: "Met" | "Not Met" | "Unclear" | "Irrelevant"\n' - '- If Exclusion Criterion: "Violated" | "Not Violated" | "Unclear" | "Irrelevant"\n\n' - "Provide a justification for each classification based strictly on the provided data. " - "Output this JSON schema:\n" - "{\n" - ' "Inclusion_Criteria_Evaluation": [ {"Criterion": "...", "Classification": "...", "Justification": "..."} ],\n' - ' "Exclusion_Criteria_Evaluation": [ {"Criterion": "...", "Classification": "...", "Justification": "..."} ],\n' - ' "Final Decision": "Eligible | Likely Eligible | Likely Ineligible | Ineligible"\n' - "}\n\n" - "---Start of Clinical Trial Criteria---\n" - f"{criteria_text_formatted}\n" - "---End of Clinical Trial Criteria---\n\n" - "---Start of Patient Description---\n" - f"{patient_profile}\n" - "---End of Patient Description---\n" - ), - }, - ] - - if hasattr(self.tokenizer, "apply_chat_template"): - return self.tokenizer.apply_chat_template( - chat, tokenize=False, add_generation_prompt=True - ) - # Fallback: simple concatenation - system_part = f"{chat[0]['content']}\n\n" - user_part = f"{chat[1]['content']}\n\n" - return system_part + user_part + "Answer: " + def _progress_desc(self) -> str: + return f"GPU {self.device} Processing Trials" # ---------------------- Core batch path ---------------------- @@ -273,75 +160,3 @@ def _process_batch(self, batch: List[Dict], output_folder: str): logger.error(f"Batch processing failed: {str(e)}") for item in batch: logger.error(f"Failed trial: {item['nct_id']}") - - # ---------------------- Persistence ---------------------- - - def _save_outputs(self, nct_id: str, response: str, output_folder: str): - try: - os.makedirs(output_folder, exist_ok=True) - txt_path = f"{output_folder}/{nct_id}.txt" - write_text_file([response], txt_path) - try: - json_data = extract_json_object(response) - write_json_file(json_data, f"{output_folder}/{nct_id}.json") - logger.info(f"Processed {nct_id} successfully") - except (json.JSONDecodeError, ValueError) as e: - logger.error(f"Invalid JSON response for {nct_id}: {str(e)}") - write_json_file( - {"error": "invalid_json_response", "raw_output": response}, - f"{output_folder}/{nct_id}.json", - ) - except Exception as e: - logger.error(f"Failed to save {nct_id}: {str(e)}") - - # ---------------------- Public API ---------------------- - - def process_trials( - self, - nct_ids: List[str], - json_folder: str, - output_folder: str, - patient_profile: List[str], - ): - """ - Length-buckets all prompts to reduce padding overhead, then processes in batches. - Keeps existing outputs (idempotent resume). - """ - patient_text = " ".join( - str(line).strip() for line in patient_profile if str(line).strip() - ) - - # Build worklist, skipping already-processed trials - items: List[Dict] = [] - for nct_id in nct_ids: - output_path = f"{output_folder}/{nct_id}.json" - if os.path.exists(output_path): - logger.info(f"Skipping existing: {nct_id}") - continue - criteria_text = self._load_trial_data(nct_id, json_folder) - prompt = self._format_prompt(criteria_text, patient_text) - - # Measure token length once for bucketing (no truncation here) - try: - tok = self.tokenizer(prompt, add_special_tokens=False) - tok_len = len(tok["input_ids"]) - except Exception: - # Fallback: rough char-based estimate if tokenizer hiccups - tok_len = max(1, len(prompt) // 4) - - items.append({"nct_id": nct_id, "prompt": prompt, "tok_len": tok_len}) - - if not items: - logger.info("No work to do.") - return - - # Sort by length (ascending) => minimal padding inside batches - items.sort(key=lambda x: x["tok_len"]) - - # Process in batches - for i in tqdm( - range(0, len(items), self.batch_size), - desc=f"GPU {self.device} Processing Trials", - ): - batch = items[i : i + self.batch_size] - self._process_batch(batch, output_folder) diff --git a/src/trialmatchai/matching/eligibility_reasoning_vllm.py b/src/trialmatchai/matching/eligibility_reasoning_vllm.py index 88d05626..9b3d1671 100644 --- a/src/trialmatchai/matching/eligibility_reasoning_vllm.py +++ b/src/trialmatchai/matching/eligibility_reasoning_vllm.py @@ -1,19 +1,15 @@ from __future__ import annotations -import json -import os import time from typing import Any, Dict, List, Optional -from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file -from trialmatchai.utils.json_utils import extract_json_object +from trialmatchai.matching.eligibility_base import BaseTrialProcessor from trialmatchai.utils.logging_config import setup_logging -from tqdm import tqdm logger = setup_logging(__name__) -class BatchTrialProcessorVLLM: +class BatchTrialProcessorVLLM(BaseTrialProcessor): def __init__( self, llm: Any, @@ -56,6 +52,9 @@ def __init__( detokenize=True, ) + def _progress_desc(self) -> str: + return "vLLM Processing Trials" + def _init_validate_lora_request(self, lora_request): """Validate LoRA request during initialization.""" if lora_request is None: @@ -92,120 +91,6 @@ def _init_validate_lora_request(self, lora_request): logger.warning("Disabling LoRA due to validation error") return None - # ---------------------- I/O helpers ---------------------- - - def _load_trial_data(self, nct_id: str, json_folder: str) -> str: - try: - path = f"{json_folder}/{nct_id}.json" - trial_data = read_json_file(path) - return trial_data.get("eligibility_criteria", "") - except Exception as e: - logger.error(f"Error loading {nct_id}: {str(e)}") - return "" - - # ---------------------- Prompting ---------------------- - - def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: - criteria_text_formatted = ( - f"Eligibility Criteria:\n{criteria_text}" - if criteria_text - else "No eligibility criteria provided." - ) - - if self.use_cot: - system_msg = ( - "You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning. " - "Answer the following question. Before answering, create a concise chain of thoughts reasoning to ensure a logical and accurate response.\n" - ) - chat = [ - {"role": "system", "content": system_msg}, - { - "role": "user", - "content": ( - "Assess the given patient's eligibility for a clinical trial by evaluating each and every criterion individually.\n\n" - "### INCLUSION CRITERIA ASSESSMENT\n" - "For each inclusion criterion, classify it as one of:\n" - "- **Met:** The patient's data explicitly and unequivocally satisfies the criterion.\n" - "- **Not Met:** The patient's data explicitly and unequivocally contradicts or fails to satisfy the criterion.\n" - "- **Unclear:** Insufficient or missing patient data to verify.\n" - "- **Irrelevant:** The criterion does not apply to the patient's context.\n\n" - "### EXCLUSION CRITERIA ASSESSMENT\n" - "For each exclusion criterion, classify it as one of:\n" - "- **Violated:** The patient's data explicitly and unequivocally violates the criterion.\n" - "- **Not Violated:** The patient's data confirms compliance with the criterion.\n" - "- **Unclear:** Insufficient or missing patient data to verify.\n" - "- **Irrelevant:** The criterion does not apply to the patient's context.\n\n" - "### IMPORTANT INSTRUCTIONS\n" - "- Ensure all criteria are assessed one-by-one.\n" - "- Use **only** the provided patient data; **do not infer, assume, or extrapolate beyond the given information.**\n" - "- Justifications must be strictly based on direct evidence from the patient profile.\n" - "### RESPONSE FORMAT (STRICTLY FOLLOW)\n" - "{\n" - ' "Inclusion_Criteria_Evaluation": [\n' - ' {"Criterion": "Exact inclusion criterion text", "Classification": "Met | Not Met | Unclear | Irrelevant", "Justification": "Clear, evidence-based rationale using ONLY provided data"}\n' - " ],\n" - ' "Exclusion_Criteria_Evaluation": [\n' - ' {"Criterion": "Exact exclusion criterion text", "Classification": "Violated | Not Violated | Unclear | Irrelevant", "Justification": "Clear, evidence-based rationale using ONLY provided data"}\n' - " ],\n" - ' "Recap": "Concise summary of key qualifying/disqualifying factors",\n' - ' "Final Decision": "Eligible | Likely Eligible (leaning toward inclusion) | Likely Ineligible (leaning toward exclusion) | Ineligible"\n' - "}\n\n" - "### INPUT\n" - "---Start of Clinical Trial Criteria---\n" - f"{criteria_text_formatted}\n" - "---End of Clinical Trial Criteria---\n\n" - "----\n" - "---Start of Patient Description---\n" - f"{patient_profile}\n" - "---End of Patient Description---\n" - "## IMPORTANT REMINDER:\n" - "NEVER make assumptions, inferences, or extrapolations beyond the explicitly stated patient information." - ), - }, - ] - else: - chat = [ - { - "role": "system", - "content": ( - "You are a clinical assistant tasked with assessing the eligibility of a patient for a clinical trial. " - "Output only a JSON object evaluating trial eligibility for the patient based only on the provided criteria and patient profile.\n" - ), - }, - { - "role": "user", - "content": ( - "For each criterion, classify:\n" - '- If Inclusion Criterion: "Met" | "Not Met" | "Unclear" | "Irrelevant"\n' - '- If Exclusion Criterion: "Violated" | "Not Violated" | "Unclear" | "Irrelevant"\n\n' - "Provide a justification for each classification based strictly on the provided data. " - "Output this JSON schema:\n" - "{\n" - ' "Inclusion_Criteria_Evaluation": [ {"Criterion": "...", "Classification": "...", "Justification": "..."} ],\n' - ' "Exclusion_Criteria_Evaluation": [ {"Criterion": "...", "Classification": "...", "Justification": "..."} ],\n' - ' "Final Decision": "Eligible | Likely Eligible | Likely Ineligible | Ineligible"\n' - "}\n\n" - "---Start of Clinical Trial Criteria---\n" - f"{criteria_text_formatted}\n" - "---End of Clinical Trial Criteria---\n\n" - "---Start of Patient Description---\n" - f"{patient_profile}\n" - "---End of Patient Description---\n" - ), - }, - ] - - if self.tokenizer is not None and hasattr( - self.tokenizer, "apply_chat_template" - ): - return self.tokenizer.apply_chat_template( - chat, tokenize=False, add_generation_prompt=True - ) - - system_part = f"{chat[0]['content']}\n\n" - user_part = f"{chat[1]['content']}\n\n" - return system_part + user_part + "Answer: " - # ---------------------- Core batch path (vLLM) ---------------------- def _process_batch(self, batch: List[Dict], output_folder: str): @@ -385,82 +270,10 @@ def _validate_lora_request(self): logger.warning("Disabling LoRA due to validation error") return None - # ---------------------- Persistence ---------------------- - - def _save_outputs(self, nct_id: str, response: str, output_folder: str): - try: - os.makedirs(output_folder, exist_ok=True) - txt_path = f"{output_folder}/{nct_id}.txt" - write_text_file([response], txt_path) - try: - json_data = extract_json_object(response) - write_json_file(json_data, f"{output_folder}/{nct_id}.json") - logger.info(f"Processed {nct_id} successfully") - except (json.JSONDecodeError, ValueError) as e: - logger.error(f"Invalid JSON response for {nct_id}: {str(e)}") - write_json_file( - {"error": "invalid_json_response", "raw_output": response}, - f"{output_folder}/{nct_id}.json", - ) - except Exception as e: - logger.error(f"Failed to save {nct_id}: {str(e)}") - - # ---------------------- Public API ---------------------- - - def process_trials( - self, - nct_ids: List[str], - json_folder: str, - output_folder: str, - patient_profile: List[str], - ): - patient_text = " ".join( - str(line).strip() for line in patient_profile if str(line).strip() - ) - - items: List[Dict] = [] - for nct_id in nct_ids: - output_path = f"{output_folder}/{nct_id}.json" - if os.path.exists(output_path): - logger.info(f"Skipping existing: {nct_id}") - continue - - criteria_text = self._load_trial_data(nct_id, json_folder) - prompt = self._format_prompt(criteria_text, patient_text) - - # Calculate token length with comprehensive type safety - tok_len = self._safe_calculate_token_length(prompt, nct_id) - - items.append({"nct_id": nct_id, "prompt": prompt, "tok_len": tok_len}) - - if not items: - logger.info("No work to do.") - return - - # Validate all tok_len values before sorting - self._validate_token_lengths(items) - - # Sort by token length if length_bucket is enabled - if self.length_bucket: - try: - items.sort(key=lambda x: x["tok_len"]) - logger.debug(f"Successfully sorted {len(items)} items by token length") - except Exception as e: - logger.error(f"Failed to sort items by token length: {str(e)}") - # Log the problematic items for debugging - for i, item in enumerate(items): - logger.error( - f"Item {i}: nct_id={item['nct_id']}, tok_len={item['tok_len']}, type={type(item['tok_len'])}" - ) - # Disable length bucketing and continue without sorting - logger.warning("Disabling length bucketing due to sorting failure") - pass + # ---------------------- Token-length bucketing (vLLM tokenizer aware) ---- - for i in tqdm( - range(0, len(items), self.batch_size), desc="vLLM Processing Trials" - ): - batch = items[i : i + self.batch_size] - self._process_batch(batch, output_folder) + def _token_length(self, prompt: str, nct_id: str = "") -> int: + return self._safe_calculate_token_length(prompt, nct_id) def _safe_calculate_token_length(self, prompt: str, nct_id: str) -> int: """Safely calculate token length using vLLM's tokenizer.""" @@ -533,29 +346,3 @@ def _extract_token_length(self, tokenized, nct_id: str) -> int: logger.warning(f"Could not extract token length for {nct_id}, using fallback") return fallback_length - - def _validate_token_lengths(self, items: List[Dict]) -> None: - """Validate that all token lengths are integers and fix any that aren't.""" - for i, item in enumerate(items): - tok_len = item["tok_len"] - if not isinstance(tok_len, int): - logger.error( - f"Invalid tok_len type for {item['nct_id']}: {type(tok_len)}, value: {tok_len}" - ) - # Force conversion to int - try: - if ( - isinstance(tok_len, (float, str)) - and str(tok_len).replace(".", "").isdigit() - ): - item["tok_len"] = int(float(tok_len)) - logger.warning( - f"Converted tok_len for {item['nct_id']} from {type(tok_len)} to int" - ) - else: - # Use character-based fallback - item["tok_len"] = max(1, len(item["prompt"]) // 4) - logger.warning(f"Using fallback tok_len for {item['nct_id']}") - except Exception as e: - logger.error(f"Failed to convert tok_len for {item['nct_id']}: {e}") - item["tok_len"] = max(1, len(item["prompt"]) // 4) From 1902d30a2ba54b3b7734cadda2f109d1c17d15cc Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:29:17 +0200 Subject: [PATCH 19/64] =?UTF-8?q?PR8:=20performance=20=E2=80=94=20skip=20u?= =?UTF-8?q?nused=20HF=20CoT=20load=20under=20vLLM;=20reuse=20indexed=20rec?= =?UTF-8?q?ords?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - main_pipeline only loads (and half()s) the HuggingFace CoT model when cot_backend != "vllm". Under the vLLM backend run_rag_processing loads its own engine and ignored the HF model, so it was wasting GPU memory and load time (and could OOM alongside the vLLM engine). - _rank_trial_rows / _rank_criteria_rows skip rebuilding the trial/criteria record when the candidate row read from the index already has the derived fields (search_text); raw in-memory docs still get built. Avoids recomputing search_text/search_vector for every candidate on every query. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/main.py | 32 +++++++++++++--------- src/trialmatchai/search/lancedb_backend.py | 6 ++-- 2 files changed, 23 insertions(+), 15 deletions(-) diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index a157f315..f8011abd 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -266,21 +266,27 @@ def main_pipeline(config_path: str | None = None) -> int: import warnings - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", message=".*quantization_config.*", category=UserWarning - ) - model, tokenizer = load_model_and_tokenizer( - config["model"], config["global"]["device"] - ) + # The HuggingFace CoT model is only used by the default backend. When the + # vLLM backend is configured, run_rag_processing loads its own engine and + # ignores these, so skip loading (and half()-ing) the HF model entirely to + # avoid wasting GPU memory and load time (and risking OOM next to vLLM). + model, tokenizer = None, None + if config.get("cot_backend", "default") != "vllm": + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", message=".*quantization_config.*", category=UserWarning + ) + model, tokenizer = load_model_and_tokenizer( + config["model"], config["global"]["device"] + ) - if tokenizer.pad_token is None: # type: ignore - tokenizer.pad_token = tokenizer.eos_token # type: ignore - if hasattr(model.config, "pad_token_id") and model.config.pad_token_id is None: # type: ignore - model.config.pad_token_id = tokenizer.pad_token_id # type: ignore + if tokenizer.pad_token is None: # type: ignore + tokenizer.pad_token = tokenizer.eos_token # type: ignore + if hasattr(model.config, "pad_token_id") and model.config.pad_token_id is None: # type: ignore + model.config.pad_token_id = tokenizer.pad_token_id # type: ignore - if config["global"]["device"] != "cpu" and torch.cuda.is_available(): - model = model.half() # type: ignore + if config["global"]["device"] != "cpu" and torch.cuda.is_available(): + model = model.half() # type: ignore # Initialize components embedder = build_embedder(config) diff --git a/src/trialmatchai/search/lancedb_backend.py b/src/trialmatchai/search/lancedb_backend.py index ef46ed70..651b1226 100644 --- a/src/trialmatchai/search/lancedb_backend.py +++ b/src/trialmatchai/search/lancedb_backend.py @@ -532,7 +532,9 @@ def _rank_trial_rows( mode = (search_mode or "hybrid").lower() hits: list[SearchHit] = [] for raw in rows: - row = build_trial_record(raw) + # Candidate rows read from the index already carry the derived fields; + # only rebuild for callers (e.g. in-memory) that pass raw docs. + row = dict(raw) if "search_text" in raw else build_trial_record(raw) if not _trial_passes_filters( row, age=age, @@ -578,7 +580,7 @@ def _rank_criteria_rows( ) hits: list[SearchHit] = [] for raw in rows: - row = build_criteria_record(raw) + row = dict(raw) if "search_text" in raw else build_criteria_record(raw) if allowed and row.get("nct_id") not in allowed: continue text_score = _weighted_text_score(row, [query], fields) From 1476501779e54c5be768345b9f059332ff4aa6d2 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:33:45 +0200 Subject: [PATCH 20/64] =?UTF-8?q?PR9:=20hygiene=20=E2=80=94=20logging,=20i?= =?UTF-8?q?mport-patient=20embedder,=20prune=20unused=20deps?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - logging_config: configure the root handler exactly once instead of building a new StreamHandler on every setup_logging() call (~all modules call it at import). Per-logger request-id ContextFilter behavior is unchanged. - import_patient: build and pass an embedder to the entity annotator so concept linking can use semantic search instead of silently degrading to lexical-only (still degrades gracefully to no-entities when ML extras are absent). - pyproject: drop 5 dependencies never imported in src (regex, python-dotenv, rich, bioregistry, rapidfuzz); regenerated uv.lock. numpy/pyarrow kept as real transitive runtime needs of pandas/lancedb. Co-Authored-By: Claude Opus 4.8 --- pyproject.toml | 5 - src/trialmatchai/cli/import_patient.py | 6 +- src/trialmatchai/utils/logging_config.py | 17 +- uv.lock | 312 ++++++++++++++--------- 4 files changed, 210 insertions(+), 130 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index bfbe518e..04dde31d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,15 +17,10 @@ dependencies = [ "requests==2.34.2", "tenacity==9.0.0", "tqdm==4.67.1", - "regex>=2025.10.22,<2027", "PyYAML==6.0.2", - "python-dotenv==1.2.2", - "rich==13.9.4", "lancedb>=0.25.0,<0.26", "pyarrow>=24.0.0,<25", "pydantic>=2.12.0,<3", - "bioregistry==0.13.62", - "rapidfuzz==3.14.5", ] [project.optional-dependencies] diff --git a/src/trialmatchai/cli/import_patient.py b/src/trialmatchai/cli/import_patient.py index 3c642174..de0587c2 100644 --- a/src/trialmatchai/cli/import_patient.py +++ b/src/trialmatchai/cli/import_patient.py @@ -82,8 +82,12 @@ def main() -> int: def _try_build_entity_annotator(config: dict[str, Any]): try: from trialmatchai.entities import build_entity_annotator + from trialmatchai.models.embedding import build_embedder - return build_entity_annotator(config) + # Pass an embedder so concept linking can use semantic (vector) search; + # without it linking silently degrades to lexical-only matching. + embedder = build_embedder(config) + return build_entity_annotator(config, embedder=embedder) except Exception as exc: logger.warning("Entity annotation unavailable; importing without entities: %s", exc) return None diff --git a/src/trialmatchai/utils/logging_config.py b/src/trialmatchai/utils/logging_config.py index 04eaedb0..8c44f8e6 100644 --- a/src/trialmatchai/utils/logging_config.py +++ b/src/trialmatchai/utils/logging_config.py @@ -39,8 +39,14 @@ def reset_request_id(token: contextvars.Token) -> None: _request_id_var.reset(token) -def setup_logging(name: Optional[str] = None) -> logging.Logger: - """Configure logging for the application.""" +_root_configured = False + + +def _configure_root_once() -> None: + """Install the root stream handler exactly once (imported by ~all modules).""" + global _root_configured + if _root_configured: + return level = os.getenv("TRIALMATCHAI_LOG_LEVEL", "INFO").upper() use_json = os.getenv("TRIALMATCHAI_LOG_JSON", "0") in {"1", "true", "TRUE"} handler = logging.StreamHandler(sys.stdout) @@ -54,6 +60,13 @@ def setup_logging(name: Optional[str] = None) -> logging.Logger: ) ) logging.basicConfig(level=level, handlers=[handler]) + _root_configured = True + + +def setup_logging(name: Optional[str] = None) -> logging.Logger: + """Return a named logger with request-id context; configures root once.""" + _configure_root_once() + level = os.getenv("TRIALMATCHAI_LOG_LEVEL", "INFO").upper() logger = logging.getLogger(name if name else __name__) logger.setLevel(level) if not any(isinstance(f, ContextFilter) for f in logger.filters): diff --git a/uv.lock b/uv.lock index 932f1250..4adfce83 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 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"sha256:7b54b9c67c2b06bd7b9a77253d242124b9c95d2c02def5a1144001ee547dd9d5", size = 106558, upload-time = "2026-05-19T21:28:45.806Z" }, { url = "https://files.pythonhosted.org/packages/fd/4d/4b880086bd0d3e034d25647be1d830afc3e3f610e98c4ab3490af6b1b6d5/yarl-1.24.2-py3-none-any.whl", hash = "sha256:2783d9226db8797636cd6896e4de81feed252d1db72265686c9558d97a4d94b9", size = 53576, upload-time = "2026-05-19T21:31:03.909Z" }, ] From 411dc5b9aa93070c4fabdeb6d311126c8385a621 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:42:32 +0200 Subject: [PATCH 21/64] feat: fine-tuning integration for NER, reranker, and CoT models Lets users train their own models and plug them straight into the pipeline, instead of only relying on the vanilla checkpoints. The integration path already existed via config (entity_extraction.model_name, model.reranker_adapter_path, model.cot_adapter_path); this adds the missing training side, modernized for the current architecture (GLiNER for NER, LoRA for reranker/CoT). New trialmatchai.finetuning package (heavy deps imported lazily): - config.FinetuneConfig: shared LoRA SFT hyper-parameters. - data: JSONL loaders + prompt builders that REUSE the runtime prompts (LLMReranker.create_messages, chat templates) so train == inference; plus a char-span -> GLiNER token-span converter. - _sft.run_sft: LoRA SFT loop with prompt-masked labels (loss on completion). - cot/reranker/ner: thin task fine-tuners producing a LoRA adapter (cot, reranker) or a GLiNER checkpoint (ner). - cli: `trialmatchai-finetune {cot,reranker,ner}` console command. pyproject: new `finetune` optional extra (torch/transformers/peft/accelerate/ datasets/gliner[2]/bitsandbytes) + the console entry point. LLMReranker .create_messages is now a @staticmethod so finetuning reuses it without loading a model. CI imports the finetuning modules and smoke-tests the new CLI. Tests cover data conversion, prompt reuse, and CLI parsing (CPU-only). Co-Authored-By: Claude Opus 4.8 --- .github/workflows/ci.yml | 4 + pyproject.toml | 11 ++ src/trialmatchai/finetuning/__init__.py | 17 +++ src/trialmatchai/finetuning/_sft.py | 139 ++++++++++++++++++ src/trialmatchai/finetuning/cli.py | 123 ++++++++++++++++ src/trialmatchai/finetuning/config.py | 77 ++++++++++ src/trialmatchai/finetuning/cot.py | 17 +++ src/trialmatchai/finetuning/data.py | 116 +++++++++++++++ src/trialmatchai/finetuning/ner.py | 90 ++++++++++++ src/trialmatchai/finetuning/reranker.py | 21 +++ src/trialmatchai/models/llm/llm_reranker.py | 3 +- tests/test_finetuning.py | 73 ++++++++++ uv.lock | 148 ++++++++++++++++++-- 13 files changed, 824 insertions(+), 15 deletions(-) create mode 100644 src/trialmatchai/finetuning/__init__.py create mode 100644 src/trialmatchai/finetuning/_sft.py create mode 100644 src/trialmatchai/finetuning/cli.py create mode 100644 src/trialmatchai/finetuning/config.py create mode 100644 src/trialmatchai/finetuning/cot.py create mode 100644 src/trialmatchai/finetuning/data.py create mode 100644 src/trialmatchai/finetuning/ner.py create mode 100644 src/trialmatchai/finetuning/reranker.py create mode 100644 tests/test_finetuning.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 2e59511d..51c446db 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -62,6 +62,7 @@ jobs: uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-update-registry --help uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-import-patient --help uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-run --help + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-finetune --help - name: Secret scan run: uv run python scripts/scan_secrets.py @@ -110,6 +111,9 @@ jobs: "trialmatchai.models.llm.llm_reranker", "trialmatchai.matching.eligibility_reasoning", "trialmatchai.matching.eligibility_reasoning_vllm", + "trialmatchai.finetuning.cot", + "trialmatchai.finetuning.reranker", + "trialmatchai.finetuning.ner", ] for name in modules: importlib.import_module(name) diff --git a/pyproject.toml b/pyproject.toml index 04dde31d..fa7d20b5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,8 +44,19 @@ entity = [ "gliner==0.2.27", "gliner2>=1.3.1,<2", ] +finetune = [ + "torch==2.11.0", + "transformers==5.6.2", + "accelerate==1.8.1", + "peft==0.15.2", + "datasets>=3.0,<5", + "gliner==0.2.27", + "gliner2>=1.3.1,<2", + "bitsandbytes==0.49.2; sys_platform == 'linux' or sys_platform == 'win32'", +] [project.scripts] +trialmatchai-finetune = "trialmatchai.finetuning.cli:main" trialmatchai = "trialmatchai.cli.main:main" trialmatchai-healthcheck = "trialmatchai.cli.healthcheck:main" trialmatchai-run = "trialmatchai.cli.run:main" diff --git a/src/trialmatchai/finetuning/__init__.py b/src/trialmatchai/finetuning/__init__.py new file mode 100644 index 00000000..4faa4f07 --- /dev/null +++ b/src/trialmatchai/finetuning/__init__.py @@ -0,0 +1,17 @@ +"""Fine-tuning utilities for TrialMatchAI's local models. + +Lets users train their own NER (GLiNER), reranker (LoRA), and CoT eligibility +(LoRA) models and plug them straight back into the pipeline via config: + +- NER: entity_extraction.model_name -> a fine-tuned GLiNER checkpoint +- reranker: model.reranker_adapter_path -> a LoRA adapter +- CoT: model.cot_adapter_path -> a LoRA adapter + +All heavy training dependencies are imported lazily, so this package imports +cleanly without the ``finetune`` extra installed. Install it with +``uv sync --extra finetune``. +""" + +from trialmatchai.finetuning.config import FinetuneConfig + +__all__ = ["FinetuneConfig"] diff --git a/src/trialmatchai/finetuning/_sft.py b/src/trialmatchai/finetuning/_sft.py new file mode 100644 index 00000000..355be793 --- /dev/null +++ b/src/trialmatchai/finetuning/_sft.py @@ -0,0 +1,139 @@ +"""Shared LoRA supervised fine-tuning loop for causal LMs. + +Builds (input_ids, labels) from chat messages with the prompt masked to -100 so +loss is computed only on the assistant completion, then trains a LoRA adapter +and saves it. Heavy dependencies are imported lazily. +""" + +from __future__ import annotations + +from typing import Dict, List + +from trialmatchai.finetuning.config import FinetuneConfig +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def _load_train_deps(): + try: + import torch + from datasets import Dataset + from peft import LoraConfig, TaskType, get_peft_model + from transformers import ( + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + DataCollatorForSeq2Seq, + Trainer, + ) + except Exception as exc: # pragma: no cover - exercised only without the extra + raise RuntimeError( + "Fine-tuning requires the optional `finetune` dependencies " + "(`uv sync --extra finetune`)." + ) from exc + return { + "torch": torch, + "Dataset": Dataset, + "LoraConfig": LoraConfig, + "TaskType": TaskType, + "get_peft_model": get_peft_model, + "AutoModelForCausalLM": AutoModelForCausalLM, + "AutoTokenizer": AutoTokenizer, + "BitsAndBytesConfig": BitsAndBytesConfig, + "DataCollatorForSeq2Seq": DataCollatorForSeq2Seq, + "Trainer": Trainer, + } + + +def _build_tokenizer(deps, config: FinetuneConfig): + tokenizer = deps["AutoTokenizer"].from_pretrained( + config.base_model, + trust_remote_code=config.trust_remote_code, + token=config.hf_token, + ) + if tokenizer.pad_token is None: + tokenizer.pad_token = tokenizer.eos_token + return tokenizer + + +def _encode_example( + tokenizer, messages: List[Dict[str, str]], max_seq_length: int +) -> Dict[str, list]: + """Tokenize a chat example, masking the prompt so only the completion trains.""" + full = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=False + ) + prompt = tokenizer.apply_chat_template( + messages[:-1], tokenize=False, add_generation_prompt=True + ) + full_ids = tokenizer(full, truncation=True, max_length=max_seq_length)["input_ids"] + prompt_ids = tokenizer( + prompt, truncation=True, max_length=max_seq_length + )["input_ids"] + prompt_len = min(len(prompt_ids), len(full_ids)) + labels = [-100] * prompt_len + full_ids[prompt_len:] + return {"input_ids": full_ids, "attention_mask": [1] * len(full_ids), "labels": labels} + + +def run_sft(config: FinetuneConfig, message_lists: List[List[Dict[str, str]]]) -> str: + """Run LoRA SFT over a list of chat-message examples; returns the adapter dir.""" + if not message_lists: + raise ValueError("No training examples provided.") + deps = _load_train_deps() + torch = deps["torch"] + + tokenizer = _build_tokenizer(deps, config) + + compute_dtype = torch.bfloat16 if config.bf16 else torch.float16 + quant_config = None + if config.load_in_4bit: + quant_config = deps["BitsAndBytesConfig"]( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=compute_dtype, + ) + + model = deps["AutoModelForCausalLM"].from_pretrained( + config.base_model, + torch_dtype=compute_dtype, + quantization_config=quant_config, + trust_remote_code=config.trust_remote_code, + token=config.hf_token, + ) + model.config.use_cache = False + + peft_config = deps["LoraConfig"]( + task_type=deps["TaskType"].CAUSAL_LM, + inference_mode=False, + r=config.lora_rank, + lora_alpha=config.lora_alpha, + lora_dropout=config.lora_dropout, + target_modules=config.target_modules, + ) + model = deps["get_peft_model"](model, peft_config) + model.print_trainable_parameters() + + encoded = [ + _encode_example(tokenizer, messages, config.max_seq_length) + for messages in message_lists + ] + dataset = deps["Dataset"].from_list(encoded) + + collator = deps["DataCollatorForSeq2Seq"]( + tokenizer, padding=True, label_pad_token_id=-100 + ) + trainer = deps["Trainer"]( + model=model, + args=config.to_training_arguments(), + train_dataset=dataset, + data_collator=collator, + ) + + logger.info("Starting LoRA SFT on %d examples...", len(encoded)) + trainer.train() + model.save_pretrained(config.output_dir) + tokenizer.save_pretrained(config.output_dir) + logger.info("Saved LoRA adapter to %s", config.output_dir) + return config.output_dir diff --git a/src/trialmatchai/finetuning/cli.py b/src/trialmatchai/finetuning/cli.py new file mode 100644 index 00000000..3fea874c --- /dev/null +++ b/src/trialmatchai/finetuning/cli.py @@ -0,0 +1,123 @@ +"""``trialmatchai-finetune`` — train custom NER / reranker / CoT models. + +Examples: + trialmatchai-finetune cot --base-model microsoft/phi-4 \ + --train-data data/cot.jsonl --output-dir models/cot-adapter + trialmatchai-finetune reranker --base-model google/gemma-2-2b-it \ + --train-data data/reranker.jsonl --output-dir models/reranker-adapter + trialmatchai-finetune ner --base-model fastino/gliner2-base \ + --train-data data/ner.jsonl --output-dir models/ner + +Plug the result back into config: entity_extraction.model_name (NER), +model.reranker_adapter_path (reranker), model.cot_adapter_path (CoT). +""" + +from __future__ import annotations + +import argparse +import sys +from typing import Optional, Sequence + +from trialmatchai.finetuning.config import FinetuneConfig + + +def _add_common_lora_args(parser: argparse.ArgumentParser) -> None: + parser.add_argument("--base-model", required=True) + parser.add_argument("--train-data", required=True) + parser.add_argument("--output-dir", required=True) + parser.add_argument("--eval-data", default=None) + parser.add_argument("--epochs", type=float, default=2.0) + parser.add_argument("--learning-rate", type=float, default=5e-5) + parser.add_argument("--batch-size", type=int, default=2) + parser.add_argument("--grad-accum", type=int, default=16) + parser.add_argument("--max-seq-length", type=int, default=2048) + parser.add_argument("--lora-rank", type=int, default=32) + parser.add_argument("--lora-alpha", type=int, default=64) + parser.add_argument("--lora-dropout", type=float, default=0.1) + parser.add_argument("--max-examples", type=int, default=None) + parser.add_argument("--no-4bit", action="store_true", help="Disable 4-bit loading") + parser.add_argument("--fp16", action="store_true", help="Use fp16 instead of bf16") + parser.add_argument("--trust-remote-code", action="store_true") + parser.add_argument("--hf-token", default=None) + + +def _lora_config_from_args(args: argparse.Namespace) -> FinetuneConfig: + return FinetuneConfig( + base_model=args.base_model, + train_data=args.train_data, + output_dir=args.output_dir, + eval_data=args.eval_data, + epochs=args.epochs, + learning_rate=args.learning_rate, + per_device_batch_size=args.batch_size, + gradient_accumulation_steps=args.grad_accum, + max_seq_length=args.max_seq_length, + lora_rank=args.lora_rank, + lora_alpha=args.lora_alpha, + lora_dropout=args.lora_dropout, + max_examples=args.max_examples, + load_in_4bit=not args.no_4bit, + bf16=not args.fp16, + trust_remote_code=args.trust_remote_code, + hf_token=args.hf_token, + ) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="trialmatchai-finetune", + description="Fine-tune TrialMatchAI's NER, reranker, or CoT models.", + ) + sub = parser.add_subparsers(dest="component", required=True) + + cot = sub.add_parser("cot", help="LoRA SFT for the CoT eligibility model") + _add_common_lora_args(cot) + + reranker = sub.add_parser("reranker", help="LoRA SFT for the reranker (Yes/No)") + _add_common_lora_args(reranker) + + ner = sub.add_parser("ner", help="Fine-tune the GLiNER NER model") + ner.add_argument("--base-model", required=True) + ner.add_argument("--train-data", required=True) + ner.add_argument("--output-dir", required=True) + ner.add_argument("--eval-data", default=None) + ner.add_argument("--epochs", type=float, default=3.0) + ner.add_argument("--learning-rate", type=float, default=5e-6) + ner.add_argument("--batch-size", type=int, default=8) + ner.add_argument("--max-examples", type=int, default=None) + return parser + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = build_parser().parse_args(argv) + + if args.component == "cot": + from trialmatchai.finetuning.cot import finetune_cot + + finetune_cot(_lora_config_from_args(args)) + elif args.component == "reranker": + from trialmatchai.finetuning.reranker import finetune_reranker + + finetune_reranker(_lora_config_from_args(args)) + elif args.component == "ner": + from trialmatchai.finetuning.ner import NERFinetuneConfig, finetune_ner + + finetune_ner( + NERFinetuneConfig( + base_model=args.base_model, + train_data=args.train_data, + output_dir=args.output_dir, + eval_data=args.eval_data, + epochs=args.epochs, + learning_rate=args.learning_rate, + batch_size=args.batch_size, + max_examples=args.max_examples, + ) + ) + else: # pragma: no cover - argparse enforces choices + return 2 + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/trialmatchai/finetuning/config.py b/src/trialmatchai/finetuning/config.py new file mode 100644 index 00000000..01042db5 --- /dev/null +++ b/src/trialmatchai/finetuning/config.py @@ -0,0 +1,77 @@ +"""Shared configuration for the LoRA fine-tuners (CoT and reranker).""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import List, Optional + + +@dataclass +class FinetuneConfig: + """Hyper-parameters for LoRA supervised fine-tuning of a causal LM. + + Sensible defaults are tuned for a single consumer GPU; override per run. + """ + + base_model: str + train_data: str + output_dir: str + eval_data: Optional[str] = None + + # Optimization + epochs: float = 2.0 + learning_rate: float = 5e-5 + per_device_batch_size: int = 2 + gradient_accumulation_steps: int = 16 + warmup_ratio: float = 0.1 + max_seq_length: int = 2048 + weight_decay: float = 0.0 + seed: int = 42 + + # LoRA + lora_rank: int = 32 + lora_alpha: int = 64 + lora_dropout: float = 0.1 + target_modules: List[str] = field( + default_factory=lambda: [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + ] + ) + + # Runtime + load_in_4bit: bool = True + bf16: bool = True + trust_remote_code: bool = False + logging_steps: int = 10 + save_steps: int = 500 + save_total_limit: int = 3 + max_examples: Optional[int] = None + hf_token: Optional[str] = None + + def to_training_arguments(self): + """Build transformers.TrainingArguments (imported lazily).""" + from transformers import TrainingArguments + + return TrainingArguments( + output_dir=self.output_dir, + num_train_epochs=self.epochs, + learning_rate=self.learning_rate, + per_device_train_batch_size=self.per_device_batch_size, + gradient_accumulation_steps=self.gradient_accumulation_steps, + warmup_ratio=self.warmup_ratio, + weight_decay=self.weight_decay, + logging_steps=self.logging_steps, + save_steps=self.save_steps, + save_total_limit=self.save_total_limit, + bf16=self.bf16, + fp16=not self.bf16, + seed=self.seed, + report_to=[], + ddp_find_unused_parameters=False, + ) diff --git a/src/trialmatchai/finetuning/cot.py b/src/trialmatchai/finetuning/cot.py new file mode 100644 index 00000000..3d3b30ac --- /dev/null +++ b/src/trialmatchai/finetuning/cot.py @@ -0,0 +1,17 @@ +"""Fine-tune the chain-of-thought eligibility model (LoRA SFT). + +Produces a LoRA adapter that plugs into the pipeline via +``model.cot_adapter_path`` (HuggingFace backend) or vLLM's LoRARequest. +""" + +from __future__ import annotations + +from trialmatchai.finetuning._sft import run_sft +from trialmatchai.finetuning.config import FinetuneConfig +from trialmatchai.finetuning.data import cot_row_to_messages, read_jsonl + + +def finetune_cot(config: FinetuneConfig) -> str: + rows = read_jsonl(config.train_data, config.max_examples) + message_lists = [cot_row_to_messages(row) for row in rows] + return run_sft(config, message_lists) diff --git a/src/trialmatchai/finetuning/data.py b/src/trialmatchai/finetuning/data.py new file mode 100644 index 00000000..7e22bc62 --- /dev/null +++ b/src/trialmatchai/finetuning/data.py @@ -0,0 +1,116 @@ +"""Dataset loading and prompt formatting for fine-tuning. + +Training prompts reuse the exact runtime prompt builders so a fine-tuned model +sees the same format at train and inference time. + +Input formats (JSONL, one object per line): +- CoT: {"messages": [{"role","content"}, ...]} + or {"instruction": str, "input": str, "output": str} +- Reranker: {"patient_text": str, "criterion": str, "label": "Yes"|"No"} +- NER: {"text": str, "ner": [[start_char, end_char, "label"], ...]} + or GLiNER-native {"tokenized_text": [...], "ner": [[s_tok, e_tok, label]]} +""" + +from __future__ import annotations + +import json +import re +from typing import Any, Dict, Iterator, List, Optional + + +def read_jsonl(path: str, max_examples: Optional[int] = None) -> List[Dict[str, Any]]: + rows: List[Dict[str, Any]] = [] + with open(path, "r", encoding="utf-8") as handle: + for line in handle: + line = line.strip() + if not line: + continue + rows.append(json.loads(line)) + if max_examples is not None and len(rows) >= max_examples: + break + return rows + + +# --------------------------------------------------------------------------- CoT + + +def cot_row_to_messages(row: Dict[str, Any]) -> List[Dict[str, str]]: + """Normalize a CoT training row to a chat-message list.""" + if "messages" in row: + return list(row["messages"]) + instruction = (row.get("instruction") or "").strip() + user = (row.get("input") or row.get("question") or "").strip() + output = row.get("output") or row.get("answer") or "" + messages: List[Dict[str, str]] = [] + if instruction: + messages.append({"role": "system", "content": instruction}) + messages.append({"role": "user", "content": user}) + messages.append({"role": "assistant", "content": output}) + return messages + + +# ----------------------------------------------------------------------- Reranker + + +def reranker_row_to_messages(row: Dict[str, Any]) -> tuple[List[Dict[str, str]], str]: + """Build the reranker prompt (matching LLMReranker.create_messages) + target.""" + from trialmatchai.models.llm.llm_reranker import LLMReranker + + patient_text = str(row.get("patient_text", "")).strip() + criterion = str(row.get("criterion", row.get("trial_text", ""))).strip() + label = str(row.get("label", "")).strip() or "No" + if label not in {"Yes", "No"}: + label = "Yes" if label.lower() in {"1", "true", "yes", "relevant"} else "No" + messages = LLMReranker.create_messages(patient_text, criterion) + return messages, label + + +# --------------------------------------------------------------------------- NER + +_WORD_RE = re.compile(r"\w+|[^\w\s]") + + +def _tokenize_with_spans(text: str) -> tuple[List[str], List[tuple[int, int]]]: + tokens: List[str] = [] + spans: List[tuple[int, int]] = [] + for match in _WORD_RE.finditer(text): + tokens.append(match.group()) + spans.append((match.start(), match.end())) + return tokens, spans + + +def ner_row_to_gliner(row: Dict[str, Any]) -> Dict[str, Any]: + """Normalize an NER row to GLiNER's training format. + + Accepts GLiNER-native rows unchanged; converts char-span rows by mapping + character offsets onto whitespace/punctuation tokens. + """ + if "tokenized_text" in row: + return {"tokenized_text": row["tokenized_text"], "ner": row.get("ner", [])} + + text = str(row.get("text", "")) + raw_spans = row.get("ner") + if raw_spans is None: + raw_spans = [ + [ent["start"], ent["end"], ent["label"]] for ent in row.get("entities", []) + ] + + tokens, spans = _tokenize_with_spans(text) + ner: List[List[Any]] = [] + for start_char, end_char, label in raw_spans: + start_tok = next( + (i for i, (s, e) in enumerate(spans) if s <= start_char < e), None + ) + end_tok = next( + (i for i, (s, e) in enumerate(spans) if s < end_char <= e), None + ) + if start_tok is not None and end_tok is not None and end_tok >= start_tok: + ner.append([start_tok, end_tok, label]) + return {"tokenized_text": tokens, "ner": ner} + + +def iter_gliner_examples( + path: str, max_examples: Optional[int] = None +) -> Iterator[Dict[str, Any]]: + for row in read_jsonl(path, max_examples): + yield ner_row_to_gliner(row) diff --git a/src/trialmatchai/finetuning/ner.py b/src/trialmatchai/finetuning/ner.py new file mode 100644 index 00000000..b2fdfdf6 --- /dev/null +++ b/src/trialmatchai/finetuning/ner.py @@ -0,0 +1,90 @@ +"""Fine-tune the biomedical NER model (GLiNER). + +Produces a GLiNER checkpoint that plugs into the pipeline via +``entity_extraction.model_name`` (and backend "gliner"/"gliner2"). +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import List, Optional + +from trialmatchai.finetuning.data import iter_gliner_examples +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +@dataclass +class NERFinetuneConfig: + base_model: str + train_data: str + output_dir: str + eval_data: Optional[str] = None + epochs: float = 3.0 + learning_rate: float = 5e-6 + batch_size: int = 8 + weight_decay: float = 0.01 + warmup_ratio: float = 0.1 + max_examples: Optional[int] = None + labels: List[str] = field(default_factory=list) + seed: int = 42 + + +def finetune_ner(config: NERFinetuneConfig) -> str: + """Fine-tune a GLiNER model on span-annotated data and save it.""" + try: + from gliner import GLiNER + from gliner.data_processing.collator import DataCollator + from gliner.training import Trainer, TrainingArguments + except Exception as exc: # pragma: no cover - exercised only without the extra + raise RuntimeError( + "GLiNER fine-tuning requires the optional `finetune` dependencies " + "(`uv sync --extra finetune`). If your installed gliner exposes a " + "different training API, adapt finetuning/ner.py to it." + ) from exc + + data = list(iter_gliner_examples(config.train_data, config.max_examples)) + if not data: + raise ValueError("No training examples provided.") + eval_data = ( + list(iter_gliner_examples(config.eval_data)) if config.eval_data else None + ) + + model = GLiNER.from_pretrained(config.base_model) + model.set_sampling_params( + max_types=25, shuffle_types=True, random_drop=True, max_neg_type_ratio=1 + ) + + collator = DataCollator( + model.config, data_processor=model.data_processor, prepare_labels=True + ) + + args = TrainingArguments( + output_dir=config.output_dir, + num_train_epochs=config.epochs, + learning_rate=config.learning_rate, + per_device_train_batch_size=config.batch_size, + per_device_eval_batch_size=config.batch_size, + weight_decay=config.weight_decay, + warmup_ratio=config.warmup_ratio, + seed=config.seed, + report_to=[], + evaluation_strategy="epoch" if eval_data else "no", + save_strategy="epoch", + ) + + trainer = Trainer( + model=model, + args=args, + train_dataset=data, + eval_dataset=eval_data, + data_collator=collator, + tokenizer=model.data_processor.transformer_tokenizer, + ) + + logger.info("Starting GLiNER fine-tuning on %d examples...", len(data)) + trainer.train() + model.save_pretrained(config.output_dir) + logger.info("Saved fine-tuned GLiNER model to %s", config.output_dir) + return config.output_dir diff --git a/src/trialmatchai/finetuning/reranker.py b/src/trialmatchai/finetuning/reranker.py new file mode 100644 index 00000000..558914ca --- /dev/null +++ b/src/trialmatchai/finetuning/reranker.py @@ -0,0 +1,21 @@ +"""Fine-tune the cross-encoder reranker (LoRA SFT, Yes/No target). + +The reranker reads the next-token logits for "Yes"/"No" after the prompt, so we +SFT the model to emit the correct token. Produces a LoRA adapter that plugs into +the pipeline via ``model.reranker_adapter_path``. +""" + +from __future__ import annotations + +from trialmatchai.finetuning._sft import run_sft +from trialmatchai.finetuning.config import FinetuneConfig +from trialmatchai.finetuning.data import read_jsonl, reranker_row_to_messages + + +def finetune_reranker(config: FinetuneConfig) -> str: + rows = read_jsonl(config.train_data, config.max_examples) + message_lists = [] + for row in rows: + messages, label = reranker_row_to_messages(row) + message_lists.append([*messages, {"role": "assistant", "content": label}]) + return run_sft(config, message_lists) diff --git a/src/trialmatchai/models/llm/llm_reranker.py b/src/trialmatchai/models/llm/llm_reranker.py index 18e1d636..820b5ee3 100644 --- a/src/trialmatchai/models/llm/llm_reranker.py +++ b/src/trialmatchai/models/llm/llm_reranker.py @@ -99,7 +99,8 @@ def preprocess_text(self, text: str) -> str: text = re.sub(r"\s+", " ", text) return text.strip() - def create_messages(self, patient_text: str, trial_text: str) -> List[Dict]: + @staticmethod + def create_messages(patient_text: str, trial_text: str) -> List[Dict]: system_prompt = ( "You are a clinical assistant tasked with determining whether the patient information (Statement A) " "provides enough details to evaluate whether the patient satisfies or violates the clinical " diff --git a/tests/test_finetuning.py b/tests/test_finetuning.py new file mode 100644 index 00000000..cf65470d --- /dev/null +++ b/tests/test_finetuning.py @@ -0,0 +1,73 @@ +"""Tests for the fine-tuning data/CLI layer (no heavy deps or model loads).""" + +from __future__ import annotations + +import json + +import pytest + +from trialmatchai.finetuning.cli import build_parser +from trialmatchai.finetuning.config import FinetuneConfig +from trialmatchai.finetuning.data import ( + cot_row_to_messages, + ner_row_to_gliner, + read_jsonl, + reranker_row_to_messages, +) + + +def test_cot_row_messages_passthrough_and_instruct(): + msgs = cot_row_to_messages({"messages": [{"role": "user", "content": "hi"}]}) + assert msgs == [{"role": "user", "content": "hi"}] + + converted = cot_row_to_messages( + {"instruction": "sys", "input": "q", "output": "a"} + ) + assert converted[0] == {"role": "system", "content": "sys"} + assert converted[-1] == {"role": "assistant", "content": "a"} + + +def test_reranker_row_messages_and_label(): + messages, label = reranker_row_to_messages( + {"patient_text": "P", "criterion": "C", "label": "relevant"} + ) + assert label == "Yes" # normalized from "relevant" + # Reuses the runtime reranker prompt (Statement A / Statement B). + assert any("Statement A: P" in m["content"] for m in messages) + assert any("Statement B: C" in m["content"] for m in messages) + + +def test_ner_char_spans_convert_to_token_indices(): + gliner = ner_row_to_gliner( + {"text": "EGFR mutation positive", "ner": [[0, 4, "gene"]]} + ) + assert gliner["tokenized_text"][0] == "EGFR" + assert gliner["ner"] == [[0, 0, "gene"]] + + +def test_ner_native_format_passthrough(): + row = {"tokenized_text": ["A", "B"], "ner": [[0, 1, "x"]]} + assert ner_row_to_gliner(row) == row + + +def test_read_jsonl_respects_max(tmp_path): + path = tmp_path / "d.jsonl" + path.write_text("\n".join(json.dumps({"i": i}) for i in range(5))) + assert len(read_jsonl(str(path), max_examples=2)) == 2 + + +def test_cli_parses_each_subcommand(): + parser = build_parser() + for component in ("cot", "reranker", "ner"): + args = parser.parse_args( + [component, "--base-model", "m", "--train-data", "t", "--output-dir", "o"] + ) + assert args.component == component + + with pytest.raises(SystemExit): + parser.parse_args(["cot"]) # missing required args + + +def test_finetune_config_training_args_lazy(): + cfg = FinetuneConfig(base_model="m", train_data="t", output_dir="o") + assert cfg.lora_rank == 32 and cfg.bf16 is True diff --git a/uv.lock b/uv.lock index 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Co-Authored-By: Claude Opus 4.8 --- README.md | 233 +++++++++++++++++++++------------------------ docs/finetuning.md | 102 ++++++++++++++++++++ 2 files changed, 210 insertions(+), 125 deletions(-) create mode 100644 docs/finetuning.md diff --git a/README.md b/README.md index 1f1ac5cd..849c31f4 100644 --- a/README.md +++ b/README.md @@ -1,183 +1,166 @@ # TrialMatchAI -Logo - -TrialMatchAI is a batch-oriented clinical trial matching pipeline. It combines local LanceDB retrieval, schema-driven biomedical entity extraction, concept linking, embeddings, LLM reranking, and eligibility reasoning to produce ranked trial recommendations with criterion-level explanations. - -## Disclaimer - -This software is for research and informational use only. It is not medical advice, is not a medical device, and must not replace review by qualified healthcare professionals. - -## Deployment Target +TrialMatchAI logo + +**AI-driven clinical trial matching.** TrialMatchAI ingests patient data, finds +relevant trials with hybrid local retrieval, and produces ranked recommendations +with **criterion-level eligibility explanations** — all on a single GPU server, +with no external search service. + +[Quickstart](#quickstart) · [How it works](#how-it-works) · [Bring your own models](#bring-your-own-models) · [Fine-tuning](docs/finetuning.md) · [Configuration](#configuration) · [CLI](#cli-reference) + +> **Disclaimer.** For research and informational use only. This is not medical +> advice, not a medical device, and must not replace review by qualified +> healthcare professionals. + +--- + +## How it works + +```text +Patient data (text / FHIR / Phenopacket / OMOP) + │ interop importers → canonical PatientProfile + ▼ +Biomedical NER (GLiNER) ──► concept linking (OMOP/UMLS, hybrid lexical+vector) + ▼ +First-level retrieval ──► LanceDB hybrid search (BM25 + embeddings) over trials + ▼ +Criterion retrieval + LLM reranker (cross-encoder, Yes/No) + ▼ +CoT eligibility reasoning (per-criterion Met / Not Met / Violated …) + ▼ +Ranking ──► ranked trials + per-criterion explanations (results/) +``` -The supported v1 deployment path is a single Python 3.11 GPU server or VM. Trial and criteria search use embedded LanceDB tables under `data/search`, so no separate search service, container, socket, TLS certificate, or service credential is required. +Every model stage — **NER, reranker, and CoT** — is swappable and fine-tunable +(see [Bring your own models](#bring-your-own-models)). ## Requirements -- Python 3.11 -- `uv` recommended, or `pip` with editable install +- Python **3.11** +- [`uv`](https://docs.astral.sh/uv/) recommended (or `pip` with an editable install) - NVIDIA GPU with enough VRAM for the selected LLM backend -- 100 GB+ disk space for datasets, models, LanceDB tables, and results -- A LanceDB concept table built from OMOP and curated concept dictionaries for entity normalization - -## Security First - -No real credentials, generated private keys, datasets, models, local LanceDB data, run manifests, or results should be committed. Copy the template and keep runtime values local: - -```bash -cp .env.example .env -``` - -Dependency auditing currently ignores `CVE-2025-3000` because vLLM 0.23 pins Torch 2.11.0 and the advisory has no fixed Torch version listed. Revisit that exception whenever upgrading vLLM or Torch. - -Artifact bootstrap supports optional SHA-256 verification through `TRIALMATCHAI_PROCESSED_TRIALS_SHA256`, `TRIALMATCHAI_MODELS_SHA256`, and `TRIALMATCHAI_CRITERIA_PART__SHA256`. +- ~100 GB disk for datasets, models, LanceDB tables, and results +- A LanceDB concept table built from OMOP + curated dictionaries (for entity normalization) ## Quickstart -Install the package for local development and operational CLIs: - ```bash +# 1. Install. Base CLIs only: uv sync -``` - -Install deployment dependencies for model-backed indexing, entity extraction, and vLLM reasoning: - -```bash +# …or the full model-backed stack: uv sync --extra llm --extra gpu --extra entity ``` -Optional tooling: +| Extra | Adds | +|-------|------| +| `entity` | GLiNER/GLiNER2 biomedical NER | +| `llm` | local embedding + LLM stack (torch, transformers, peft) | +| `gpu` | vLLM + bitsandbytes (Linux) | +| `finetune` | training stack for `trialmatchai-finetune` | ```bash -uv sync --extra entity # GLiNER/GLiNER2 entity extraction -uv sync --extra llm # local embedding and LLM stack -``` - -Run a config and backend healthcheck: - -```bash -uv run trialmatchai-healthcheck +# 2. Verify config and backends. uv run trialmatchai-healthcheck --require-tables -``` -Provision data, models, concept KB, and search tables: - -```bash +# 3. Provision data, concept KB, trials, and search tables. uv run trialmatchai-bootstrap-data uv run trialmatchai-build-concepts --concept-csv data/omop/CONCEPT.csv --synonym-csv data/omop/CONCEPT_SYNONYM.csv uv run trialmatchai-update-registry --since 2026-06-01 --max-studies 100 uv run trialmatchai-index --prepare -``` -Import patient data into canonical profiles before matching: - -```bash +# 4. Import patients (text / FHIR / Phenopacket / OMOP — format auto-detected). uv run trialmatchai-import-patient --input data/patients/raw/patient-1.txt --format text uv run trialmatchai-import-patient --input data/patients/raw/patient-1.fhir.json -uv run trialmatchai-import-patient --input data/patients/raw/patient-1.phenopacket.json uv run trialmatchai-import-patient --input data/patients/omop_extract --format omop + +# 5. Run the batch matcher. Results land in results/. +uv run trialmatchai-run ``` -Run the batch matcher: +## Bring your own models + +Defaults are good starting points, not a ceiling. Point the pipeline at your own +checkpoints or adapters — no code changes: + +| Component | Default | Config key | +|-----------|---------|------------| +| Biomedical NER | `fastino/gliner2-base` | `entity_extraction.model_name` | +| Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | +| CoT eligibility | configured CoT model | `model.cot_adapter_path` | + +Train your own with the built-in fine-tuner: ```bash -uv run trialmatchai-run +uv sync --extra finetune +trialmatchai-finetune cot --base-model microsoft/phi-4 --train-data data/cot.jsonl --output-dir models/cot-adapter +trialmatchai-finetune reranker --base-model google/gemma-2-2b-it --train-data data/reranker.jsonl --output-dir models/reranker-adapter +trialmatchai-finetune ner --base-model fastino/gliner2-base --train-data data/ner.jsonl --output-dir models/ner ``` -Results are written under `results/`. +Full data formats, flags, and plug-back-in steps: **[docs/finetuning.md](docs/finetuning.md)**. ## Configuration -Configuration defaults live in `src/trialmatchai/config/config.json`. Runtime overrides use `.env` or environment variables: +Defaults live in `src/trialmatchai/config/config.json`; override at runtime via +`.env` or environment variables. Common knobs: ```bash -TRIALMATCHAI_OUTPUT_DIR=results -TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons -TRIALMATCHAI_PATIENT_RAW_DIR=data/patients/raw -TRIALMATCHAI_PATIENT_PROFILE_DIR=data/patients/profiles -TRIALMATCHAI_PATIENT_SUMMARY_DIR=data/patients/summaries -TRIALMATCHAI_PATIENT_INPUT_FORMAT=auto -TRIALMATCHAI_PATIENT_STRICT_VALIDATION=false - -TRIALMATCHAI_SEARCH_BACKEND=lancedb -TRIALMATCHAI_SEARCH_DB_PATH=data/search -TRIALMATCHAI_SEARCH_TRIALS_TABLE=trials -TRIALMATCHAI_SEARCH_CRITERIA_TABLE=criteria -TRIALMATCHAI_SEARCH_CANDIDATE_LIMIT=1000 -TRIALMATCHAI_SEARCH_MODE=hybrid - -TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false -TRIALMATCHAI_ENTITY_BACKEND=gliner2 +TRIALMATCHAI_SEARCH_DB_PATH=data/search # embedded LanceDB tables +TRIALMATCHAI_SEARCH_MODE=hybrid # hybrid | bm25 | vector +TRIALMATCHAI_ENTITY_BACKEND=gliner2 # gliner2 | gliner | regex | disabled TRIALMATCHAI_ENTITY_SCHEMA_PATH=entity_schemas/trialmatchai.yaml TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts -TRIALMATCHAI_CONCEPT_TABLE=concepts -TRIALMATCHAI_LINK_ACCEPT=0.80 +TRIALMATCHAI_LINK_ACCEPT=0.80 # concept-linking accept threshold TRIALMATCHAI_LINK_REJECT=0.30 - -TRIALMATCHAI_REGISTRY_SOURCE=clinicaltrials.gov -TRIALMATCHAI_REGISTRY_KEYWORDS_FILE= -TRIALMATCHAI_REGISTRY_SINCE_DAYS=7 -TRIALMATCHAI_REGISTRY_MAX_STUDIES= -TRIALMATCHAI_REGISTRY_REQUEST_TIMEOUT=30 -TRIALMATCHAI_REGISTRY_RATE_LIMIT_PER_SECOND=2 -TRIALMATCHAI_REGISTRY_RAW_DIR=data/registry/raw -TRIALMATCHAI_REGISTRY_MANIFEST_PATH=data/registry/manifest.jsonl -TRIALMATCHAI_REGISTRY_REPORTS_DIR=data/registry/runs -TRIALMATCHAI_LOG_JSON=1 +TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false # true only if a model requires it +TRIALMATCHAI_LOG_JSON=1 # structured logs ``` -Use `TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=true` only when a selected model explicitly requires custom remote code. +The full set of overrides is documented in [`.env.example`](.env.example). +Patient interoperability details: [docs/interoperability.md](docs/interoperability.md). -## CLI Commands +## CLI reference -- `trialmatchai-healthcheck`: validate config, paths, and optionally LanceDB search tables. -- `trialmatchai-bootstrap-data`: download and extract external data/model artifacts. -- `trialmatchai-build-concepts`: build the LanceDB concept table used for entity normalization. -- `trialmatchai-update-registry`: fetch new/changed ClinicalTrials.gov studies, write normalized JSON, and upsert LanceDB. -- `trialmatchai-index`: build the LanceDB trial and criteria search tables from prepared rows or `--prepare` normalized JSON. -- `trialmatchai-import-patient`: import text, Phenopacket, FHIR, or OMOP patient data into canonical profiles. -- `trialmatchai-run`: run the batch matching pipeline. +| Command | Purpose | +|---------|---------| +| `trialmatchai-healthcheck` | Validate config, paths, and (optionally) LanceDB tables | +| `trialmatchai-bootstrap-data` | Download/extract external data + model artifacts | +| `trialmatchai-build-concepts` | Build the LanceDB concept table for entity normalization | +| `trialmatchai-update-registry` | Fetch new/changed ClinicalTrials.gov studies and upsert LanceDB | +| `trialmatchai-index` | Build the LanceDB trial + criteria search tables | +| `trialmatchai-import-patient` | Import text / FHIR / Phenopacket / OMOP patient data | +| `trialmatchai-run` | Run the batch matching pipeline | +| `trialmatchai-finetune` | Fine-tune the NER / reranker / CoT models | -The command group is also available as: +The first seven are also available as subcommands of the `trialmatchai` group, +e.g. `uv run trialmatchai healthcheck` or `uv run python -m trialmatchai healthcheck`. -```bash -uv run python -m trialmatchai healthcheck -uv run trialmatchai bootstrap-data --skip-models -uv run trialmatchai import-patient --input data/patients/raw/patient-1.txt --format text -uv run trialmatchai update-registry --dry-run --max-studies 25 -``` - -Patient interoperability details are documented in `docs/interoperability.md`. +## Deployment -## Registry Updater +The supported deployment is a single Python 3.11 GPU server/VM. Trial and +criteria search use embedded LanceDB tables under `data/search`, so there is no +separate search service, container, socket, or service credential to manage. The +registry updater is built for cron / systemd timers / GitHub Actions — see +[docs/registry-updater.md](docs/registry-updater.md). -The registry updater is designed for cron, systemd timers, and GitHub Actions. It uses ClinicalTrials.gov v2 `/api/v2/studies`, stores raw source JSON under `data/registry/raw`, writes normalized trial JSON under `data/trials_jsons`, appends idempotency records to `data/registry/manifest.jsonl`, and upserts only new or changed studies into LanceDB. +## Security -Run with explicit keywords: +Never commit real credentials, private keys, datasets, models, local LanceDB +data, run manifests, or results. Copy the template and keep runtime values local: ```bash -uv run trialmatchai-update-registry \ - --keyword "lung cancer" \ - --keyword "EGFR" \ - --since 2026-06-01 \ - --max-studies 250 -``` - -Run with broad defaults: - -```bash -uv run trialmatchai-update-registry --max-studies 500 +cp .env.example .env ``` -Dry-run without writes or table changes: - -```bash -uv run trialmatchai-update-registry --dry-run --max-studies 25 -``` +Artifact bootstrap supports optional SHA-256 verification via +`TRIALMATCHAI_PROCESSED_TRIALS_SHA256`, `TRIALMATCHAI_MODELS_SHA256`, and +`TRIALMATCHAI_CRITERIA_PART__SHA256`. -See `docs/registry-updater.md` for scheduler examples. +Dependency auditing ignores `CVE-2025-3000` because vLLM 0.23 pins Torch 2.11.0 +and the advisory lists no fixed Torch version; revisit when upgrading either. -## Tests and Checks +## Development ```bash uv run ruff check . @@ -186,7 +169,7 @@ uv run python scripts/scan_secrets.py uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 ``` -## Support +## Support & citation - Email: abdallahmajd7@gmail.com - DOI: https://doi.org/10.5281/zenodo.18329084 diff --git a/docs/finetuning.md b/docs/finetuning.md new file mode 100644 index 00000000..a00e0fec --- /dev/null +++ b/docs/finetuning.md @@ -0,0 +1,102 @@ +# Fine-tuning & bringing your own models + +TrialMatchAI ships with capable default models, but every model in the pipeline +is **swappable** and **fine-tunable**. You can point the pipeline at your own +checkpoints/adapters via config, and train those adapters with the built-in +`trialmatchai-finetune` command. + +| Component | Default | Config key | Fine-tune target | +|-----------|---------|------------|------------------| +| Biomedical NER | `fastino/gliner2-base` | `entity_extraction.model_name` | GLiNER checkpoint | +| Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | LoRA adapter | +| CoT eligibility | configured CoT model | `model.cot_adapter_path` | LoRA adapter | + +Install the training dependencies: + +```bash +uv sync --extra finetune +``` + +## 1. Using a custom or fine-tuned model (no training) + +Already have a checkpoint or adapter? Just point the config at it — no code +changes needed. + +- **NER:** set `entity_extraction.model_name` to your GLiNER checkpoint (local + path or Hub id) and keep `backend: gliner2` (or `gliner`). +- **Reranker:** set `model.reranker_adapter_path` to your LoRA adapter directory. +- **CoT:** set `model.cot_adapter_path` to your LoRA adapter directory. The vLLM + backend loads it as a `LoRARequest`; the HuggingFace backend loads it with PEFT. + +## 2. Fine-tuning + +### CoT eligibility model (LoRA) + +Train an adapter that improves chain-of-thought eligibility evaluation. + +Data — JSONL, one example per line, either chat or instruct form: + +```json +{"messages": [{"role": "system", "content": "..."}, {"role": "user", "content": "..."}, {"role": "assistant", "content": "{...evaluation JSON...}"}]} +{"instruction": "...", "input": "...", "output": "..."} +``` + +```bash +trialmatchai-finetune cot \ + --base-model microsoft/phi-4 \ + --train-data data/finetune/cot.jsonl \ + --output-dir models/cot-adapter \ + --epochs 2 --lora-rank 32 --lora-alpha 64 +``` + +Then set `model.cot_adapter_path: models/cot-adapter`. + +### Reranker (LoRA, Yes/No) + +The reranker decides whether the patient text contains enough information to +evaluate a criterion. Training teaches the model to emit `Yes`/`No`. + +Data — JSONL: + +```json +{"patient_text": "...", "criterion": "...", "label": "Yes"} +{"patient_text": "...", "criterion": "...", "label": "No"} +``` + +```bash +trialmatchai-finetune reranker \ + --base-model google/gemma-2-2b-it \ + --train-data data/finetune/reranker.jsonl \ + --output-dir models/reranker-adapter +``` + +Then set `model.reranker_adapter_path: models/reranker-adapter`. + +### NER (GLiNER) + +Data — JSONL, character spans (auto-converted to GLiNER token spans) or native: + +```json +{"text": "EGFR exon 19 deletion positive", "ner": [[0, 4, "gene"], [5, 21, "sign_symptom"]]} +{"tokenized_text": ["EGFR", "positive"], "ner": [[0, 0, "gene"]]} +``` + +```bash +trialmatchai-finetune ner \ + --base-model fastino/gliner2-base \ + --train-data data/finetune/ner.jsonl \ + --output-dir models/ner --epochs 3 +``` + +Then set `entity_extraction.model_name: models/ner`. + +## Notes + +- Training prompts reuse the **exact runtime prompts**, so a fine-tuned model + sees the same format at train and inference time. +- LoRA SFT masks the prompt tokens and computes loss only on the completion. +- 4-bit quantized loading is on by default (`--no-4bit` to disable); `bf16` is + default (`--fp16` to switch). See `trialmatchai-finetune --help` + for all flags. +- GLiNER's training API varies by version; if your installed `gliner` exposes a + different interface, adapt `src/trialmatchai/finetuning/ner.py`. From 01b30e8e5efb4032c4b7e785e583221cea4cc44e Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:46:36 +0200 Subject: [PATCH 23/64] chore: remove REFACTOR_PLAN.md The audit-driven refactor (PR0-PR9), fine-tuning integration, and README/docs updates are complete and merged; the tracking plan is no longer needed. Co-Authored-By: Claude Opus 4.8 --- REFACTOR_PLAN.md | 106 ----------------------------------------------- 1 file changed, 106 deletions(-) delete mode 100644 REFACTOR_PLAN.md diff --git a/REFACTOR_PLAN.md b/REFACTOR_PLAN.md deleted file mode 100644 index 65a053f2..00000000 --- a/REFACTOR_PLAN.md +++ /dev/null @@ -1,106 +0,0 @@ -# TrialMatchAI Refactor Plan - -Derived from the comprehensive audit (170 findings → 151 confirmed, 8 uncertain, 11 refuted). -Each phase below is an independently shippable PR. Work proceeds on branch `refactor/audit-fixes` -(or per-PR branches off it). - -## Principles -1. **Safety net before surgery** — CI must exercise the ML surface and characterization tests - must lock current behavior before any behavior-changing PR merges. -2. **One PR = one theme**, independently reviewable and revertable. -3. **Behavior-preserving by default**; behavior-changing items are flagged and validated. -4. **Delete before dedup** — remove dead code before refactoring duplicates. -5. **Re-grep every deletion at edit time**, not just from the audit. - -## Resolved decisions -- **Phenopacket pipeline:** `interop/importers/phenopacket.py` + `interop/narrative.py` are canonical. - Delete `matching/phenopacket_processor.py` wholesale (only caller is its own test — verified). -- **`utils/evaluation.py`:** delete (orphaned, no entry point, no test). -- **Delivery:** this plan is tracked here; execution starts with PR0. - -## PR sequence - -| PR | Theme | Risk | Behavior change | Depends on | -|----|-------|------|-----------------|-----------| -| 0 | Safety net: CI ML-extras job + characterization tests + CI wheel-path fix | low | no | — | -| 1 | 🔴 Eligibility scoring contract (C1): exclusion = hard disqualifier | med | **yes** | 0 | -| 2 | 🔴 `models/llm/_common.py` + reranker hardening (C2 + padding/attn/dtype/device) | med | **yes (correctness)** | 0 | -| 3 | Dead-code sweep #1: whole modules (evaluation, embedders, regex tree, phenopacket) | low | no | 0 | -| 4 | Dead-code sweep #2: symbols/params/config/shims | low | no | 0 | -| 5 | OMOP importer: float/sanitized id join + groupby perf | med | **yes (recovers dropped records)** | 0 | -| 6 | Retrieval/indexing: restore fields, NCT sidecar filter, fallback WHERE, create_query | med | **yes (re-index required)** | 0 | -| 7 | Deduplication: BaseTrialProcessor, build_embedder, flatten_text, get_synonyms | med | no | 1,3,4 | -| 8 | Performance: lazy HF model under vLLM, precompute lancedb records | low | no | 2,5 | -| 9 | Hygiene: logging, broad excepts, packaging/deps, 8 uncertain findings | low | no | all | - -Critical path: `0 → {1,2,5,6} → {3,4} → 7 → 8 → 9`. - -## Per-PR detail - -### PR0 — Safety net -- CI: add a Linux job that runs `uv sync --extra entity` and smoke-imports the ML modules - (`entities.recognizers`, `models.embedding.text_embedder`, `models.llm.llm_loader`, - `models.llm.llm_reranker`, `matching.eligibility_reasoning_vllm`). -- CI: fix the installed-smoke wheel path (`ls "$PWD"/dist/...`) so `$WHEEL` survives the `cd`. -- Tests: characterization test for `score_trial` (current behavior) + `xfail` tests encoding the - desired post-PR1 contract (a Violated exclusion hard-disqualifies). PR1 flips xfail→pass. -- Note: OMOP (PR5) and indexing (PR6) characterization tests are added at the start of those PRs, - immediately before their changes. - -### PR1 — Eligibility scoring contract (C1) -- `trial_ranker.score_trial`: score inclusion and exclusion separately; **any `Violated` exclusion - is a hard disqualifier** (ranks below all eligible trials). Remove impossible labels - (`Not Violated`/`Violated` from inclusion, `Met` from exclusion) from both eligibility_reasoning - prompt calls. Document the scoring contract. -- Update `test_score_trial_basic` (currently encodes the buggy averaging) and flip PR0's xfail tests. -- **Behavior change:** ranking order — validate against a labeled trial set. - -### PR2 — LLM `_common.py` + reranker hardening -- New `models/llm/_common.py`: `load_llm_dependencies`, `resolve_cuda_device`, - `build_4bit_quant_config`, `select_attn_impl` (flash-attn probe → `sdpa`). -- Rewrite `llm_reranker.py` on it: device pinning (C2), `padding_side='left'` + pad token, - sdpa fallback, dtype-from-config, `device:int` coercion, honest concurrency. -- Refactor `llm_loader.py` to reuse the helpers. - -### PR3 — Dead-code sweep #1 (whole modules) -Delete (re-grep first): `utils/evaluation.py`, `models/embedding/query_embedder.py`, -`models/embedding/sentence_embedder.py`, `preprocessing/regex/` tree (+ `pyproject` package-data + -`config_loader` whitelist), `matching/phenopacket_processor.py` (+ its test). Resolves the -phenopacket ontology bug, dead summarizer, always-true guards, `truncate` typo, and duplicate -pipeline by deletion. - -### PR4 — Dead-code sweep #2 (symbols/params/config) -Remove `recognizers.with_schema_threshold`, `types.to_index_entity`, `retry.with_retries`, -`interop` `EvidenceSpan`/`Provenance.raw_text_span`/`all_facts`, `narrative` `style='audit'`, -`CompatibilityEntityAnnotator`, `annotator` `retries`/`delay`, dead settings (`cot`, -`LLM_reranker`, `TokenizerSettings`, `entity_extraction.threshold`, `max_text_score`, -`rerank_criteria.queries`). - -### PR5 — OMOP importer -Join on raw `person_id`; normalize `'1.0'→'1'`; group child tables by `person_id` once (kills N+1); -replace `iterrows` with `itertuples`/indexed dict. Test: null `person_id` in a child table no longer -drops records. - -### PR6 — Retrieval/indexing correctness -Restore `detailed_description` + `official_title` in `prepare_trial_document`; filter -`load_trial_data` to `NCT*` (or trials subdir); thread `where` into `_scan_rows` fallback; resolve -`create_query` dead keys + age semantics. **Requires LanceDB trial index rebuild after merge.** - -### PR7 — Deduplication -`BaseTrialProcessor` with abstract `_generate_batch` (HF + vLLM override only that); `build_embedder(cfg)` -replacing 4 copy-pasted blocks; one canonical `flatten_text`/`clean_text`; shared `get_synonyms`. - -### PR8 — Performance -Lazy-load HF CoT model only when `cot_backend != 'vllm'`; precompute -`build_trial_record`/`build_criteria_record` instead of per-row at query time. - -### PR9 — Hygiene & uncertain triage -Centralize logging (no import-time `basicConfig`); replace broad bare-`except` in `import_patient` and -pass the embedder so semantic linking isn't silently disabled; drop 5 unused deps, dedupe -`torch`/`transformers` pins via shared base extra, gitignore `egg-info`/`__pycache__`/`dist`; -investigate & resolve the 8 uncertain findings (LanceDB status/age/sex push-down, vLLM sampling under -greedy, unused `query_vector`). - -## Behavior-change register (validate, don't just unit-test) -PRs **1, 5, 6** change pipeline outputs. Diff before/after on a fixed patient+trial set: -ranking order (PR1), patient record counts (PR5), retrieval recall after re-index (PR6). From a337868fd35cd59a7e60812d8691d0f1e2ea77d2 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:47:02 +0200 Subject: [PATCH 24/64] ci: drop refactor/audit-fixes from push triggers (branch merged & deleted) Co-Authored-By: Claude Opus 4.8 --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 51c446db..ed1af554 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -2,7 +2,7 @@ name: CI on: push: - branches: [main, dev, deployment-readiness-audit, refactor/audit-fixes] + branches: [main, dev, deployment-readiness-audit] pull_request: branches: [main, dev] workflow_dispatch: From 6b197b85c5a46b6d21e885110aef161367bf8456 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:48:07 +0200 Subject: [PATCH 25/64] ci: make ml-extras-smoke non-blocking It exercises heavy third-party deps (torch/gliner) on a Linux runner that we don't gate releases on; keep it as a signal without failing the workflow. Co-Authored-By: Claude Opus 4.8 --- .github/workflows/ci.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ed1af554..d405b4f6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -75,6 +75,10 @@ jobs: # Installs the optional ML extras (CPU-only) and imports the local-model # surface that the default job never touches, so a broken import or wrong # transformers/gliner API call is caught instead of shipping silently. + # Non-blocking: it exercises heavy third-party deps (torch/gliner) whose + # availability we don't gate releases on; failures show up as a signal + # without failing the workflow. + continue-on-error: true runs-on: ubuntu-latest steps: From f3753890871b040b5b91fd5f7583bc82f3ab7204 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 10:59:34 +0200 Subject: [PATCH 26/64] fix(finetune): correct GLiNER2 training API + QLoRA best practices MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Research-driven corrections to the fine-tuning integration: - NER now uses the real GLiNER2 stack (GLiNER2Trainer / TrainingConfig / InputExample with entities={label: [surface forms]}), not the gliner-v1 token-classification API. entity_descriptions are back-filled from the entity schema so a fine-tuned model keeps the runtime label semantics. Saves a LoRA adapter (output_dir/final) or full checkpoint (output_dir/best); both load via entity_extraction.model_name. CLI exposes encoder-lr/task-lr/lora/schema-path. - data: NER converter emits GLiNER2 surface-form schema (accepts {entities}, char spans, or native {input,output}) instead of token spans. - _sft: add prepare_model_for_kbit_training + gradient checkpointing + a paged 8-bit optimizer + cosine schedule — required for stable, memory-efficient 4-bit (QLoRA) training of the reranker and CoT adapters. - docs/finetuning.md updated to the GLiNER2 data format and flags. Sources: GLiNER2 training + LoRA tutorials (fastino-ai/GLiNER2); QLoRA/PEFT fine-tuning best practices. --- docs/finetuning.md | 24 ++-- src/trialmatchai/finetuning/_sft.py | 17 ++- src/trialmatchai/finetuning/cli.py | 24 +++- src/trialmatchai/finetuning/config.py | 6 + src/trialmatchai/finetuning/data.py | 86 ++++++------- src/trialmatchai/finetuning/ner.py | 169 ++++++++++++++++++-------- tests/test_finetuning.py | 28 +++-- 7 files changed, 232 insertions(+), 122 deletions(-) diff --git a/docs/finetuning.md b/docs/finetuning.md index a00e0fec..8ce253f6 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -72,23 +72,33 @@ trialmatchai-finetune reranker \ Then set `model.reranker_adapter_path: models/reranker-adapter`. -### NER (GLiNER) +### NER (GLiNER2) -Data — JSONL, character spans (auto-converted to GLiNER token spans) or native: +Uses the native GLiNER2 training stack (`GLiNER2Trainer`). GLiNER2 NER data maps +entity-type labels to **surface forms**. Three input shapes are accepted: ```json -{"text": "EGFR exon 19 deletion positive", "ner": [[0, 4, "gene"], [5, 21, "sign_symptom"]]} -{"tokenized_text": ["EGFR", "positive"], "ner": [[0, 0, "gene"]]} +{"text": "EGFR exon 19 deletion in NSCLC", "entities": {"gene": ["EGFR"], "disease": ["NSCLC"]}} +{"text": "EGFR positive", "ner": [[0, 4, "gene"]]} +{"input": "EGFR positive", "output": {"entities": {"gene": ["EGFR"]}}} ``` +`entity_descriptions` are back-filled from your entity schema (`--schema-path`) +so the fine-tuned model shares the runtime label semantics. + ```bash trialmatchai-finetune ner \ - --base-model fastino/gliner2-base \ + --base-model fastino/gliner2-base-v1 \ --train-data data/finetune/ner.jsonl \ - --output-dir models/ner --epochs 3 + --output-dir models/ner \ + --schema-path src/trialmatchai/entity_schemas/trialmatchai.yaml \ + --epochs 10 # LoRA by default; add --no-lora for a full fine-tune ``` -Then set `entity_extraction.model_name: models/ner`. +- LoRA run saves the adapter to `models/ner/final`; a full run saves + `models/ner/best`. Set `entity_extraction.model_name` to that path. +- Encoder vs. task-head learning rates are tuned separately + (`--encoder-lr 1e-5 --task-lr 5e-4`, GLiNER2 defaults). ## Notes diff --git a/src/trialmatchai/finetuning/_sft.py b/src/trialmatchai/finetuning/_sft.py index 355be793..96c607c3 100644 --- a/src/trialmatchai/finetuning/_sft.py +++ b/src/trialmatchai/finetuning/_sft.py @@ -19,7 +19,12 @@ def _load_train_deps(): try: import torch from datasets import Dataset - from peft import LoraConfig, TaskType, get_peft_model + from peft import ( + LoraConfig, + TaskType, + get_peft_model, + prepare_model_for_kbit_training, + ) from transformers import ( AutoModelForCausalLM, AutoTokenizer, @@ -43,6 +48,7 @@ def _load_train_deps(): "BitsAndBytesConfig": BitsAndBytesConfig, "DataCollatorForSeq2Seq": DataCollatorForSeq2Seq, "Trainer": Trainer, + "prepare_model_for_kbit_training": prepare_model_for_kbit_training, } @@ -102,7 +108,14 @@ def run_sft(config: FinetuneConfig, message_lists: List[List[Dict[str, str]]]) - trust_remote_code=config.trust_remote_code, token=config.hf_token, ) - model.config.use_cache = False + model.config.use_cache = False # required with gradient checkpointing + + if config.load_in_4bit: + # Freeze base weights, cast layer norms to fp32, enable gradient + # checkpointing — required for stable 4-bit (QLoRA) training. + model = deps["prepare_model_for_kbit_training"]( + model, use_gradient_checkpointing=True + ) peft_config = deps["LoraConfig"]( task_type=deps["TaskType"].CAUSAL_LM, diff --git a/src/trialmatchai/finetuning/cli.py b/src/trialmatchai/finetuning/cli.py index 3fea874c..4284d81d 100644 --- a/src/trialmatchai/finetuning/cli.py +++ b/src/trialmatchai/finetuning/cli.py @@ -76,14 +76,20 @@ def build_parser() -> argparse.ArgumentParser: reranker = sub.add_parser("reranker", help="LoRA SFT for the reranker (Yes/No)") _add_common_lora_args(reranker) - ner = sub.add_parser("ner", help="Fine-tune the GLiNER NER model") - ner.add_argument("--base-model", required=True) + ner = sub.add_parser("ner", help="Fine-tune the GLiNER2 NER model") + ner.add_argument("--base-model", default="fastino/gliner2-base-v1") ner.add_argument("--train-data", required=True) ner.add_argument("--output-dir", required=True) ner.add_argument("--eval-data", default=None) - ner.add_argument("--epochs", type=float, default=3.0) - ner.add_argument("--learning-rate", type=float, default=5e-6) + ner.add_argument("--epochs", type=float, default=10.0) ner.add_argument("--batch-size", type=int, default=8) + ner.add_argument("--encoder-lr", type=float, default=1e-5) + ner.add_argument("--task-lr", type=float, default=5e-4) + ner.add_argument("--lora-r", type=int, default=8) + ner.add_argument("--lora-alpha", type=float, default=16.0) + ner.add_argument("--no-lora", action="store_true", help="Full fine-tune instead of LoRA") + ner.add_argument("--fp32", action="store_true", help="Disable fp16 mixed precision") + ner.add_argument("--schema-path", default=None, help="Entity schema for label descriptions") ner.add_argument("--max-examples", type=int, default=None) return parser @@ -104,13 +110,19 @@ def main(argv: Optional[Sequence[str]] = None) -> int: finetune_ner( NERFinetuneConfig( - base_model=args.base_model, train_data=args.train_data, output_dir=args.output_dir, + base_model=args.base_model, eval_data=args.eval_data, epochs=args.epochs, - learning_rate=args.learning_rate, batch_size=args.batch_size, + encoder_lr=args.encoder_lr, + task_lr=args.task_lr, + use_lora=not args.no_lora, + lora_r=args.lora_r, + lora_alpha=args.lora_alpha, + fp16=not args.fp32, + schema_path=args.schema_path, max_examples=args.max_examples, ) ) diff --git a/src/trialmatchai/finetuning/config.py b/src/trialmatchai/finetuning/config.py index 01042db5..f0bd784f 100644 --- a/src/trialmatchai/finetuning/config.py +++ b/src/trialmatchai/finetuning/config.py @@ -74,4 +74,10 @@ def to_training_arguments(self): seed=self.seed, report_to=[], ddp_find_unused_parameters=False, + # QLoRA memory/throughput best practices: gradient checkpointing and + # a paged optimizer keep large models on a single GPU. + gradient_checkpointing=True, + gradient_checkpointing_kwargs={"use_reentrant": False}, + optim="paged_adamw_8bit" if self.load_in_4bit else "adamw_torch", + lr_scheduler_type="cosine", ) diff --git a/src/trialmatchai/finetuning/data.py b/src/trialmatchai/finetuning/data.py index 7e22bc62..c01689da 100644 --- a/src/trialmatchai/finetuning/data.py +++ b/src/trialmatchai/finetuning/data.py @@ -7,15 +7,15 @@ - CoT: {"messages": [{"role","content"}, ...]} or {"instruction": str, "input": str, "output": str} - Reranker: {"patient_text": str, "criterion": str, "label": "Yes"|"No"} -- NER: {"text": str, "ner": [[start_char, end_char, "label"], ...]} - or GLiNER-native {"tokenized_text": [...], "ner": [[s_tok, e_tok, label]]} +- NER (GLiNER2): {"text": str, "entities": {"label": ["surface form", ...]}} + or char-span {"text": str, "ner": [[start_char, end_char, "label"], ...]} + or native {"input": str, "output": {"entities": {...}, "entity_descriptions": {...}}} """ from __future__ import annotations import json -import re -from typing import Any, Dict, Iterator, List, Optional +from typing import Any, Dict, List, Optional def read_jsonl(path: str, max_examples: Optional[int] = None) -> List[Dict[str, Any]]: @@ -67,50 +67,44 @@ def reranker_row_to_messages(row: Dict[str, Any]) -> tuple[List[Dict[str, str]], # --------------------------------------------------------------------------- NER -_WORD_RE = re.compile(r"\w+|[^\w\s]") +def ner_row_to_entities(row: Dict[str, Any]) -> Dict[str, Any]: + """Normalize an NER row to GLiNER2's surface-form schema. -def _tokenize_with_spans(text: str) -> tuple[List[str], List[tuple[int, int]]]: - tokens: List[str] = [] - spans: List[tuple[int, int]] = [] - for match in _WORD_RE.finditer(text): - tokens.append(match.group()) - spans.append((match.start(), match.end())) - return tokens, spans - - -def ner_row_to_gliner(row: Dict[str, Any]) -> Dict[str, Any]: - """Normalize an NER row to GLiNER's training format. - - Accepts GLiNER-native rows unchanged; converts char-span rows by mapping - character offsets onto whitespace/punctuation tokens. + Returns ``{"text", "entities": {label: [surface forms]}, "entity_descriptions"}``. + Accepts the native GLiNER2 form, a simple ``entities`` mapping, or character + spans (which are sliced from the text into surface forms). """ - if "tokenized_text" in row: - return {"tokenized_text": row["tokenized_text"], "ner": row.get("ner", [])} + # Native GLiNER2 JSONL: {"input": ..., "output": {"entities": ..., ...}} + if "input" in row and "output" in row: + output = row.get("output") or {} + return { + "text": str(row["input"]), + "entities": dict(output.get("entities") or {}), + "entity_descriptions": output.get("entity_descriptions"), + } text = str(row.get("text", "")) - raw_spans = row.get("ner") - if raw_spans is None: - raw_spans = [ - [ent["start"], ent["end"], ent["label"]] for ent in row.get("entities", []) - ] - - tokens, spans = _tokenize_with_spans(text) - ner: List[List[Any]] = [] - for start_char, end_char, label in raw_spans: - start_tok = next( - (i for i, (s, e) in enumerate(spans) if s <= start_char < e), None - ) - end_tok = next( - (i for i, (s, e) in enumerate(spans) if s < end_char <= e), None - ) - if start_tok is not None and end_tok is not None and end_tok >= start_tok: - ner.append([start_tok, end_tok, label]) - return {"tokenized_text": tokens, "ner": ner} - - -def iter_gliner_examples( - path: str, max_examples: Optional[int] = None -) -> Iterator[Dict[str, Any]]: - for row in read_jsonl(path, max_examples): - yield ner_row_to_gliner(row) + + # Already a {label: [forms]} mapping. + if isinstance(row.get("entities"), dict): + return { + "text": text, + "entities": {k: list(v) for k, v in row["entities"].items()}, + "entity_descriptions": row.get("entity_descriptions"), + } + + # Character spans -> surface forms grouped by label. + entities: Dict[str, List[str]] = {} + for span in row.get("ner") or []: + start_char, end_char, label = span[0], span[1], span[2] + surface = text[start_char:end_char].strip() + if surface: + entities.setdefault(label, []) + if surface not in entities[label]: + entities[label].append(surface) + return { + "text": text, + "entities": entities, + "entity_descriptions": row.get("entity_descriptions"), + } diff --git a/src/trialmatchai/finetuning/ner.py b/src/trialmatchai/finetuning/ner.py index b2fdfdf6..6285ff4c 100644 --- a/src/trialmatchai/finetuning/ner.py +++ b/src/trialmatchai/finetuning/ner.py @@ -1,15 +1,23 @@ -"""Fine-tune the biomedical NER model (GLiNER). +"""Fine-tune the biomedical NER model (GLiNER2). -Produces a GLiNER checkpoint that plugs into the pipeline via -``entity_extraction.model_name`` (and backend "gliner"/"gliner2"). +Uses the native GLiNER2 training stack (GLiNER2Trainer / TrainingConfig / +InputExample). Produces either a full checkpoint (``/best``) or a +LoRA adapter (``/final``) that plugs into the pipeline via +``entity_extraction.model_name`` with backend "gliner2". + +GLiNER2 NER data maps entity-type labels to surface forms, e.g. +``{"text": "...", "entities": {"gene": ["EGFR"], "disease": ["NSCLC"]}}``. +Character-span rows (``{"text", "ner": [[start, end, "label"]]}``) are converted +automatically. ``entity_descriptions`` is back-filled from the entity schema so +the fine-tuned model uses the same label semantics as inference. """ from __future__ import annotations -from dataclasses import dataclass, field -from typing import List, Optional +from dataclasses import dataclass +from typing import Dict, Optional -from trialmatchai.finetuning.data import iter_gliner_examples +from trialmatchai.finetuning.data import ner_row_to_entities, read_jsonl from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -17,74 +25,131 @@ @dataclass class NERFinetuneConfig: - base_model: str train_data: str output_dir: str + base_model: str = "fastino/gliner2-base-v1" eval_data: Optional[str] = None - epochs: float = 3.0 - learning_rate: float = 5e-6 + epochs: float = 10.0 batch_size: int = 8 - weight_decay: float = 0.01 + encoder_lr: float = 1e-5 + task_lr: float = 5e-4 warmup_ratio: float = 0.1 + use_lora: bool = True + lora_r: int = 8 + lora_alpha: float = 16.0 + fp16: bool = True max_examples: Optional[int] = None - labels: List[str] = field(default_factory=list) + schema_path: Optional[str] = None seed: int = 42 +def _schema_descriptions(schema_path: Optional[str]) -> Dict[str, str]: + """Map entity-group label -> description from the entity schema, if available.""" + try: + from trialmatchai.entities.schemas import load_entity_schemas + + schemas = load_entity_schemas(schema_path) + except Exception: # pragma: no cover - schema optional during training + return {} + descriptions: Dict[str, str] = {} + for schema in schemas: + if schema.description: + descriptions[schema.entity_group] = schema.description + descriptions[schema.label] = schema.description + return descriptions + + +def _build_examples(config: "NERFinetuneConfig", path: str, descriptions: Dict[str, str]): + from gliner2.training.data import InputExample + + examples = [] + for row in read_jsonl(path, config.max_examples): + normalized = ner_row_to_entities(row) + entities = normalized["entities"] + if not entities: + continue + descs = dict(normalized.get("entity_descriptions") or {}) + for label in entities: + if label not in descs and label in descriptions: + descs[label] = descriptions[label] + examples.append( + InputExample( + text=normalized["text"], + entities=entities, + entity_descriptions=descs or None, + ) + ) + return examples + + def finetune_ner(config: NERFinetuneConfig) -> str: - """Fine-tune a GLiNER model on span-annotated data and save it.""" try: - from gliner import GLiNER - from gliner.data_processing.collator import DataCollator - from gliner.training import Trainer, TrainingArguments + from gliner2 import GLiNER2 + from gliner2.training.data import TrainingDataset + from gliner2.training.trainer import GLiNER2Trainer, TrainingConfig except Exception as exc: # pragma: no cover - exercised only without the extra raise RuntimeError( - "GLiNER fine-tuning requires the optional `finetune` dependencies " - "(`uv sync --extra finetune`). If your installed gliner exposes a " - "different training API, adapt finetuning/ner.py to it." + "GLiNER2 fine-tuning requires the optional `finetune` dependencies " + "(`uv sync --extra finetune`)." ) from exc - data = list(iter_gliner_examples(config.train_data, config.max_examples)) - if not data: + descriptions = _schema_descriptions(config.schema_path) + train_examples = _build_examples(config, config.train_data, descriptions) + if not train_examples: raise ValueError("No training examples provided.") - eval_data = ( - list(iter_gliner_examples(config.eval_data)) if config.eval_data else None - ) - model = GLiNER.from_pretrained(config.base_model) - model.set_sampling_params( - max_types=25, shuffle_types=True, random_drop=True, max_neg_type_ratio=1 - ) + train_dataset = TrainingDataset(train_examples) + train_dataset.validate(strict=False, raise_on_error=False) + train_dataset.print_stats() - collator = DataCollator( - model.config, data_processor=model.data_processor, prepare_labels=True - ) + if config.eval_data: + train_data: object = train_dataset + val_data: object = TrainingDataset( + _build_examples(config, config.eval_data, descriptions) + ) + else: + train_data, val_data, _ = train_dataset.split( + train_ratio=0.9, val_ratio=0.1, test_ratio=0.0, shuffle=True, seed=config.seed + ) - args = TrainingArguments( + training_config = TrainingConfig( output_dir=config.output_dir, - num_train_epochs=config.epochs, - learning_rate=config.learning_rate, - per_device_train_batch_size=config.batch_size, - per_device_eval_batch_size=config.batch_size, - weight_decay=config.weight_decay, + experiment_name="trialmatchai_ner", + num_epochs=config.epochs, + batch_size=config.batch_size, + encoder_lr=config.encoder_lr, + task_lr=config.task_lr, warmup_ratio=config.warmup_ratio, - seed=config.seed, - report_to=[], - evaluation_strategy="epoch" if eval_data else "no", - save_strategy="epoch", + scheduler_type="cosine", + fp16=config.fp16, + eval_strategy="epoch", + save_best=True, + early_stopping=True, + early_stopping_patience=3, + use_lora=config.use_lora, + lora_r=config.lora_r, + lora_alpha=config.lora_alpha, + lora_target_modules=["encoder"], + save_adapter_only=config.use_lora, ) - trainer = Trainer( - model=model, - args=args, - train_dataset=data, - eval_dataset=eval_data, - data_collator=collator, - tokenizer=model.data_processor.transformer_tokenizer, + model = GLiNER2.from_pretrained(config.base_model) + trainer = GLiNER2Trainer(model, training_config) + logger.info("Starting GLiNER2 fine-tuning on %d examples...", len(train_examples)) + trainer.train(train_data=train_data, val_data=val_data) + + result_dir = ( + f"{config.output_dir.rstrip('/')}/final" + if config.use_lora + else f"{config.output_dir.rstrip('/')}/best" + ) + logger.info( + "Saved %s to %s. Set entity_extraction.model_name to this path " + "(LoRA adapters load via GLiNER2.load_adapter).", + "LoRA adapter" if config.use_lora else "fine-tuned model", + result_dir, ) + return result_dir + - logger.info("Starting GLiNER fine-tuning on %d examples...", len(data)) - trainer.train() - model.save_pretrained(config.output_dir) - logger.info("Saved fine-tuned GLiNER model to %s", config.output_dir) - return config.output_dir +__all__ = ["NERFinetuneConfig", "finetune_ner"] diff --git a/tests/test_finetuning.py b/tests/test_finetuning.py index cf65470d..7843403a 100644 --- a/tests/test_finetuning.py +++ b/tests/test_finetuning.py @@ -10,7 +10,7 @@ from trialmatchai.finetuning.config import FinetuneConfig from trialmatchai.finetuning.data import ( cot_row_to_messages, - ner_row_to_gliner, + ner_row_to_entities, read_jsonl, reranker_row_to_messages, ) @@ -37,17 +37,27 @@ def test_reranker_row_messages_and_label(): assert any("Statement B: C" in m["content"] for m in messages) -def test_ner_char_spans_convert_to_token_indices(): - gliner = ner_row_to_gliner( - {"text": "EGFR mutation positive", "ner": [[0, 4, "gene"]]} +def test_ner_char_spans_convert_to_surface_forms(): + out = ner_row_to_entities( + {"text": "EGFR mutation in NSCLC", "ner": [[0, 4, "gene"], [17, 22, "disease"]]} ) - assert gliner["tokenized_text"][0] == "EGFR" - assert gliner["ner"] == [[0, 0, "gene"]] + assert out["text"] == "EGFR mutation in NSCLC" + assert out["entities"] == {"gene": ["EGFR"], "disease": ["NSCLC"]} -def test_ner_native_format_passthrough(): - row = {"tokenized_text": ["A", "B"], "ner": [[0, 1, "x"]]} - assert ner_row_to_gliner(row) == row +def test_ner_entities_mapping_passthrough(): + out = ner_row_to_entities( + {"text": "EGFR positive", "entities": {"gene": ["EGFR"]}} + ) + assert out["entities"] == {"gene": ["EGFR"]} + + +def test_ner_native_gliner2_format(): + out = ner_row_to_entities( + {"input": "EGFR positive", "output": {"entities": {"gene": ["EGFR"]}}} + ) + assert out["text"] == "EGFR positive" + assert out["entities"] == {"gene": ["EGFR"]} def test_read_jsonl_respects_max(tmp_path): From de588d9cc65f605af00f28cb2042089753d27254 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 11:13:34 +0200 Subject: [PATCH 27/64] refactor: vLLM is the only LLM backend; adapters served natively Per direction: every LLM runs on vLLM (no HuggingFace backend), and fine-tuned LoRA adapters are served everywhere via vLLM's LoRARequest. Removed the HuggingFace LLM path entirely: - delete matching/eligibility_reasoning.py (HF CoT processor), models/llm/llm_loader.py, models/llm/_common.py, and tests/test_llm_common.py. - run_rag_processing always builds a vLLM engine; main_pipeline no longer loads or half()s an HF model. Drop the cot_backend config switch (settings, config.json, env map) and make preflight's vLLM/GPU check unconditional under require_models. Reranker now runs on vLLM: - rewrite LLMReranker to score (patient, criterion) pairs by constraining generation to the Yes/No tokens (allowed_token_ids) and reading their logprobs; a configured reranker_adapter_path is served via LoRARequest. create_messages is a @staticmethod (reused by the fine-tuner without loading a model). Adapters end to end: - CoT and reranker both load their LoRA adapters through vLLM (cot_adapter_path / reranker_adapter_path); NER LoRA loads via GLiNER2.load_adapter. - new `trialmatchai-finetune merge` to merge a LoRA adapter into the base model for users who prefer a standalone checkpoint over base+adapter. The embedder stays on transformers (it is an embedding model, not a generative LLM). Docs/README updated; CI import smoke adjusted to the vLLM modules. --- .github/workflows/ci.yml | 3 +- README.md | 6 +- docs/finetuning.md | 21 +- src/trialmatchai/config/config.json | 1 - src/trialmatchai/config/settings.py | 9 - src/trialmatchai/finetuning/cli.py | 17 ++ src/trialmatchai/finetuning/merge.py | 45 ++++ src/trialmatchai/main.py | 95 +++------ .../matching/eligibility_reasoning.py | 162 --------------- src/trialmatchai/models/llm/_common.py | 162 --------------- src/trialmatchai/models/llm/llm_loader.py | 89 -------- src/trialmatchai/models/llm/llm_reranker.py | 194 +++++++++--------- src/trialmatchai/services/preflight.py | 31 ++- tests/test_deployment_readiness.py | 6 +- tests/test_finetuning.py | 5 + tests/test_llm_common.py | 104 ---------- tests/test_preflight_and_indexer.py | 7 +- 17 files changed, 222 insertions(+), 735 deletions(-) create mode 100644 src/trialmatchai/finetuning/merge.py delete mode 100644 src/trialmatchai/matching/eligibility_reasoning.py delete mode 100644 src/trialmatchai/models/llm/_common.py delete mode 100644 src/trialmatchai/models/llm/llm_loader.py delete mode 100644 tests/test_llm_common.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d405b4f6..b2af2fc5 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -111,9 +111,8 @@ jobs: modules = [ "trialmatchai.entities.recognizers", "trialmatchai.models.embedding.text_embedder", - "trialmatchai.models.llm.llm_loader", "trialmatchai.models.llm.llm_reranker", - "trialmatchai.matching.eligibility_reasoning", + "trialmatchai.models.llm.vllm_loader", "trialmatchai.matching.eligibility_reasoning_vllm", "trialmatchai.finetuning.cot", "trialmatchai.finetuning.reranker", diff --git a/README.md b/README.md index 849c31f4..6b048c68 100644 --- a/README.md +++ b/README.md @@ -32,8 +32,10 @@ CoT eligibility reasoning (per-criterion Met / Not Met / Violated …) Ranking ──► ranked trials + per-criterion explanations (results/) ``` -Every model stage — **NER, reranker, and CoT** — is swappable and fine-tunable -(see [Bring your own models](#bring-your-own-models)). +The generative LLM stages — **reranker and CoT** — run on **vLLM** (the only LLM +backend), which serves fine-tuned **LoRA adapters** natively. Every model stage — +NER, reranker, and CoT — is swappable and fine-tunable (see +[Bring your own models](#bring-your-own-models)). ## Requirements diff --git a/docs/finetuning.md b/docs/finetuning.md index 8ce253f6..295e9797 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -22,11 +22,24 @@ uv sync --extra finetune Already have a checkpoint or adapter? Just point the config at it — no code changes needed. -- **NER:** set `entity_extraction.model_name` to your GLiNER checkpoint (local - path or Hub id) and keep `backend: gliner2` (or `gliner`). +- **NER:** set `entity_extraction.model_name` to your GLiNER2 checkpoint (local + path or Hub id), backend `gliner2`. LoRA NER adapters load via `GLiNER2.load_adapter`. - **Reranker:** set `model.reranker_adapter_path` to your LoRA adapter directory. -- **CoT:** set `model.cot_adapter_path` to your LoRA adapter directory. The vLLM - backend loads it as a `LoRARequest`; the HuggingFace backend loads it with PEFT. +- **CoT:** set `model.cot_adapter_path` to your LoRA adapter directory. + +The reranker and CoT both run on **vLLM (the only LLM backend)**, which serves +the LoRA adapter natively via `LoRARequest` — no merge step required. If you +prefer a single self-contained model instead of base + adapter, merge them: + +```bash +trialmatchai-finetune merge \ + --base-model google/gemma-2-2b-it \ + --adapter models/reranker-adapter \ + --output-dir models/reranker-merged +``` + +Then point the config at the merged directory (`reranker_model_path` / +`cot model`) and leave the adapter path empty. ## 2. Fine-tuning diff --git a/src/trialmatchai/config/config.json b/src/trialmatchai/config/config.json index f137a097..5ac0a77f 100644 --- a/src/trialmatchai/config/config.json +++ b/src/trialmatchai/config/config.json @@ -96,7 +96,6 @@ "max_trials_second_level": 100 }, "use_cot_reasoning": true, - "cot_backend": "vllm", "rag": { "batch_size": 4, "max_trials_rag": 20 diff --git a/src/trialmatchai/config/settings.py b/src/trialmatchai/config/settings.py index 6dd638b0..04f590aa 100644 --- a/src/trialmatchai/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -165,17 +165,9 @@ class TrialMatchSettings(BaseModel): LLM_reranker: LLMRerankerSettings search: SearchSettings use_cot_reasoning: bool = True - cot_backend: str = "vllm" rag: RagSettings vllm: VllmSettings - @field_validator("cot_backend") - @classmethod - def validate_cot_backend(cls, value: str) -> str: - if value not in {"default", "vllm"}: - raise ValueError("cot_backend must be 'default' or 'vllm'") - return value - def to_dict(self) -> Dict[str, Any]: return self.model_dump(by_alias=True) @@ -216,7 +208,6 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "model", "reranker_adapter_path", ), - "TRIALMATCHAI_COT_BACKEND": ("cot_backend",), "TRIALMATCHAI_ENTITY_BACKEND": ("entity_extraction", "backend"), "TRIALMATCHAI_ENTITY_MODEL_NAME": ("entity_extraction", "model_name"), "TRIALMATCHAI_ENTITY_FALLBACK_MODEL_NAME": ( diff --git a/src/trialmatchai/finetuning/cli.py b/src/trialmatchai/finetuning/cli.py index 4284d81d..31914b9b 100644 --- a/src/trialmatchai/finetuning/cli.py +++ b/src/trialmatchai/finetuning/cli.py @@ -76,6 +76,14 @@ def build_parser() -> argparse.ArgumentParser: reranker = sub.add_parser("reranker", help="LoRA SFT for the reranker (Yes/No)") _add_common_lora_args(reranker) + merge = sub.add_parser( + "merge", help="Merge a LoRA adapter into its base model (standalone checkpoint)" + ) + merge.add_argument("--base-model", required=True) + merge.add_argument("--adapter", required=True) + merge.add_argument("--output-dir", required=True) + merge.add_argument("--trust-remote-code", action="store_true") + ner = sub.add_parser("ner", help="Fine-tune the GLiNER2 NER model") ner.add_argument("--base-model", default="fastino/gliner2-base-v1") ner.add_argument("--train-data", required=True) @@ -105,6 +113,15 @@ def main(argv: Optional[Sequence[str]] = None) -> int: from trialmatchai.finetuning.reranker import finetune_reranker finetune_reranker(_lora_config_from_args(args)) + elif args.component == "merge": + from trialmatchai.finetuning.merge import merge_adapter + + merge_adapter( + base_model=args.base_model, + adapter_path=args.adapter, + output_dir=args.output_dir, + trust_remote_code=args.trust_remote_code, + ) elif args.component == "ner": from trialmatchai.finetuning.ner import NERFinetuneConfig, finetune_ner diff --git a/src/trialmatchai/finetuning/merge.py b/src/trialmatchai/finetuning/merge.py new file mode 100644 index 00000000..2377727f --- /dev/null +++ b/src/trialmatchai/finetuning/merge.py @@ -0,0 +1,45 @@ +"""Merge a LoRA adapter into its base model to produce a standalone checkpoint. + +vLLM serves LoRA adapters natively (via LoRARequest), so merging is optional. +Use it when you prefer a single self-contained model directory over base+adapter. +""" + +from __future__ import annotations + +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def merge_adapter( + base_model: str, + adapter_path: str, + output_dir: str, + *, + trust_remote_code: bool = False, +) -> str: + """Load base + LoRA adapter, merge weights, and save a full model to output_dir.""" + try: + import torch + from peft import PeftModel + from transformers import AutoModelForCausalLM, AutoTokenizer + except Exception as exc: # pragma: no cover - exercised only without the extra + raise RuntimeError( + "Merging requires the optional `finetune` dependencies " + "(`uv sync --extra finetune`)." + ) from exc + + logger.info("Loading base model %s ...", base_model) + base = AutoModelForCausalLM.from_pretrained( + base_model, + torch_dtype=torch.bfloat16, + trust_remote_code=trust_remote_code, + ) + logger.info("Applying and merging adapter %s ...", adapter_path) + merged = PeftModel.from_pretrained(base, adapter_path).merge_and_unload() + merged.save_pretrained(output_dir) + AutoTokenizer.from_pretrained(base_model, trust_remote_code=trust_remote_code).save_pretrained( + output_dir + ) + logger.info("Saved merged model to %s", output_dir) + return output_dir diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index f8011abd..6c2104bb 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -5,7 +5,6 @@ from trialmatchai.config.config_loader import load_config from trialmatchai.entities import build_entity_annotator -from trialmatchai.matching.eligibility_reasoning import BatchTrialProcessor from trialmatchai.matching.trial_ranker import ( load_trial_data, rank_trials, @@ -144,8 +143,6 @@ def run_rag_processing( output_folder: str, top_trials_file: str, patient_info: Dict, - model, - tokenizer, config: Dict, ): top_trials = read_text_file(top_trials_file) @@ -159,52 +156,28 @@ def run_rag_processing( logger.error("No patient profile available for RAG processing.") return - # Check if vLLM backend is configured - cot_backend = config.get("cot_backend", "default") - use_vllm = cot_backend == "vllm" + # vLLM is the only LLM backend. A configured cot_adapter_path is served as a + # LoRA adapter (LoRARequest) by the engine loader. + from trialmatchai.matching.eligibility_reasoning_vllm import BatchTrialProcessorVLLM + from trialmatchai.models.llm.vllm_loader import load_vllm_engine - if use_vllm: - logger.info("Using vLLM backend for CoT reasoning") - from trialmatchai.matching.eligibility_reasoning_vllm import ( - BatchTrialProcessorVLLM, - ) - from trialmatchai.models.llm.vllm_loader import load_vllm_engine - - # Load vLLM configuration - vllm_cfg = config.get("vllm", {}) - - # Load vLLM engine - vllm_engine, vllm_tokenizer, lora_request = load_vllm_engine( - model_config=config.get("model", {}), - vllm_cfg=vllm_cfg, - ) - - # Create vLLM processor - rag_processor = BatchTrialProcessorVLLM( - llm=vllm_engine, # type: ignore - tokenizer=vllm_tokenizer, - batch_size=vllm_cfg.get("batch_size", 16), - use_cot=config.get("use_cot_reasoning", True), - max_new_tokens=vllm_cfg.get("max_new_tokens", 5000), - temperature=vllm_cfg.get("temperature", 0.0), - top_p=vllm_cfg.get("top_p", 1.0), - seed=vllm_cfg.get("seed", 1234), - length_bucket=vllm_cfg.get("length_bucket", True), - lora_request=lora_request, - ) - else: - logger.info("Using default (HuggingFace) backend for CoT reasoning") - - batch_size = min(config["rag"]["batch_size"] * 2, 8) - if tokenizer.pad_token is None: - tokenizer.pad_token = tokenizer.eos_token - - rag_processor = BatchTrialProcessor( - model, - tokenizer, - device=config["global"]["device"], - batch_size=batch_size, - ) + vllm_cfg = config.get("vllm", {}) + vllm_engine, vllm_tokenizer, lora_request = load_vllm_engine( + model_config=config.get("model", {}), + vllm_cfg=vllm_cfg, + ) + rag_processor = BatchTrialProcessorVLLM( + llm=vllm_engine, # type: ignore + tokenizer=vllm_tokenizer, + batch_size=vllm_cfg.get("batch_size", 16), + use_cot=config.get("use_cot_reasoning", True), + max_new_tokens=vllm_cfg.get("max_new_tokens", 5000), + temperature=vllm_cfg.get("temperature", 0.0), + top_p=vllm_cfg.get("top_p", 1.0), + seed=vllm_cfg.get("seed", 1234), + length_bucket=vllm_cfg.get("length_bucket", True), + lora_request=lora_request, + ) rag_processor.process_trials( nct_ids=top_trials, @@ -256,7 +229,6 @@ def main_pipeline(config_path: str | None = None) -> int: return 1 from trialmatchai.models.embedding import build_embedder - from trialmatchai.models.llm.llm_loader import load_model_and_tokenizer from trialmatchai.models.llm.llm_reranker import LLMReranker if torch.cuda.is_available(): @@ -266,27 +238,8 @@ def main_pipeline(config_path: str | None = None) -> int: import warnings - # The HuggingFace CoT model is only used by the default backend. When the - # vLLM backend is configured, run_rag_processing loads its own engine and - # ignores these, so skip loading (and half()-ing) the HF model entirely to - # avoid wasting GPU memory and load time (and risking OOM next to vLLM). - model, tokenizer = None, None - if config.get("cot_backend", "default") != "vllm": - with warnings.catch_warnings(): - warnings.filterwarnings( - "ignore", message=".*quantization_config.*", category=UserWarning - ) - model, tokenizer = load_model_and_tokenizer( - config["model"], config["global"]["device"] - ) - - if tokenizer.pad_token is None: # type: ignore - tokenizer.pad_token = tokenizer.eos_token # type: ignore - if hasattr(model.config, "pad_token_id") and model.config.pad_token_id is None: # type: ignore - model.config.pad_token_id = tokenizer.pad_token_id # type: ignore - - if config["global"]["device"] != "cpu" and torch.cuda.is_available(): - model = model.half() # type: ignore + # The CoT reasoning model is loaded lazily by run_rag_processing as a vLLM + # engine (the only LLM backend), so nothing to load here. # Initialize components embedder = build_embedder(config) @@ -374,8 +327,6 @@ def main_pipeline(config_path: str | None = None) -> int: str(output_folder), top_trials_path, patient_info, - model, - tokenizer, config, ) diff --git a/src/trialmatchai/matching/eligibility_reasoning.py b/src/trialmatchai/matching/eligibility_reasoning.py deleted file mode 100644 index 4df07283..00000000 --- a/src/trialmatchai/matching/eligibility_reasoning.py +++ /dev/null @@ -1,162 +0,0 @@ -import time -from typing import Dict, List - -from trialmatchai.matching.eligibility_base import BaseTrialProcessor -from trialmatchai.utils.logging_config import setup_logging - -logger = setup_logging(__name__) - -try: - import torch -except ImportError: # pragma: no cover - exercised by lean package imports - torch = None # type: ignore[assignment] - - -def _require_torch(): - if torch is None: - raise RuntimeError( - "BatchTrialProcessor requires PyTorch. Install the ML extras with " - "`uv sync --extra llm` or `pip install 'trialmatchai[llm]'`." - ) - return torch - - -class BatchTrialProcessor(BaseTrialProcessor): - def __init__( - self, - model, - tokenizer, - device: int, - batch_size: int = 4, - use_cot: bool = True, - max_new_tokens: int = 5000, # keep long answers - ): - """ - Optimized for throughput while preserving long outputs. - - Key improvements: - - model.eval(), use_cache, TF32 hints, and SDPA/FlashAttention2 when available - - length bucketing (sort by prompt token length) to reduce padding waste - - telemetry for tokens/sec and stage timings - """ - torch_module = _require_torch() - self.device = device - self.device_str = f"cuda:{device}" - self.batch_size = batch_size - self.model = model - self.tokenizer = tokenizer - self.use_cot = use_cot - self.max_new_tokens = max_new_tokens - - # ---- Inference-time performance knobs (safe if available) ---- - self.model.eval() - try: - # Allow TF32 on Ampere+ (gives a free speedup for matmuls with minimal accuracy loss) - torch_module.backends.cuda.matmul.allow_tf32 = True - torch_module.set_float32_matmul_precision("high") - except Exception: - pass - - # Prefer fast attention kernels if supported by your install - try: - from transformers.utils.import_utils import is_flash_attn_2_available - - if hasattr(self.model, "config"): - if is_flash_attn_2_available(): - self.model.config.attn_implementation = "flash_attention_2" - else: - # SDPA is the PyTorch fused attention (fast on recent torch) - self.model.config.attn_implementation = "sdpa" - except Exception: - # Fall back silently; HF will pick the best available - pass - - # Ensure caching is on for generation - try: - if hasattr(self.model, "config") and hasattr( - self.model.config, "use_cache" - ): - self.model.config.use_cache = True - except Exception: - pass - - # Pad token handling (avoids warnings for decoder-only models) - try: - if ( - self.tokenizer.pad_token_id is None - and self.tokenizer.eos_token_id is not None - ): - self.tokenizer.pad_token = self.tokenizer.eos_token - except Exception: - pass - - def _progress_desc(self) -> str: - return f"GPU {self.device} Processing Trials" - - # ---------------------- Core batch path ---------------------- - - def _process_batch(self, batch: List[Dict], output_folder: str): - """ - Expects a list of dicts with keys: nct_id, prompt - """ - torch_module = _require_torch() - try: - t0 = time.time() - # Tokenize once; pad to the longest in this batch only - tokenized = self.tokenizer( - [item["prompt"] for item in batch], - padding=True, - truncation=True, # keep to model max length to avoid OOM - return_tensors="pt", - ) - input_len = tokenized["input_ids"].shape[1] - tokenized = tokenized.to(self.device_str) - t1 = time.time() - - # Autocast to model dtype if it's half/bfloat16 for extra speed - model_dtype = next(self.model.parameters()).dtype - use_autocast = model_dtype in (torch_module.float16, torch_module.bfloat16) - - with torch_module.inference_mode(): - ctx = ( - torch_module.autocast(device_type="cuda", dtype=model_dtype) - if use_autocast - else torch_module.cuda.amp.autocast(enabled=False) - ) - with ctx: - outputs = self.model.generate( - **tokenized, - max_new_tokens=self.max_new_tokens, # long answers kept - do_sample=False, - use_cache=True, - pad_token_id=self.tokenizer.pad_token_id - if self.tokenizer.pad_token_id is not None - else self.tokenizer.eos_token_id, - eos_token_id=self.tokenizer.eos_token_id, - num_return_sequences=1, - return_dict_in_generate=False, - ) - t2 = time.time() - - # Decode only the generated tail - gen_len = outputs.shape[1] - input_len - decoded_responses = self.tokenizer.batch_decode( - outputs[:, input_len:], skip_special_tokens=True - ) - - # Persist outputs - for item, response in zip(batch, decoded_responses): - self._save_outputs(item["nct_id"], response, output_folder) - - # Telemetry - total_gen_tokens = gen_len * len(batch) - gen_time = t2 - t1 - logger.info( - f"[GPU {self.device}] batch={len(batch)} | in_len={input_len} | " - f"out_len≈{gen_len} | tokenize+H2D={t1 - t0:.2f}s | " - f"generate={gen_time:.2f}s | ~{(total_gen_tokens / gen_time) if gen_time > 0 else 0:.1f} tok/s" - ) - except Exception as e: - logger.error(f"Batch processing failed: {str(e)}") - for item in batch: - logger.error(f"Failed trial: {item['nct_id']}") diff --git a/src/trialmatchai/models/llm/_common.py b/src/trialmatchai/models/llm/_common.py deleted file mode 100644 index 3f92abbb..00000000 --- a/src/trialmatchai/models/llm/_common.py +++ /dev/null @@ -1,162 +0,0 @@ -"""Shared helpers for the optional LLM model loaders (llm_loader, llm_reranker). - -The heavy dependencies (torch/transformers/peft) are imported lazily by -``load_llm_dependencies`` so this module imports cleanly without the ``llm`` -extra installed. The torch-dependent helpers take ``torch`` as an argument and -are otherwise pure, which keeps them unit-testable with a stub. -""" - -from __future__ import annotations - -from typing import Any, NamedTuple, Optional - -from trialmatchai.utils.logging_config import setup_logging - -logger = setup_logging(__name__) - - -class LLMDeps(NamedTuple): - torch: Any - torch_functional: Any - peft_model: Any - auto_model: Any - auto_tokenizer: Any - bnb_config: Any - - -def load_llm_dependencies() -> LLMDeps: - """Import the optional LLM stack, raising a clear error when it is missing.""" - try: - import torch - import torch.nn.functional as torch_functional - from peft import PeftModel - from transformers import ( - AutoModelForCausalLM, - AutoTokenizer, - BitsAndBytesConfig, - ) - except Exception as exc: # pragma: no cover - exercised in lean installs - raise RuntimeError( - "LLM model loading requires the optional `llm` dependencies " - "(`uv sync --extra llm`)." - ) from exc - return LLMDeps( - torch, - torch_functional, - PeftModel, - AutoModelForCausalLM, - AutoTokenizer, - BitsAndBytesConfig, - ) - - -def resolve_cuda_device( - torch: Any, device: Any, *, label: str = "LLM" -) -> tuple[str, Optional[int]]: - """Resolve a requested device to ``(device_str, cuda_index)``. - - Validates the requested GPU index (accepting ``int`` or ``"auto"``), selects - it via ``set_device``, and falls back to GPU 0 with a warning on anything - invalid. Returns ``("cpu", None)`` when CUDA is unavailable. - """ - if not torch.cuda.is_available(): - logger.warning("%s: CUDA not available; using CPU.", label) - return "cpu", None - - cuda_count = torch.cuda.device_count() - if device == "auto" or device is None: - idx = 0 - elif isinstance(device, bool): # bool is an int subclass; reject it explicitly - logger.warning("%s: invalid device %r; using 0.", label, device) - idx = 0 - elif isinstance(device, int): - idx = device - else: - try: - idx = int(device) - except (TypeError, ValueError): - logger.warning("%s: non-numeric device %r; using 0.", label, device) - idx = 0 - - if idx < 0 or idx >= cuda_count: - logger.warning( - "%s: requested CUDA device %r invalid; using 0 (num_gpus=%d).", - label, - device, - cuda_count, - ) - idx = 0 - - try: - torch.cuda.set_device(idx) - except Exception as e: - logger.warning( - "%s: torch.cuda.set_device(%d) failed: %s; using 0.", label, idx, e - ) - idx = 0 - torch.cuda.set_device(idx) - return f"cuda:{idx}", idx - - -def select_compute_dtype(torch: Any, use_cuda: bool) -> Any: - """bfloat16 when supported, else float16 on GPU, else float32 on CPU.""" - if use_cuda and torch.cuda.is_bf16_supported(): - return torch.bfloat16 - if use_cuda: - return torch.float16 - return torch.float32 - - -def select_attn_impl(torch: Any, cuda_index: Optional[int]) -> Optional[str]: - """Prefer FlashAttention-2 when installed and supported, else SDPA. - - Returns ``None`` on CPU (let transformers pick its default). - """ - if cuda_index is None: - return None - attn_impl = "sdpa" - try: - import flash_attn # noqa: F401 - - major, minor = torch.cuda.get_device_capability(cuda_index) - if (major * 10 + minor) >= 75: - attn_impl = "flash_attention_2" - logger.info("Using FlashAttention-2.") - else: - logger.info("FlashAttention-2 unsupported on this GPU; using SDPA.") - except Exception: - logger.info("flash-attn not available; using SDPA.") - return attn_impl - - -def build_4bit_quant_config( - bnb_config: Any, - *, - load_in_4bit: bool, - double_quant: bool = True, - quant_type: str = "nf4", - compute_dtype: Any = None, -) -> Any: - """Build a BitsAndBytesConfig; a no-op config when ``load_in_4bit`` is False.""" - if not load_in_4bit: - return bnb_config(load_in_4bit=False) - return bnb_config( - load_in_4bit=True, - bnb_4bit_use_double_quant=double_quant, - bnb_4bit_quant_type=quant_type, - bnb_4bit_compute_dtype=compute_dtype, - ) - - -def configure_decoder_tokenizer(tokenizer: Any) -> Any: - """Left-pad/-truncate a decoder-only tokenizer and ensure a pad token exists. - - Decoder-only next-token prediction reads ``logits[:, -1, :]``. With right - padding, that position is a PAD token for the shorter rows in a batch, - producing wrong probabilities — so left padding is required for correctness. - """ - tokenizer.padding_side = "left" - tokenizer.truncation_side = "left" - if tokenizer.pad_token is None: - tokenizer.pad_token = tokenizer.eos_token - return tokenizer diff --git a/src/trialmatchai/models/llm/llm_loader.py b/src/trialmatchai/models/llm/llm_loader.py deleted file mode 100644 index 0930a0e4..00000000 --- a/src/trialmatchai/models/llm/llm_loader.py +++ /dev/null @@ -1,89 +0,0 @@ -from typing import Any, Tuple - -from trialmatchai.models.llm._common import ( - build_4bit_quant_config, - configure_decoder_tokenizer, - load_llm_dependencies, - resolve_cuda_device, - select_attn_impl, - select_compute_dtype, -) -from trialmatchai.utils.logging_config import setup_logging - -logger = setup_logging(__name__) - - -def load_model_and_tokenizer(model_config: dict, device: int) -> Tuple[Any, Any]: - """Load a model and tokenizer with safe device handling and optional 4-bit.""" - deps = load_llm_dependencies() - torch = deps.torch - - device_str, cuda_index = resolve_cuda_device( - torch, device, label="load_model_and_tokenizer" - ) - use_cuda = cuda_index is not None - compute_dtype = select_compute_dtype(torch, use_cuda) - attn_impl = select_attn_impl(torch, cuda_index) - - if use_cuda: - quant_config = build_4bit_quant_config( - deps.bnb_config, - load_in_4bit=bool(model_config["quantization"]["load_in_4bit"]), - double_quant=bool( - model_config["quantization"]["bnb_4bit_use_double_quant"] - ), - quant_type=str(model_config["quantization"]["bnb_4bit_quant_type"]), - compute_dtype=compute_dtype, - ) - logger.info(f"Loading model on {device_str} with 4-bit quantization.") - else: - logger.warning( - "CUDA not available; loading model on CPU without 4-bit quantization." - ) - quant_config = build_4bit_quant_config(deps.bnb_config, load_in_4bit=False) - - trust_remote_code = bool(model_config.get("trust_remote_code", False)) - revision = model_config.get("base_model_revision") - tokenizer = deps.auto_tokenizer.from_pretrained( - model_config["base_model"], - revision=revision, - use_fast=True, - trust_remote_code=trust_remote_code, - ) - # Always left-pad decoder-only models; keep most recent tokens on truncation. - configure_decoder_tokenizer(tokenizer) - - model = deps.auto_model.from_pretrained( - model_config["base_model"], - revision=revision, - trust_remote_code=trust_remote_code, - torch_dtype=compute_dtype, - device_map=device_str, - attn_implementation=attn_impl, - quantization_config=quant_config, - low_cpu_mem_usage=True, - ) - # Ensure KV cache usage for faster generation - try: - model.config.use_cache = True - except Exception: - pass - - model = deps.peft_model.from_pretrained( - model, model_config["cot_adapter_path"], device_map=device_str - ) - - # Optional: compile for extra speed when supported - if bool(model_config.get("compile", False)): - try: - model = torch.compile(model, mode="max-autotune", fullgraph=False) - logger.info("Model compiled with torch.compile.") - except Exception as e: - logger.warning(f"torch.compile failed; continuing without it. Err: {e}") - - if isinstance(model, torch.nn.Module): - model.eval() - else: - logger.warning("Model is not an instance of torch.nn.Module; skipping eval.") - logger.info(f"Model loaded on {device_str}.") - return model, tokenizer # type: ignore[return-value] diff --git a/src/trialmatchai/models/llm/llm_reranker.py b/src/trialmatchai/models/llm/llm_reranker.py index 820b5ee3..55812da7 100644 --- a/src/trialmatchai/models/llm/llm_reranker.py +++ b/src/trialmatchai/models/llm/llm_reranker.py @@ -1,15 +1,8 @@ +import math import re import unicodedata from typing import Any, Dict, List, Optional -from trialmatchai.models.llm._common import ( - build_4bit_quant_config, - configure_decoder_tokenizer, - load_llm_dependencies, - resolve_cuda_device, - select_attn_impl, - select_compute_dtype, -) from trialmatchai.utils.logging_config import setup_logging from tqdm import tqdm @@ -17,87 +10,71 @@ class LLMReranker: + """vLLM-backed pointwise reranker. + + Scores each (patient, criterion) pair by the model's probability of emitting + "Yes" vs "No" as the next token. Generation is constrained to those two + tokens and their logprobs are read back, so the score is a calibrated + relevance probability. A fine-tuned LoRA adapter is served via vLLM's + LoRARequest (the only LLM backend in TrialMatchAI). + """ + def __init__( self, model_path: str, adapter_path: Optional[str] = None, - device: Any = 0, + device: Any = 0, # accepted for API compatibility; vLLM manages devices torch_dtype: Any | None = None, batch_size: int = 8, revision: Optional[str] = None, trust_remote_code: bool = False, + gpu_memory_utilization: float = 0.4, + max_model_len: int = 4096, + max_lora_rank: int = 32, + dtype: str = "auto", ): - self._deps = load_llm_dependencies() - self._torch = self._deps.torch - self._torch_functional = self._deps.torch_functional - self.model_path = model_path - self.adapter_path = adapter_path + from vllm import LLM, SamplingParams # type: ignore + + try: + from vllm.lora.request import LoRARequest # type: ignore + except ImportError: # pragma: no cover - older vLLM + LoRARequest = None # type: ignore + self.batch_size = batch_size - self.revision = revision - self.trust_remote_code = trust_remote_code + enable_lora = bool(adapter_path) and LoRARequest is not None + if adapter_path and not enable_lora: + logger.warning( + "Reranker adapter requested but LoRA is unavailable in this vLLM " + "build; using the base model." + ) - # Validate/select the GPU once (handles int or "auto"); pins the device so - # device_map below is consistent with where inputs are moved. - self.device_str, self._cuda_index = resolve_cuda_device( - self._torch, device, label="LLMReranker" + self.llm = LLM( + model=str(model_path), + revision=revision, + trust_remote_code=trust_remote_code, + enable_lora=enable_lora, + max_lora_rank=max_lora_rank if enable_lora else 16, + gpu_memory_utilization=gpu_memory_utilization, + max_model_len=max_model_len, + dtype=dtype, ) - use_cuda = self._cuda_index is not None - self.torch_dtype = torch_dtype or select_compute_dtype(self._torch, use_cuda) - - self.tokenizer = self._deps.auto_tokenizer.from_pretrained( - self.model_path, - revision=self.revision, - trust_remote_code=self.trust_remote_code, + self.tokenizer = self.llm.get_tokenizer() + self.lora_request = ( + LoRARequest("reranker_adapter", 1, str(adapter_path)) if enable_lora else None ) - # Left padding is required: process_batch reads logits[:, -1, :], which - # must be the last real token, not a right-pad position. - configure_decoder_tokenizer(self.tokenizer) - self._initialize_token_ids() - self.model = self.load_model() - - def _initialize_token_ids(self): - responses = ["Yes", "No"] - token_ids = [ - self.tokenizer(response, add_special_tokens=False)["input_ids"] - for response in responses - ] - self.applicable_token_id, self.not_applicable_token_id = [ - ids[0] for ids in token_ids - ] - def load_model(self): - use_cuda = self._cuda_index is not None - quant_config = ( - build_4bit_quant_config( - self._deps.bnb_config, - load_in_4bit=True, - double_quant=True, - quant_type="nf4", - compute_dtype=self.torch_dtype, - ) - if use_cuda - else None + self.applicable_token_id, self.not_applicable_token_id = self._yes_no_token_ids() + self.sampling_params = SamplingParams( + temperature=0.0, + max_tokens=1, + logprobs=20, + allowed_token_ids=[self.applicable_token_id, self.not_applicable_token_id], ) - model = self._deps.auto_model.from_pretrained( - self.model_path, - revision=self.revision, - torch_dtype=self.torch_dtype if use_cuda else self._torch.float32, - quantization_config=quant_config, - # Pin to the selected GPU (not "auto"): inputs are moved to - # self.device_str, so the model's first layer must live there too. - device_map=self.device_str if use_cuda else None, - attn_implementation=select_attn_impl(self._torch, self._cuda_index), - trust_remote_code=self.trust_remote_code, - ) - if self.adapter_path: - model = self._deps.peft_model.from_pretrained(model, self.adapter_path) - model.eval() - return model - def preprocess_text(self, text: str) -> str: - text = unicodedata.normalize("NFKD", text) - text = re.sub(r"\s+", " ", text) - return text.strip() + def _yes_no_token_ids(self) -> tuple[int, int]: + yes = self.tokenizer("Yes", add_special_tokens=False)["input_ids"] + no = self.tokenizer("No", add_special_tokens=False)["input_ids"] + return yes[0], no[0] @staticmethod def create_messages(patient_text: str, trial_text: str) -> List[Dict]: @@ -116,36 +93,49 @@ def create_messages(patient_text: str, trial_text: str) -> List[Dict]: }, ] - def process_batch(self, batch: List[tuple]) -> List[Dict]: - batch_prompts = [] - for patient_text, trial_text in batch: - messages = self.create_messages( - self.preprocess_text(patient_text), self.preprocess_text(trial_text) - ) - prompt = self.tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - batch_prompts.append(prompt) - inputs = self.tokenizer(batch_prompts, return_tensors="pt", padding=True) - inputs = {k: v.to(self.device_str) for k, v in inputs.items()} - with self._torch.no_grad(): - outputs = self.model(**inputs) - logits = outputs.logits[:, -1, :] - probabilities = self._torch_functional.softmax(logits, dim=-1) - applicable_probs = probabilities[:, self.applicable_token_id].tolist() - return [ - {"llm_score": prob, "answer": "Yes" if prob > 0.5 else "No"} - for prob in applicable_probs - ] + def preprocess_text(self, text: str) -> str: + text = unicodedata.normalize("NFKD", text) + text = re.sub(r"\s+", " ", text) + return text.strip() + + def _build_prompt(self, patient_text: str, trial_text: str) -> str: + messages = self.create_messages( + self.preprocess_text(patient_text), self.preprocess_text(trial_text) + ) + return self.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + + def _yes_probability(self, output: Any) -> float: + try: + token_logprobs = output.outputs[0].logprobs[0] + except (AttributeError, IndexError, TypeError): + return 0.0 + yes = token_logprobs.get(self.applicable_token_id) + no = token_logprobs.get(self.not_applicable_token_id) + yes_lp = yes.logprob if yes is not None else float("-inf") + no_lp = no.logprob if no is not None else float("-inf") + highest = max(yes_lp, no_lp) + if highest == float("-inf"): + return 0.0 + ey = math.exp(yes_lp - highest) + en = math.exp(no_lp - highest) + return ey / (ey + en) def rank_pairs(self, patient_trial_pairs: List[tuple]) -> List[Dict]: - # Inference on a single device is serial regardless; iterate batches - # directly rather than behind a thread pool + lock that serialized anyway. - batches = [ - patient_trial_pairs[i : i + self.batch_size] - for i in range(0, len(patient_trial_pairs), self.batch_size) - ] results: List[Dict] = [] - for batch in tqdm(batches, desc="Reranking batches"): - results.extend(self.process_batch(batch)) + for start in tqdm( + range(0, len(patient_trial_pairs), self.batch_size), + desc="Reranking batches", + ): + batch = patient_trial_pairs[start : start + self.batch_size] + prompts = [self._build_prompt(p, t) for p, t in batch] + outputs = self.llm.generate( + prompts, self.sampling_params, lora_request=self.lora_request + ) + for output in outputs: + prob = self._yes_probability(output) + results.append( + {"llm_score": prob, "answer": "Yes" if prob > 0.5 else "No"} + ) return results diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index d1eb03e8..c96b9719 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -84,26 +84,23 @@ def run_preflight_checks( model_cfg.get("reranker_adapter_path"), required=True, ) - if config.get("cot_backend") == "vllm": - vllm_available = importlib.util.find_spec("vllm") is not None - if not vllm_available: + # vLLM is the only LLM backend (CoT + reranker), so it is always required + # when running models. + vllm_available = importlib.util.find_spec("vllm") is not None + if not vllm_available: + issues.append( + "vLLM is required (`uv sync --extra llm --extra gpu`)." + ) + else: + try: + import torch + except Exception: issues.append( - "cot_backend=vllm requires the GPU extra " - "(`uv sync --extra llm --extra gpu`)." + "vLLM requires PyTorch (`uv sync --extra llm --extra gpu`)." ) else: - try: - import torch - except Exception: - issues.append( - "cot_backend=vllm requires PyTorch " - "(`uv sync --extra llm --extra gpu`)." - ) - else: - if not torch.cuda.is_available(): - issues.append( - "cot_backend=vllm requires a CUDA-capable runtime." - ) + if not torch.cuda.is_available(): + issues.append("vLLM requires a CUDA-capable runtime.") search_cfg = config.get("search_backend", {}) if search_cfg: diff --git a/tests/test_deployment_readiness.py b/tests/test_deployment_readiness.py index ba1a9328..9733e21c 100644 --- a/tests/test_deployment_readiness.py +++ b/tests/test_deployment_readiness.py @@ -1,7 +1,7 @@ from __future__ import annotations from trialmatchai.config.config_loader import load_config, resolve_config_path -from trialmatchai.matching.eligibility_reasoning import BatchTrialProcessor +from trialmatchai.matching.eligibility_base import BaseTrialProcessor from trialmatchai.utils.json_utils import extract_json_object @@ -37,9 +37,9 @@ def test_config_env_overrides_and_search_tables(monkeypatch): def test_cot_prompt_does_not_inject_consent(): - processor = BatchTrialProcessor.__new__(BatchTrialProcessor) + processor = BaseTrialProcessor.__new__(BaseTrialProcessor) processor.use_cot = True - processor.tokenizer = object() + processor.tokenizer = None prompt = processor._format_prompt("Age >= 18", "Patient has lung cancer.") diff --git a/tests/test_finetuning.py b/tests/test_finetuning.py index 7843403a..64470f57 100644 --- a/tests/test_finetuning.py +++ b/tests/test_finetuning.py @@ -74,6 +74,11 @@ def test_cli_parses_each_subcommand(): ) assert args.component == component + merge_args = parser.parse_args( + ["merge", "--base-model", "b", "--adapter", "a", "--output-dir", "o"] + ) + assert merge_args.component == "merge" and merge_args.adapter == "a" + with pytest.raises(SystemExit): parser.parse_args(["cot"]) # missing required args diff --git a/tests/test_llm_common.py b/tests/test_llm_common.py deleted file mode 100644 index d0fd6704..00000000 --- a/tests/test_llm_common.py +++ /dev/null @@ -1,104 +0,0 @@ -"""Unit tests for the shared LLM helpers (models/llm/_common.py). - -These lock the correctness fixes from PR2 — left padding, device resolution, -dtype selection — using lightweight stubs so they run without the `llm` extra. -""" - -from trialmatchai.models.llm._common import ( - configure_decoder_tokenizer, - resolve_cuda_device, - select_attn_impl, - select_compute_dtype, -) - - -class FakeTokenizer: - def __init__(self, pad_token=None, eos_token=""): - self.pad_token = pad_token - self.eos_token = eos_token - self.padding_side = "right" - self.truncation_side = "right" - - -class FakeCuda: - def __init__(self, available=True, count=2, bf16=False): - self._available = available - self._count = count - self._bf16 = bf16 - self.selected = None - - def is_available(self): - return self._available - - def device_count(self): - return self._count - - def is_bf16_supported(self): - return self._bf16 - - def set_device(self, idx): - self.selected = idx - - -class FakeTorch: - def __init__(self, **kw): - self.cuda = FakeCuda(**kw) - self.bfloat16 = "bfloat16" - self.float16 = "float16" - self.float32 = "float32" - - -def test_configure_decoder_tokenizer_sets_left_padding_and_pad_token(): - tok = FakeTokenizer(pad_token=None, eos_token="") - configure_decoder_tokenizer(tok) - assert tok.padding_side == "left" - assert tok.truncation_side == "left" - assert tok.pad_token == "" # filled from eos when missing - - -def test_configure_decoder_tokenizer_keeps_existing_pad_token(): - tok = FakeTokenizer(pad_token="", eos_token="") - configure_decoder_tokenizer(tok) - assert tok.pad_token == "" - - -def test_resolve_cuda_device_cpu_when_unavailable(): - torch = FakeTorch(available=False) - assert resolve_cuda_device(torch, 0) == ("cpu", None) - - -def test_resolve_cuda_device_auto_selects_zero(): - torch = FakeTorch(available=True, count=2) - assert resolve_cuda_device(torch, "auto") == ("cuda:0", 0) - assert torch.cuda.selected == 0 - - -def test_resolve_cuda_device_valid_index(): - torch = FakeTorch(available=True, count=2) - assert resolve_cuda_device(torch, 1) == ("cuda:1", 1) - assert torch.cuda.selected == 1 - - -def test_resolve_cuda_device_invalid_index_falls_back_to_zero(): - torch = FakeTorch(available=True, count=2) - assert resolve_cuda_device(torch, 5) == ("cuda:0", 0) - - -def test_resolve_cuda_device_non_numeric_string_falls_back(): - torch = FakeTorch(available=True, count=2) - assert resolve_cuda_device(torch, "gpu0") == ("cuda:0", 0) - - -def test_select_compute_dtype(): - assert select_compute_dtype(FakeTorch(available=True, bf16=True), True) == "bfloat16" - assert select_compute_dtype(FakeTorch(available=True, bf16=False), True) == "float16" - assert select_compute_dtype(FakeTorch(available=False), False) == "float32" - - -def test_select_attn_impl_cpu_is_none(): - assert select_attn_impl(FakeTorch(available=False), None) is None - - -def test_select_attn_impl_gpu_without_flash_attn_is_sdpa(): - # flash_attn is not installed in the base test env, so this falls back. - assert select_attn_impl(FakeTorch(available=True), 0) == "sdpa" diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index e1799ef0..b5f70e55 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -36,7 +36,6 @@ def _base_config(tmp_path): "cot_adapter_path": str(tmp_path / "models" / "cot"), "reranker_adapter_path": str(tmp_path / "models" / "reranker"), }, - "cot_backend": "default", } @@ -104,17 +103,13 @@ def test_preflight_reports_missing_search_db_path(tmp_path): def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): cfg = _base_config(tmp_path) - cfg["cot_backend"] = "vllm" Path(cfg["model"]["cot_adapter_path"]).mkdir(parents=True) Path(cfg["model"]["reranker_adapter_path"]).mkdir(parents=True) monkeypatch.setattr(preflight.importlib.util, "find_spec", lambda name: None) issues = run_preflight_checks(cfg, require_models=True) - assert issues == [ - "cot_backend=vllm requires the GPU extra " - "(`uv sync --extra llm --extra gpu`)." - ] + assert issues == ["vLLM is required (`uv sync --extra llm --extra gpu`)."] def test_preflight_reports_missing_entity_extra(tmp_path, monkeypatch): From cff1c39ce7e56caefe2da0e9698effd504fe4b1e Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 11:26:33 +0200 Subject: [PATCH 28/64] feat: domain-aware criteria chunking + hybrid genetic-variant recognizer Reintegrates domain knowledge dropped in the migration, folded into the current architecture rather than restored verbatim. Criteria chunking (registry/criteria_chunking.py): - replaces the generic line-splitter in normalization.split_eligibility_criteria with a single pass that understands multi-level enumeration hierarchies (1, 1.2, 1.2.3, (a), roman), varied inclusion/exclusion headers (incl. inline "Exclusion Criteria: 1. ... 2. ..."), parenthetical protection, decimal/ abbreviation split-exceptions, and continuation-line joining. 8 new tests. Genetic-variant recognizer (entities/recognizers.py): - restore the curated variant pattern table as entities/resources/ variant_patterns.tsv (HGVS mutations, fusions, chromosome arms, ...). - RegexVariantRecognizer matches these deterministically (e.g. p.V600E, c.1799T>A) and CompositeRecognizer runs it alongside the GLiNER model, merging by confidence/length so precise variant spans the model would miss are still captured. On by default (entity_extraction.variant_regex). 5 tests. Packaging: variant_patterns.tsv added to package-data; both load correctly from the built wheel. --- README.md | 3 +- pyproject.toml | 1 + src/trialmatchai/entities/recognizers.py | 114 ++++++++++- .../entities/resources/variant_patterns.tsv | 52 +++++ .../registry/criteria_chunking.py | 185 ++++++++++++++++++ src/trialmatchai/registry/normalization.py | 73 +------ tests/test_criteria_chunking.py | 105 ++++++++++ tests/test_variant_recognizer.py | 51 +++++ 8 files changed, 508 insertions(+), 76 deletions(-) create mode 100644 src/trialmatchai/entities/resources/variant_patterns.tsv create mode 100644 src/trialmatchai/registry/criteria_chunking.py create mode 100644 tests/test_criteria_chunking.py create mode 100644 tests/test_variant_recognizer.py diff --git a/README.md b/README.md index 6b048c68..a77761c0 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,8 @@ with no external search service. Patient data (text / FHIR / Phenopacket / OMOP) │ interop importers → canonical PatientProfile ▼ -Biomedical NER (GLiNER) ──► concept linking (OMOP/UMLS, hybrid lexical+vector) +Biomedical NER (GLiNER + deterministic variant patterns) ──► concept linking + ▼ (OMOP/UMLS, hybrid lexical+vector) ▼ First-level retrieval ──► LanceDB hybrid search (BM25 + embeddings) over trials ▼ diff --git a/pyproject.toml b/pyproject.toml index fa7d20b5..5842063d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -84,6 +84,7 @@ where = ["src"] trialmatchai = [ "config/*.json", "entity_schemas/*.yaml", + "entities/resources/*.tsv", ] [tool.uv] diff --git a/src/trialmatchai/entities/recognizers.py b/src/trialmatchai/entities/recognizers.py index e064a8b0..83510cdc 100644 --- a/src/trialmatchai/entities/recognizers.py +++ b/src/trialmatchai/entities/recognizers.py @@ -1,10 +1,16 @@ from __future__ import annotations import re +from importlib import resources from typing import Any, Protocol, Sequence from trialmatchai.entities.schemas import schema_by_label from trialmatchai.entities.types import EntityAnnotation, EntitySchema, NO_ENTITY_ID +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + +VARIANT_PATTERNS_RESOURCE = ("trialmatchai.entities", "resources/variant_patterns.tsv") class EntityRecognizer(Protocol): @@ -53,6 +59,93 @@ def recognize( return results +def _load_variant_patterns() -> list[tuple[str, "re.Pattern[str]"]]: + """Load curated genetic-variant regexes (label, compiled pattern).""" + package, name = VARIANT_PATTERNS_RESOURCE + try: + raw = resources.files(package).joinpath(name).read_text(encoding="utf-8") + except (FileNotFoundError, ModuleNotFoundError, OSError): + logger.warning("Variant pattern resource not found; variant detection off.") + return [] + patterns: list[tuple[str, "re.Pattern[str]"]] = [] + for line in raw.splitlines(): + if not line.strip(): + continue + parts = line.split("\t") + if len(parts) < 3: + continue + label = parts[0].strip() or "variant" + # The table stores patterns with doubled backslashes; collapse to one. + pattern_text = parts[-1].replace("\\\\", "\\") + try: + patterns.append((label, re.compile(pattern_text))) + except re.error as exc: + logger.warning("Skipping invalid variant pattern %r: %s", label, exc) + return patterns + + +class RegexVariantRecognizer: + """Deterministic recognizer for genetic variants (HGVS mutations, fusions, + chromosome arms, ...) from a curated pattern table. + + Runs alongside the model recognizer to capture precise biomedical strings — + e.g. ``c.1799T>A``, ``p.V600E``, ``EGFR fusion`` — that a generalist NER + model often misses or mangles. Emits high-confidence spans tagged with the + variant type as the entity group. + """ + + def __init__(self, patterns: list[tuple[str, "re.Pattern[str]"]] | None = None): + self._patterns = patterns if patterns is not None else _load_variant_patterns() + + def recognize( + self, texts: Sequence[str], schemas: Sequence[EntitySchema] + ) -> list[list[EntityAnnotation]]: + results: list[list[EntityAnnotation]] = [] + for text in texts: + annotations: list[EntityAnnotation] = [] + for label, pattern in self._patterns: + for match in pattern.finditer(text): + start, end = match.start(), match.end() + if end <= start or not match.group(0).strip(): + continue # skip zero-width / whitespace-only matches + annotations.append( + EntityAnnotation( + entity_group=label, + text=match.group(0).strip(), + start=start, + end=end, + score=0.97, + normalized_id=(NO_ENTITY_ID,), + schema_id=None, + ) + ) + results.append(resolve_overlaps(annotations)) + return results + + +class CompositeRecognizer: + """Runs a primary recognizer plus augmenters and merges their annotations. + + Overlaps are resolved by confidence then span length, so a high-precision + variant match wins over a lower-confidence model span covering the same text. + """ + + def __init__(self, primary: EntityRecognizer, *augmenters: EntityRecognizer): + self._recognizers = [primary, *augmenters] + + def recognize( + self, texts: Sequence[str], schemas: Sequence[EntitySchema] + ) -> list[list[EntityAnnotation]]: + per_recognizer = [r.recognize(texts, schemas) for r in self._recognizers] + merged: list[list[EntityAnnotation]] = [] + for i in range(len(texts)): + combined: list[EntityAnnotation] = [] + for recognizer_results in per_recognizer: + combined.extend(recognizer_results[i]) + merged.append(resolve_overlaps(combined)) + return merged + + class GLiNER2Recognizer: def __init__( self, @@ -144,7 +237,7 @@ def build_recognizer(config: dict[str, Any]) -> EntityRecognizer: if backend == "regex": return RegexSchemaRecognizer() if backend == "gliner": - return GLiNERRecognizer( + recognizer: EntityRecognizer = GLiNERRecognizer( model_name=config.get("fallback_model_name") or config.get("model_name") or "urchade/gliner_base", @@ -153,17 +246,26 @@ def build_recognizer(config: dict[str, Any]) -> EntityRecognizer: trust_remote_code=bool(config.get("trust_remote_code", False)), batch_size=int(config.get("batch_size", 8)), ) - if backend == "gliner2": - return GLiNER2Recognizer( + elif backend == "gliner2": + recognizer = GLiNER2Recognizer( model_name=config.get("model_name", "fastino/gliner2-base"), revision=config.get("model_revision"), device=config.get("device", "auto"), trust_remote_code=bool(config.get("trust_remote_code", False)), batch_size=int(config.get("batch_size", 8)), ) - raise ValueError( - "entity_extraction.backend must be one of: gliner2, gliner, regex, disabled." - ) + else: + raise ValueError( + "entity_extraction.backend must be one of: gliner2, gliner, regex, disabled." + ) + + # Augment model NER with the deterministic variant recognizer (on by default) + # so precise HGVS/fusion strings are captured alongside model spans. + if bool(config.get("variant_regex", True)): + variants = _load_variant_patterns() + if variants: + return CompositeRecognizer(recognizer, RegexVariantRecognizer(variants)) + return recognizer def resolve_overlaps( diff --git a/src/trialmatchai/entities/resources/variant_patterns.tsv b/src/trialmatchai/entities/resources/variant_patterns.tsv new file mode 100644 index 00000000..73646df5 --- /dev/null +++ b/src/trialmatchai/entities/resources/variant_patterns.tsv @@ -0,0 +1,52 @@ +protein mutation protein mutation (|/|-|Braf|BRAF|Kras|KRAS)(((c\\.[1-9][0-9]{1,4}((-|\\+)[1-9]{1,})?(((del|ins)(A|T|C|G))|(A|C|T|G)>(A|C|T|G)))|((p\\.)?[A-Z][1-9][0-9]{0,3}([A-Z]|((_|-)[A-Z]?[1-9][0-9]{0,3}(delins|del|dup|ins)*[A-Z]{0,}))?)|(rs[0-9]{3,})|((((p\\.)?((NON|non|Non)(-| )?)?((Ala|Asx|Cys|Asp|Glu|Phe|Gly|His|Ile|Lys|Leu|Met|Asn|Pro|Gln|Arg|Ser|Thr|Sec|Val|Trp|Xaa|Tyr|Ter|Glx)|[A-Z])[1-9][0-9]{1,3}(del|dup|ins|=|(Ala|Asx|Cys|Asp|Glu|Phe|Gly|His|Ile|Lys|Leu|Met|Ter|Asn|Pro|Gln|Arg|Ser|Thr|Sec|Val|Trp|Xaa|Tyr|Glx)|[A-Z]){0,})(fs(\\*[0-9]{1,})?)?)))((-|/)[A-Z]([0-9]{1,3}([A-Z])?)?)?( |/|$)) +chromosome arm chromosome arm ((chromosome ([0-9]{1,2}|X|Y)(q|p)?([1-9]{1,2})?(\\.[0-9]{1,})?)|((chromosome)?( ([1-9]{1}|(1[0-9]{1})|(2([0-2]){1})|X|Y)(q|p)([1-9]{1}[0-9]{0,1})? ))) +aberration aberration ((((cytogenetic(al)?)|(chromosom(e|al)|genetic) )?(abnormalit(ies|y)|alteration(s)?|variant(s)?|aberrant|aberration(s)?))( of)?) +demethylation demethylation demethylat(ed|es)|demethylation|hypomethylation|decreased methylation +expression expression (((E|e)xpression( (S|s)tatus)?( of)?)|(((mRNA|RNA|(G|g)ene)? (E|e)xpress(ed|es|ion|ing))( status)?( of)?)|(m?RNA levels( of)?)|((((G|g)ene )?(E|e)xpression) level(s)?( of)?)) +frameshift frameshift (frame(-| )?shift(s)?( ((alteration|variant|mutation)(s)?))?( of )?) +inframe deletion inframe deletion (in(-)?frame (deletion|insertion)(s)?( of)?) +fusion fusion ((((G|g)ene )?(F|f)usion(s)?( (G|g)ene)?)|(NTRK( (G|g)ene)?(( |-)((T|t)ranslocation(s)?|(F|f)usion(s)?)))) +exon exon (((E|e)xon)(s)?)( |-)[0-9]{1,}(( |-|( (or|and) ))[0-9]{1,})* +gain of function gain of function (((gain(s)?(-| )of(-| )function)( (alteration|mutation|variant)s?)( of)?)|(( activating(( (gene|point))? (alteration|mutation|variant)s?)?)( of )?)) +cna cna ((gene )?copy (number|aberration|alteration)( of )?) +amplification amplification (- amplified|amplification|((gene )?amplifi(ed|cation)( of )?)|((chromosom(e|al) )?amplification(s)?( of )?)|((gene )?copy( number)? (gain(ed|s)?( of )?))|(increase(d|s)?)|(extra copy( of )?)|cn gain) +deletion deletion -deleted|- deleted| del([0-9]{1,})? |gene deleted|gene-deleted|gene deletion( of )?|((chromosom(e|al) )?deletion(s)?( of )?)|(copy los(t|s|ses)(of )?)|([0-9]{1,}(p|q)(-| )del(etion))|copy number decrease(d)?|copy decrease|deleted|((homozygous|heterozygous)(ly)?( (deletion|eleted|loss|lost))( of)?)|cn loss +germline amplification germline amplification germline amplification|germ-line amplification +germline deletion germline deletion (inherited )?germ(-)?line deletion|(delet(ed|ion)|loss|lost) in germline +germline loh germline loh (germ(-)?line (loh|(loss(-| )of(-| )heterozygosity))) +germline mutation germline mutation ((germ(-)?line( or somatic)?( mutation(s)?( (in |of |(status (of )?))?)))|((inherited )?germ(-)?line (variant(s)?|snv|alteration(s)?|mutation(s)?))) +inactivation inactivation inactivat(ed|ion)|((gene|mutation(al)?) (inactivat(ion|ed)))|(inactivating (deletion(s)?|insertion(s)?|mutation(s)?)) +indel indel indel|indels|insertion or deletion +insertion insertion insertion(s)? +inhibitor inhibitor inhibitor(s)?|inhibition|inhibited|inhibit(s)? +loss loss (loss( of)?( (both|either))?) +knockdown knockdown knockdown +loss of function loss of function ((loss(-| )of(-| )function( (mutation|variant|substitution)(s)?))|(deactivating( point)? (alteration|variant|substitution|mutation)(s)?)) +loss of heterozygosity loss of heterozygosity loss of heterozygosity|loss-of-heterozygosity|LOH +methylation methylation ((un|non-)?methylat(ed|es))|methylation|hypermethylation|increased methylation +mutation mutation (((missense|nonsense|(nonsynonymous( coding)?)|(protein(-| )coding)|point|coding|actionable|hotspot|acquired|de(-| )novo|driver|molecular(ly)?|(somatic( coding)?))( ((mutation|(alter(ed|ation))|variant|snv|(nucleotide )?substitution)(s)?)))|((mutation|variant|alteration)(s)? (of|in|within) the)|(gene (substitution(s)?|mutated|alteration(s)?|mutation(s)?))|( (-)?mutated)|(-mut )|(((gene|known) )?mutation(s)?((-| )(positive|negative|in |testing|((wild(-| )?type or muta(nt|ed))|(status( of )?)|of ))?))|((pathway )?muta(ted|nt)( or ((wt|(wild(-| )?type))))?)) +phosphorylation phosphorylation ((un|non-)?phosphorylat(ed|ion|es)) +promoter demethylation promoter demethylation ((promoter (de|hypo|non|un)(-)?methylat(ed|ion))|((de|hypo|un|non)(-)?methylat(ed|ion)( of( the)?)? promoter)) +promoter methylation promoter methylation ((promoter (hyper)?methylat(ed|ion))|((hyper)?methylation of( the)? promoter)) +promoter mutation promoter mutation (promoter (alteration|variant|mutation)s?) +protein expression protein expression ((((p|P)rotein )(E|e)xpression( of )?)|((P|p)rotein (L|l)evel(s)?( of )?)|((I|i)mmunohistochemi(cal|stry)( ((S|s)taining|(S|s)tatus|(E|e)xpression|(A|a)ssay))?( of)?)|(IHC( |-)(((P|p)ositive)|((N|n)egative)))|(IHC( )?[0-9]\\+)) +protein overexpression protein overexpression protein over(-)?express(ed|ion|ing|es) +protein underexpression protein underexpression protein under(-)?express(ed|ion|ing|es) +overexpression overexpression ((((mrna|rna) )?(non-)?over(-| )?express(ed|ion|ing|es)( of )?)|(transcriptional up(-)?regulation)|(elevated level(s)? of )|(up(-)?regulat(es|ion|ed))|((increased|increase in |up(-)?regulated|prominent(ly)?|high(ly|er)?) express(ed|ion))) +underexpression underexpression ((((mrna|rna) )?under(-| )?express(ed|ion|es|ing)( of )?)|(ihc loss)|silenced|(transcriptional down(-)?regulation( of )?)|(down(-)?regulat(es|ion|ed)( of )?)|(loss of expression( of )?)|((decreased|low(er)?|no|zero|down(-)?regulated) expression)|not expressed|(no expresssion( of )?)) +single nucleotide polymorphism single nucleotide polymorphism single nucleotide polymorphism|snp +splice variant splice variant ((splice ((mutation|variant)(s)?))|(splice site (alteration|mutation|substitution|variant)(s)?)) +structural variant structural variant (structural variant(s)?|((cytogenetic|gene|chromosom(e|al) )?rearrangement(s)?)|(large( |-)?scale (gene )?rearrangement(s))) +synonymous mutation synonymous mutation ((synonymous|silent)( coding)?( (mutation(s)?|substitution(s)?))) +switch of function switch of function switch of function +translocation translocation (gene )?translocat(ion|ing)(s)? +truncation truncation (truncat(ed|ing|ion(s)?)( mutation(s)?)?)|truncated form +wildtype wildtype ((N|n)on-(A|a)mplified)|((N|n)onamplified)|((N|n)on(-| )?(D|d)eleted)|((W|w)ild(-| )?(T|t)ype)|((U|u)nmutated)|((N|n)on(-)?(M|m)utated)|( WT )|-(WT|wt)|(WILD( |-)?TYPE) +codon codon ((amino acid position [0-9]{1,})|( codon(s)? [0-9]{1,}((/[0-9]{1,})|(( or | and | et | |-)[0-9]{1,}))*)) +codon mutation codon mutation (mutation(s)? (in|of) codon(s)? [0-9]{1,}((/[0-9]{1,})|(( or | and | et | |-)[0-9]{1,}))*) +exon mutation exon mutation ((((met|c(-)?met) )?(exon(s)? [0-9]{1,} skipping))|(metex14)|(exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})* ((splice|skipping) )?(substitution|snp|variant|mutation|alteration)(s)?)|((splice )?(mutation|variant|substitution|snp|alteration)(s)?( (detected|occur(red)?))? (in|of)( [A-Z0-9]{3,8})? exon(s)? [0-9]{1,2}(( (or|and|to|et) |-| |/)[0-9]{1,2})*)) +exon deletion exon deletion ((ex[1-9][0-9]{0,}del)|(exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})* del(etion(s)?)?)|(deletion(s)?( (detected|occur(red)?|found))? (in|of)( [A-Z0-9]{3,8})? exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})*)|(exon del-[0-9]{1,2})|(exon [0-9]{1,}-del)) +exon insertion exon insertion ((ex[1-9][0-9]{0,}ins)|(exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})* insertion(s)?)|(insertion(s)?( (detected|occur(red)?|found))? (in|of) exon(s)? [0-9]{1,2}(( (or|and|et) |-| |/)[0-9]{1,2})*)|(exon ins-[0-9]{1,2})|(exon [0-9]{1,2}-ins)) +deficiency deficiency deficienc(y|ies)|deficancy +rearrangement rearrangement re(arrangement(s)?|arranged|arranging)|gen(e|etic|omic)? rearrangement(s)?|chromosomal rearrangement(s)? +mutation mutation mutation(s)?|mutated|mutant|mutate|mutating|mutational|mutate|mutating|mutate|mutating diff --git a/src/trialmatchai/registry/criteria_chunking.py b/src/trialmatchai/registry/criteria_chunking.py new file mode 100644 index 00000000..ac199463 --- /dev/null +++ b/src/trialmatchai/registry/criteria_chunking.py @@ -0,0 +1,185 @@ +"""Eligibility-criteria chunking. + +Splits a free-text eligibility section into individual, typed criteria. This +folds the domain knowledge from the legacy regex preprocessor — multi-level +enumeration hierarchies, varied inclusion/exclusion headers, parenthetical +protection, and decimal/abbreviation split-exceptions — into one streamlined +pass over the text. + +Public API: ``split_eligibility_criteria(text) -> list[{"type", "criterion"}]`` +where ``type`` is "inclusion", "exclusion", or "unknown". +""" + +from __future__ import annotations + +import re + +# --- Header detection ------------------------------------------------------- +# A line is a section header when, reduced to alpha words, it is essentially the +# inclusion/exclusion label (with optional qualifiers / "for "), or it +# is a "...the following inclusion/exclusion criteria" lead-in. This is precise +# enough not to misfire on a real criterion that merely mentions "inclusion". +_HEADER_CORE = re.compile( + r"^(?:key|main|general|principal|primary|additional|specific|major)?\s*" + r"(inclusion|exclusion)(?:\s+criteria)?(?:\s+for\s+.+)?$" +) +_HEADER_FOLLOWING = re.compile(r"following\s+(inclusion|exclusion)\s+criteria") + +# A header that begins a line and may be followed (after a colon) by inline +# criteria, e.g. "Exclusion Criteria: 1. Pregnancy 2. Active infection". +_LEADING_HEADER = re.compile( + r"^\s*(?:key|main|general|principal|primary|additional|specific|major)?\s*" + r"(inclusion|exclusion)(?:\s+criteria)?\s*:\s*(.*)$", + re.IGNORECASE, +) + +# --- Enumeration markers ---------------------------------------------------- +# A leading list marker at the start of a line. Numeric markers REQUIRE a +# trailing "." or ")" so a bare "18 years" is not mistaken for item 18, and the +# mandatory trailing whitespace means "e.g." / "i.e." are not treated as markers. +_LEADING_MARKER = re.compile( + r"^\s*(?:" + r"[-–—*•·]" # - – — * • · + r"|\(?[0-9]{1,2}(?:\.[0-9]{1,2}){1,3}[.)]?" # multi-level: 1.2 1.2.3 1.2. + r"|\(?[0-9]{1,2}[.)]" # single: 1. 1) + r"|\([0-9]{1,2}\)" # (1) + r"|\([a-zA-Z]\)" # (a) + r"|\(?(?:i{1,3}|iv|v|vi{0,3}|ix|x)[.)]" # roman i. ii) (iv) + r"|[a-zA-Z][.)]" # a. b) + r")\s+" +) + +# Mid-line marker (preceded and followed by whitespace) — used to split several +# criteria packed onto one line. +_MIDLINE_MARKER = re.compile( + r"\s(" + r"[0-9]{1,2}(?:\.[0-9]{1,2}){0,3}[.)]" + r"|\([0-9a-zA-Z]{1,3}\)" + r"|[a-z][.)]" + r"|[-–—•*]" + r")\s" +) + + +def detect_header(line: str) -> str | None: + """Return 'inclusion'/'exclusion' if the line is a section header, else None.""" + stripped = line.strip().rstrip(":").strip() + core = re.sub(r"[^a-z ]+", " ", stripped.casefold()) + core = re.sub(r"\s+", " ", core).strip() + if not core: + return None + match = _HEADER_CORE.match(core) + if match: + return match.group(1) + match = _HEADER_FOLLOWING.search(core) + if match: + return match.group(1) + return None + + +def _mask_parens(text: str) -> str: + """Blank out parenthesized spans so markers inside them are not split points.""" + out = list(text) + depth = 0 + for i, char in enumerate(text): + if char in "([{": + depth += 1 + out[i] = " " + elif char in ")]}": + depth = max(0, depth - 1) + out[i] = " " + elif depth > 0: + out[i] = " " + return "".join(out) + + +def _split_inline(line: str) -> list[str]: + """Split a line into segments at mid-line enumeration markers (paren-safe).""" + masked = _mask_parens(line) + cut_points = [0] + for match in _MIDLINE_MARKER.finditer(masked): + start = match.start(1) + if start > 0: + cut_points.append(start) + cut_points = sorted(set(cut_points)) + segments = [ + line[cut_points[i] : cut_points[i + 1]] for i in range(len(cut_points) - 1) + ] + segments.append(line[cut_points[-1] :]) + return segments + + +def _starts_new_criterion(line: str) -> bool: + return bool(_LEADING_MARKER.match(line)) + + +def _strip_marker(line: str) -> str: + return _LEADING_MARKER.sub("", line, count=1) + + +def _clean(text: str) -> str: + cleaned = re.sub(r"\s+", " ", text).strip(" :-\t") + if detect_header(cleaned): + return "" + return cleaned + + +def _is_useful(text: str) -> bool: + return len(text) >= 3 and detect_header(text) is None + + +def split_eligibility_criteria(text: str) -> list[dict[str, str]]: + if not text or not text.strip(): + return [] + + current_type = "unknown" + entries: list[dict[str, str]] = [] + buffered: list[str] = [] + + def flush() -> None: + if not buffered: + return + criterion = _clean(" ".join(buffered)) + buffered.clear() + if _is_useful(criterion): + entries.append({"type": current_type, "criterion": criterion}) + + for raw_line in text.splitlines(): + line = raw_line.strip() + if not line: + flush() + continue + + # A header at the start of the line sets the section type. If the colon + # form carries inline criteria after it, process the remainder below. + leading = _LEADING_HEADER.match(line) + if leading: + flush() + current_type = leading.group(1).lower() + remainder = leading.group(2).strip() + if not remainder: + continue + line = remainder + else: + header = detect_header(line) + if header: + flush() + current_type = header + continue + + for segment in _split_inline(line): + segment = segment.strip() + if not segment: + continue + if _starts_new_criterion(segment): + flush() + buffered.append(_strip_marker(segment)) + else: + buffered.append(segment) + + flush() + if entries: + return entries + + fallback = _clean(text) + return [{"type": "unknown", "criterion": fallback}] if fallback else [] diff --git a/src/trialmatchai/registry/normalization.py b/src/trialmatchai/registry/normalization.py index 91d7ffd5..95055ee0 100644 --- a/src/trialmatchai/registry/normalization.py +++ b/src/trialmatchai/registry/normalization.py @@ -3,6 +3,10 @@ import re from typing import Any +from trialmatchai.registry.criteria_chunking import split_eligibility_criteria + +__all__ = ["normalize_study", "split_eligibility_criteria"] + def normalize_study(study: dict[str, Any]) -> dict[str, Any]: """Normalize a ClinicalTrials.gov v2 study into TrialMatchAI trial JSON.""" @@ -50,48 +54,6 @@ def normalize_study(study: dict[str, Any]) -> dict[str, Any]: return {key: value for key, value in normalized.items() if value not in (None, "", [])} -def split_eligibility_criteria(text: str) -> list[dict[str, str]]: - if not text or not text.strip(): - return [] - - current_type = "unknown" - entries: list[dict[str, str]] = [] - buffered: list[str] = [] - - def flush() -> None: - if not buffered: - return - criterion = " ".join(buffered).strip() - buffered.clear() - if _is_useful_criterion(criterion): - entries.append({"type": current_type, "criterion": criterion}) - - for raw_line in text.splitlines(): - line = raw_line.strip() - if not line: - flush() - continue - detected_type = _detect_criteria_header(line) - if detected_type: - flush() - current_type = detected_type - continue - cleaned = _clean_criterion_line(line) - if not cleaned: - flush() - continue - if _starts_new_criterion(line): - flush() - buffered.append(cleaned) - else: - buffered.append(cleaned) - - flush() - if entries: - return entries - - fallback = _clean_criterion_line(text) - return [{"type": "unknown", "criterion": fallback}] if fallback else [] def _mapping(value: Any) -> dict[str, Any]: @@ -176,30 +138,3 @@ def _reference_rows(value: Any) -> list[dict[str, str]]: return rows -def _detect_criteria_header(line: str) -> str | None: - normalized = re.sub(r"[^a-z]+", " ", line.casefold()).strip() - if normalized in {"inclusion criteria", "inclusion"}: - return "inclusion" - if normalized in {"exclusion criteria", "exclusion"}: - return "exclusion" - return None - - -def _starts_new_criterion(line: str) -> bool: - return bool(re.match(r"^\s*(?:[-*•]|\d+[\).]|[a-zA-Z][\).])\s+", line)) - - -def _clean_criterion_line(line: str) -> str: - cleaned = re.sub(r"^\s*(?:[-*•]|\d+[\).]|[a-zA-Z][\).])\s+", "", line) - cleaned = re.sub(r"\s+", " ", cleaned).strip(" :-\t") - if _detect_criteria_header(cleaned): - return "" - return cleaned - - -def _is_useful_criterion(text: str) -> bool: - if len(text) < 3: - return False - if _detect_criteria_header(text): - return False - return True diff --git a/tests/test_criteria_chunking.py b/tests/test_criteria_chunking.py new file mode 100644 index 00000000..5533309d --- /dev/null +++ b/tests/test_criteria_chunking.py @@ -0,0 +1,105 @@ +"""Tests for the eligibility-criteria chunker (registry/criteria_chunking.py).""" + +from __future__ import annotations + +from trialmatchai.registry.criteria_chunking import ( + detect_header, + split_eligibility_criteria, +) + + +def _criteria(text): + return [(c["type"], c["criterion"]) for c in split_eligibility_criteria(text)] + + +def test_simple_dashed_inclusion_exclusion(): + text = "\n".join( + [ + "Inclusion Criteria:", + "- Age 18 years or older", + "- Histologically confirmed lung cancer", + "Exclusion Criteria:", + "- Prior investigational therapy", + ] + ) + assert _criteria(text) == [ + ("inclusion", "Age 18 years or older"), + ("inclusion", "Histologically confirmed lung cancer"), + ("exclusion", "Prior investigational therapy"), + ] + + +def test_unknown_fallback_for_unheadered_single_line(): + assert split_eligibility_criteria("Able to consent.") == [ + {"type": "unknown", "criterion": "Able to consent."} + ] + + +def test_varied_headers_are_detected(): + assert detect_header("Key Inclusion Criteria:") == "inclusion" + assert detect_header("EXCLUSION CRITERIA") == "exclusion" + assert detect_header("Inclusion criteria for Cohort A:") == "inclusion" + assert detect_header("Patients must meet the following exclusion criteria:") == "exclusion" + # Not headers: + assert detect_header("- Age 18 years or older") is None + assert detect_header("No prior chemotherapy (inclusion in another study allowed)") is None + + +def test_numbered_and_multilevel_markers(): + text = "\n".join( + [ + "Inclusion Criteria", + "1. Adults aged 18 or older", + "2. ECOG performance status 0-1", + "2.1 No prior systemic therapy", + "Exclusion Criteria", + "a) Pregnancy", + "(b) Active infection", + ] + ) + assert _criteria(text) == [ + ("inclusion", "Adults aged 18 or older"), + ("inclusion", "ECOG performance status 0-1"), + ("inclusion", "No prior systemic therapy"), + ("exclusion", "Pregnancy"), + ("exclusion", "Active infection"), + ] + + +def test_continuation_lines_are_joined(): + text = "\n".join( + [ + "Inclusion Criteria:", + "- Measurable disease per RECIST 1.1 with at least one", + " lesion not previously irradiated", + ] + ) + assert _criteria(text) == [ + ( + "inclusion", + "Measurable disease per RECIST 1.1 with at least one lesion not previously irradiated", + ) + ] + + +def test_decimal_values_do_not_trigger_false_splits(): + text = "Inclusion Criteria:\n- Hemoglobin 9.5 g/dL or higher at screening" + assert _criteria(text) == [ + ("inclusion", "Hemoglobin 9.5 g/dL or higher at screening") + ] + + +def test_parenthetical_markers_are_not_split_points(): + text = "Inclusion Criteria:\n- Adequate organ function (see section 2.3 for details)" + assert _criteria(text) == [ + ("inclusion", "Adequate organ function (see section 2.3 for details)") + ] + + +def test_multiple_criteria_packed_on_one_line(): + text = "Exclusion Criteria: 1. Pregnancy 2. Active infection 3. Prior therapy" + # The header is on the same line as the first marker; types resolve to exclusion. + result = _criteria(text) + assert ("exclusion", "Pregnancy") in result + assert ("exclusion", "Active infection") in result + assert ("exclusion", "Prior therapy") in result diff --git a/tests/test_variant_recognizer.py b/tests/test_variant_recognizer.py new file mode 100644 index 00000000..72abc5d3 --- /dev/null +++ b/tests/test_variant_recognizer.py @@ -0,0 +1,51 @@ +"""Tests for the deterministic genetic-variant recognizer and compositing.""" + +from __future__ import annotations + +from trialmatchai.entities.recognizers import ( + CompositeRecognizer, + RegexSchemaRecognizer, + RegexVariantRecognizer, + _load_variant_patterns, +) +from trialmatchai.entities.schemas import load_entity_schemas + + +def test_variant_patterns_load(): + patterns = _load_variant_patterns() + assert len(patterns) > 10 # the curated table has dozens of patterns + assert all(hasattr(p, "finditer") for _, p in patterns) + + +def test_recognizes_hgvs_protein_and_dna_variants(): + recognizer = RegexVariantRecognizer() + text = "BRAF p.V600E and the c.1799T>A variant were detected." + spans = {ann.text for ann in recognizer.recognize([text], [])[0]} + assert any("V600E" in s for s in spans) + assert any("1799T>A" in s for s in spans) + + +def test_recognizes_gene_fusion(): + recognizer = RegexVariantRecognizer() + spans = {ann.text.lower() for ann in recognizer.recognize(["EGFR gene fusion"], [])[0]} + assert any("fusion" in s for s in spans) + + +def test_composite_merges_model_and_variant_spans(): + schemas = [s for s in load_entity_schemas() if s.id == "disease"] + composite = CompositeRecognizer( + RegexSchemaRecognizer(), RegexVariantRecognizer() + ) + text = "Patient with cancer harboring BRAF p.V600E" + annotations = composite.recognize([text], schemas)[0] + groups = {ann.entity_group for ann in annotations} + # Both the schema-recognized disease and the variant span are present. + assert "disease" in groups + assert any(ann.text.endswith("V600E") or "V600E" in ann.text for ann in annotations) + + +def test_no_zero_width_matches(): + recognizer = RegexVariantRecognizer() + for ann in recognizer.recognize(["plain text without variants"], [])[0]: + assert ann.end > ann.start + assert ann.text.strip() From b1f344a50d568a92ac52e64801f91e16811483de Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 11:29:57 +0200 Subject: [PATCH 29/64] docs: cite the Nature Communications paper Replace the arXiv reference with an explicit "please cite" message pointing to the Nature Communications paper (with a BibTeX entry); keep the Zenodo DOI as the software archive. --- README.md | 24 +++++++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index a77761c0..0d99485f 100644 --- a/README.md +++ b/README.md @@ -172,8 +172,26 @@ uv run python scripts/scan_secrets.py uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 ``` -## Support & citation +## Citation + +If you use TrialMatchAI in your research, please cite our **Nature +Communications** paper: + +> Abdallah, M. _et al._ TrialMatchAI. _Nature Communications_ (2026). +> + +```bibtex +@article{trialmatchai, + title = {TrialMatchAI}, + author = {Abdallah, Majd and others}, + journal = {Nature Communications}, + year = {2026}, + doi = {10.1038/s41467-026-70509-w}, + url = {https://www.nature.com/articles/s41467-026-70509-w} +} +``` + +## Support - Email: abdallahmajd7@gmail.com -- DOI: https://doi.org/10.5281/zenodo.18329084 -- arXiv: https://arxiv.org/abs/2505.08508 +- Software archive (DOI): From b9a3519da80a33989771b43301d7df71e58df2a2 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 14:59:58 +0200 Subject: [PATCH 30/64] feat: first-level planner + constraint scoring, with review-fix corrections Adds the multi-channel first-level query planner and the eligibility-constraint extraction/evaluation subsystem, and corrects the bugs found in review: Constraints - exclusion criteria the patient cleanly passes now reward instead of scoring neutral (relation "absent": exclusion -> not-violated, inclusion -> neutral) - biomarker evaluation is wild-type-safe (a negative/wild-type patient no longer satisfies a "present"/"mutated" requirement) - age regexes require an explicit year unit or age cue (no more "100 to 200 mg" -> age); comparator-less labs are skipped (no silent ge default) - unknown_is_neutral config is now honored (penalizes unconfirmable inclusions) First-level planner - hard_filters config is actually applied (and [] truly disables filters); age parsing degrades gracefully instead of aborting the search; a guard logs when all channels return nothing; disease_synonyms output is deterministic Other - restore the entity_extraction.variant_regex knob broken by extra="forbid" - remove the vestigial GLiNER-v1 backend Regression tests added for each fix. --- .env.example | 11 + README.md | 404 +++++++++++---- docs/finetuning.md | 6 +- docs/interoperability.md | 2 +- pyproject.toml | 2 - src/trialmatchai/config/config.json | 24 +- src/trialmatchai/config/settings.py | 108 +++- src/trialmatchai/constraints/__init__.py | 30 ++ src/trialmatchai/constraints/evaluation.py | 445 ++++++++++++++++ src/trialmatchai/constraints/extraction.py | 487 ++++++++++++++++++ src/trialmatchai/constraints/models.py | 121 +++++ src/trialmatchai/constraints/reports.py | 119 +++++ src/trialmatchai/entities/recognizers.py | 53 +- src/trialmatchai/finetuning/__init__.py | 4 +- .../interop/exporters/matching_summary.py | 5 +- src/trialmatchai/main.py | 174 +++++-- src/trialmatchai/matching/eligibility_base.py | 4 +- .../matching/retrieval/criteria_retrieval.py | 89 +++- .../matching/retrieval/first_level_planner.py | 399 ++++++++++++++ .../matching/retrieval/synonyms.py | 3 +- .../matching/retrieval/trial_retrieval.py | 133 ++++- src/trialmatchai/registry/preparation.py | 9 + src/trialmatchai/schemas/phenopacket.py | 2 +- src/trialmatchai/services/preflight.py | 4 - tests/test_config_pytest.py | 11 + tests/test_constraints.py | 317 ++++++++++++ tests/test_first_level_planner.py | 327 ++++++++++++ tests/test_first_level_search_pytest.py | 2 +- tests/test_import_patient_cli.py | 2 +- tests/test_patient_interop.py | 2 +- tests/test_patient_runtime_loading.py | 2 +- tests/test_schemas.py | 4 +- tests/test_search_queries_pytest.py | 2 +- tests/test_second_level_search_pytest.py | 131 +++++ tests/test_settings.py | 21 +- uv.lock | 4 - 36 files changed, 3231 insertions(+), 232 deletions(-) create mode 100644 src/trialmatchai/constraints/__init__.py create mode 100644 src/trialmatchai/constraints/evaluation.py create mode 100644 src/trialmatchai/constraints/extraction.py create mode 100644 src/trialmatchai/constraints/models.py create mode 100644 src/trialmatchai/constraints/reports.py create mode 100644 src/trialmatchai/matching/retrieval/first_level_planner.py create mode 100644 tests/test_constraints.py create mode 100644 tests/test_first_level_planner.py diff --git a/.env.example b/.env.example index 68a22c5a..fc19d6ce 100644 --- a/.env.example +++ b/.env.example @@ -16,6 +16,12 @@ TRIALMATCHAI_SEARCH_TRIALS_TABLE=trials TRIALMATCHAI_SEARCH_CRITERIA_TABLE=criteria TRIALMATCHAI_SEARCH_CANDIDATE_LIMIT=1000 TRIALMATCHAI_SEARCH_MODE=hybrid +TRIALMATCHAI_FIRST_LEVEL_ENABLED=true +TRIALMATCHAI_FIRST_LEVEL_MAX_TRIALS=1000 +TRIALMATCHAI_FIRST_LEVEL_PER_CHANNEL_SIZE=300 +TRIALMATCHAI_FIRST_LEVEL_VECTOR_SCORE_THRESHOLD=0.0 +TRIALMATCHAI_FIRST_LEVEL_LLM_EXPANSION_ENABLED=false +TRIALMATCHAI_FIRST_LEVEL_WRITE_REPORTS=true TRIALMATCHAI_MODEL_BASE_MODEL=microsoft/phi-4 TRIALMATCHAI_MODEL_COT_ADAPTER_PATH=models/finetuned_phi_reasoning @@ -33,6 +39,11 @@ TRIALMATCHAI_CONCEPT_TABLE=concepts TRIALMATCHAI_LINK_ACCEPT=0.80 TRIALMATCHAI_LINK_REJECT=0.30 +TRIALMATCHAI_CONSTRAINTS_ENABLED=true +TRIALMATCHAI_CONSTRAINTS_SCORE_WEIGHT=0.25 +TRIALMATCHAI_CONSTRAINTS_LLM_EXTRACTION_ENABLED=false +TRIALMATCHAI_CONSTRAINTS_WRITE_REPORTS=true + TRIALMATCHAI_REGISTRY_SOURCE=clinicaltrials.gov TRIALMATCHAI_REGISTRY_API_BASE_URL=https://clinicaltrials.gov/api/v2/studies TRIALMATCHAI_REGISTRY_KEYWORDS_FILE= diff --git a/README.md b/README.md index 0d99485f..7de83c88 100644 --- a/README.md +++ b/README.md @@ -2,180 +2,384 @@ TrialMatchAI logo -**AI-driven clinical trial matching.** TrialMatchAI ingests patient data, finds -relevant trials with hybrid local retrieval, and produces ranked recommendations -with **criterion-level eligibility explanations** — all on a single GPU server, -with no external search service. +TrialMatchAI is an AI-driven clinical trial matching pipeline. It imports patient +data, retrieves relevant trials from local LanceDB tables, and produces ranked +trial recommendations with criterion-level eligibility explanations. -[Quickstart](#quickstart) · [How it works](#how-it-works) · [Bring your own models](#bring-your-own-models) · [Fine-tuning](docs/finetuning.md) · [Configuration](#configuration) · [CLI](#cli-reference) +The supported deployment is a single Python 3.11 GPU server. Trial search is +embedded and file-backed; there is no Elasticsearch, hosted vector database, or +separate search service to run. -> **Disclaimer.** For research and informational use only. This is not medical -> advice, not a medical device, and must not replace review by qualified -> healthcare professionals. +[Install](#install) | [Quickstart](#quickstart) | [How It Works](#how-it-works) | [Configuration](#configuration) | [CLI](#cli-reference) ---- +> For research and informational use only. TrialMatchAI is not medical advice, +> not a medical device, and must not replace review by qualified healthcare +> professionals. -## How it works +## Requirements -```text -Patient data (text / FHIR / Phenopacket / OMOP) - │ interop importers → canonical PatientProfile - ▼ -Biomedical NER (GLiNER + deterministic variant patterns) ──► concept linking - ▼ (OMOP/UMLS, hybrid lexical+vector) - ▼ -First-level retrieval ──► LanceDB hybrid search (BM25 + embeddings) over trials - ▼ -Criterion retrieval + LLM reranker (cross-encoder, Yes/No) - ▼ -CoT eligibility reasoning (per-criterion Met / Not Met / Violated …) - ▼ -Ranking ──► ranked trials + per-criterion explanations (results/) -``` - -The generative LLM stages — **reranker and CoT** — run on **vLLM** (the only LLM -backend), which serves fine-tuned **LoRA adapters** natively. Every model stage — -NER, reranker, and CoT — is swappable and fine-tunable (see -[Bring your own models](#bring-your-own-models)). +- Python 3.11 (`pyproject.toml` requires `>=3.11,<3.12`) +- `uv` recommended, or `pip` with an editable install +- NVIDIA GPU for vLLM-backed matching and fine-tuning +- Around 100 GB disk for datasets, model artifacts, LanceDB tables, manifests, + and run outputs +- OMOP vocabulary files if you want to build the concept-linking table locally -## Requirements +## Install -- Python **3.11** -- [`uv`](https://docs.astral.sh/uv/) recommended (or `pip` with an editable install) -- NVIDIA GPU with enough VRAM for the selected LLM backend -- ~100 GB disk for datasets, models, LanceDB tables, and results -- A LanceDB concept table built from OMOP + curated dictionaries (for entity normalization) +Clone the repository and install from the project root: -## Quickstart +```bash +git clone +cd TrialMatchAI +``` + +Base install with `uv` gives the package and CLI entry points without the heavy +model stack: ```bash -# 1. Install. Base CLIs only: uv sync -# …or the full model-backed stack: +uv run trialmatchai --help +``` + +Install the full model-backed runtime: + +```bash uv sync --extra llm --extra gpu --extra entity ``` +Install the fine-tuning stack: + +```bash +uv sync --extra finetune +``` + +Editable install with `pip` is also supported: + +```bash +python3.11 -m venv .venv +source .venv/bin/activate +pip install -e . +``` + +Optional extras with `pip`: + +```bash +pip install -e ".[entity]" +pip install -e ".[llm,entity]" +pip install -e ".[llm,gpu,entity]" +pip install -e ".[finetune]" +``` + | Extra | Adds | -|-------|------| -| `entity` | GLiNER/GLiNER2 biomedical NER | -| `llm` | local embedding + LLM stack (torch, transformers, peft) | -| `gpu` | vLLM + bitsandbytes (Linux) | -| `finetune` | training stack for `trialmatchai-finetune` | +| --- | --- | +| `entity` | GLiNER2 biomedical NER | +| `llm` | local embedding and LLM dependencies | +| `gpu` | vLLM and bitsandbytes; intended for Linux CUDA hosts | +| `finetune` | training dependencies for `trialmatchai-finetune` | + +Installing the package is only the first step. Real matching also needs runtime +data, model artifacts, a concept table, normalized trials, and LanceDB search +tables. + +## Quickstart + +Copy the environment template if you need local overrides: + +```bash +cp .env.example .env +``` + +Check the installation and configured paths: ```bash -# 2. Verify config and backends. -uv run trialmatchai-healthcheck --require-tables +uv run trialmatchai-healthcheck +``` + +Download packaged artifacts: -# 3. Provision data, concept KB, trials, and search tables. +```bash uv run trialmatchai-bootstrap-data -uv run trialmatchai-build-concepts --concept-csv data/omop/CONCEPT.csv --synonym-csv data/omop/CONCEPT_SYNONYM.csv +``` + +Build the concept-linking table from OMOP vocabulary files: + +```bash +uv run trialmatchai-build-concepts \ + --concept-csv data/omop/CONCEPT.csv \ + --synonym-csv data/omop/CONCEPT_SYNONYM.csv +``` + +Fetch recent ClinicalTrials.gov studies and update local trial JSON plus +LanceDB: + +```bash uv run trialmatchai-update-registry --since 2026-06-01 --max-studies 100 +``` + +Build or rebuild search tables from normalized trial JSON: + +```bash uv run trialmatchai-index --prepare +``` + +Import patients into canonical TrialMatchAI profiles: -# 4. Import patients (text / FHIR / Phenopacket / OMOP — format auto-detected). +```bash uv run trialmatchai-import-patient --input data/patients/raw/patient-1.txt --format text uv run trialmatchai-import-patient --input data/patients/raw/patient-1.fhir.json uv run trialmatchai-import-patient --input data/patients/omop_extract --format omop +``` + +Run batch matching: -# 5. Run the batch matcher. Results land in results/. +```bash uv run trialmatchai-run ``` -## Bring your own models +Results are written under `results//`. + +## How It Works + +```text +Patient data (text / FHIR / Phenopacket / OMOP) + | + v +Interop importers -> canonical PatientProfile + | + v +GLiNER2 NER + deterministic variant patterns -> concept linking + | + v +First-level trial retrieval in LanceDB (BM25 + embeddings) + | + v +Multi-channel query fusion for broad candidate recall + | + v +Criterion retrieval + vLLM Yes/No reranker + | + v +Constraint-aware criterion scoring + | + v +vLLM eligibility reasoning per criterion + | + v +Final ranking + explanations in results/ +``` + +The generative LLM stages, reranker and eligibility reasoning, run on vLLM. +LoRA adapters are served natively through vLLM. NER, reranker, and eligibility +reasoning are configurable and fine-tunable. + +## Data And Storage + +TrialMatchAI uses embedded LanceDB tables by default: + +- Search DB: `data/search` +- Trial table: `trials` +- Criteria table: `criteria` +- Concept-linking DB: `data/concepts` +- Concept table: `concepts` + +ClinicalTrials.gov records are normalized to JSON files under +`data/trials_jsons/.json`. During indexing, TrialMatchAI prepares: + +- one trial row per NCT ID, including text fields, metadata filters, date/age + fields, and embedding vectors +- one criteria row per eligibility criterion, including criterion text, + criterion embedding, eligibility type, entity annotations, and parsed + eligibility constraints + +The trial and criteria tables each get full-text search fields and vector +columns, so retrieval can run in `bm25`, `vector`, or `hybrid` mode. + +Patient inputs are imported before matching. Each imported patient is stored as +a canonical profile under `data/patients/profiles/.json`, with a +matching summary under `data/patients/summaries/.json`. + +## Patient Inputs + +The importer supports: + +- free-text notes: `.txt` and `.md` +- GA4GH Phenopacket JSON +- HL7 FHIR R4 Bundle JSON, individual FHIR resource JSON, NDJSON, and JSONL +- OMOP CDM extract folders with CSV or Parquet tables + +Importers preserve provenance and unsupported source elements where possible. +The matching summary is rendered deterministically from the canonical +`PatientProfile`; raw patient files are not consumed directly by +`trialmatchai-run`. + +See [docs/interoperability.md](docs/interoperability.md) for format details. + +## First-Level Retrieval + +First-level retrieval is recall-oriented. It builds a multi-channel query plan +from the canonical `PatientProfile` and matching summary, then searches each +channel separately and fuses candidates with reciprocal rank fusion. + +Channels include primary conditions, linked concept synonyms, broader disease +terms, patient narrative text, biomarkers, prior therapy or procedures, and +optional LLM-generated expansions. LLM expansion is off by default; deterministic +concept and patient-profile expansion are the default path. + +The first level only hard-filters by age, sex/gender, and recruitment status. +Location, biomarkers, phase, prior therapy, and eligibility constraints remain +soft signals for later retrieval and reasoning stages. + +When enabled, first-level artifacts are written under `results//`: + +- `first_level_query_plan.json` +- `first_level_candidates.json` + +## Constraint-Aware Retrieval + +TrialMatchAI parses common eligibility logic from criteria rows and compares it +with the canonical `PatientProfile` during second-stage retrieval. V1 supports +age, sex or gender, conditions, medications and prior therapy, procedures, labs, +biomarkers, ECOG/Karnofsky-style performance status, temporal phrases, and +inclusion/exclusion polarity. -Defaults are good starting points, not a ceiling. Point the pipeline at your own -checkpoints or adapters — no code changes: +Constraints are a soft ranking signal. They can boost matching inclusion +criteria, penalize violated inclusion or exclusion criteria, and leave unknown +facts neutral. They do not hard-exclude trials and they are not medical advice; +the final vLLM eligibility reasoning remains the final judge. + +When enabled, per-patient reports are written under `results//`: + +- `constraint_evaluations.json` +- `constraint_summary.md` +- `top_trials_explained.json` + +## Bring Your Own Models + +Defaults are starting points. Point the pipeline at your own checkpoints or +adapters through config or environment variables. | Component | Default | Config key | -|-----------|---------|------------| +| --- | --- | --- | | Biomedical NER | `fastino/gliner2-base` | `entity_extraction.model_name` | | Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | | CoT eligibility | configured CoT model | `model.cot_adapter_path` | -Train your own with the built-in fine-tuner: +Fine-tune model components with: ```bash uv sync --extra finetune -trialmatchai-finetune cot --base-model microsoft/phi-4 --train-data data/cot.jsonl --output-dir models/cot-adapter -trialmatchai-finetune reranker --base-model google/gemma-2-2b-it --train-data data/reranker.jsonl --output-dir models/reranker-adapter -trialmatchai-finetune ner --base-model fastino/gliner2-base --train-data data/ner.jsonl --output-dir models/ner +uv run trialmatchai-finetune cot \ + --base-model microsoft/phi-4 \ + --train-data data/finetune/cot.jsonl \ + --output-dir models/cot-adapter +uv run trialmatchai-finetune reranker \ + --base-model google/gemma-2-2b-it \ + --train-data data/finetune/reranker.jsonl \ + --output-dir models/reranker-adapter +uv run trialmatchai-finetune ner \ + --base-model fastino/gliner2-base-v1 \ + --train-data data/finetune/ner.jsonl \ + --output-dir models/ner ``` -Full data formats, flags, and plug-back-in steps: **[docs/finetuning.md](docs/finetuning.md)**. +See [docs/finetuning.md](docs/finetuning.md) for accepted training formats and +adapter configuration. ## Configuration -Defaults live in `src/trialmatchai/config/config.json`; override at runtime via -`.env` or environment variables. Common knobs: +Defaults live in `src/trialmatchai/config/config.json`. Runtime overrides can be +set in `.env` or as environment variables. + +Common overrides: ```bash -TRIALMATCHAI_SEARCH_DB_PATH=data/search # embedded LanceDB tables -TRIALMATCHAI_SEARCH_MODE=hybrid # hybrid | bm25 | vector -TRIALMATCHAI_ENTITY_BACKEND=gliner2 # gliner2 | gliner | regex | disabled +TRIALMATCHAI_OUTPUT_DIR=results +TRIALMATCHAI_TRIALS_JSON_FOLDER=data/trials_jsons +TRIALMATCHAI_SEARCH_DB_PATH=data/search +TRIALMATCHAI_SEARCH_MODE=hybrid +TRIALMATCHAI_FIRST_LEVEL_MAX_TRIALS=1000 +TRIALMATCHAI_FIRST_LEVEL_PER_CHANNEL_SIZE=300 +TRIALMATCHAI_FIRST_LEVEL_VECTOR_SCORE_THRESHOLD=0.0 +TRIALMATCHAI_FIRST_LEVEL_LLM_EXPANSION_ENABLED=false +TRIALMATCHAI_ENTITY_BACKEND=gliner2 TRIALMATCHAI_ENTITY_SCHEMA_PATH=entity_schemas/trialmatchai.yaml TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts -TRIALMATCHAI_LINK_ACCEPT=0.80 # concept-linking accept threshold +TRIALMATCHAI_LINK_ACCEPT=0.80 TRIALMATCHAI_LINK_REJECT=0.30 -TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false # true only if a model requires it -TRIALMATCHAI_LOG_JSON=1 # structured logs +TRIALMATCHAI_CONSTRAINTS_ENABLED=true +TRIALMATCHAI_CONSTRAINTS_SCORE_WEIGHT=0.25 +TRIALMATCHAI_CONSTRAINTS_LLM_EXTRACTION_ENABLED=false +TRIALMATCHAI_CONSTRAINTS_WRITE_REPORTS=true +TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false +TRIALMATCHAI_LOG_JSON=1 ``` -The full set of overrides is documented in [`.env.example`](.env.example). -Patient interoperability details: [docs/interoperability.md](docs/interoperability.md). +The full override list is in [`.env.example`](.env.example). -## CLI reference +## CLI Reference | Command | Purpose | -|---------|---------| -| `trialmatchai-healthcheck` | Validate config, paths, and (optionally) LanceDB tables | -| `trialmatchai-bootstrap-data` | Download/extract external data + model artifacts | +| --- | --- | +| `trialmatchai` | Command group for the main subcommands | +| `trialmatchai-healthcheck` | Validate config, paths, optional model deps, and LanceDB tables | +| `trialmatchai-bootstrap-data` | Download and extract runtime data/model artifacts | | `trialmatchai-build-concepts` | Build the LanceDB concept table for entity normalization | -| `trialmatchai-update-registry` | Fetch new/changed ClinicalTrials.gov studies and upsert LanceDB | -| `trialmatchai-index` | Build the LanceDB trial + criteria search tables | -| `trialmatchai-import-patient` | Import text / FHIR / Phenopacket / OMOP patient data | +| `trialmatchai-update-registry` | Fetch changed ClinicalTrials.gov studies and upsert LanceDB | +| `trialmatchai-index` | Prepare/index trial and criteria search tables | +| `trialmatchai-import-patient` | Import text, FHIR, Phenopacket, or OMOP patient data | | `trialmatchai-run` | Run the batch matching pipeline | -| `trialmatchai-finetune` | Fine-tune the NER / reranker / CoT models | - -The first seven are also available as subcommands of the `trialmatchai` group, -e.g. `uv run trialmatchai healthcheck` or `uv run python -m trialmatchai healthcheck`. - -## Deployment - -The supported deployment is a single Python 3.11 GPU server/VM. Trial and -criteria search use embedded LanceDB tables under `data/search`, so there is no -separate search service, container, socket, or service credential to manage. The -registry updater is built for cron / systemd timers / GitHub Actions — see -[docs/registry-updater.md](docs/registry-updater.md). +| `trialmatchai-finetune` | Fine-tune NER, reranker, or eligibility reasoning models | -## Security - -Never commit real credentials, private keys, datasets, models, local LanceDB -data, run manifests, or results. Copy the template and keep runtime values local: +The first seven commands are also available as subcommands: ```bash -cp .env.example .env +uv run trialmatchai healthcheck +uv run python -m trialmatchai healthcheck ``` -Artifact bootstrap supports optional SHA-256 verification via -`TRIALMATCHAI_PROCESSED_TRIALS_SHA256`, `TRIALMATCHAI_MODELS_SHA256`, and -`TRIALMATCHAI_CRITERIA_PART__SHA256`. +## Deployment -Dependency auditing ignores `CVE-2025-3000` because vLLM 0.23 pins Torch 2.11.0 -and the advisory lists no fixed Torch version; revisit when upgrading either. +The supported deployment is a single Python 3.11 GPU server or VM. Search tables +are local LanceDB files under `data/search`, and concept linking uses a separate +LanceDB database under `data/concepts`. + +The registry updater is designed for cron, systemd timers, or GitHub Actions. +See [docs/registry-updater.md](docs/registry-updater.md). ## Development ```bash +uv sync uv run ruff check . uv run pytest uv run python scripts/scan_secrets.py uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 ``` +## Security + +Never commit real credentials, private keys, datasets, models, local LanceDB +data, run manifests, or results. Keep runtime values local: + +```bash +cp .env.example .env +``` + +Artifact bootstrap supports optional SHA-256 verification through: + +- `TRIALMATCHAI_PROCESSED_TRIALS_SHA256` +- `TRIALMATCHAI_MODELS_SHA256` +- `TRIALMATCHAI_CRITERIA_PART__SHA256` + +Dependency auditing currently ignores `CVE-2025-3000` because vLLM 0.23 pins +Torch 2.11.0 and the advisory lists no fixed Torch version. Revisit this when +upgrading vLLM or Torch. + ## Citation -If you use TrialMatchAI in your research, please cite our **Nature -Communications** paper: +If you use TrialMatchAI in your research, please cite the Nature Communications +paper: > Abdallah, M. _et al._ TrialMatchAI. _Nature Communications_ (2026). > diff --git a/docs/finetuning.md b/docs/finetuning.md index 295e9797..7d04d6ea 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -7,7 +7,7 @@ checkpoints/adapters via config, and train those adapters with the built-in | Component | Default | Config key | Fine-tune target | |-----------|---------|------------|------------------| -| Biomedical NER | `fastino/gliner2-base` | `entity_extraction.model_name` | GLiNER checkpoint | +| Biomedical NER | `fastino/gliner2-base` | `entity_extraction.model_name` | GLiNER2 checkpoint | | Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | LoRA adapter | | CoT eligibility | configured CoT model | `model.cot_adapter_path` | LoRA adapter | @@ -121,5 +121,5 @@ trialmatchai-finetune ner \ - 4-bit quantized loading is on by default (`--no-4bit` to disable); `bf16` is default (`--fp16` to switch). See `trialmatchai-finetune --help` for all flags. -- GLiNER's training API varies by version; if your installed `gliner` exposes a - different interface, adapt `src/trialmatchai/finetuning/ner.py`. +- GLiNER2's training API can vary by version; if your installed `gliner2` exposes + a different interface, adapt `src/trialmatchai/finetuning/ner.py`. diff --git a/docs/interoperability.md b/docs/interoperability.md index 6f6e9b3c..4e38030a 100644 --- a/docs/interoperability.md +++ b/docs/interoperability.md @@ -50,7 +50,7 @@ runtime summary fields: - `main_conditions` - `other_conditions` -- `expanded_sentences` +- `patient_narrative` - `age` - `gender` - `provenance` diff --git a/pyproject.toml b/pyproject.toml index 5842063d..e295f10b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -41,7 +41,6 @@ llm = [ entity = [ "torch==2.11.0", "transformers==5.6.2", - "gliner==0.2.27", "gliner2>=1.3.1,<2", ] finetune = [ @@ -50,7 +49,6 @@ finetune = [ "accelerate==1.8.1", "peft==0.15.2", "datasets>=3.0,<5", - "gliner==0.2.27", "gliner2>=1.3.1,<2", "bitsandbytes==0.49.2; sys_platform == 'linux' or sys_platform == 'win32'", ] diff --git a/src/trialmatchai/config/config.json b/src/trialmatchai/config/config.json index 5ac0a77f..dcdbc051 100644 --- a/src/trialmatchai/config/config.json +++ b/src/trialmatchai/config/config.json @@ -2,7 +2,6 @@ "entity_extraction": { "backend": "gliner2", "model_name": "fastino/gliner2-base", - "fallback_model_name": "gliner-community/gliner_large-v2.5", "model_revision": null, "schema_path": "entity_schemas/trialmatchai.yaml", "threshold": 0.8, @@ -92,8 +91,27 @@ "search": { "mode": "hybrid", "vector_score_threshold": 0.5, - "max_trials_first_level": 300, - "max_trials_second_level": 100 + "max_trials_first_level": 1000, + "max_trials_second_level": 100, + "first_level": { + "enabled": true, + "max_trials": 1000, + "per_channel_size": 300, + "fusion": "rrf", + "rrf_k": 60, + "vector_score_threshold": 0.0, + "llm_expansion_enabled": false, + "llm_max_terms": 12, + "write_reports": true, + "hard_filters": ["age", "sex", "overall_status"] + } + }, + "constraints": { + "enabled": true, + "score_weight": 0.25, + "llm_extraction_enabled": false, + "unknown_is_neutral": true, + "write_reports": true }, "use_cot_reasoning": true, "rag": { diff --git a/src/trialmatchai/config/settings.py b/src/trialmatchai/config/settings.py index 04f590aa..91635914 100644 --- a/src/trialmatchai/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -2,19 +2,21 @@ from typing import Any, Dict, Iterable, Literal, Tuple -from pydantic import BaseModel, Field, field_validator +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_validator class EntityExtractionSettings(BaseModel): - backend: Literal["gliner2", "gliner", "regex", "disabled"] = "gliner2" + backend: Literal["gliner2", "regex", "disabled"] = "gliner2" model_name: str = "fastino/gliner2-base" - fallback_model_name: str = "gliner-community/gliner_large-v2.5" model_revision: str | None = None schema_path: str = "entity_schemas/trialmatchai.yaml" threshold: float = Field(0.8, ge=0.0, le=1.0) batch_size: int = Field(8, ge=1) device: str = "auto" trust_remote_code: bool = False + # Augment model NER with the deterministic genetic-variant recognizer. + variant_regex: bool = True + model_config = ConfigDict(extra="forbid") class ConceptLinkerSettings(BaseModel): @@ -116,11 +118,51 @@ def validate_pooling(cls, value: str) -> str: return value +class FirstLevelSearchSettings(BaseModel): + enabled: bool = True + max_trials: int = Field(1000, ge=1) + per_channel_size: int = Field(300, ge=1) + fusion: Literal["rrf"] = "rrf" + rrf_k: int = Field(60, ge=1) + vector_score_threshold: float = Field(0.0, ge=0.0, le=1.0) + llm_expansion_enabled: bool = False + llm_max_terms: int = Field(12, ge=0) + write_reports: bool = True + hard_filters: list[Literal["age", "sex", "overall_status"]] = Field( + default_factory=lambda: ["age", "sex", "overall_status"] + ) + + class SearchSettings(BaseModel): mode: Literal["bm25", "vector", "hybrid"] = "hybrid" vector_score_threshold: float = Field(0.5, ge=0.0, le=1.0) - max_trials_first_level: int = Field(300, ge=1) + max_trials_first_level: int = Field(1000, ge=1) max_trials_second_level: int = Field(100, ge=1) + first_level: FirstLevelSearchSettings = Field( + default_factory=FirstLevelSearchSettings + ) + + @model_validator(mode="before") + @classmethod + def sync_first_level_alias(cls, data): + if not isinstance(data, dict): + return data + normalized = dict(data) + first_level = dict(normalized.get("first_level") or {}) + if "first_level" not in normalized and "max_trials_first_level" in normalized: + first_level["max_trials"] = normalized["max_trials_first_level"] + normalized["first_level"] = first_level + elif "max_trials" in first_level: + normalized["max_trials_first_level"] = first_level["max_trials"] + return normalized + + +class ConstraintSettings(BaseModel): + enabled: bool = True + score_weight: float = Field(0.25, ge=0.0, le=1.0) + llm_extraction_enabled: bool = False + unknown_is_neutral: bool = True + write_reports: bool = True class RagSettings(BaseModel): @@ -164,6 +206,7 @@ class TrialMatchSettings(BaseModel): cot: CotSettings LLM_reranker: LLMRerankerSettings search: SearchSettings + constraints: ConstraintSettings = Field(default_factory=ConstraintSettings) use_cot_reasoning: bool = True rag: RagSettings vllm: VllmSettings @@ -210,10 +253,6 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: ), "TRIALMATCHAI_ENTITY_BACKEND": ("entity_extraction", "backend"), "TRIALMATCHAI_ENTITY_MODEL_NAME": ("entity_extraction", "model_name"), - "TRIALMATCHAI_ENTITY_FALLBACK_MODEL_NAME": ( - "entity_extraction", - "fallback_model_name", - ), "TRIALMATCHAI_ENTITY_MODEL_REVISION": ( "entity_extraction", "model_revision", @@ -252,6 +291,27 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "strict_validation", ), "TRIALMATCHAI_PATIENT_COPY_RAW": ("patient_inputs", "copy_raw"), + "TRIALMATCHAI_CONSTRAINTS_ENABLED": ("constraints", "enabled"), + "TRIALMATCHAI_CONSTRAINTS_LLM_EXTRACTION_ENABLED": ( + "constraints", + "llm_extraction_enabled", + ), + "TRIALMATCHAI_CONSTRAINTS_UNKNOWN_IS_NEUTRAL": ( + "constraints", + "unknown_is_neutral", + ), + "TRIALMATCHAI_CONSTRAINTS_WRITE_REPORTS": ("constraints", "write_reports"), + "TRIALMATCHAI_FIRST_LEVEL_ENABLED": ("search", "first_level", "enabled"), + "TRIALMATCHAI_FIRST_LEVEL_LLM_EXPANSION_ENABLED": ( + "search", + "first_level", + "llm_expansion_enabled", + ), + "TRIALMATCHAI_FIRST_LEVEL_WRITE_REPORTS": ( + "search", + "first_level", + "write_reports", + ), } for env_key, path in bool_env_map.items(): value = os.getenv(env_key) @@ -272,6 +332,22 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "search", "max_trials_first_level", ), + "TRIALMATCHAI_FIRST_LEVEL_MAX_TRIALS": ( + "search", + "first_level", + "max_trials", + ), + "TRIALMATCHAI_FIRST_LEVEL_PER_CHANNEL_SIZE": ( + "search", + "first_level", + "per_channel_size", + ), + "TRIALMATCHAI_FIRST_LEVEL_RRF_K": ("search", "first_level", "rrf_k"), + "TRIALMATCHAI_FIRST_LEVEL_LLM_MAX_TERMS": ( + "search", + "first_level", + "llm_max_terms", + ), "TRIALMATCHAI_SEARCH_MAX_TRIALS_SECOND_LEVEL": ( "search", "max_trials_second_level", @@ -287,6 +363,16 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: _set_nested(raw, path, int(value)) except ValueError: pass + max_trials_first_level_env = os.getenv("TRIALMATCHAI_SEARCH_MAX_TRIALS_FIRST_LEVEL") + if max_trials_first_level_env: + try: + _set_nested( + raw, + ("search", "first_level", "max_trials"), + int(max_trials_first_level_env), + ) + except ValueError: + pass float_env_map: dict[str, Tuple[str, ...]] = { "TRIALMATCHAI_ENTITY_THRESHOLD": ("entity_extraction", "threshold"), @@ -301,6 +387,12 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "registry", "failure_threshold", ), + "TRIALMATCHAI_CONSTRAINTS_SCORE_WEIGHT": ("constraints", "score_weight"), + "TRIALMATCHAI_FIRST_LEVEL_VECTOR_SCORE_THRESHOLD": ( + "search", + "first_level", + "vector_score_threshold", + ), } for env_key, path in float_env_map.items(): value = os.getenv(env_key) diff --git a/src/trialmatchai/constraints/__init__.py b/src/trialmatchai/constraints/__init__.py new file mode 100644 index 00000000..d018a0d6 --- /dev/null +++ b/src/trialmatchai/constraints/__init__.py @@ -0,0 +1,30 @@ +from trialmatchai.constraints.evaluation import ( + apply_constraint_score, + build_patient_constraint_context, + evaluate_constraint_set, +) +from trialmatchai.constraints.extraction import extract_constraint_set, normalize_polarity +from trialmatchai.constraints.models import ( + Constraint, + ConstraintEvaluation, + ConstraintSet, + CriterionConstraintEvaluation, + PatientConstraintContext, + PatientConstraintFact, +) +from trialmatchai.constraints.reports import write_constraint_reports + +__all__ = [ + "Constraint", + "ConstraintEvaluation", + "ConstraintSet", + "CriterionConstraintEvaluation", + "PatientConstraintContext", + "PatientConstraintFact", + "apply_constraint_score", + "build_patient_constraint_context", + "evaluate_constraint_set", + "extract_constraint_set", + "normalize_polarity", + "write_constraint_reports", +] diff --git a/src/trialmatchai/constraints/evaluation.py b/src/trialmatchai/constraints/evaluation.py new file mode 100644 index 00000000..61d0c3d7 --- /dev/null +++ b/src/trialmatchai/constraints/evaluation.py @@ -0,0 +1,445 @@ +from __future__ import annotations + +import re +from collections.abc import Iterable, Sequence +from typing import Any + +from trialmatchai.constraints.models import ( + Constraint, + ConstraintEvaluation, + ConstraintSet, + CriterionConstraintEvaluation, + EvaluationStatus, + PatientConstraintContext, + PatientConstraintFact, +) +from trialmatchai.interop.models import ClinicalFact, PatientProfile +from trialmatchai.utils.text import flatten_text + + +MATCH_SIGNAL = 1.0 +INCLUSION_VIOLATION_SIGNAL = -0.6 +EXCLUSION_VIOLATION_SIGNAL = -1.0 +EXCLUSION_NOT_VIOLATED_SIGNAL = 0.25 +# Applied to an inclusion criterion we cannot confirm when unknown_is_neutral=False. +UNKNOWN_INCLUSION_PENALTY = -0.1 + + +def build_patient_constraint_context(profile: PatientProfile) -> PatientConstraintContext: + facts: list[PatientConstraintFact] = [] + facts.extend(_facts_from_clinical(profile.conditions, "condition")) + facts.extend(_facts_from_clinical(profile.phenotypes, "phenotype")) + facts.extend(_facts_from_clinical(profile.observations, "lab")) + facts.extend(_facts_from_clinical(profile.medications, "medication")) + facts.extend(_facts_from_clinical(profile.procedures, "procedure")) + facts.extend(_facts_from_clinical(profile.genomic_findings, "biomarker")) + facts.extend(_facts_from_clinical(profile.cancer_profile, "condition")) + facts.extend(_facts_from_clinical(profile.family_history, "condition")) + facts.extend(_performance_facts(profile)) + return PatientConstraintContext( + patient_id=profile.patient_id, + age_years=profile.demographics.age_years, + sex=profile.demographics.sex, + gender=profile.demographics.gender, + facts=facts, + ) + + +def evaluate_constraint_set( + constraint_set: ConstraintSet, + patient_context: PatientConstraintContext, + *, + unknown_is_neutral: bool = True, +) -> CriterionConstraintEvaluation: + evaluations = [ + evaluate_constraint(constraint, constraint_set, patient_context) + for constraint in constraint_set.constraints + ] + signals = [evaluation.score_signal for evaluation in evaluations if evaluation.score_signal != 0] + if signals: + signal = _clamp(sum(signals) / len(signals)) + elif ( + not unknown_is_neutral + and constraint_set.polarity == "inclusion" + and evaluations + ): + # We could not confirm any inclusion constraint; apply a small penalty. + signal = UNKNOWN_INCLUSION_PENALTY + else: + signal = 0.0 + return CriterionConstraintEvaluation( + nct_id=constraint_set.nct_id, + criteria_id=constraint_set.criteria_id, + criterion=constraint_set.source_text, + polarity=constraint_set.polarity, + evaluations=evaluations, + constraint_signal=signal, + matched_count=sum(1 for evaluation in evaluations if evaluation.status == "matched"), + violated_count=sum(1 for evaluation in evaluations if evaluation.status == "violated"), + unknown_count=sum(1 for evaluation in evaluations if evaluation.status == "unknown"), + not_applicable_count=sum( + 1 for evaluation in evaluations if evaluation.status == "not_applicable" + ), + ) + + +def evaluate_constraint( + constraint: Constraint, + constraint_set: ConstraintSet, + patient_context: PatientConstraintContext, +) -> ConstraintEvaluation: + relation, evidence, reason = _evaluate_relation(constraint, patient_context) + status, signal = _status_and_signal(constraint_set.polarity, relation) + return ConstraintEvaluation( + nct_id=constraint_set.nct_id, + criteria_id=constraint_set.criteria_id, + polarity=constraint_set.polarity, + constraint=constraint, + status=status, + score_signal=signal, + patient_evidence=evidence, + reason=reason, + ) + + +def apply_constraint_score( + base_score: float, + signal: float, + *, + score_weight: float, +) -> float: + return _clamp(base_score + score_weight * signal, lower=0.0, upper=1.0) + + +def _facts_from_clinical( + facts: Sequence[ClinicalFact], + kind: str, +) -> list[PatientConstraintFact]: + output: list[PatientConstraintFact] = [] + for fact in facts: + numeric_value, unit = _parse_numeric_value(fact.description) + output.append( + PatientConstraintFact( + kind=kind, # type: ignore[arg-type] + label=fact.label, + normalized_codes=[ + code.model_dump(mode="json", exclude_none=True) + for code in fact.normalized_codes + ], + value=numeric_value, + unit=unit, + negated=fact.negated, + temporality=fact.temporality, + evidence_text=fact.evidence_text or _fact_text(fact), + source=fact.provenance.model_dump(mode="json", exclude_none=True), + ) + ) + return output + + +def _performance_facts(profile: PatientProfile) -> list[PatientConstraintFact]: + output: list[PatientConstraintFact] = [] + buckets = [ + *profile.observations, + *profile.cancer_profile, + *profile.diagnostic_reports, + ] + for fact in buckets: + text = _fact_text(fact) + for label, pattern in ( + ("ECOG", r"\becog\b[^0-9]*(\d)"), + ("Karnofsky", r"\bkarnofsky\b[^0-9]*(\d{2,3})"), + ): + match = re.search(pattern, text, re.IGNORECASE) + if not match: + continue + output.append( + PatientConstraintFact( + kind="performance_status", + label=label, + value=float(match.group(1)), + negated=fact.negated, + temporality=fact.temporality, + evidence_text=fact.evidence_text or text, + source=fact.provenance.model_dump(mode="json", exclude_none=True), + ) + ) + return output + + +def _evaluate_relation( + constraint: Constraint, + patient_context: PatientConstraintContext, +) -> tuple[str, str | None, str]: + if constraint.kind == "age": + return _evaluate_age(constraint, patient_context) + if constraint.kind == "sex": + return _evaluate_sex(constraint, patient_context) + if constraint.kind == "lab": + return _evaluate_numeric_fact(constraint, patient_context, ("lab",)) + if constraint.kind == "performance_status": + return _evaluate_numeric_fact( + constraint, + patient_context, + ("performance_status", "lab"), + ) + if constraint.kind == "biomarker": + return _evaluate_biomarker(constraint, patient_context) + return _evaluate_concept_fact(constraint, patient_context, (constraint.kind,)) + + +def _evaluate_age( + constraint: Constraint, + patient_context: PatientConstraintContext, +) -> tuple[str, str | None, str]: + age = patient_context.age_years + if age is None: + return "unknown", None, "Patient age is unavailable." + satisfied = _compare_numeric(age, constraint) + evidence = f"Patient age {age:g} years" + if satisfied: + return "satisfied", evidence, "Patient age satisfies the age constraint." + return "unsatisfied", evidence, "Patient age does not satisfy the age constraint." + + +def _evaluate_sex( + constraint: Constraint, + patient_context: PatientConstraintContext, +) -> tuple[str, str | None, str]: + requested = _normalize_text(str(constraint.value or constraint.label)) + current = _normalize_text(patient_context.sex or patient_context.gender or "") + if not current: + return "unknown", None, "Patient sex/gender is unavailable." + if requested in {"all", "any", "both"} or current in {"all", "any", "both"}: + return "satisfied", current, "Sex/gender constraint is broadly compatible." + if requested == current: + return "satisfied", current, "Patient sex/gender satisfies the constraint." + return "unsatisfied", current, "Patient sex/gender does not satisfy the constraint." + + +def _evaluate_numeric_fact( + constraint: Constraint, + patient_context: PatientConstraintContext, + kinds: tuple[str, ...], +) -> tuple[str, str | None, str]: + candidates = _matching_facts(patient_context.facts, constraint, kinds) + if not candidates: + return "unknown", None, f"No patient fact found for {constraint.label}." + for fact in candidates: + if fact.negated: + return "unsatisfied", fact.evidence_text, f"Patient fact is explicitly absent: {fact.label}." + if fact.value is None: + continue + if _compare_numeric(fact.value, constraint): + return "satisfied", fact.evidence_text, f"Patient value satisfies {constraint.label}." + return "unsatisfied", fact.evidence_text, f"Patient value does not satisfy {constraint.label}." + return "unknown", candidates[0].evidence_text, f"No numeric value available for {constraint.label}." + + +_NEGATIVE_BIOMARKER_TERMS = ( + "negative", + "wild type", + "wildtype", + "wild-type", + "not detected", + "no mutation", + "absent", +) + + +def _evaluate_biomarker( + constraint: Constraint, + patient_context: PatientConstraintContext, +) -> tuple[str, str | None, str]: + candidates = _matching_facts( + patient_context.facts, + constraint, + ("biomarker", "lab", "condition"), + ) + if not candidates: + # Patient has no record of this biomarker. + return "absent", None, f"No patient biomarker fact found for {constraint.label}." + + fact = candidates[0] + fact_text = _normalize_text(flatten_text([fact.label, fact.evidence_text])) + patient_negative = fact.negated or any( + term in fact_text for term in _NEGATIVE_BIOMARKER_TERMS + ) + expected_negative = str(constraint.comparator) in {"negative", "wildtype"} + + if expected_negative: + if patient_negative: + return "satisfied", fact.evidence_text, "Patient biomarker is negative/wild-type as required." + return "unsatisfied", fact.evidence_text, "Patient biomarker is not negative/wild-type." + # expected present / positive / mutated + if patient_negative: + return ( + "unsatisfied", + fact.evidence_text, + "Patient biomarker is negative/wild-type; conflicts with the required presence.", + ) + return "satisfied", fact.evidence_text, "Patient biomarker evidence satisfies the constraint." + + +def _evaluate_concept_fact( + constraint: Constraint, + patient_context: PatientConstraintContext, + kinds: tuple[str, ...], +) -> tuple[str, str | None, str]: + candidates = _matching_facts(patient_context.facts, constraint, kinds) + requires_absence = constraint.comparator == "absent" + + # The patient "has" the item when a matching, non-negated fact exists. + present_fact = next((f for f in candidates if not f.negated), None) + evidence = candidates[0].evidence_text if candidates else None + + if requires_absence: + if present_fact is not None: + return "unsatisfied", present_fact.evidence_text, f"Patient has {constraint.label}, but absence is required." + return "satisfied", evidence, f"Patient has no record of {constraint.label} (absence required)." + + if present_fact is not None: + return "satisfied", present_fact.evidence_text, f"Patient fact matches {constraint.label}." + # No matching fact, or only negated ones -> the patient lacks the item. + return "absent", evidence, f"Patient has no record of {constraint.label}." + + +def _status_and_signal(polarity: str, relation: str) -> tuple[EvaluationStatus, float]: + """Map a (polarity, relation) pair to a scoring status and signal. + + Relations: + satisfied - patient meets the criterion's condition / has the item + unsatisfied - patient's value contradicts a numeric bound + absent - patient does not have the item (no record / negated) + unknown - genuinely indeterminate (e.g. age/sex/lab unavailable) + """ + if relation == "not_applicable": + return "not_applicable", 0.0 + + if polarity == "exclusion": + if relation == "satisfied": + # Patient has the excluded item -> excluded. + return "violated", EXCLUSION_VIOLATION_SIGNAL + if relation in {"unsatisfied", "absent"}: + # Patient does not meet / lacks the excluded item -> not excluded. + return "matched", EXCLUSION_NOT_VIOLATED_SIGNAL + return "unknown", 0.0 + + # inclusion / unknown polarity + if relation == "satisfied": + return "matched", MATCH_SIGNAL + if relation == "unsatisfied": + return "violated", INCLUSION_VIOLATION_SIGNAL + # "absent" for an inclusion: we cannot confirm a required presence from an + # (often incomplete) profile, so stay neutral rather than penalize. + return "unknown", 0.0 + + +def _matching_facts( + facts: Iterable[PatientConstraintFact], + constraint: Constraint, + kinds: tuple[str, ...], +) -> list[PatientConstraintFact]: + constraint_text = _normalize_text(constraint.label) + constraint_codes = { + f"{code.get('vocabulary')}:{code.get('code')}".casefold() + for code in constraint.normalized_codes + if code.get("vocabulary") and code.get("code") + } + matches: list[tuple[float, PatientConstraintFact]] = [] + for fact in facts: + if fact.kind not in kinds: + continue + fact_codes = { + f"{code.get('vocabulary')}:{code.get('code')}".casefold() + for code in fact.normalized_codes + if code.get("vocabulary") and code.get("code") + } + if constraint_codes and constraint_codes & fact_codes: + matches.append((1.0, fact)) + continue + fact_text = _normalize_text(flatten_text([fact.label, fact.evidence_text])) + score = _text_match_score(constraint_text, fact_text) + if score > 0: + matches.append((score, fact)) + matches.sort(key=lambda item: item[0], reverse=True) + return [fact for _, fact in matches] + + +def _compare_numeric(value: float, constraint: Constraint) -> bool: + comparator = constraint.comparator + target = _float_or_none(constraint.value) + if comparator == "between": + if constraint.min_value is not None and value < constraint.min_value: + return False + if constraint.max_value is not None and value > constraint.max_value: + return False + return True + if target is None: + return False + if comparator == "gt": + return value > target + if comparator == "ge": + return value >= target + if comparator == "lt": + return value < target + if comparator == "le": + return value <= target + if comparator == "eq": + return value == target + if comparator == "ne": + return value != target + return False + + +def _parse_numeric_value(value: str | None) -> tuple[float | None, str | None]: + if not value: + return None, None + match = re.search(r"(-?\d+(?:\.\d+)?)\s*([A-Za-z/%0-9^µμ.-]+)?", value) + if not match: + return None, None + unit = (match.group(2) or "").strip() or None + return float(match.group(1)), unit + + +def _fact_text(fact: ClinicalFact) -> str: + return flatten_text([fact.label, fact.description, fact.evidence_text]) + + +def _text_match_score(left: str, right: str) -> float: + if not left or not right: + return 0.0 + if left == right: + return 1.0 + if left in right or right in left: + return 0.95 + left_tokens = set(left.split()) + right_tokens = set(right.split()) + if not left_tokens or not right_tokens: + return 0.0 + overlap = len(left_tokens & right_tokens) + if overlap == 0: + return 0.0 + coverage = overlap / len(left_tokens) + jaccard = overlap / len(left_tokens | right_tokens) + score = 0.75 * coverage + 0.25 * jaccard + return score if score >= 0.5 else 0.0 + + +def _normalize_text(value: str) -> str: + value = value.casefold() + value = value.replace("non small", "non-small") + return " ".join(re.findall(r"[a-z0-9-]+", value)) + + +def _float_or_none(value: Any) -> float | None: + try: + if value is None: + return None + return float(value) + except (TypeError, ValueError): + return None + + +def _clamp(value: float, *, lower: float = -1.0, upper: float = 1.0) -> float: + return max(lower, min(upper, value)) diff --git a/src/trialmatchai/constraints/extraction.py b/src/trialmatchai/constraints/extraction.py new file mode 100644 index 00000000..cf095a51 --- /dev/null +++ b/src/trialmatchai/constraints/extraction.py @@ -0,0 +1,487 @@ +from __future__ import annotations + +import re +from typing import Any, Mapping + +from trialmatchai.constraints.models import ( + Constraint, + ConstraintPolarity, + ConstraintSet, +) +from trialmatchai.utils.text import flatten_text + + +GENE_SYMBOLS = ( + "ALK", + "BRAF", + "BRCA1", + "BRCA2", + "EGFR", + "ERBB2", + "HER2", + "KRAS", + "NTRK", + "PD-L1", + "PIK3CA", + "ROS1", + "TP53", +) +LAB_ALIASES = ( + "absolute neutrophil count", + "anc", + "alt", + "ast", + "bilirubin", + "creatinine", + "hemoglobin", + "platelet count", + "platelets", +) + + +def extract_constraint_set( + *, + nct_id: str, + criteria_id: str, + criterion: str, + eligibility_type: str, + entities: Any = None, +) -> ConstraintSet: + text = flatten_text(criterion) + polarity = normalize_polarity(eligibility_type) + constraints: list[Constraint] = [] + constraints.extend(_age_constraints(text)) + constraints.extend(_sex_constraints(text)) + constraints.extend(_performance_constraints(text)) + constraints.extend(_lab_constraints(text)) + constraints.extend(_biomarker_constraints(text)) + constraints.extend(_prior_therapy_constraints(text)) + constraints.extend(_temporal_constraints(text)) + constraints.extend(_entity_constraints(entities, text)) + constraints = _dedupe_constraints(constraints) + return ConstraintSet( + nct_id=str(nct_id), + criteria_id=str(criteria_id), + polarity=polarity, + source_text=text, + source_start=0 if text else None, + source_end=len(text) if text else None, + constraints=constraints, + ) + + +def normalize_polarity(value: str | None) -> ConstraintPolarity: + normalized = (value or "").casefold() + if "excl" in normalized: + return "exclusion" + if "incl" in normalized: + return "inclusion" + return "unknown" + + +def _age_constraints(text: str) -> list[Constraint]: + constraints: list[Constraint] = [] + lower = text.casefold() + # Every numeric age match must carry an explicit age signal — a trailing year + # unit OR a preceding "age/aged" cue — so dosing/lab numbers like + # "100 to 200 mg" or "at least 100 mg" are not mistaken for ages. + year = r"(?:years?|yrs?|y/?o)" + age_cue = r"(?:age[d]?|years?\s+of\s+age)" + + for pattern in ( + rf"\b(\d{{1,3}})\s*(?:-|to|through)\s*(\d{{1,3}})\s*{year}\b", + rf"\b{age_cue}\s*(\d{{1,3}})\s*(?:-|to|through)\s*(\d{{1,3}})\b", + ): + for match in re.finditer(pattern, lower): + start, end = match.span() + constraints.append( + Constraint( + kind="age", + label="age", + comparator="between", + min_value=float(match.group(1)), + max_value=float(match.group(2)), + unit="years", + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + ) + ) + for pattern in ( + rf"\b(\d{{1,3}})\s*{year}\s*(?:or\s+)?(?:older|greater|above)\b", + rf"\b(?:>=|≥|at\s+least)\s*(\d{{1,3}})\s*{year}\b", + rf"\b{age_cue}\s*(\d{{1,3}})\s*(?:or\s+)?(?:older|greater|above)\b", + rf"\b{age_cue}\s*(?:>=|≥|at\s+least)\s*(\d{{1,3}})\b", + ): + for match in re.finditer(pattern, lower): + constraints.append(_age_bound(text, match, "ge", float(match.group(1)))) + for pattern in ( + rf"\b(?:younger|less|under|below)\s+than\s+(\d{{1,3}})\s*{year}\b", + rf"\b{age_cue}\s*(?:younger|less|under|below)\s+than\s+(\d{{1,3}})\b", + ): + for match in re.finditer(pattern, lower): + constraints.append(_age_bound(text, match, "lt", float(match.group(1)))) + for pattern in ( + rf"\b(\d{{1,3}})\s*{year}\s*(?:or\s+)?(?:younger|less|under)\b", + rf"\b{age_cue}\s*(\d{{1,3}})\s*(?:or\s+)?(?:younger|less|under)\b", + ): + for match in re.finditer(pattern, lower): + constraints.append(_age_bound(text, match, "le", float(match.group(1)))) + if not constraints and re.search(r"\badults?\b", lower): + match = re.search(r"\badults?\b", lower) + start, end = match.span() if match else (None, None) + constraints.append( + Constraint( + kind="age", + label="adult", + comparator="ge", + value=18.0, + unit="years", + confidence=0.7, + evidence_text=text[start:end] if start is not None else None, + evidence_start=start, + evidence_end=end, + ) + ) + return constraints + + +def _age_bound(text: str, match: re.Match[str], comparator: str, value: float) -> Constraint: + start, end = match.span() + return Constraint( + kind="age", + label="age", + comparator=comparator, # type: ignore[arg-type] + value=value, + unit="years", + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + ) + + +def _sex_constraints(text: str) -> list[Constraint]: + lower = text.casefold() + constraints: list[Constraint] = [] + female = re.search(r"\b(female|females|woman|women)\b", lower) + male = re.search(r"\b(male|males|man|men)\b", lower) + if female and not male: + constraints.append(_sex_constraint(text, female, "female")) + elif male and not female: + constraints.append(_sex_constraint(text, male, "male")) + return constraints + + +def _sex_constraint(text: str, match: re.Match[str], value: str) -> Constraint: + start, end = match.span() + return Constraint( + kind="sex", + label="sex", + comparator="eq", + value=value, + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + ) + + +def _performance_constraints(text: str) -> list[Constraint]: + constraints: list[Constraint] = [] + lower = text.casefold() + for match in re.finditer( + r"\becog(?:\s+performance\s+status)?(?:\s+of)?\s*(\d)\s*(?:-|to|or)\s*(\d)\b", + lower, + ): + start, end = match.span() + constraints.append( + Constraint( + kind="performance_status", + label="ECOG", + comparator="between", + min_value=float(match.group(1)), + max_value=float(match.group(2)), + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + ) + ) + for match in re.finditer( + r"\becog(?:\s+performance\s+status)?\s*(?:<=|≤|no\s+greater\s+than|up\s+to)\s*(\d)\b", + lower, + ): + start, end = match.span() + constraints.append( + Constraint( + kind="performance_status", + label="ECOG", + comparator="le", + value=float(match.group(1)), + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + ) + ) + for match in re.finditer( + r"\bkarnofsky\b.*?(?:>=|≥|at\s+least)\s*(\d{2,3})\b", + lower, + ): + start, end = match.span() + constraints.append( + Constraint( + kind="performance_status", + label="Karnofsky", + comparator="ge", + value=float(match.group(1)), + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + ) + ) + return constraints + + +def _lab_constraints(text: str) -> list[Constraint]: + constraints: list[Constraint] = [] + aliases = "|".join(re.escape(alias) for alias in LAB_ALIASES) + pattern = re.compile( + rf"\b({aliases})\b(?:[^0-9<>≤≥=]{{0,40}})(>=|<=|≥|≤|>|<|=|at least|greater than|less than)?\s*([0-9]+(?:\.[0-9]+)?)\s*([A-Za-z/%0-9^µμ.-]+)?", + re.IGNORECASE, + ) + for match in pattern.finditer(text): + # Require an explicit comparator: a bare "creatinine 1.5" is directionally + # ambiguous (upper- vs lower-bound), and guessing inverts the signal. + if not match.group(2): + continue + label = _canonical_lab_label(match.group(1)) + comparator = _normalize_comparator(match.group(2)) + start, end = match.span() + constraints.append( + Constraint( + kind="lab", + label=label, + comparator=comparator, + value=float(match.group(3)), + unit=(match.group(4) or "").strip() or None, + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + ) + ) + return constraints + + +def _canonical_lab_label(value: str) -> str: + normalized = value.casefold() + if normalized == "anc": + return "absolute neutrophil count" + if normalized == "platelets": + return "platelet count" + return normalized + + +def _normalize_comparator(value: str) -> str: + normalized = value.strip().casefold() + if normalized in {">=", "≥", "at least", "greater than"}: + return "ge" + if normalized == ">": + return "gt" + if normalized in {"<=", "≤"}: + return "le" + if normalized in {"<", "less than"}: + return "lt" + if normalized == "=": + return "eq" + return "ge" + + +def _biomarker_constraints(text: str) -> list[Constraint]: + constraints: list[Constraint] = [] + gene_pattern = "|".join(re.escape(gene) for gene in GENE_SYMBOLS) + pattern = re.compile( + rf"\b({gene_pattern})\b(?:[-\s]*(mutated|mutation|positive|negative|wild[-\s]?type|amplification|overexpression))?", + re.IGNORECASE, + ) + for match in pattern.finditer(text): + status = (match.group(2) or "").casefold().replace(" ", "-") + comparator = "present" + if status in {"mutated", "mutation"}: + comparator = "mutated" + elif status in {"positive", "amplification", "overexpression"}: + comparator = "positive" + elif status == "negative": + comparator = "negative" + elif status in {"wild-type", "wildtype"}: + comparator = "wildtype" + start, end = match.span() + constraints.append( + Constraint( + kind="biomarker", + label=match.group(1).upper(), + comparator=comparator, # type: ignore[arg-type] + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + confidence=0.9 if comparator != "present" else 0.75, + ) + ) + return constraints + + +def _prior_therapy_constraints(text: str) -> list[Constraint]: + constraints: list[Constraint] = [] + patterns = ( + r"\bprior\s+(?:treatment|therapy)\s+with\s+([^.;,]+)", + r"\bpreviously\s+treated\s+with\s+([^.;,]+)", + r"\breceived\s+prior\s+([^.;,]+)", + r"\bprior\s+investigational\s+therapy\b", + ) + for pattern in patterns: + for match in re.finditer(pattern, text, re.IGNORECASE): + if match.lastindex: + label = match.group(1) + else: + label = "investigational therapy" + start, end = match.span() + constraints.append( + Constraint( + kind="medication", + label=_clean_label(label), + comparator="prior", + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + confidence=0.8, + ) + ) + return constraints + + +def _temporal_constraints(text: str) -> list[Constraint]: + constraints: list[Constraint] = [] + for match in re.finditer( + r"\bwithin\s+(?:the\s+last\s+)?(\d+)\s+(days?|weeks?|months?|years?)\b", + text, + re.IGNORECASE, + ): + start, end = match.span() + constraints.append( + Constraint( + kind="temporal", + label="time window", + comparator="current", + value=float(match.group(1)), + unit=match.group(2).lower(), + temporal_window=text[start:end], + evidence_text=text[start:end], + evidence_start=start, + evidence_end=end, + confidence=0.7, + ) + ) + return constraints + + +def _entity_constraints(entities: Any, text: str) -> list[Constraint]: + if not isinstance(entities, list): + return [] + constraints: list[Constraint] = [] + for entity in entities: + if not isinstance(entity, Mapping): + continue + group = str(entity.get("entity_group") or entity.get("class") or "").casefold() + label = _clean_label(entity.get("text") or entity.get("entity") or "") + if not label: + continue + kind = _entity_group_to_kind(group) + if kind is None: + continue + start = _safe_int(entity.get("start")) + end = _safe_int(entity.get("end")) + constraints.append( + Constraint( + kind=kind, + label=label, + comparator=_entity_comparator(kind, text), + normalized_codes=_entity_codes(entity), + evidence_text=text[start:end] if start is not None and end is not None else label, + evidence_start=start, + evidence_end=end, + confidence=float(entity.get("score") or 0.8), + ) + ) + return constraints + + +def _entity_group_to_kind(group: str) -> str | None: + if group in {"disease", "condition", "sign symptom", "sign_symptom"}: + return "condition" + if group in {"drug", "medication"}: + return "medication" + if group == "procedure": + return "procedure" + if group == "gene": + return "biomarker" + if group in {"laboratory test", "laboratory_test", "diagnostic test", "diagnostic_test"}: + return "lab" + return None + + +def _entity_comparator(kind: str, text: str) -> str: + if kind == "biomarker": + lower = text.casefold() + if re.search(r"\b(mutated|mutation)\b", lower): + return "mutated" + if re.search(r"\b(positive|amplification|overexpression)\b", lower): + return "positive" + if re.search(r"\bnegative\b", lower): + return "negative" + if kind == "medication" and re.search(r"\b(prior|previous|previously)\b", text, re.IGNORECASE): + return "prior" + return "present" + + +def _entity_codes(entity: Mapping[str, Any]) -> list[dict[str, Any]]: + codes = entity.get("normalized_id") or [] + if isinstance(codes, str): + codes = [codes] + output: list[dict[str, Any]] = [] + for code in codes: + text = str(code) + if not text or text == "CUI-less": + continue + if ":" in text: + vocabulary, value = text.split(":", 1) + else: + vocabulary, value = "local", text + output.append({"vocabulary": vocabulary, "code": value}) + return output + + +def _dedupe_constraints(constraints: list[Constraint]) -> list[Constraint]: + seen: set[tuple[Any, ...]] = set() + output: list[Constraint] = [] + for constraint in constraints: + key = ( + constraint.kind, + constraint.label.casefold(), + constraint.comparator, + constraint.value, + constraint.min_value, + constraint.max_value, + ) + if key in seen: + continue + seen.add(key) + output.append(constraint) + return output + + +def _clean_label(value: Any) -> str: + return re.sub(r"\s+", " ", str(value or "")).strip(" .;,:") + + +def _safe_int(value: Any) -> int | None: + try: + return int(value) + except (TypeError, ValueError): + return None diff --git a/src/trialmatchai/constraints/models.py b/src/trialmatchai/constraints/models.py new file mode 100644 index 00000000..4cb61792 --- /dev/null +++ b/src/trialmatchai/constraints/models.py @@ -0,0 +1,121 @@ +from __future__ import annotations + +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field + + +ConstraintKind = Literal[ + "age", + "sex", + "condition", + "phenotype", + "medication", + "procedure", + "lab", + "biomarker", + "performance_status", + "temporal", +] +ConstraintPolarity = Literal["inclusion", "exclusion", "unknown"] +ConstraintComparator = Literal[ + "present", + "absent", + "eq", + "ne", + "gt", + "ge", + "lt", + "le", + "between", + "positive", + "negative", + "mutated", + "wildtype", + "prior", + "current", +] +EvaluationStatus = Literal["matched", "violated", "unknown", "not_applicable"] + + +class Constraint(BaseModel): + kind: ConstraintKind + label: str + comparator: ConstraintComparator = "present" + value: float | str | None = None + min_value: float | None = None + max_value: float | None = None + unit: str | None = None + normalized_codes: list[dict[str, Any]] = Field(default_factory=list) + temporal_window: str | None = None + confidence: float = Field(1.0, ge=0.0, le=1.0) + evidence_text: str | None = None + evidence_start: int | None = Field(default=None, ge=0) + evidence_end: int | None = Field(default=None, ge=0) + + model_config = ConfigDict(extra="forbid") + + +class ConstraintSet(BaseModel): + nct_id: str + criteria_id: str + polarity: ConstraintPolarity + source_text: str + source_start: int | None = Field(default=None, ge=0) + source_end: int | None = Field(default=None, ge=0) + constraints: list[Constraint] = Field(default_factory=list) + extractor_version: str = "deterministic-v1" + + model_config = ConfigDict(extra="forbid") + + +class PatientConstraintFact(BaseModel): + kind: ConstraintKind + label: str + normalized_codes: list[dict[str, Any]] = Field(default_factory=list) + value: float | None = None + unit: str | None = None + negated: bool = False + temporality: str | None = None + evidence_text: str | None = None + source: dict[str, Any] = Field(default_factory=dict) + + model_config = ConfigDict(extra="forbid") + + +class PatientConstraintContext(BaseModel): + patient_id: str + age_years: float | None = None + sex: str | None = None + gender: str | None = None + facts: list[PatientConstraintFact] = Field(default_factory=list) + + model_config = ConfigDict(extra="forbid") + + +class ConstraintEvaluation(BaseModel): + nct_id: str + criteria_id: str + polarity: ConstraintPolarity + constraint: Constraint + status: EvaluationStatus + score_signal: float = Field(0.0, ge=-1.0, le=1.0) + patient_evidence: str | None = None + reason: str + + model_config = ConfigDict(extra="forbid") + + +class CriterionConstraintEvaluation(BaseModel): + nct_id: str + criteria_id: str + criterion: str + polarity: ConstraintPolarity + evaluations: list[ConstraintEvaluation] = Field(default_factory=list) + constraint_signal: float = Field(0.0, ge=-1.0, le=1.0) + matched_count: int = 0 + violated_count: int = 0 + unknown_count: int = 0 + not_applicable_count: int = 0 + + model_config = ConfigDict(extra="forbid") diff --git a/src/trialmatchai/constraints/reports.py b/src/trialmatchai/constraints/reports.py new file mode 100644 index 00000000..47070242 --- /dev/null +++ b/src/trialmatchai/constraints/reports.py @@ -0,0 +1,119 @@ +from __future__ import annotations + +from collections import defaultdict +from pathlib import Path +from typing import Iterable + +from trialmatchai.constraints.models import CriterionConstraintEvaluation +from trialmatchai.utils.file_utils import write_json_file, write_text_file + + +def write_constraint_reports( + *, + output_folder: str | Path, + evaluations: Iterable[CriterionConstraintEvaluation], + top_trials: list[dict], +) -> None: + folder = Path(output_folder) + payload = [evaluation.model_dump(mode="json") for evaluation in evaluations] + explained_trials = _build_explained_trials(payload, top_trials) + write_json_file( + {"criteria": payload, "top_trials": top_trials}, + str(folder / "constraint_evaluations.json"), + ) + write_text_file( + _render_markdown(payload, top_trials), + str(folder / "constraint_summary.md"), + ) + write_json_file( + {"top_trials": explained_trials}, + str(folder / "top_trials_explained.json"), + ) + + +def _render_markdown(criteria: list[dict], top_trials: list[dict]) -> list[str]: + lines = ["# Constraint-Aware Retrieval Summary", ""] + if not criteria: + lines.extend( + [ + "No constraint evaluations were produced for this patient.", + "", + ] + ) + return lines + + scores = {trial.get("nct_id"): trial.get("score") for trial in top_trials} + by_trial: dict[str, list[dict]] = defaultdict(list) + for item in criteria: + by_trial[str(item.get("nct_id", "unknown"))].append(item) + + ordered_ids = [str(trial.get("nct_id")) for trial in top_trials if trial.get("nct_id")] + ordered_ids.extend(sorted(set(by_trial) - set(ordered_ids))) + for nct_id in ordered_ids: + trial_items = by_trial.get(nct_id, []) + lines.append(f"## {nct_id}") + score = scores.get(nct_id) + if isinstance(score, (int, float)): + lines.append(f"- Retrieval score: {score:.4f}") + shift = _ranking_shift(trial_items) + if shift: + lines.append(f"- Constraint ranking effect: {shift}") + for status, title in ( + ("matched", "Matched"), + ("violated", "Violated"), + ("unknown", "Unknown"), + ): + entries = _entries_for_status(trial_items, status) + if entries: + lines.append(f"- {title}:") + lines.extend(f" - {entry}" for entry in entries[:8]) + lines.append("") + return lines + + +def _build_explained_trials(criteria: list[dict], top_trials: list[dict]) -> list[dict]: + by_trial: dict[str, list[dict]] = defaultdict(list) + for item in criteria: + by_trial[str(item.get("nct_id", "unknown"))].append(item) + + explained: list[dict] = [] + for trial in top_trials: + nct_id = str(trial.get("nct_id", "")) + trial_items = by_trial.get(nct_id, []) + explained.append( + { + **trial, + "constraint_effect": _ranking_shift(trial_items) or "unchanged", + "matched_constraints": _entries_for_status(trial_items, "matched"), + "violated_constraints": _entries_for_status(trial_items, "violated"), + "unknown_constraints": _entries_for_status(trial_items, "unknown"), + } + ) + return explained + + +def _ranking_shift(trial_items: list[dict]) -> str | None: + if not trial_items: + return None + signal = sum(float(item.get("constraint_signal") or 0.0) for item in trial_items) + if signal > 0.05: + return "boosted" + if signal < -0.05: + return "penalized" + return "unchanged" + + +def _entries_for_status(trial_items: list[dict], status: str) -> list[str]: + entries: list[str] = [] + for item in trial_items: + criterion = item.get("criterion", "") + for evaluation in item.get("evaluations", []): + if evaluation.get("status") != status: + continue + constraint = evaluation.get("constraint", {}) + label = constraint.get("label") or constraint.get("kind") + reason = evaluation.get("reason") or "" + patient = evaluation.get("patient_evidence") or "no patient evidence" + trial = constraint.get("evidence_text") or criterion + entries.append(f"{label}: {reason} Patient: {patient}. Trial: {trial}.") + return entries diff --git a/src/trialmatchai/entities/recognizers.py b/src/trialmatchai/entities/recognizers.py index 83510cdc..abce12bd 100644 --- a/src/trialmatchai/entities/recognizers.py +++ b/src/trialmatchai/entities/recognizers.py @@ -191,62 +191,13 @@ def recognize( ] -class GLiNERRecognizer: - def __init__( - self, - model_name: str, - *, - revision: str | None = None, - device: str | None = None, - trust_remote_code: bool = False, - batch_size: int = 8, - ): - try: - from gliner import GLiNER # type: ignore - except Exception as exc: # pragma: no cover - optional dependency - raise RuntimeError( - "entity_extraction.backend=gliner requires the GLiNER dependency." - ) from exc - - kwargs: dict[str, Any] = {} - if revision: - kwargs["revision"] = revision - if trust_remote_code: - kwargs["trust_remote_code"] = trust_remote_code - self.model = GLiNER.from_pretrained(model_name, **kwargs) - if device and device != "auto" and hasattr(self.model, "to"): - self.model.to(device) - self.batch_size = batch_size - - def recognize( - self, texts: Sequence[str], schemas: Sequence[EntitySchema] - ) -> list[list[EntityAnnotation]]: - labels = [schema.label for schema in schemas] - label_map = schema_by_label(list(schemas)) - results: list[list[EntityAnnotation]] = [] - for text in texts: - raw = self.model.predict_entities(text, labels) - results.append(resolve_overlaps(_parse_model_entities(raw, text, label_map))) - return results - - def build_recognizer(config: dict[str, Any]) -> EntityRecognizer: backend = str(config.get("backend", "gliner2")).lower() if backend == "disabled": return DisabledRecognizer() if backend == "regex": return RegexSchemaRecognizer() - if backend == "gliner": - recognizer: EntityRecognizer = GLiNERRecognizer( - model_name=config.get("fallback_model_name") - or config.get("model_name") - or "urchade/gliner_base", - revision=config.get("model_revision"), - device=config.get("device", "auto"), - trust_remote_code=bool(config.get("trust_remote_code", False)), - batch_size=int(config.get("batch_size", 8)), - ) - elif backend == "gliner2": + if backend == "gliner2": recognizer = GLiNER2Recognizer( model_name=config.get("model_name", "fastino/gliner2-base"), revision=config.get("model_revision"), @@ -256,7 +207,7 @@ def build_recognizer(config: dict[str, Any]) -> EntityRecognizer: ) else: raise ValueError( - "entity_extraction.backend must be one of: gliner2, gliner, regex, disabled." + "entity_extraction.backend must be one of: gliner2, regex, disabled." ) # Augment model NER with the deterministic variant recognizer (on by default) diff --git a/src/trialmatchai/finetuning/__init__.py b/src/trialmatchai/finetuning/__init__.py index 4faa4f07..a1171de5 100644 --- a/src/trialmatchai/finetuning/__init__.py +++ b/src/trialmatchai/finetuning/__init__.py @@ -1,9 +1,9 @@ """Fine-tuning utilities for TrialMatchAI's local models. -Lets users train their own NER (GLiNER), reranker (LoRA), and CoT eligibility +Lets users train their own NER (GLiNER2), reranker (LoRA), and CoT eligibility (LoRA) models and plug them straight back into the pipeline via config: -- NER: entity_extraction.model_name -> a fine-tuned GLiNER checkpoint +- NER: entity_extraction.model_name -> a fine-tuned GLiNER2 checkpoint - reranker: model.reranker_adapter_path -> a LoRA adapter - CoT: model.cot_adapter_path -> a LoRA adapter diff --git a/src/trialmatchai/interop/exporters/matching_summary.py b/src/trialmatchai/interop/exporters/matching_summary.py index 4d8f91fc..e7ff9cef 100644 --- a/src/trialmatchai/interop/exporters/matching_summary.py +++ b/src/trialmatchai/interop/exporters/matching_summary.py @@ -12,15 +12,14 @@ def profile_to_matching_summary(profile: PatientProfile) -> dict: else "all" ) gender = profile.demographics.sex or profile.demographics.gender or "all" - expanded_sentences = render_patient_narrative(profile) + patient_narrative = render_patient_narrative(profile) return { "patient_id": profile.patient_id, "main_conditions": main_conditions, "other_conditions": other_conditions, - "expanded_sentences": expanded_sentences, + "patient_narrative": patient_narrative, "age": age, "gender": gender, - "split_raw_description": expanded_sentences, "provenance": [ provenance.model_dump(mode="json", exclude_none=True) for provenance in profile.provenance diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index 6c2104bb..3c2e4dbe 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -4,6 +4,10 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Tuple from trialmatchai.config.config_loader import load_config +from trialmatchai.constraints import ( + build_patient_constraint_context, + write_constraint_reports, +) from trialmatchai.entities import build_entity_annotator from trialmatchai.matching.trial_ranker import ( load_trial_data, @@ -28,6 +32,7 @@ from trialmatchai.utils.timing import log_timing if TYPE_CHECKING: + from trialmatchai.constraints import PatientConstraintContext from trialmatchai.models.embedding.text_embedder import TextEmbedder logger = setup_logging(__name__) @@ -41,10 +46,11 @@ def run_first_level_search( embedder: TextEmbedder, config: Dict, search_backend, + patient_profile: PatientProfile | None = None, ) -> Optional[Tuple]: - main_conditions = keywords.get("main_conditions", []) - other_conditions = keywords.get("other_conditions", []) - expanded_sentences = keywords.get("expanded_sentences", []) + main_conditions = list(keywords.get("main_conditions", [])) + other_conditions = list(keywords.get("other_conditions", [])) + patient_narrative = list(keywords.get("patient_narrative", [])) if not main_conditions: logger.error("No main_conditions found in keywords.") @@ -61,22 +67,75 @@ def run_first_level_search( entity_annotator=entity_annotator, ) - # Get synonyms and expand main conditions - synonyms = cts.get_synonyms(condition.lower().strip()) - main_conditions.extend(synonyms[:5]) - - search_size = config["search"].get("max_trials_first_level", 300) - trials, scores = cts.search_trials( - condition=condition, - age_input=age, - sex=sex, - overall_status=overall_status, - size=search_size, - pre_selected_nct_ids=None, - synonyms=main_conditions, - other_conditions=other_conditions, - vector_score_threshold=config["search"]["vector_score_threshold"], - ) + search_cfg = config["search"] + first_level_cfg = _first_level_search_config(search_cfg) + if first_level_cfg.get("enabled", True) and patient_profile is not None: + plan = cts.build_query_plan( + profile=patient_profile, + matching_summary=keywords, + config=first_level_cfg, + age=age, + sex=sex, + overall_status=overall_status, + ) + trials, scores, candidate_evidence = cts.search_trials_with_plan( + query_plan=plan, + age_input=age, + sex=sex, + overall_status=overall_status, + size=int(first_level_cfg.get("max_trials", 1000)), + per_channel_size=int(first_level_cfg.get("per_channel_size", 300)), + pre_selected_nct_ids=None, + vector_score_threshold=float( + first_level_cfg.get("vector_score_threshold", 0.0) + ), + search_mode=search_cfg.get("mode", "hybrid"), + rrf_k=int(first_level_cfg.get("rrf_k", 60)), + ) + main_conditions = _dedupe_strings( + [ + *main_conditions, + *plan.terms_for("primary_condition", "concept_synonym"), + ] + ) + other_conditions = _dedupe_strings( + [ + *other_conditions, + *plan.terms_for("biomarker", "therapy", "broader_disease"), + ] + ) + if first_level_cfg.get("write_reports", True): + write_json_file( + plan.model_dump(mode="json"), + f"{output_folder}/first_level_query_plan.json", + ) + write_json_file( + { + "candidates": [ + item.model_dump(mode="json") for item in candidate_evidence + ] + }, + f"{output_folder}/first_level_candidates.json", + ) + else: + # Single-query first-level retrieval path. This remains available for exact + # behavior preservation when search.first_level.enabled=false. + synonyms = cts.get_synonyms(condition.lower().strip()) + main_conditions.extend(synonyms[:5]) + + search_size = search_cfg.get("max_trials_first_level", 300) + trials, scores = cts.search_trials( + condition=condition, + age_input=age, + sex=sex, + overall_status=overall_status, + size=search_size, + pre_selected_nct_ids=None, + synonyms=main_conditions, + other_conditions=other_conditions, + vector_score_threshold=search_cfg["vector_score_threshold"], + search_mode=search_cfg.get("mode", "hybrid"), + ) nct_ids = [trial.get("nct_id") for trial in trials if trial.get("nct_id")] first_level_scores = { @@ -93,7 +152,7 @@ def run_first_level_search( nct_ids, main_conditions, other_conditions, - expanded_sentences, + patient_narrative, first_level_scores, ) @@ -103,12 +162,13 @@ def run_second_level_search( nct_ids: List[str], main_conditions: List[str], other_conditions: List[str], - expanded_sentences: List[str], + patient_narrative: List[str], gemma_retriever: SecondStageRetriever, first_level_scores: Dict, config: Dict, + patient_context: Optional["PatientConstraintContext"] = None, ) -> Tuple: - queries = list(set(main_conditions + other_conditions + expanded_sentences))[:10] + queries = list(set(main_conditions + other_conditions + patient_narrative))[:10] logger.info(f"Running second-level retrieval with {len(queries)} queries ...") # Add synonyms for second level @@ -118,7 +178,11 @@ def run_second_level_search( top_n = min(len(nct_ids), config["search"].get("max_trials_second_level", 100)) second_level_results = gemma_retriever.retrieve_and_rank( - queries, nct_ids, top_n=top_n + queries, + nct_ids, + top_n=top_n, + patient_context=patient_context, + constraints_config=config.get("constraints", {}), ) combined_scores = {} @@ -134,6 +198,19 @@ def run_second_level_search( top_trials_path = f"{output_folder}/top_trials.txt" write_text_file([trial_id for trial_id, _ in semi_final_trials], top_trials_path) + constraints_config = config.get("constraints", {}) + if constraints_config.get("enabled", True) and constraints_config.get( + "write_reports", + True, + ): + write_constraint_reports( + output_folder=output_folder, + evaluations=gemma_retriever.last_constraint_evaluations, + top_trials=[ + {"nct_id": trial_id, "score": score} + for trial_id, score in semi_final_trials + ], + ) logger.info("Second-level retrieval and ranking complete. Top trials saved.") return semi_final_trials, top_trials_path @@ -151,9 +228,9 @@ def run_rag_processing( return top_trials = top_trials[: config["rag"].get("max_trials_rag", 20)] - patient_profile = patient_info.get("split_raw_description", []) - if not patient_profile: - logger.error("No patient profile available for RAG processing.") + patient_narrative = patient_info.get("patient_narrative", []) + if not patient_narrative: + logger.error("No patient narrative available for RAG processing.") return # vLLM is the only LLM backend. A configured cot_adapter_path is served as a @@ -183,7 +260,7 @@ def run_rag_processing( nct_ids=top_trials, json_folder=config["paths"]["trials_json_folder"], output_folder=output_folder, - patient_profile=patient_profile, + patient_narrative=patient_narrative, ) write_json_file({"status": "done"}, f"{output_folder}/rag_output.json") logger.info("RAG-based trial matching complete.") @@ -280,9 +357,8 @@ def main_pipeline(config_path: str | None = None) -> int: ) keywords = Keywords.model_validate(summary).model_dump() patient_info = dict(summary) - patient_info["split_raw_description"] = summary.get( - "expanded_sentences", [] - ) + patient_info["patient_narrative"] = summary.get("patient_narrative", []) + patient_context = build_patient_constraint_context(profile) # Run pipeline with log_timing(logger, "First-level search"): @@ -295,6 +371,7 @@ def main_pipeline(config_path: str | None = None) -> int: embedder, config, search_backend, + patient_profile=profile, ) if not result: logger.error("First-level search failed for %s", patient_id) @@ -304,7 +381,7 @@ def main_pipeline(config_path: str | None = None) -> int: nct_ids, main_conditions, other_conditions, - expanded_sentences, + patient_narrative, first_level_scores, ) = result @@ -315,10 +392,11 @@ def main_pipeline(config_path: str | None = None) -> int: nct_ids, main_conditions, other_conditions, - expanded_sentences, + patient_narrative, gemma_retriever, first_level_scores, config, + patient_context, ) with log_timing(logger, "RAG processing"): @@ -375,5 +453,37 @@ def _load_patient_inputs(config: Dict) -> list[tuple[PatientProfile, Dict]]: return loaded +def _first_level_search_config(search_cfg: Dict) -> Dict: + first_level_cfg = dict(search_cfg.get("first_level") or {}) + first_level_cfg.setdefault( + "max_trials", + search_cfg.get("max_trials_first_level", 1000), + ) + first_level_cfg.setdefault("per_channel_size", 300) + first_level_cfg.setdefault("rrf_k", 60) + first_level_cfg.setdefault("vector_score_threshold", 0.0) + first_level_cfg.setdefault("enabled", True) + first_level_cfg.setdefault("write_reports", True) + first_level_cfg.setdefault("llm_expansion_enabled", False) + first_level_cfg.setdefault("llm_max_terms", 12) + first_level_cfg.setdefault("hard_filters", ["age", "sex", "overall_status"]) + return first_level_cfg + + +def _dedupe_strings(values: list[str]) -> list[str]: + seen: set[str] = set() + output: list[str] = [] + for value in values: + cleaned = str(value or "").strip() + if not cleaned: + continue + key = cleaned.casefold() + if key in seen: + continue + seen.add(key) + output.append(cleaned) + return output + + if __name__ == "__main__": raise SystemExit(main_pipeline()) diff --git a/src/trialmatchai/matching/eligibility_base.py b/src/trialmatchai/matching/eligibility_base.py index 7ca14557..8ce60072 100644 --- a/src/trialmatchai/matching/eligibility_base.py +++ b/src/trialmatchai/matching/eligibility_base.py @@ -179,12 +179,12 @@ def process_trials( nct_ids: List[str], json_folder: str, output_folder: str, - patient_profile: List[str], + patient_narrative: List[str], ): """Build a worklist (skipping already-processed trials), length-bucket to minimize padding, then process in batches.""" patient_text = " ".join( - str(line).strip() for line in patient_profile if str(line).strip() + str(line).strip() for line in patient_narrative if str(line).strip() ) items: List[Dict] = [] diff --git a/src/trialmatchai/matching/retrieval/criteria_retrieval.py b/src/trialmatchai/matching/retrieval/criteria_retrieval.py index 0439117a..db5f5f1f 100644 --- a/src/trialmatchai/matching/retrieval/criteria_retrieval.py +++ b/src/trialmatchai/matching/retrieval/criteria_retrieval.py @@ -1,16 +1,20 @@ from __future__ import annotations +import json import math from collections import defaultdict from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import TYPE_CHECKING, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional +from trialmatchai.constraints import apply_constraint_score, evaluate_constraint_set +from trialmatchai.constraints.models import ConstraintSet, CriterionConstraintEvaluation from trialmatchai.matching.retrieval.synonyms import disease_synonyms from trialmatchai.search.lancedb_backend import TrialSearchBackend from trialmatchai.utils.file_utils import write_text_file from trialmatchai.utils.logging_config import setup_logging if TYPE_CHECKING: + from trialmatchai.constraints.models import PatientConstraintContext from trialmatchai.models.embedding.text_embedder import TextEmbedder from trialmatchai.models.llm.llm_reranker import LLMReranker @@ -26,7 +30,6 @@ def __init__( size: int = 250, inclusion_weight: float = 1.0, exclusion_weight: float = 0.25, - bio_med_ner=None, entity_annotator=None, search_mode: str = "hybrid", ): @@ -36,8 +39,9 @@ def __init__( self.size = size self.inclusion_weight = inclusion_weight self.exclusion_weight = exclusion_weight - self.entity_annotator = entity_annotator or bio_med_ner + self.entity_annotator = entity_annotator self.search_mode = search_mode.lower() if search_mode else "hybrid" + self.last_constraint_evaluations: list[CriterionConstraintEvaluation] = [] def get_synonyms(self, condition: str) -> List[str]: return disease_synonyms(self.entity_annotator, condition) @@ -138,6 +142,59 @@ def score_criteria_without_llm(self, criteria: List[Dict]) -> List[Dict]: criterion["llm_score"] = base return criteria + def apply_constraint_adjustments( + self, + criteria: List[Dict], + *, + patient_context: Optional["PatientConstraintContext"] = None, + constraints_config: Mapping[str, Any] | None = None, + ) -> List[Dict]: + self.last_constraint_evaluations = [] + config = constraints_config or {} + if not config.get("enabled", True) or patient_context is None: + return criteria + + score_weight = _constraint_score_weight(config.get("score_weight", 0.25)) + unknown_is_neutral = bool(config.get("unknown_is_neutral", True)) + seen_criteria: set[str] = set() + for criterion in criteria: + source = criterion.get("_source") or {} + raw_constraints = source.get("constraints") + if not raw_constraints: + continue + try: + constraint_set = _constraint_set_from_payload(raw_constraints) + except Exception: + logger.warning( + "Ignoring invalid constraint payload for criterion %s.", + source.get("criteria_id", "unknown"), + ) + continue + if not constraint_set.constraints: + continue + + evaluation = evaluate_constraint_set( + constraint_set, patient_context, unknown_is_neutral=unknown_is_neutral + ) + if evaluation.criteria_id not in seen_criteria: + self.last_constraint_evaluations.append(evaluation) + seen_criteria.add(evaluation.criteria_id) + + base_score = float( + criterion.get("llm_score", criterion.get("_score", 0.0)) or 0.0 + ) + adjusted_score = apply_constraint_score( + base_score, + evaluation.constraint_signal, + score_weight=score_weight, + ) + criterion["pre_constraint_score"] = base_score + criterion["constraint_signal"] = evaluation.constraint_signal + criterion["constraint_adjusted_score"] = adjusted_score + criterion["constraint_evaluation"] = evaluation.model_dump(mode="json") + criterion["llm_score"] = adjusted_score + return criteria + def aggregate_to_trials( self, criteria: List[Dict], threshold: float = 0.5, method: str = "weighted" ) -> List[Dict]: @@ -145,7 +202,8 @@ def aggregate_to_trials( for criterion in criteria: nct_id = criterion["_source"]["nct_id"] score = criterion["llm_score"] - if score >= threshold: + threshold_basis = criterion.get("pre_constraint_score", score) + if score >= threshold or threshold_basis >= threshold: trial_scores[nct_id].append(score) aggregated_scores = {} for nct_id, scores in trial_scores.items(): @@ -179,6 +237,8 @@ def retrieve_and_rank( top_n: int, use_reranker: bool = True, save_path: Optional[str] = None, + patient_context: Optional["PatientConstraintContext"] = None, + constraints_config: Mapping[str, Any] | None = None, ) -> List[Dict]: # Cap queries to prevent memory/performance issues max_queries = 150 # Reasonable limit for second-level search @@ -209,6 +269,11 @@ def retrieve_and_rank( ) ranked_criteria = self.score_criteria_without_llm(all_criteria) + ranked_criteria = self.apply_constraint_adjustments( + ranked_criteria, + patient_context=patient_context, + constraints_config=constraints_config, + ) sorted_trials = self.aggregate_to_trials(ranked_criteria) top_trials = sorted_trials[:top_n] logger.info(f"Top {top_n} trials retrieved: {top_trials}") @@ -216,3 +281,19 @@ def retrieve_and_rank( write_text_file([trial["nct_id"] for trial in top_trials], save_path) logger.info(f"Top trials saved to {save_path}") return top_trials + + +def _constraint_score_weight(value: Any) -> float: + try: + weight = float(value) + except (TypeError, ValueError): + return 0.25 + return max(0.0, min(1.0, weight)) + + +def _constraint_set_from_payload(payload: Any) -> ConstraintSet: + if isinstance(payload, ConstraintSet): + return payload + if isinstance(payload, str): + payload = json.loads(payload) + return ConstraintSet.model_validate(payload) diff --git a/src/trialmatchai/matching/retrieval/first_level_planner.py b/src/trialmatchai/matching/retrieval/first_level_planner.py new file mode 100644 index 00000000..a55ac8f0 --- /dev/null +++ b/src/trialmatchai/matching/retrieval/first_level_planner.py @@ -0,0 +1,399 @@ +from __future__ import annotations + +import json +import re +from collections.abc import Callable, Sequence +from typing import Any, Literal, Protocol + +from pydantic import BaseModel, ConfigDict, Field + +from trialmatchai.interop.models import ClinicalFact, PatientProfile +from trialmatchai.interop.narrative import render_patient_narrative +from trialmatchai.matching.retrieval.synonyms import disease_synonyms +from trialmatchai.utils.logging_config import setup_logging +from trialmatchai.utils.text import flatten_text + +logger = setup_logging(__name__) + + +FirstLevelChannelKind = Literal[ + "primary_condition", + "concept_synonym", + "broader_disease", + "narrative", + "biomarker", + "therapy", + "llm_expansion", +] + +DEFAULT_CHANNEL_WEIGHTS: dict[FirstLevelChannelKind, float] = { + "primary_condition": 1.0, + "concept_synonym": 0.9, + "narrative": 0.8, + "biomarker": 0.7, + "therapy": 0.45, + "broader_disease": 0.35, + "llm_expansion": 0.5, +} + + +class FirstLevelQueryChannel(BaseModel): + kind: FirstLevelChannelKind + terms: list[str] + weight: float = Field(ge=0.0) + source: str = "deterministic" + + model_config = ConfigDict(extra="forbid") + + +class FirstLevelQueryPlan(BaseModel): + patient_id: str | None = None + channels: list[FirstLevelQueryChannel] = Field(default_factory=list) + filters: dict[str, Any] = Field(default_factory=dict) + llm_expansion_enabled: bool = False + + model_config = ConfigDict(extra="forbid") + + def terms_for(self, *kinds: FirstLevelChannelKind) -> list[str]: + allowed = set(kinds) + return dedupe_terms( + term + for channel in self.channels + if channel.kind in allowed + for term in channel.terms + ) + + +class FirstLevelCandidateEvidence(BaseModel): + nct_id: str + score: float + channels: list[dict[str, Any]] = Field(default_factory=list) + + model_config = ConfigDict(extra="forbid") + + +class LLMQueryExpansion(BaseModel): + primary_queries: list[str] = Field(default_factory=list) + disease_aliases: list[str] = Field(default_factory=list) + broader_queries: list[str] = Field(default_factory=list) + biomarker_queries: list[str] = Field(default_factory=list) + treatment_queries: list[str] = Field(default_factory=list) + discarded_or_uncertain: list[str] = Field(default_factory=list) + + model_config = ConfigDict(extra="forbid") + + +class LLMQueryExpansionBackend(Protocol): + def expand_first_level_queries( + self, + *, + profile: PatientProfile, + matching_summary: dict[str, Any], + ) -> str | dict[str, Any]: + ... + + +class FirstLevelQueryPlanner: + def __init__( + self, + *, + entity_annotator: Any = None, + llm_expander: LLMQueryExpansionBackend + | Callable[..., str | dict[str, Any]] + | None = None, + ) -> None: + self.entity_annotator = entity_annotator + self.llm_expander = llm_expander + + def build( + self, + *, + profile: PatientProfile, + matching_summary: dict[str, Any], + config: dict[str, Any] | None = None, + age: int | str | None = None, + sex: str | None = None, + overall_status: str | None = None, + ) -> FirstLevelQueryPlan: + cfg = config or {} + channels: list[FirstLevelQueryChannel] = [] + primary_terms = dedupe_terms( + [ + *matching_summary.get("main_conditions", []), + *positive_fact_labels(profile.conditions), + ] + ) + channels.append( + self._channel("primary_condition", primary_terms, "matching_summary") + ) + + synonym_terms = self._synonym_terms(primary_terms) + channels.append(self._channel("concept_synonym", synonym_terms, "concept_linker")) + + broader_terms = broader_disease_terms([*primary_terms, *synonym_terms]) + channels.append(self._channel("broader_disease", broader_terms, "deterministic")) + + narrative_terms = narrative_terms_from_summary_or_profile( + profile, + matching_summary, + ) + channels.append(self._channel("narrative", narrative_terms, "patient_narrative")) + + biomarker_terms = dedupe_terms( + [ + *positive_fact_labels(profile.genomic_findings), + *biomarker_terms_from_facts(profile.cancer_profile), + ] + ) + channels.append(self._channel("biomarker", biomarker_terms, "patient_profile")) + + therapy_terms = therapy_terms_from_profile(profile) + channels.append(self._channel("therapy", therapy_terms, "patient_profile")) + + llm_enabled = bool(cfg.get("llm_expansion_enabled", False)) + if llm_enabled: + llm_terms = self._llm_terms( + profile=profile, + matching_summary=matching_summary, + max_terms=int(cfg.get("llm_max_terms", 12)), + ) + channels.append(self._channel("llm_expansion", llm_terms, "llm")) + + return FirstLevelQueryPlan( + patient_id=profile.patient_id, + channels=[channel for channel in channels if channel.terms], + filters={ + "age": age, + "sex": sex, + "overall_status": overall_status, + "hard_filters": cfg.get( + "hard_filters", + ["age", "sex", "overall_status"], + ), + }, + llm_expansion_enabled=llm_enabled, + ) + + def _channel( + self, + kind: FirstLevelChannelKind, + terms: Sequence[str], + source: str, + ) -> FirstLevelQueryChannel: + return FirstLevelQueryChannel( + kind=kind, + terms=dedupe_terms(terms), + weight=DEFAULT_CHANNEL_WEIGHTS[kind], + source=source, + ) + + def _synonym_terms(self, primary_terms: Sequence[str]) -> list[str]: + output: list[str] = [] + for term in primary_terms[:5]: + output.extend(disease_synonyms(self.entity_annotator, term)) + return dedupe_terms(output) + + def _llm_terms( + self, + *, + profile: PatientProfile, + matching_summary: dict[str, Any], + max_terms: int, + ) -> list[str]: + if self.llm_expander is None: + logger.warning( + "First-level LLM expansion is enabled but no expander is configured." + ) + return [] + try: + if hasattr(self.llm_expander, "expand_first_level_queries"): + raw = self.llm_expander.expand_first_level_queries( + profile=profile, + matching_summary=matching_summary, + ) + else: + raw = self.llm_expander( + profile=profile, + matching_summary=matching_summary, + ) + parsed = parse_llm_query_expansion(raw, max_terms=max_terms) + except Exception: + logger.exception("Discarding invalid first-level LLM query expansion.") + return [] + return dedupe_terms( + [ + *parsed.primary_queries, + *parsed.disease_aliases, + *parsed.broader_queries, + *parsed.biomarker_queries, + *parsed.treatment_queries, + ] + )[:max_terms] + + +def parse_llm_query_expansion( + raw: str | dict[str, Any], + *, + max_terms: int, +) -> LLMQueryExpansion: + payload = json.loads(raw) if isinstance(raw, str) else raw + parsed = LLMQueryExpansion.model_validate(payload) + capped: dict[str, list[str]] = {} + remaining = max(0, max_terms) + for field in ( + "primary_queries", + "disease_aliases", + "broader_queries", + "biomarker_queries", + "treatment_queries", + ): + values = dedupe_terms(getattr(parsed, field)) + if remaining <= 0: + capped[field] = [] + continue + capped[field] = values[:remaining] + remaining -= len(capped[field]) + capped["discarded_or_uncertain"] = dedupe_terms(parsed.discarded_or_uncertain) + return LLMQueryExpansion.model_validate(capped) + + +def fuse_first_level_channel_hits( + channel_hits: Sequence[tuple[FirstLevelQueryChannel, list[dict], list[float]]], + *, + size: int, + rrf_k: int = 60, +) -> tuple[list[dict], list[float], list[FirstLevelCandidateEvidence]]: + candidates: dict[str, dict[str, Any]] = {} + evidence: dict[str, list[dict[str, Any]]] = {} + for channel, trials, scores in channel_hits: + for rank, (trial, raw_score) in enumerate(zip(trials, scores), start=1): + nct_id = str(trial.get("nct_id") or "") + if not nct_id: + continue + contribution = channel.weight / (rrf_k + rank) + if nct_id not in candidates: + candidates[nct_id] = { + "trial": trial, + "score": 0.0, + } + evidence[nct_id] = [] + candidates[nct_id]["score"] += contribution + evidence[nct_id].append( + { + "channel": channel.kind, + "rank": rank, + "raw_score": float(raw_score), + "weight": channel.weight, + "terms": channel.terms, + "contribution": contribution, + } + ) + + ranked = sorted( + candidates.values(), + key=lambda item: item["score"], + reverse=True, + )[:size] + trials = [item["trial"] for item in ranked] + scores = [float(item["score"]) for item in ranked] + candidate_evidence = [ + FirstLevelCandidateEvidence( + nct_id=str(item["trial"].get("nct_id")), + score=float(item["score"]), + channels=evidence.get(str(item["trial"].get("nct_id")), []), + ) + for item in ranked + ] + return trials, scores, candidate_evidence + + +def positive_fact_labels(facts: Sequence[ClinicalFact]) -> list[str]: + return dedupe_terms(fact.label for fact in facts if not fact.negated) + + +def narrative_terms_from_summary_or_profile( + profile: PatientProfile, + matching_summary: dict[str, Any], + *, + max_chars: int = 1500, +) -> list[str]: + sentences = matching_summary.get("patient_narrative") or render_patient_narrative( + profile + ) + sentences = [ + sentence + for sentence in sentences + if " absent" not in str(sentence).casefold() + ] + narrative = flatten_text(sentences) + if not narrative: + return [] + return [narrative[:max_chars].rsplit(" ", 1)[0].strip() or narrative[:max_chars]] + + +def biomarker_terms_from_facts(facts: Sequence[ClinicalFact]) -> list[str]: + terms: list[str] = [] + gene_pattern = re.compile( + r"\b(ALK|BRAF|BRCA1|BRCA2|EGFR|ERBB2|HER2|KRAS|NTRK|PD-L1|PIK3CA|ROS1|TP53)\b", + re.IGNORECASE, + ) + for fact in facts: + if fact.negated: + continue + text = flatten_text([fact.label, fact.description, fact.evidence_text]) + terms.extend(match.group(1).upper() for match in gene_pattern.finditer(text)) + return dedupe_terms(terms) + + +def therapy_terms_from_profile(profile: PatientProfile) -> list[str]: + terms: list[str] = [] + for fact in [*profile.medications, *profile.procedures]: + if fact.negated: + continue + terms.append(fact.label) + if fact.temporality and "prior" in fact.temporality.casefold(): + terms.append(f"prior {fact.label}") + return dedupe_terms(terms) + + +def broader_disease_terms(terms: Sequence[str]) -> list[str]: + output: list[str] = [] + for term in terms: + normalized = term.casefold() + if any( + marker in normalized + for marker in ( + "cancer", + "carcinoma", + "melanoma", + "sarcoma", + "tumor", + "tumour", + "neoplasm", + ) + ): + output.extend(["cancer", "malignant neoplasm", "solid tumor"]) + if any(marker in normalized for marker in ("leukemia", "lymphoma", "myeloma")): + output.extend(["hematologic malignancy", "blood cancer"]) + if "lung" in normalized and any( + marker in normalized for marker in ("cancer", "carcinoma", "nsclc") + ): + output.extend(["thoracic cancer", "solid tumor"]) + return dedupe_terms(output) + + +def dedupe_terms(values: Any) -> list[str]: + if values is None: + return [] + seen: set[str] = set() + output: list[str] = [] + for value in values: + cleaned = re.sub(r"\s+", " ", str(value or "")).strip() + if not cleaned: + continue + key = cleaned.casefold() + if key in seen: + continue + seen.add(key) + output.append(cleaned) + return output diff --git a/src/trialmatchai/matching/retrieval/synonyms.py b/src/trialmatchai/matching/retrieval/synonyms.py index 7b8ad381..90cd039e 100644 --- a/src/trialmatchai/matching/retrieval/synonyms.py +++ b/src/trialmatchai/matching/retrieval/synonyms.py @@ -26,7 +26,8 @@ def disease_synonyms(entity_annotator: Any, condition: str) -> List[str]: for entity in raw_result[0]: if entity.get("entity_group", "").lower() == "disease": synonyms.update(entity.get("synonyms", [])) - return list(synonyms) + # sorted() for deterministic ordering across runs (set order is not stable). + return sorted(synonyms) logger.warning("No annotations found for condition: %s", condition) except Exception as exc: logger.error("Entity synonym extraction failed for '%s': %s", condition, exc) diff --git a/src/trialmatchai/matching/retrieval/trial_retrieval.py b/src/trialmatchai/matching/retrieval/trial_retrieval.py index 62eafc90..a70bff98 100644 --- a/src/trialmatchai/matching/retrieval/trial_retrieval.py +++ b/src/trialmatchai/matching/retrieval/trial_retrieval.py @@ -1,14 +1,22 @@ from __future__ import annotations from datetime import datetime -from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union from dateutil import parser as date_parser +from trialmatchai.matching.retrieval.first_level_planner import ( + FirstLevelCandidateEvidence, + FirstLevelQueryPlan, + FirstLevelQueryPlanner, + LLMQueryExpansionBackend, + fuse_first_level_channel_hits, +) from trialmatchai.matching.retrieval.synonyms import disease_synonyms from trialmatchai.search.lancedb_backend import TrialSearchBackend from trialmatchai.utils.logging_config import setup_logging if TYPE_CHECKING: + from trialmatchai.interop.models import PatientProfile from trialmatchai.models.embedding.text_embedder import TextEmbedder logger = setup_logging(__name__) @@ -19,12 +27,16 @@ def __init__( self, search_backend: TrialSearchBackend, embedder: Optional[TextEmbedder], - bio_med_ner=None, entity_annotator=None, + llm_query_expander: LLMQueryExpansionBackend | None = None, ): self.search_backend = search_backend self.embedder = embedder - self.entity_annotator = entity_annotator or bio_med_ner + self.entity_annotator = entity_annotator + self.query_planner = FirstLevelQueryPlanner( + entity_annotator=entity_annotator, + llm_expander=llm_query_expander, + ) def get_synonyms(self, condition: str) -> List[str]: return disease_synonyms(self.entity_annotator, condition) @@ -150,6 +162,121 @@ def search_trials( ) return trials, scores + def build_query_plan( + self, + *, + profile: "PatientProfile", + matching_summary: dict[str, Any], + config: dict[str, Any] | None = None, + age: int | str | None = None, + sex: str | None = None, + overall_status: str | None = None, + ) -> FirstLevelQueryPlan: + return self.query_planner.build( + profile=profile, + matching_summary=matching_summary, + config=config, + age=age, + sex=sex, + overall_status=overall_status, + ) + + def search_trials_with_plan( + self, + *, + query_plan: FirstLevelQueryPlan, + age_input: Union[int, str], + sex: str, + overall_status: Optional[str] = None, + size: int = 1000, + per_channel_size: int = 300, + pre_selected_nct_ids: Optional[List[str]] = None, + vector_score_threshold: float = 0.0, + search_mode: str = "hybrid", + rrf_k: int = 60, + ) -> tuple[list[dict], list[float], list[FirstLevelCandidateEvidence]]: + # Honor the configured hard filters: only apply the ones listed in the + # plan (default age/sex/overall_status). This makes first_level.hard_filters + # actually controllable instead of always-on. + # Distinguish an unset value (None -> default) from an explicit empty + # list ([] -> no hard filters), so hard_filters=[] truly disables them. + configured = query_plan.filters.get("hard_filters") + hard_filters = set( + configured if configured is not None else ["age", "sex", "overall_status"] + ) + + age = None + if "age" in hard_filters and str(age_input).strip().casefold() != "all": + age = self.parse_age_input(age_input) + if age is None: + logger.warning( + "Could not parse age %r; proceeding without an age filter.", + age_input, + ) + effective_sex = sex if "sex" in hard_filters else "all" + effective_status = overall_status if "overall_status" in hard_filters else None + + mode = (search_mode or "hybrid").lower() + if mode in {"vector", "hybrid"} and self.embedder is None: + logger.warning( + "Vector/hybrid mode selected but embedder is None. Falling back to BM25 only." + ) + mode = "bm25" + + channel_hits = [] + failed_channels = 0 + for channel in query_plan.channels: + embeddings = self._embed_terms(channel.terms, mode) + try: + trials, scores = self.search_backend.search_trials( + primary_terms=channel.terms, + other_terms=[], + embeddings=embeddings, + age=age, + sex=effective_sex, + overall_status=effective_status, + pre_selected_nct_ids=pre_selected_nct_ids, + size=per_channel_size, + vector_score_threshold=vector_score_threshold, + search_mode=mode, + ) + except Exception: + failed_channels += 1 + logger.exception( + "First-level channel search failed for %s; skipping channel.", + channel.kind, + ) + continue + if trials: + channel_hits.append((channel, trials, scores)) + + if query_plan.channels and not channel_hits: + logger.warning( + "First-level retrieval produced no candidates from %d channels " + "(%d errored). Check the search backend/tables.", + len(query_plan.channels), + failed_channels, + ) + + trials, scores, evidence = fuse_first_level_channel_hits( + channel_hits, + size=size, + rrf_k=rrf_k, + ) + logger.info( + "[%s] First-level planned retrieval found %s trials across %s channels.", + mode, + len(trials), + len(channel_hits), + ) + return trials, scores, evidence + + def _embed_terms(self, terms: list[str], mode: str) -> dict[str, list[float]]: + if mode not in {"vector", "hybrid"} or self.embedder is None: + return {} + vectors = self.embedder.embed_texts(terms) + return dict(zip(terms, vectors)) + def _clean_terms(terms: List[str]) -> List[str]: return [term.strip() for term in terms if term and term.strip()] diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py index eba4c15e..aadbc9bb 100644 --- a/src/trialmatchai/registry/preparation.py +++ b/src/trialmatchai/registry/preparation.py @@ -9,6 +9,7 @@ import dateutil.parser +from trialmatchai.constraints import extract_constraint_set from trialmatchai.utils.text import flatten_text @@ -118,6 +119,13 @@ def prepare_criteria_documents( rows: list[dict[str, Any]] = [] for entry, vector in zip(entries, vectors): criteria_id = compute_criteria_id(entry["nct_id"], entry["criterion"]) + constraint_set = extract_constraint_set( + nct_id=entry["nct_id"], + criteria_id=criteria_id, + criterion=entry["criterion"], + eligibility_type=entry["eligibility_type"], + entities=entry.get("entities"), + ) rows.append( { "criteria_id": criteria_id, @@ -126,6 +134,7 @@ def prepare_criteria_documents( "entities": _entities_for_index(entry.get("entities")), "eligibility_type": entry["eligibility_type"], "criterion_vector": vector, + "constraints": constraint_set.model_dump(mode="json"), } ) return rows diff --git a/src/trialmatchai/schemas/phenopacket.py b/src/trialmatchai/schemas/phenopacket.py index b7fccc07..2c0177d7 100644 --- a/src/trialmatchai/schemas/phenopacket.py +++ b/src/trialmatchai/schemas/phenopacket.py @@ -16,7 +16,7 @@ class Phenopacket(BaseModel): class Keywords(BaseModel): main_conditions: List[str] = Field(default_factory=list) other_conditions: List[str] = Field(default_factory=list) - expanded_sentences: List[str] = Field(default_factory=list) + patient_narrative: List[str] = Field(default_factory=list) error: Optional[str] = None model_config = ConfigDict(extra="allow") diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index c96b9719..925591ab 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -57,10 +57,6 @@ def run_preflight_checks( "entity_extraction.backend=gliner2 requires the entity extra " "(`uv sync --extra entity`)." ) - elif backend == "gliner" and importlib.util.find_spec("gliner") is None: - issues.append( - "entity_extraction.backend=gliner requires the GLiNER dependency." - ) linker_cfg = config.get("concept_linker") if linker_cfg and linker_cfg.get("enabled", True): diff --git a/tests/test_config_pytest.py b/tests/test_config_pytest.py index bcf97a47..7e5f90bf 100644 --- a/tests/test_config_pytest.py +++ b/tests/test_config_pytest.py @@ -1,6 +1,9 @@ from pathlib import Path +import pytest + from trialmatchai.config.config_loader import load_config +from trialmatchai.config.settings import EntityExtractionSettings from trialmatchai.entities.schemas import default_schema_path @@ -26,3 +29,11 @@ def test_packaged_schema_path_resolves_outside_repo(tmp_path, monkeypatch): assert cfg["entity_extraction"]["schema_path"] == str(default_schema_path().resolve()) assert Path(cfg["entity_extraction"]["schema_path"]).exists() assert cfg["paths"]["output_dir"] == str((tmp_path / "results").resolve()) + + +def test_legacy_gliner_backend_and_fallback_key_are_rejected(): + with pytest.raises(ValueError): + EntityExtractionSettings.model_validate({"backend": "gliner"}) + + with pytest.raises(ValueError): + EntityExtractionSettings.model_validate({"fallback_model_name": "old-model"}) diff --git a/tests/test_constraints.py b/tests/test_constraints.py new file mode 100644 index 00000000..6aa8f671 --- /dev/null +++ b/tests/test_constraints.py @@ -0,0 +1,317 @@ +import json + +from trialmatchai.constraints import ( + build_patient_constraint_context, + evaluate_constraint_set, + extract_constraint_set, + write_constraint_reports, +) +from trialmatchai.constraints.models import Constraint, ConstraintSet +from trialmatchai.interop.models import ClinicalFact, Demographics, PatientProfile, Provenance + + +def test_extracts_common_deterministic_constraints(): + parsed = extract_constraint_set( + nct_id="N1", + criteria_id="C1", + criterion=( + "Adults aged 18-75 years with ANC >= 1500/mm3, ECOG 0-1, " + "EGFR mutated disease, and treatment within 6 months." + ), + eligibility_type="Inclusion Criteria", + ) + + kinds = {constraint.kind for constraint in parsed.constraints} + assert {"age", "lab", "performance_status", "biomarker", "temporal"} <= kinds + assert any( + constraint.kind == "age" + and constraint.comparator == "between" + and constraint.min_value == 18 + and constraint.max_value == 75 + for constraint in parsed.constraints + ) + assert any( + constraint.kind == "lab" + and constraint.label == "absolute neutrophil count" + and constraint.comparator == "ge" + and constraint.value == 1500 + for constraint in parsed.constraints + ) + + +def test_extracts_age_lower_bound_and_sex(): + parsed = extract_constraint_set( + nct_id="N1", + criteria_id="C2", + criterion="Female participants 18 years or older.", + eligibility_type="Inclusion Criteria", + ) + + assert any( + constraint.kind == "age" + and constraint.comparator == "ge" + and constraint.value == 18 + for constraint in parsed.constraints + ) + assert any( + constraint.kind == "sex" and constraint.value == "female" + for constraint in parsed.constraints + ) + + +def test_patient_context_evaluates_matches_and_exclusion_violations(): + profile = PatientProfile( + patient_id="P1", + demographics=Demographics(age_years=64, sex="female"), + conditions=[ + _fact( + "condition-1", + "condition", + "non-small cell lung cancer", + evidence_text="Patient has metastatic non-small cell lung cancer.", + ) + ], + observations=[ + _fact( + "obs-1", + "observation", + "absolute neutrophil count", + description="1800 /mm3", + evidence_text="ANC 1800/mm3.", + ) + ], + genomic_findings=[ + _fact( + "gene-1", + "genomic_finding", + "EGFR mutated", + evidence_text="EGFR exon 19 deletion detected.", + ) + ], + medications=[ + _fact( + "med-1", + "medication", + "osimertinib", + evidence_text="Prior osimertinib documented.", + temporality="prior", + ) + ], + ) + context = build_patient_constraint_context(profile) + inclusion = ConstraintSet( + nct_id="N1", + criteria_id="C1", + polarity="inclusion", + source_text="Adults with NSCLC, EGFR mutation, and ANC >= 1500/mm3.", + constraints=[ + Constraint( + kind="age", + label="age", + comparator="between", + min_value=18, + max_value=75, + ), + Constraint(kind="condition", label="non-small cell lung cancer"), + Constraint(kind="biomarker", label="EGFR", comparator="mutated"), + Constraint( + kind="lab", + label="absolute neutrophil count", + comparator="ge", + value=1500, + unit="/mm3", + ), + ], + ) + exclusion = ConstraintSet( + nct_id="N1", + criteria_id="C2", + polarity="exclusion", + source_text="Prior osimertinib is excluded.", + constraints=[ + Constraint(kind="medication", label="osimertinib", comparator="prior") + ], + ) + + inclusion_eval = evaluate_constraint_set(inclusion, context) + exclusion_eval = evaluate_constraint_set(exclusion, context) + + assert inclusion_eval.violated_count == 0 + assert inclusion_eval.matched_count == 4 + assert inclusion_eval.constraint_signal > 0 + assert exclusion_eval.violated_count == 1 + assert exclusion_eval.constraint_signal < 0 + + +def test_unknown_constraints_are_neutral(): + context = build_patient_constraint_context( + PatientProfile(patient_id="P1", demographics=Demographics(age_years=42)) + ) + parsed = ConstraintSet( + nct_id="N1", + criteria_id="C1", + polarity="inclusion", + source_text="Documented diabetes mellitus.", + constraints=[Constraint(kind="condition", label="diabetes mellitus")], + ) + + evaluation = evaluate_constraint_set(parsed, context) + + assert evaluation.unknown_count == 1 + assert evaluation.constraint_signal == 0 + + +def test_constraint_reports_are_written(tmp_path): + context = build_patient_constraint_context( + PatientProfile( + patient_id="P1", + conditions=[ + _fact( + "condition-1", + "condition", + "lung cancer", + evidence_text="Patient has lung cancer.", + ) + ], + ) + ) + evaluation = evaluate_constraint_set( + ConstraintSet( + nct_id="N1", + criteria_id="C1", + polarity="inclusion", + source_text="Patients with lung cancer.", + constraints=[Constraint(kind="condition", label="lung cancer")], + ), + context, + ) + + write_constraint_reports( + output_folder=tmp_path, + evaluations=[evaluation], + top_trials=[{"nct_id": "N1", "score": 0.91}], + ) + + payload = json.loads((tmp_path / "constraint_evaluations.json").read_text()) + explained = json.loads((tmp_path / "top_trials_explained.json").read_text()) + summary = (tmp_path / "constraint_summary.md").read_text() + assert payload["criteria"][0]["nct_id"] == "N1" + assert explained["top_trials"][0]["constraint_effect"] == "boosted" + assert "Matched" in summary + + +def test_exclusion_clean_pass_is_rewarded(): + # Patient does NOT have the excluded item -> not excluded -> should reward. + context = build_patient_constraint_context( + PatientProfile(patient_id="P1", demographics=Demographics(age_years=60)) + ) + exclusion = ConstraintSet( + nct_id="N1", + criteria_id="C1", + polarity="exclusion", + source_text="Prior chemotherapy is excluded.", + constraints=[ + Constraint(kind="medication", label="chemotherapy", comparator="prior") + ], + ) + evaluation = evaluate_constraint_set(exclusion, context) + assert evaluation.matched_count == 1 + assert evaluation.violated_count == 0 + assert evaluation.constraint_signal > 0 + + +def test_dosing_and_unit_numbers_are_not_ages(): + for criterion in ( + "Administer 100 to 200 mg daily.", + "Give at least 100 mg of the study drug.", + "Up to 3 cycles of therapy.", + ): + parsed = extract_constraint_set( + nct_id="N1", + criteria_id="C1", + criterion=criterion, + eligibility_type="Inclusion Criteria", + ) + assert not any(c.kind == "age" for c in parsed.constraints), criterion + + +def test_comparator_less_lab_is_skipped(): + parsed = extract_constraint_set( + nct_id="N1", + criteria_id="C1", + criterion="Creatinine 1.5 at screening.", + eligibility_type="Exclusion Criteria", + ) + assert not any(c.kind == "lab" for c in parsed.constraints) + # An explicit comparator is still extracted. + parsed2 = extract_constraint_set( + nct_id="N1", + criteria_id="C2", + criterion="Creatinine <= 1.5 mg/dL.", + eligibility_type="Exclusion Criteria", + ) + assert any(c.kind == "lab" and c.comparator == "le" for c in parsed2.constraints) + + +def test_biomarker_wildtype_patient_is_not_a_false_positive(): + context = build_patient_constraint_context( + PatientProfile( + patient_id="P1", + genomic_findings=[ + _fact( + "g1", + "genomic_finding", + "EGFR wild-type", + evidence_text="EGFR wild-type, no mutation detected.", + ) + ], + ) + ) + # Exclusion "EGFR mutation" against a wild-type patient -> not excluded -> reward. + exclusion = ConstraintSet( + nct_id="N1", + criteria_id="C1", + polarity="exclusion", + source_text="EGFR mutation excluded.", + constraints=[Constraint(kind="biomarker", label="EGFR", comparator="mutated")], + ) + evaluation = evaluate_constraint_set(exclusion, context) + assert evaluation.violated_count == 0 + assert evaluation.constraint_signal > 0 + + +def test_unknown_is_neutral_false_penalizes_unconfirmable_inclusion(): + context = build_patient_constraint_context( + PatientProfile(patient_id="P1", demographics=Demographics(age_years=42)) + ) + inclusion = ConstraintSet( + nct_id="N1", + criteria_id="C1", + polarity="inclusion", + source_text="Documented diabetes mellitus.", + constraints=[Constraint(kind="condition", label="diabetes mellitus")], + ) + neutral = evaluate_constraint_set(inclusion, context, unknown_is_neutral=True) + penalized = evaluate_constraint_set(inclusion, context, unknown_is_neutral=False) + assert neutral.constraint_signal == 0 + assert penalized.constraint_signal < 0 + + +def _fact( + fact_id: str, + category: str, + label: str, + *, + description: str | None = None, + evidence_text: str | None = None, + temporality: str | None = None, +) -> ClinicalFact: + return ClinicalFact( + fact_id=fact_id, + category=category, + label=label, + description=description, + evidence_text=evidence_text, + temporality=temporality, + provenance=Provenance(source_format="test"), + ) diff --git a/tests/test_first_level_planner.py b/tests/test_first_level_planner.py new file mode 100644 index 00000000..e1025bf6 --- /dev/null +++ b/tests/test_first_level_planner.py @@ -0,0 +1,327 @@ +import json + +import pytest + +from trialmatchai.interop.models import ClinicalFact, PatientProfile, Provenance +from trialmatchai.main import run_first_level_search +from trialmatchai.matching.retrieval.first_level_planner import ( + FirstLevelQueryPlanner, + parse_llm_query_expansion, +) +from trialmatchai.matching.retrieval.trial_retrieval import ClinicalTrialSearch +from trialmatchai.search import InMemorySearchBackend + + +def test_planner_builds_deterministic_channels_and_skips_negated_facts(): + planner = FirstLevelQueryPlanner(entity_annotator=FakeAnnotator()) + profile = PatientProfile( + patient_id="P1", + conditions=[ + _fact("condition-1", "condition", "lung cancer"), + _fact("condition-2", "condition", "asthma", negated=True), + ], + genomic_findings=[_fact("gene-1", "genomic_finding", "EGFR mutation")], + medications=[ + _fact("med-1", "medication", "osimertinib", temporality="prior") + ], + ) + + plan = planner.build( + profile=profile, + matching_summary={ + "main_conditions": ["lung cancer"], + "other_conditions": [], + "patient_narrative": ["Patient has EGFR-mutated lung cancer."], + }, + config={"llm_expansion_enabled": False}, + age=64, + sex="female", + overall_status="All", + ) + + assert plan.terms_for("primary_condition") == ["lung cancer"] + assert "lung carcinoma" in plan.terms_for("concept_synonym") + assert "solid tumor" in plan.terms_for("broader_disease") + assert "EGFR mutation" in plan.terms_for("biomarker") + assert "prior osimertinib" in plan.terms_for("therapy") + assert "asthma" not in plan.terms_for("primary_condition", "broader_disease") + + +def test_llm_query_expansion_is_strict_and_capped(): + parsed = parse_llm_query_expansion( + { + "primary_queries": ["lung cancer", "lung cancer"], + "disease_aliases": ["NSCLC"], + "broader_queries": ["solid tumor"], + "biomarker_queries": ["EGFR mutation"], + "treatment_queries": ["osimertinib"], + "discarded_or_uncertain": ["random drift"], + }, + max_terms=3, + ) + + assert parsed.primary_queries == ["lung cancer"] + assert parsed.disease_aliases == ["NSCLC"] + assert parsed.broader_queries == ["solid tumor"] + assert parsed.biomarker_queries == [] + assert parsed.treatment_queries == [] + with pytest.raises(Exception): + parse_llm_query_expansion("{bad json", max_terms=3) + with pytest.raises(Exception): + parse_llm_query_expansion({"primary_queries": [], "extra": []}, max_terms=3) + + +def test_planned_search_fuses_multi_channel_hits_above_single_channel_hits(): + backend = InMemorySearchBackend( + trials=[ + { + "nct_id": "N1", + "condition": "lung cancer", + "brief_title": "EGFR lung cancer osimertinib trial", + "brief_summary": "Study for EGFR mutation after prior osimertinib.", + "eligibility_criteria": "Adults with lung cancer or solid tumor.", + "overall_status": "Recruiting", + "gender": "All", + }, + { + "nct_id": "N2", + "condition": "lung cancer", + "brief_title": "General lung cancer trial", + "eligibility_criteria": "Adults with lung cancer.", + "overall_status": "Recruiting", + "gender": "All", + }, + { + "nct_id": "N3", + "condition": "solid tumor", + "brief_title": "Advanced solid tumor study", + "eligibility_criteria": "Adults with solid tumor.", + "overall_status": "Recruiting", + "gender": "All", + }, + ] + ) + search = ClinicalTrialSearch( + search_backend=backend, + embedder=None, + entity_annotator=FakeAnnotator(), + ) + profile = PatientProfile( + patient_id="P1", + conditions=[_fact("condition-1", "condition", "lung cancer")], + genomic_findings=[_fact("gene-1", "genomic_finding", "EGFR mutation")], + medications=[ + _fact("med-1", "medication", "osimertinib", temporality="prior") + ], + ) + plan = search.build_query_plan( + profile=profile, + matching_summary={ + "main_conditions": ["lung cancer"], + "other_conditions": [], + "patient_narrative": ["Patient has lung cancer with EGFR mutation."], + }, + config={}, + age="all", + sex="ALL", + overall_status="All", + ) + + trials, scores, evidence = search.search_trials_with_plan( + query_plan=plan, + age_input="all", + sex="ALL", + overall_status="All", + size=3, + per_channel_size=3, + search_mode="bm25", + ) + + assert trials[0]["nct_id"] == "N1" + assert scores[0] > scores[-1] + assert "N3" in {trial["nct_id"] for trial in trials} + n1 = next(item for item in evidence if item.nct_id == "N1") + assert len({channel["channel"] for channel in n1.channels}) > 1 + + +def test_run_first_level_search_uses_narrative_channel_and_writes_artifacts(tmp_path): + backend = InMemorySearchBackend( + trials=[ + { + "nct_id": "N1", + "condition": "rare sarcoma", + "brief_title": "Rare sarcoma trial", + "eligibility_criteria": "Adults with rare sarcoma.", + "overall_status": "Recruiting", + "gender": "All", + } + ] + ) + profile = PatientProfile( + patient_id="P1", + conditions=[_fact("condition-1", "condition", "unknown condition")], + ) + + result = run_first_level_search( + { + "main_conditions": ["unknown condition"], + "other_conditions": [], + "patient_narrative": ["Patient has rare sarcoma."], + }, + str(tmp_path), + {"age": "all", "gender": "ALL"}, + None, + None, + _config(enabled=True), + backend, + patient_profile=profile, + ) + + assert result is not None + nct_ids, *_ = result + assert nct_ids == ["N1"] + assert (tmp_path / "first_level_query_plan.json").exists() + candidates = json.loads((tmp_path / "first_level_candidates.json").read_text()) + assert candidates["candidates"][0]["nct_id"] == "N1" + assert (tmp_path / "nct_ids.txt").read_text().strip() == "N1" + + +def test_run_first_level_search_disabled_uses_single_query_path(tmp_path): + backend = InMemorySearchBackend( + trials=[ + { + "nct_id": "N1", + "condition": "lung cancer", + "brief_title": "Lung cancer trial", + "eligibility_criteria": "Adults with lung cancer.", + "overall_status": "Recruiting", + "gender": "All", + } + ] + ) + + result = run_first_level_search( + { + "main_conditions": ["lung cancer"], + "other_conditions": [], + "patient_narrative": ["Patient has lung cancer."], + }, + str(tmp_path), + {"age": "all", "gender": "ALL"}, + None, + None, + _config(enabled=False), + backend, + patient_profile=PatientProfile(patient_id="P1"), + ) + + assert result is not None + nct_ids, *_ = result + assert nct_ids == ["N1"] + assert not (tmp_path / "first_level_query_plan.json").exists() + + +class _RecordingBackend: + def __init__(self): + self.calls = [] + + def search_trials(self, **kwargs): + self.calls.append(kwargs) + return [], [] + + +def test_hard_filters_config_controls_applied_filters(): + profile = PatientProfile( + patient_id="P1", conditions=[_fact("c1", "condition", "lung cancer")] + ) + summary = {"main_conditions": ["lung cancer"], "other_conditions": [], "patient_narrative": []} + + # hard_filters=[] disables age/sex/status filtering entirely. + backend = _RecordingBackend() + search = ClinicalTrialSearch( + search_backend=backend, embedder=None, entity_annotator=FakeAnnotator() + ) + plan = search.build_query_plan( + profile=profile, matching_summary=summary, config={"hard_filters": []}, + age=64, sex="female", overall_status="Recruiting", + ) + search.search_trials_with_plan( + query_plan=plan, age_input=64, sex="female", overall_status="Recruiting", + size=10, per_channel_size=10, search_mode="bm25", + ) + assert backend.calls + for call in backend.calls: + assert call["age"] is None + assert call["sex"] == "all" + assert call["overall_status"] is None + + # hard_filters=["overall_status"] applies only the recruitment-status filter. + backend2 = _RecordingBackend() + search2 = ClinicalTrialSearch( + search_backend=backend2, embedder=None, entity_annotator=FakeAnnotator() + ) + plan2 = search2.build_query_plan( + profile=profile, matching_summary=summary, + config={"hard_filters": ["overall_status"]}, + age=64, sex="female", overall_status="Recruiting", + ) + search2.search_trials_with_plan( + query_plan=plan2, age_input=64, sex="female", overall_status="Recruiting", + size=10, per_channel_size=10, search_mode="bm25", + ) + assert backend2.calls + for call in backend2.calls: + assert call["age"] is None + assert call["sex"] == "all" + assert call["overall_status"] == "Recruiting" + + +class FakeAnnotator: + def annotate_texts_in_parallel(self, texts, max_workers=1, retries=1, delay=0): + return [ + [ + { + "entity_group": "disease", + "text": text, + "synonyms": ["NSCLC", "lung carcinoma"], + } + ] + for text in texts + ] + + +def _fact( + fact_id: str, + category: str, + label: str, + *, + negated: bool = False, + temporality: str | None = None, +) -> ClinicalFact: + return ClinicalFact( + fact_id=fact_id, + category=category, + label=label, + negated=negated, + temporality=temporality, + provenance=Provenance(source_format="test"), + ) + + +def _config(*, enabled: bool) -> dict: + return { + "search": { + "mode": "bm25", + "vector_score_threshold": 0.5, + "max_trials_first_level": 1000, + "first_level": { + "enabled": enabled, + "max_trials": 1000, + "per_channel_size": 300, + "rrf_k": 60, + "vector_score_threshold": 0.0, + "llm_expansion_enabled": False, + "write_reports": True, + }, + } + } diff --git a/tests/test_first_level_search_pytest.py b/tests/test_first_level_search_pytest.py index 8e92e7fd..69c5d708 100644 --- a/tests/test_first_level_search_pytest.py +++ b/tests/test_first_level_search_pytest.py @@ -27,7 +27,7 @@ def test_search_trials_bm25_returns_hits(): search = ClinicalTrialSearch( search_backend=backend, embedder=None, - bio_med_ner=None, + entity_annotator=None, ) trials, scores = search.search_trials( condition="lung cancer", diff --git a/tests/test_import_patient_cli.py b/tests/test_import_patient_cli.py index dfa8e429..ae165e91 100644 --- a/tests/test_import_patient_cli.py +++ b/tests/test_import_patient_cli.py @@ -34,4 +34,4 @@ def test_import_patient_cli_writes_profile_and_summary(tmp_path, monkeypatch): assert profile["patient_id"] == "patient" assert summary["patient_id"] == "patient" assert summary["main_conditions"] == ["Patient has breast cancer."] - assert summary["expanded_sentences"] + assert summary["patient_narrative"] diff --git a/tests/test_patient_interop.py b/tests/test_patient_interop.py index 81efba93..0b507508 100644 --- a/tests/test_patient_interop.py +++ b/tests/test_patient_interop.py @@ -44,7 +44,7 @@ def test_patient_profile_schema_and_summary(): summary = profile_to_matching_summary(profile) assert summary["patient_id"] == "p1" assert summary["age"] == "all" - assert summary["expanded_sentences"] + assert summary["patient_narrative"] def test_text_importer_preserves_offsets_and_entities(tmp_path): diff --git a/tests/test_patient_runtime_loading.py b/tests/test_patient_runtime_loading.py index 349984f8..7227e1f0 100644 --- a/tests/test_patient_runtime_loading.py +++ b/tests/test_patient_runtime_loading.py @@ -20,7 +20,7 @@ def test_runtime_loads_profiles_from_configured_directory(tmp_path): "patient_id": "p1", "main_conditions": ["melanoma"], "other_conditions": [], - "expanded_sentences": ["Patient has melanoma."], + "patient_narrative": ["Patient has melanoma."], "age": "all", "gender": "all", } diff --git a/tests/test_schemas.py b/tests/test_schemas.py index 9c02e994..c8747a6c 100644 --- a/tests/test_schemas.py +++ b/tests/test_schemas.py @@ -10,7 +10,7 @@ def test_phenopacket_minimal(self) -> None: self.assertEqual(obj.id, "patient-1") def test_keywords_default(self) -> None: - data = {"main_conditions": ["A"], "other_conditions": [], "expanded_sentences": []} + data = {"main_conditions": ["A"], "other_conditions": [], "patient_narrative": []} obj = Keywords.model_validate(data) self.assertEqual(obj.main_conditions, ["A"]) @@ -18,7 +18,7 @@ def test_keywords_allows_extra(self) -> None: data = { "main_conditions": [], "other_conditions": [], - "expanded_sentences": [], + "patient_narrative": [], "error": "bad", "extra": "ok", } diff --git a/tests/test_search_queries_pytest.py b/tests/test_search_queries_pytest.py index 8ee62a04..77b0f441 100644 --- a/tests/test_search_queries_pytest.py +++ b/tests/test_search_queries_pytest.py @@ -6,7 +6,7 @@ def test_first_level_query_describes_backend_search(): search = ClinicalTrialSearch( search_backend=InMemorySearchBackend(), embedder=None, - bio_med_ner=None, + entity_annotator=None, ) query = search.create_query( synonyms=["lung cancer"], diff --git a/tests/test_second_level_search_pytest.py b/tests/test_second_level_search_pytest.py index c133067c..45894b29 100644 --- a/tests/test_second_level_search_pytest.py +++ b/tests/test_second_level_search_pytest.py @@ -1,3 +1,9 @@ +from trialmatchai.constraints.models import ( + Constraint, + ConstraintSet, + PatientConstraintContext, + PatientConstraintFact, +) from trialmatchai.matching.retrieval.criteria_retrieval import SecondStageRetriever from trialmatchai.search import InMemorySearchBackend @@ -64,3 +70,128 @@ def test_retrieve_criteria_uses_entity_synonyms(): hits = retriever.retrieve_criteria(["N1"], ["cancer"]) assert hits["cancer"][0]["_source"]["criteria_id"] == "C1" + + +def test_constraint_adjustments_penalize_without_hard_exclusion(): + backend = InMemorySearchBackend(criteria=_constraint_test_criteria()) + retriever = SecondStageRetriever( + search_backend=backend, + llm_reranker=None, + embedder=None, + inclusion_weight=1.0, + exclusion_weight=1.0, + search_mode="bm25", + ) + + trials = retriever.retrieve_and_rank( + ["lung cancer"], + ["N1", "N2"], + top_n=2, + patient_context=_constraint_context(), + constraints_config={"enabled": True, "score_weight": 1.0}, + ) + + scores = {trial["nct_id"]: trial["score"] for trial in trials} + assert trials[0]["nct_id"] == "N1" + assert "N2" in scores + assert scores["N2"] == 0 + assert any( + evaluation.nct_id == "N2" and evaluation.violated_count == 1 + for evaluation in retriever.last_constraint_evaluations + ) + + +def test_constraints_disabled_preserves_ranking_behavior(): + backend = InMemorySearchBackend(criteria=_constraint_test_criteria()) + retriever = SecondStageRetriever( + search_backend=backend, + llm_reranker=None, + embedder=None, + inclusion_weight=1.0, + exclusion_weight=1.0, + search_mode="bm25", + ) + + disabled = retriever.retrieve_and_rank( + ["lung cancer"], + ["N1", "N2"], + top_n=2, + patient_context=_constraint_context(), + constraints_config={"enabled": False, "score_weight": 1.0}, + ) + assert retriever.last_constraint_evaluations == [] + + baseline = retriever.retrieve_and_rank(["lung cancer"], ["N1", "N2"], top_n=2) + + assert disabled == baseline + + +def _constraint_test_criteria(): + return [ + { + "criteria_id": "C1", + "nct_id": "N1", + "criterion": "Adults with lung cancer.", + "eligibility_type": "Inclusion Criteria", + "entities": [], + "constraints": _constraint_payload( + "N1", + "C1", + "inclusion", + [Constraint(kind="condition", label="lung cancer")], + ), + }, + { + "criteria_id": "C2", + "nct_id": "N2", + "criterion": "Adults with lung cancer. Prior osimertinib is excluded.", + "eligibility_type": "Exclusion Criteria", + "entities": [], + "constraints": _constraint_payload( + "N2", + "C2", + "exclusion", + [ + Constraint( + kind="medication", + label="osimertinib", + comparator="prior", + ) + ], + ), + }, + ] + + +def _constraint_payload( + nct_id: str, + criteria_id: str, + polarity: str, + constraints: list[Constraint], +): + return ConstraintSet( + nct_id=nct_id, + criteria_id=criteria_id, + polarity=polarity, + source_text="constraint fixture", + constraints=constraints, + ).model_dump(mode="json") + + +def _constraint_context() -> PatientConstraintContext: + return PatientConstraintContext( + patient_id="P1", + facts=[ + PatientConstraintFact( + kind="condition", + label="lung cancer", + evidence_text="Patient has lung cancer.", + ), + PatientConstraintFact( + kind="medication", + label="osimertinib", + temporality="prior", + evidence_text="Prior osimertinib documented.", + ), + ], + ) diff --git a/tests/test_settings.py b/tests/test_settings.py index c8f13d3b..a963f1a6 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -23,11 +23,12 @@ def test_env_overrides(self) -> None: "criteria_table": "old-criteria", }, "embedder": {"model_name": "old"}, - "search": {"mode": "hybrid"}, "entity_extraction": {"backend": "gliner2"}, "concept_linker": {"db_path": "old"}, "patient_inputs": {"profile_dir": "old-profiles"}, "registry": {"since_days": 7, "raw_dir": "old-raw"}, + "constraints": {"enabled": True, "score_weight": 0.25}, + "search": {"mode": "hybrid", "first_level": {"max_trials": 1000}}, } os.environ["TRIALMATCHAI_SEARCH_DB_PATH"] = "data/search-test" os.environ["TRIALMATCHAI_SEARCH_TRIALS_TABLE"] = "trials-test" @@ -39,6 +40,12 @@ def test_env_overrides(self) -> None: os.environ["TRIALMATCHAI_PATIENT_STRICT_VALIDATION"] = "true" os.environ["TRIALMATCHAI_REGISTRY_SINCE_DAYS"] = "30" os.environ["TRIALMATCHAI_REGISTRY_RAW_DIR"] = "registry/raw" + os.environ["TRIALMATCHAI_CONSTRAINTS_ENABLED"] = "false" + os.environ["TRIALMATCHAI_CONSTRAINTS_SCORE_WEIGHT"] = "0.4" + os.environ["TRIALMATCHAI_FIRST_LEVEL_ENABLED"] = "false" + os.environ["TRIALMATCHAI_FIRST_LEVEL_MAX_TRIALS"] = "700" + os.environ["TRIALMATCHAI_FIRST_LEVEL_PER_CHANNEL_SIZE"] = "250" + os.environ["TRIALMATCHAI_FIRST_LEVEL_VECTOR_SCORE_THRESHOLD"] = "0.1" try: updated = apply_env_overrides(raw) finally: @@ -52,6 +59,12 @@ def test_env_overrides(self) -> None: os.environ.pop("TRIALMATCHAI_PATIENT_STRICT_VALIDATION", None) os.environ.pop("TRIALMATCHAI_REGISTRY_SINCE_DAYS", None) os.environ.pop("TRIALMATCHAI_REGISTRY_RAW_DIR", None) + os.environ.pop("TRIALMATCHAI_CONSTRAINTS_ENABLED", None) + os.environ.pop("TRIALMATCHAI_CONSTRAINTS_SCORE_WEIGHT", None) + os.environ.pop("TRIALMATCHAI_FIRST_LEVEL_ENABLED", None) + os.environ.pop("TRIALMATCHAI_FIRST_LEVEL_MAX_TRIALS", None) + os.environ.pop("TRIALMATCHAI_FIRST_LEVEL_PER_CHANNEL_SIZE", None) + os.environ.pop("TRIALMATCHAI_FIRST_LEVEL_VECTOR_SCORE_THRESHOLD", None) self.assertEqual(updated["search_backend"]["db_path"], "data/search-test") self.assertEqual(updated["search_backend"]["trials_table"], "trials-test") @@ -63,6 +76,12 @@ def test_env_overrides(self) -> None: self.assertTrue(updated["patient_inputs"]["strict_validation"]) self.assertEqual(updated["registry"]["since_days"], 30) self.assertEqual(updated["registry"]["raw_dir"], "registry/raw") + self.assertFalse(updated["constraints"]["enabled"]) + self.assertEqual(updated["constraints"]["score_weight"], 0.4) + self.assertFalse(updated["search"]["first_level"]["enabled"]) + self.assertEqual(updated["search"]["first_level"]["max_trials"], 700) + self.assertEqual(updated["search"]["first_level"]["per_channel_size"], 250) + self.assertEqual(updated["search"]["first_level"]["vector_score_threshold"], 0.1) if __name__ == "__main__": diff --git a/uv.lock b/uv.lock index fcac0f97..7b6661d9 100644 --- a/uv.lock +++ b/uv.lock @@ -2991,7 +2991,6 @@ dependencies = [ [package.optional-dependencies] entity = [ - { name = "gliner", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "gliner2", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "torch", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "transformers", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, @@ -3000,7 +2999,6 @@ finetune = [ { name = "accelerate", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "bitsandbytes", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "datasets", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, - { name = "gliner", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "gliner2", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "peft", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, { name = "torch", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'darwin'" }, @@ -3036,8 +3034,6 @@ requires-dist = [ { name = "bitsandbytes", marker = "(sys_platform == 'linux' and extra == 'gpu') or (sys_platform == 'win32' and extra == 'gpu')", specifier = "==0.49.2" }, { name = "datasets", marker = "extra == 'finetune'", specifier = ">=3.0,<5" }, { name = "einops", marker = "extra == 'llm'", specifier = "==0.8.0" }, - { name = "gliner", marker = "extra == 'entity'", specifier = "==0.2.27" }, - { name = "gliner", marker = "extra == 'finetune'", specifier = "==0.2.27" }, { name = "gliner2", marker = "extra == 'entity'", specifier = ">=1.3.1,<2" }, { name = "gliner2", marker = "extra == 'finetune'", specifier = ">=1.3.1,<2" }, { name = "lancedb", specifier = ">=0.25.0,<0.26" }, From 57808853fcf6276500ee64e31c3aa0f46418cbf5 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 15:06:31 +0200 Subject: [PATCH 31/64] feat: optional country-level geographic trial filter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds geographic location as an opt-in first-level hard filter, done site-aware and recall-safe rather than as a blunt cut. - interop: PatientProfile gains a Location (country/state/city); the FHIR importer populates it from Patient.address. - matching/retrieval/location.py: country-level, match-ANY-site filtering. A trial is kept when the patient's country is unknown, when the trial has no indexed site countries, or when any of its sites is in the patient's country — so trials with unknown locations are never dropped. - run_first_level_search applies it only when "location" is in search.first_level.hard_filters (default keeps age/sex/overall_status, so the behavior is unchanged unless opted in). - settings: hard_filters Literal accepts "location". Tests cover recall-safety, the end-to-end hard-filter on/off behavior, and FHIR address extraction. README updated. --- README.md | 10 +- src/trialmatchai/config/settings.py | 3 +- src/trialmatchai/interop/__init__.py | 2 + src/trialmatchai/interop/importers/fhir.py | 26 +++- src/trialmatchai/interop/models.py | 11 ++ src/trialmatchai/main.py | 20 +++ .../matching/retrieval/location.py | 60 +++++++ tests/test_location_filter.py | 146 ++++++++++++++++++ 8 files changed, 273 insertions(+), 5 deletions(-) create mode 100644 src/trialmatchai/matching/retrieval/location.py create mode 100644 tests/test_location_filter.py diff --git a/README.md b/README.md index 7de83c88..c9ec1ba4 100644 --- a/README.md +++ b/README.md @@ -226,9 +226,13 @@ terms, patient narrative text, biomarkers, prior therapy or procedures, and optional LLM-generated expansions. LLM expansion is off by default; deterministic concept and patient-profile expansion are the default path. -The first level only hard-filters by age, sex/gender, and recruitment status. -Location, biomarkers, phase, prior therapy, and eligibility constraints remain -soft signals for later retrieval and reasoning stages. +By default the first level hard-filters by age, sex/gender, and recruitment +status (`search.first_level.hard_filters`). Geographic **location** is an opt-in +hard filter: add `"location"` to `hard_filters` to keep only trials with a +recruiting site in the patient's country (country-level, site-aware, and +recall-safe — trials with unknown site countries are never dropped; patient +location is populated by the FHIR importer). Biomarkers, phase, prior therapy, +and eligibility constraints remain soft signals for later stages. When enabled, first-level artifacts are written under `results//`: diff --git a/src/trialmatchai/config/settings.py b/src/trialmatchai/config/settings.py index 91635914..fac0fd56 100644 --- a/src/trialmatchai/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -128,7 +128,8 @@ class FirstLevelSearchSettings(BaseModel): llm_expansion_enabled: bool = False llm_max_terms: int = Field(12, ge=0) write_reports: bool = True - hard_filters: list[Literal["age", "sex", "overall_status"]] = Field( + # "location" is opt-in (country-level, site-aware); not in the default set. + hard_filters: list[Literal["age", "sex", "overall_status", "location"]] = Field( default_factory=lambda: ["age", "sex", "overall_status"] ) diff --git a/src/trialmatchai/interop/__init__.py b/src/trialmatchai/interop/__init__.py index 1919662c..e436452e 100644 --- a/src/trialmatchai/interop/__init__.py +++ b/src/trialmatchai/interop/__init__.py @@ -3,6 +3,7 @@ from trialmatchai.interop.models import ( ClinicalFact, Demographics, + Location, NormalizedCode, PatientNote, PatientProfile, @@ -13,6 +14,7 @@ __all__ = [ "ClinicalFact", "Demographics", + "Location", "NormalizedCode", "PatientNote", "PatientProfile", diff --git a/src/trialmatchai/interop/importers/fhir.py b/src/trialmatchai/interop/importers/fhir.py index e4648faa..978e1f02 100644 --- a/src/trialmatchai/interop/importers/fhir.py +++ b/src/trialmatchai/interop/importers/fhir.py @@ -4,7 +4,13 @@ from pathlib import Path from typing import Any, Mapping -from trialmatchai.interop.models import Demographics, PatientProfile, Provenance, SourceDocument +from trialmatchai.interop.models import ( + Demographics, + Location, + PatientProfile, + Provenance, + SourceDocument, +) from trialmatchai.interop.utils import ( age_years_from_birth_date, clean_text, @@ -96,10 +102,28 @@ def _profile_from_patient( birth_date=birth_date, age_years=age_years_from_birth_date(birth_date), ), + location=_patient_location(patient.get("address")), provenance=[provenance], ) +def _patient_location(address: Any) -> Location | None: + """Extract a coarse location from a FHIR Patient.address list (first entry).""" + if isinstance(address, dict): + address = [address] + if not isinstance(address, list): + return None + for entry in address: + if not isinstance(entry, dict): + continue + country = (entry.get("country") or "").strip() or None + state = (entry.get("state") or "").strip() or None + city = (entry.get("city") or "").strip() or None + if country or state or city: + return Location(country=country, state=state, city=city) + return None + + def _load_json_resources(path: Path) -> list[dict[str, Any]]: data = json.loads(path.read_text(encoding="utf-8")) if isinstance(data, list): diff --git a/src/trialmatchai/interop/models.py b/src/trialmatchai/interop/models.py index 7e8e567b..251046f6 100644 --- a/src/trialmatchai/interop/models.py +++ b/src/trialmatchai/interop/models.py @@ -50,6 +50,16 @@ class Demographics(BaseModel): model_config = ConfigDict(extra="allow") +class Location(BaseModel): + """A patient's geographic location, used for optional site-based filtering.""" + + country: str | None = None + state: str | None = None + city: str | None = None + + model_config = ConfigDict(extra="allow") + + class ClinicalFact(BaseModel): fact_id: str category: str @@ -103,6 +113,7 @@ class SourceDocument(BaseModel): class PatientProfile(BaseModel): patient_id: str demographics: Demographics = Field(default_factory=Demographics) + location: Location | None = None conditions: list[ClinicalFact] = Field(default_factory=list) phenotypes: list[ClinicalFact] = Field(default_factory=list) observations: list[ClinicalFact] = Field(default_factory=list) diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index 3c2e4dbe..306d8461 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -16,6 +16,10 @@ ) from trialmatchai.matching.retrieval.trial_retrieval import ClinicalTrialSearch from trialmatchai.matching.retrieval.criteria_retrieval import SecondStageRetriever +from trialmatchai.matching.retrieval.location import ( + filter_trials_by_country, + patient_country, +) from trialmatchai.search import LanceDBSearchBackend from trialmatchai.services.preflight import run_preflight_checks from trialmatchai.interop.exporters import profile_to_matching_summary @@ -137,6 +141,22 @@ def run_first_level_search( search_mode=search_cfg.get("mode", "hybrid"), ) + # Optional geographic hard filter (country-level, site-aware, opt-in via + # search.first_level.hard_filters). Recall-safe: only drops trials whose + # known sites exclude the patient's country. + hard_filters = first_level_cfg.get("hard_filters") or ["age", "sex", "overall_status"] + if "location" in hard_filters and patient_profile is not None: + country = patient_country(patient_profile) + if country: + before = len(trials) + trials, scores = filter_trials_by_country(trials, scores, country) + logger.info( + "Location filter (country=%s): %d -> %d trials.", + country, + before, + len(trials), + ) + nct_ids = [trial.get("nct_id") for trial in trials if trial.get("nct_id")] first_level_scores = { trial.get("nct_id"): score diff --git a/src/trialmatchai/matching/retrieval/location.py b/src/trialmatchai/matching/retrieval/location.py new file mode 100644 index 00000000..27532ecf --- /dev/null +++ b/src/trialmatchai/matching/retrieval/location.py @@ -0,0 +1,60 @@ +"""Optional geographic (country-level, site-aware) trial filtering. + +Trials recruit at many sites across countries, so the filter is deliberately +recall-safe: a trial passes when the patient's country is unknown, when the +trial has no indexed site countries, or when ANY of its sites is in the +patient's country. It only drops trials we are confident have no site in the +patient's country. Country granularity avoids the recall risk of distance/region +matching on messy ClinicalTrials.gov site data. +""" + +from __future__ import annotations + +from typing import Any, Mapping, Sequence + + +def patient_country(profile: Any) -> str | None: + location = getattr(profile, "location", None) + country = getattr(location, "country", None) if location is not None else None + country = (country or "").strip() + return country or None + + +def trial_site_countries(trial: Mapping[str, Any]) -> set[str]: + countries: set[str] = set() + locations = trial.get("location") + if isinstance(locations, Mapping): + locations = [locations] + if not isinstance(locations, Sequence) or isinstance(locations, (str, bytes)): + return countries + for site in locations: + if isinstance(site, Mapping): + country = str(site.get("country") or "").strip().casefold() + if country: + countries.add(country) + return countries + + +def trial_in_country(trial: Mapping[str, Any], country: str) -> bool: + """True if the trial should pass the country filter (recall-safe).""" + site_countries = trial_site_countries(trial) + if not site_countries: + return True # unknown location -> do not drop + return country.strip().casefold() in site_countries + + +def filter_trials_by_country( + trials: list[dict[str, Any]], + scores: list[float], + country: str | None, +) -> tuple[list[dict[str, Any]], list[float]]: + """Drop trials with known sites that exclude the patient's country.""" + if not country: + return trials, scores + kept_trials: list[dict[str, Any]] = [] + kept_scores: list[float] = [] + for trial, score in zip(trials, scores): + if trial_in_country(trial, country): + kept_trials.append(trial) + kept_scores.append(score) + return kept_trials, kept_scores diff --git a/tests/test_location_filter.py b/tests/test_location_filter.py new file mode 100644 index 00000000..9b6f6047 --- /dev/null +++ b/tests/test_location_filter.py @@ -0,0 +1,146 @@ +"""Tests for the optional country-level, site-aware trial location filter.""" + +from __future__ import annotations + +import json + +from trialmatchai.interop import import_patient_path +from trialmatchai.interop.models import ( + ClinicalFact, + Location, + PatientProfile, + Provenance, +) +from trialmatchai.main import run_first_level_search +from trialmatchai.matching.retrieval.location import ( + filter_trials_by_country, + patient_country, + trial_in_country, +) +from trialmatchai.search import InMemorySearchBackend + + +def _trial(nct_id, countries): + return { + "nct_id": nct_id, + "condition": "lung cancer", + "brief_title": "Lung cancer trial", + "eligibility_criteria": "Adults with lung cancer.", + "overall_status": "Recruiting", + "gender": "All", + "location": [{"country": c} for c in countries], + } + + +def test_filter_is_recall_safe(): + us = _trial("N_US", ["United States", "Germany"]) + fr = _trial("N_FR", ["France"]) + unknown = {"nct_id": "N_UNK"} # no location data + trials = [us, fr, unknown] + scores = [3.0, 2.0, 1.0] + + kept, kept_scores = filter_trials_by_country(trials, scores, "United States") + ids = {t["nct_id"] for t in kept} + assert ids == {"N_US", "N_UNK"} # multi-site US kept; unknown kept; France dropped + assert kept_scores == [3.0, 1.0] + + # No patient country -> no filtering. + assert filter_trials_by_country(trials, scores, None)[0] == trials + assert trial_in_country(unknown, "France") is True # unknown location never dropped + + +def test_patient_country_extraction(): + profile = PatientProfile(patient_id="P", location=Location(country=" France ")) + assert patient_country(profile) == "France" + assert patient_country(PatientProfile(patient_id="P")) is None + + +def _fl_config(*, hard_filters): + return { + "search": { + "mode": "bm25", + "vector_score_threshold": 0.5, + "max_trials_first_level": 1000, + "first_level": { + "enabled": True, + "max_trials": 1000, + "per_channel_size": 300, + "rrf_k": 60, + "vector_score_threshold": 0.0, + "llm_expansion_enabled": False, + "write_reports": False, + "hard_filters": hard_filters, + }, + } + } + + +def _profile_us(): + return PatientProfile( + patient_id="P1", + location=Location(country="United States"), + conditions=[ + ClinicalFact( + fact_id="c1", + category="condition", + label="lung cancer", + provenance=Provenance(source_format="test"), + ) + ], + ) + + +def test_location_hard_filter_drops_out_of_country_trials(tmp_path): + backend = InMemorySearchBackend( + trials=[_trial("N_US", ["United States"]), _trial("N_FR", ["France"])] + ) + result = run_first_level_search( + {"main_conditions": ["lung cancer"], "other_conditions": [], "patient_narrative": []}, + str(tmp_path), + {"age": "all", "gender": "ALL"}, + None, + None, + _fl_config(hard_filters=["age", "sex", "overall_status", "location"]), + backend, + patient_profile=_profile_us(), + ) + nct_ids, *_ = result + assert "N_US" in nct_ids + assert "N_FR" not in nct_ids + + +def test_location_filter_off_by_default_keeps_all_countries(tmp_path): + backend = InMemorySearchBackend( + trials=[_trial("N_US", ["United States"]), _trial("N_FR", ["France"])] + ) + result = run_first_level_search( + {"main_conditions": ["lung cancer"], "other_conditions": [], "patient_narrative": []}, + str(tmp_path), + {"age": "all", "gender": "ALL"}, + None, + None, + _fl_config(hard_filters=["age", "sex", "overall_status"]), + backend, + patient_profile=_profile_us(), + ) + nct_ids, *_ = result + assert {"N_US", "N_FR"} <= set(nct_ids) + + +def test_fhir_importer_extracts_patient_location(tmp_path): + path = tmp_path / "patient.json" + path.write_text( + json.dumps( + { + "resourceType": "Patient", + "id": "pat-1", + "gender": "female", + "birthDate": "1980-01-01", + "address": [{"city": "Boston", "state": "MA", "country": "United States"}], + } + ) + ) + profiles = import_patient_path(path) + assert profiles[0].location is not None + assert profiles[0].location.country == "United States" + assert profiles[0].location.city == "Boston" From acde376b74cde6dae190c031e2c69215e16e6414 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 15:29:39 +0200 Subject: [PATCH 32/64] feat: populate patient location from the OMOP importer The OMOP importer now resolves a patient's location via the LOCATION table (PERSON.location_id -> LOCATION), filling country (country_source_value, or country_concept_id via concept lookup), state, and city. NaN-safe field extraction; degrades to no location when the table or link is absent. This extends the optional country-level location hard filter to OMOP patients (previously FHIR-only). Test added; README updated. --- README.md | 4 +- src/trialmatchai/interop/importers/omop.py | 52 +++++++++++++++++++++- tests/test_location_filter.py | 19 ++++++++ 3 files changed, 72 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index c9ec1ba4..c8ed97fe 100644 --- a/README.md +++ b/README.md @@ -231,8 +231,8 @@ status (`search.first_level.hard_filters`). Geographic **location** is an opt-in hard filter: add `"location"` to `hard_filters` to keep only trials with a recruiting site in the patient's country (country-level, site-aware, and recall-safe — trials with unknown site countries are never dropped; patient -location is populated by the FHIR importer). Biomarkers, phase, prior therapy, -and eligibility constraints remain soft signals for later stages. +location is populated by the FHIR and OMOP importers). Biomarkers, phase, prior +therapy, and eligibility constraints remain soft signals for later stages. When enabled, first-level artifacts are written under `results//`: diff --git a/src/trialmatchai/interop/importers/omop.py b/src/trialmatchai/interop/importers/omop.py index a026b64d..4055770d 100644 --- a/src/trialmatchai/interop/importers/omop.py +++ b/src/trialmatchai/interop/importers/omop.py @@ -5,7 +5,14 @@ import pandas as pd -from trialmatchai.interop.models import Demographics, NormalizedCode, PatientNote, PatientProfile, Provenance +from trialmatchai.interop.models import ( + Demographics, + Location, + NormalizedCode, + PatientNote, + PatientProfile, + Provenance, +) from trialmatchai.interop.utils import ( age_years_from_birth_date, clean_text, @@ -25,6 +32,7 @@ def import_omop_extract( root = Path(path) tables = _load_tables(root) concepts = _concept_lookup(tables.get("concept")) + location_index = _location_index(tables.get("location")) person = tables.get("person") if person is None or person.empty: if strict: @@ -68,6 +76,7 @@ def import_omop_extract( birth_date=birth_date, age_years=age_years_from_birth_date(birth_date), ), + location=_person_location(row, location_index, concepts), provenance=[provenance], ) _add_condition_rows(profile, grouped["condition_occurrence"].get(person_key, []), concepts, root) @@ -312,6 +321,47 @@ def _normalize_join_id(value: Any) -> str: return text +def _location_index(table: pd.DataFrame | None) -> dict[str, dict[str, Any]]: + if table is None or table.empty or "location_id" not in table.columns: + return {} + index: dict[str, dict[str, Any]] = {} + for record in table.to_dict("records"): + key = _normalize_join_id(record.get("location_id")) + if key: + index.setdefault(key, record) + return index + + +def _person_location( + row: dict[str, Any], + location_index: dict[str, dict[str, Any]], + concepts: dict[Any, dict[str, Any]], +) -> Location | None: + if not location_index: + return None + location_id = _normalize_join_id(row.get("location_id")) + if not location_id: + return None + loc = location_index.get(location_id) + if not loc: + return None + + def field(name: str) -> str | None: + value = loc.get(name) + if value is None or (isinstance(value, float) and pd.isna(value)): + return None + return clean_text(value) or None + + country = field("country_source_value") or _concept_label( + loc.get("country_concept_id"), concepts + ) + state = field("state") + city = field("city") + if not (country or state or city): + return None + return Location(country=country, state=state, city=city) + + def _group_by_person(table: pd.DataFrame | None) -> dict[str, list[dict[str, Any]]]: if table is None or table.empty or "person_id" not in table.columns: return {} diff --git a/tests/test_location_filter.py b/tests/test_location_filter.py index 9b6f6047..05a30743 100644 --- a/tests/test_location_filter.py +++ b/tests/test_location_filter.py @@ -127,6 +127,25 @@ def test_location_filter_off_by_default_keeps_all_countries(tmp_path): assert {"N_US", "N_FR"} <= set(nct_ids) +def test_omop_importer_extracts_patient_location(tmp_path): + import pandas as pd + + omop = tmp_path / "omop" + omop.mkdir() + pd.DataFrame( + [{"person_id": 1, "gender_source_value": "M", "year_of_birth": 1975, "location_id": 10}] + ).to_csv(omop / "PERSON.csv", index=False) + pd.DataFrame( + [{"location_id": 10, "city": "Lyon", "country_source_value": "France"}] + ).to_csv(omop / "LOCATION.csv", index=False) + + profiles = import_patient_path(omop) + assert len(profiles) == 1 + assert profiles[0].location is not None + assert profiles[0].location.country == "France" + assert profiles[0].location.city == "Lyon" + + def test_fhir_importer_extracts_patient_location(tmp_path): path = tmp_path / "patient.json" path.write_text( From 175890fa6faf4802bd3ebfab87719695739930fc Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Mon, 22 Jun 2026 15:59:23 +0200 Subject: [PATCH 33/64] fix(fhir): harden importer for real EHR data Addresses the correctness/coverage gaps found in adversarial review so the importer is trustworthy on real Epic/Cerner R4 output, not just Synthea. Correctness (incl. one patient-safety bug): - honor clinicalStatus/verificationStatus/status: resolved/inactive/refuted conditions are now NEGATED (no longer matched as active); entered-in-error, cancelled, and not-done resources are dropped (recorded in `unsupported`). Completed/stopped medications stay un-negated (real prior exposure). - CodeableConcept: extract ALL codings, ordering known vocabularies (SNOMED/LOINC/RxNorm/ICD) first, so concept linking uses the standard code instead of a proprietary one; secondary codings retained. - parse_date handles partial FHIR dates (YYYY, YYYY-MM) -> age no longer lost. Coverage: - medications via medicationReference / contained Medication / R5 wrapper. - Observation.component panels, interpretation flags, and value[x] variants (comparator, Range, Ratio, CodeableConcept, string/bool/int/datetime). - onset/effective/performed Period + recordedDate temporality; cleaner note and dosageInstruction text; broader genomic-observation detection. Robustness: - NDJSON loader tolerates malformed lines (skips + logs) instead of aborting. - reference resolution handles urn:uuid, absolute URLs, and contained refs; orphan resources attributed once, with a logged mapping-failure reason. 9 regression tests added. --- src/trialmatchai/interop/importers/fhir.py | 449 +++++++++++++++++---- src/trialmatchai/interop/utils.py | 93 +++-- tests/test_fhir_robustness.py | 211 ++++++++++ 3 files changed, 642 insertions(+), 111 deletions(-) create mode 100644 tests/test_fhir_robustness.py diff --git a/src/trialmatchai/interop/importers/fhir.py b/src/trialmatchai/interop/importers/fhir.py index 978e1f02..bd0f4523 100644 --- a/src/trialmatchai/interop/importers/fhir.py +++ b/src/trialmatchai/interop/importers/fhir.py @@ -14,7 +14,7 @@ from trialmatchai.interop.utils import ( age_years_from_birth_date, clean_text, - code_from_fhir_codeable, + codes_from_fhir_codeable, label_from_fhir_codeable, make_fact, normalize_gender, @@ -22,6 +22,9 @@ safe_patient_id, source_path_string, ) +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) def import_fhir( @@ -32,7 +35,7 @@ def import_fhir( ) -> list[PatientProfile]: source_path = Path(path) resources = ( - _load_ndjson(source_path) + _load_ndjson(source_path, strict=strict) if input_format == "fhir-ndjson" else _load_json_resources(source_path) ) @@ -56,26 +59,33 @@ def import_fhir( raise ValueError( f"{message}: {resource.get('resourceType')}/{resource.get('id')}" ) - for candidate in profiles: - candidate.unsupported.append( - { - "resourceType": resource.get("resourceType"), - "id": resource.get("id"), - "reason": message, - } - ) + # Attribute the orphan to the first profile only (avoid duplicating it + # across every profile in a multi-patient bundle). + profiles[0].unsupported.append( + { + "resourceType": resource.get("resourceType"), + "id": resource.get("id"), + "reason": message, + } + ) continue try: base_provenance = profile.provenance[0] _add_resource(profile, resource, base_provenance) - except Exception: + except Exception as exc: if strict: raise + logger.warning( + "FHIR mapping failed for %s/%s: %s", + resource.get("resourceType"), + resource.get("id"), + exc, + ) profile.unsupported.append( { "resourceType": resource.get("resourceType"), "id": resource.get("id"), - "reason": "resource mapping failed", + "reason": f"resource mapping failed: {exc}", } ) return profiles @@ -124,6 +134,9 @@ def _patient_location(address: Any) -> Location | None: return None +# --------------------------------------------------------------------------- I/O + + def _load_json_resources(path: Path) -> list[dict[str, Any]]: data = json.loads(path.read_text(encoding="utf-8")) if isinstance(data, list): @@ -143,15 +156,33 @@ def _load_json_resources(path: Path) -> list[dict[str, Any]]: return [data] -def _load_ndjson(path: Path) -> list[dict[str, Any]]: - resources = [] +def _load_ndjson(path: Path, *, strict: bool = False) -> list[dict[str, Any]]: + resources: list[dict[str, Any]] = [] with path.open("r", encoding="utf-8") as handle: - for line in handle: - if line.strip(): - resources.append(json.loads(line)) + for line_number, line in enumerate(handle, start=1): + line = line.strip() + if not line: + continue + try: + obj = json.loads(line) + except json.JSONDecodeError as exc: + if strict: + raise + logger.warning( + "Skipping malformed NDJSON line %d in %s: %s", + line_number, + path.name, + exc, + ) + continue + if isinstance(obj, dict): + resources.append(obj) return resources +# ------------------------------------------------------------ reference resolution + + def _profiles_by_reference( patients: list[Mapping[str, Any]], profiles: list[PatientProfile], @@ -165,6 +196,8 @@ def _profiles_by_reference( full_url = str(patient.get("_bundle_full_url") or "").strip() if full_url: mapping[full_url] = profile + if full_url.casefold().startswith("urn:uuid:"): + mapping[full_url.split(":")[-1]] = profile return mapping @@ -175,12 +208,34 @@ def _profile_for_resource( ) -> PatientProfile | None: reference = _patient_reference(resource) if reference: - return profiles_by_reference.get(reference) + for candidate in _reference_candidates(reference): + if candidate in profiles_by_reference: + return profiles_by_reference[candidate] if len(profiles) == 1: return profiles[0] return None +def _reference_candidates(reference: str) -> list[str]: + """All key forms a patient reference might match (relative, absolute URL, + urn:uuid, contained).""" + ref = reference.strip() + candidates = [ref] + if ref.casefold().startswith("urn:uuid:"): + candidates.append(ref.split(":")[-1]) + if ref.startswith("#"): + candidates.append(ref[1:]) + if "/" in ref: + parts = ref.rstrip("/").split("/") + tail = parts[-1] + candidates.append(tail) + if len(parts) >= 2 and parts[-2].casefold() == "patient": + candidates.append(f"Patient/{tail}") + # de-duplicate, preserve order + seen: set[str] = set() + return [c for c in candidates if c and not (c in seen or seen.add(c))] + + def _patient_reference(resource: Mapping[str, Any]) -> str | None: for key in ("subject", "patient", "beneficiary"): reference = _reference_value(resource.get(key)) @@ -197,6 +252,78 @@ def _reference_value(value: Any) -> str | None: return None +# ------------------------------------------------------------------ status / dates + +# Statuses that mean the resource is an error or did not happen -> drop entirely. +_STATUS_DROP = {"entered-in-error", "cancelled", "not-done", "nullified", "declined"} +# clinicalStatus values that mean the item is no longer present -> negate. +_INACTIVE_CLINICAL = {"resolved", "inactive", "remission"} + + +def _status_code(value: Any) -> str: + if isinstance(value, Mapping): + for coding in value.get("coding") or []: + code = str((coding or {}).get("code") or "").strip().casefold() + if code: + return code + return clean_text(value.get("text")).casefold() + return str(value or "").strip().casefold() + + +def _resource_disposition(resource_type: str, resource: Mapping[str, Any]) -> tuple[str, bool]: + """Return ("drop"|"keep", negated) from FHIR status fields. + + - entered-in-error / cancelled / not-done -> drop (error or did not happen) + - verificationStatus refuted -> keep but negated + - clinicalStatus resolved/inactive on Condition/Allergy -> keep but negated + (medications stay un-negated: a completed/stopped drug is real prior exposure) + """ + status = _status_code(resource.get("status")) + verification = _status_code(resource.get("verificationStatus")) + clinical = _status_code(resource.get("clinicalStatus")) + + if status in _STATUS_DROP or verification == "entered-in-error": + return "drop", False + if verification == "refuted": + return "keep", True + if resource_type in {"Condition", "AllergyIntolerance"} and clinical in _INACTIVE_CLINICAL: + return "keep", True + return "keep", False + + +def _temporality(resource: Mapping[str, Any], keys: tuple[str, ...]) -> str | None: + for key in keys: + value = resource.get(key) + if not value: + continue + if isinstance(value, str): + return clean_text(value) or None + if isinstance(value, Mapping): + if value.get("start") or value.get("end"): + span = f"{value.get('start', '')} to {value.get('end', '')}" + return clean_text(span).strip(" to") or None + if value.get("value") is not None: + return clean_text(f"{value.get('value')} {value.get('unit', '')}") or None + return None + + +def _annotations_text(value: Any) -> str | None: + if isinstance(value, Mapping): + value = [value] + if not isinstance(value, list): + return None + texts = [ + clean_text((note or {}).get("text")) + for note in value + if isinstance(note, Mapping) + ] + joined = "; ".join(text for text in texts if text) + return joined or None + + +# ----------------------------------------------------------------- resource router + + def _add_resource( profile: PatientProfile, resource: Mapping[str, Any], @@ -205,33 +332,43 @@ def _add_resource( resource_type = resource.get("resourceType") if resource_type in {None, "Patient"}: return + + disposition, negated = _resource_disposition(str(resource_type), resource) provenance = base_provenance.model_copy( - update={ - "source_resource": f"{resource_type}/{resource.get('id', 'unknown')}", - } + update={"source_resource": f"{resource_type}/{resource.get('id', 'unknown')}"} ) + if disposition == "drop": + profile.unsupported.append( + { + "resourceType": resource_type, + "id": resource.get("id"), + "reason": "dropped: status indicates error or did-not-happen", + } + ) + return + if resource_type == "Condition": - _add_condition(profile, resource, provenance) + _add_condition(profile, resource, provenance, negated) elif resource_type == "Observation": - _add_observation(profile, resource, provenance) + _add_observation(profile, resource, provenance, negated) elif resource_type in { "MedicationRequest", "MedicationStatement", "MedicationAdministration", }: - _add_medication(profile, resource, provenance) + _add_medication(profile, resource, provenance, negated) elif resource_type == "Procedure": - _add_procedure(profile, resource, provenance) + _add_procedure(profile, resource, provenance, negated) elif resource_type == "DiagnosticReport": _add_diagnostic_report(profile, resource, provenance) elif resource_type == "DocumentReference": _add_document_reference(profile, resource, provenance) elif resource_type == "AllergyIntolerance": - _add_allergy(profile, resource, provenance) + _add_allergy(profile, resource, provenance, negated) elif resource_type == "FamilyMemberHistory": _add_family_history(profile, resource, provenance) elif resource_type in {"MolecularSequence", "GenomicStudy"}: - _add_genomic(profile, resource, provenance) + _add_genomic(profile, resource, provenance, negated) elif resource_type == "Specimen": _add_specimen(profile, resource, provenance) else: @@ -248,20 +385,26 @@ def _add_condition( profile: PatientProfile, resource: Mapping[str, Any], provenance: Provenance, + negated: bool, ) -> None: - code = code_from_fhir_codeable(resource.get("code") or {}) - label = label_from_fhir_codeable(resource.get("code") or {}) + codeable = resource.get("code") or {} + codes = codes_from_fhir_codeable(codeable) + label = label_from_fhir_codeable(codeable) if not label: return profile.conditions.append( make_fact( category="condition", label=label, - original_code=code, + original_code=codes[0] if codes else None, + normalized_codes=codes or None, provenance=provenance, - description=clean_text(resource.get("note")) or None, - temporality=clean_text(resource.get("onsetDateTime")) or None, - negated=_is_negated(resource), + description=_annotations_text(resource.get("note")), + temporality=_temporality( + resource, + ("onsetDateTime", "onsetPeriod", "onsetAge", "onsetString", "recordedDate"), + ), + negated=negated, ) ) @@ -270,21 +413,26 @@ def _add_observation( profile: PatientProfile, resource: Mapping[str, Any], provenance: Provenance, + negated: bool, ) -> None: - code = code_from_fhir_codeable(resource.get("code") or {}) - label = label_from_fhir_codeable(resource.get("code") or {}) + codeable = resource.get("code") or {} + codes = codes_from_fhir_codeable(codeable) + label = label_from_fhir_codeable(codeable) if not label: return - value = _observation_value(resource) category = "genomic_finding" if _is_genomic_observation(resource) else "observation" profile.add_fact( make_fact( category=category, label=label, - original_code=code, + original_code=codes[0] if codes else None, + normalized_codes=codes or None, provenance=provenance, - description=value, - temporality=clean_text(resource.get("effectiveDateTime")) or None, + description=_observation_value(resource), + temporality=_temporality( + resource, ("effectiveDateTime", "effectivePeriod", "effectiveInstant") + ), + negated=negated, ) ) @@ -293,28 +441,24 @@ def _add_medication( profile: PatientProfile, resource: Mapping[str, Any], provenance: Provenance, + negated: bool, ) -> None: - medication = ( - resource.get("medicationCodeableConcept") - or resource.get("medication") - or resource.get("contained") - or {} - ) - code = code_from_fhir_codeable(medication) if isinstance(medication, Mapping) else None - label = ( - label_from_fhir_codeable(medication) - if isinstance(medication, Mapping) - else clean_text(medication) - ) + codes, label = _medication_codes_label(resource) if not label: return profile.medications.append( make_fact( category="medication", label=label, - original_code=code, + original_code=codes[0] if codes else None, + normalized_codes=codes or None, provenance=provenance, - description=clean_text(resource.get("dosageInstruction")) or None, + description=_dosage_text(resource.get("dosageInstruction")), + temporality=_temporality( + resource, + ("authoredOn", "effectiveDateTime", "effectivePeriod"), + ), + negated=negated, ) ) @@ -323,17 +467,23 @@ def _add_procedure( profile: PatientProfile, resource: Mapping[str, Any], provenance: Provenance, + negated: bool, ) -> None: - code = code_from_fhir_codeable(resource.get("code") or {}) - label = label_from_fhir_codeable(resource.get("code") or {}) + codeable = resource.get("code") or {} + codes = codes_from_fhir_codeable(codeable) + label = label_from_fhir_codeable(codeable) if label: profile.procedures.append( make_fact( category="procedure", label=label, - original_code=code, + original_code=codes[0] if codes else None, + normalized_codes=codes or None, provenance=provenance, - temporality=clean_text(resource.get("performedDateTime")) or None, + temporality=_temporality( + resource, ("performedDateTime", "performedPeriod", "performedString") + ), + negated=negated, ) ) @@ -343,16 +493,16 @@ def _add_diagnostic_report( resource: Mapping[str, Any], provenance: Provenance, ) -> None: - code = code_from_fhir_codeable(resource.get("code") or {}) - label = label_from_fhir_codeable(resource.get("code") or {}) or clean_text( - resource.get("conclusion") - ) + codeable = resource.get("code") or {} + codes = codes_from_fhir_codeable(codeable) + label = label_from_fhir_codeable(codeable) or clean_text(resource.get("conclusion")) if label: profile.diagnostic_reports.append( make_fact( category="diagnostic_report", label=label, - original_code=code, + original_code=codes[0] if codes else None, + normalized_codes=codes or None, provenance=provenance, description=clean_text(resource.get("conclusion")) or None, ) @@ -379,17 +529,21 @@ def _add_allergy( profile: PatientProfile, resource: Mapping[str, Any], provenance: Provenance, + negated: bool, ) -> None: - code = code_from_fhir_codeable(resource.get("code") or {}) - label = label_from_fhir_codeable(resource.get("code") or {}) + codeable = resource.get("code") or {} + codes = codes_from_fhir_codeable(codeable) + label = label_from_fhir_codeable(codeable) if label: profile.conditions.append( make_fact( category="condition", label=f"Allergy: {label}", - original_code=code, + original_code=codes[0] if codes else None, + normalized_codes=codes or None, provenance=provenance, - extra={"clinical_status": resource.get("clinicalStatus")}, + negated=negated, + extra={"clinical_status": _status_code(resource.get("clinicalStatus")) or None}, ) ) @@ -415,7 +569,7 @@ def _add_family_history( category="family_history", label=label, provenance=provenance, - extra={"relationship": relationship, "conditions": conditions}, + extra={"relationship": relationship}, ) ) @@ -424,14 +578,19 @@ def _add_genomic( profile: PatientProfile, resource: Mapping[str, Any], provenance: Provenance, + negated: bool = False, ) -> None: - label = clean_text(resource.get("id") or resource.get("type") or "Genomic finding") + label = ( + label_from_fhir_codeable(resource.get("type") or {}) + or clean_text(resource.get("id")) + or "Genomic finding" + ) profile.genomic_findings.append( make_fact( category="genomic_finding", label=label, provenance=provenance, - extra=dict(resource), + negated=negated, ) ) @@ -450,35 +609,151 @@ def _add_specimen( category="diagnostic_report", label=f"Specimen: {label}", provenance=provenance, - extra=dict(resource), ) ) +# ------------------------------------------------------------------- value helpers + + +def _medication_codes_label(resource: Mapping[str, Any]): + """Resolve a medication's codes + label across R4/R5 shapes and references.""" + codeable = resource.get("medicationCodeableConcept") + if not isinstance(codeable, Mapping): + medication = resource.get("medication") + if isinstance(medication, Mapping): + # R5 wraps as medication.concept; otherwise treat as the concept itself. + concept = medication.get("concept") + codeable = concept if isinstance(concept, Mapping) else medication + if isinstance(codeable, Mapping) and (codeable.get("coding") or codeable.get("text")): + label = label_from_fhir_codeable(codeable) + if label: + return codes_from_fhir_codeable(codeable), label + + # medicationReference (R4) / medication.reference (R5), incl. contained Medication. + reference = resource.get("medicationReference") + if not isinstance(reference, Mapping) and isinstance(resource.get("medication"), Mapping): + reference = resource["medication"].get("reference") + if isinstance(reference, Mapping): + contained = _resolve_contained(resource, reference.get("reference")) + if contained is not None: + contained_code = contained.get("code") or {} + label = label_from_fhir_codeable(contained_code) or clean_text( + reference.get("display") + ) + if label: + return codes_from_fhir_codeable(contained_code), label + display = clean_text(reference.get("display")) + if display: + return [], display + return [], None + + +def _resolve_contained(resource: Mapping[str, Any], reference: Any) -> Mapping[str, Any] | None: + if not isinstance(reference, str) or not reference.startswith("#"): + return None + target = reference[1:] + for item in resource.get("contained") or []: + if isinstance(item, Mapping) and str(item.get("id")) == target: + return item + return None + + +def _dosage_text(value: Any) -> str | None: + if isinstance(value, Mapping): + value = [value] + if not isinstance(value, list): + return None + texts = [ + clean_text((dosage or {}).get("text")) + for dosage in value + if isinstance(dosage, Mapping) + ] + joined = "; ".join(text for text in texts if text) + return joined or None + + def _observation_value(resource: Mapping[str, Any]) -> str | None: - if resource.get("valueQuantity"): - value = resource["valueQuantity"] - return clean_text( - f"{value.get('value', '')} {value.get('unit') or value.get('code') or ''}" - ) - if resource.get("valueCodeableConcept"): - return label_from_fhir_codeable(resource.get("valueCodeableConcept") or {}) - for key in ("valueString", "valueBoolean", "valueInteger", "valueDateTime"): - if key in resource: - return clean_text(resource.get(key)) + parts: list[str] = [] + main = _value_x(resource) + if main: + parts.append(main) + interpretation = _interpretation_text(resource.get("interpretation")) + if interpretation: + parts.append(f"[{interpretation}]") + for component in resource.get("component") or []: + if not isinstance(component, Mapping): + continue + component_label = label_from_fhir_codeable(component.get("code") or {}) + component_value = _value_x(component) + if component_label and component_value: + parts.append(f"{component_label}: {component_value}") + elif component_label: + parts.append(component_label) + elif component_value: + parts.append(component_value) + return clean_text(" ".join(parts)) or None + + +def _value_x(node: Mapping[str, Any]) -> str | None: + quantity = node.get("valueQuantity") + if isinstance(quantity, Mapping): + comparator = clean_text(quantity.get("comparator")) + unit = clean_text(quantity.get("unit") or quantity.get("code")) + value = quantity.get("value") + if value is not None or comparator: + number = f"{comparator}{value if value is not None else ''}".strip() + return clean_text(f"{number} {unit}") or None + concept = node.get("valueCodeableConcept") + if isinstance(concept, Mapping): + return label_from_fhir_codeable(concept) or None + value_range = node.get("valueRange") + if isinstance(value_range, Mapping): + low = (value_range.get("low") or {}).get("value") + high = (value_range.get("high") or {}).get("value") + span = f"{'' if low is None else low}-{'' if high is None else high}" + return clean_text(span).strip("-") or None + ratio = node.get("valueRatio") + if isinstance(ratio, Mapping): + numerator = (ratio.get("numerator") or {}).get("value") + denominator = (ratio.get("denominator") or {}).get("value") + return clean_text(f"{numerator}/{denominator}") or None + for key in ("valueString", "valueBoolean", "valueInteger", "valueDateTime", "valueTime"): + if key in node: + return clean_text(node.get(key)) or None return None -def _is_negated(resource: Mapping[str, Any]) -> bool: - verification = resource.get("verificationStatus") or {} - text = label_from_fhir_codeable(verification).casefold() - return "refuted" in text or "entered-in-error" in text +def _interpretation_text(value: Any) -> str | None: + if isinstance(value, Mapping): + value = [value] + if not isinstance(value, list): + return None + labels = [ + label_from_fhir_codeable(item) for item in value if isinstance(item, Mapping) + ] + joined = ", ".join(label for label in labels if label) + return joined or None + + +_GENOMIC_HINTS = ( + "genetic", + "genomic", + "variant", + "mutation", + "sequence variant", + "molecular", +) def _is_genomic_observation(resource: Mapping[str, Any]) -> bool: - categories = resource.get("category") or [] - text = clean_text(categories).casefold() - return "genetic" in text or "genomic" in text + haystack = " ".join( + [ + clean_text(resource.get("category")).casefold(), + label_from_fhir_codeable(resource.get("code") or {}).casefold(), + ] + ) + return any(hint in haystack for hint in _GENOMIC_HINTS) def _document_url(resource: Mapping[str, Any]) -> str | None: diff --git a/src/trialmatchai/interop/utils.py b/src/trialmatchai/interop/utils.py index ab4d67a7..130d6858 100644 --- a/src/trialmatchai/interop/utils.py +++ b/src/trialmatchai/interop/utils.py @@ -54,10 +54,32 @@ def normalize_gender(value: Any) -> str | None: def parse_date(value: Any) -> date | None: if not value: return None + text = str(value).strip() + # Full date / datetime (FHIR dateTime, with or without timezone). try: - return datetime.fromisoformat(str(value).replace("Z", "+00:00")).date() + return datetime.fromisoformat(text.replace("Z", "+00:00")).date() except ValueError: - return None + pass + # Partial FHIR dates are legal: YYYY or YYYY-MM (default to Jan / day 1). + match = re.fullmatch(r"(\d{4})(?:-(\d{2}))?(?:-(\d{2}))?", text) + if match: + try: + return date( + int(match.group(1)), + int(match.group(2) or 1), + int(match.group(3) or 1), + ) + except ValueError: + return None + # Datetime string fromisoformat couldn't parse (e.g. older Python tz forms): + # fall back to the leading YYYY-MM-DD if present. + prefix = re.match(r"(\d{4}-\d{2}-\d{2})", text) + if prefix: + try: + return date.fromisoformat(prefix.group(1)) + except ValueError: + return None + return None def age_years_from_birth_date(birth_date: date | None) -> float | None: @@ -104,33 +126,56 @@ def code_from_ontology_class(value: Mapping[str, Any] | None) -> NormalizedCode ) -def code_from_fhir_codeable(value: Mapping[str, Any] | None) -> NormalizedCode | None: - if not value: - return None +def codes_from_fhir_codeable(value: Mapping[str, Any] | None) -> list[NormalizedCode]: + """Extract all codings from a FHIR CodeableConcept, known vocabularies first. + + Real EHRs emit several codings per concept (e.g. a proprietary code plus + SNOMED/LOINC/RxNorm). We keep them all and order recognized vocabularies + first so concept linking uses the standard code rather than a local one. + """ + if not isinstance(value, Mapping) or not value: + return [] text = clean_text(value.get("text")) - codings = value.get("coding") or [] - if isinstance(codings, list) and codings: - coding = codings[0] or {} - system = str(coding.get("system") or "") - vocabulary = FHIR_SYSTEM_TO_VOCABULARY.get(system, system.rsplit("/", 1)[-1]) + codings = value.get("coding") + ranked: list[tuple[int, NormalizedCode]] = [] + for coding in codings if isinstance(codings, list) else []: + if not isinstance(coding, Mapping): + continue code = str(coding.get("code") or "").strip() + if not code: + continue + system = str(coding.get("system") or "") + known = system in FHIR_SYSTEM_TO_VOCABULARY + vocabulary = FHIR_SYSTEM_TO_VOCABULARY.get(system) or ( + system.rsplit("/", 1)[-1] if system else "FHIR" + ) label = clean_text(coding.get("display")) or text or None - if code: - return NormalizedCode( - vocabulary=vocabulary or "FHIR", - code=code, - label=label, - system=system or None, - mapping_status="exact", + ranked.append( + ( + 0 if known else 1, + NormalizedCode( + vocabulary=vocabulary or "FHIR", + code=code, + label=label, + system=system or None, + mapping_status="exact", + ), ) - if text: - return NormalizedCode( - vocabulary="FHIR", - code=text, - label=text, - mapping_status="unmapped", ) - return None + ranked.sort(key=lambda item: item[0]) # stable: known vocabularies first + codes = [code for _, code in ranked] + if not codes and text: + codes = [ + NormalizedCode( + vocabulary="FHIR", code=text, label=text, mapping_status="unmapped" + ) + ] + return codes + + +def code_from_fhir_codeable(value: Mapping[str, Any] | None) -> NormalizedCode | None: + codes = codes_from_fhir_codeable(value) + return codes[0] if codes else None def label_from_fhir_codeable(value: Mapping[str, Any] | None) -> str: diff --git a/tests/test_fhir_robustness.py b/tests/test_fhir_robustness.py new file mode 100644 index 00000000..00eb149d --- /dev/null +++ b/tests/test_fhir_robustness.py @@ -0,0 +1,211 @@ +"""Regression tests for the hardened FHIR importer (real-EHR edge cases).""" + +from __future__ import annotations + +import json + +from trialmatchai.interop.importers.fhir import ( + _medication_codes_label, + _resource_disposition, + _value_x, + import_fhir, +) +from trialmatchai.interop.utils import ( + code_from_fhir_codeable, + codes_from_fhir_codeable, + parse_date, +) + + +# --------------------------------------------------------------------- unit level + + +def test_status_disposition(): + cases = [ + ("Condition", {"clinicalStatus": {"coding": [{"code": "resolved"}]}}, ("keep", True)), + ("Condition", {"clinicalStatus": {"coding": [{"code": "inactive"}]}}, ("keep", True)), + ("Condition", {"verificationStatus": {"coding": [{"code": "refuted"}]}}, ("keep", True)), + ("Condition", {"verificationStatus": {"coding": [{"code": "entered-in-error"}]}}, ("drop", False)), + ("Observation", {"status": "entered-in-error"}, ("drop", False)), + ("Procedure", {"status": "not-done"}, ("drop", False)), + # Completed/stopped medications are real prior exposure -> kept, not negated. + ("MedicationStatement", {"status": "completed"}, ("keep", False)), + ("MedicationStatement", {"status": "stopped"}, ("keep", False)), + ("Condition", {"clinicalStatus": {"coding": [{"code": "active"}]}}, ("keep", False)), + ] + for rtype, resource, expected in cases: + assert _resource_disposition(rtype, resource) == expected, (rtype, resource) + + +def test_partial_fhir_dates(): + assert parse_date("1980") is not None and parse_date("1980").year == 1980 + assert parse_date("1980-03").month == 3 + assert parse_date("1980-03-15").day == 15 + assert parse_date("1980-03-15T09:00:00Z").year == 1980 + assert parse_date("garbage") is None + + +def test_multi_coding_prefers_known_vocabulary_and_keeps_all(): + cc = { + "coding": [ + {"system": "urn:oid:1.2.840.114350", "code": "EPIC1"}, + {"system": "http://snomed.info/sct", "code": "254637007", "display": "NSCLC"}, + ] + } + best = code_from_fhir_codeable(cc) + assert best.vocabulary == "SNOMED" and best.code == "254637007" + all_codes = codes_from_fhir_codeable(cc) + assert {c.vocabulary for c in all_codes} == {"SNOMED", "urn:oid:1.2.840.114350"} + + +def test_value_x_variants_and_comparator(): + assert _value_x({"valueQuantity": {"value": 9.5, "unit": "g/dL"}}) == "9.5 g/dL" + assert _value_x({"valueQuantity": {"comparator": "<", "value": 0.01, "unit": "ng/mL"}}) == "<0.01 ng/mL" + assert _value_x({"valueString": "positive"}) == "positive" + assert _value_x({"valueCodeableConcept": {"text": "detected"}}) == "detected" + assert _value_x({"valueRange": {"low": {"value": 1}, "high": {"value": 5}}}) == "1-5" + + +def test_medication_reference_and_contained(): + # medicationReference with display only + codes, label = _medication_codes_label( + {"medicationReference": {"reference": "Medication/123", "display": "osimertinib"}} + ) + assert label == "osimertinib" + + # contained Medication resolved by #ref + codes, label = _medication_codes_label( + { + "medicationReference": {"reference": "#med1"}, + "contained": [ + { + "resourceType": "Medication", + "id": "med1", + "code": {"coding": [{"system": "http://www.nlm.nih.gov/research/umls/rxnorm", "code": "1", "display": "pembrolizumab"}]}, + } + ], + } + ) + assert label == "pembrolizumab" + assert codes and codes[0].vocabulary == "RxNorm" + + +# -------------------------------------------------------------------- integration + + +def _import_bundle(tmp_path, entries): + path = tmp_path / "bundle.json" + path.write_text( + json.dumps( + { + "resourceType": "Bundle", + "entry": [{"resource": r} for r in entries], + } + ) + ) + return import_fhir(path, input_format="fhir") + + +def test_resolved_condition_is_negated_and_error_is_dropped(tmp_path): + profiles = _import_bundle( + tmp_path, + [ + {"resourceType": "Patient", "id": "p1", "birthDate": "1975"}, + { + "resourceType": "Condition", + "id": "c-active", + "subject": {"reference": "Patient/p1"}, + "clinicalStatus": {"coding": [{"code": "active"}]}, + "code": {"text": "lung cancer"}, + }, + { + "resourceType": "Condition", + "id": "c-resolved", + "subject": {"reference": "Patient/p1"}, + "clinicalStatus": {"coding": [{"code": "resolved"}]}, + "verificationStatus": {"coding": [{"code": "confirmed"}]}, + "code": {"text": "pneumonia"}, + }, + { + "resourceType": "Condition", + "id": "c-error", + "subject": {"reference": "Patient/p1"}, + "verificationStatus": {"coding": [{"code": "entered-in-error"}]}, + "code": {"text": "typo diagnosis"}, + }, + ], + ) + profile = profiles[0] + # Partial birthDate populated age. + assert profile.demographics.age_years is not None + by_label = {c.label: c for c in profile.conditions} + assert by_label["lung cancer"].negated is False + assert by_label["pneumonia"].negated is True # resolved -> not currently present + assert "typo diagnosis" not in by_label # entered-in-error dropped + assert any(u.get("id") == "c-error" for u in profile.unsupported) + + +def test_observation_component_and_interpretation_captured(tmp_path): + profiles = _import_bundle( + tmp_path, + [ + {"resourceType": "Patient", "id": "p1"}, + { + "resourceType": "Observation", + "id": "bp", + "subject": {"reference": "Patient/p1"}, + "status": "final", + "code": {"text": "Blood pressure"}, + "interpretation": [{"coding": [{"code": "H", "display": "High"}]}], + "component": [ + {"code": {"text": "Systolic"}, "valueQuantity": {"value": 160, "unit": "mmHg"}}, + {"code": {"text": "Diastolic"}, "valueQuantity": {"value": 95, "unit": "mmHg"}}, + ], + }, + ], + ) + obs = profiles[0].observations[0] + assert "Systolic: 160 mmHg" in obs.description + assert "Diastolic: 95 mmHg" in obs.description + assert "High" in obs.description + + +def test_ndjson_skips_malformed_lines(tmp_path): + path = tmp_path / "export.ndjson" + path.write_text( + "\n".join( + [ + json.dumps({"resourceType": "Patient", "id": "p1"}), + "{ this is not valid json", + json.dumps( + { + "resourceType": "Condition", + "subject": {"reference": "Patient/p1"}, + "code": {"text": "diabetes"}, + } + ), + ] + ) + ) + profiles = import_fhir(path, input_format="fhir-ndjson") + assert len(profiles) == 1 + assert any(c.label == "diabetes" for c in profiles[0].conditions) + + +def test_reference_resolution_urn_uuid_and_absolute_url(tmp_path): + path = tmp_path / "bundle.json" + path.write_text( + json.dumps( + { + "resourceType": "Bundle", + "entry": [ + {"fullUrl": "urn:uuid:abc-123", "resource": {"resourceType": "Patient", "id": "p1"}}, + {"resource": {"resourceType": "Condition", "subject": {"reference": "urn:uuid:abc-123"}, "code": {"text": "asthma"}}}, + {"resource": {"resourceType": "Condition", "subject": {"reference": "http://ehr.example.org/fhir/Patient/p1"}, "code": {"text": "eczema"}}}, + ], + } + ) + ) + profile = import_fhir(path, input_format="fhir")[0] + labels = {c.label for c in profile.conditions} + assert {"asthma", "eczema"} <= labels # both reference styles resolved From 2cc6d29c9dc435e6f7a23f1f7e37b4441f0ac927 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Tue, 23 Jun 2026 18:00:58 +0200 Subject: [PATCH 34/64] feat: Transformers CPU backend, finetuning improvements, and reasoning-model eligibility fixes - Add CPU-capable Transformers reranker (TransformersReranker) and eligibility processor (BatchTrialProcessorTransformers) so the pipeline runs without vLLM - Add no_think config option (rag.no_think) to disable blocks on reasoning models like Qwen3; passes enable_thinking=False to apply_chat_template and falls back to /no_think prefix for plain-prompt paths - Strip ... blocks from model output before JSON extraction so eligibility responses from thinking models are parsed correctly - Separate reranker_model_path from base_model so a lightweight model can handle second-level reranking while a larger model handles RAG eligibility reasoning - Add hf/hashing backends to EmbedderSettings, RagSettings.enabled/backend, LLMRerankerSettings.enabled, and ConceptLinkerSettings to settings schema - Finetuning: multi-GPU support, LoRA config improvements, data pipeline hardening, NER and reranker trainer updates, extended CLI flags - Registry preparation: hardened FHIR/OMOP importer, improved field normalisation - LanceDB backend: expose candidate_limit, add FTS fallback path - Preflight: extended checks for concept index, embedding model, and registry - Update default entity model to fastino/gliner2-base-v1 across config and settings - Add tests for embedding, deployment readiness, finetuning, LanceDB backend, patient runtime loading, preflight, and registry updater Co-Authored-By: Claude Sonnet 4.6 --- .env.example | 2 +- README.md | 6 +- docs/finetuning.md | 46 ++++- src/trialmatchai/config/config.json | 2 +- src/trialmatchai/config/settings.py | 8 +- src/trialmatchai/entities/recognizers.py | 37 +++- src/trialmatchai/finetuning/_sft.py | 139 ++++++++++--- src/trialmatchai/finetuning/cli.py | 64 +++++- src/trialmatchai/finetuning/config.py | 39 ++-- src/trialmatchai/finetuning/cot.py | 14 +- src/trialmatchai/finetuning/data.py | 135 +++++++++---- src/trialmatchai/finetuning/ner.py | 100 ++++++--- src/trialmatchai/finetuning/reranker.py | 17 +- src/trialmatchai/main.py | 189 +++++++++++++----- src/trialmatchai/matching/eligibility_base.py | 50 ++++- .../eligibility_reasoning_transformers.py | 123 ++++++++++++ src/trialmatchai/models/embedding/__init__.py | 3 +- .../models/embedding/text_embedder.py | 51 +++++ .../models/llm/transformers_reranker.py | 101 ++++++++++ src/trialmatchai/registry/preparation.py | 54 +++-- src/trialmatchai/search/lancedb_backend.py | 14 ++ src/trialmatchai/services/preflight.py | 88 +++++--- tests/test_deployment_readiness.py | 49 +++++ tests/test_embedding.py | 22 ++ tests/test_entities.py | 38 +++- tests/test_finetuning.py | 134 ++++++++++++- tests/test_lancedb_search_backend.py | 69 +++++++ tests/test_patient_runtime_loading.py | 55 +++++ tests/test_preflight_and_indexer.py | 28 +++ tests/test_registry_updater.py | 38 ++++ 30 files changed, 1474 insertions(+), 241 deletions(-) create mode 100644 src/trialmatchai/matching/eligibility_reasoning_transformers.py create mode 100644 src/trialmatchai/models/llm/transformers_reranker.py create mode 100644 tests/test_embedding.py diff --git a/.env.example b/.env.example index fc19d6ce..d10d303e 100644 --- a/.env.example +++ b/.env.example @@ -31,7 +31,7 @@ TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false TRIALMATCHAI_COT_BACKEND=vllm TRIALMATCHAI_ENTITY_BACKEND=gliner2 -TRIALMATCHAI_ENTITY_MODEL_NAME=fastino/gliner2-base +TRIALMATCHAI_ENTITY_MODEL_NAME=fastino/gliner2-base-v1 TRIALMATCHAI_ENTITY_SCHEMA_PATH=entity_schemas/trialmatchai.yaml TRIALMATCHAI_ENTITY_TRUST_REMOTE_CODE=false TRIALMATCHAI_CONCEPT_DB_PATH=data/concepts diff --git a/README.md b/README.md index c8ed97fe..703f713b 100644 --- a/README.md +++ b/README.md @@ -73,7 +73,7 @@ pip install -e ".[finetune]" | Extra | Adds | | --- | --- | -| `entity` | GLiNER2 biomedical NER | +| `entity` | GLiNER2 biomedical extraction | | `llm` | local embedding and LLM dependencies | | `gpu` | vLLM and bitsandbytes; intended for Linux CUDA hosts | | `finetune` | training dependencies for `trialmatchai-finetune` | @@ -148,7 +148,7 @@ Patient data (text / FHIR / Phenopacket / OMOP) Interop importers -> canonical PatientProfile | v -GLiNER2 NER + deterministic variant patterns -> concept linking +GLiNER2 entity extraction + deterministic variant patterns -> concept linking | v First-level trial retrieval in LanceDB (BM25 + embeddings) @@ -265,7 +265,7 @@ adapters through config or environment variables. | Component | Default | Config key | | --- | --- | --- | -| Biomedical NER | `fastino/gliner2-base` | `entity_extraction.model_name` | +| Biomedical extraction | `fastino/gliner2-base-v1` | `entity_extraction.model_name` | | Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | | CoT eligibility | configured CoT model | `model.cot_adapter_path` | diff --git a/docs/finetuning.md b/docs/finetuning.md index 7d04d6ea..3c58b5bf 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -7,7 +7,7 @@ checkpoints/adapters via config, and train those adapters with the built-in | Component | Default | Config key | Fine-tune target | |-----------|---------|------------|------------------| -| Biomedical NER | `fastino/gliner2-base` | `entity_extraction.model_name` | GLiNER2 checkpoint | +| Biomedical extraction | `fastino/gliner2-base-v1` | `entity_extraction.model_name` | GLiNER2 checkpoint | | Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | LoRA adapter | | CoT eligibility | configured CoT model | `model.cot_adapter_path` | LoRA adapter | @@ -22,8 +22,9 @@ uv sync --extra finetune Already have a checkpoint or adapter? Just point the config at it — no code changes needed. -- **NER:** set `entity_extraction.model_name` to your GLiNER2 checkpoint (local - path or Hub id), backend `gliner2`. LoRA NER adapters load via `GLiNER2.load_adapter`. +- **GLiNER2 extraction:** set `entity_extraction.model_name` to your GLiNER2 + checkpoint (local path or Hub id), backend `gliner2`. LoRA adapters load via + `GLiNER2.load_adapter`. - **Reranker:** set `model.reranker_adapter_path` to your LoRA adapter directory. - **CoT:** set `model.cot_adapter_path` to your LoRA adapter directory. @@ -58,12 +59,18 @@ Data — JSONL, one example per line, either chat or instruct form: trialmatchai-finetune cot \ --base-model microsoft/phi-4 \ --train-data data/finetune/cot.jsonl \ + --eval-data data/finetune/cot.eval.jsonl \ --output-dir models/cot-adapter \ --epochs 2 --lora-rank 32 --lora-alpha 64 ``` Then set `model.cot_adapter_path: models/cot-adapter`. +By default, LoRA targets all linear layers (`--target-modules all-linear`), +which works across common Gemma/Llama/Phi-style architectures. Use +`--target-modules auto` to let PEFT choose its built-in mapping, or pass a +comma-separated suffix list such as `q_proj,v_proj`. + ### Reranker (LoRA, Yes/No) The reranker decides whether the patient text contains enough information to @@ -80,24 +87,40 @@ Data — JSONL: trialmatchai-finetune reranker \ --base-model google/gemma-2-2b-it \ --train-data data/finetune/reranker.jsonl \ + --eval-data data/finetune/reranker.eval.jsonl \ --output-dir models/reranker-adapter ``` Then set `model.reranker_adapter_path: models/reranker-adapter`. -### NER (GLiNER2) +### GLiNER2 Schema Extraction -Uses the native GLiNER2 training stack (`GLiNER2Trainer`). GLiNER2 NER data maps -entity-type labels to **surface forms**. Three input shapes are accepted: +Uses the native GLiNER2 training stack (`GLiNER2Trainer`). The CLI subcommand is +still named `ner` for compatibility, but the training data can now include flat +entities, schema-based JSON structures, classifications, relations, or a mix. +Flat entity data maps entity-type labels to **surface forms**: ```json {"text": "EGFR exon 19 deletion in NSCLC", "entities": {"gene": ["EGFR"], "disease": ["NSCLC"]}} {"text": "EGFR positive", "ner": [[0, 4, "gene"]]} {"input": "EGFR positive", "output": {"entities": {"gene": ["EGFR"]}}} +{"text": "No reportable biomarker", "schema": {"entities": {"gene": []}}} +``` + +Structured JSON examples use GLiNER2's native `json_structures` and optional +`json_descriptions` keys: + +```json +{"text": "Patient has EGFR exon 19 deletion and stage IV NSCLC.", "schema": {"entities": {"gene": ["EGFR"]}, "json_structures": [{"biomarker": {"gene": "EGFR", "variant": "exon 19 deletion", "disease_stage": "stage IV"}}], "json_descriptions": {"biomarker": {"gene": "Gene symbol", "variant": "Observed alteration", "disease_stage": "Disease stage"}}}} +{"input": "Erlotinib targets EGFR.", "output": {"relations": [{"targets": {"head": "Erlotinib", "tail": "EGFR"}}]}} +{"text": "Trial requires ECOG 0-1.", "structures": {"eligibility": {"performance_status": "ECOG 0-1"}}} ``` `entity_descriptions` are back-filled from your entity schema (`--schema-path`) -so the fine-tuned model shares the runtime label semantics. +for flat entity labels, so entity examples share the runtime label semantics. +TrialMatchAI's current runtime annotator consumes the flat `entities` output; +structured JSON training data is useful for GLiNER2 adapters you call directly +or for future structured extraction integration. ```bash trialmatchai-finetune ner \ @@ -118,8 +141,11 @@ trialmatchai-finetune ner \ - Training prompts reuse the **exact runtime prompts**, so a fine-tuned model sees the same format at train and inference time. - LoRA SFT masks the prompt tokens and computes loss only on the completion. -- 4-bit quantized loading is on by default (`--no-4bit` to disable); `bf16` is - default (`--fp16` to switch). See `trialmatchai-finetune --help` - for all flags. +- `--eval-data` is optional for LoRA SFT. When supplied, evaluation loss is used + for best-checkpoint tracking; `--save-steps` must be a multiple of + `--eval-steps` (defaults align at 500). +- 4-bit quantized loading is on by default (`--no-4bit` to disable) and requires + bitsandbytes on a CUDA-capable machine; `bf16` is default (`--fp16` to switch). + See `trialmatchai-finetune --help` for all flags. - GLiNER2's training API can vary by version; if your installed `gliner2` exposes a different interface, adapt `src/trialmatchai/finetuning/ner.py`. diff --git a/src/trialmatchai/config/config.json b/src/trialmatchai/config/config.json index dcdbc051..8e102601 100644 --- a/src/trialmatchai/config/config.json +++ b/src/trialmatchai/config/config.json @@ -1,7 +1,7 @@ { "entity_extraction": { "backend": "gliner2", - "model_name": "fastino/gliner2-base", + "model_name": "fastino/gliner2-base-v1", "model_revision": null, "schema_path": "entity_schemas/trialmatchai.yaml", "threshold": 0.8, diff --git a/src/trialmatchai/config/settings.py b/src/trialmatchai/config/settings.py index fac0fd56..92ac56cc 100644 --- a/src/trialmatchai/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -7,7 +7,7 @@ class EntityExtractionSettings(BaseModel): backend: Literal["gliner2", "regex", "disabled"] = "gliner2" - model_name: str = "fastino/gliner2-base" + model_name: str = "fastino/gliner2-base-v1" model_revision: str | None = None schema_path: str = "entity_schemas/trialmatchai.yaml" threshold: float = Field(0.8, ge=0.0, le=1.0) @@ -100,6 +100,7 @@ class RegistrySettings(BaseModel): class EmbedderSettings(BaseModel): + backend: Literal["hf", "hashing"] = "hf" model_name: str = "BAAI/bge-m3" revision: str | None = None trust_remote_code: bool = False @@ -109,6 +110,7 @@ class EmbedderSettings(BaseModel): use_gpu: bool = True use_fp16: bool = False normalize: bool = True + hashing_dimensions: int = Field(64, ge=1) @field_validator("pooling") @classmethod @@ -167,6 +169,8 @@ class ConstraintSettings(BaseModel): class RagSettings(BaseModel): + enabled: bool = True + backend: Literal["vllm", "transformers"] = "vllm" batch_size: int = Field(4, ge=1) max_trials_rag: int = Field(20, ge=1) @@ -188,6 +192,8 @@ class CotSettings(BaseModel): class LLMRerankerSettings(BaseModel): + enabled: bool = True + backend: Literal["vllm", "transformers"] = "vllm" batch_size: int = Field(20, ge=1) diff --git a/src/trialmatchai/entities/recognizers.py b/src/trialmatchai/entities/recognizers.py index abce12bd..60501b87 100644 --- a/src/trialmatchai/entities/recognizers.py +++ b/src/trialmatchai/entities/recognizers.py @@ -199,7 +199,7 @@ def build_recognizer(config: dict[str, Any]) -> EntityRecognizer: return RegexSchemaRecognizer() if backend == "gliner2": recognizer = GLiNER2Recognizer( - model_name=config.get("model_name", "fastino/gliner2-base"), + model_name=config.get("model_name", "fastino/gliner2-base-v1"), revision=config.get("model_revision"), device=config.get("device", "auto"), trust_remote_code=bool(config.get("trust_remote_code", False)), @@ -252,6 +252,17 @@ def _call_extractor( schema.label: schema.description for schema in schemas } + try: + return _flatten_gliner2_entities( + model.extract_entities( + text, + schema_payload, + include_confidence=True, + include_spans=True, + ) + ) + except TypeError: + pass for kwargs in ( {"schema": schema_payload}, {"labels": labels}, @@ -267,12 +278,12 @@ def _call_extractor( def _parse_model_entities( - raw_entities: Sequence[dict[str, Any]], + raw_entities: Sequence[dict[str, Any]] | dict[str, Any], text: str, label_map: dict[str, EntitySchema], ) -> list[EntityAnnotation]: parsed: list[EntityAnnotation] = [] - for raw in raw_entities or []: + for raw in _flatten_gliner2_entities(raw_entities): label = str(raw.get("label") or raw.get("entity_group") or raw.get("type") or "") schema = label_map.get(label.casefold()) if schema is None: @@ -302,6 +313,26 @@ def _parse_model_entities( return parsed +def _flatten_gliner2_entities(raw_entities: Any) -> list[dict[str, Any]]: + if not isinstance(raw_entities, dict): + return list(raw_entities or []) + entities = raw_entities.get("entities") + if not isinstance(entities, dict): + return [] + flattened: list[dict[str, Any]] = [] + for label, values in entities.items(): + if not isinstance(values, list): + continue + for value in values: + if isinstance(value, dict): + item = dict(value) + else: + item = {"text": str(value)} + item.setdefault("label", label) + flattened.append(item) + return flattened + + def _find_span(text: str, mention: str) -> tuple[int | None, int | None]: if not mention: return None, None diff --git a/src/trialmatchai/finetuning/_sft.py b/src/trialmatchai/finetuning/_sft.py index 96c607c3..12973778 100644 --- a/src/trialmatchai/finetuning/_sft.py +++ b/src/trialmatchai/finetuning/_sft.py @@ -7,7 +7,8 @@ from __future__ import annotations -from typing import Dict, List +import importlib.util +from typing import Dict, List, Optional from trialmatchai.finetuning.config import FinetuneConfig from trialmatchai.utils.logging_config import setup_logging @@ -63,26 +64,84 @@ def _build_tokenizer(deps, config: FinetuneConfig): return tokenizer +def _validate_4bit_runtime(deps) -> None: + torch = deps["torch"] + if importlib.util.find_spec("bitsandbytes") is None: + raise RuntimeError( + "4-bit QLoRA requires bitsandbytes. Install the finetune extra on a " + "CUDA-capable Linux/Windows environment, or pass --no-4bit." + ) + if not torch.cuda.is_available(): + raise RuntimeError( + "4-bit QLoRA through bitsandbytes requires CUDA. Pass --no-4bit " + "for full/bfloat16 training on this machine." + ) + + +def _validate_messages(messages: List[Dict[str, str]]) -> None: + if len(messages) < 2: + raise ValueError("Each SFT row must contain at least one prompt and one answer.") + if messages[-1].get("role") != "assistant": + raise ValueError("Each SFT row must end with an assistant message.") + for index, message in enumerate(messages): + if message.get("role") not in {"system", "user", "assistant", "tool"}: + raise ValueError(f"Unsupported chat role at message {index}: {message!r}") + if "content" not in message: + raise ValueError(f"Missing chat content at message {index}: {message!r}") + + def _encode_example( tokenizer, messages: List[Dict[str, str]], max_seq_length: int ) -> Dict[str, list]: """Tokenize a chat example, masking the prompt so only the completion trains.""" - full = tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=False + if max_seq_length <= 0: + raise ValueError("max_seq_length must be positive.") + _validate_messages(messages) + if getattr(tokenizer, "chat_template", None) is None: + raise ValueError( + "The base model tokenizer does not define a chat template. Use a chat " + "or instruction-tuned model, or add a tokenizer chat_template before training." + ) + + full_ids = tokenizer.apply_chat_template( + messages, tokenize=True, add_generation_prompt=False ) - prompt = tokenizer.apply_chat_template( - messages[:-1], tokenize=False, add_generation_prompt=True + prompt_ids = tokenizer.apply_chat_template( + messages[:-1], tokenize=True, add_generation_prompt=True ) - full_ids = tokenizer(full, truncation=True, max_length=max_seq_length)["input_ids"] - prompt_ids = tokenizer( - prompt, truncation=True, max_length=max_seq_length - )["input_ids"] - prompt_len = min(len(prompt_ids), len(full_ids)) + if full_ids[: len(prompt_ids)] != prompt_ids: + raise ValueError( + "Tokenizer chat template did not produce a prompt prefix for the full " + "conversation; refusing to risk training on prompt tokens." + ) + + prompt_len = len(prompt_ids) labels = [-100] * prompt_len + full_ids[prompt_len:] + if len(full_ids) > max_seq_length: + full_ids = full_ids[-max_seq_length:] + labels = labels[-max_seq_length:] + if all(label == -100 for label in labels): + raise ValueError( + "Encoded example has no trainable assistant tokens. Increase " + "max_seq_length or shorten the prompt." + ) return {"input_ids": full_ids, "attention_mask": [1] * len(full_ids), "labels": labels} -def run_sft(config: FinetuneConfig, message_lists: List[List[Dict[str, str]]]) -> str: +def _encode_dataset(deps, tokenizer, message_lists, max_seq_length): + encoded = [ + _encode_example(tokenizer, messages, max_seq_length) for messages in message_lists + ] + if not encoded: + raise ValueError("No examples provided.") + return deps["Dataset"].from_list(encoded), len(encoded) + + +def run_sft( + config: FinetuneConfig, + message_lists: List[List[Dict[str, str]]], + eval_message_lists: Optional[List[List[Dict[str, str]]]] = None, +) -> str: """Run LoRA SFT over a list of chat-message examples; returns the adapter dir.""" if not message_lists: raise ValueError("No training examples provided.") @@ -94,6 +153,7 @@ def run_sft(config: FinetuneConfig, message_lists: List[List[Dict[str, str]]]) - compute_dtype = torch.bfloat16 if config.bf16 else torch.float16 quant_config = None if config.load_in_4bit: + _validate_4bit_runtime(deps) quant_config = deps["BitsAndBytesConfig"]( load_in_4bit=True, bnb_4bit_use_double_quant=True, @@ -101,12 +161,17 @@ def run_sft(config: FinetuneConfig, message_lists: List[List[Dict[str, str]]]) - bnb_4bit_compute_dtype=compute_dtype, ) + model_kwargs = { + "torch_dtype": compute_dtype, + "quantization_config": quant_config, + "trust_remote_code": config.trust_remote_code, + "token": config.hf_token, + } + if config.load_in_4bit and config.device_map: + model_kwargs["device_map"] = config.device_map + model = deps["AutoModelForCausalLM"].from_pretrained( - config.base_model, - torch_dtype=compute_dtype, - quantization_config=quant_config, - trust_remote_code=config.trust_remote_code, - token=config.hf_token, + config.base_model, **model_kwargs ) model.config.use_cache = False # required with gradient checkpointing @@ -117,22 +182,28 @@ def run_sft(config: FinetuneConfig, message_lists: List[List[Dict[str, str]]]) - model, use_gradient_checkpointing=True ) - peft_config = deps["LoraConfig"]( - task_type=deps["TaskType"].CAUSAL_LM, - inference_mode=False, - r=config.lora_rank, - lora_alpha=config.lora_alpha, - lora_dropout=config.lora_dropout, - target_modules=config.target_modules, - ) + peft_kwargs = { + "task_type": deps["TaskType"].CAUSAL_LM, + "inference_mode": False, + "r": config.lora_rank, + "lora_alpha": config.lora_alpha, + "lora_dropout": config.lora_dropout, + } + if config.target_modules is not None: + peft_kwargs["target_modules"] = config.target_modules + peft_config = deps["LoraConfig"](**peft_kwargs) model = deps["get_peft_model"](model, peft_config) model.print_trainable_parameters() - encoded = [ - _encode_example(tokenizer, messages, config.max_seq_length) - for messages in message_lists - ] - dataset = deps["Dataset"].from_list(encoded) + dataset, train_count = _encode_dataset( + deps, tokenizer, message_lists, config.max_seq_length + ) + eval_dataset = None + eval_count = 0 + if eval_message_lists is not None: + eval_dataset, eval_count = _encode_dataset( + deps, tokenizer, eval_message_lists, config.max_seq_length + ) collator = deps["DataCollatorForSeq2Seq"]( tokenizer, padding=True, label_pad_token_id=-100 @@ -141,10 +212,18 @@ def run_sft(config: FinetuneConfig, message_lists: List[List[Dict[str, str]]]) - model=model, args=config.to_training_arguments(), train_dataset=dataset, + eval_dataset=eval_dataset, data_collator=collator, ) - logger.info("Starting LoRA SFT on %d examples...", len(encoded)) + if eval_dataset is None: + logger.info("Starting LoRA SFT on %d examples...", train_count) + else: + logger.info( + "Starting LoRA SFT on %d examples with %d eval examples...", + train_count, + eval_count, + ) trainer.train() model.save_pretrained(config.output_dir) tokenizer.save_pretrained(config.output_dir) diff --git a/src/trialmatchai/finetuning/cli.py b/src/trialmatchai/finetuning/cli.py index 31914b9b..55bacac0 100644 --- a/src/trialmatchai/finetuning/cli.py +++ b/src/trialmatchai/finetuning/cli.py @@ -1,11 +1,11 @@ -"""``trialmatchai-finetune`` — train custom NER / reranker / CoT models. +"""``trialmatchai-finetune`` — train custom GLiNER2 / reranker / CoT models. Examples: trialmatchai-finetune cot --base-model microsoft/phi-4 \ --train-data data/cot.jsonl --output-dir models/cot-adapter trialmatchai-finetune reranker --base-model google/gemma-2-2b-it \ --train-data data/reranker.jsonl --output-dir models/reranker-adapter - trialmatchai-finetune ner --base-model fastino/gliner2-base \ + trialmatchai-finetune ner --base-model fastino/gliner2-base-v1 \ --train-data data/ner.jsonl --output-dir models/ner Plug the result back into config: entity_extraction.model_name (NER), @@ -21,6 +21,26 @@ from trialmatchai.finetuning.config import FinetuneConfig +def _parse_target_modules(value: Optional[str]): + if value is None: + return "all-linear" + value = value.strip() + if value.lower() in {"auto", "none"}: + return None + if "," in value: + return [part.strip() for part in value.split(",") if part.strip()] + return value + + +def _parse_device_map(value: Optional[str]) -> Optional[str]: + if value is None: + return None + value = value.strip() + if value.lower() in {"", "none", "off", "false"}: + return None + return value + + def _add_common_lora_args(parser: argparse.ArgumentParser) -> None: parser.add_argument("--base-model", required=True) parser.add_argument("--train-data", required=True) @@ -31,12 +51,29 @@ def _add_common_lora_args(parser: argparse.ArgumentParser) -> None: parser.add_argument("--batch-size", type=int, default=2) parser.add_argument("--grad-accum", type=int, default=16) parser.add_argument("--max-seq-length", type=int, default=2048) + parser.add_argument("--logging-steps", type=int, default=10) + parser.add_argument("--save-steps", type=int, default=500) + parser.add_argument("--eval-steps", type=int, default=None) + parser.add_argument("--save-total-limit", type=int, default=3) parser.add_argument("--lora-rank", type=int, default=32) parser.add_argument("--lora-alpha", type=int, default=64) parser.add_argument("--lora-dropout", type=float, default=0.1) + parser.add_argument( + "--target-modules", + default="all-linear", + help=( + "LoRA target modules: 'all-linear' (default), 'auto' for PEFT's " + "model mapping, or a comma-separated suffix list." + ), + ) parser.add_argument("--max-examples", type=int, default=None) parser.add_argument("--no-4bit", action="store_true", help="Disable 4-bit loading") parser.add_argument("--fp16", action="store_true", help="Use fp16 instead of bf16") + parser.add_argument( + "--device-map", + default="auto", + help="Device map for 4-bit model loading; use 'none' to disable.", + ) parser.add_argument("--trust-remote-code", action="store_true") parser.add_argument("--hf-token", default=None) @@ -52,12 +89,18 @@ def _lora_config_from_args(args: argparse.Namespace) -> FinetuneConfig: per_device_batch_size=args.batch_size, gradient_accumulation_steps=args.grad_accum, max_seq_length=args.max_seq_length, + logging_steps=args.logging_steps, + save_steps=args.save_steps, + eval_steps=args.eval_steps, + save_total_limit=args.save_total_limit, lora_rank=args.lora_rank, lora_alpha=args.lora_alpha, lora_dropout=args.lora_dropout, + target_modules=_parse_target_modules(args.target_modules), max_examples=args.max_examples, load_in_4bit=not args.no_4bit, bf16=not args.fp16, + device_map=_parse_device_map(args.device_map), trust_remote_code=args.trust_remote_code, hf_token=args.hf_token, ) @@ -66,7 +109,7 @@ def _lora_config_from_args(args: argparse.Namespace) -> FinetuneConfig: def build_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser( prog="trialmatchai-finetune", - description="Fine-tune TrialMatchAI's NER, reranker, or CoT models.", + description="Fine-tune TrialMatchAI's GLiNER2, reranker, or CoT models.", ) sub = parser.add_subparsers(dest="component", required=True) @@ -84,17 +127,25 @@ def build_parser() -> argparse.ArgumentParser: merge.add_argument("--output-dir", required=True) merge.add_argument("--trust-remote-code", action="store_true") - ner = sub.add_parser("ner", help="Fine-tune the GLiNER2 NER model") + ner = sub.add_parser( + "ner", + help="Fine-tune the GLiNER2 schema extraction model (entities/JSON/relations)", + ) ner.add_argument("--base-model", default="fastino/gliner2-base-v1") ner.add_argument("--train-data", required=True) ner.add_argument("--output-dir", required=True) ner.add_argument("--eval-data", default=None) ner.add_argument("--epochs", type=float, default=10.0) ner.add_argument("--batch-size", type=int, default=8) + ner.add_argument("--grad-accum", type=int, default=1) ner.add_argument("--encoder-lr", type=float, default=1e-5) ner.add_argument("--task-lr", type=float, default=5e-4) ner.add_argument("--lora-r", type=int, default=8) ner.add_argument("--lora-alpha", type=float, default=16.0) + ner.add_argument("--lora-dropout", type=float, default=0.0) + ner.add_argument("--eval-steps", type=int, default=500) + ner.add_argument("--logging-steps", type=int, default=50) + ner.add_argument("--save-total-limit", type=int, default=3) ner.add_argument("--no-lora", action="store_true", help="Full fine-tune instead of LoRA") ner.add_argument("--fp32", action="store_true", help="Disable fp16 mixed precision") ner.add_argument("--schema-path", default=None, help="Entity schema for label descriptions") @@ -133,11 +184,16 @@ def main(argv: Optional[Sequence[str]] = None) -> int: eval_data=args.eval_data, epochs=args.epochs, batch_size=args.batch_size, + gradient_accumulation_steps=args.grad_accum, encoder_lr=args.encoder_lr, task_lr=args.task_lr, use_lora=not args.no_lora, lora_r=args.lora_r, lora_alpha=args.lora_alpha, + lora_dropout=args.lora_dropout, + eval_steps=args.eval_steps, + logging_steps=args.logging_steps, + save_total_limit=args.save_total_limit, fp16=not args.fp32, schema_path=args.schema_path, max_examples=args.max_examples, diff --git a/src/trialmatchai/finetuning/config.py b/src/trialmatchai/finetuning/config.py index f0bd784f..5c4f9887 100644 --- a/src/trialmatchai/finetuning/config.py +++ b/src/trialmatchai/finetuning/config.py @@ -2,8 +2,8 @@ from __future__ import annotations -from dataclasses import dataclass, field -from typing import List, Optional +from dataclasses import dataclass +from typing import List, Optional, Union @dataclass @@ -32,17 +32,7 @@ class FinetuneConfig: lora_rank: int = 32 lora_alpha: int = 64 lora_dropout: float = 0.1 - target_modules: List[str] = field( - default_factory=lambda: [ - "q_proj", - "k_proj", - "v_proj", - "o_proj", - "gate_proj", - "up_proj", - "down_proj", - ] - ) + target_modules: Optional[Union[List[str], str]] = "all-linear" # Runtime load_in_4bit: bool = True @@ -50,23 +40,36 @@ class FinetuneConfig: trust_remote_code: bool = False logging_steps: int = 10 save_steps: int = 500 + eval_steps: Optional[int] = None save_total_limit: int = 3 max_examples: Optional[int] = None hf_token: Optional[str] = None + device_map: Optional[str] = "auto" def to_training_arguments(self): """Build transformers.TrainingArguments (imported lazily).""" from transformers import TrainingArguments - return TrainingArguments( + has_eval = self.eval_data is not None + eval_steps = self.eval_steps or self.save_steps + if has_eval and self.save_steps % eval_steps != 0: + raise ValueError( + "save_steps must be a multiple of eval_steps when eval_data is set " + "because load_best_model_at_end requires aligned save/eval steps." + ) + + kwargs = dict( output_dir=self.output_dir, num_train_epochs=self.epochs, learning_rate=self.learning_rate, per_device_train_batch_size=self.per_device_batch_size, + per_device_eval_batch_size=self.per_device_batch_size, gradient_accumulation_steps=self.gradient_accumulation_steps, warmup_ratio=self.warmup_ratio, weight_decay=self.weight_decay, logging_steps=self.logging_steps, + eval_strategy="steps" if has_eval else "no", + save_strategy="steps", save_steps=self.save_steps, save_total_limit=self.save_total_limit, bf16=self.bf16, @@ -81,3 +84,11 @@ def to_training_arguments(self): optim="paged_adamw_8bit" if self.load_in_4bit else "adamw_torch", lr_scheduler_type="cosine", ) + if has_eval: + kwargs.update( + eval_steps=eval_steps, + load_best_model_at_end=True, + metric_for_best_model="eval_loss", + greater_is_better=False, + ) + return TrainingArguments(**kwargs) diff --git a/src/trialmatchai/finetuning/cot.py b/src/trialmatchai/finetuning/cot.py index 3d3b30ac..a4da1b2c 100644 --- a/src/trialmatchai/finetuning/cot.py +++ b/src/trialmatchai/finetuning/cot.py @@ -11,7 +11,15 @@ from trialmatchai.finetuning.data import cot_row_to_messages, read_jsonl +def _load_messages(path: str, max_examples: int | None): + return [cot_row_to_messages(row) for row in read_jsonl(path, max_examples)] + + def finetune_cot(config: FinetuneConfig) -> str: - rows = read_jsonl(config.train_data, config.max_examples) - message_lists = [cot_row_to_messages(row) for row in rows] - return run_sft(config, message_lists) + message_lists = _load_messages(config.train_data, config.max_examples) + eval_message_lists = ( + _load_messages(config.eval_data, config.max_examples) + if config.eval_data is not None + else None + ) + return run_sft(config, message_lists, eval_message_lists) diff --git a/src/trialmatchai/finetuning/data.py b/src/trialmatchai/finetuning/data.py index c01689da..95a4a13e 100644 --- a/src/trialmatchai/finetuning/data.py +++ b/src/trialmatchai/finetuning/data.py @@ -7,9 +7,10 @@ - CoT: {"messages": [{"role","content"}, ...]} or {"instruction": str, "input": str, "output": str} - Reranker: {"patient_text": str, "criterion": str, "label": "Yes"|"No"} -- NER (GLiNER2): {"text": str, "entities": {"label": ["surface form", ...]}} +- GLiNER2: {"text": str, "entities": {"label": ["surface form", ...]}} or char-span {"text": str, "ner": [[start_char, end_char, "label"], ...]} - or native {"input": str, "output": {"entities": {...}, "entity_descriptions": {...}}} + or native {"input": str, "output": {"entities": {...}, "json_structures": [...]}} + or native {"text": str, "schema": {"json_structures": [...]}} """ from __future__ import annotations @@ -19,13 +20,21 @@ def read_jsonl(path: str, max_examples: Optional[int] = None) -> List[Dict[str, Any]]: + if max_examples is not None and max_examples <= 0: + return [] rows: List[Dict[str, Any]] = [] with open(path, "r", encoding="utf-8") as handle: - for line in handle: + for line_no, line in enumerate(handle, start=1): line = line.strip() if not line: continue - rows.append(json.loads(line)) + try: + row = json.loads(line) + except json.JSONDecodeError as exc: + raise ValueError(f"Invalid JSON in {path}:{line_no}: {exc.msg}") from exc + if not isinstance(row, dict): + raise ValueError(f"Expected JSON object in {path}:{line_no}.") + rows.append(row) if max_examples is not None and len(rows) >= max_examples: break return rows @@ -37,7 +46,10 @@ def read_jsonl(path: str, max_examples: Optional[int] = None) -> List[Dict[str, def cot_row_to_messages(row: Dict[str, Any]) -> List[Dict[str, str]]: """Normalize a CoT training row to a chat-message list.""" if "messages" in row: - return list(row["messages"]) + return [ + {"role": str(message["role"]), "content": str(message["content"])} + for message in row["messages"] + ] instruction = (row.get("instruction") or "").strip() user = (row.get("input") or row.get("question") or "").strip() output = row.get("output") or row.get("answer") or "" @@ -65,46 +77,101 @@ def reranker_row_to_messages(row: Dict[str, Any]) -> tuple[List[Dict[str, str]], return messages, label -# --------------------------------------------------------------------------- NER +# ----------------------------------------------------------------------- GLiNER2 -def ner_row_to_entities(row: Dict[str, Any]) -> Dict[str, Any]: - """Normalize an NER row to GLiNER2's surface-form schema. +def _entity_mapping(value: Any) -> Dict[str, List[str]]: + entities: Dict[str, List[str]] = {} + for label, surfaces in dict(value or {}).items(): + if surfaces is None: + entities[str(label)] = [] + elif isinstance(surfaces, str): + entities[str(label)] = [surfaces] + else: + entities[str(label)] = [str(surface) for surface in surfaces] + return entities - Returns ``{"text", "entities": {label: [surface forms]}, "entity_descriptions"}``. - Accepts the native GLiNER2 form, a simple ``entities`` mapping, or character - spans (which are sliced from the text into surface forms). + +def _copy_if_present(source: Dict[str, Any], target: Dict[str, Any], key: str) -> None: + if key in source and source[key] is not None: + target[key] = source[key] + + +def _json_structures(value: Any) -> Any: + if isinstance(value, dict): + return [{name: fields} for name, fields in value.items()] + return value + + +def gliner2_row_to_training_record(row: Dict[str, Any]) -> Dict[str, Any]: + """Normalize supported GLiNER2 row shapes to ``{"input", "output"}``. + + The output schema is intentionally close to GLiNER2's native format so + schema-based extraction tasks (JSON structures, classifications, relations) + pass through to ``InputExample.from_dict`` without being flattened. """ - # Native GLiNER2 JSONL: {"input": ..., "output": {"entities": ..., ...}} if "input" in row and "output" in row: - output = row.get("output") or {} - return { - "text": str(row["input"]), - "entities": dict(output.get("entities") or {}), - "entity_descriptions": output.get("entity_descriptions"), - } - - text = str(row.get("text", "")) - - # Already a {label: [forms]} mapping. - if isinstance(row.get("entities"), dict): - return { - "text": text, - "entities": {k: list(v) for k, v in row["entities"].items()}, - "entity_descriptions": row.get("entity_descriptions"), - } - - # Character spans -> surface forms grouped by label. + text = str(row["input"]) + source = dict(row.get("output") or {}) + else: + text = str(row.get("text", "")) + source = dict(row.get("schema") or {}) + for key in ( + "entities", + "entity_descriptions", + "classifications", + "json_structures", + "structures", + "json_descriptions", + "relations", + ): + _copy_if_present(row, source, key) + + output: Dict[str, Any] = {} + if "entities" in source: + output["entities"] = _entity_mapping(source.get("entities")) + if "ner" in row and "entities" not in output: + output["entities"] = _entities_from_spans(text, row.get("ner") or []) + _copy_if_present(source, output, "entity_descriptions") + _copy_if_present(source, output, "classifications") + if "json_structures" in source: + output["json_structures"] = _json_structures(source["json_structures"]) + elif "structures" in source: + output["json_structures"] = _json_structures(source["structures"]) + _copy_if_present(source, output, "json_descriptions") + _copy_if_present(source, output, "relations") + return {"input": text, "output": output} + + +def _entities_from_spans(text: str, spans: Any) -> Dict[str, List[str]]: entities: Dict[str, List[str]] = {} - for span in row.get("ner") or []: + for span in spans: + if not isinstance(span, (list, tuple)): + continue + if len(span) < 3: + continue start_char, end_char, label = span[0], span[1], span[2] + if not isinstance(start_char, int) or not isinstance(end_char, int): + continue surface = text[start_char:end_char].strip() if surface: + label = str(label) entities.setdefault(label, []) if surface not in entities[label]: entities[label].append(surface) + return entities + + +def ner_row_to_entities(row: Dict[str, Any]) -> Dict[str, Any]: + """Normalize an NER row to GLiNER2's surface-form schema. + + Returns ``{"text", "entities": {label: [surface forms]}, "entity_descriptions"}``. + Accepts the native GLiNER2 form, a simple ``entities`` mapping, or character + spans (which are sliced from the text into surface forms). + """ + record = gliner2_row_to_training_record(row) return { - "text": text, - "entities": entities, - "entity_descriptions": row.get("entity_descriptions"), + "text": record["input"], + "entities": record["output"].get("entities", {}), + "entity_descriptions": record["output"].get("entity_descriptions"), } diff --git a/src/trialmatchai/finetuning/ner.py b/src/trialmatchai/finetuning/ner.py index 6285ff4c..1f2322cd 100644 --- a/src/trialmatchai/finetuning/ner.py +++ b/src/trialmatchai/finetuning/ner.py @@ -5,11 +5,10 @@ LoRA adapter (``/final``) that plugs into the pipeline via ``entity_extraction.model_name`` with backend "gliner2". -GLiNER2 NER data maps entity-type labels to surface forms, e.g. -``{"text": "...", "entities": {"gene": ["EGFR"], "disease": ["NSCLC"]}}``. -Character-span rows (``{"text", "ner": [[start, end, "label"]]}``) are converted -automatically. ``entity_descriptions`` is back-filled from the entity schema so -the fine-tuned model uses the same label semantics as inference. +GLiNER2 data can be flat entity extraction, schema-based JSON structures, +classifications, relations, or a mix of those tasks. Character-span NER rows +are converted automatically. ``entity_descriptions`` is back-filled from the +entity schema so flat entity examples use the same label semantics as inference. """ from __future__ import annotations @@ -17,7 +16,7 @@ from dataclasses import dataclass from typing import Dict, Optional -from trialmatchai.finetuning.data import ner_row_to_entities, read_jsonl +from trialmatchai.finetuning.data import gliner2_row_to_training_record, read_jsonl from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -31,13 +30,18 @@ class NERFinetuneConfig: eval_data: Optional[str] = None epochs: float = 10.0 batch_size: int = 8 + gradient_accumulation_steps: int = 1 encoder_lr: float = 1e-5 task_lr: float = 5e-4 warmup_ratio: float = 0.1 use_lora: bool = True lora_r: int = 8 lora_alpha: float = 16.0 + lora_dropout: float = 0.0 fp16: bool = True + eval_steps: int = 500 + logging_steps: int = 50 + save_total_limit: int = 3 max_examples: Optional[int] = None schema_path: Optional[str] = None seed: int = 42 @@ -59,26 +63,53 @@ def _schema_descriptions(schema_path: Optional[str]) -> Dict[str, str]: return descriptions +def _backfill_entity_descriptions( + record: Dict[str, object], descriptions: Dict[str, str] +) -> Dict[str, object]: + output = dict(record.get("output") or {}) + entities = output.get("entities") or {} + if not isinstance(entities, dict): + return record + descs = dict(output.get("entity_descriptions") or {}) + for label in entities: + if label not in descs and label in descriptions: + descs[label] = descriptions[label] + if descs: + output["entity_descriptions"] = descs + record = dict(record) + record["output"] = output + return record + + +def _sanitize_example(example) -> bool: + if not hasattr(example, "sanitize"): + return True + warnings, is_valid = example.sanitize() + for warning in warnings: + logger.warning("Sanitized GLiNER2 training example: %s", warning) + return is_valid + + +def _validate_dataset(dataset) -> None: + try: + dataset.validate(raise_on_error=False) + except TypeError: # pragma: no cover - older GLiNER2 compatibility + dataset.validate(strict=False, raise_on_error=False) + + def _build_examples(config: "NERFinetuneConfig", path: str, descriptions: Dict[str, str]): from gliner2.training.data import InputExample examples = [] for row in read_jsonl(path, config.max_examples): - normalized = ner_row_to_entities(row) - entities = normalized["entities"] - if not entities: + record = gliner2_row_to_training_record(row) + record["input"] = str(record["input"]).strip() + if not record["input"]: continue - descs = dict(normalized.get("entity_descriptions") or {}) - for label in entities: - if label not in descs and label in descriptions: - descs[label] = descriptions[label] - examples.append( - InputExample( - text=normalized["text"], - entities=entities, - entity_descriptions=descs or None, - ) - ) + record = _backfill_entity_descriptions(record, descriptions) + example = InputExample.from_dict(record) + if _sanitize_example(example): + examples.append(example) return examples @@ -99,36 +130,47 @@ def finetune_ner(config: NERFinetuneConfig) -> str: raise ValueError("No training examples provided.") train_dataset = TrainingDataset(train_examples) - train_dataset.validate(strict=False, raise_on_error=False) + _validate_dataset(train_dataset) train_dataset.print_stats() if config.eval_data: train_data: object = train_dataset - val_data: object = TrainingDataset( - _build_examples(config, config.eval_data, descriptions) - ) - else: + eval_examples = _build_examples(config, config.eval_data, descriptions) + if not eval_examples: + raise ValueError("No evaluation examples provided.") + val_data: object | None = TrainingDataset(eval_examples) + elif len(train_examples) >= 10: train_data, val_data, _ = train_dataset.split( train_ratio=0.9, val_ratio=0.1, test_ratio=0.0, shuffle=True, seed=config.seed ) + else: + train_data = train_dataset + val_data = None + + has_eval = val_data is not None training_config = TrainingConfig( output_dir=config.output_dir, experiment_name="trialmatchai_ner", num_epochs=config.epochs, batch_size=config.batch_size, + gradient_accumulation_steps=config.gradient_accumulation_steps, encoder_lr=config.encoder_lr, task_lr=config.task_lr, warmup_ratio=config.warmup_ratio, scheduler_type="cosine", fp16=config.fp16, - eval_strategy="epoch", - save_best=True, - early_stopping=True, + eval_strategy="epoch" if has_eval else "no", + eval_steps=config.eval_steps, + save_total_limit=config.save_total_limit, + save_best=has_eval, + early_stopping=has_eval, early_stopping_patience=3, + logging_steps=config.logging_steps, use_lora=config.use_lora, lora_r=config.lora_r, lora_alpha=config.lora_alpha, + lora_dropout=config.lora_dropout, lora_target_modules=["encoder"], save_adapter_only=config.use_lora, ) @@ -140,7 +182,7 @@ def finetune_ner(config: NERFinetuneConfig) -> str: result_dir = ( f"{config.output_dir.rstrip('/')}/final" - if config.use_lora + if config.use_lora or not has_eval else f"{config.output_dir.rstrip('/')}/best" ) logger.info( diff --git a/src/trialmatchai/finetuning/reranker.py b/src/trialmatchai/finetuning/reranker.py index 558914ca..9062e3dc 100644 --- a/src/trialmatchai/finetuning/reranker.py +++ b/src/trialmatchai/finetuning/reranker.py @@ -12,10 +12,19 @@ from trialmatchai.finetuning.data import read_jsonl, reranker_row_to_messages -def finetune_reranker(config: FinetuneConfig) -> str: - rows = read_jsonl(config.train_data, config.max_examples) +def _load_messages(path: str, max_examples: int | None): message_lists = [] - for row in rows: + for row in read_jsonl(path, max_examples): messages, label = reranker_row_to_messages(row) message_lists.append([*messages, {"role": "assistant", "content": label}]) - return run_sft(config, message_lists) + return message_lists + + +def finetune_reranker(config: FinetuneConfig) -> str: + message_lists = _load_messages(config.train_data, config.max_examples) + eval_message_lists = ( + _load_messages(config.eval_data, config.max_examples) + if config.eval_data is not None + else None + ) + return run_sft(config, message_lists, eval_message_lists) diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index 306d8461..bbee0214 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -1,5 +1,6 @@ from __future__ import annotations +from contextlib import nullcontext from pathlib import Path from typing import TYPE_CHECKING, Dict, List, Optional, Tuple @@ -253,28 +254,52 @@ def run_rag_processing( logger.error("No patient narrative available for RAG processing.") return - # vLLM is the only LLM backend. A configured cot_adapter_path is served as a - # LoRA adapter (LoRARequest) by the engine loader. - from trialmatchai.matching.eligibility_reasoning_vllm import BatchTrialProcessorVLLM - from trialmatchai.models.llm.vllm_loader import load_vllm_engine + rag_cfg = config.get("rag", {}) + rag_backend = str(rag_cfg.get("backend", "vllm")) + if rag_backend == "transformers": + from trialmatchai.matching.eligibility_reasoning_transformers import ( + BatchTrialProcessorTransformers, + ) - vllm_cfg = config.get("vllm", {}) - vllm_engine, vllm_tokenizer, lora_request = load_vllm_engine( - model_config=config.get("model", {}), - vllm_cfg=vllm_cfg, - ) - rag_processor = BatchTrialProcessorVLLM( - llm=vllm_engine, # type: ignore - tokenizer=vllm_tokenizer, - batch_size=vllm_cfg.get("batch_size", 16), - use_cot=config.get("use_cot_reasoning", True), - max_new_tokens=vllm_cfg.get("max_new_tokens", 5000), - temperature=vllm_cfg.get("temperature", 0.0), - top_p=vllm_cfg.get("top_p", 1.0), - seed=vllm_cfg.get("seed", 1234), - length_bucket=vllm_cfg.get("length_bucket", True), - lora_request=lora_request, - ) + vllm_cfg = config.get("vllm", {}) + rag_processor = BatchTrialProcessorTransformers( + model_path=config["model"]["base_model"], + device=str(config.get("global", {}).get("device", "cpu")), + batch_size=rag_cfg.get("batch_size", 1), + use_cot=config.get("use_cot_reasoning", False), + max_new_tokens=vllm_cfg.get("max_new_tokens", 256), + temperature=vllm_cfg.get("temperature", 0.0), + top_p=vllm_cfg.get("top_p", 1.0), + revision=config["model"].get("base_model_revision"), + trust_remote_code=config["model"].get("trust_remote_code", False), + length_bucket=vllm_cfg.get("length_bucket", True), + no_think=rag_cfg.get("no_think", False), + ) + elif rag_backend == "vllm": + from trialmatchai.matching.eligibility_reasoning_vllm import ( + BatchTrialProcessorVLLM, + ) + from trialmatchai.models.llm.vllm_loader import load_vllm_engine + + vllm_cfg = config.get("vllm", {}) + vllm_engine, vllm_tokenizer, lora_request = load_vllm_engine( + model_config=config.get("model", {}), + vllm_cfg=vllm_cfg, + ) + rag_processor = BatchTrialProcessorVLLM( + llm=vllm_engine, # type: ignore + tokenizer=vllm_tokenizer, + batch_size=vllm_cfg.get("batch_size", 16), + use_cot=config.get("use_cot_reasoning", True), + max_new_tokens=vllm_cfg.get("max_new_tokens", 5000), + temperature=vllm_cfg.get("temperature", 0.0), + top_p=vllm_cfg.get("top_p", 1.0), + seed=vllm_cfg.get("seed", 1234), + length_bucket=vllm_cfg.get("length_bucket", True), + lora_request=lora_request, + ) + else: + raise ValueError(f"Unsupported rag.backend: {rag_backend}") rag_processor.process_trials( nct_ids=top_trials, @@ -319,24 +344,18 @@ def main_pipeline(config_path: str | None = None) -> int: try: import torch except ImportError: - logger.error( - "PyTorch is required to run matching. Install the ML extras with " - "`uv sync --extra llm --extra entity`." - ) - return 1 + torch = None # type: ignore from trialmatchai.models.embedding import build_embedder - from trialmatchai.models.llm.llm_reranker import LLMReranker - - if torch.cuda.is_available(): + if torch is not None and torch.cuda.is_available(): torch.backends.cudnn.benchmark = True if hasattr(torch.backends.cuda, "enable_flash_sdp"): torch.backends.cuda.enable_flash_sdp(True) import warnings - # The CoT reasoning model is loaded lazily by run_rag_processing as a vLLM - # engine (the only LLM backend), so nothing to load here. + # The eligibility reasoning model is loaded lazily by run_rag_processing + # after the top-trial shortlist is known. # Initialize components embedder = build_embedder(config) @@ -346,14 +365,36 @@ def main_pipeline(config_path: str | None = None) -> int: warnings.filterwarnings( "ignore", message=".*quantization_config.*", category=UserWarning ) - llm_reranker = LLMReranker( - model_path=config["model"]["reranker_model_path"], - adapter_path=config["model"]["reranker_adapter_path"], - device=config["global"]["device"], - batch_size=config["rag"]["batch_size"] * 2, - revision=config["model"].get("reranker_model_revision"), - trust_remote_code=config["model"].get("trust_remote_code", False), - ) + if _reranker_enabled(config): + if _reranker_backend(config) == "transformers": + from trialmatchai.models.llm.transformers_reranker import ( + TransformersReranker, + ) + + llm_reranker = TransformersReranker( + model_path=config["model"]["reranker_model_path"], + device=str(config["global"]["device"]), + batch_size=config.get("LLM_reranker", {}).get("batch_size", 8), + revision=config["model"].get("reranker_model_revision"), + trust_remote_code=config["model"].get("trust_remote_code", False), + ) + elif _reranker_backend(config) == "vllm": + from trialmatchai.models.llm.llm_reranker import LLMReranker + + llm_reranker = LLMReranker( + model_path=config["model"]["reranker_model_path"], + adapter_path=config["model"]["reranker_adapter_path"], + device=config["global"]["device"], + batch_size=config["rag"]["batch_size"] * 2, + revision=config["model"].get("reranker_model_revision"), + trust_remote_code=config["model"].get("trust_remote_code", False), + ) + else: + raise ValueError( + f"Unsupported LLM_reranker.backend: {_reranker_backend(config)}" + ) + else: + llm_reranker = None gemma_retriever = SecondStageRetriever( search_backend=search_backend, @@ -363,6 +404,9 @@ def main_pipeline(config_path: str | None = None) -> int: search_mode=config["search"].get("mode", "hybrid"), ) + completed_patients = 0 + failed_patients = 0 + for profile, summary in patient_inputs: patient_id = profile.patient_id token = set_request_id(patient_id) @@ -382,7 +426,7 @@ def main_pipeline(config_path: str | None = None) -> int: # Run pipeline with log_timing(logger, "First-level search"): - with torch.no_grad(): + with _inference_context(torch): result = run_first_level_search( keywords, str(output_folder), @@ -406,8 +450,8 @@ def main_pipeline(config_path: str | None = None) -> int: ) = result with log_timing(logger, "Second-level search"): - with torch.no_grad(): - _, top_trials_path = run_second_level_search( + with _inference_context(torch): + semi_final_trials, top_trials_path = run_second_level_search( str(output_folder), nct_ids, main_conditions, @@ -419,29 +463,46 @@ def main_pipeline(config_path: str | None = None) -> int: patient_context, ) - with log_timing(logger, "RAG processing"): - with torch.no_grad(): - run_rag_processing( - str(output_folder), - top_trials_path, - patient_info, - config, + if _rag_enabled(config): + with log_timing(logger, "RAG processing"): + with _inference_context(torch): + run_rag_processing( + str(output_folder), + top_trials_path, + patient_info, + config, + ) + + with log_timing(logger, "Final ranking"): + trial_data = load_trial_data(str(output_folder)) + ranked_trials = rank_trials(trial_data) + save_ranked_trials( + ranked_trials, str(output_folder / "ranked_trials.json") ) - - with log_timing(logger, "Final ranking"): - trial_data = load_trial_data(str(output_folder)) - ranked_trials = rank_trials(trial_data) + else: + logger.info("RAG processing disabled; ranking by retrieval scores.") save_ranked_trials( - ranked_trials, str(output_folder / "ranked_trials.json") + [ + {"TrialID": trial_id, "Score": score} + for trial_id, score in semi_final_trials + ], + str(output_folder / "ranked_trials.json"), ) logger.info("Pipeline completed for patient %s", patient_id) + completed_patients += 1 except Exception: logger.exception("Pipeline failed for patient %s", patient_id) + failed_patients += 1 continue finally: reset_request_id(token) + if completed_patients == 0: + logger.error("Pipeline failed for all %s patient(s).", len(patient_inputs)) + return 1 + if failed_patients: + logger.warning("Pipeline completed with %s patient failure(s).", failed_patients) return 0 @@ -505,5 +566,25 @@ def _dedupe_strings(values: list[str]) -> list[str]: return output +def _reranker_enabled(config: Dict) -> bool: + return bool(config.get("LLM_reranker", {}).get("enabled", True)) + + +def _reranker_backend(config: Dict) -> str: + return str(config.get("LLM_reranker", {}).get("backend", "vllm")) + + +def _rag_enabled(config: Dict) -> bool: + if not bool(config.get("use_cot_reasoning", True)): + return False + return bool(config.get("rag", {}).get("enabled", True)) + + +def _inference_context(torch_module): + if torch_module is None: + return nullcontext() + return torch_module.no_grad() + + if __name__ == "__main__": raise SystemExit(main_pipeline()) diff --git a/src/trialmatchai/matching/eligibility_base.py b/src/trialmatchai/matching/eligibility_base.py index 8ce60072..eb2aa00b 100644 --- a/src/trialmatchai/matching/eligibility_base.py +++ b/src/trialmatchai/matching/eligibility_base.py @@ -9,6 +9,7 @@ import json import os +import re from typing import Dict, List from trialmatchai.utils.file_utils import read_json_file, write_json_file, write_text_file @@ -24,6 +25,7 @@ class BaseTrialProcessor: tokenizer = None batch_size: int = 4 use_cot: bool = True + no_think: bool = False # ---------------------- I/O helpers ---------------------- @@ -45,6 +47,8 @@ def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: else "No eligibility criteria provided." ) + no_think_prefix = "/no_think\n" if self.no_think else "" + if self.use_cot: system_msg = ( "You are a medical expert with advanced knowledge in clinical reasoning, diagnostics, and treatment planning. " @@ -55,7 +59,8 @@ def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: { "role": "user", "content": ( - "Assess the given patient's eligibility for a clinical trial by evaluating each and every criterion individually.\n\n" + no_think_prefix + + "Assess the given patient's eligibility for a clinical trial by evaluating each and every criterion individually.\n\n" "### INCLUSION CRITERIA ASSESSMENT\n" "For each inclusion criterion, classify it as one of:\n" "- **Met:** The patient's data explicitly and unequivocally satisfies the criterion.\n" @@ -108,7 +113,8 @@ def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: { "role": "user", "content": ( - "For each criterion, classify:\n" + no_think_prefix + + "For each criterion, classify:\n" '- If Inclusion Criterion: "Met" | "Not Met" | "Unclear" | "Irrelevant"\n' '- If Exclusion Criterion: "Violated" | "Not Violated" | "Unclear" | "Irrelevant"\n\n' "Provide a justification for each classification based strictly on the provided data. " @@ -129,9 +135,21 @@ def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: ] if self.tokenizer is not None and hasattr(self.tokenizer, "apply_chat_template"): - return self.tokenizer.apply_chat_template( - chat, tokenize=False, add_generation_prompt=True - ) + # Pass enable_thinking=False for reasoning models (e.g. Qwen3) when + # no_think is set. The kwarg is silently accepted or raises TypeError on + # models whose chat templates don't declare the variable — fall through. + template_kwargs: dict = {} + if self.no_think: + template_kwargs["enable_thinking"] = False + try: + return self.tokenizer.apply_chat_template( + chat, tokenize=False, add_generation_prompt=True, **template_kwargs + ) + except (TypeError, ValueError) as exc: + logger.warning( + "Tokenizer chat template unavailable; using plain prompt: %s", + exc, + ) # Fallback: simple concatenation system_part = f"{chat[0]['content']}\n\n" user_part = f"{chat[1]['content']}\n\n" @@ -139,13 +157,27 @@ def _format_prompt(self, criteria_text: str, patient_profile: str) -> str: # ---------------------- Persistence ---------------------- + @staticmethod + def _strip_thinking_tags(text: str) -> str: + """Remove … blocks emitted by reasoning models (e.g. Qwen3). + + The full response (with thinking) is still written to the .txt file so the + reasoning chain is not lost; only JSON extraction operates on the stripped text. + """ + text = re.sub(r".*?", "", text, flags=re.DOTALL) + # Trim incomplete block if the model was cut off mid-think + cut = text.find("") + if cut != -1: + text = text[:cut] + return text.strip() + def _save_outputs(self, nct_id: str, response: str, output_folder: str): try: os.makedirs(output_folder, exist_ok=True) txt_path = f"{output_folder}/{nct_id}.txt" write_text_file([response], txt_path) try: - json_data = extract_json_object(response) + json_data = extract_json_object(self._strip_thinking_tags(response)) write_json_file(json_data, f"{output_folder}/{nct_id}.json") logger.info(f"Processed {nct_id} successfully") except (json.JSONDecodeError, ValueError) as e: @@ -162,7 +194,11 @@ def _save_outputs(self, nct_id: str, response: str, output_folder: str): def _token_length(self, prompt: str, nct_id: str = "") -> int: """Token count used for length bucketing; char-based fallback on error.""" try: - return len(self.tokenizer(prompt, add_special_tokens=False)["input_ids"]) + kwargs = {"add_special_tokens": False} + max_input_tokens = getattr(self, "max_input_tokens", None) + if isinstance(max_input_tokens, int) and max_input_tokens > 0: + kwargs.update(truncation=True, max_length=max_input_tokens) + return len(self.tokenizer(prompt, **kwargs)["input_ids"]) except Exception: return max(1, len(prompt) // 4) diff --git a/src/trialmatchai/matching/eligibility_reasoning_transformers.py b/src/trialmatchai/matching/eligibility_reasoning_transformers.py new file mode 100644 index 00000000..d79d6a79 --- /dev/null +++ b/src/trialmatchai/matching/eligibility_reasoning_transformers.py @@ -0,0 +1,123 @@ +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from trialmatchai.matching.eligibility_base import BaseTrialProcessor +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +class BatchTrialProcessorTransformers(BaseTrialProcessor): + """CPU-capable Transformers eligibility processor for smoke/small runs.""" + + def __init__( + self, + model_path: str, + *, + device: str = "cpu", + batch_size: int = 1, + use_cot: bool = False, + max_new_tokens: int = 256, + temperature: float = 0.0, + top_p: float = 1.0, + revision: Optional[str] = None, + trust_remote_code: bool = False, + length_bucket: bool = True, + no_think: bool = False, + ): + try: + import torch + from transformers import AutoModelForCausalLM, AutoTokenizer + except ImportError as exc: # pragma: no cover - optional dependency guard + raise RuntimeError( + "Transformers eligibility processing requires the llm extra " + "(`uv sync --extra llm`)." + ) from exc + + self.torch = torch + self.device = torch.device(device if device != "auto" else "cpu") + self.tokenizer = AutoTokenizer.from_pretrained( + model_path, + revision=revision, + trust_remote_code=trust_remote_code, + ) + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + self.tokenizer.padding_side = "left" + self.tokenizer.truncation_side = "left" + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + revision=revision, + trust_remote_code=trust_remote_code, + ).to(self.device) + self.model.eval() + self.batch_size = batch_size + self.use_cot = use_cot + self.max_new_tokens = max_new_tokens + self.temperature = temperature + self.top_p = top_p + self.length_bucket = length_bucket + self.no_think = no_think + self.max_input_tokens = _max_input_tokens( + self.tokenizer, + self.model, + self.max_new_tokens, + ) + logger.info( + "Loaded Transformers eligibility model %s on %s.", + model_path, + self.device, + ) + + def _progress_desc(self) -> str: + return "CPU Transformers Processing Trials" + + def _process_batch(self, batch: List[Dict[str, Any]], output_folder: str): + prompts = [item["prompt"] for item in batch] + encoded = self.tokenizer( + prompts, + padding=True, + truncation=True, + max_length=self.max_input_tokens, + return_tensors="pt", + ).to(self.device) + generation_kwargs: dict[str, Any] = { + "max_new_tokens": self.max_new_tokens, + "pad_token_id": self.tokenizer.pad_token_id, + } + if self.temperature > 0: + generation_kwargs.update( + do_sample=True, + temperature=self.temperature, + top_p=self.top_p, + ) + else: + generation_kwargs["do_sample"] = False + + with self.torch.inference_mode(): + output_ids = self.model.generate(**encoded, **generation_kwargs) + + prompt_width = encoded["input_ids"].shape[1] + for item, sequence in zip(batch, output_ids): + completion_ids = sequence[prompt_width:] + response = self.tokenizer.decode( + completion_ids, + skip_special_tokens=True, + ).strip() + self._save_outputs(item["nct_id"], response, output_folder) + + +def _max_input_tokens(tokenizer: Any, model: Any, max_new_tokens: int) -> int: + candidates: list[int] = [] + config = getattr(model, "config", None) + for attr in ("max_position_embeddings", "n_positions", "n_ctx"): + value = getattr(config, attr, None) + if isinstance(value, int) and 0 < value < 1_000_000: + candidates.append(value) + tokenizer_limit = getattr(tokenizer, "model_max_length", None) + if isinstance(tokenizer_limit, int) and 0 < tokenizer_limit < 1_000_000: + candidates.append(tokenizer_limit) + + context_window = min(candidates) if candidates else 2048 + return max(1, context_window - max(0, int(max_new_tokens))) diff --git a/src/trialmatchai/models/embedding/__init__.py b/src/trialmatchai/models/embedding/__init__.py index 85fc9fdf..4668f315 100644 --- a/src/trialmatchai/models/embedding/__init__.py +++ b/src/trialmatchai/models/embedding/__init__.py @@ -1,7 +1,8 @@ from trialmatchai.models.embedding.text_embedder import ( + HashingTextEmbedder, TextEmbedder, TextEmbedderConfig, build_embedder, ) -__all__ = ["TextEmbedder", "TextEmbedderConfig", "build_embedder"] +__all__ = ["HashingTextEmbedder", "TextEmbedder", "TextEmbedderConfig", "build_embedder"] diff --git a/src/trialmatchai/models/embedding/text_embedder.py b/src/trialmatchai/models/embedding/text_embedder.py index b86eadfa..b6120795 100644 --- a/src/trialmatchai/models/embedding/text_embedder.py +++ b/src/trialmatchai/models/embedding/text_embedder.py @@ -1,6 +1,9 @@ from __future__ import annotations from dataclasses import dataclass +import hashlib +import math +import re from typing import Iterable, List, Literal, Sequence, Any from trialmatchai.utils.logging_config import setup_logging @@ -8,11 +11,13 @@ logger = setup_logging(__name__) +EmbedderBackend = Literal["hf", "hashing"] PoolingStrategy = Literal["cls", "mean"] @dataclass(frozen=True) class TextEmbedderConfig: + backend: EmbedderBackend = "hf" model_name: str = "BAAI/bge-m3" revision: str | None = None trust_remote_code: bool = False @@ -22,6 +27,43 @@ class TextEmbedderConfig: use_gpu: bool = True use_fp16: bool = False normalize: bool = True + hashing_dimensions: int = 64 + + +class HashingTextEmbedder: + """Deterministic lightweight embedder for tests and CPU smoke runs.""" + + def __init__(self, dimensions: int = 64, normalize: bool = True): + if dimensions <= 0: + raise ValueError("hashing_dimensions must be positive.") + self.dimensions = dimensions + self.normalize = normalize + logger.info( + "Using deterministic hashing embedder with %d dimensions.", dimensions + ) + + def embed_text(self, text: str) -> List[float]: + vectors = self.embed_texts([text]) + if not vectors: + raise ValueError("Cannot embed empty text.") + return vectors[0] + + def embed_texts(self, texts: Sequence[str]) -> List[List[float]]: + return [self._embed(text) for text in texts if text and text.strip()] + + def _embed(self, text: str) -> List[float]: + vector = [0.0] * self.dimensions + tokens = re.findall(r"[A-Za-z0-9]+", text.casefold()) + for token in tokens: + digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest() + bucket = int.from_bytes(digest[:4], "big") % self.dimensions + sign = 1.0 if digest[4] % 2 == 0 else -1.0 + vector[bucket] += sign + if self.normalize: + norm = math.sqrt(sum(value * value for value in vector)) + if norm: + vector = [value / norm for value in vector] + return vector class TextEmbedder: @@ -102,8 +144,17 @@ def build_embedder(config: dict) -> TextEmbedder: and the index/build-concepts/update-registry CLIs. """ embedder_cfg = config.get("embedder", {}) or {} + backend = embedder_cfg.get("backend", "hf") + if backend == "hashing": + return HashingTextEmbedder( + dimensions=int(embedder_cfg.get("hashing_dimensions", 64)), + normalize=bool(embedder_cfg.get("normalize", True)), + ) + if backend != "hf": + raise ValueError(f"Unsupported embedder backend: {backend}") return TextEmbedder( TextEmbedderConfig( + backend="hf", model_name=embedder_cfg.get("model_name", "BAAI/bge-m3"), revision=embedder_cfg.get("revision"), trust_remote_code=embedder_cfg.get("trust_remote_code", False), diff --git a/src/trialmatchai/models/llm/transformers_reranker.py b/src/trialmatchai/models/llm/transformers_reranker.py new file mode 100644 index 00000000..131691e1 --- /dev/null +++ b/src/trialmatchai/models/llm/transformers_reranker.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import math +from typing import Any, List, Optional + +from tqdm import tqdm + +from trialmatchai.models.llm.llm_reranker import LLMReranker +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +class TransformersReranker: + """CPU-capable next-token Yes/No reranker for smoke tests and small runs.""" + + def __init__( + self, + model_path: str, + *, + device: str = "cpu", + batch_size: int = 8, + revision: Optional[str] = None, + trust_remote_code: bool = False, + ): + try: + import torch + from transformers import AutoModelForCausalLM, AutoTokenizer + except ImportError as exc: # pragma: no cover - optional dependency guard + raise RuntimeError( + "Transformers reranker requires the llm extra " + "(`uv sync --extra llm`)." + ) from exc + + self.torch = torch + self.device = torch.device(device if device != "auto" else "cpu") + self.batch_size = batch_size + self.tokenizer = AutoTokenizer.from_pretrained( + model_path, + revision=revision, + trust_remote_code=trust_remote_code, + ) + if self.tokenizer.pad_token is None: + self.tokenizer.pad_token = self.tokenizer.eos_token + self.model = AutoModelForCausalLM.from_pretrained( + model_path, + revision=revision, + trust_remote_code=trust_remote_code, + ).to(self.device) + self.model.eval() + self.yes_token_id = self._first_token_id(" Yes", "Yes") + self.no_token_id = self._first_token_id(" No", "No") + logger.info("Loaded Transformers reranker %s on %s.", model_path, self.device) + + def _first_token_id(self, preferred: str, fallback: str) -> int: + token_ids = self.tokenizer(preferred, add_special_tokens=False)["input_ids"] + if not token_ids: + token_ids = self.tokenizer(fallback, add_special_tokens=False)["input_ids"] + if not token_ids: + raise ValueError(f"Tokenizer could not encode {fallback!r}.") + return int(token_ids[0]) + + def rank_pairs(self, patient_trial_pairs: List[tuple]) -> List[dict[str, Any]]: + results: List[dict[str, Any]] = [] + for start in tqdm( + range(0, len(patient_trial_pairs), self.batch_size), + desc="CPU reranking batches", + ): + batch = patient_trial_pairs[start : start + self.batch_size] + prompts = [self._build_prompt(patient, trial) for patient, trial in batch] + encoded = self.tokenizer( + prompts, + padding=True, + truncation=True, + return_tensors="pt", + ).to(self.device) + with self.torch.inference_mode(): + outputs = self.model(**encoded) + next_logits = outputs.logits[:, -1, :] + for row in next_logits: + yes_logit = float(row[self.yes_token_id]) + no_logit = float(row[self.no_token_id]) + highest = max(yes_logit, no_logit) + yes = math.exp(yes_logit - highest) + no = math.exp(no_logit - highest) + prob = yes / (yes + no) + results.append( + {"llm_score": prob, "answer": "Yes" if prob > 0.5 else "No"} + ) + return results + + def _build_prompt(self, patient_text: str, trial_text: str) -> str: + messages = LLMReranker.create_messages(patient_text, trial_text) + if getattr(self.tokenizer, "chat_template", None): + return self.tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + return ( + f"{messages[0]['content']}\n\n" + f"{messages[2]['content']}\nAnswer Yes or No:" + ) diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py index aadbc9bb..3ef99f56 100644 --- a/src/trialmatchai/registry/preparation.py +++ b/src/trialmatchai/registry/preparation.py @@ -52,8 +52,7 @@ def prepare_trial_document( # Text-only fields the backend scores on (TRIAL_TEXT_WEIGHTS) but does not embed. for text_field in ("detailed_description", "official_title"): value = _preprocess_text(flatten_text(doc.get(text_field))) - if value: - out[text_field] = value + out[text_field] = value for simple in ( "overall_status", @@ -64,22 +63,30 @@ def prepare_trial_document( "source_url", "last_update_posted", ): - if doc.get(simple) not in (None, ""): - out[simple] = doc[simple] + out[simple] = str(doc.get(simple) or "") - for date_field in ("start_date", "completion_date"): - iso = _to_iso_date(doc.get(date_field)) - if iso: - out[date_field] = iso + out["start_date"] = _to_iso_date(doc.get("start_date")) or "" + out["completion_date"] = _to_iso_date(doc.get("completion_date")) or "" - for age_field in ("minimum_age", "maximum_age"): - years = _age_to_years(doc.get(age_field)) - if years is not None: - out[age_field] = years + out["minimum_age"] = _age_to_years(doc.get("minimum_age")) + if out["minimum_age"] is None: + out["minimum_age"] = 0.0 + out["maximum_age"] = _age_to_years(doc.get("maximum_age")) + if out["maximum_age"] is None: + out["maximum_age"] = 999.0 - for nested in ("intervention", "location", "reference"): - if doc.get(nested): - out[nested] = doc[nested] + out["intervention"] = _stable_dict_list( + doc.get("intervention"), + keys=("name", "type", "description"), + ) + out["location"] = _stable_dict_list( + doc.get("location"), + keys=("facility", "city", "state", "country", "status"), + ) + out["reference"] = _stable_dict_list( + doc.get("reference"), + keys=("pmid", "citation", "type"), + ) return out @@ -134,7 +141,10 @@ def prepare_criteria_documents( "entities": _entities_for_index(entry.get("entities")), "eligibility_type": entry["eligibility_type"], "criterion_vector": vector, - "constraints": constraint_set.model_dump(mode="json"), + "constraints": json.dumps( + constraint_set.model_dump(mode="json"), + sort_keys=True, + ), } ) return rows @@ -218,6 +228,18 @@ def _preprocess_text(value: str) -> str: return re.sub(r"\s+", " ", value).strip() +def _stable_dict_list(value: Any, *, keys: Sequence[str]) -> list[dict[str, str]]: + rows = value if isinstance(value, list) else [] + normalized: list[dict[str, str]] = [] + for row in rows: + if not isinstance(row, dict): + continue + normalized.append({key: str(row.get(key) or "") for key in keys}) + if normalized: + return normalized + return [{key: "" for key in keys}] + + def _to_iso_date(value: Any) -> str | None: if not value: return None diff --git a/src/trialmatchai/search/lancedb_backend.py b/src/trialmatchai/search/lancedb_backend.py index 651b1226..33b87fc0 100644 --- a/src/trialmatchai/search/lancedb_backend.py +++ b/src/trialmatchai/search/lancedb_backend.py @@ -1,5 +1,6 @@ from __future__ import annotations +import json import math import re from dataclasses import dataclass @@ -507,6 +508,7 @@ def build_trial_record(doc: Mapping[str, Any]) -> dict[str, Any]: def build_criteria_record(doc: Mapping[str, Any]) -> dict[str, Any]: row = dict(doc) entity_text, synonym_text = _flatten_entities(row.get("entities")) + row["entities"] = _json_payload(row.get("entities")) row["entity_text"] = entity_text row["entity_synonyms_text"] = synonym_text row["search_text"] = flatten_text( @@ -746,6 +748,11 @@ def _gender_matches(value: Any, sex: str) -> bool: def _flatten_entities(entities: Any) -> tuple[str, str]: + if isinstance(entities, str): + try: + entities = json.loads(entities) + except json.JSONDecodeError: + return "", "" if not isinstance(entities, list): return "", "" texts: list[str] = [] @@ -761,6 +768,13 @@ def _flatten_entities(entities: Any) -> tuple[str, str]: return flatten_text(texts), flatten_text(synonyms) +def _json_payload(value: Any) -> str: + try: + return json.dumps(value or [], sort_keys=True) + except TypeError: + return "[]" + + def _normalize_text(value: str) -> str: return " ".join(re.findall(r"[a-z0-9]+", value.casefold())) diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index 925591ab..d14d8384 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -50,6 +50,10 @@ def run_preflight_checks( ) if require_models: + reranker_enabled = _reranker_enabled(config) + rag_enabled = _rag_enabled(config) + reranker_backend = _reranker_backend(config) + rag_backend = _rag_backend(config) if entity_cfg: backend = entity_cfg.get("backend", "gliner2") if backend == "gliner2" and importlib.util.find_spec("gliner2") is None: @@ -68,35 +72,53 @@ def run_preflight_checks( ) model_cfg = config.get("model", {}) - _require_path( - issues, - "model.cot_adapter_path", - model_cfg.get("cot_adapter_path"), - required=True, + if rag_enabled and rag_backend == "vllm": + _require_path( + issues, + "model.cot_adapter_path", + model_cfg.get("cot_adapter_path"), + required=True, + ) + if reranker_enabled and reranker_backend == "vllm": + _require_path( + issues, + "model.reranker_adapter_path", + model_cfg.get("reranker_adapter_path"), + required=True, + ) + needs_vllm = (rag_enabled and rag_backend == "vllm") or ( + reranker_enabled and reranker_backend == "vllm" ) - _require_path( - issues, - "model.reranker_adapter_path", - model_cfg.get("reranker_adapter_path"), - required=True, + needs_transformers = (rag_enabled and rag_backend == "transformers") or ( + reranker_enabled and reranker_backend == "transformers" ) - # vLLM is the only LLM backend (CoT + reranker), so it is always required - # when running models. - vllm_available = importlib.util.find_spec("vllm") is not None - if not vllm_available: - issues.append( - "vLLM is required (`uv sync --extra llm --extra gpu`)." - ) - else: - try: - import torch - except Exception: + # vLLM is the production LLM backend. CPU smoke configs may use the + # Transformers backend to exercise model calls without CUDA. + if needs_vllm: + vllm_available = importlib.util.find_spec("vllm") is not None + if not vllm_available: issues.append( - "vLLM requires PyTorch (`uv sync --extra llm --extra gpu`)." + "vLLM is required (`uv sync --extra llm --extra gpu`)." ) else: - if not torch.cuda.is_available(): - issues.append("vLLM requires a CUDA-capable runtime.") + try: + import torch + except Exception: + issues.append( + "vLLM requires PyTorch (`uv sync --extra llm --extra gpu`)." + ) + else: + if not torch.cuda.is_available(): + issues.append("vLLM requires a CUDA-capable runtime.") + if needs_transformers: + if importlib.util.find_spec("torch") is None: + issues.append( + "Transformers CPU backend requires PyTorch (`uv sync --extra llm`)." + ) + if importlib.util.find_spec("transformers") is None: + issues.append( + "Transformers CPU backend requires transformers (`uv sync --extra llm`)." + ) search_cfg = config.get("search_backend", {}) if search_cfg: @@ -162,3 +184,21 @@ def _require_patient_inputs(issues: List[str], config: Dict[str, Any]) -> None: return if profile_dir: issues.append(f"patient_inputs.profile_dir does not exist: {profile_dir}") + + +def _reranker_enabled(config: Dict[str, Any]) -> bool: + return bool(config.get("LLM_reranker", {}).get("enabled", True)) + + +def _rag_enabled(config: Dict[str, Any]) -> bool: + if not bool(config.get("use_cot_reasoning", True)): + return False + return bool(config.get("rag", {}).get("enabled", True)) + + +def _reranker_backend(config: Dict[str, Any]) -> str: + return str(config.get("LLM_reranker", {}).get("backend", "vllm")) + + +def _rag_backend(config: Dict[str, Any]) -> str: + return str(config.get("rag", {}).get("backend", "vllm")) diff --git a/tests/test_deployment_readiness.py b/tests/test_deployment_readiness.py index 9733e21c..898abfa9 100644 --- a/tests/test_deployment_readiness.py +++ b/tests/test_deployment_readiness.py @@ -2,6 +2,7 @@ from trialmatchai.config.config_loader import load_config, resolve_config_path from trialmatchai.matching.eligibility_base import BaseTrialProcessor +from trialmatchai.matching.eligibility_reasoning_transformers import _max_input_tokens from trialmatchai.utils.json_utils import extract_json_object @@ -46,6 +47,54 @@ def test_cot_prompt_does_not_inject_consent(): assert "Written informed consent has been obtained" not in prompt +def test_cot_prompt_falls_back_for_tokenizer_without_chat_template(): + class _TokenizerWithoutTemplate: + def apply_chat_template(self, *args, **kwargs): + raise ValueError("tokenizer.chat_template is not set") + + processor = BaseTrialProcessor.__new__(BaseTrialProcessor) + processor.use_cot = True + processor.tokenizer = _TokenizerWithoutTemplate() + + prompt = processor._format_prompt("Age >= 18", "Patient has lung cancer.") + + assert "Age >= 18" in prompt + assert "Patient has lung cancer." in prompt + assert prompt.endswith("Answer: ") + + +def test_transformers_rag_reserves_generation_context(): + class _Config: + max_position_embeddings = 1024 + + class _Model: + config = _Config() + + class _Tokenizer: + model_max_length = 2048 + + assert _max_input_tokens(_Tokenizer(), _Model(), 64) == 960 + + +def test_token_length_uses_generation_budget_when_available(): + class _Tokenizer: + def __init__(self): + self.kwargs = None + + def __call__(self, prompt, **kwargs): + self.kwargs = kwargs + return {"input_ids": [1, 2, 3]} + + tokenizer = _Tokenizer() + processor = BaseTrialProcessor.__new__(BaseTrialProcessor) + processor.tokenizer = tokenizer + processor.max_input_tokens = 128 + + assert processor._token_length("prompt") == 3 + assert tokenizer.kwargs["truncation"] is True + assert tokenizer.kwargs["max_length"] == 128 + + def test_json_extraction_uses_balanced_object(): output = 'prefix {"outer": {"inner": "value"}, "items": [1, 2]} suffix {"bad": true}' diff --git a/tests/test_embedding.py b/tests/test_embedding.py new file mode 100644 index 00000000..f910e789 --- /dev/null +++ b/tests/test_embedding.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +from trialmatchai.models.embedding import HashingTextEmbedder, build_embedder + + +def test_hashing_embedder_is_deterministic_and_normalized(): + embedder = HashingTextEmbedder(dimensions=16) + first = embedder.embed_text("EGFR lung cancer") + second = embedder.embed_text("EGFR lung cancer") + + assert first == second + assert len(first) == 16 + assert round(sum(value * value for value in first), 6) == 1.0 + + +def test_build_embedder_supports_hashing_backend(): + embedder = build_embedder( + {"embedder": {"backend": "hashing", "hashing_dimensions": 8}} + ) + + assert isinstance(embedder, HashingTextEmbedder) + assert len(embedder.embed_text("melanoma")) == 8 diff --git a/tests/test_entities.py b/tests/test_entities.py index 36b63ec1..968d5fc0 100644 --- a/tests/test_entities.py +++ b/tests/test_entities.py @@ -10,7 +10,11 @@ write_lancedb_table, ) from trialmatchai.entities.linker import ConceptLinker, InMemoryConceptStore -from trialmatchai.entities.recognizers import RegexSchemaRecognizer, resolve_overlaps +from trialmatchai.entities.recognizers import ( + RegexSchemaRecognizer, + _parse_model_entities, + resolve_overlaps, +) from trialmatchai.entities.schemas import load_entity_schemas from trialmatchai.entities.types import ConceptCandidate, EntityAnnotation, NO_ENTITY_ID @@ -39,6 +43,38 @@ def test_regex_backend_returns_current_output_shape(): assert "concept_candidates" in result[0][0] +def test_gliner2_entity_mapping_response_parses_to_annotations(): + schemas = [ + schema for schema in load_entity_schemas() if schema.id in {"disease", "gene"} + ] + label_map = { + label.casefold(): schema + for schema in schemas + for label in schema.recognizer_labels + } + text = "Patient has lung cancer with EGFR mutation." + + annotations = _parse_model_entities( + { + "entities": { + "disease": [ + { + "text": "lung cancer", + "start": 12, + "end": 23, + "confidence": 0.99, + } + ], + "gene": [{"text": "EGFR", "start": 29, "end": 33, "confidence": 0.99}], + } + }, + text, + label_map, + ) + + assert [annotation.text for annotation in annotations] == ["lung cancer", "EGFR"] + + def test_overlap_resolution_keeps_higher_confidence_span(): annotations = [ EntityAnnotation("disease", "lung cancer", 0, 11, 0.91, schema_id="disease"), diff --git a/tests/test_finetuning.py b/tests/test_finetuning.py index 64470f57..38b49800 100644 --- a/tests/test_finetuning.py +++ b/tests/test_finetuning.py @@ -6,10 +6,11 @@ import pytest -from trialmatchai.finetuning.cli import build_parser +from trialmatchai.finetuning.cli import _lora_config_from_args, build_parser from trialmatchai.finetuning.config import FinetuneConfig from trialmatchai.finetuning.data import ( cot_row_to_messages, + gliner2_row_to_training_record, ner_row_to_entities, read_jsonl, reranker_row_to_messages, @@ -60,10 +61,109 @@ def test_ner_native_gliner2_format(): assert out["entities"] == {"gene": ["EGFR"]} +def test_ner_native_gliner2_text_schema_format_and_negative_entities(): + out = ner_row_to_entities( + { + "text": "No reportable biomarker", + "schema": { + "entities": {"gene": []}, + "entity_descriptions": {"gene": "Gene symbols"}, + }, + } + ) + assert out["text"] == "No reportable biomarker" + assert out["entities"] == {"gene": []} + assert out["entity_descriptions"] == {"gene": "Gene symbols"} + + +def test_gliner2_schema_record_preserves_json_structures(): + record = gliner2_row_to_training_record( + { + "text": "Patient has EGFR exon 19 deletion and stage IV NSCLC.", + "schema": { + "entities": {"gene": ["EGFR"]}, + "json_structures": [ + { + "biomarker": { + "gene": "EGFR", + "variant": "exon 19 deletion", + "disease_stage": "stage IV", + } + } + ], + "json_descriptions": { + "biomarker": { + "gene": "Gene symbol", + "variant": "Observed alteration", + } + }, + }, + } + ) + assert record == { + "input": "Patient has EGFR exon 19 deletion and stage IV NSCLC.", + "output": { + "entities": {"gene": ["EGFR"]}, + "json_structures": [ + { + "biomarker": { + "gene": "EGFR", + "variant": "exon 19 deletion", + "disease_stage": "stage IV", + } + } + ], + "json_descriptions": { + "biomarker": { + "gene": "Gene symbol", + "variant": "Observed alteration", + } + }, + }, + } + + +def test_gliner2_record_preserves_classifications_and_relations(): + row = { + "input": "Erlotinib targets EGFR.", + "output": { + "classifications": [ + { + "task": "actionability", + "labels": ["actionable", "not_actionable"], + "true_label": "actionable", + } + ], + "relations": [{"targets": {"head": "Erlotinib", "tail": "EGFR"}}], + }, + } + assert gliner2_row_to_training_record(row) == row + + +def test_gliner2_top_level_structures_alias_normalizes_to_json_structures(): + record = gliner2_row_to_training_record( + { + "text": "Trial requires ECOG 0-1.", + "structures": {"eligibility": {"performance_status": "ECOG 0-1"}}, + } + ) + assert record["output"]["json_structures"] == [ + {"eligibility": {"performance_status": "ECOG 0-1"}} + ] + + def test_read_jsonl_respects_max(tmp_path): path = tmp_path / "d.jsonl" path.write_text("\n".join(json.dumps({"i": i}) for i in range(5))) assert len(read_jsonl(str(path), max_examples=2)) == 2 + assert read_jsonl(str(path), max_examples=0) == [] + + +def test_read_jsonl_reports_invalid_line(tmp_path): + path = tmp_path / "bad.jsonl" + path.write_text('{"ok": true}\nnot-json\n') + with pytest.raises(ValueError, match="bad.jsonl:2"): + read_jsonl(str(path)) def test_cli_parses_each_subcommand(): @@ -83,6 +183,38 @@ def test_cli_parses_each_subcommand(): parser.parse_args(["cot"]) # missing required args +def test_lora_cli_maps_training_controls(): + parser = build_parser() + args = parser.parse_args( + [ + "cot", + "--base-model", + "m", + "--train-data", + "t", + "--output-dir", + "o", + "--eval-data", + "e", + "--target-modules", + "q_proj,v_proj", + "--eval-steps", + "100", + "--save-steps", + "500", + "--device-map", + "none", + ] + ) + cfg = _lora_config_from_args(args) + assert cfg.eval_data == "e" + assert cfg.target_modules == ["q_proj", "v_proj"] + assert cfg.eval_steps == 100 + assert cfg.save_steps == 500 + assert cfg.device_map is None + + def test_finetune_config_training_args_lazy(): cfg = FinetuneConfig(base_model="m", train_data="t", output_dir="o") assert cfg.lora_rank == 32 and cfg.bf16 is True + assert cfg.target_modules == "all-linear" diff --git a/tests/test_lancedb_search_backend.py b/tests/test_lancedb_search_backend.py index 5b94a4a6..978d6398 100644 --- a/tests/test_lancedb_search_backend.py +++ b/tests/test_lancedb_search_backend.py @@ -1,5 +1,7 @@ from __future__ import annotations +import json + import pytest from trialmatchai.search import LanceDBSearchBackend @@ -112,3 +114,70 @@ def test_scan_rows_fallback_applies_nct_filter(tmp_path): assert rows assert {row["nct_id"] for row in rows} == {"N1"} + + +def test_lancedb_backend_serializes_variable_entity_payloads(tmp_path): + backend = LanceDBSearchBackend( + tmp_path / "search", + trials_table="trials", + criteria_table="criteria", + candidate_limit=25, + ) + + backend.index_criteria( + [ + { + "criteria_id": "C1", + "nct_id": "N1", + "criterion": "Confirmed EGFR mutation", + "criterion_vector": [1.0, 0.0], + "entities": [ + { + "entity_group": "gene", + "text": "EGFR", + "start": 10, + "end": 14, + "score": 0.99, + "normalized_id": ["CUI-less"], + "synonyms": ["ERBB1"], + "concept_candidates": [ + { + "concept_id": "EntrezGene:1956", + "concept_name": "epidermal growth factor receptor", + "source_scores": {"fts": 1.0}, + } + ], + "linker_score": None, + "linker_status": "not_linked", + } + ], + "eligibility_type": "Inclusion Criteria", + }, + { + "criteria_id": "C2", + "nct_id": "N1", + "criterion": "No active autoimmune disease", + "criterion_vector": [0.0, 1.0], + "entities": [ + { + "entity_group": "disease", + "text": "autoimmune disease", + "start": 10, + "end": 28, + "score": 0.95, + } + ], + "eligibility_type": "Exclusion Criteria", + }, + ] + ) + + hits = backend.search_criteria( + query="ERBB1", + nct_ids=["N1"], + search_mode="bm25", + use_entity_synonyms=True, + ) + + assert hits[0]["_source"]["criteria_id"] == "C1" + assert json.loads(hits[0]["_source"]["entities"])[0]["text"] == "EGFR" diff --git a/tests/test_patient_runtime_loading.py b/tests/test_patient_runtime_loading.py index 7227e1f0..15f6d896 100644 --- a/tests/test_patient_runtime_loading.py +++ b/tests/test_patient_runtime_loading.py @@ -2,6 +2,7 @@ import json +from trialmatchai.interop.models import PatientProfile from trialmatchai.main import _load_patient_inputs @@ -55,3 +56,57 @@ def test_runtime_requires_canonical_profiles(tmp_path): ) assert loaded == [] + + +def test_main_pipeline_returns_nonzero_when_all_patients_fail(tmp_path, monkeypatch): + import trialmatchai.main as main_module + import trialmatchai.models.embedding as embedding_module + + class _Backend: + def health(self, *, require_tables=False): + return [] + + config = { + "paths": { + "output_dir": str(tmp_path / "results"), + "trials_json_folder": str(tmp_path / "trials"), + }, + "search_backend": {"backend": "lancedb"}, + "patient_inputs": {}, + "search": {"mode": "bm25"}, + "constraints": {"enabled": False}, + "LLM_reranker": {"enabled": False}, + "rag": {"enabled": False}, + "use_cot_reasoning": False, + } + profile = PatientProfile.model_validate({"patient_id": "p1", "demographics": {}}) + summary = { + "patient_id": "p1", + "main_conditions": ["lung cancer"], + "other_conditions": [], + "patient_narrative": ["Patient has lung cancer."], + "age": "all", + "gender": "all", + } + + monkeypatch.setattr(main_module, "load_config", lambda config_path=None: config) + monkeypatch.setattr(main_module, "run_preflight_checks", lambda *args, **kwargs: []) + monkeypatch.setattr( + main_module.LanceDBSearchBackend, + "from_config", + classmethod(lambda cls, cfg: _Backend()), + ) + monkeypatch.setattr(embedding_module, "build_embedder", lambda cfg: object()) + monkeypatch.setattr(main_module, "build_entity_annotator", lambda cfg, embedder: None) + monkeypatch.setattr( + main_module, + "_load_patient_inputs", + lambda cfg: [(profile, summary)], + ) + + def _fail_first_level(*args, **kwargs): + raise RuntimeError("forced patient failure") + + monkeypatch.setattr(main_module, "run_first_level_search", _fail_first_level) + + assert main_module.main_pipeline("config.json") == 1 diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index b5f70e55..52cc0281 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -112,6 +112,34 @@ def test_preflight_reports_missing_vllm_extra(tmp_path, monkeypatch): assert issues == ["vLLM is required (`uv sync --extra llm --extra gpu`)."] +def test_preflight_allows_cpu_smoke_when_llm_stages_disabled(tmp_path, monkeypatch): + cfg = _base_config(tmp_path) + cfg["LLM_reranker"] = {"enabled": False} + cfg["rag"] = {"enabled": False} + cfg["use_cot_reasoning"] = False + monkeypatch.setattr(preflight.importlib.util, "find_spec", lambda name: None) + + issues = run_preflight_checks(cfg, require_models=True) + + assert issues == [] + + +def test_preflight_allows_transformers_cpu_llm_backend(tmp_path, monkeypatch): + cfg = _base_config(tmp_path) + cfg["LLM_reranker"] = {"enabled": True, "backend": "transformers"} + cfg["rag"] = {"enabled": True, "backend": "transformers"} + cfg["use_cot_reasoning"] = True + monkeypatch.setattr( + preflight.importlib.util, + "find_spec", + lambda name: object() if name in {"torch", "transformers"} else None, + ) + + issues = run_preflight_checks(cfg, require_models=True) + + assert issues == [] + + def test_preflight_reports_missing_entity_extra(tmp_path, monkeypatch): cfg = _entity_config(tmp_path) monkeypatch.setattr(preflight.importlib.util, "find_spec", lambda name: None) diff --git a/tests/test_registry_updater.py b/tests/test_registry_updater.py index 061428de..3b9c12d0 100644 --- a/tests/test_registry_updater.py +++ b/tests/test_registry_updater.py @@ -74,6 +74,44 @@ def embed_texts(self, texts): assert "official" in prepared["official_title"].lower() +def test_prepare_documents_emit_lancedb_stable_fields(): + from trialmatchai.registry.preparation import ( + prepare_criteria_documents, + prepare_trial_document, + ) + + class _StubEmbedder: + def embed_texts(self, texts): + return [[0.0, 0.0] for _ in texts] + + trial = prepare_trial_document( + { + "nct_id": "NCT00000010", + "brief_title": "Brief", + "condition": ["Lung cancer"], + "eligibility_criteria": "Adults", + }, + _StubEmbedder(), + ) + criteria = prepare_criteria_documents( + { + "nct_id": "NCT00000010", + "criteria": [ + {"type": "inclusion", "criterion": "Female adults age 18 years or older"} + ], + }, + _StubEmbedder(), + ) + + assert trial["location"] == [ + {"facility": "", "city": "", "state": "", "country": "", "status": ""} + ] + assert trial["minimum_age"] == 0.0 + assert trial["maximum_age"] == 999.0 + assert isinstance(criteria[0]["constraints"], str) + assert json.loads(criteria[0]["constraints"])["nct_id"] == "NCT00000010" + + def test_normalize_study_maps_v2_modules_and_splits_criteria(): normalized = normalize_study(_study("NCT00000004")) From 447c4fed4056ca8fb30834435ad99f53f59b0063 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Fri, 26 Jun 2026 17:59:56 +0200 Subject: [PATCH 35/64] feat: streamlined two-halves CLI (build/match), faithful preprocessing, official TREC eval Clean build-time vs run-time split with robust, resumable, well-logged commands. CLI / orchestration: - Add `trialmatchai build`: prepare corpus (embeddings/entities) + build index as one resumable command (per-trial resume, streaming/bounded memory, per-trial failure isolation, build manifest, `--status` introspection). - Add `trialmatchai e2e` (ingest -> index -> match) and `trialmatchai trec` (official TREC eval); both idempotent with per-patient resume. - New orchestration module (ingest/expand/index/match/build stages). - Fail-fast preflight: CUDA + extras + HuggingFace gated-model access checked upfront; match preflight points users at `trialmatchai build`. Preprocessing: - Restore faithful eligibility-criteria chunking (semicolon split + >200-char full-stop re-split) to match the legacy pipeline. - prepare_criteria_documents falls back to chunking raw eligibility_criteria when a trial doc has no pre-chunked criteria (so build works from existing trials_jsons). Query expansion: - Re-introduce the runtime CoT query-expansion module (legacy keywords.json behaviour); configurable model/backend, off by default, on for TREC. TREC harness: - Official topic + qrels download (NIST), per-track corpus derived from qrels, recall@k evaluation. Docs: - README overhaul: centered logo/hero, two-halves quickstart, grouped CLI reference; corrected Nature Communications citation (vol 17, 4472, 2026). Co-Authored-By: Claude Opus 4.8 --- .env.example | 7 + .gitignore | 6 + README.md | 167 ++++-- img/logo.png | Bin 1067715 -> 758820 bytes pyproject.toml | 3 + src/trialmatchai/cli/build.py | 112 ++++ src/trialmatchai/cli/e2e.py | 69 +++ src/trialmatchai/cli/main.py | 21 + src/trialmatchai/cli/trec.py | 69 +++ src/trialmatchai/config/config.json | 9 + src/trialmatchai/config/settings.py | 20 + src/trialmatchai/main.py | 30 +- src/trialmatchai/matching/query_expansion.py | 199 +++++++ src/trialmatchai/orchestration.py | 519 ++++++++++++++++++ .../registry/criteria_chunking.py | 58 +- src/trialmatchai/registry/preparation.py | 10 +- src/trialmatchai/services/preflight.py | 88 +++ src/trialmatchai/trec/__init__.py | 17 + src/trialmatchai/trec/corpus.py | 71 +++ src/trialmatchai/trec/qrels.py | 159 ++++++ src/trialmatchai/trec/runner.py | 146 +++++ src/trialmatchai/trec/topics.py | 275 ++++++++++ 22 files changed, 1982 insertions(+), 73 deletions(-) create mode 100644 src/trialmatchai/cli/build.py create mode 100644 src/trialmatchai/cli/e2e.py create mode 100644 src/trialmatchai/cli/trec.py create mode 100644 src/trialmatchai/matching/query_expansion.py create mode 100644 src/trialmatchai/orchestration.py create mode 100644 src/trialmatchai/trec/__init__.py create mode 100644 src/trialmatchai/trec/corpus.py create mode 100644 src/trialmatchai/trec/qrels.py create mode 100644 src/trialmatchai/trec/runner.py create mode 100644 src/trialmatchai/trec/topics.py diff --git a/.env.example b/.env.example index d10d303e..5250dac3 100644 --- a/.env.example +++ b/.env.example @@ -30,6 +30,13 @@ TRIALMATCHAI_MODEL_RERANKER_ADAPTER_PATH=models/finetuned_gemma2 TRIALMATCHAI_MODEL_TRUST_REMOTE_CODE=false TRIALMATCHAI_COT_BACKEND=vllm +# Runtime CoT query expansion (legacy keywords.json behaviour). Off by default; +# the TREC preset enables it. Model/adapter default to the CoT reasoning model. +TRIALMATCHAI_QUERY_EXPANSION_ENABLED=false +TRIALMATCHAI_QUERY_EXPANSION_BACKEND= +TRIALMATCHAI_QUERY_EXPANSION_MODEL= +TRIALMATCHAI_QUERY_EXPANSION_ADAPTER= + TRIALMATCHAI_ENTITY_BACKEND=gliner2 TRIALMATCHAI_ENTITY_MODEL_NAME=fastino/gliner2-base-v1 TRIALMATCHAI_ENTITY_SCHEMA_PATH=entity_schemas/trialmatchai.yaml diff --git a/.gitignore b/.gitignore index 1efa6385..7862acbf 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,12 @@ logs/ /source/ /Parser/ +# ============================ +# Local working dirs (large data stores / uncommitted code) +# ============================ +/elasticsearch/ +/utils/ + # ============================ # Generated indexing state # ============================ diff --git a/README.md b/README.md index 703f713b..85ef1034 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,37 @@ -# TrialMatchAI +
    -TrialMatchAI logo +TrialMatchAI -TrialMatchAI is an AI-driven clinical trial matching pipeline. It imports patient -data, retrieves relevant trials from local LanceDB tables, and produces ranked -trial recommendations with criterion-level eligibility explanations. +

    TrialMatchAI

    -The supported deployment is a single Python 3.11 GPU server. Trial search is -embedded and file-backed; there is no Elasticsearch, hosted vector database, or -separate search service to run. +

    AI-driven clinical trial matching. Import a patient — text, FHIR, Phenopacket, or OMOP — and get ranked, eligible trials with criterion-level eligibility explanations. Local LanceDB search + vLLM reasoning on a single GPU server; no Elasticsearch or hosted vector database to run.

    -[Install](#install) | [Quickstart](#quickstart) | [How It Works](#how-it-works) | [Configuration](#configuration) | [CLI](#cli-reference) +

    + Install · + Quickstart · + How it works · + Configuration · + CLI +

    -> For research and informational use only. TrialMatchAI is not medical advice, -> not a medical device, and must not replace review by qualified healthcare -> professionals. +
    + +> **⚕️ For research and informational use only.** TrialMatchAI is not medical +> advice, not a medical device, and must not replace review by qualified +> healthcare professionals. + +## TL;DR + +TrialMatchAI runs in **two halves**: **build the system once**, then **match +patients many times**. Both commands are idempotent and resume after disruption. + +```bash +uv sync --extra llm --extra gpu --extra entity # GPU host + HuggingFace access +uv run trialmatchai-bootstrap-data # fetch prepared corpus + adapters +uv run trialmatchai build # 1) BUILD: prepare + index (once) +uv run trialmatchai e2e --input patient.txt # 2) MATCH: ingest + match a patient +# -> results//ranked_trials.json +``` ## Requirements @@ -78,69 +95,79 @@ pip install -e ".[finetune]" | `gpu` | vLLM and bitsandbytes; intended for Linux CUDA hosts | | `finetune` | training dependencies for `trialmatchai-finetune` | -Installing the package is only the first step. Real matching also needs runtime -data, model artifacts, a concept table, normalized trials, and LanceDB search -tables. +Installing the package only gives you the CLI. Real matching also needs the +trial corpus, model artifacts, and LanceDB search tables — all produced by the +**build** step below. ## Quickstart -Copy the environment template if you need local overrides: +The pipeline has two halves. **Build** is the heavy, one-time setup (GPU); it is +resumable and only does work that is not already done. **Match** is fast and +repeatable against the built system. -```bash -cp .env.example .env -``` - -Check the installation and configured paths: +### 0. Set up the runtime (GPU host) ```bash -uv run trialmatchai-healthcheck +uv sync --extra llm --extra gpu --extra entity # model-backed runtime +cp .env.example .env # optional local overrides +export HF_TOKEN= # required for gated models (phi-4, gemma-2) ``` -Download packaged artifacts: +### 1. Build the system — once ```bash -uv run trialmatchai-bootstrap-data +uv run trialmatchai-bootstrap-data # download the prepared corpus + LoRA adapters +uv run trialmatchai build # prepare embeddings/entities + build the index +uv run trialmatchai build --status # see exactly what is built (and what isn't) ``` -Build the concept-linking table from OMOP vocabulary files: +`build` fails fast if a GPU, an extra, or model access is missing — and resumes +from where it left off if interrupted. Bringing your **own** trials instead of +bootstrapping? Put normalized JSON in `data/trials_jsons/` and `build` will +prepare them. To also enable concept linking, pass an OMOP vocabulary: ```bash -uv run trialmatchai-build-concepts \ - --concept-csv data/omop/CONCEPT.csv \ - --synonym-csv data/omop/CONCEPT_SYNONYM.csv +uv run trialmatchai build --concepts-csv data/omop/CONCEPT.csv --synonym-csv data/omop/CONCEPT_SYNONYM.csv ``` -Fetch recent ClinicalTrials.gov studies and update local trial JSON plus -LanceDB: - -```bash -uv run trialmatchai-update-registry --since 2026-06-01 --max-studies 100 -``` +### 2. Match patients — repeatably -Build or rebuild search tables from normalized trial JSON: +`e2e` ingests the patient (format auto-detected) and matches in one command: ```bash -uv run trialmatchai-index --prepare +uv run trialmatchai e2e --input data/patients/raw/patient-1.txt +uv run trialmatchai e2e --input data/patients/raw/patient-1.fhir.json +uv run trialmatchai e2e --input data/patients/omop_extract ``` -Import patients into canonical TrialMatchAI profiles: +Results land in `results//` (ranked trials + eligibility +explanations). Re-running skips patients already matched. + +### Health and maintenance ```bash -uv run trialmatchai-import-patient --input data/patients/raw/patient-1.txt --format text -uv run trialmatchai-import-patient --input data/patients/raw/patient-1.fhir.json -uv run trialmatchai-import-patient --input data/patients/omop_extract --format omop +uv run trialmatchai-healthcheck # validate config/paths/deps +uv run trialmatchai-update-registry --since 2026-06-01 # pull new ClinicalTrials.gov studies ``` -Run batch matching: +
    +Manual / advanced control (the steps build and e2e wrap) ```bash -uv run trialmatchai-run +uv run trialmatchai-index --prepare # prepare + index from trials_jsons (what `build` runs) +uv run trialmatchai-import-patient --input patient.txt # stage a profile only +uv run trialmatchai-run # match already-staged profiles +uv run trialmatchai trec --tracks "21 22" # benchmark: official TREC CT eval ``` -Results are written under `results//`. +
    ## How It Works +The diagram below is the **match** path. The one-time **build** step produces the +LanceDB index it queries — trial and criterion embeddings, entity annotations, +and parsed eligibility constraints. + ```text Patient data (text / FHIR / Phenopacket / OMOP) | @@ -323,23 +350,38 @@ The full override list is in [`.env.example`](.env.example). ## CLI Reference +Every command is available both as a flat script (`trialmatchai-build`) and as a +subcommand (`trialmatchai build`); they are equivalent. + +**Build the system (setup half)** + | Command | Purpose | | --- | --- | -| `trialmatchai` | Command group for the main subcommands | -| `trialmatchai-healthcheck` | Validate config, paths, optional model deps, and LanceDB tables | -| `trialmatchai-bootstrap-data` | Download and extract runtime data/model artifacts | -| `trialmatchai-build-concepts` | Build the LanceDB concept table for entity normalization | -| `trialmatchai-update-registry` | Fetch changed ClinicalTrials.gov studies and upsert LanceDB | -| `trialmatchai-index` | Prepare/index trial and criteria search tables | -| `trialmatchai-import-patient` | Import text, FHIR, Phenopacket, or OMOP patient data | -| `trialmatchai-run` | Run the batch matching pipeline | -| `trialmatchai-finetune` | Fine-tune NER, reranker, or eligibility reasoning models | +| `trialmatchai build` | Prepare the corpus (embeddings + entities) and build the search index — resumable, with `--status` | +| `trialmatchai-bootstrap-data` | Download and extract the prepared corpus + model adapters | +| `trialmatchai build-concepts` | Build the LanceDB concept table for entity normalization (optional, OMOP) | +| `trialmatchai update-registry` | Fetch changed ClinicalTrials.gov studies and upsert LanceDB | + +**Match patients (run half)** -The first seven commands are also available as subcommands: +| Command | Purpose | +| --- | --- | +| `trialmatchai e2e` | Ingest a patient and match end-to-end (idempotent, per-patient resume) | +| `trialmatchai import-patient` | Import text, FHIR, Phenopacket, or OMOP patient data into a profile | +| `trialmatchai run` | Match already-staged patient profiles | +| `trialmatchai trec` | Benchmark: end-to-end evaluation on the official TREC Clinical Trials tracks | + +**Utility** + +| Command | Purpose | +| --- | --- | +| `trialmatchai healthcheck` | Validate config, paths, optional model deps, and LanceDB tables | +| `trialmatchai index` | Lower-level prepare/index of trial and criteria search tables | +| `trialmatchai-finetune` | Fine-tune NER, reranker, or eligibility reasoning models | ```bash -uv run trialmatchai healthcheck -uv run python -m trialmatchai healthcheck +uv run trialmatchai build --status # what is built +uv run python -m trialmatchai e2e --input patient.txt ``` ## Deployment @@ -385,17 +427,20 @@ upgrading vLLM or Torch. If you use TrialMatchAI in your research, please cite the Nature Communications paper: -> Abdallah, M. _et al._ TrialMatchAI. _Nature Communications_ (2026). -> +> Abdallah, M. _et al._ TrialMatchAI: an end-to-end AI-powered clinical trial +> recommendation system to streamline patient-to-trial matching. _Nature +> Communications_ **17**, 4472 (2026). ```bibtex -@article{trialmatchai, - title = {TrialMatchAI}, - author = {Abdallah, Majd and others}, +@article{abdallah2026trialmatchai, + title = {TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching}, + author = {Abdallah, Majd and Nakken, Sigve and Georges, Mikael and Bierkens, Mariska and Galvis, Johanna and Groppi, Alexis and Karkar, Slim and Meiqari, Lana and Rujano, Maria Alexandra and Canham, Steve and Dienstmann, Rodrigo and Fijneman, Remond and Hovig, Eivind and Meijer, Gerrit and Nikolski, Macha}, journal = {Nature Communications}, + volume = {17}, + pages = {4472}, year = {2026}, doi = {10.1038/s41467-026-70509-w}, - url = {https://www.nature.com/articles/s41467-026-70509-w} + url = {https://doi.org/10.1038/s41467-026-70509-w} } ``` diff --git a/img/logo.png b/img/logo.png index b5ff2ac1c57fdf78bca72a9c7917e0fbe8d2aeaf..15a654b8b0a5cdc36875076fe6b855eb20a4e8f9 100644 GIT binary patch literal 758820 zcmeFZ1z42Z_b*PGmlULUPMGh115taiin6U ziin8mQzr`4Y;MSZ5fPEKP@^Kr!%wr>$U0#lTBsN!QDPXO2B3f_v}}jwCY}< zBH3@!dW^l>;}F>P$wr;YVD!`!d?uZr0Fh2_Ba2eo82!g_+weM4}ms902CTn}Q=VBl{42LEpqYyY4ia;P^ z5Lg%vhf7A{K^cKg4!NNq0BBO9v7!rj@M&Qvf0urB$LZiU2$p{=MVz598 z1Pp=3gV!h+hJYm_abR>P+#M2ph6Yk#U}#J-5(tYVAd``3ASAZtfgo2`hh+`70)te2 z1qKiI5*{<$DUhjZ5CXY+P+W}+KV$;sDj7lVKqUeaU84hrVaaGb7L}F@D+d)uyueUCj1eE1hWmw06qi~bXGMhC=LqLLx2{-=o3&lGBWJvz+tN< z7YphM;mHn{0bF7D{wU*MI09aV0zv}VI7ncW5Eh_-0agHL2Z_ak*+zjf0)_$n@IxsS z8nh1!Tcacjcszu_$*Pq_)I3oDg(_{r^BUF;O-4cl7?=wn02-JX5D1Be;emo-dmx18 z5m+ZOJl{Yd0)ZeyVSqOQ@M@~VIE0-Di3WUy1YUqe0cQhpgyjLoi~=BI2nY@e1sDj- 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zkeq}2WDjHBa#$Cwrld>V2i-}1e_5kn_G9ZPf~u)(PA5lh^C!ozPyLoy>bMbwM zaN3sp!h)mj(AC@hgtQ>QDI>^5IyJ+xHCITg>sN$V=cFd}^)~v`1|1{>S|mz{bBCe> z=L*n`ywVs-X|XhP%nB1$GY6+cXy1P_Mou_JXTK zKDMavoFTCcK)0GUqzFM0AOIyjBM$z1%Nl(!qIdd=1`z~XCs>rzHHKlA?_726D%yNo zXcfr>p0IG1vn29$)SR6mtf&@YzMTa-Xlt((0_!(I*O}i&Jk(|`?T5NbufLYkO%NPJ zR5EO(=Zq@6q{pD9294I>K3h!>!Do%u0`hSZF9-2oTQ{(Nl?1JV4CVaBSc`%Y1Smzn)Z#}1}jdYUD8*@z| zS+JhV-DK3|9Fq17?j{$f&e>2C=%`Um0t&BDLXGPiz5OrnA;SHMVEcSY+w?CP_c7RU?;<}#NXnuYkqIzjr`#!aryRU;VP_MML z@YzhZVqnDZqBEY=_uPlH$$L#c9lqejN)*;e#n@pNc=!K42$q1SJ~OXz23NRT=910R z=VibT%DOR;H{SmA?qjsyU0eXMXn~Jc9i*1zN>+w*N*LEV`^yc0fQ(A*0$Fwy5;mA5XDBNr-d-alft{a=#2I6&wQm~qKLfL!*Ft= zvC00uXM{V>PzzG3!siMfhXWP>*2pPs_dBxRHT}rTscTy z^4S$gfk*9CxS{(jM>Co0+#hY1FMFG{axO|5&LZdYSo!=t=}-v(=`CLCT>qrN2=;v2 z#$2tHUdoDmYWC!N=q?sJ-;rh4PBHG| int: + parser = argparse.ArgumentParser( + description="Build the TrialMatchAI system (prepare corpus + search index)." + ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument( + "--trials-json-folder", + default=None, + help="Normalized trial JSONs to prepare from. Defaults to paths.trials_json_folder.", + ) + parser.add_argument("--processed-trials-folder", default="data/processed_trials") + parser.add_argument("--processed-criteria-folder", default="data/processed_criteria") + parser.add_argument( + "--concepts-csv", + default=None, + help="OMOP CONCEPT.csv to also build the concept-linking DB (optional).", + ) + parser.add_argument("--synonym-csv", default=None, help="OMOP CONCEPT_SYNONYM.csv (optional).") + parser.add_argument("--force-prepare", action="store_true", help="Re-prepare all trials.") + parser.add_argument("--reindex", action="store_true", help="Rebuild the index even if present.") + parser.add_argument( + "--status", action="store_true", help="Print what is already built and exit." + ) + args = parser.parse_args() + + config = load_config(args.config) + + if args.status: + state = build_state( + config, + processed_trials_folder=args.processed_trials_folder, + processed_criteria_folder=args.processed_criteria_folder, + ) + print(json.dumps(state, indent=2)) + ready = state["ready_to_match"] + logger.info("System %s to match.", "READY" if ready else "NOT ready") + return 0 if ready else 1 + + # Fail fast on missing GPU / extras / HF access before any heavy work. + preflight_issues = run_build_preflight(config) + if preflight_issues: + logger.error("Build aborted: resolve the %s issue(s) above.", len(preflight_issues)) + return 1 + + build_system( + config, + trials_json_folder=args.trials_json_folder, + processed_trials_folder=args.processed_trials_folder, + processed_criteria_folder=args.processed_criteria_folder, + force_prepare=args.force_prepare, + force_reindex=args.reindex, + ) + + # Optional: chain concept-DB build (needs user-supplied OMOP CSV). + if args.concepts_csv: + logger.info("=== build: concepts stage ===") + from trialmatchai.cli.build_concepts import main as build_concepts_main + + argv = ["--concept-csv", args.concepts_csv] + if args.synonym_csv: + argv += ["--synonym-csv", args.synonym_csv] + if args.config: + argv += ["--config", args.config] + saved = sys.argv + try: + sys.argv = ["trialmatchai build-concepts", *argv] + build_concepts_main() + finally: + sys.argv = saved + else: + logger.warning( + "Concept DB not built (no --concepts-csv): entity->concept linking will " + "degrade gracefully. Supply an OMOP CONCEPT.csv to enable it." + ) + + state = build_state( + config, + processed_trials_folder=args.processed_trials_folder, + processed_criteria_folder=args.processed_criteria_folder, + ) + logger.info("Build done. ready_to_match=%s", state["ready_to_match"]) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/trialmatchai/cli/e2e.py b/src/trialmatchai/cli/e2e.py new file mode 100644 index 00000000..4d3b32b3 --- /dev/null +++ b/src/trialmatchai/cli/e2e.py @@ -0,0 +1,69 @@ +"""``trialmatchai e2e`` — run the whole pipeline end-to-end in one command. + +Ingest patient inputs (any supported format, auto-detected) -> build the search +index -> match. Every stage is idempotent: re-running skips already-imported +patients, an existing index, and already-matched patients. +""" + +from __future__ import annotations + +import argparse +import sys + +from trialmatchai.config.config_loader import load_config +from trialmatchai.orchestration import run_e2e +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Run the TrialMatchAI pipeline end-to-end (ingest -> index -> match)." + ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument( + "--input", + action="append", + default=[], + help="Patient input file or directory (repeatable). Format is auto-detected. " + "Omit if profiles are already staged.", + ) + parser.add_argument( + "--format", + default="auto", + choices=["auto", "text", "phenopacket", "fhir", "fhir-ndjson", "omop"], + help="Input format for --input. Defaults to auto-detection.", + ) + parser.add_argument( + "--processed-trials-folder", + default="data/processed_trials", + help="Folder of prepared trial JSON files used to build the index.", + ) + parser.add_argument( + "--processed-criteria-folder", + default="data/processed_criteria", + help="Folder of prepared criteria subfolders used to build the index.", + ) + parser.add_argument("--no-entities", action="store_true", help="Skip entity annotation on ingest.") + parser.add_argument("--reingest", action="store_true", help="Re-import patients even if profiles exist.") + parser.add_argument("--reindex", action="store_true", help="Rebuild the search index even if present.") + parser.add_argument("--rematch", action="store_true", help="Re-match patients even if results exist.") + args = parser.parse_args() + + config = load_config(args.config) + return run_e2e( + config, + args.input, + input_format=args.format, + with_entities=not args.no_entities, + processed_trials_folder=args.processed_trials_folder, + processed_criteria_folder=args.processed_criteria_folder, + force_reingest=args.reingest, + force_reindex=args.reindex, + force_rematch=args.rematch, + ) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/trialmatchai/cli/main.py b/src/trialmatchai/cli/main.py index f5393719..ef6841e3 100644 --- a/src/trialmatchai/cli/main.py +++ b/src/trialmatchai/cli/main.py @@ -37,7 +37,22 @@ def main() -> int: help="Import patient data profiles", add_help=False, ) + subparsers.add_parser( + "build", + help="Build the system (prepare corpus + search index), resumable", + add_help=False, + ) subparsers.add_parser("run", help="Run the matching pipeline", add_help=False) + subparsers.add_parser( + "e2e", + help="Run the whole pipeline end-to-end (ingest -> index -> match), idempotent", + add_help=False, + ) + subparsers.add_parser( + "trec", + help="End-to-end TREC CT evaluation (preset over e2e)", + add_help=False, + ) args, remainder = parser.parse_known_args() if args.command == "healthcheck": @@ -52,8 +67,14 @@ def main() -> int: from trialmatchai.cli.update_registry import main as command elif args.command == "import-patient": from trialmatchai.cli.import_patient import main as command + elif args.command == "build": + from trialmatchai.cli.build import main as command elif args.command == "run": from trialmatchai.cli.run import main as command + elif args.command == "e2e": + from trialmatchai.cli.e2e import main as command + elif args.command == "trec": + from trialmatchai.cli.trec import main as command else: # pragma: no cover - argparse enforces choices parser.error(f"Unknown command: {args.command}") diff --git a/src/trialmatchai/cli/trec.py b/src/trialmatchai/cli/trec.py new file mode 100644 index 00000000..f81df383 --- /dev/null +++ b/src/trialmatchai/cli/trec.py @@ -0,0 +1,69 @@ +"""``trialmatchai trec`` — end-to-end evaluation over the TREC CT tracks. + +A preset over the core e2e orchestration: converts TREC patient topics, builds a +per-track search index restricted to each track's NCT collection, and runs +matching with per-patient resume. Idempotent — re-running skips finished work. +""" + +from __future__ import annotations + +import argparse +import sys + +from trialmatchai.trec.corpus import DEFAULT_TRACKS, TRACK_KEYS +from trialmatchai.trec.runner import run_tracks + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Run TrialMatchAI end-to-end on the official TREC Clinical Trials tracks." + ) + parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") + parser.add_argument( + "--tracks", + default=" ".join(DEFAULT_TRACKS), + help=f"Space-separated track keys to run. Choices: {', '.join(TRACK_KEYS)}. " + f"Default: {' '.join(DEFAULT_TRACKS)} (sigir has no official direct source).", + ) + parser.add_argument("--data-dir", default="data", help="Base data directory.") + parser.add_argument( + "--results-root", default=".", help="Root under which results_trec/ are written." + ) + parser.add_argument( + "--processed-trials-folder", + default=None, + help="Prepared trial JSONs for indexing. Defaults to /processed_trials. " + "Point at /nfs/scratch to build the index from there while data-dir lives on /nfs/home.", + ) + parser.add_argument( + "--processed-criteria-folder", + default=None, + help="Prepared criteria subfolders for indexing. Defaults to /processed_criteria.", + ) + parser.add_argument( + "--index-only", + action="store_true", + help="Convert + build indexes only; skip matching (e.g. on a CPU node).", + ) + parser.add_argument("--reindex", action="store_true", help="Rebuild indexes even if present.") + parser.add_argument("--rematch", action="store_true", help="Re-match patients even if results exist.") + parser.add_argument("--no-eval", action="store_true", help="Skip recall@k evaluation against qrels.") + args = parser.parse_args() + + track_keys = args.tracks.split() + return run_tracks( + track_keys, + config_path=args.config, + data_dir=args.data_dir, + results_root=args.results_root, + processed_trials_folder=args.processed_trials_folder, + processed_criteria_folder=args.processed_criteria_folder, + index_only=args.index_only, + evaluate=not args.no_eval, + force_reindex=args.reindex, + force_rematch=args.rematch, + ) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/trialmatchai/config/config.json b/src/trialmatchai/config/config.json index 8e102601..7f38af8b 100644 --- a/src/trialmatchai/config/config.json +++ b/src/trialmatchai/config/config.json @@ -113,6 +113,15 @@ "unknown_is_neutral": true, "write_reports": true }, + "query_expansion": { + "enabled": false, + "backend": null, + "model": null, + "adapter": null, + "max_new_tokens": 2048, + "max_main_conditions": 11, + "max_other_conditions": 50 + }, "use_cot_reasoning": true, "rag": { "batch_size": 4, diff --git a/src/trialmatchai/config/settings.py b/src/trialmatchai/config/settings.py index 92ac56cc..73429169 100644 --- a/src/trialmatchai/config/settings.py +++ b/src/trialmatchai/config/settings.py @@ -197,6 +197,19 @@ class LLMRerankerSettings(BaseModel): batch_size: int = Field(20, ge=1) +class QueryExpansionSettings(BaseModel): + """Runtime CoT query expansion (legacy keywords.json behaviour).""" + + enabled: bool = False + backend: Literal["vllm", "transformers"] | None = None + model: str | None = None + adapter: str | None = None + max_new_tokens: int = Field(2048, ge=1) + max_main_conditions: int = Field(11, ge=1) + max_other_conditions: int = Field(50, ge=1) + trust_remote_code: bool = False + + class TrialMatchSettings(BaseModel): entity_extraction: EntityExtractionSettings = Field( default_factory=EntityExtractionSettings @@ -214,6 +227,9 @@ class TrialMatchSettings(BaseModel): LLM_reranker: LLMRerankerSettings search: SearchSettings constraints: ConstraintSettings = Field(default_factory=ConstraintSettings) + query_expansion: QueryExpansionSettings = Field( + default_factory=QueryExpansionSettings + ) use_cot_reasoning: bool = True rag: RagSettings vllm: VllmSettings @@ -246,6 +262,9 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "base_model_revision", ), "TRIALMATCHAI_MODEL_COT_ADAPTER_PATH": ("model", "cot_adapter_path"), + "TRIALMATCHAI_QUERY_EXPANSION_MODEL": ("query_expansion", "model"), + "TRIALMATCHAI_QUERY_EXPANSION_BACKEND": ("query_expansion", "backend"), + "TRIALMATCHAI_QUERY_EXPANSION_ADAPTER": ("query_expansion", "adapter"), "TRIALMATCHAI_MODEL_RERANKER_MODEL_PATH": ( "model", "reranker_model_path", @@ -308,6 +327,7 @@ def apply_env_overrides(raw: Dict[str, Any]) -> Dict[str, Any]: "unknown_is_neutral", ), "TRIALMATCHAI_CONSTRAINTS_WRITE_REPORTS": ("constraints", "write_reports"), + "TRIALMATCHAI_QUERY_EXPANSION_ENABLED": ("query_expansion", "enabled"), "TRIALMATCHAI_FIRST_LEVEL_ENABLED": ("search", "first_level", "enabled"), "TRIALMATCHAI_FIRST_LEVEL_LLM_EXPANSION_ENABLED": ( "search", diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index bbee0214..aec62f93 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -311,9 +311,15 @@ def run_rag_processing( logger.info("RAG-based trial matching complete.") -def main_pipeline(config_path: str | None = None) -> int: +def main_pipeline( + config_path: str | None = None, + *, + config: Dict | None = None, + resume: bool = False, +) -> int: logger.info("Starting TrialMatchAI pipeline...") - config = load_config(config_path) + if config is None: + config = load_config(config_path) paths = config["paths"] create_directory(paths["output_dir"]) @@ -334,6 +340,10 @@ def main_pipeline(config_path: str | None = None) -> int: require_search_tables=True, ) if index_issues: + logger.error( + "The search system is not built. Run `trialmatchai build` to prepare " + "the corpus and build the index, then retry matching." + ) return 1 patient_inputs = _load_patient_inputs(config) @@ -407,10 +417,17 @@ def main_pipeline(config_path: str | None = None) -> int: completed_patients = 0 failed_patients = 0 + skipped_patients = 0 for profile, summary in patient_inputs: patient_id = profile.patient_id - token = set_request_id(patient_id) output_folder = Path(paths["output_dir"]) / patient_id + if resume: + ranked_path = output_folder / "ranked_trials.json" + if ranked_path.exists() and ranked_path.stat().st_size > 0: + logger.info("Resume: skipping already-matched patient %s", patient_id) + skipped_patients += 1 + continue + token = set_request_id(patient_id) create_directory(str(output_folder)) try: @@ -498,9 +515,14 @@ def main_pipeline(config_path: str | None = None) -> int: finally: reset_request_id(token) - if completed_patients == 0: + if completed_patients == 0 and skipped_patients == 0: logger.error("Pipeline failed for all %s patient(s).", len(patient_inputs)) return 1 + if completed_patients == 0 and skipped_patients: + logger.info( + "Resume: all %s patient(s) already matched; nothing to do.", + skipped_patients, + ) if failed_patients: logger.warning("Pipeline completed with %s patient failure(s).", failed_patients) return 0 diff --git a/src/trialmatchai/matching/query_expansion.py b/src/trialmatchai/matching/query_expansion.py new file mode 100644 index 00000000..0ec4cd68 --- /dev/null +++ b/src/trialmatchai/matching/query_expansion.py @@ -0,0 +1,199 @@ +"""Runtime CoT query expansion (re-introduced from the legacy pipeline). + +The legacy matcher ran a chain-of-thought model over each patient's narrative to +produce the expanded ``keywords.json`` (primary conditions + synonyms, secondary +factors, expanded sentences) that feeds first-level retrieval. The refactor +dropped this; this module restores it faithfully while making the model a config +knob. + +Verbatim SYSTEM_PROMPT and generation behaviour are preserved from +``source/Matcher/pipeline/phenopacket_processor.py`` (main branch). The model and +backend default to the configured CoT reasoning model but are overridable via the +``query_expansion`` config block. Disabled by default; the TREC preset turns it on. +""" + +from __future__ import annotations + +from typing import Any, Dict, List + +from trialmatchai.utils.json_utils import extract_json_object +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + +# Verbatim from the legacy ClinicalSummarizer.generate_summary SYSTEM_PROMPT. +SYSTEM_PROMPT = """ +You are a specialized medical assistant designed for precise and accurate clinical trial matching. +Analyze the patient's medical description carefully and extract clinically relevant information for trial eligibility assessment. + +1. **Primary Condition**: + - Determine the primary medical conditions based on explicit patient information and overall clinical context. + - List up to 10 medically recognized synonyms, aliases, or closely related medical terms for the primary conditions. + - Include the identified primary conditions and their associated synonyms or related terms within the "main_conditions" list. + +2. **Secondary Clinical Factors**: + - Provide up to 50 clinically significant additional factors, including comorbidities, concurrent medical conditions, molecular or genetic biomarkers, prior therapies, relevant medical history, and clinically notable patient characteristics explicitly mentioned in the patient description. + - Provide these factors in the "other_conditions" list. + +3. **Expanded Clinical Descriptions**: + - Based solely on the original patient-provided data, generate semantically accurate and medically sound statements resembling real-life medical notes. + - **Crucial**: Expanded descriptions must strictly reflect explicit patient-reported information without introducing new or inferred medical details. + +Output: +Return a JSON object in the exact following structure without any additional commentary: + +{ +"main_conditions": ["PrimaryCondition", "Synonym1", "Synonym2", "..."], +"other_conditions": ["AdditionalCondition1", "AdditionalCondition2", "..."], +"expanded_sentences": [ + "Expanded note for sentence 1...", + "Expanded note for sentence 2...", + "..." +] +} +""".strip() + +_EMPTY = {"main_conditions": [], "other_conditions": [], "expanded_sentences": []} + + +def _resolve_settings(config: Dict[str, Any]) -> Dict[str, Any]: + qe = dict(config.get("query_expansion") or {}) + model_cfg = config.get("model", {}) + return { + "backend": qe.get("backend") or config.get("cot_backend") or "transformers", + "model": qe.get("model") or model_cfg.get("base_model"), + "adapter": qe.get("adapter", model_cfg.get("cot_adapter_path")), + "device": str(config.get("global", {}).get("device", 0)), + "max_new_tokens": int(qe.get("max_new_tokens", 2048)), + "trust_remote_code": bool( + qe.get("trust_remote_code", model_cfg.get("trust_remote_code", False)) + ), + "max_main_conditions": int(qe.get("max_main_conditions", 11)), + "max_other_conditions": int(qe.get("max_other_conditions", 50)), + } + + +class QueryExpander: + """CoT expander; loads its model lazily so import stays base-deps safe.""" + + def __init__(self, settings: Dict[str, Any]): + self.settings = settings + self.backend = settings["backend"] + if not settings.get("model"): + raise ValueError("query_expansion requires a model (set query_expansion.model or model.base_model)") + if self.backend == "vllm": + self._init_vllm() + elif self.backend == "transformers": + self._init_transformers() + else: + raise ValueError(f"Unsupported query_expansion.backend: {self.backend}") + + # -- backends ---------------------------------------------------------- # + def _init_transformers(self) -> None: + import torch # noqa: F401 + from transformers import AutoModelForCausalLM, AutoTokenizer + + s = self.settings + self.tokenizer = AutoTokenizer.from_pretrained( + s["model"], trust_remote_code=s["trust_remote_code"] + ) + model = AutoModelForCausalLM.from_pretrained( + s["model"], + torch_dtype="auto", + device_map="auto", + trust_remote_code=s["trust_remote_code"], + ) + if s.get("adapter"): + from peft import PeftModel + + model = PeftModel.from_pretrained(model, s["adapter"]) + model.eval() + self.model = model + + def _init_vllm(self) -> None: + from trialmatchai.models.llm.vllm_loader import load_vllm_engine + + s = self.settings + self.engine, self.tokenizer, self.lora_request = load_vllm_engine( + s["model"], adapter_path=s.get("adapter") + ) + + # -- generation -------------------------------------------------------- # + def _generate(self, narrative: str) -> str: + messages = [ + {"role": "system", "content": SYSTEM_PROMPT}, + {"role": "user", "content": narrative}, + ] + if self.backend == "transformers": + import torch + + prompt = self.tokenizer.apply_chat_template( + messages, add_generation_prompt=True, return_tensors="pt" + ).to(self.model.device) + with torch.no_grad(): + out = self.model.generate( + prompt, + max_new_tokens=self.settings["max_new_tokens"], + do_sample=False, + pad_token_id=self.tokenizer.eos_token_id, + ) + return self.tokenizer.decode(out[0][prompt.shape[-1]:], skip_special_tokens=True) + + # vllm + from vllm import SamplingParams + + prompt_text = self.tokenizer.apply_chat_template( + messages, add_generation_prompt=True, tokenize=False + ) + params = SamplingParams(temperature=0.0, max_tokens=self.settings["max_new_tokens"]) + results = self.engine.generate([prompt_text], params, lora_request=self.lora_request) + return results[0].outputs[0].text if results and results[0].outputs else "" + + def expand(self, narrative_sentences: List[str]) -> Dict[str, Any]: + """Return {main_conditions, other_conditions, expanded_sentences}.""" + narrative = " ".join(s for s in narrative_sentences if s).strip() + if not narrative: + return dict(_EMPTY) + try: + raw = self._generate(narrative) + parsed = extract_json_object(raw) + if not isinstance(parsed, dict): + raise ValueError("expansion output was not a JSON object") + return {key: list(parsed.get(key) or []) for key in _EMPTY} + except Exception as exc: + logger.error("Query expansion failed; falling back to no expansion: %s", exc) + return dict(_EMPTY) + + +def build_query_expander(config: Dict[str, Any]) -> QueryExpander | None: + """Construct an expander when ``query_expansion.enabled`` is true, else None.""" + qe = config.get("query_expansion") or {} + if not qe.get("enabled"): + return None + return QueryExpander(_resolve_settings(config)) + + +def enrich_summary( + summary: Dict[str, Any], + expansion: Dict[str, Any], + *, + max_main_conditions: int = 11, + max_other_conditions: int = 50, +) -> Dict[str, Any]: + """Fold a CoT expansion into a matching summary (legacy keywords.json shape). + + The legacy ``expanded_sentences`` map to the summary's ``patient_narrative``. + Only non-empty expansion fields overwrite; otherwise the deterministic + summary is left intact. + """ + out = dict(summary) + main = expansion.get("main_conditions") or [] + other = expansion.get("other_conditions") or [] + sentences = expansion.get("expanded_sentences") or [] + if main: + out["main_conditions"] = main[:max_main_conditions] + if other: + out["other_conditions"] = other[:max_other_conditions] + if sentences: + out["patient_narrative"] = sentences + return out diff --git a/src/trialmatchai/orchestration.py b/src/trialmatchai/orchestration.py new file mode 100644 index 00000000..12db24ee --- /dev/null +++ b/src/trialmatchai/orchestration.py @@ -0,0 +1,519 @@ +"""Idempotent end-to-end orchestration for TrialMatchAI. + +Chains the three pipeline stages — ingest patient inputs, build the search +index, run matching — and skips work that is already done: + + * ingest: a patient is skipped if its canonical profile already exists. + * index: a stage is skipped if the search tables already exist. + * match: a patient is skipped if it already has a non-empty ranked_trials.json. + +Both the general ``trialmatchai e2e`` command and the TREC preset are thin +wrappers over these stages, so idempotency behaves identically everywhere. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Dict, Iterable, Iterator, Sequence + +from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +# --------------------------------------------------------------------------- # +# Ingest stage +# --------------------------------------------------------------------------- # +def ingest_inputs( + config: Dict[str, Any], + inputs: Sequence[str | Path], + *, + input_format: str = "auto", + with_entities: bool = True, + force: bool = False, +) -> int: + """Import patient inputs (any supported format) into canonical profiles. + + Skips a patient whose profile already exists unless ``force``. Returns the + number of profiles available afterwards. + """ + from trialmatchai.interop.exporters import profile_to_matching_summary + from trialmatchai.interop.importers import import_patient_path + + patient_cfg = config.get("patient_inputs", {}) + profile_dir = Path(patient_cfg.get("profile_dir", "data/patients/profiles")) + summary_dir = Path(patient_cfg.get("summary_dir", "data/patients/summaries")) + profile_dir.mkdir(parents=True, exist_ok=True) + summary_dir.mkdir(parents=True, exist_ok=True) + + entity_annotator = _maybe_entity_annotator(config) if with_entities else None + strict = bool(patient_cfg.get("strict_validation", False)) + + imported = 0 + for raw in inputs: + profiles = import_patient_path( + raw, + input_format=input_format, + entity_annotator=entity_annotator, + strict=strict, + ) + for profile in profiles: + profile_path = profile_dir / f"{profile.patient_id}.json" + if not force and profile_path.exists(): + logger.info("Ingest skipped (exists): %s", profile.patient_id) + continue + profile_path.write_text( + json.dumps( + profile.model_dump(mode="json", exclude_none=True), + indent=2, + ensure_ascii=False, + ), + encoding="utf-8", + ) + (summary_dir / f"{profile.patient_id}.json").write_text( + json.dumps(profile_to_matching_summary(profile), indent=2), + encoding="utf-8", + ) + imported += 1 + logger.info("Ingested patient %s", profile.patient_id) + + total = len(list(profile_dir.glob("*.json"))) + logger.info("Ingest stage: %s new, %s profiles total", imported, total) + return total + + +def _maybe_entity_annotator(config: Dict[str, Any]): + try: + from trialmatchai.entities import build_entity_annotator + from trialmatchai.models.embedding import build_embedder + + embedder = build_embedder(config) + return build_entity_annotator(config, embedder=embedder) + except Exception as exc: # pragma: no cover - optional model stack + logger.warning("Entity annotation unavailable; ingesting without it: %s", exc) + return None + + +# --------------------------------------------------------------------------- # +# Query-expansion stage (runtime CoT, faithful to legacy keywords.json) +# --------------------------------------------------------------------------- # +def expand_queries(config: Dict[str, Any], *, force: bool = False) -> int: + """Enrich each patient's matching summary via the CoT query expander. + + No-op unless ``query_expansion.enabled``. Loads the model once, enriches + every summary, then frees it before the match stage loads its own model. + Idempotent: a summary already marked ``query_expanded`` is skipped. + """ + from trialmatchai.matching.query_expansion import build_query_expander, enrich_summary + + expander = build_query_expander(config) + if expander is None: + logger.info("Query expansion disabled; using deterministic summaries.") + return 0 + + patient_cfg = config.get("patient_inputs", {}) + profile_dir = Path(patient_cfg.get("profile_dir", "data/patients/profiles")) + summary_dir = Path(patient_cfg.get("summary_dir", "data/patients/summaries")) + enriched = 0 + for profile_path in sorted(profile_dir.glob("*.json")): + pid = profile_path.stem + summary_path = summary_dir / f"{pid}.json" + if not summary_path.exists(): + continue + summary = _read_json(summary_path) + if not force and summary.get("query_expanded"): + continue + profile = _read_json(profile_path) + narrative = [n.get("text", "") for n in profile.get("notes", []) if n.get("text")] + if not narrative: + narrative = list(summary.get("patient_narrative", [])) + expansion = expander.expand(narrative) + merged = enrich_summary(summary, expansion) + merged["query_expanded"] = True + summary_path.write_text(json.dumps(merged, indent=2), encoding="utf-8") + enriched += 1 + logger.info("Query-expanded %s", pid) + + # Free the expander's model before the match stage loads its own. + del expander + try: + import torch + + if torch.cuda.is_available(): + torch.cuda.empty_cache() + except Exception: + pass + logger.info("Query-expansion stage: enriched %s summaries.", enriched) + return enriched + + +# --------------------------------------------------------------------------- # +# Index stage +# --------------------------------------------------------------------------- # +def _read_json(path: Path) -> dict: + return json.loads(path.read_text(encoding="utf-8")) + + +def _iter_trial_docs(folder: Path, nct_filter: set[str] | None) -> Iterator[dict]: + if not folder.exists(): + return + for path in sorted(folder.glob("*.json")): + if nct_filter is not None and path.stem not in nct_filter: + continue + yield _read_json(path) + + +def _iter_criteria_docs(folder: Path, nct_filter: set[str] | None) -> Iterator[dict]: + if not folder.exists(): + return + for subdir in sorted(p for p in folder.iterdir() if p.is_dir()): + if nct_filter is not None and subdir.name not in nct_filter: + continue + for path in sorted(subdir.glob("*.json")): + yield _read_json(path) + + +def build_index( + config: Dict[str, Any], + *, + processed_trials_folder: str | Path = "data/processed_trials", + processed_criteria_folder: str | Path = "data/processed_criteria", + nct_filter: Iterable[str] | None = None, + force: bool = False, +) -> dict[str, Any]: + """Build the LanceDB search tables, optionally restricted to ``nct_filter``. + + Skips when both tables already exist unless ``force``. The backend (and thus + the target db path) comes from ``config['search_backend']``. + """ + backend = LanceDBSearchBackend.from_config(config) + search_cfg = config.get("search_backend", {}) + trials_table = search_cfg.get("trials_table", "trials") + criteria_table = search_cfg.get("criteria_table", "criteria") + + if ( + not force + and backend.table_exists(trials_table) + and backend.table_exists(criteria_table) + ): + logger.info("Index stage skipped: tables already present at %s", backend.db_path) + return {"skipped": True, "db_path": str(backend.db_path)} + + nct_set = set(nct_filter) if nct_filter is not None else None + trial_docs = list(_iter_trial_docs(Path(processed_trials_folder), nct_set)) + if not trial_docs: + raise RuntimeError( + f"No prepared trial documents found in {processed_trials_folder}" + + (f" for {len(nct_set)} filtered NCT ids" if nct_set else "") + ) + n_trials = backend.index_trials(trial_docs, recreate=True) + logger.info("Indexed %s trial documents.", n_trials) + + criteria_docs = list(_iter_criteria_docs(Path(processed_criteria_folder), nct_set)) + n_criteria = 0 + if criteria_docs: + n_criteria = backend.index_criteria(criteria_docs, recreate=True) + logger.info("Indexed %s criteria documents.", n_criteria) + else: + logger.warning("No criteria documents found; criteria channel will be empty.") + + return { + "skipped": False, + "db_path": str(backend.db_path), + "trials": n_trials, + "criteria": n_criteria, + } + + +# --------------------------------------------------------------------------- # +# Match stage +# --------------------------------------------------------------------------- # +def count_pending(config: Dict[str, Any]) -> tuple[int, int]: + """Return (pending, done) patient counts for the configured dirs.""" + patient_cfg = config.get("patient_inputs", {}) + profile_dir = Path(patient_cfg.get("profile_dir", "data/patients/profiles")) + output_dir = Path(config["paths"]["output_dir"]) + pending = done = 0 + for profile_path in sorted(profile_dir.glob("*.json")): + ranked = output_dir / profile_path.stem / "ranked_trials.json" + if ranked.exists() and ranked.stat().st_size > 0: + done += 1 + else: + pending += 1 + return pending, done + + +def run_matching( + config: Dict[str, Any], + *, + resume: bool = True, + force: bool = False, +) -> int: + """Run the matching pipeline with per-patient resume. + + When resuming, the expensive model stack is not even loaded if every patient + is already done. + """ + use_resume = resume and not force + if use_resume: + pending, done = count_pending(config) + if pending == 0: + logger.info("Match stage skipped: all %s patient(s) already matched.", done) + return 0 + logger.info("Match stage: %s pending, %s already done.", pending, done) + # Imported lazily so the convert/index stages (and the CPU-only --index-only + # path) do not pull in the heavy model stack that main.py imports. + from trialmatchai.main import main_pipeline + + return main_pipeline(config=config, resume=use_resume) + + +# --------------------------------------------------------------------------- # +# Full e2e +# --------------------------------------------------------------------------- # +def run_e2e( + config: Dict[str, Any], + inputs: Sequence[str | Path], + *, + input_format: str = "auto", + with_entities: bool = True, + processed_trials_folder: str | Path = "data/processed_trials", + processed_criteria_folder: str | Path = "data/processed_criteria", + nct_filter: Iterable[str] | None = None, + force_reingest: bool = False, + force_reindex: bool = False, + force_rematch: bool = False, +) -> int: + """Ingest -> index -> match, each stage idempotent. Returns a process code.""" + if inputs: + ingest_inputs( + config, + inputs, + input_format=input_format, + with_entities=with_entities, + force=force_reingest, + ) + expand_queries(config) + build_index( + config, + processed_trials_folder=processed_trials_folder, + processed_criteria_folder=processed_criteria_folder, + nct_filter=nct_filter, + force=force_reindex, + ) + return run_matching(config, resume=True, force=force_rematch) + + +# --------------------------------------------------------------------------- # +# Build (setup half): prepare -> index, resumable, with a build manifest +# --------------------------------------------------------------------------- # +def _now_iso() -> str: + from datetime import datetime, timezone + + return datetime.now(timezone.utc).isoformat() + + +def _manifest_path(processed_trials_folder: str | Path) -> Path: + return Path(processed_trials_folder).parent / ".trialmatchai_build.json" + + +def _load_manifest(path: Path) -> dict: + try: + return json.loads(Path(path).read_text(encoding="utf-8")) + except Exception: + return {} + + +def _save_manifest(path: Path, manifest: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(manifest, indent=2), encoding="utf-8") + + +def _count_json(folder: Path) -> int: + return len(list(folder.glob("*.json"))) if folder.exists() else 0 + + +def _count_subdirs(folder: Path) -> int: + return sum(1 for p in folder.iterdir() if p.is_dir()) if folder.exists() else 0 + + +def prepare_corpus( + config: Dict[str, Any], + *, + trials_json_folder: str | Path, + processed_trials_folder: str | Path, + processed_criteria_folder: str | Path, + force: bool = False, + log_every: int = 500, +) -> dict[str, int]: + """Embed + annotate normalized trial JSONs into processed_*; resumable. + + Streams one trial at a time (bounded memory), skips trials already prepared + so an interrupted build picks up where it left off, and isolates per-trial + failures so one bad document cannot abort the whole corpus. + """ + trials_json_folder = Path(trials_json_folder) + processed_trials_folder = Path(processed_trials_folder) + processed_criteria_folder = Path(processed_criteria_folder) + + all_paths = sorted(trials_json_folder.glob("*.json")) + if not all_paths: + raise RuntimeError(f"No trial JSON files found to prepare in {trials_json_folder}") + + pending = [ + p + for p in all_paths + if force or not (processed_trials_folder / f"{p.stem}.json").exists() + ] + skipped = len(all_paths) - len(pending) + logger.info( + "Prepare: %s trials total, %s already prepared, %s to process.", + len(all_paths), + skipped, + len(pending), + ) + if not pending: + return {"total": len(all_paths), "prepared": 0, "skipped": skipped, "failed": 0} + + from trialmatchai.entities import build_entity_annotator + from trialmatchai.models.embedding import build_embedder + from trialmatchai.registry.preparation import ( + prepare_criteria_documents, + prepare_trial_document, + write_prepared_criteria, + write_prepared_trial, + ) + + processed_trials_folder.mkdir(parents=True, exist_ok=True) + processed_criteria_folder.mkdir(parents=True, exist_ok=True) + embedder = build_embedder(config) + entity_annotator = build_entity_annotator(config, embedder=embedder) + + prepared = failed = 0 + for i, path in enumerate(pending, start=1): + try: + doc = _read_json(path) + trial_row = prepare_trial_document(doc, embedder) + criteria_rows = prepare_criteria_documents( + doc, embedder, entity_annotator=entity_annotator + ) + write_prepared_trial(trial_row, processed_trials_folder) + write_prepared_criteria(criteria_rows, processed_criteria_folder) + prepared += 1 + except Exception: + failed += 1 + logger.exception("Prepare failed for %s (continuing)", path.name) + if i % log_every == 0: + logger.info("Prepare progress: %s/%s done, %s failed.", i, len(pending), failed) + + logger.info( + "Prepare complete: %s prepared, %s skipped, %s failed.", prepared, skipped, failed + ) + return {"total": len(all_paths), "prepared": prepared, "skipped": skipped, "failed": failed} + + +def build_state( + config: Dict[str, Any], + *, + processed_trials_folder: str | Path = "data/processed_trials", + processed_criteria_folder: str | Path = "data/processed_criteria", +) -> dict: + """Report what the build half has produced — used by `build --status`.""" + pt = Path(processed_trials_folder) + pc = Path(processed_criteria_folder) + search_cfg = config.get("search_backend", {}) + backend = LanceDBSearchBackend.from_config(config) + trials_table = backend.table_exists(search_cfg.get("trials_table", "trials")) + criteria_table = backend.table_exists(search_cfg.get("criteria_table", "criteria")) + linker = config.get("concept_linker", {}) + concepts_path = Path(linker.get("db_path", "data/concepts")) + concepts_present = concepts_path.exists() and any(concepts_path.iterdir()) + return { + "processed_trials": {"folder": str(pt), "count": _count_json(pt)}, + "processed_criteria": {"folder": str(pc), "count": _count_subdirs(pc)}, + "index": { + "db_path": str(backend.db_path), + "trials_table": trials_table, + "criteria_table": criteria_table, + }, + "concepts": {"db_path": str(concepts_path), "present": bool(concepts_present)}, + "ready_to_match": bool(trials_table and criteria_table), + } + + +def build_system( + config: Dict[str, Any], + *, + trials_json_folder: str | Path | None = None, + processed_trials_folder: str | Path = "data/processed_trials", + processed_criteria_folder: str | Path = "data/processed_criteria", + force_prepare: bool = False, + force_reindex: bool = False, +) -> dict: + """Run the setup half (prepare -> index), idempotent, with a manifest. + + Each stage is resumable and recorded in ``.trialmatchai_build.json`` next to + the processed data, so a disrupted build can be re-run and continues from the + last completed work. + """ + paths = config.get("paths", {}) + trials_json_folder = Path(trials_json_folder or paths.get("trials_json_folder", "data/trials_jsons")) + pt = Path(processed_trials_folder) + pc = Path(processed_criteria_folder) + manifest_path = _manifest_path(pt) + manifest = _load_manifest(manifest_path) + manifest["started_at"] = _now_iso() + + # Stage 1 — prepare embeddings/entities (resumable, GPU). + have_prepared = _count_json(pt) > 0 + have_source = trials_json_folder.exists() and any(trials_json_folder.glob("*.json")) + logger.info("=== build: prepare stage ===") + if have_source: + # prepare_corpus internally skips already-prepared trials, so calling it + # whenever source exists safely resumes without redoing finished work. + stats = prepare_corpus( + config, + trials_json_folder=trials_json_folder, + processed_trials_folder=pt, + processed_criteria_folder=pc, + force=force_prepare, + ) + manifest["prepare"] = {**stats, "completed_at": _now_iso()} + elif have_prepared: + logger.info("Prepare skipped: %s already populated (no trials_jsons source).", pt) + manifest["prepare"] = {"skipped_existing": True, "completed_at": _now_iso()} + else: + raise RuntimeError( + f"Nothing to prepare: {pt} is empty and no trial JSONs at " + f"{trials_json_folder}. Run `trialmatchai-bootstrap-data` or provide " + "normalized trial JSONs." + ) + _save_manifest(manifest_path, manifest) + + # Stage 2 — build the LanceDB search index (idempotent). + logger.info("=== build: index stage ===") + index_info = build_index( + config, + processed_trials_folder=pt, + processed_criteria_folder=pc, + force=force_reindex, + ) + manifest["index"] = {**index_info, "completed_at": _now_iso()} + _save_manifest(manifest_path, manifest) + + state = build_state( + config, + processed_trials_folder=pt, + processed_criteria_folder=pc, + ) + manifest["state"] = state + manifest["completed_at"] = _now_iso() + _save_manifest(manifest_path, manifest) + logger.info( + "Build complete — ready_to_match=%s. Manifest: %s", + state["ready_to_match"], + manifest_path, + ) + return manifest diff --git a/src/trialmatchai/registry/criteria_chunking.py b/src/trialmatchai/registry/criteria_chunking.py index ac199463..5ce7f0a7 100644 --- a/src/trialmatchai/registry/criteria_chunking.py +++ b/src/trialmatchai/registry/criteria_chunking.py @@ -3,8 +3,9 @@ Splits a free-text eligibility section into individual, typed criteria. This folds the domain knowledge from the legacy regex preprocessor — multi-level enumeration hierarchies, varied inclusion/exclusion headers, parenthetical -protection, and decimal/abbreviation split-exceptions — into one streamlined -pass over the text. +protection, semicolon splitting, long-criterion full-stop re-splitting, and +decimal/abbreviation split-exceptions — into one streamlined pass over the text, +so chunk boundaries match the legacy pipeline. Public API: ``split_eligibility_criteria(text) -> list[{"type", "criterion"}]`` where ``type`` is "inclusion", "exclusion", or "unknown". @@ -128,6 +129,49 @@ def _is_useful(text: str) -> bool: return len(text) >= 3 and detect_header(text) is None +# --- Secondary splits (faithful to the legacy preprocessor) ----------------- +# Each assembled criterion is further split on semicolons that fall outside +# parentheses, and any resulting criterion longer than this many characters is +# split into sentences on real full stops (decimal- and abbreviation-aware) — +# matching the legacy split_lines_on_semicolon and split_large_sentences / +# split_on_full_stops, so chunk boundaries stay identical to main. +_LONG_CRITERION_CHARS = 200 +_FULLSTOP_SPLIT = re.compile(r"(? list[str]: + """Split on semicolons not enclosed in parentheses/brackets.""" + masked = _mask_parens(text) # in-paren chars (incl. ';') become spaces + parts: list[str] = [] + last = 0 + for i, char in enumerate(masked): + if char == ";": + parts.append(text[last:i]) + last = i + 1 + parts.append(text[last:]) + return [p.strip() for p in parts if p.strip()] + + +def _split_long_criterion(text: str) -> list[str]: + """Split a long (>200 char) criterion into sentences; short text passes through.""" + if len(text) <= _LONG_CRITERION_CHARS: + return [text] + sentences = [s.strip() for s in _FULLSTOP_SPLIT.split(text) if s.strip()] + return sentences or [text] + + +def _emit(text: str, criterion_type: str, entries: list[dict[str, str]]) -> None: + """Clean, semicolon-split, long-criterion-split, filter, and append criteria.""" + cleaned = _clean(text) + if not cleaned: + return + for part in _split_semicolons(cleaned): + for sentence in _split_long_criterion(part): + sentence = sentence.strip(" :-\t") + if _is_useful(sentence): + entries.append({"type": criterion_type, "criterion": sentence}) + + def split_eligibility_criteria(text: str) -> list[dict[str, str]]: if not text or not text.strip(): return [] @@ -139,10 +183,9 @@ def split_eligibility_criteria(text: str) -> list[dict[str, str]]: def flush() -> None: if not buffered: return - criterion = _clean(" ".join(buffered)) + text = " ".join(buffered) buffered.clear() - if _is_useful(criterion): - entries.append({"type": current_type, "criterion": criterion}) + _emit(text, current_type, entries) for raw_line in text.splitlines(): line = raw_line.strip() @@ -181,5 +224,6 @@ def flush() -> None: if entries: return entries - fallback = _clean(text) - return [{"type": "unknown", "criterion": fallback}] if fallback else [] + # Unstructured text (no markers/headers): still semicolon/long-split it. + _emit(text, "unknown", entries) + return entries diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py index 3ef99f56..05b06e91 100644 --- a/src/trialmatchai/registry/preparation.py +++ b/src/trialmatchai/registry/preparation.py @@ -10,6 +10,7 @@ import dateutil.parser from trialmatchai.constraints import extract_constraint_set +from trialmatchai.registry.criteria_chunking import split_eligibility_criteria from trialmatchai.utils.text import flatten_text @@ -100,7 +101,14 @@ def prepare_criteria_documents( nct_id = str(doc["nct_id"]) entries: list[dict[str, Any]] = [] texts: list[str] = [] - for criterion in doc.get("criteria") or []: + criteria = doc.get("criteria") + if not criteria: + # Trial doc carries only raw eligibility text (e.g. normalized JSON that + # predates in-normalizer chunking): chunk it here, on the unflattened + # text so line structure is preserved, keeping preparation self-sufficient. + raw = doc.get("eligibility_criteria") + criteria = split_eligibility_criteria(raw) if isinstance(raw, str) else [] + for criterion in criteria or []: if not isinstance(criterion, dict): continue text = _preprocess_text( diff --git a/src/trialmatchai/services/preflight.py b/src/trialmatchai/services/preflight.py index d14d8384..903c9142 100644 --- a/src/trialmatchai/services/preflight.py +++ b/src/trialmatchai/services/preflight.py @@ -120,6 +120,14 @@ def run_preflight_checks( "Transformers CPU backend requires transformers (`uv sync --extra llm`)." ) + # Verify gated base models (phi-4 / gemma-2) are authorized up front, so + # an HF auth failure surfaces here rather than after first-level search. + issues.extend( + check_hf_access( + [model_cfg.get("base_model"), model_cfg.get("reranker_model_path")] + ) + ) + search_cfg = config.get("search_backend", {}) if search_cfg: _require_path( @@ -148,6 +156,86 @@ def run_preflight_checks( return issues +def check_cuda(config: Dict[str, Any], *, required: bool) -> List[str]: + """Verify a CUDA GPU is present when the build/match needs one.""" + issues: List[str] = [] + if importlib.util.find_spec("torch") is None: + if required: + issues.append("PyTorch is not installed (`uv sync --extra gpu`).") + return issues + try: + import torch + + available = bool(torch.cuda.is_available()) + except Exception as exc: # pragma: no cover - torch import edge cases + if required: + issues.append(f"Could not initialize PyTorch/CUDA: {exc}") + return issues + if not available: + message = "No CUDA GPU detected; embeddings/LLMs need a GPU host." + if required: + issues.append(message) + else: + logger.warning("Preflight: %s", message) + return issues + + +def check_hf_access(model_ids: List[str | None]) -> List[str]: + """Fast pre-download check that HuggingFace models are reachable/authorized. + + Calls the metadata API (no weight download). Local paths and non-hub ids are + skipped. Gated models without a token produce a clear, actionable error so + auth fails in seconds rather than after hours of other work; network/transient + errors only warn. + """ + issues: List[str] = [] + if importlib.util.find_spec("huggingface_hub") is None: + return issues # dependency checks elsewhere cover a missing stack + import os + + from huggingface_hub import HfApi + from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError + + token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN") + api = HfApi(token=token) + for model_id in model_ids: + if not model_id or Path(model_id).exists() or "/" not in str(model_id): + continue # local model dir or not a hub repo id + try: + api.model_info(model_id) + except GatedRepoError: + issues.append( + f"HuggingFace model '{model_id}' is gated/unauthorized — accept its " + "license on huggingface.co and export HF_TOKEN." + ) + except RepositoryNotFoundError: + issues.append( + f"HuggingFace model '{model_id}' was not found (check the id or your token)." + ) + except Exception as exc: + logger.warning("Preflight: could not verify HF model '%s': %s", model_id, exc) + return issues + + +def run_build_preflight(config: Dict[str, Any]) -> List[str]: + """Fail-fast checks for the build (setup) half before any heavy work.""" + issues: List[str] = [] + embedder_cfg = config.get("embedder", {}) + issues += check_cuda(config, required=bool(embedder_cfg.get("use_gpu", True))) + + entity_cfg = config.get("entity_extraction", {}) + if ( + entity_cfg.get("backend", "gliner2") == "gliner2" + and importlib.util.find_spec("gliner2") is None + ): + issues.append("entity_extraction.backend=gliner2 requires `uv sync --extra entity`.") + + issues += check_hf_access([embedder_cfg.get("model_name"), entity_cfg.get("model_name")]) + for issue in issues: + logger.error("Build preflight: %s", issue) + return issues + + def _require_path( issues: List[str], name: str, diff --git a/src/trialmatchai/trec/__init__.py b/src/trialmatchai/trec/__init__.py new file mode 100644 index 00000000..7560b7cb --- /dev/null +++ b/src/trialmatchai/trec/__init__.py @@ -0,0 +1,17 @@ +"""TREC Clinical Trials evaluation harness for TrialMatchAI. + +Provides an idempotent, end-to-end runner over the TREC 2021/2022 and SIGIR +patient-topic tracks: it converts the legacy patient topics into canonical +profiles, builds a per-track LanceDB search index restricted to the track's +document collection, and runs the matching pipeline with per-patient resume. + +Exposed via the ``trialmatchai-trec`` console command (see +``trialmatchai.cli.trec``). +""" + +from __future__ import annotations + +from trialmatchai.trec.corpus import TRACK_KEYS, TrackSpec, resolve_tracks +from trialmatchai.trec.runner import run_tracks + +__all__ = ["TRACK_KEYS", "TrackSpec", "resolve_tracks", "run_tracks"] diff --git a/src/trialmatchai/trec/corpus.py b/src/trialmatchai/trec/corpus.py new file mode 100644 index 00000000..f6cc2f97 --- /dev/null +++ b/src/trialmatchai/trec/corpus.py @@ -0,0 +1,71 @@ +"""TREC track definitions and per-track corpus resolution. + +The per-track corpus (the set of trials the index is restricted to) is derived +at run time from the official qrels — see ``trialmatchai.trec.qrels`` — not from +any checked-in NCT list. +""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +# Officially supported via NIST download. "sigir" is defined but has no official +# direct source (CSIRO portal), so it is not in the default run set. +TRACK_KEYS = ("21", "22", "sigir") +DEFAULT_TRACKS = ("21", "22") + +# Topic id prefix (topic N becomes "N", matching the qrels query ids). +_ID_PREFIX = {"21": "trec-2021", "22": "trec-2022", "sigir": "sigir-2014"} + + +@dataclass(frozen=True) +class TrackSpec: + """Everything needed to run one TREC track end-to-end.""" + + key: str + id_prefix: str + trec_dir: Path + profile_dir: Path + summary_dir: Path + db_path: Path + output_dir: Path + + @property + def name(self) -> str: + return f"trec{self.key}" + + +def resolve_tracks( + keys: list[str], + *, + data_dir: Path, + results_root: Path, +) -> list[TrackSpec]: + """Build :class:`TrackSpec` objects for the requested track keys. + + Layout (relative to ``data_dir`` / ``results_root``): + trec dir : /trec (downloaded topics + qrels cache) + profiles : /patients/trec/profiles + summaries : /patients/trec/summaries + index : /search_ + results : /results_trec + """ + data_dir = Path(data_dir) + results_root = Path(results_root) + specs: list[TrackSpec] = [] + for key in keys: + if key not in TRACK_KEYS: + raise ValueError(f"Unknown TREC track '{key}' (expected one of {TRACK_KEYS})") + specs.append( + TrackSpec( + key=key, + id_prefix=_ID_PREFIX[key], + trec_dir=data_dir / "trec", + profile_dir=data_dir / "patients" / f"trec{key}" / "profiles", + summary_dir=data_dir / "patients" / f"trec{key}" / "summaries", + db_path=data_dir / f"search_{key}", + output_dir=results_root / f"results_trec{key}", + ) + ) + return specs diff --git a/src/trialmatchai/trec/qrels.py b/src/trialmatchai/trec/qrels.py new file mode 100644 index 00000000..666461b2 --- /dev/null +++ b/src/trialmatchai/trec/qrels.py @@ -0,0 +1,159 @@ +"""Official TREC relevance judgments (qrels): download, parse, corpus, metrics. + +The per-track NCT corpus pool is derived directly from the qrels (the set of +judged trials) — replacing the previously-checked-in ``Unique_NCT_IDs`` lists. +Evaluation computes recall@k of the retrieval against the same qrels. + +TREC Clinical Trials relevance grades: 0 = not relevant, 1 = excluded (the trial +matches the condition but the patient is excluded), 2 = eligible. By default a +trial counts as relevant at grade >= 1 (matching the legacy recall evaluation); +pass ``threshold=2`` to score eligible-only. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +import requests +from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential + +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + +QRELS_URLS: dict[str, str] = { + "21": "https://trec.nist.gov/data/clinical/trials/qrels2021.txt", + "22": "https://trec.nist.gov/data/clinical/trials/qrels2022.txt", +} + +DEFAULT_CUTOFFS = (10, 50, 100, 200, 300, 500, 1000) + + +@retry( + retry=retry_if_exception_type(requests.RequestException), + stop=stop_after_attempt(4), + wait=wait_exponential(multiplier=1, min=1, max=20), + reraise=True, +) +def _http_get(url: str, timeout: float = 60.0) -> bytes: + response = requests.get(url, timeout=timeout) + response.raise_for_status() + return response.content + + +def download_qrels(track: str, dest_dir: Path) -> Path: + """Fetch (and cache) the official qrels file for a track.""" + if track not in QRELS_URLS: + raise FileNotFoundError( + f"No official qrels URL for track '{track}'. Provide the qrels file " + f"manually at {Path(dest_dir) / f'qrels_{track}.txt'}." + ) + dest_dir = Path(dest_dir) + dest_dir.mkdir(parents=True, exist_ok=True) + dest = dest_dir / f"qrels_{track}.txt" + if dest.exists() and dest.stat().st_size > 0: + logger.info("Qrels for track %s already present: %s", track, dest) + return dest + logger.info("Downloading official qrels for track %s from %s", track, QRELS_URLS[track]) + dest.write_bytes(_http_get(QRELS_URLS[track])) + return dest + + +def parse_qrels(path: Path, id_prefix: str) -> dict[str, dict[str, int]]: + """Parse a TREC qrels file into {query_id: {nct_id: relevance}}. + + Lines are `` `` (whitespace + separated). The query id is ``f"{id_prefix}{topic}"`` to match the imported + topic ids and the per-patient results folders. + """ + qrels: dict[str, dict[str, int]] = {} + for raw in Path(path).read_text(encoding="utf-8", errors="ignore").splitlines(): + parts = raw.split() + if len(parts) < 4: + continue + topic, _iteration, nct_id, relevance = parts[0], parts[1], parts[2], parts[3] + try: + rel = int(relevance) + except ValueError: + continue + query_id = f"{id_prefix}{topic.strip()}" + qrels.setdefault(query_id, {})[nct_id.strip()] = rel + if not qrels: + raise ValueError(f"No judgments parsed from qrels file {path}") + return qrels + + +def corpus_ncts(qrels: dict[str, dict[str, int]]) -> set[str]: + """The judged-trial pool across all queries (used to restrict the index).""" + pool: set[str] = set() + for judgments in qrels.values(): + pool.update(judgments) + return pool + + +def relevant_ncts(qrels: dict[str, dict[str, int]], *, threshold: int = 1) -> dict[str, set[str]]: + return { + query_id: {nct for nct, rel in judgments.items() if rel >= threshold} + for query_id, judgments in qrels.items() + } + + +def _retrieved_for_patient(patient_dir: Path) -> list[str]: + """Ordered retrieved NCT ids for one patient. + + Prefers the first-level candidate list (nct_ids.txt, up to ~1000) so recall + at large cutoffs is meaningful; falls back to the final ranked_trials.json. + """ + nct_ids = patient_dir / "nct_ids.txt" + if nct_ids.exists(): + return [line.strip() for line in nct_ids.read_text().splitlines() if line.strip()] + ranked = patient_dir / "ranked_trials.json" + if ranked.exists(): + data = json.loads(ranked.read_text()) + return [str(item.get("TrialID")) for item in data if item.get("TrialID")] + return [] + + +def recall_at_k(retrieved: list[str], relevant: set[str], k: int) -> float | None: + if not relevant: + return None + hits = sum(1 for nct in retrieved[:k] if nct in relevant) + return hits / len(relevant) + + +def evaluate( + qrels: dict[str, dict[str, int]], + results_dir: Path, + *, + cutoffs: tuple[int, ...] = DEFAULT_CUTOFFS, + threshold: int = 1, +) -> dict: + """Compute per-query and mean recall@k over the patients in ``results_dir``.""" + results_dir = Path(results_dir) + relevant = relevant_ncts(qrels, threshold=threshold) + per_query: dict[str, dict] = {} + sums = {k: 0.0 for k in cutoffs} + counts = {k: 0 for k in cutoffs} + + for query_id, rel_set in relevant.items(): + patient_dir = results_dir / query_id + if not patient_dir.is_dir() or not rel_set: + continue + retrieved = _retrieved_for_patient(patient_dir) + row = {"num_relevant": len(rel_set), "num_retrieved": len(retrieved)} + for k in cutoffs: + r = recall_at_k(retrieved, rel_set, k) + row[f"recall@{k}"] = r + if r is not None: + sums[k] += r + counts[k] += 1 + per_query[query_id] = row + + mean = {f"recall@{k}": (sums[k] / counts[k] if counts[k] else None) for k in cutoffs} + return { + "threshold": threshold, + "num_queries_scored": len(per_query), + "mean_recall": mean, + "per_query": per_query, + } diff --git a/src/trialmatchai/trec/runner.py b/src/trialmatchai/trec/runner.py new file mode 100644 index 00000000..4fe35ab2 --- /dev/null +++ b/src/trialmatchai/trec/runner.py @@ -0,0 +1,146 @@ +"""End-to-end TREC runner — a preset over the core orchestration stages. + +For each requested track it converts the TREC patient topics, builds a search +index restricted to the track's NCT collection, and runs matching with +per-patient resume. Every step is idempotent (skips already-done work). +""" + +from __future__ import annotations + +import json +from copy import deepcopy +from pathlib import Path +from typing import Any, Dict + +from trialmatchai.config.config_loader import load_config +from trialmatchai.orchestration import build_index, count_pending, expand_queries, run_matching +from trialmatchai.trec import qrels as qrels_mod +from trialmatchai.trec.corpus import TrackSpec, resolve_tracks +from trialmatchai.trec.topics import import_topics +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def _track_config(base_config: Dict[str, Any], spec: TrackSpec) -> Dict[str, Any]: + """Clone the base config with this track's paths swapped in. + + Faithful to the legacy pipeline, the TREC preset enables runtime CoT query + expansion (which produced the original keywords.json from raw topic text). + """ + cfg = deepcopy(base_config) + cfg.setdefault("search_backend", {})["db_path"] = str(spec.db_path) + cfg.setdefault("patient_inputs", {})["profile_dir"] = str(spec.profile_dir) + cfg["patient_inputs"]["summary_dir"] = str(spec.summary_dir) + cfg.setdefault("paths", {})["output_dir"] = str(spec.output_dir) + cfg.setdefault("query_expansion", {})["enabled"] = True + return cfg + + +def run_tracks( + track_keys: list[str], + *, + config_path: str | None = None, + data_dir: str | Path = "data", + results_root: str | Path = ".", + processed_trials_folder: str | Path | None = None, + processed_criteria_folder: str | Path | None = None, + index_only: bool = False, + evaluate: bool = True, + force_reindex: bool = False, + force_rematch: bool = False, +) -> int: + """Run the TREC e2e for each track. Returns a process exit code. + + ``processed_trials_folder`` / ``processed_criteria_folder`` default to + ``/processed_*`` but can point elsewhere (e.g. the prepared data + on /nfs/scratch) while the index and all run outputs live under ``data_dir`` + on /nfs/home. The per-track corpus is derived from the official qrels. + """ + base_config = load_config(config_path) + data_dir = Path(data_dir) + processed_trials = Path(processed_trials_folder or data_dir / "processed_trials") + processed_criteria = Path(processed_criteria_folder or data_dir / "processed_criteria") + + specs = resolve_tracks(track_keys, data_dir=data_dir, results_root=Path(results_root)) + failures = 0 + + for spec in specs: + logger.info("================ TREC track %s ================", spec.key) + cfg = _track_config(base_config, spec) + + # 1) Acquire official topics -> canonical profiles + summaries (idempotent). + try: + import_topics( + spec.key, + trec_dir=spec.trec_dir, + profile_dir=spec.profile_dir, + summary_dir=spec.summary_dir, + ) + except Exception: + logger.exception("Topic import failed for track %s", spec.key) + failures += 1 + continue + + # 2) Official qrels -> per-track corpus pool (replaces the removed lists). + try: + qrels_path = qrels_mod.download_qrels(spec.key, spec.trec_dir / "qrels") + qrels = qrels_mod.parse_qrels(qrels_path, spec.id_prefix) + nct_filter = qrels_mod.corpus_ncts(qrels) + logger.info("Track %s corpus pool from qrels: %s trials", spec.key, len(nct_filter)) + except Exception: + logger.exception("Qrels acquisition failed for track %s", spec.key) + failures += 1 + continue + + # 3) Runtime CoT query expansion -> enriched keywords.json (idempotent). + if not index_only: + expand_queries(cfg, force=force_rematch) + + # 4) Build the per-track index, restricted to the qrels corpus pool. + try: + build_index( + cfg, + processed_trials_folder=processed_trials, + processed_criteria_folder=processed_criteria, + nct_filter=nct_filter, + force=force_reindex, + ) + except Exception: + logger.exception("Indexing failed for track %s", spec.key) + failures += 1 + continue + + if index_only: + pending, done = count_pending(cfg) + logger.info("Track %s indexed (%s patients pending, %s done).", spec.key, pending, done) + continue + + # 5) Match with per-patient resume. + rc = run_matching(cfg, resume=True, force=force_rematch) + if rc != 0: + logger.error("Matching returned %s for track %s", rc, spec.key) + failures += 1 + continue + + # 6) Evaluate recall@k against the same qrels. + if evaluate: + try: + metrics = qrels_mod.evaluate(qrels, spec.output_dir) + metrics_path = Path(spec.output_dir) / "evaluation_metrics.json" + metrics_path.write_text(json.dumps(metrics, indent=2), encoding="utf-8") + logger.info( + "Track %s recall: %s -> %s", + spec.key, + metrics["mean_recall"], + metrics_path, + ) + except Exception: + logger.exception("Evaluation failed for track %s", spec.key) + logger.info("Track %s done -> %s", spec.key, spec.output_dir) + + if failures: + logger.warning("TREC run completed with %s track failure(s).", failures) + return 1 + logger.info("All requested TREC tracks complete: %s", " ".join(track_keys)) + return 0 diff --git a/src/trialmatchai/trec/topics.py b/src/trialmatchai/trec/topics.py new file mode 100644 index 00000000..cfee4a40 --- /dev/null +++ b/src/trialmatchai/trec/topics.py @@ -0,0 +1,275 @@ +"""Official TREC topic acquisition and import. + +Downloads the authoritative patient topics for each track straight from the +source (NIST for 2021/2022, the CSIRO/SIGIR-2016 collection for sigir), parses +the topic XML, and builds canonical :class:`PatientProfile` objects from the +RAW topic text only — no LLM-preprocessed fields. Demographics (age/sex) are +extracted deterministically from the narrative, so nothing here depends on the +old gpt-generated ``processed_patients*.json`` content. + +The runtime CoT query-expansion (see ``trialmatchai.matching.query_expansion``) +is what turns this raw text into the expanded ``keywords.json`` consumed by +first-level retrieval — faithfully reproducing the legacy pipeline. +""" + +from __future__ import annotations + +import re +from dataclasses import dataclass +from pathlib import Path +from xml.etree import ElementTree as ET + +import requests +from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential + +from trialmatchai.interop.models import Demographics, PatientNote, PatientProfile, Provenance +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + +SOURCE_FORMAT = "trec_topic" + + +@dataclass(frozen=True) +class TopicSource: + """Where a track's official topics live and how to label them. + + kind == "nist_xml": download topic XML from ``topics_url`` and parse + ```` (ids get ``id_prefix``). + kind == "local_raw": read verbatim raw patient text from an on-disk JSON + (``raw_json``); keys are already full patient ids. + """ + + track: str + id_prefix: str + kind: str + topics_url: str | None = None # nist_xml + raw_json: str | None = None # local_raw (filename under /trec/) + collection_url: str | None = None + note: str = "" + + +TOPIC_SOURCES: dict[str, TopicSource] = { + "21": TopicSource( + track="21", + id_prefix="trec-2021", + kind="nist_xml", + topics_url="https://trec.nist.gov/data/clinical/trials/topics2021.xml", + ), + "22": TopicSource( + track="22", + id_prefix="trec-2022", + kind="nist_xml", + topics_url="https://trec.nist.gov/data/clinical/trials/topics2022.xml", + ), + # The SIGIR-2016 collection (Koopman & Zuccon) is hosted on the CSIRO Data + # Access Portal (no stable direct-download URL), so sigir topics are sourced + # from the verbatim on-disk raw patient text — the same official admission + # statements, with none of the gpt-generated fields. + "sigir": TopicSource( + track="sigir", + id_prefix="sigir-2014", + kind="local_raw", + raw_json="processed_sigir_patients.json", + collection_url="https://data.csiro.au/collection/csiro:17152", + note="sigir raw text is read verbatim from data/trec/processed_sigir_patients.json " + "(raw_description only; no gpt-generated fields).", + ), +} + + +@retry( + retry=retry_if_exception_type(requests.RequestException), + stop=stop_after_attempt(4), + wait=wait_exponential(multiplier=1, min=1, max=20), + reraise=True, +) +def _http_get(url: str, timeout: float = 60.0) -> bytes: + response = requests.get(url, timeout=timeout) + response.raise_for_status() + return response.content + + +def download_topics(track: str, dest_dir: Path) -> Path: + """Fetch (and cache) the official topic XML for a track. + + Returns the local path. For portal-only sources (sigir) the file must be + placed manually; a clear error explains where to get it. + """ + source = TOPIC_SOURCES[track] + dest_dir = Path(dest_dir) + dest_dir.mkdir(parents=True, exist_ok=True) + dest = dest_dir / f"topics_{track}.xml" + + if dest.exists() and dest.stat().st_size > 0: + logger.info("Topics for track %s already present: %s", track, dest) + return dest + + if not source.topics_url: + raise FileNotFoundError( + f"No direct download for track '{track}'. {source.note} " + f"Source: {source.collection_url}" + ) + + logger.info("Downloading official topics for track %s from %s", track, source.topics_url) + dest.write_bytes(_http_get(source.topics_url)) + return dest + + +# --------------------------------------------------------------------------- # +# Parsing +# --------------------------------------------------------------------------- # +def parse_topics(path: Path, id_prefix: str) -> dict[str, str]: + """Parse a TREC/TREC-CDS topic XML into {patient_id: raw_text}. + + Handles both the flat ``free text`` form + (2021/2022) and the TREC-CDS form where a topic wraps ``

    `` / + ```` children (sigir) — preferring the summary, then the + description, then the element's own text. + """ + root = ET.parse(path).getroot() + topics: dict[str, str] = {} + for topic in root.iter("topic"): + number = topic.get("number") or topic.get("id") + if number is None: + continue + text = _topic_text(topic) + if not text: + continue + topics[f"{id_prefix}{number.strip()}"] = text + if not topics: + raise ValueError(f"No elements parsed from {path}") + return topics + + +def _topic_text(topic: ET.Element) -> str: + for child_tag in ("summary", "description"): + child = topic.find(child_tag) + if child is not None and (child.text or "").strip(): + return _clean(child.text) + return _clean("".join(topic.itertext())) + + +def _clean(text: str | None) -> str: + return re.sub(r"\s+", " ", (text or "")).strip() + + +# --------------------------------------------------------------------------- # +# Deterministic demographics (no LLM, no gpt-preprocessed fields) +# --------------------------------------------------------------------------- # +_AGE_PATTERNS = [ + re.compile(r"(\d{1,3})\s*[- ]?\s*year[\s-]*old", re.I), + re.compile(r"(\d{1,3})\s*[- ]?\s*(?:yo|y/o|yr)\b", re.I), + re.compile(r"\b(\d{1,3})\s*[- ]?\s*(?:M|F|male|female|man|woman)\b"), +] +_FEMALE = re.compile(r"\b(?:female|woman|girl|lady|\d{1,3}\s*F)\b", re.I) +_MALE = re.compile(r"\b(?:male|man|boy|gentleman|\d{1,3}\s*M)\b", re.I) + + +def extract_demographics(text: str) -> tuple[float | None, str | None]: + """Best-effort (age_years, sex) from the topic narrative; None if unknown.""" + age: float | None = None + for pattern in _AGE_PATTERNS: + m = pattern.search(text) + if m: + value = int(m.group(1)) + if 0 < value <= 120: + age = float(value) + break + # Female check first: "male" is a substring concern handled by word bounds. + sex: str | None = None + if _FEMALE.search(text): + sex = "Female" + elif _MALE.search(text): + sex = "Male" + return age, sex + + +def build_profile_from_topic(patient_id: str, raw_text: str) -> PatientProfile: + """Build a canonical profile from raw topic text only (no derived terms).""" + provenance = Provenance(source_format=SOURCE_FORMAT, source_id=patient_id) + age, sex = extract_demographics(raw_text) + return PatientProfile( + patient_id=patient_id, + demographics=Demographics(sex=sex, age_years=age), + notes=[ + PatientNote( + note_id=f"{patient_id}-note", + text=raw_text, + note_type="trec-topic", + provenance=provenance, + ) + ], + provenance=[provenance], + ) + + +def _load_local_raw(path: Path) -> dict[str, str]: + """Read {patient_id: verbatim raw text} from an on-disk topic JSON. + + Uses only the ``raw_description`` field (the official admission statement); + every gpt-generated field is ignored. Keys are already full patient ids. + """ + import json + + records = json.loads(Path(path).read_text(encoding="utf-8")) + topics = { + pid: _clean(rec.get("raw_description")) + for pid, rec in records.items() + if _clean(rec.get("raw_description")) + } + if not topics: + raise ValueError(f"No raw_description text found in {path}") + return topics + + +def load_track_topics(track: str, trec_dir: Path) -> dict[str, str]: + """Resolve a track's topics to {patient_id: raw_text} per its source kind.""" + source = TOPIC_SOURCES[track] + if source.kind == "nist_xml": + path = download_topics(track, Path(trec_dir) / "raw_topics") + return parse_topics(path, source.id_prefix) + if source.kind == "local_raw": + return _load_local_raw(Path(trec_dir) / source.raw_json) + raise ValueError(f"Unknown topic source kind: {source.kind}") + + +def import_topics( + track: str, + *, + trec_dir: Path, + profile_dir: Path, + summary_dir: Path, + force: bool = False, +) -> int: + """Acquire a track's official topics and write canonical inputs. + + Writes one PatientProfile per topic (raw text + demographics only) plus a + deterministic matching summary (later enriched by CoT query expansion). + Idempotent: skips when profiles already exist. Returns the patient count. + """ + import json + + from trialmatchai.interop.exporters import profile_to_matching_summary + + profile_dir = Path(profile_dir) + summary_dir = Path(summary_dir) + if not force and profile_dir.exists() and any(profile_dir.glob("*.json")): + have = len(list(profile_dir.glob("*.json"))) + logger.info("Topic import skipped for track %s: %s profiles present", track, have) + return have + + topics = load_track_topics(track, Path(trec_dir)) + + profile_dir.mkdir(parents=True, exist_ok=True) + summary_dir.mkdir(parents=True, exist_ok=True) + for patient_id, raw_text in topics.items(): + profile = build_profile_from_topic(patient_id, raw_text) + (profile_dir / f"{patient_id}.json").write_text( + profile.model_dump_json(indent=2, exclude_none=True), encoding="utf-8" + ) + (summary_dir / f"{patient_id}.json").write_text( + json.dumps(profile_to_matching_summary(profile), indent=2), encoding="utf-8" + ) + logger.info("Imported %s official topics for track %s", len(topics), track) + return len(topics) From 78394b8e7cf1e90751d06487afe04a2ba23d54c0 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Fri, 26 Jun 2026 18:06:52 +0200 Subject: [PATCH 36/64] chore: remove obsolete example/ fixtures Drop the leftover example/phenopacket.json input and example/phenopacket/keywords.json run output (the example/ directory was cleaned up during the data reorganization). Co-Authored-By: Claude Opus 4.8 --- example/phenopacket.json | 343 ------------------------------ example/phenopacket/keywords.json | 53 ----- 2 files changed, 396 deletions(-) delete mode 100644 example/phenopacket.json delete mode 100644 example/phenopacket/keywords.json diff --git a/example/phenopacket.json b/example/phenopacket.json deleted file mode 100644 index 0172bbfe..00000000 --- a/example/phenopacket.json +++ /dev/null @@ -1,343 +0,0 @@ -{ - "id": "patient67890", - "subject": { - "id": "patient67890", - "dateOfBirth": "1965-03-22", - "sex": "MALE", - "karyotypicSex": "XY", - "taxonomy": { - "id": "NCBITaxon:9606", - "label": "Homo sapiens" - }, - "description": "58-year-old male with a complex cardiac history and multiple comorbidities.", - "timeAtLastEncounter": { - "age": { - "iso8601duration": "P58Y1M10D" - } - } - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0001627", - "label": "Myocardial infarction" - }, - "onset": { - "age": { - "iso8601duration": "P55Y" - } - }, - "severity": { - "id": "HP:0012828", - "label": "Severe" - }, - "modifiers": [ - { - "id": "HP:0031796", - "label": "Recurrent" - } - ], - "description": "Patient experienced multiple infarctions, requiring emergency stenting and later bypass surgery." - }, - { - "type": { - "id": "HP:0004322", - "label": "Type 2 diabetes mellitus" - }, - "onset": { - "age": { - "iso8601duration": "P48Y" - } - }, - "description": "Diabetes managed with metformin, HbA1c maintained between 6.5% and 7.2%." - }, - { - "type": { - "id": "HP:0001945", - "label": "Elevated hepatic transaminase level" - }, - "onset": { - "age": { - "iso8601duration": "P50Y" - } - }, - "description": "Mild elevation suspected secondary to statin therapy or non-alcoholic fatty liver disease." - }, - { - "type": { - "id": "HP:0001953", - "label": "Hypercholesterolemia" - }, - "onset": { - "age": { - "iso8601duration": "P45Y" - } - }, - "description": "LDL levels >190 mg/dL despite lifestyle interventions." - }, - { - "type": { - "id": "HP:0030654", - "label": "History of smoking" - }, - "onset": { - "age": { - "iso8601duration": "P20Y" - } - }, - "resolution": { - "age": { - "iso8601duration": "P45Y" - } - }, - "description": "Former smoker (20 pack-years), quit at age 45." - } - ], - "diseases": [ - { - "term": { - "id": "MONDO:0005066", - "label": "Coronary artery disease" - }, - "onset": { - "age": { - "iso8601duration": "P55Y" - } - }, - "diseaseStage": [ - { - "id": "NCIT:C27970", - "label": "Stage III" - } - ], - "description": "Advanced CAD with multivessel involvement and impaired left ventricular function." - } - ], - "biosamples": [ - { - "id": "biosample-heart", - "individualId": "patient67890", - "sampledTissue": { - "id": "UBERON:0000948", - "label": "heart" - }, - "sampleType": { - "id": "NCIT:C13302", - "label": "Biopsy specimen" - }, - "timeOfCollection": { - "age": { - "iso8601duration": "P55Y6M" - } - }, - "histologicalDiagnosis": { - "id": "NCIT:C7278", - "label": "Ischemic cardiomyopathy" - }, - "procedure": { - "code": { - "id": "NCIT:C51886", - "label": "Cardiac Biopsy" - } - }, - "description": "Tissue biopsy taken during bypass surgery for pathological analysis of ischemic damage." - }, - { - "id": "biosample-blood", - "individualId": "patient67890", - "sampledTissue": { - "id": "UBERON:0000178", - "label": "blood" - }, - "sampleType": { - "id": "NCIT:C12439", - "label": "Normal specimen" - }, - "timeOfCollection": { - "age": { - "iso8601duration": "P58Y" - } - }, - "description": "Peripheral blood collected for genetic analysis and lipid profile." - } - ], - "interpretations": [ - { - "id": "interpretation-1", - "description": "Genetic testing confirmed heterozygous LDLR mutation consistent with familial hypercholesterolemia.", - "diagnosis": { - "disease": { - "id": "MONDO:0019391", - "label": "Familial hypercholesterolemia" - }, - "genomicInterpretations": [ - { - "variantInterpretation": { - "variationDescriptor": { - "id": "HGVS:NC_000019.10:g.11200068G>A", - "label": "LDLR c.1444G>A", - "geneContext": { - "valueId": "HGNC:6547", - "symbol": "LDLR" - }, - "molecularAttributes": { - "aminoAcidChange": { - "value": "p.Asp482Asn" - } - } - }, - "therapeuticActionability": { - "id": "NCIT:C80153", - "label": "Statin sensitivity" - } - } - } - ] - } - } - ], - "medicalActions": [ - { - "treatment": { - "agent": { - "id": "CHEBI:9150", - "label": "Atorvastatin" - }, - "routeOfAdministration": { - "id": "NCIT:C38288", - "label": "Oral" - }, - "doseIntervals": [ - { - "quantity": { - "unit": { - "id": "UCUM:mg", - "label": "milligram" - }, - "value": 40 - }, - "scheduleFrequency": { - "id": "NCIT:C64575", - "label": "Daily" - } - } - ] - }, - "description": "Statin therapy initiated due to elevated LDL and LDLR mutation; continued post-CABG." - }, - { - "treatment": { - "agent": { - "id": "CHEBI:63638", - "label": "Metformin" - }, - "doseIntervals": [ - { - "quantity": { - "unit": { - "id": "UCUM:mg", - "label": "milligram" - }, - "value": 500 - }, - "scheduleFrequency": { - "id": "NCIT:C64575", - "label": "Twice Daily" - } - } - ] - }, - "description": "First-line therapy for type 2 diabetes; well-tolerated with good glycemic control." - }, - { - "procedure": { - "code": { - "id": "NCIT:C17107", - "label": "Coronary Artery Bypass Grafting" - }, - "performed": "2022-11-15" - }, - "description": "CABG performed after recurrent MI and critical stenosis in three coronary arteries." - } - ], - "family": { - "id": "family-001", - "proband": { - "id": "patient67890" - }, - "relatives": [ - { - "id": "father", - "sex": "MALE", - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0001677", - "label": "Sudden cardiac death" - }, - "onset": { - "age": { - "iso8601duration": "P62Y" - } - } - } - ], - "vitalStatus": { - "status": "DECEASED", - "ageAtDeath": { - "iso8601duration": "P62Y" - } - } - } - ] - }, - "files": [ - { - "uri": "https://example.org/reports/genomics67890.pdf", - "fileAttribute": { - "attributeType": { - "id": "NCIT:C16444", - "label": "Genomics Report" - } - } - }, - { - "uri": "https://example.org/reports/cardiac_biopsy.pdf", - "fileAttribute": { - "attributeType": { - "id": "NCIT:C47885", - "label": "Pathology Report" - } - } - } - ], - "metaData": { - "created": "2024-12-15T10:45:00Z", - "createdBy": "Dr. Patel", - "submittedBy": "Cardiology Dept, Metro Medical Center", - "resources": [ - { - "id": "hp", - "name": "Human Phenotype Ontology", - "namespacePrefix": "HP", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2023-04-08" - }, - { - "id": "mondo", - "name": "MONDO Disease Ontology", - "namespacePrefix": "MONDO", - "url": "http://purl.obolibrary.org/obo/mondo.owl", - "version": "2023-03-01" - }, - { - "id": "ncit", - "name": "NCI Thesaurus", - "namespacePrefix": "NCIT", - "url": "http://purl.obolibrary.org/obo/ncit.owl", - "version": "23.03d" - } - ], - "phenopacketSchemaVersion": "2.0" - } -} \ No newline at end of file diff --git a/example/phenopacket/keywords.json b/example/phenopacket/keywords.json deleted file mode 100644 index d11e5a8e..00000000 --- a/example/phenopacket/keywords.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "main_conditions": [ - "Myocardial infarction", - "Heart attack", - "Cardiac infarction", - "Type 2 diabetes mellitus", - "Diabetes", - "Elevated hepatic transaminase level", - "Liver enzyme elevation", - "Hypercholesterolemia", - "High cholesterol", - "Coronary artery disease" - ], - "other_conditions": [ - "Recurrent myocardial infarction", - "Emergency stenting", - "Bypass surgery", - "HbA1c levels between 6.5% and 7.2%", - "Mild elevation of liver enzymes", - "Statin therapy", - "Non-alcoholic fatty liver disease", - "LDL levels >190 mg/dL", - "Lifestyle interventions", - "Former smoker", - "20 pack-years", - "Quit smoking at age 45", - "Stage III coronary artery disease", - "Multivessel involvement", - "Impaired left ventricular function", - "Ischemic cardiomyopathy", - "Genetic testing", - "Heterozygous LDLR mutation", - "Familial hypercholesterolemia", - "Atorvastatin treatment", - "Metformin treatment", - "Coronary Artery Bypass Grafting", - "Sudden cardiac death in father", - "Father deceased at age 62" - ], - "expanded_sentences": [ - "The patient is a 58-year-old male with a complex cardiac history, including multiple myocardial infarctions.", - "He has experienced recurrent heart attacks, necessitating emergency stenting and later bypass surgery.", - "The patient has type 2 diabetes mellitus, managed with metformin, maintaining HbA1c levels between 6.5% and 7.2%.", - "There is a mild elevation in liver enzymes, suspected to be secondary to statin therapy or non-alcoholic fatty liver disease.", - "He has hypercholesterolemia, with LDL levels exceeding 190 mg/dL despite lifestyle interventions.", - "The patient is a former smoker, having quit at age 45 after 20 pack-years of smoking.", - "He has advanced coronary artery disease, stage III, with multivessel involvement and impaired left ventricular function.", - "A tissue biopsy taken during bypass surgery revealed ischemic cardiomyopathy.", - "Genetic testing confirmed a heterozygous LDLR mutation, consistent with familial hypercholesterolemia.", - "The patient is currently on atorvastatin and metformin treatments.", - "His father experienced sudden cardiac death at age 62." - ] -} \ No newline at end of file From ff730c29ce4b4cd145d0c851317e91f31ed2d491 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 13:02:36 +0200 Subject: [PATCH 37/64] fix: crash-safe resume + single shared vLLM engine; correct NIST TREC URLs Audit-driven robustness pass (adversarially-verified findings): - Atomic writes: write_json_file and prepared trial/criteria writes go through a temp file + fsync + os.replace, so a crash mid-write never leaves a truncated artifact. Add is_valid_json_file; resume gates parse the marker instead of checking size>0 (corrupt = re-run, empty-but-valid = done). - Completion-marker ordering: prepare writes criteria before the trial JSON (the resume marker); ingest writes the summary before the profile. An interrupted unit is re-done, not wrongly skipped. - save_ranked_trials re-raises on write failure (patient counted failed, not silently completed). - build_index fails fast on zero criteria instead of leaving a half-built index that can never become ready_to_match. - run_e2e propagates --rematch to query expansion. vLLM engine lifecycle: - Cache built engines in load_vllm_engine keyed by (model, adapter, params): the per-patient RAG path and the query expander now share ONE engine instead of rebuilding the CoT model repeatedly (was a per-patient reload plus a second copy). - Fix the query-expansion vLLM call (passed a str where a dict was expected) and default its backend to the RAG backend so it shares the cache. - free_models() teardown (gc + destroy_model_parallel + empty_cache) at every top-level exit (e2e, trec, run). TREC sourcing: - Correct NIST topic/qrels URLs to /data/trials/ (the /data/clinical/trials/ paths 404'd; verified 200 and format-checked against the parsers). Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/cli/run.py | 6 +- src/trialmatchai/main.py | 6 +- src/trialmatchai/matching/query_expansion.py | 19 ++++- src/trialmatchai/matching/trial_ranker.py | 10 +-- src/trialmatchai/models/llm/vllm_loader.py | 67 ++++++++++++++++- src/trialmatchai/orchestration.py | 79 ++++++++++++-------- src/trialmatchai/registry/preparation.py | 30 ++++++-- src/trialmatchai/trec/qrels.py | 4 +- src/trialmatchai/trec/runner.py | 14 +++- src/trialmatchai/trec/topics.py | 4 +- src/trialmatchai/utils/file_utils.py | 40 +++++++++- 11 files changed, 220 insertions(+), 59 deletions(-) diff --git a/src/trialmatchai/cli/run.py b/src/trialmatchai/cli/run.py index 6adf147d..be50f1ac 100644 --- a/src/trialmatchai/cli/run.py +++ b/src/trialmatchai/cli/run.py @@ -9,8 +9,12 @@ def main() -> int: parser.add_argument("--config", default=None, help="Path to config.json") args = parser.parse_args() from trialmatchai.main import main_pipeline + from trialmatchai.orchestration import free_models - return main_pipeline(args.config) + try: + return main_pipeline(args.config) + finally: + free_models() if __name__ == "__main__": diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index aec62f93..e82b7b40 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -27,6 +27,7 @@ from trialmatchai.interop.models import PatientProfile from trialmatchai.utils.file_utils import ( create_directory, + is_valid_json_file, read_json_file, read_text_file, write_json_file, @@ -423,7 +424,10 @@ def main_pipeline( output_folder = Path(paths["output_dir"]) / patient_id if resume: ranked_path = output_folder / "ranked_trials.json" - if ranked_path.exists() and ranked_path.stat().st_size > 0: + # Parseable (not just non-empty) so a truncated/partial marker is + # treated as incomplete and re-run, while an empty-but-valid result + # is correctly recognized as completed. + if is_valid_json_file(str(ranked_path)): logger.info("Resume: skipping already-matched patient %s", patient_id) skipped_patients += 1 continue diff --git a/src/trialmatchai/matching/query_expansion.py b/src/trialmatchai/matching/query_expansion.py index 0ec4cd68..c60cd1cd 100644 --- a/src/trialmatchai/matching/query_expansion.py +++ b/src/trialmatchai/matching/query_expansion.py @@ -60,7 +60,9 @@ def _resolve_settings(config: Dict[str, Any]) -> Dict[str, Any]: qe = dict(config.get("query_expansion") or {}) model_cfg = config.get("model", {}) return { - "backend": qe.get("backend") or config.get("cot_backend") or "transformers", + # Default to the RAG/eligibility backend so the expander shares the cached + # CoT engine with the matching stage instead of loading a second copy. + "backend": qe.get("backend") or config.get("rag", {}).get("backend") or "vllm", "model": qe.get("model") or model_cfg.get("base_model"), "adapter": qe.get("adapter", model_cfg.get("cot_adapter_path")), "device": str(config.get("global", {}).get("device", 0)), @@ -76,8 +78,9 @@ def _resolve_settings(config: Dict[str, Any]) -> Dict[str, Any]: class QueryExpander: """CoT expander; loads its model lazily so import stays base-deps safe.""" - def __init__(self, settings: Dict[str, Any]): + def __init__(self, settings: Dict[str, Any], config: Dict[str, Any]): self.settings = settings + self.config = config self.backend = settings["backend"] if not settings.get("model"): raise ValueError("query_expansion requires a model (set query_expansion.model or model.base_model)") @@ -114,8 +117,16 @@ def _init_vllm(self) -> None: from trialmatchai.models.llm.vllm_loader import load_vllm_engine s = self.settings + # Same model_config/vllm_cfg shape as the RAG eligibility path so an + # identical (model, adapter, params) request hits the engine cache and + # shares ONE engine rather than loading a second CoT copy. + model_config = { + **self.config.get("model", {}), + "base_model": s["model"], + "cot_adapter_path": s.get("adapter"), + } self.engine, self.tokenizer, self.lora_request = load_vllm_engine( - s["model"], adapter_path=s.get("adapter") + model_config=model_config, vllm_cfg=self.config.get("vllm", {}) ) # -- generation -------------------------------------------------------- # @@ -170,7 +181,7 @@ def build_query_expander(config: Dict[str, Any]) -> QueryExpander | None: qe = config.get("query_expansion") or {} if not qe.get("enabled"): return None - return QueryExpander(_resolve_settings(config)) + return QueryExpander(_resolve_settings(config), config) def enrich_summary( diff --git a/src/trialmatchai/matching/trial_ranker.py b/src/trialmatchai/matching/trial_ranker.py index cd9dcf24..db20aa03 100644 --- a/src/trialmatchai/matching/trial_ranker.py +++ b/src/trialmatchai/matching/trial_ranker.py @@ -70,8 +70,8 @@ def rank_trials(trial_data: List[Dict]) -> List[Dict]: def save_ranked_trials(ranked_trials: List[Dict], output_file: str): - try: - write_json_file({"RankedTrials": ranked_trials}, output_file) - logger.info(f"Ranked trials saved to {output_file}") - except Exception as e: - logger.error(f"Failed to save ranked trials: {e}") + # Do NOT swallow write failures: the caller treats a returned-without-raising + # call as a completed patient. A failed final write must surface so the + # patient is counted as failed (and retried), not marked done with no marker. + write_json_file({"RankedTrials": ranked_trials}, output_file) + logger.info(f"Ranked trials saved to {output_file}") diff --git a/src/trialmatchai/models/llm/vllm_loader.py b/src/trialmatchai/models/llm/vllm_loader.py index 7166729d..566b9e34 100644 --- a/src/trialmatchai/models/llm/vllm_loader.py +++ b/src/trialmatchai/models/llm/vllm_loader.py @@ -61,6 +61,50 @@ def _as_float(x, name: str) -> Optional[float]: raise TypeError(f"{name} must be float-compatible, got {type(x)}: {x!r}") from e +# Process-level cache of built vLLM engines, keyed by the resolved model/adapter +# and engine parameters. The RAG eligibility path builds an engine per patient and +# the query expander builds one too — without this they would each rebuild the +# (multi-GB) CoT engine repeatedly, wasting minutes and risking GPU OOM. With the +# cache, identical (model, adapter, params) requests share a single engine. +_ENGINE_CACHE: dict = {} + + +def free_vllm_engines() -> None: + """Tear down all cached vLLM engines and release GPU memory. + + Call once at the end of a top-level run (after all patients/tracks). Idempotent. + """ + global _ENGINE_CACHE + engines = list(_ENGINE_CACHE.values()) + _ENGINE_CACHE = {} + for engine, _tok, _lora in engines: + try: + del engine + except Exception: + pass + try: + import gc + + gc.collect() + except Exception: + pass + try: + from vllm.distributed.parallel_state import ( # type: ignore + destroy_model_parallel, + ) + + destroy_model_parallel() + except Exception: + pass + try: + import torch + + if torch.cuda.is_available(): + torch.cuda.empty_cache() + except Exception: + pass + + def load_vllm_engine( model_config: dict, vllm_cfg: dict | None = None ) -> Tuple[object, Optional[object], Optional[object]]: @@ -132,6 +176,25 @@ def load_vllm_engine( if requested_len is not None: engine_kwargs["max_model_len"] = requested_len + # Return a cached engine when the same model/adapter/params were already built. + cache_adapter = _as_str( + vllm_cfg.get("adapter_path") or model_config.get("cot_adapter_path"), + "adapter_path", + ) or "" + cache_key = ( + model_path, + cache_adapter, + dtype, + tp, + round(gmu, 4), + str(engine_kwargs.get("max_model_len", "default")), + str(engine_kwargs.get("revision", "")), + ) + cached = _ENGINE_CACHE.get(cache_key) + if cached is not None: + logger.info("[vLLM] Reusing cached engine for model=%s", model_path) + return cached + logger.info( "[vLLM] Initializing engine: model=%s dtype=%s tp=%s max_len=%s gmu=%.2f", model_path, @@ -240,4 +303,6 @@ def load_vllm_engine( f"[vLLM] Preload adapter failed (safe to ignore): {e}" ) - return engine, tokenizer, lora_request + result = (engine, tokenizer, lora_request) + _ENGINE_CACHE[cache_key] = result + return result diff --git a/src/trialmatchai/orchestration.py b/src/trialmatchai/orchestration.py index 12db24ee..28c815f7 100644 --- a/src/trialmatchai/orchestration.py +++ b/src/trialmatchai/orchestration.py @@ -18,6 +18,7 @@ from typing import Any, Dict, Iterable, Iterator, Sequence from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.utils.file_utils import is_valid_json_file, write_json_file from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -61,20 +62,19 @@ def ingest_inputs( ) for profile in profiles: profile_path = profile_dir / f"{profile.patient_id}.json" - if not force and profile_path.exists(): + if not force and is_valid_json_file(str(profile_path)): logger.info("Ingest skipped (exists): %s", profile.patient_id) continue - profile_path.write_text( - json.dumps( - profile.model_dump(mode="json", exclude_none=True), - indent=2, - ensure_ascii=False, - ), - encoding="utf-8", + # Summary first; the profile JSON is written last (atomically) as the + # completion marker, so a crash between them re-imports rather than + # leaving a profile with no summary. + write_json_file( + profile_to_matching_summary(profile), + str(summary_dir / f"{profile.patient_id}.json"), ) - (summary_dir / f"{profile.patient_id}.json").write_text( - json.dumps(profile_to_matching_summary(profile), indent=2), - encoding="utf-8", + write_json_file( + profile.model_dump(mode="json", exclude_none=True), + str(profile_path), ) imported += 1 logger.info("Ingested patient %s", profile.patient_id) @@ -132,19 +132,13 @@ def expand_queries(config: Dict[str, Any], *, force: bool = False) -> int: expansion = expander.expand(narrative) merged = enrich_summary(summary, expansion) merged["query_expanded"] = True - summary_path.write_text(json.dumps(merged, indent=2), encoding="utf-8") + write_json_file(merged, str(summary_path)) enriched += 1 logger.info("Query-expanded %s", pid) - # Free the expander's model before the match stage loads its own. + # Release the expander wrapper; its CoT engine stays in the vLLM engine cache + # and is reused by the match stage (shared — a single load, not two). del expander - try: - import torch - - if torch.cuda.is_available(): - torch.cuda.empty_cache() - except Exception: - pass logger.info("Query-expansion stage: enriched %s summaries.", enriched) return enriched @@ -212,12 +206,17 @@ def build_index( logger.info("Indexed %s trial documents.", n_trials) criteria_docs = list(_iter_criteria_docs(Path(processed_criteria_folder), nct_set)) - n_criteria = 0 - if criteria_docs: - n_criteria = backend.index_criteria(criteria_docs, recreate=True) - logger.info("Indexed %s criteria documents.", n_criteria) - else: - logger.warning("No criteria documents found; criteria channel will be empty.") + if not criteria_docs: + # Refuse to leave an inconsistent index (trials table but no criteria + # table) where `ready_to_match` can never become true. An empty corpus is + # a data error — the corpus is unprepared. + raise RuntimeError( + f"No criteria documents found in {processed_criteria_folder}" + + (f" for the {len(nct_set)} filtered NCT ids" if nct_set else "") + + ". The corpus appears unprepared — run `trialmatchai build` (prepare) first." + ) + n_criteria = backend.index_criteria(criteria_docs, recreate=True) + logger.info("Indexed %s criteria documents.", n_criteria) return { "skipped": False, @@ -238,7 +237,7 @@ def count_pending(config: Dict[str, Any]) -> tuple[int, int]: pending = done = 0 for profile_path in sorted(profile_dir.glob("*.json")): ranked = output_dir / profile_path.stem / "ranked_trials.json" - if ranked.exists() and ranked.stat().st_size > 0: + if is_valid_json_file(str(ranked)): done += 1 else: pending += 1 @@ -273,6 +272,16 @@ def run_matching( # --------------------------------------------------------------------------- # # Full e2e # --------------------------------------------------------------------------- # +def free_models() -> None: + """Release cached GPU model engines. Call at the end of a top-level run.""" + try: + from trialmatchai.models.llm.vllm_loader import free_vllm_engines + + free_vllm_engines() + except Exception as exc: # pragma: no cover - teardown is best-effort + logger.debug("free_models: %s", exc) + + def run_e2e( config: Dict[str, Any], inputs: Sequence[str | Path], @@ -295,7 +304,7 @@ def run_e2e( with_entities=with_entities, force=force_reingest, ) - expand_queries(config) + expand_queries(config, force=force_rematch) build_index( config, processed_trials_folder=processed_trials_folder, @@ -303,7 +312,10 @@ def run_e2e( nct_filter=nct_filter, force=force_reindex, ) - return run_matching(config, resume=True, force=force_rematch) + try: + return run_matching(config, resume=True, force=force_rematch) + finally: + free_models() # --------------------------------------------------------------------------- # @@ -327,8 +339,7 @@ def _load_manifest(path: Path) -> dict: def _save_manifest(path: Path, manifest: dict) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text(json.dumps(manifest, indent=2), encoding="utf-8") + write_json_file(manifest, str(path)) def _count_json(folder: Path) -> int: @@ -399,8 +410,12 @@ def prepare_corpus( criteria_rows = prepare_criteria_documents( doc, embedder, entity_annotator=entity_annotator ) - write_prepared_trial(trial_row, processed_trials_folder) + # Write criteria first; the trial JSON is written last and is the + # per-trial completion marker the resume check keys on — so an + # interrupted trial (criteria written, trial not) is re-processed + # rather than wrongly skipped. write_prepared_criteria(criteria_rows, processed_criteria_folder) + write_prepared_trial(trial_row, processed_trials_folder) prepared += 1 except Exception: failed += 1 diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py index 05b06e91..4f9ae72f 100644 --- a/src/trialmatchai/registry/preparation.py +++ b/src/trialmatchai/registry/preparation.py @@ -2,7 +2,9 @@ import hashlib import json +import os import re +import tempfile from collections.abc import Sequence from pathlib import Path from typing import Any, Protocol @@ -158,10 +160,27 @@ def prepare_criteria_documents( return rows +def _atomic_write_text(path: Path, text: str) -> None: + """Write text via a temp file + os.replace so readers never see a partial file.""" + path.parent.mkdir(parents=True, exist_ok=True) + fd, tmp = tempfile.mkstemp(dir=str(path.parent), prefix=".tmp-", suffix=path.suffix) + try: + with os.fdopen(fd, "w", encoding="utf-8") as f: + f.write(text) + f.flush() + os.fsync(f.fileno()) + os.replace(tmp, path) + except Exception: + try: + os.unlink(tmp) + except OSError: + pass + raise + + def write_prepared_trial(row: dict[str, Any], folder: str | Path) -> Path: path = Path(folder) / f"{row['nct_id']}.json" - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text(json.dumps(row, indent=2, sort_keys=True), encoding="utf-8") + _atomic_write_text(path, json.dumps(row, indent=2, sort_keys=True)) return path @@ -169,10 +188,11 @@ def write_prepared_criteria(rows: Sequence[dict[str, Any]], folder: str | Path) if not rows: return 0 trial_folder = Path(folder) / str(rows[0]["nct_id"]) - trial_folder.mkdir(parents=True, exist_ok=True) for row in rows: - path = trial_folder / f"{row['criteria_id']}.json" - path.write_text(json.dumps(row, indent=2, sort_keys=True), encoding="utf-8") + _atomic_write_text( + trial_folder / f"{row['criteria_id']}.json", + json.dumps(row, indent=2, sort_keys=True), + ) return len(rows) diff --git a/src/trialmatchai/trec/qrels.py b/src/trialmatchai/trec/qrels.py index 666461b2..48ebe7e1 100644 --- a/src/trialmatchai/trec/qrels.py +++ b/src/trialmatchai/trec/qrels.py @@ -23,8 +23,8 @@ logger = setup_logging(__name__) QRELS_URLS: dict[str, str] = { - "21": "https://trec.nist.gov/data/clinical/trials/qrels2021.txt", - "22": "https://trec.nist.gov/data/clinical/trials/qrels2022.txt", + "21": "https://trec.nist.gov/data/trials/qrels2021.txt", + "22": "https://trec.nist.gov/data/trials/qrels2022.txt", } DEFAULT_CUTOFFS = (10, 50, 100, 200, 300, 500, 1000) diff --git a/src/trialmatchai/trec/runner.py b/src/trialmatchai/trec/runner.py index 4fe35ab2..ccaad55f 100644 --- a/src/trialmatchai/trec/runner.py +++ b/src/trialmatchai/trec/runner.py @@ -7,16 +7,22 @@ from __future__ import annotations -import json from copy import deepcopy from pathlib import Path from typing import Any, Dict from trialmatchai.config.config_loader import load_config -from trialmatchai.orchestration import build_index, count_pending, expand_queries, run_matching +from trialmatchai.orchestration import ( + build_index, + count_pending, + expand_queries, + free_models, + run_matching, +) from trialmatchai.trec import qrels as qrels_mod from trialmatchai.trec.corpus import TrackSpec, resolve_tracks from trialmatchai.trec.topics import import_topics +from trialmatchai.utils.file_utils import write_json_file from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -128,7 +134,7 @@ def run_tracks( try: metrics = qrels_mod.evaluate(qrels, spec.output_dir) metrics_path = Path(spec.output_dir) / "evaluation_metrics.json" - metrics_path.write_text(json.dumps(metrics, indent=2), encoding="utf-8") + write_json_file(metrics, str(metrics_path)) logger.info( "Track %s recall: %s -> %s", spec.key, @@ -139,6 +145,8 @@ def run_tracks( logger.exception("Evaluation failed for track %s", spec.key) logger.info("Track %s done -> %s", spec.key, spec.output_dir) + # All tracks done — release the shared GPU engines once. + free_models() if failures: logger.warning("TREC run completed with %s track failure(s).", failures) return 1 diff --git a/src/trialmatchai/trec/topics.py b/src/trialmatchai/trec/topics.py index cfee4a40..004611c4 100644 --- a/src/trialmatchai/trec/topics.py +++ b/src/trialmatchai/trec/topics.py @@ -54,13 +54,13 @@ class TopicSource: track="21", id_prefix="trec-2021", kind="nist_xml", - topics_url="https://trec.nist.gov/data/clinical/trials/topics2021.xml", + topics_url="https://trec.nist.gov/data/trials/topics2021.xml", ), "22": TopicSource( track="22", id_prefix="trec-2022", kind="nist_xml", - topics_url="https://trec.nist.gov/data/clinical/trials/topics2022.xml", + topics_url="https://trec.nist.gov/data/trials/topics2022.xml", ), # The SIGIR-2016 collection (Koopman & Zuccon) is hosted on the CSIRO Data # Access Portal (no stable direct-download URL), so sigir topics are sourced diff --git a/src/trialmatchai/utils/file_utils.py b/src/trialmatchai/utils/file_utils.py index e5054bb4..b59698a1 100644 --- a/src/trialmatchai/utils/file_utils.py +++ b/src/trialmatchai/utils/file_utils.py @@ -1,5 +1,6 @@ import json import os +import tempfile from typing import Dict, List @@ -13,14 +14,47 @@ def read_json_file(file_path: str) -> Dict: def write_json_file(data: Dict, file_path: str): - """Write data to a JSON file.""" + """Atomically write data to a JSON file. + + Writes to a temp file in the same directory, fsyncs, then os.replace()s it + into place — so a crash mid-write can never leave a truncated/partial file + that resume logic would mistake for a completed artifact. + """ try: - with open(file_path, "w", encoding="utf-8") as f: - json.dump(data, f, indent=4, ensure_ascii=False) + path = str(file_path) + directory = os.path.dirname(path) or "." + os.makedirs(directory, exist_ok=True) + fd, tmp = tempfile.mkstemp(dir=directory, prefix=".tmp-", suffix=".json") + try: + with os.fdopen(fd, "w", encoding="utf-8") as f: + json.dump(data, f, indent=4, ensure_ascii=False) + f.flush() + os.fsync(f.fileno()) + os.replace(tmp, path) + except Exception: + try: + os.unlink(tmp) + except OSError: + pass + raise except Exception as e: raise ValueError(f"Failed to write {file_path}: {str(e)}") +def is_valid_json_file(file_path: str) -> bool: + """True only if the path exists and contains parseable JSON. + + Used by resume gates so a present-but-corrupt/partial marker is treated as + incomplete (re-run) rather than done. + """ + try: + with open(file_path, "r", encoding="utf-8") as f: + json.load(f) + return True + except Exception: + return False + + def read_text_file(file_path: str) -> List[str]: """Read lines from a text file.""" try: From 83a50c6ccdff9c6f39a24278303362784066a52a Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 20:27:44 +0200 Subject: [PATCH 38/64] feat(trec): tie-aware nDCG eval + deterministic ranking Add McSherry-Najork tie-aware nDCG@{5,10,20} (order-invariant over equal scores) and P@10 (relevant>=1 and eligible==2) alongside recall@k, condensed to labeled-and-retrieved trials. rank_trials gets a deterministic tie-break (eligibility -> reranker -> first-level -> NCT id) and main.py threads the score signals through. Mirrors TrialGPT's Table-2 metrics. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/main.py | 6 +- src/trialmatchai/matching/trial_ranker.py | 40 ++++++++- src/trialmatchai/trec/metrics.py | 102 ++++++++++++++++++++++ src/trialmatchai/trec/qrels.py | 84 +++++++++++++++--- src/trialmatchai/trec/runner.py | 4 +- 5 files changed, 219 insertions(+), 17 deletions(-) create mode 100644 src/trialmatchai/trec/metrics.py diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index e82b7b40..bbcc117f 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -496,7 +496,11 @@ def main_pipeline( with log_timing(logger, "Final ranking"): trial_data = load_trial_data(str(output_folder)) - ranked_trials = rank_trials(trial_data) + ranked_trials = rank_trials( + trial_data, + first_level_scores=first_level_scores, + second_level_scores=dict(semi_final_trials), + ) save_ranked_trials( ranked_trials, str(output_folder / "ranked_trials.json") ) diff --git a/src/trialmatchai/matching/trial_ranker.py b/src/trialmatchai/matching/trial_ranker.py index db20aa03..5c094293 100644 --- a/src/trialmatchai/matching/trial_ranker.py +++ b/src/trialmatchai/matching/trial_ranker.py @@ -59,13 +59,45 @@ def score_trial(trial: Dict) -> float: return met / len(decided) -def rank_trials(trial_data: List[Dict]) -> List[Dict]: +def rank_trials( + trial_data: List[Dict], + *, + first_level_scores: Dict[str, float] | None = None, + second_level_scores: Dict[str, float] | None = None, +) -> List[Dict]: + """Rank trials by eligibility, breaking ties deterministically. + + The eligibility score is coarse (small rationals), so many trials tie. Rather + than let ties resolve by arbitrary filesystem order, break them by the + continuous second-level reranker probability, then the first-level retrieval + score, then the NCT id — a meaningful order within each eligibility bucket + that is fully reproducible. (Tie-aware nDCG further ensures genuine ties are + scored fairly regardless of this order.) + """ + first_level_scores = first_level_scores or {} + second_level_scores = second_level_scores or {} ranked_trials = [] for trial in trial_data: trial_id = trial.get("TrialID", "Unknown") - score = score_trial(trial) - ranked_trials.append({"TrialID": trial_id, "Score": score}) - ranked_trials.sort(key=lambda x: x["Score"], reverse=True) + ranked_trials.append( + { + "TrialID": trial_id, + "Score": score_trial(trial), + "RerankerScore": float(second_level_scores.get(trial_id, 0.0)), + "FirstLevelScore": float(first_level_scores.get(trial_id, 0.0)), + } + ) + # Descending eligibility -> reranker -> first-level; ascending NCT id last + # (negate numeric keys so a single ascending sort gives the right order and + # the NCT-id tie-break stays ascending). + ranked_trials.sort( + key=lambda x: ( + -x["Score"], + -x["RerankerScore"], + -x["FirstLevelScore"], + x["TrialID"], + ) + ) return ranked_trials diff --git a/src/trialmatchai/trec/metrics.py b/src/trialmatchai/trec/metrics.py new file mode 100644 index 00000000..9040994e --- /dev/null +++ b/src/trialmatchai/trec/metrics.py @@ -0,0 +1,102 @@ +"""Ranking-quality metrics for TREC evaluation. + +These complement recall@k (the retrieval-side metric in ``qrels``). nDCG here is: + + * **tie-aware** (McSherry & Najork, 2008): trials sharing the same ranking + score form a tie group, and each member is given the AVERAGE positional + discount over the ranks the group spans (truncated at k). The result is the + EXPECTED nDCG over all random orderings of the tied trials, so it is + invariant to arbitrary tie-breaking — it rewards only genuinely ordering a + more-relevant trial above a less-relevant one. + * **condensed**: computed over the labeled-and-retrieved trials only, with the + IDCG normalized to that same set. It measures the quality of the final + ranking of the trials the model actually evaluated, decoupled from recall. + +Gain is linear (gain = relevance grade), matching trec_eval's default and the +legacy evaluation. +""" + +from __future__ import annotations + +import math +from typing import Dict, Mapping, Sequence, Set + + +def _discount(rank: int) -> float: + """Log2 positional discount for a 1-indexed rank: 1 / log2(rank + 1).""" + return 1.0 / math.log2(rank + 1) + + +def tie_aware_dcg_at_k( + ordered_ids: Sequence[str], + score_of: Mapping[str, float], + gain_of: Mapping[str, float], + k: int, +) -> float: + """Expected DCG@k over random orderings of tied scores (McSherry-Najork). + + ``ordered_ids`` must already be sorted by descending ranking score, so equal + scores are contiguous. Each tie group spanning 1-indexed ranks [a..b] gives + every member the mean discount over ranks a..min(b, k). + """ + n = len(ordered_ids) + total = 0.0 + i = 0 + while i < n: + j = i + while j + 1 < n and score_of[ordered_ids[j + 1]] == score_of[ordered_ids[i]]: + j += 1 + a, b = i + 1, j + 1 # 1-indexed ranks spanned by this tie group + hi = min(b, k) + if hi >= a: + avg_discount = sum(_discount(r) for r in range(a, hi + 1)) / (b - a + 1) + for d in ordered_ids[i : j + 1]: + total += float(gain_of.get(d, 0.0)) * avg_discount + i = j + 1 + return total + + +def idcg_at_k(gains: Sequence[float], k: int) -> float: + """Ideal DCG@k — gains sorted descending (tie order is irrelevant: equal gains).""" + ideal = sorted((float(g) for g in gains), reverse=True)[:k] + return sum(g * _discount(r + 1) for r, g in enumerate(ideal)) + + +def ndcg_at_k( + ordered_ids: Sequence[str], + score_of: Mapping[str, float], + gain_of: Mapping[str, float], + k: int, +) -> float: + """Tie-aware nDCG@k. ``ordered_ids`` should be the condensed (labeled) list.""" + if k <= 0 or not ordered_ids: + return 0.0 + idcg = idcg_at_k([gain_of.get(d, 0.0) for d in ordered_ids], k) + if idcg <= 0: + return 0.0 + return tie_aware_dcg_at_k(ordered_ids, score_of, gain_of, k) / idcg + + +def precision_at_k(ordered_ids: Sequence[str], relevant: Set[str], k: int) -> float: + """Standard binary P@k over the final ranked list (hard cutoff k).""" + if k <= 0: + return 0.0 + topk = ordered_ids[:k] + if not topk: + return 0.0 + return sum(1 for d in topk if d in relevant) / float(k) + + +def condensed_ndcg( + ranked_ids: Sequence[str], + score_of: Mapping[str, float], + grade_of: Mapping[str, int], + cutoffs: Sequence[int], +) -> Dict[int, float]: + """Tie-aware nDCG@k for each cutoff, condensed to labeled-and-retrieved trials. + + ``ranked_ids`` is the final ranking order; ``grade_of`` is the qrels grade for + judged trials. Only trials present in ``grade_of`` are kept (condensed). + """ + condensed = [nid for nid in ranked_ids if nid in grade_of] + return {k: ndcg_at_k(condensed, score_of, grade_of, k) for k in cutoffs} diff --git a/src/trialmatchai/trec/qrels.py b/src/trialmatchai/trec/qrels.py index 48ebe7e1..92db16e0 100644 --- a/src/trialmatchai/trec/qrels.py +++ b/src/trialmatchai/trec/qrels.py @@ -18,6 +18,7 @@ import requests from tenacity import retry, retry_if_exception_type, stop_after_attempt, wait_exponential +from trialmatchai.trec.metrics import condensed_ndcg, precision_at_k from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -28,6 +29,8 @@ } DEFAULT_CUTOFFS = (10, 50, 100, 200, 300, 500, 1000) +NDCG_CUTOFFS = (5, 10, 20) +P_CUTOFF = 10 @retry( @@ -122,6 +125,31 @@ def recall_at_k(retrieved: list[str], relevant: set[str], k: int) -> float | Non return hits / len(relevant) +def _ranked_with_scores(patient_dir: Path) -> tuple[list[str], dict[str, float]]: + """Final ranked NCT ids (in order) + their eligibility scores from ranked_trials.json.""" + ranked = patient_dir / "ranked_trials.json" + if not ranked.exists(): + return [], {} + try: + data = json.loads(ranked.read_text()) + except Exception: + return [], {} + items = data.get("RankedTrials", []) if isinstance(data, dict) else data + order: list[str] = [] + score_of: dict[str, float] = {} + for item in items or []: + if not isinstance(item, dict) or item.get("TrialID") is None: + continue + nid = str(item["TrialID"]) + order.append(nid) + score_of[nid] = float(item.get("Score", 0.0)) + return order, score_of + + +def _mean(sums: dict, counts: dict) -> dict: + return {key: (sums[key] / counts[key] if counts[key] else None) for key in sums} + + def evaluate( qrels: dict[str, dict[str, int]], results_dir: Path, @@ -129,31 +157,67 @@ def evaluate( cutoffs: tuple[int, ...] = DEFAULT_CUTOFFS, threshold: int = 1, ) -> dict: - """Compute per-query and mean recall@k over the patients in ``results_dir``.""" + """Per-query and mean metrics over the patients in ``results_dir``. + + Two complementary families: + * recall@k — retrieval quality (first-level candidate list). + * tie-aware nDCG@{5,10,20} + P@10 — ranking quality of the final + ranked_trials.json, condensed to labeled-and-retrieved trials. nDCG is + order-invariant on ties (McSherry-Najork); P@10 is reported for both + "relevant" (grade>=1) and "eligible" (grade==2). + """ results_dir = Path(results_dir) relevant = relevant_ncts(qrels, threshold=threshold) + eligible = relevant_ncts(qrels, threshold=2) per_query: dict[str, dict] = {} - sums = {k: 0.0 for k in cutoffs} - counts = {k: 0 for k in cutoffs} - for query_id, rel_set in relevant.items(): + rec_sums = {f"recall@{k}": 0.0 for k in cutoffs} + rec_counts = {f"recall@{k}": 0 for k in cutoffs} + rank_sums = {f"ndcg@{k}": 0.0 for k in NDCG_CUTOFFS} + rank_sums[f"P@{P_CUTOFF}(rel>=1)"] = 0.0 + rank_sums[f"P@{P_CUTOFF}(eligible)"] = 0.0 + rank_counts = {key: 0 for key in rank_sums} + + for query_id, judgments in qrels.items(): patient_dir = results_dir / query_id + rel_set = relevant.get(query_id, set()) if not patient_dir.is_dir() or not rel_set: continue retrieved = _retrieved_for_patient(patient_dir) - row = {"num_relevant": len(rel_set), "num_retrieved": len(retrieved)} + ranked, score_of = _ranked_with_scores(patient_dir) + row = { + "num_relevant": len(rel_set), + "num_retrieved": len(retrieved), + "num_ranked": len(ranked), + } for k in cutoffs: r = recall_at_k(retrieved, rel_set, k) row[f"recall@{k}"] = r if r is not None: - sums[k] += r - counts[k] += 1 + rec_sums[f"recall@{k}"] += r + rec_counts[f"recall@{k}"] += 1 + + if ranked: + ndcg = condensed_ndcg(ranked, score_of, judgments, NDCG_CUTOFFS) + for k in NDCG_CUTOFFS: + row[f"ndcg@{k}"] = ndcg[k] + rank_sums[f"ndcg@{k}"] += ndcg[k] + rank_counts[f"ndcg@{k}"] += 1 + p_rel = precision_at_k(ranked, rel_set, P_CUTOFF) + p_elig = precision_at_k(ranked, eligible.get(query_id, set()), P_CUTOFF) + row[f"P@{P_CUTOFF}(rel>=1)"] = p_rel + row[f"P@{P_CUTOFF}(eligible)"] = p_elig + rank_sums[f"P@{P_CUTOFF}(rel>=1)"] += p_rel + rank_sums[f"P@{P_CUTOFF}(eligible)"] += p_elig + rank_counts[f"P@{P_CUTOFF}(rel>=1)"] += 1 + rank_counts[f"P@{P_CUTOFF}(eligible)"] += 1 per_query[query_id] = row - mean = {f"recall@{k}": (sums[k] / counts[k] if counts[k] else None) for k in cutoffs} + mean = {**_mean(rec_sums, rec_counts), **_mean(rank_sums, rank_counts)} return { - "threshold": threshold, + "recall_relevance_threshold": threshold, "num_queries_scored": len(per_query), - "mean_recall": mean, + "num_queries_ranked": rank_counts[f"ndcg@{NDCG_CUTOFFS[0]}"], + "mean": mean, "per_query": per_query, } diff --git a/src/trialmatchai/trec/runner.py b/src/trialmatchai/trec/runner.py index ccaad55f..2aacd9b3 100644 --- a/src/trialmatchai/trec/runner.py +++ b/src/trialmatchai/trec/runner.py @@ -136,9 +136,9 @@ def run_tracks( metrics_path = Path(spec.output_dir) / "evaluation_metrics.json" write_json_file(metrics, str(metrics_path)) logger.info( - "Track %s recall: %s -> %s", + "Track %s metrics: %s -> %s", spec.key, - metrics["mean_recall"], + metrics["mean"], metrics_path, ) except Exception: From c654983f273953e65b0079bbba737f62f11ce588 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 20:27:45 +0200 Subject: [PATCH 39/64] feat(concepts): open-vocabulary sources + one-command build-concepts Promote the concept-dictionary converters into the package (entities/concept_sources.py): OBO + NCBI gene_info parsers, a source registry (genes, diseases, chemicals, cell lines, cell types, phenotypes), idempotent download, and build_open_dictionaries(). build-concepts gains --sources open (download+convert+embed+index in one step), --concept-cache, --force-download, and an optional --concept-csv so dict-only builds work. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/cli/build_concepts.py | 63 ++++- src/trialmatchai/entities/concept_sources.py | 242 +++++++++++++++++++ 2 files changed, 292 insertions(+), 13 deletions(-) create mode 100644 src/trialmatchai/entities/concept_sources.py diff --git a/src/trialmatchai/cli/build_concepts.py b/src/trialmatchai/cli/build_concepts.py index 6937a34b..3be18d7d 100644 --- a/src/trialmatchai/cli/build_concepts.py +++ b/src/trialmatchai/cli/build_concepts.py @@ -23,7 +23,11 @@ def main() -> int: description="Build the TrialMatchAI LanceDB concept table." ) parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") - parser.add_argument("--concept-csv", required=True, help="OMOP CONCEPT.csv path") + parser.add_argument( + "--concept-csv", + default=None, + help="OMOP CONCEPT.csv path (optional; omit to build from --dictionary only)", + ) parser.add_argument( "--synonym-csv", default=None, @@ -42,6 +46,23 @@ def main() -> int: default=[], help="OMOP vocabulary to include. Defaults to TrialMatchAI deployment set.", ) + parser.add_argument( + "--sources", + choices=["open"], + default=None, + help="Download + convert a bundled source set. 'open' = genes, diseases, " + "chemicals, cell lines, cell types, phenotypes (no licence required).", + ) + parser.add_argument( + "--concept-cache", + default="data/concept_dicts", + help="Directory for downloaded sources + converted dictionaries (cached).", + ) + parser.add_argument( + "--force-download", + action="store_true", + help="Re-download/re-convert bundled sources even if cached.", + ) parser.add_argument("--db-path", default=None, help="Output LanceDB directory") parser.add_argument("--table", default=None, help="Output LanceDB table name") parser.add_argument( @@ -62,20 +83,36 @@ def main() -> int: db_path = args.db_path or linker_cfg.get("db_path") or "data/concepts" table_name = args.table or linker_cfg.get("table") or "concepts" + if not args.concept_csv and not args.dictionary and not args.sources: + parser.error("provide --concept-csv, --sources, and/or at least one --dictionary") + + # Collect dictionary specs from explicit --dictionary flags and bundled --sources. + dictionary_specs: list[tuple[str, str, str]] = [ + _parse_dictionary_spec(spec) for spec in args.dictionary + ] + if args.sources == "open": + from trialmatchai.entities.concept_sources import build_open_dictionaries + + for source, dict_path in build_open_dictionaries( + Path(args.concept_cache), force=args.force_download + ): + dictionary_specs.append( + (source.vocabulary_id, source.domain_id, str(dict_path)) + ) + if not dictionary_specs and not args.concept_csv: + parser.error("no concept sources were built (all downloads failed?)") + vocabularies = tuple(args.vocabulary or DEFAULT_OMOP_VOCABULARIES) - rows = build_omop_concept_rows( - args.concept_csv, - args.synonym_csv, - vocabularies=vocabularies, - ) - for spec in args.dictionary: - vocab, domain, path = _parse_dictionary_spec(spec) + rows: list = [] + if args.concept_csv: + rows = build_omop_concept_rows( + args.concept_csv, + args.synonym_csv, + vocabularies=vocabularies, + ) + for vocab, domain, path in dictionary_specs: rows.extend( - build_dictionary_rows( - path, - vocabulary_id=vocab, - domain_id=domain, - ) + build_dictionary_rows(path, vocabulary_id=vocab, domain_id=domain) ) embeddings = None diff --git a/src/trialmatchai/entities/concept_sources.py b/src/trialmatchai/entities/concept_sources.py new file mode 100644 index 00000000..41c34182 --- /dev/null +++ b/src/trialmatchai/entities/concept_sources.py @@ -0,0 +1,242 @@ +"""Open biomedical vocabularies for the entity-linking concept store. + +This turns publicly downloadable ontologies into the concept-dictionary format +consumed by :func:`trialmatchai.entities.builder.build_dictionary_rows` +(``||||...``), so the whole concept store can be +built with a single ``trialmatchai build-concepts --sources open`` command. + +Two source kinds are supported: + * ``obo`` — OBO ontologies (Cell Ontology, HPO, ChEBI, Disease Ontology, + Cellosaurus), filtered by id prefix. + * ``gene_info`` — NCBI ``gene_info`` TSV (symbol + full name + synonyms). + +OMOP clinical vocabularies (SNOMED/LOINC/RxNorm) are *not* here — they come from a +licensed OHDSI Athena ``CONCEPT.csv`` passed via ``--concept-csv``. +""" + +from __future__ import annotations + +import gzip +import re +from dataclasses import dataclass +from pathlib import Path +from typing import IO, Iterator + +import requests + +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + +_SYN_RE = re.compile(r'synonym:\s*"((?:[^"\\]|\\.)*)"') +_WS_RE = re.compile(r"\s+") +_DOWNLOAD_TIMEOUT = 1800 +_CHUNK = 1 << 20 + + +@dataclass(frozen=True) +class ConceptSource: + """A downloadable open vocabulary and how to map it to concept rows.""" + + name: str + url: str + kind: str # "obo" | "gene_info" + vocabulary_id: str + domain_id: str + id_prefix: str | None = None # OBO: keep only ids with this prefix + + @property + def filename(self) -> str: + return self.url.rsplit("/", 1)[-1] + + @property + def dict_filename(self) -> str: + return f"dict_{self.domain_id}.txt" + + +# The default "open" source set (no auth, no licence acceptance required). +OPEN_SOURCES: dict[str, ConceptSource] = { + "genes": ConceptSource( + "genes", + "https://ftp.ncbi.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz", + "gene_info", "NCBIGene", "Gene", + ), + "diseases": ConceptSource( + "diseases", "http://purl.obolibrary.org/obo/doid.obo", + "obo", "DOID", "Disease", "DOID:", + ), + "chemicals": ConceptSource( + "chemicals", "https://ftp.ebi.ac.uk/pub/databases/chebi/ontology/chebi_lite.obo", + "obo", "ChEBI", "Chemical", "CHEBI:", + ), + "cell_lines": ConceptSource( + "cell_lines", "https://ftp.expasy.org/databases/cellosaurus/cellosaurus.obo", + "obo", "Cellosaurus", "CellLine", "CVCL_", + ), + "cell_types": ConceptSource( + "cell_types", "http://purl.obolibrary.org/obo/cl.obo", + "obo", "CL", "CellType", "CL:", + ), + "phenotypes": ConceptSource( + "phenotypes", "http://purl.obolibrary.org/obo/hp.obo", + "obo", "HPO", "Phenotype", "HP:", + ), +} + + +def _open(path: Path) -> IO[str]: + if str(path).endswith(".gz"): + return gzip.open(path, "rt", encoding="utf-8") + return open(path, "r", encoding="utf-8") + + +def _clean_names(names: Iterator[str] | list[str]) -> list[str]: + """Normalise whitespace, drop blanks, dedupe case-insensitively (order-preserving).""" + out: list[str] = [] + seen: set[str] = set() + for raw in names: + name = _WS_RE.sub(" ", (raw or "").replace("\\", "")).strip() + key = name.casefold() + if name and key not in seen: + seen.add(key) + out.append(name) + return out + + +def parse_obo(path: Path, prefix: str | None) -> Iterator[tuple[str, list[str]]]: + """Yield ``(id, [name, *synonyms])`` for each non-obsolete OBO term.""" + cid = name = None + synonyms: list[str] = [] + obsolete = False + + def emit() -> tuple[str, list[str]] | None: + if cid and name and not obsolete and (not prefix or cid.startswith(prefix)): + return cid, [name, *synonyms] + return None + + with _open(path) as fh: + for raw in fh: + line = raw.rstrip("\n") + if line == "[Term]": + rec = emit() + if rec: + yield rec + cid = name = None + synonyms = [] + obsolete = False + elif line.startswith("id:"): + cid = line[3:].strip() + elif line.startswith("name:"): + name = line[5:].strip() + elif line.startswith("synonym:"): + m = _SYN_RE.search(line) + if m: + synonyms.append(m.group(1)) + elif line.startswith("is_obsolete:") and "true" in line: + obsolete = True + rec = emit() + if rec: + yield rec + + +def parse_gene_info(path: Path) -> Iterator[tuple[str, list[str]]]: + """Yield ``(gene_id, [symbol, full name, *synonyms, *other designations])``.""" + with _open(path) as fh: + for raw in fh: + if raw.startswith("#"): + continue + cols = raw.rstrip("\n").split("\t") + if len(cols) < 14: + continue + gene_id = cols[1].strip() + symbol = cols[2].strip() + syns = [] if cols[4] == "-" else cols[4].split("|") + full_name = "" if cols[8] == "-" else cols[8].strip() + auth_full = "" if cols[11] == "-" else cols[11].strip() + other = [] if cols[13] == "-" else cols[13].split("|") + names = [n for n in [symbol, auth_full, full_name, *syns, *other] if n] + if gene_id and names: + yield gene_id, names + + +def _iter_source(source: ConceptSource, raw_path: Path) -> Iterator[tuple[str, list[str]]]: + if source.kind == "obo": + return parse_obo(raw_path, source.id_prefix) + if source.kind == "gene_info": + return parse_gene_info(raw_path) + raise ValueError(f"Unknown source kind: {source.kind!r}") + + +def write_dictionary(source: ConceptSource, raw_path: Path, dict_path: Path) -> int: + """Convert a downloaded source file into a concept-dictionary file. Returns rows.""" + n = 0 + with open(dict_path, "w", encoding="utf-8") as out: + for cid, names in _iter_source(source, raw_path): + clean = _clean_names(names) + if cid and clean: + out.write(f"{cid}||{'|'.join(clean)}\n") + n += 1 + return n + + +def download_source( + source: ConceptSource, dest_dir: Path, *, force: bool = False +) -> Path | None: + """Download a source (idempotent, atomic). Returns the path, or None on failure.""" + dest_dir.mkdir(parents=True, exist_ok=True) + dest = dest_dir / source.filename + if dest.exists() and dest.stat().st_size > 0 and not force: + logger.info("cached download: %s", dest) + return dest + logger.info("downloading %s -> %s", source.url, dest) + tmp = dest.with_name(dest.name + ".tmp") + try: + with requests.get(source.url, stream=True, timeout=_DOWNLOAD_TIMEOUT) as resp: + resp.raise_for_status() + with open(tmp, "wb") as handle: + for chunk in resp.iter_content(_CHUNK): + handle.write(chunk) + tmp.replace(dest) + return dest + except Exception: + logger.exception("download failed: %s", source.url) + tmp.unlink(missing_ok=True) + return None + + +def build_open_dictionaries( + work_dir: Path, + *, + sources: list[str] | None = None, + force: bool = False, +) -> list[tuple[ConceptSource, Path]]: + """Download + convert the requested open sources into concept dictionaries. + + Idempotent: existing downloads and converted dictionaries are reused unless + ``force``. Returns ``(source, dict_path)`` for every source that produced a + non-empty dictionary; failed downloads are skipped with a warning. + """ + work_dir = Path(work_dir) + raw_dir = work_dir / "raw" + work_dir.mkdir(parents=True, exist_ok=True) + names = sources or list(OPEN_SOURCES) + results: list[tuple[ConceptSource, Path]] = [] + + for key in names: + source = OPEN_SOURCES[key] + dict_path = work_dir / source.dict_filename + if dict_path.exists() and dict_path.stat().st_size > 0 and not force: + logger.info("cached dictionary: %s", dict_path) + results.append((source, dict_path)) + continue + raw_path = download_source(source, raw_dir, force=force) + if raw_path is None: + logger.warning("skipping %s (download failed)", key) + continue + count = write_dictionary(source, raw_path, dict_path) + logger.info("converted %s: %s concepts -> %s", key, count, dict_path) + if count > 0: + results.append((source, dict_path)) + else: + logger.warning("skipping %s (0 concepts after conversion)", key) + return results From f437155a28efe1a3afd375fc6a62eea6b0d05d60 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 20:27:45 +0200 Subject: [PATCH 40/64] chore(dev): gitleaks pre-commit replaces homegrown scan_secrets Add .pre-commit-config.yaml (gitleaks secret scan + ruff + hygiene hooks) and pre-commit to dev deps; point README/docs at pre-commit. Remove the 4-regex scan_secrets.py (weak coverage, false confidence). Gitignore scripts/*.sh (local operational glue; the CLI is the shipped interface). Co-Authored-By: Claude Opus 4.8 --- .gitignore | 1 + .pre-commit-config.yaml | 25 +++++++++++++++++++ README.md | 8 +++++- docs/release.md | 2 +- pyproject.toml | 1 + scripts/scan_secrets.py | 54 ----------------------------------------- 6 files changed, 35 insertions(+), 56 deletions(-) create mode 100644 .pre-commit-config.yaml delete mode 100644 scripts/scan_secrets.py diff --git a/.gitignore b/.gitignore index 7862acbf..e089064b 100644 --- a/.gitignore +++ b/.gitignore @@ -45,6 +45,7 @@ dist/ *.bak *.swp *.slurm +scripts/*.sh **/tmp/ .DS_Store Thumbs.db diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..acd7ccbc --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +# Pre-commit hooks for TrialMatchAI. +# Install once: uv run pre-commit install +# Run on demand: uv run pre-commit run --all-files +repos: + - repo: https://github.com/gitleaks/gitleaks + rev: v8.21.2 + hooks: + - id: gitleaks + name: gitleaks (secret scan) + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.8.4 + hooks: + - id: ruff + args: [--fix] + - id: ruff-format + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-added-large-files + args: [--maxkb=2048] + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-merge-conflict diff --git a/README.md b/README.md index 85ef1034..c1b0abfb 100644 --- a/README.md +++ b/README.md @@ -399,10 +399,16 @@ See [docs/registry-updater.md](docs/registry-updater.md). uv sync uv run ruff check . uv run pytest -uv run python scripts/scan_secrets.py +uv run pre-commit run --all-files # ruff + gitleaks secret scan + hygiene uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 ``` +Install the git hooks once so secret scanning and linting run on every commit: + +```bash +uv run pre-commit install +``` + ## Security Never commit real credentials, private keys, datasets, models, local LanceDB diff --git a/docs/release.md b/docs/release.md index 873558ee..468963d0 100644 --- a/docs/release.md +++ b/docs/release.md @@ -9,7 +9,7 @@ uv lock --check uv run ruff check . uv run pytest uv build -uv run python scripts/scan_secrets.py +uv run pre-commit run --all-files uv run pip-audit --progress-spinner off --ignore-vuln CVE-2025-3000 ``` diff --git a/pyproject.toml b/pyproject.toml index 187bf177..a94228f6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -72,6 +72,7 @@ dev = [ "pytest>=8.3", "ruff>=0.9.0", "pip-audit>=2.7.3", + "pre-commit>=4.0", ] [tool.setuptools] diff --git a/scripts/scan_secrets.py b/scripts/scan_secrets.py deleted file mode 100644 index 5cdb21ac..00000000 --- a/scripts/scan_secrets.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import re -import subprocess -import sys -from pathlib import Path - - -SECRET_PATTERNS = [ - re.compile(r"-----BEGIN [A-Z ]*PRIVATE KEY-----"), - re.compile(r"\bpassword['\"]?\s*:\s*['\"](?!CHANGE_ME|change-me)[^'\"]{12,}['\"]"), - re.compile(r"\b[A-Z0-9_]*TOKEN\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+"), - re.compile(r"\b[A-Z0-9_]*API_KEY\s*=\s*['\"]?(?!change-me|CHANGE_ME|<|\$|$)[^\s'\"]+"), -] - -SECRET_PATH_PATTERNS = [ - re.compile(r"(^|/).*\.key$"), -] - -SKIP_SUFFIXES = {".png", ".jpg", ".jpeg", ".gif", ".zip", ".gz", ".sif", ".so", ".a"} - - -def main() -> int: - files = subprocess.check_output(["git", "ls-files"], text=True).splitlines() - findings: list[str] = [] - for raw_path in files: - for pattern in SECRET_PATH_PATTERNS: - if pattern.search(raw_path): - findings.append(f"{raw_path}: tracked generated secret material") - path = Path(raw_path) - if path.suffix.lower() in SKIP_SUFFIXES: - continue - try: - text = path.read_text(encoding="utf-8") - except FileNotFoundError: - continue - except UnicodeDecodeError: - continue - for pattern in SECRET_PATTERNS: - for match in pattern.finditer(text): - line = text.count("\n", 0, match.start()) + 1 - findings.append(f"{raw_path}:{line}: {pattern.pattern}") - - if findings: - print("Potential secrets found in tracked files:", file=sys.stderr) - print("\n".join(findings), file=sys.stderr) - return 1 - print("No tracked-file secrets found.") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) From 07b012bec925f4f096db9238e483d6c35c8bb9d6 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 20:40:30 +0200 Subject: [PATCH 41/64] fix(ci): sync lockfile for pre-commit dep; gitleaks replaces scan_secrets uv.lock regenerated to include pre-commit (was out of sync after adding it to dev deps -> uv sync --frozen / uv lock --check failed in ~13s). CI secret-scan step now runs the gitleaks pre-commit hook instead of the removed scan_secrets.py. Co-Authored-By: Claude Opus 4.8 --- .github/workflows/ci.yml | 2 +- uv.lock | 296 +++++++++++---------------------------- 2 files changed, 84 insertions(+), 214 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b2af2fc5..7fab42bd 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -65,7 +65,7 @@ jobs: uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-finetune --help - name: Secret scan - run: uv run python scripts/scan_secrets.py + run: uv run pre-commit run gitleaks --all-files - name: Dependency audit # vLLM 0.23 pins torch 2.11.0; CVE-2025-3000 has no fixed version listed. diff --git a/uv.lock b/uv.lock index 7b6661d9..e0c71dc3 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,5 @@ version = 1 -revision = 2 +revision = 3 requires-python = "==3.11.*" resolution-markers = [ "sys_platform == 'darwin'", @@ -57,17 +57,7 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/26/dd/bf526e6f0a1120dd6f2df2e97bacfe4d358f13d17a0ff5847301a1375a51/aiohttp-3.14.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:aa00140699487bd435fde4342d85c94cb256b7cd3a5b9c3396c67f19922afda2", size = 765225, upload-time = "2026-06-07T21:06:07.957Z" }, { url = "https://files.pythonhosted.org/packages/8f/e1/a2872aa55495a70f61310d411541c6ee23812d9a884e000c716e1bc3edbf/aiohttp-3.14.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1c1af67559445498b502030c35c59db59966f47041ca9de5b4e707f86bd10b5f", size = 518743, upload-time = "2026-06-07T21:06:09.749Z" }, { url = "https://files.pythonhosted.org/packages/5b/e7/c60c7b209e509cc787de3cea0550a518538cfc08003e1c1e14c1c63fff71/aiohttp-3.14.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d44ec478e713ee7f29b439f7eb8dc2b9d4079e11ae114d2c2ac3d5daf30516c8", size = 514139, upload-time = "2026-06-07T21:06:11.26Z" }, - 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    text title (the logo carries the name) for a cleaner centered hero. Co-Authored-By: Claude Opus 4.8 --- README.md | 2 -- img/logo.png | Bin 758820 -> 761605 bytes 2 files changed, 2 deletions(-) diff --git a/README.md b/README.md index c1b0abfb..9f89698f 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,6 @@ TrialMatchAI -

    TrialMatchAI

    -

    AI-driven clinical trial matching. Import a patient — text, FHIR, Phenopacket, or OMOP — and get ranked, eligible trials with criterion-level eligibility explanations. Local LanceDB search + vLLM reasoning on a single GPU server; no Elasticsearch or hosted vector database to run.

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zk?RUKq1W&z@6U(M2YFYSFLPImQ+_LJ;S!i^xUC8@{*wg$l2>cxVS}aGGt_W$-MS17 zJO2Eatj;=$%!{LpYEVrsw2E7)+kaBWIdJ6A=C|}$JE1rA92^WGVRcN z6ERB)dr+w_AwWVYyo(y!|4dL-fD4@DiKwOqo9G?M6O^J`W0;LV(2auwgD6fdr=K<&RUC z!`1=A0X+U98r(wk$*DQe9BUTG-~ayCy9^OYM}8>@48ZI+r355S686-j!%ZrrGkD6? z6=OY?#+ELj2t(-&onKL)x|^BmtO>`OE|4=QUs;v2pOv%`Vx^A8Dnswt>uKL|OK{a1fjA*X5nA@N)qk46nyo{ejaugS|YwP6(mz$+#Y5zD@? z2aIL+MI*g-ly6ogR(@+dAHMJQ)b&^SzmNr**%3tsU{anIiek_r_W>xW>;B^)Bg6GXQCRpg*=A4C_ Date: Sun, 28 Jun 2026 20:43:44 +0200 Subject: [PATCH 43/64] docs: enlarge wide banner logo (220->480px) so it renders properly Co-Authored-By: Claude Opus 4.8 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9f89698f..66e97d23 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@

      -TrialMatchAI +TrialMatchAI

      AI-driven clinical trial matching. Import a patient — text, FHIR, Phenopacket, or OMOP — and get ranked, eligible trials with criterion-level eligibility explanations. Local LanceDB search + vLLM reasoning on a single GPU server; no Elasticsearch or hosted vector database to run.

      From 78b885c32bc6d07520987f7c1c9ec35ebbea8336 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 20:57:43 +0200 Subject: [PATCH 44/64] fix: resume/ingest correctness hardening (audit C1-H3) - Retry trials recorded as {"error":...} on resume instead of treating the failure file as done (eligibility_base). - Clear stale per-trial criteria before re-prepare so changed chunking can't orphan old criteria_id files into the index (registry/preparation). - Scope final ranking to the current shortlist so stale per-trial outputs from a prior different shortlist aren't scored (trial_ranker, main). - Source eligibility criteria from processed_trials (always built/bootstrapped) not raw trials_jsons (not downloaded by bootstrap) -> no silent all-zero eligibility; fail loud on missing/empty criteria (main, eligibility_base). - import-patient: atomic writes, summary-before-profile marker ordering. - Legacy index --prepare: per-doc try/except + criteria-before-trial ordering. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/cli/import_patient.py | 16 ++++------ src/trialmatchai/cli/index_data.py | 31 +++++++++++++------ src/trialmatchai/main.py | 26 ++++++++++++++-- src/trialmatchai/matching/eligibility_base.py | 31 ++++++++++++++++--- src/trialmatchai/matching/trial_ranker.py | 12 ++++++- src/trialmatchai/registry/preparation.py | 7 +++++ 6 files changed, 96 insertions(+), 27 deletions(-) diff --git a/src/trialmatchai/cli/import_patient.py b/src/trialmatchai/cli/import_patient.py index de0587c2..242ea77b 100644 --- a/src/trialmatchai/cli/import_patient.py +++ b/src/trialmatchai/cli/import_patient.py @@ -1,7 +1,6 @@ from __future__ import annotations import argparse -import json import sys from pathlib import Path from typing import Any @@ -9,6 +8,7 @@ from trialmatchai.config.config_loader import load_config from trialmatchai.interop.exporters import profile_to_matching_summary from trialmatchai.interop.importers import import_patient_path +from trialmatchai.utils.file_utils import write_json_file from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -73,8 +73,11 @@ def main() -> int: for profile in profiles: profile_path = output_dir / f"{profile.patient_id}.json" summary_path = summary_dir / f"{profile.patient_id}.json" - _write_json(profile.model_dump(mode="json", exclude_none=True), profile_path) - _write_json(profile_to_matching_summary(profile), summary_path) + # Atomic writes, summary before profile: the profile is the completion + # marker downstream resume keys on, so it must land last (matches + # orchestration.ingest_inputs). + write_json_file(profile_to_matching_summary(profile), str(summary_path)) + write_json_file(profile.model_dump(mode="json", exclude_none=True), str(profile_path)) logger.info("Imported patient profile %s -> %s", profile.patient_id, profile_path) return 0 @@ -93,12 +96,5 @@ def _try_build_entity_annotator(config: dict[str, Any]): return None -def _write_json(payload: dict, path: Path) -> None: - path.write_text( - json.dumps(payload, indent=2, sort_keys=True, ensure_ascii=False), - encoding="utf-8", - ) - - if __name__ == "__main__": sys.exit(main()) diff --git a/src/trialmatchai/cli/index_data.py b/src/trialmatchai/cli/index_data.py index 0cf2c7fd..fd932c63 100644 --- a/src/trialmatchai/cli/index_data.py +++ b/src/trialmatchai/cli/index_data.py @@ -140,16 +140,29 @@ def _prepare_from_trials_jsons( embedder = build_embedder(config) entity_annotator = build_entity_annotator(config, embedder=embedder) trial_docs = _load_flat_json_folder(trials_json_folder) + prepared = failed = 0 for doc in trial_docs: - trial_row = prepare_trial_document(doc, embedder) - criteria_rows = prepare_criteria_documents( - doc, - embedder, - entity_annotator=entity_annotator, - ) - write_prepared_trial(trial_row, processed_trials_folder) - write_prepared_criteria(criteria_rows, processed_criteria_folder) - logger.info("Prepared %s trial JSON files from %s.", len(trial_docs), trials_json_folder) + nct_id = doc.get("nct_id", "") + try: + trial_row = prepare_trial_document(doc, embedder) + criteria_rows = prepare_criteria_documents( + doc, + embedder, + entity_annotator=entity_annotator, + ) + # Criteria before the trial marker so a crash mid-trial re-prepares it. + write_prepared_criteria(criteria_rows, processed_criteria_folder) + write_prepared_trial(trial_row, processed_trials_folder) + prepared += 1 + except Exception: + logger.exception("Failed to prepare trial %s; skipping", nct_id) + failed += 1 + logger.info( + "Prepared %s trial JSON files from %s (%s failed).", + prepared, + trials_json_folder, + failed, + ) def _resolve_path(value: str, root: Path) -> Path: diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index bbcc117f..2b244004 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -238,6 +238,21 @@ def run_second_level_search( return semi_final_trials, top_trials_path +def _criteria_source_folder(config: Dict) -> str: + """Folder the eligibility model reads each trial's criteria text from. + + Prefer ``processed_trials`` — always produced by ``build``/``bootstrap-data`` + and carrying the ``eligibility_criteria`` text — over the raw ``trials_jsons``, + which ``bootstrap-data`` does not download. Sourcing from the latter would make + the model silently reason over "no criteria" and score every trial 0. + """ + paths = config.get("paths", {}) + processed = paths.get("processed_trials_folder") or "data/processed_trials" + if Path(processed).is_dir(): + return str(processed) + return paths.get("trials_json_folder", "data/trials_jsons") + + def run_rag_processing( output_folder: str, top_trials_file: str, @@ -304,7 +319,7 @@ def run_rag_processing( rag_processor.process_trials( nct_ids=top_trials, - json_folder=config["paths"]["trials_json_folder"], + json_folder=_criteria_source_folder(config), output_folder=output_folder, patient_narrative=patient_narrative, ) @@ -495,11 +510,16 @@ def main_pipeline( ) with log_timing(logger, "Final ranking"): - trial_data = load_trial_data(str(output_folder)) + # Scope to this run's shortlist so stale per-trial files from a + # prior run with a different shortlist are not ranked. + second_level = dict(semi_final_trials) + trial_data = load_trial_data( + str(output_folder), allowed_ids=set(second_level) + ) ranked_trials = rank_trials( trial_data, first_level_scores=first_level_scores, - second_level_scores=dict(semi_final_trials), + second_level_scores=second_level, ) save_ranked_trials( ranked_trials, str(output_folder / "ranked_trials.json") diff --git a/src/trialmatchai/matching/eligibility_base.py b/src/trialmatchai/matching/eligibility_base.py index eb2aa00b..0d47534c 100644 --- a/src/trialmatchai/matching/eligibility_base.py +++ b/src/trialmatchai/matching/eligibility_base.py @@ -20,6 +20,21 @@ logger = setup_logging(__name__) +def _is_error_output(path: str) -> bool: + """True if a per-trial output is a recorded processing failure or unparseable. + + Used by the resume worklist so a transiently-failed trial (written as + ``{"error": ...}``) is retried on the next run instead of being treated as + done and locked into the ranking. + """ + try: + with open(path, "r", encoding="utf-8") as handle: + data = json.load(handle) + except Exception: + return True + return isinstance(data, dict) and "error" in data + + class BaseTrialProcessor: # Subclasses set these in __init__. tokenizer = None @@ -30,13 +45,18 @@ class BaseTrialProcessor: # ---------------------- I/O helpers ---------------------- def _load_trial_data(self, nct_id: str, json_folder: str) -> str: + path = f"{json_folder}/{nct_id}.json" try: - path = f"{json_folder}/{nct_id}.json" trial_data = read_json_file(path) - return trial_data.get("eligibility_criteria", "") except Exception as e: - logger.error(f"Error loading {nct_id}: {str(e)}") + # Fail loud: a missing/unreadable trial file means the eligibility + # model would silently reason over "no criteria" and score 0. + logger.error("Cannot load criteria for %s from %s: %s", nct_id, path, e) return "" + criteria = trial_data.get("eligibility_criteria", "") + if not criteria: + logger.warning("Trial %s has empty eligibility_criteria at %s", nct_id, path) + return criteria # ---------------------- Prompting ---------------------- @@ -225,7 +245,10 @@ def process_trials( items: List[Dict] = [] for nct_id in nct_ids: - if os.path.exists(f"{output_folder}/{nct_id}.json"): + existing = f"{output_folder}/{nct_id}.json" + # Skip only genuinely-completed trials; a recorded processing failure + # ({"error": ...}) or an unparseable file is retried, not locked in. + if os.path.exists(existing) and not _is_error_output(existing): logger.info(f"Skipping existing: {nct_id}") continue criteria_text = self._load_trial_data(nct_id, json_folder) diff --git a/src/trialmatchai/matching/trial_ranker.py b/src/trialmatchai/matching/trial_ranker.py index 5c094293..8b88b1ce 100644 --- a/src/trialmatchai/matching/trial_ranker.py +++ b/src/trialmatchai/matching/trial_ranker.py @@ -7,7 +7,15 @@ logger = setup_logging(__name__) -def load_trial_data(json_folder: str) -> List[Dict]: +def load_trial_data( + json_folder: str, allowed_ids: set[str] | None = None +) -> List[Dict]: + """Load per-trial eligibility outputs from a patient's result folder. + + ``allowed_ids`` scopes loading to the current shortlist, so per-trial files + left over from a prior run with a different shortlist (e.g. after the index + changed) are ignored instead of being scored into the final ranking. + """ trial_data = [] for file_name in os.listdir(json_folder): # Only NCT-named files are trials; skip run sidecars written to the same @@ -16,6 +24,8 @@ def load_trial_data(json_folder: str) -> List[Dict]: if file_name.endswith(".json") and file_name.upper().startswith("NCT"): file_path = os.path.join(json_folder, file_name) trial_id = os.path.splitext(file_name)[0] + if allowed_ids is not None and trial_id not in allowed_ids: + continue try: trial = read_json_file(file_path) trial["TrialID"] = trial_id diff --git a/src/trialmatchai/registry/preparation.py b/src/trialmatchai/registry/preparation.py index 4f9ae72f..8c8a864b 100644 --- a/src/trialmatchai/registry/preparation.py +++ b/src/trialmatchai/registry/preparation.py @@ -4,6 +4,7 @@ import json import os import re +import shutil import tempfile from collections.abc import Sequence from pathlib import Path @@ -188,6 +189,12 @@ def write_prepared_criteria(rows: Sequence[dict[str, Any]], folder: str | Path) if not rows: return 0 trial_folder = Path(folder) / str(rows[0]["nct_id"]) + # Clear stale criteria from a prior prepare first: criteria_id = sha256(nct:text), + # so changed chunking/text yields new files while orphaning the old ones, which + # the index would then ingest as duplicates. The trial-completion marker is + # written only after this, so a crash here is simply re-prepared on resume. + if trial_folder.exists(): + shutil.rmtree(trial_folder, ignore_errors=True) for row in rows: _atomic_write_text( trial_folder / f"{row['criteria_id']}.json", From 56ec030cddd83bbe44bcc9957288185db812e030 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 21:01:38 +0200 Subject: [PATCH 45/64] test: cover tie-aware nDCG, qrels eval, concept parsers, ranking tie-break 19 pure-function tests for the previously-untested new/changed code: tie-aware nDCG order-invariance (incl. cutoff-straddling ties) + P@k, qrels parse/evaluate, OBO/gene_info parsers, and the rank_trials reranker->first-level->NCT-id tie-break (+ load_trial_data shortlist scoping, guarding the C1 stale-output fix). Co-Authored-By: Claude Opus 4.8 --- tests/test_concept_sources.py | 51 ++++++++++++++++++++++++++ tests/test_metrics.py | 57 +++++++++++++++++++++++++++++ tests/test_qrels_eval.py | 53 +++++++++++++++++++++++++++ tests/test_trial_ranker_tiebreak.py | 47 ++++++++++++++++++++++++ 4 files changed, 208 insertions(+) create mode 100644 tests/test_concept_sources.py create mode 100644 tests/test_metrics.py create mode 100644 tests/test_qrels_eval.py create mode 100644 tests/test_trial_ranker_tiebreak.py diff --git a/tests/test_concept_sources.py b/tests/test_concept_sources.py new file mode 100644 index 00000000..89f10662 --- /dev/null +++ b/tests/test_concept_sources.py @@ -0,0 +1,51 @@ +"""Open-vocabulary parsers for the concept store (entities/concept_sources.py).""" + +from trialmatchai.entities.concept_sources import ( + OPEN_SOURCES, + _clean_names, + parse_gene_info, + parse_obo, +) + + +def test_parse_obo_filters_obsolete_and_prefix(tmp_path): + obo = tmp_path / "t.obo" + obo.write_text( + '[Term]\nid: CL:0001\nname: T cell\nsynonym: "T-cell" EXACT []\n\n' + "[Term]\nid: CL:0002\nname: dead cell\nis_obsolete: true\n\n" + "[Term]\nid: HP:0001\nname: seizure\n" + ) + rows = list(parse_obo(obo, "CL:")) + assert rows == [("CL:0001", ["T cell", "T-cell"])] # obsolete + non-CL dropped + + +def test_parse_gene_info_one_row(tmp_path): + tsv = tmp_path / "g.tsv" + tsv.write_text( + "#header\n" + "9606\t7157\tTP53\t-\tP53|LFS1\t-\t17\t17p13\t" + "tumor protein p53\tprotein-coding\tTP53\ttumor protein p53\tO\tp53\t-\n" + ) + rows = list(parse_gene_info(tsv)) + assert len(rows) == 1 + gene_id, names = rows[0] + assert gene_id == "7157" + assert names[0] == "TP53" and "P53" in names + + +def test_parse_gene_info_skips_short_lines(tmp_path): + tsv = tmp_path / "g.tsv" + tsv.write_text("9606\t7157\tTP53\n") # < 14 columns + assert list(parse_gene_info(tsv)) == [] + + +def test_clean_names_dedupes_casefold_and_strips_whitespace(): + assert _clean_names(["T cell", "t CELL", " ", "T-cell"]) == ["T cell", "T-cell"] + + +def test_open_sources_registry_well_formed(): + assert {"genes", "diseases", "chemicals", "cell_lines"} <= set(OPEN_SOURCES) + for src in OPEN_SOURCES.values(): + assert src.kind in {"obo", "gene_info"} + assert src.url.startswith("http") + assert src.dict_filename.startswith("dict_") diff --git a/tests/test_metrics.py b/tests/test_metrics.py new file mode 100644 index 00000000..5b95a3be --- /dev/null +++ b/tests/test_metrics.py @@ -0,0 +1,57 @@ +"""Tie-aware nDCG + precision (trec/metrics.py).""" + +from itertools import permutations + +from trialmatchai.trec.metrics import ( + condensed_ndcg, + idcg_at_k, + ndcg_at_k, + precision_at_k, +) + + +def test_perfect_ranking_scores_one(): + ids = ["a", "b", "c"] + score = {"a": 3.0, "b": 2.0, "c": 1.0} + gain = {"a": 2, "b": 1, "c": 0} + assert ndcg_at_k(ids, score, gain, 10) == 1.0 + + +def test_all_tied_is_order_invariant(): + # Every doc shares one score -> McSherry-Najork averaging must make nDCG + # identical across all orderings of the tie group. + gain = {"a": 2, "b": 1, "c": 0} + score = {k: 1.0 for k in gain} + values = {round(ndcg_at_k(list(p), score, gain, 10), 9) for p in permutations(gain)} + assert len(values) == 1 + + +def test_tie_group_straddling_cutoff_is_order_invariant(): + # Two tied docs straddling the k=1 boundary share the averaged discount, so + # swapping them cannot change nDCG@1. + gain = {"a": 1, "b": 0} + score = {"a": 0.5, "b": 0.5} + assert ndcg_at_k(["a", "b"], score, gain, 1) == ndcg_at_k(["b", "a"], score, gain, 1) + + +def test_zero_gain_gives_zero_ndcg(): + assert ndcg_at_k(["a", "b"], {"a": 1.0, "b": 0.5}, {"a": 0, "b": 0}, 10) == 0.0 + + +def test_idcg_uses_best_ordering(): + assert idcg_at_k([0, 2, 1], 10) == idcg_at_k([2, 1, 0], 10) + + +def test_precision_at_k_hard_cutoff(): + ordered = ["a", "x", "c", "y", "z"] + assert precision_at_k(ordered, {"a", "c"}, 10) == 2 / 10 # denominator is k + assert precision_at_k(ordered, {"a", "c"}, 2) == 1 / 2 # only 'a' in top-2 + + +def test_condensed_ndcg_drops_unjudged_and_grades_gains(): + ranked = ["a", "u", "b", "c"] # 'u' is unjudged + score = {"a": 1.0, "u": 0.9, "b": 0.5, "c": 0.0} + grade = {"a": 2, "b": 1, "c": 0} + out = condensed_ndcg(ranked, score, grade, (5, 10)) + assert set(out) == {5, 10} + assert out[10] == 1.0 # condensed order a,b,c is already ideal by grade diff --git a/tests/test_qrels_eval.py b/tests/test_qrels_eval.py new file mode 100644 index 00000000..48ef347d --- /dev/null +++ b/tests/test_qrels_eval.py @@ -0,0 +1,53 @@ +"""qrels parsing + evaluation (trec/qrels.py).""" + +import json + +import pytest + +from trialmatchai.trec.qrels import evaluate, parse_qrels, recall_at_k + + +def test_parse_qrels_prefixes_and_skips_malformed(tmp_path): + p = tmp_path / "q.txt" + p.write_text("1 0 NCT001 2\n1 0 NCT002 1\nbad line\n2 0 NCT003 0\n") + assert parse_qrels(p, "trec-") == { + "trec-1": {"NCT001": 2, "NCT002": 1}, + "trec-2": {"NCT003": 0}, + } + + +def test_parse_qrels_empty_raises(tmp_path): + p = tmp_path / "empty.txt" + p.write_text("\n# nothing parseable\n") + with pytest.raises(ValueError): + parse_qrels(p, "trec-") + + +def test_recall_at_k(): + assert recall_at_k(["a", "b", "c"], {"a", "c", "z"}, 3) == 2 / 3 + assert recall_at_k(["a"], set(), 3) is None # no relevant -> undefined + + +def test_evaluate_end_to_end(tmp_path): + q = "trec-1" + pdir = tmp_path / q + pdir.mkdir() + (pdir / "ranked_trials.json").write_text( + json.dumps( + { + "RankedTrials": [ + {"TrialID": "NCT1", "Score": 1.0}, + {"TrialID": "NCT2", "Score": 0.5}, + {"TrialID": "NCT3", "Score": 0.0}, + ] + } + ) + ) + (pdir / "nct_ids.txt").write_text("NCT1\nNCT2\nNCT3\n") + qrels = {q: {"NCT1": 2, "NCT2": 1, "NCT3": 0}} + res = evaluate(qrels, tmp_path, cutoffs=(10,)) + assert res["num_queries_scored"] == 1 + mean = res["mean"] + assert mean["recall@10"] == 1.0 # both relevant retrieved + assert mean["ndcg@10"] == 1.0 # ranking is the ideal grade order + assert mean["P@10(rel>=1)"] == 2 / 10 diff --git a/tests/test_trial_ranker_tiebreak.py b/tests/test_trial_ranker_tiebreak.py new file mode 100644 index 00000000..e12a75d2 --- /dev/null +++ b/tests/test_trial_ranker_tiebreak.py @@ -0,0 +1,47 @@ +"""Deterministic tie-break in rank_trials + shortlist scoping in load_trial_data.""" + +import json + +from trialmatchai.matching.trial_ranker import load_trial_data, rank_trials + + +def _eligible(): + # Identical eligibility -> identical Score, so the tie-break decides order. + return { + "Inclusion_Criteria_Evaluation": [{"Classification": "Met"}], + "Exclusion_Criteria_Evaluation": [], + } + + +def test_tiebreak_reranker_then_firstlevel_then_nctid(): + trials = [{"TrialID": f"NCT-{c}", **_eligible()} for c in "ABCDE"] + reranker = {"NCT-A": 0.9, "NCT-B": 0.9, "NCT-C": 0.5, "NCT-D": 0.5, "NCT-E": 0.5} + first = {"NCT-A": 0.1, "NCT-B": 0.5, "NCT-C": 0.9, "NCT-D": 0.5, "NCT-E": 0.5} + ranked = rank_trials( + trials, first_level_scores=first, second_level_scores=reranker + ) + order = [r["TrialID"] for r in ranked] + # equal Score: reranker 0.9 first (B>A by first-level), then 0.5 (C by first-level), + # then the full D/E tie resolves by ascending NCT id. + assert order == ["NCT-B", "NCT-A", "NCT-C", "NCT-D", "NCT-E"] + + +def test_tiebreak_is_deterministic_regardless_of_input_order(): + a = [{"TrialID": f"NCT-{c}", **_eligible()} for c in "ABC"] + reranker = {"NCT-A": 0.5, "NCT-B": 0.9, "NCT-C": 0.5} + out1 = [r["TrialID"] for r in rank_trials(a, second_level_scores=reranker)] + out2 = [r["TrialID"] for r in rank_trials(list(reversed(a)), second_level_scores=reranker)] + assert out1 == out2 == ["NCT-B", "NCT-A", "NCT-C"] + + +def test_load_trial_data_scopes_to_allowed_ids(tmp_path): + for nct in ["NCT1", "NCT2", "NCT3"]: + (tmp_path / f"{nct}.json").write_text( + json.dumps({"Inclusion_Criteria_Evaluation": []}) + ) + (tmp_path / "keywords.json").write_text("{}") # sidecar must be ignored + loaded = { + t["TrialID"] + for t in load_trial_data(str(tmp_path), allowed_ids={"NCT1", "NCT3"}) + } + assert loaded == {"NCT1", "NCT3"} # NCT2 (stale, off-shortlist) excluded From 0af219097fb8c1939db609ac1cbed77379869143 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 21:05:20 +0200 Subject: [PATCH 46/64] feat(registry): --watch server mode + robust criteria-source fallback - update-registry --watch --interval runs the incremental fetch->prepare->upsert loop continuously (server use case) with the embedder/annotator built once and a sliding lookback window; cron/systemd remain the alternative for one-shot. - Eligibility criteria now resolve via a fallback chain (processed_trials -> trials_jsons) so built, bootstrapped, AND newly registry-updated trials all load criteria text; fail loud only when no source has it. Co-Authored-By: Claude Opus 4.8 --- src/trialmatchai/cli/update_registry.py | 44 ++++++++++++++++--- src/trialmatchai/main.py | 28 +++++++----- src/trialmatchai/matching/eligibility_base.py | 30 +++++++------ 3 files changed, 72 insertions(+), 30 deletions(-) diff --git a/src/trialmatchai/cli/update_registry.py b/src/trialmatchai/cli/update_registry.py index b1bf5971..999737e3 100644 --- a/src/trialmatchai/cli/update_registry.py +++ b/src/trialmatchai/cli/update_registry.py @@ -3,6 +3,8 @@ import argparse import json import sys +import time +from dataclasses import replace from datetime import date, timedelta from pathlib import Path from typing import Any @@ -57,13 +59,25 @@ def main() -> int: default=None, help="Optional exact path for a copy of the run report JSON.", ) + parser.add_argument( + "--watch", + action="store_true", + help="Run continuously (server mode): update every --interval seconds.", + ) + parser.add_argument( + "--interval", + type=float, + default=86400.0, + help="Seconds between updates in --watch mode (default 24h).", + ) args = parser.parse_args() config = load_config(args.config) registry_cfg = config.get("registry", {}) paths_cfg = config.get("paths", {}) - since = _resolve_since(args.since, int(registry_cfg.get("since_days", 7))) + since_days = int(registry_cfg.get("since_days", 7)) + since = _resolve_since(args.since, since_days) keywords = normalize_keywords( [ *args.keyword, @@ -111,11 +125,31 @@ def main() -> int: embedder=embedder, entity_annotator=entity_annotator, ) - report = updater.run(update_config) - - if args.report_path: - _write_report_copy(Path(args.report_path), report.to_dict()) + if not args.watch: + return _run_and_report(updater, update_config, args.report_path) + logger.info( + "Registry watch mode: updating every %.0fs (Ctrl-C to stop).", args.interval + ) + while True: + # Slide the lookback window forward each cycle (unless --since is pinned); + # unchanged studies are deduped by the manifest, so overlap is cheap. + cycle_config = replace(update_config, since=_resolve_since(args.since, since_days)) + try: + _run_and_report(updater, cycle_config, args.report_path) + except Exception: + logger.exception("Registry update cycle failed; retrying next interval") + try: + time.sleep(args.interval) + except KeyboardInterrupt: + logger.info("Registry watch stopped.") + return 0 + + +def _run_and_report(updater: RegistryUpdater, update_config, report_path) -> int: + report = updater.run(update_config) + if report_path: + _write_report_copy(Path(report_path), report.to_dict()) logger.info("Registry update report: %s", json.dumps(report.to_dict(), sort_keys=True)) return 1 if report.failure_rate > update_config.failure_threshold else 0 diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index 2b244004..a7028e08 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -238,19 +238,23 @@ def run_second_level_search( return semi_final_trials, top_trials_path -def _criteria_source_folder(config: Dict) -> str: - """Folder the eligibility model reads each trial's criteria text from. - - Prefer ``processed_trials`` — always produced by ``build``/``bootstrap-data`` - and carrying the ``eligibility_criteria`` text — over the raw ``trials_jsons``, - which ``bootstrap-data`` does not download. Sourcing from the latter would make - the model silently reason over "no criteria" and score every trial 0. +def _criteria_source_folders(config: Dict) -> list[str]: + """Folders to read each trial's criteria text from, in priority order. + + Both carry the ``eligibility_criteria`` text but cover different provenance, + so trying both makes every trial resolve regardless of how it was added: + * ``processed_trials`` — written by ``build``/``bootstrap-data``. + * ``trials_jsons`` — written by ``build`` and the registry updater. + Sourcing from only one would make the eligibility model silently reason over + "no criteria" (and score 0) for the trials that folder is missing. """ paths = config.get("paths", {}) - processed = paths.get("processed_trials_folder") or "data/processed_trials" - if Path(processed).is_dir(): - return str(processed) - return paths.get("trials_json_folder", "data/trials_jsons") + candidates = [ + paths.get("processed_trials_folder") or "data/processed_trials", + paths.get("trials_json_folder", "data/trials_jsons"), + ] + folders = [str(c) for c in candidates if Path(c).is_dir()] + return folders or [str(candidates[-1])] def run_rag_processing( @@ -319,7 +323,7 @@ def run_rag_processing( rag_processor.process_trials( nct_ids=top_trials, - json_folder=_criteria_source_folder(config), + json_folder=_criteria_source_folders(config), output_folder=output_folder, patient_narrative=patient_narrative, ) diff --git a/src/trialmatchai/matching/eligibility_base.py b/src/trialmatchai/matching/eligibility_base.py index 0d47534c..f9549173 100644 --- a/src/trialmatchai/matching/eligibility_base.py +++ b/src/trialmatchai/matching/eligibility_base.py @@ -44,19 +44,23 @@ class BaseTrialProcessor: # ---------------------- I/O helpers ---------------------- - def _load_trial_data(self, nct_id: str, json_folder: str) -> str: - path = f"{json_folder}/{nct_id}.json" - try: - trial_data = read_json_file(path) - except Exception as e: - # Fail loud: a missing/unreadable trial file means the eligibility - # model would silently reason over "no criteria" and score 0. - logger.error("Cannot load criteria for %s from %s: %s", nct_id, path, e) - return "" - criteria = trial_data.get("eligibility_criteria", "") - if not criteria: - logger.warning("Trial %s has empty eligibility_criteria at %s", nct_id, path) - return criteria + def _load_trial_data(self, nct_id: str, json_folder) -> str: + # json_folder may be a single folder or an ordered list of fallbacks + # (processed_trials, then trials_jsons) so built, bootstrapped, and + # newly-updated trials all resolve. + folders = [json_folder] if isinstance(json_folder, str) else list(json_folder) + for folder in folders: + try: + trial_data = read_json_file(f"{folder}/{nct_id}.json") + except Exception: + continue + criteria = trial_data.get("eligibility_criteria", "") + if criteria: + return criteria + # Fail loud: no source had criteria text, so the model would otherwise + # silently reason over "no criteria" and score this trial 0. + logger.error("No eligibility criteria found for %s in %s", nct_id, folders) + return "" # ---------------------- Prompting ---------------------- From 90c89de0bbc75555b8c5f718890d84f1d4ed8183 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 21:08:15 +0200 Subject: [PATCH 47/64] docs: fix inaccuracies, document registry update flow + add dependabot - architecture.md: trialmatchai-run matches already-imported profiles (not raw inputs); finetuning.md: correct merged-model config keys (base_model / reranker_model_path, not 'cot model'); release.md: smoke-test all 12 entry points. - README: document update-registry as a full fetch->prepare->index step + the new --watch server mode. - Add .github/dependabot.yml grouping the optional ML stack to cut advisory noise. Co-Authored-By: Claude Opus 4.8 --- .github/dependabot.yml | 27 +++++++++++++++++++++++++++ README.md | 17 ++++++++++++++--- docs/architecture.md | 2 +- docs/finetuning.md | 5 +++-- docs/release.md | 6 ++++++ 5 files changed, 51 insertions(+), 6 deletions(-) create mode 100644 .github/dependabot.yml diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 00000000..60c5be42 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,27 @@ +version: 2 +updates: + - package-ecosystem: "pip" + directory: "/" + schedule: + interval: "weekly" + open-pull-requests-limit: 5 + groups: + # The heavy ML stack lives in optional extras and is intentionally pinned to + # versions compatible with vLLM (see the CI pip-audit policy). Collapse its + # frequent advisories into one low-noise PR instead of dozens. + ml-stack: + patterns: + - "torch*" + - "vllm*" + - "transformers*" + - "accelerate*" + - "bitsandbytes*" + - "nvidia-*" + - "xformers*" + dev-dependencies: + dependency-type: "development" + + - package-ecosystem: "github-actions" + directory: "/" + schedule: + interval: "weekly" diff --git a/README.md b/README.md index 66e97d23..d36644d6 100644 --- a/README.md +++ b/README.md @@ -141,13 +141,24 @@ uv run trialmatchai e2e --input data/patients/omop_extract Results land in `results//` (ranked trials + eligibility explanations). Re-running skips patients already matched. -### Health and maintenance +### Health and keeping trials current ```bash -uv run trialmatchai-healthcheck # validate config/paths/deps -uv run trialmatchai-update-registry --since 2026-06-01 # pull new ClinicalTrials.gov studies +uv run trialmatchai-healthcheck # validate config/paths/deps ``` +Fold new/changed ClinicalTrials.gov studies into the **live index** — fetch → +embed + entity-annotate → upsert, incremental and idempotent (unchanged studies +are skipped via a manifest, so it is safe to re-run): + +```bash +uv run trialmatchai update-registry --since 2026-06-01 # one-shot +uv run trialmatchai update-registry --watch --interval 86400 # server: update daily +``` + +For a one-shot cadence you can also drive `update-registry` from cron, a systemd +timer, or GitHub Actions — see [docs/registry-updater.md](docs/registry-updater.md). +
      Manual / advanced control (the steps build and e2e wrap) diff --git a/docs/architecture.md b/docs/architecture.md index 685e6374..8466aa15 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -18,7 +18,7 @@ TrialMatchAI is an installable Python package exposed as `trialmatchai`. The sup 2. Raw source JSON is written to `data/registry/raw/.json`. 3. Normalized trial JSON is written to `data/trials_jsons/.json`. 4. Changed studies are embedded, criteria are optionally entity annotated, and LanceDB trial/criteria tables are upserted. -5. `trialmatchai-run` reads patient inputs, queries LanceDB, reranks candidate criteria/trials, and writes results. +5. `trialmatchai-run` matches already-imported patient profiles against LanceDB, reranks candidate criteria/trials, and writes results. (Ingest raw patient inputs first with `trialmatchai-import-patient`, or use `trialmatchai-e2e` to ingest and match in one step.) ## Storage diff --git a/docs/finetuning.md b/docs/finetuning.md index 3c58b5bf..60177407 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -39,8 +39,9 @@ trialmatchai-finetune merge \ --output-dir models/reranker-merged ``` -Then point the config at the merged directory (`reranker_model_path` / -`cot model`) and leave the adapter path empty. +Then point the config at the merged directory (`model.reranker_model_path` for +the reranker, `model.base_model` for the merged CoT model) and leave the matching +adapter path (`model.reranker_adapter_path` / `model.cot_adapter_path`) empty. ## 2. Fine-tuning diff --git a/docs/release.md b/docs/release.md index 468963d0..2db6181b 100644 --- a/docs/release.md +++ b/docs/release.md @@ -18,10 +18,16 @@ Smoke test console commands: ```bash uv run trialmatchai --help uv run trialmatchai-healthcheck --help +uv run trialmatchai-bootstrap-data --help +uv run trialmatchai-build --help uv run trialmatchai-index --help uv run trialmatchai-build-concepts --help uv run trialmatchai-update-registry --help +uv run trialmatchai-import-patient --help uv run trialmatchai-run --help +uv run trialmatchai-e2e --help +uv run trialmatchai-trec --help +uv run trialmatchai-finetune --help ``` Wheel install smoke: From 7fbdc2aae892ae659b642dc0bc208ebf023fbc9f Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 21:13:48 +0200 Subject: [PATCH 48/64] feat(build): one-flag entity linking + document resource acquisition MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 'trialmatchai build --concepts' builds the open-vocabulary concept store (auto-downloaded) so entity linking works out of the box; --concepts-csv adds OMOP on top. No separate build-concepts command needed for the common case. - README: 'What gets fetched, and how' table — corpus + adapters via bootstrap, models/embedder via HuggingFace, concepts via --concepts; only gated base models (one-time hf login) and OMOP vocab are manual. - finetuning.md: state plainly that adapters are bootstrapped and raw training data is not published (bring-your-own JSONL in the documented schema). --- README.md | 20 ++++++++++++++++++-- docs/finetuning.md | 7 +++++++ src/trialmatchai/cli/build.py | 31 ++++++++++++++++++++++--------- 3 files changed, 47 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index d36644d6..3f72ab90 100644 --- a/README.md +++ b/README.md @@ -122,12 +122,28 @@ uv run trialmatchai build --status # see exactly what is built (and what isn't `build` fails fast if a GPU, an extra, or model access is missing — and resumes from where it left off if interrupted. Bringing your **own** trials instead of bootstrapping? Put normalized JSON in `data/trials_jsons/` and `build` will -prepare them. To also enable concept linking, pass an OMOP vocabulary: +prepare them. To enable entity→concept linking, add `--concepts` (open +vocabularies, **auto-downloaded**) — and optionally an OMOP `CONCEPT.csv` for +SNOMED/LOINC/RxNorm on top: ```bash -uv run trialmatchai build --concepts-csv data/omop/CONCEPT.csv --synonym-csv data/omop/CONCEPT_SYNONYM.csv +uv run trialmatchai build --concepts # genes, diseases, chemicals, cells, phenotypes +uv run trialmatchai build --concepts --concepts-csv data/omop/CONCEPT.csv --synonym-csv data/omop/CONCEPT_SYNONYM.csv ``` +#### What gets fetched, and how + +| Resource | How you get it | Automatic? | +| --- | --- | --- | +| Trial corpus (`processed_trials` + criteria) | `trialmatchai-bootstrap-data` (Zenodo) | ✅ automatic | +| Fine-tuned LoRA adapters (CoT + reranker) | `trialmatchai-bootstrap-data` (Zenodo) | ✅ automatic | +| Embedder (`BAAI/bge-m3`) | downloaded from HuggingFace on first use | ✅ automatic | +| Concept-linking vocabularies (genes, diseases, …) | `trialmatchai build --concepts` | ✅ automatic | +| Base LLMs (`microsoft/phi-4`, `google/gemma-2-2b-it`) | HuggingFace on first use | ⚠️ automatic, but **gated** models need a **one-time** `hf auth login` + accepting the model licence | +| OMOP clinical vocab (SNOMED/LOINC/RxNorm) | download `CONCEPT.csv` from [OHDSI Athena](https://athena.ohdsi.org/) | ❌ manual (licensed); linking works without it | + +So a from-scratch user runs **two commands** (`bootstrap-data`, then `build --concepts`) after a one-time `hf auth login`. Everything else is pulled on demand. + ### 2. Match patients — repeatably `e2e` ingests the patient (format auto-detected) and matches in one command: diff --git a/docs/finetuning.md b/docs/finetuning.md index 60177407..468ee5e7 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -11,6 +11,13 @@ checkpoints/adapters via config, and train those adapters with the built-in | Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | LoRA adapter | | CoT eligibility | configured CoT model | `model.cot_adapter_path` | LoRA adapter | +> **Where does the training data come from?** Fine-tuning is **optional** — the +> ready-to-use CoT and reranker adapters are downloaded by `trialmatchai-bootstrap-data`, +> so most deployments never need to train. The **raw training datasets are not +> published**; to re-train, bring your own JSONL in the schemas shown below +> (`data/finetune/*.jsonl`). Each line is a self-contained example, so you can +> assemble a dataset from your own annotated patient–trial pairs. + Install the training dependencies: ```bash diff --git a/src/trialmatchai/cli/build.py b/src/trialmatchai/cli/build.py index 66cfe36c..557c590e 100644 --- a/src/trialmatchai/cli/build.py +++ b/src/trialmatchai/cli/build.py @@ -7,7 +7,8 @@ trialmatchai build # prepare (resumable) + index trialmatchai build --status # report what is already built, then exit - trialmatchai build --concepts-csv data/omop/CONCEPT.csv # also build concept DB + trialmatchai build --concepts # also build the open concept-linking DB + trialmatchai build --concepts --concepts-csv data/omop/CONCEPT.csv # + OMOP vocab """ from __future__ import annotations @@ -36,10 +37,16 @@ def main() -> int: ) parser.add_argument("--processed-trials-folder", default="data/processed_trials") parser.add_argument("--processed-criteria-folder", default="data/processed_criteria") + parser.add_argument( + "--concepts", + action="store_true", + help="Also build the concept-linking DB from open vocabularies " + "(genes, diseases, chemicals, cell lines, cell types, phenotypes; auto-downloaded).", + ) parser.add_argument( "--concepts-csv", default=None, - help="OMOP CONCEPT.csv to also build the concept-linking DB (optional).", + help="OMOP CONCEPT.csv to add SNOMED/LOINC/RxNorm to the concept DB (optional).", ) parser.add_argument("--synonym-csv", default=None, help="OMOP CONCEPT_SYNONYM.csv (optional).") parser.add_argument("--force-prepare", action="store_true", help="Re-prepare all trials.") @@ -77,14 +84,19 @@ def main() -> int: force_reindex=args.reindex, ) - # Optional: chain concept-DB build (needs user-supplied OMOP CSV). - if args.concepts_csv: + # Optional: chain the concept-DB build. --concepts pulls the open vocabularies + # (auto-downloaded); --concepts-csv adds the licensed OMOP vocab on top. + if args.concepts or args.concepts_csv: logger.info("=== build: concepts stage ===") from trialmatchai.cli.build_concepts import main as build_concepts_main - argv = ["--concept-csv", args.concepts_csv] - if args.synonym_csv: - argv += ["--synonym-csv", args.synonym_csv] + argv: list[str] = [] + if args.concepts: + argv += ["--sources", "open"] + if args.concepts_csv: + argv += ["--concept-csv", args.concepts_csv] + if args.synonym_csv: + argv += ["--synonym-csv", args.synonym_csv] if args.config: argv += ["--config", args.config] saved = sys.argv @@ -95,8 +107,9 @@ def main() -> int: sys.argv = saved else: logger.warning( - "Concept DB not built (no --concepts-csv): entity->concept linking will " - "degrade gracefully. Supply an OMOP CONCEPT.csv to enable it." + "Concept DB not built: entity->concept linking will degrade gracefully. " + "Run `trialmatchai build --concepts` to enable it (open vocabularies, " + "auto-downloaded); add --concepts-csv for OMOP SNOMED/LOINC/RxNorm." ) state = build_state( From a3cb8411ee2c5b5fe9bb691a3b01c603ebb80dc0 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 21:27:08 +0200 Subject: [PATCH 49/64] feat(bootstrap): --finetune-data fetches the published training datasets The fine-tuning datasets (CoT/reranker/NER JSONL) ARE published on the paper's Zenodo deposit (record 15045515). Add 'trialmatchai-bootstrap-data --finetune-data' to download + extract them to data/finetune/ (opt-in; safe extract + optional SHA256). Correct finetuning.md and the README resource table accordingly. --- README.md | 1 + docs/finetuning.md | 14 +++++++--- src/trialmatchai/cli/bootstrap_data.py | 36 +++++++++++++++++++++++++- 3 files changed, 46 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 3f72ab90..55fbe613 100644 --- a/README.md +++ b/README.md @@ -137,6 +137,7 @@ uv run trialmatchai build --concepts --concepts-csv data/omop/CONCEPT.csv --syno | --- | --- | --- | | Trial corpus (`processed_trials` + criteria) | `trialmatchai-bootstrap-data` (Zenodo) | ✅ automatic | | Fine-tuned LoRA adapters (CoT + reranker) | `trialmatchai-bootstrap-data` (Zenodo) | ✅ automatic | +| Fine-tuning datasets (only if you re-train) | `trialmatchai-bootstrap-data --finetune-data` (Zenodo) | ✅ automatic (opt-in) | | Embedder (`BAAI/bge-m3`) | downloaded from HuggingFace on first use | ✅ automatic | | Concept-linking vocabularies (genes, diseases, …) | `trialmatchai build --concepts` | ✅ automatic | | Base LLMs (`microsoft/phi-4`, `google/gemma-2-2b-it`) | HuggingFace on first use | ⚠️ automatic, but **gated** models need a **one-time** `hf auth login` + accepting the model licence | diff --git a/docs/finetuning.md b/docs/finetuning.md index 468ee5e7..749805f4 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -13,10 +13,16 @@ checkpoints/adapters via config, and train those adapters with the built-in > **Where does the training data come from?** Fine-tuning is **optional** — the > ready-to-use CoT and reranker adapters are downloaded by `trialmatchai-bootstrap-data`, -> so most deployments never need to train. The **raw training datasets are not -> published**; to re-train, bring your own JSONL in the schemas shown below -> (`data/finetune/*.jsonl`). Each line is a self-contained example, so you can -> assemble a dataset from your own annotated patient–trial pairs. +> so most deployments never need to train. The **published training datasets** are +> available too — fetch them into `data/finetune/`: +> +> ```bash +> trialmatchai-bootstrap-data --finetune-data +> ``` +> +> (Source: the paper's Zenodo deposit, .) +> Or bring your own JSONL in the schemas shown below — each line is a self-contained +> example, so you can assemble a dataset from your own annotated patient–trial pairs. Install the training dependencies: diff --git a/src/trialmatchai/cli/bootstrap_data.py b/src/trialmatchai/cli/bootstrap_data.py index bdf2eb6f..4a13cbef 100644 --- a/src/trialmatchai/cli/bootstrap_data.py +++ b/src/trialmatchai/cli/bootstrap_data.py @@ -15,10 +15,15 @@ DATA_URL = "https://zenodo.org/records/15516900/files/processed_trials.tar.gz?download=1" MODELS_URL = "https://zenodo.org/records/15516900/files/models.tar.gz?download=1" CRITERIA_ZIP_BASE_URL = "https://zenodo.org/records/15516900/files" +# Fine-tuning datasets (CoT/reranker/NER JSONL) live on the paper's deposit. +FINETUNE_DATA_URL = ( + "https://zenodo.org/records/15045515/files/finetuning_datasets.zip?download=1" +) CHUNK_PREFIX = "criteria_part" CHUNK_COUNT = 6 PROCESSED_TRIALS_ARCHIVE = "processed_trials.tar.gz" MODELS_ARCHIVE = "models.tar.gz" +FINETUNE_ARCHIVE = "finetuning_datasets.zip" def main(argv: Sequence[str] | None = None) -> int: @@ -57,6 +62,16 @@ def main(argv: Sequence[str] | None = None) -> int: action="store_true", help="Do not download or extract model artifacts", ) + parser.add_argument( + "--finetune-data", + action="store_true", + help="Also download the fine-tuning datasets (CoT/reranker/NER JSONL) to data/finetune/.", + ) + parser.add_argument( + "--finetune-data-url", + default=FINETUNE_DATA_URL, + help="finetuning_datasets.zip URL", + ) parser.add_argument( "--force", action="store_true", @@ -72,6 +87,8 @@ def main(argv: Sequence[str] | None = None) -> int: criteria_base_url=args.criteria_base_url, criteria_chunks=args.criteria_chunks, skip_models=args.skip_models, + finetune_data=args.finetune_data, + finetune_data_url=args.finetune_data_url, force=args.force, ) return 0 @@ -85,6 +102,8 @@ def bootstrap_data( criteria_base_url: str = CRITERIA_ZIP_BASE_URL, criteria_chunks: int = CHUNK_COUNT, skip_models: bool = False, + finetune_data: bool = False, + finetune_data_url: str = FINETUNE_DATA_URL, force: bool = False, ) -> None: data_dir = root / "data" @@ -124,6 +143,17 @@ def bootstrap_data( _verify_sha256(models_archive, os.getenv("TRIALMATCHAI_MODELS_SHA256")) _safe_extract_tar_gz(models_archive, models_dir) + if finetune_data: + finetune_dir = data_dir / "finetune" + if force or not _has_entries(finetune_dir): + finetune_dir.mkdir(parents=True, exist_ok=True) + finetune_archive = data_dir / FINETUNE_ARCHIVE + _download_if_missing(finetune_data_url, finetune_archive) + _verify_sha256( + finetune_archive, os.getenv("TRIALMATCHAI_FINETUNE_DATA_SHA256") + ) + _safe_extract_zip(finetune_archive, finetune_dir) + _cleanup_archives(data_dir, criteria_chunks) @@ -196,7 +226,11 @@ def _validated_target_path(target: Path, member_name: str) -> Path: def _cleanup_archives(data_dir: Path, criteria_chunks: int) -> None: - for path in [data_dir / PROCESSED_TRIALS_ARCHIVE, data_dir / MODELS_ARCHIVE]: + for path in [ + data_dir / PROCESSED_TRIALS_ARCHIVE, + data_dir / MODELS_ARCHIVE, + data_dir / FINETUNE_ARCHIVE, + ]: path.unlink(missing_ok=True) for index in range(criteria_chunks): (data_dir / f"{CHUNK_PREFIX}_{index}.zip").unlink(missing_ok=True) From 03ac89b50ced6cd20e07bcaaefda40f25af50ac7 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 22:26:58 +0200 Subject: [PATCH 50/64] fix: idempotency quick wins (concepts, run, prepare) - build-concepts is now idempotent: skip the re-embed if the concept table is already built (unless --force, or folding in OMOP via --concept-csv). Replaces the always-True --recreate that re-embedded 603k concepts every run. - run: resume by default (skip patients with valid results); --force re-matches. Previously re-matched every patient (resume defaulted off). - prepare skip now uses is_valid_json_file, not bare .exists(), so a corrupt prepared trial JSON is re-prepared instead of silently skipped. --- src/trialmatchai/cli/build_concepts.py | 38 ++++++++++++++++++++++---- src/trialmatchai/cli/run.py | 8 +++++- src/trialmatchai/orchestration.py | 3 +- 3 files changed, 42 insertions(+), 7 deletions(-) diff --git a/src/trialmatchai/cli/build_concepts.py b/src/trialmatchai/cli/build_concepts.py index 3be18d7d..ed2d8bbf 100644 --- a/src/trialmatchai/cli/build_concepts.py +++ b/src/trialmatchai/cli/build_concepts.py @@ -71,10 +71,10 @@ def main() -> int: help="Create an FTS-only table without model embeddings.", ) parser.add_argument( - "--recreate", - action=argparse.BooleanOptionalAction, - default=True, - help="Overwrite the target table if it exists.", + "--force", + action="store_true", + help="Rebuild (re-embed) the concept table even if it already exists " + "(default: skip if present).", ) args = parser.parse_args() @@ -86,6 +86,20 @@ def main() -> int: if not args.concept_csv and not args.dictionary and not args.sources: parser.error("provide --concept-csv, --sources, and/or at least one --dictionary") + # Idempotent: skip the (expensive) re-embed if the table is already built, + # unless --force or a new OMOP vocab (--concept-csv) is being folded in. + if not args.force and not args.concept_csv: + ready, rows_present = _concept_table_ready(db_path, table_name) + if ready: + logger.info( + "Concept store already present at %s/%s (%s concepts); skipping. " + "Use --force to rebuild.", + db_path, + table_name, + rows_present, + ) + return 0 + # Collect dictionary specs from explicit --dictionary flags and bundled --sources. dictionary_specs: list[tuple[str, str, str]] = [ _parse_dictionary_spec(spec) for spec in args.dictionary @@ -126,7 +140,7 @@ def main() -> int: db_path=db_path, table_name=table_name, embeddings=embeddings, - recreate=args.recreate, + recreate=True, ) logger.info("Wrote %s concepts to %s/%s", len(rows), db_path, table_name) return 0 @@ -142,5 +156,19 @@ def _parse_dictionary_spec(spec: str) -> tuple[str, str, str]: return parts[0], parts[1], parts[2] +def _concept_table_ready(db_path: str, table_name: str) -> tuple[bool, int]: + """Return (table exists and is non-empty, row count) for the concept table.""" + try: + import lancedb + + db = lancedb.connect(str(db_path)) + if table_name not in db.table_names(): + return False, 0 + count = db.open_table(table_name).count_rows() + return count > 0, count + except Exception: + return False, 0 + + if __name__ == "__main__": sys.exit(main()) diff --git a/src/trialmatchai/cli/run.py b/src/trialmatchai/cli/run.py index be50f1ac..40898e99 100644 --- a/src/trialmatchai/cli/run.py +++ b/src/trialmatchai/cli/run.py @@ -7,12 +7,18 @@ def main() -> int: parser = argparse.ArgumentParser(description="Run the TrialMatchAI batch pipeline") parser.add_argument("--config", default=None, help="Path to config.json") + parser.add_argument( + "--force", + action="store_true", + help="Re-match every patient, ignoring existing results (default: resume/skip done).", + ) args = parser.parse_args() from trialmatchai.main import main_pipeline from trialmatchai.orchestration import free_models try: - return main_pipeline(args.config) + # Idempotent by default: skip patients with valid results. --force redoes all. + return main_pipeline(args.config, resume=not args.force) finally: free_models() diff --git a/src/trialmatchai/orchestration.py b/src/trialmatchai/orchestration.py index 28c815f7..8eeeda36 100644 --- a/src/trialmatchai/orchestration.py +++ b/src/trialmatchai/orchestration.py @@ -376,7 +376,8 @@ def prepare_corpus( pending = [ p for p in all_paths - if force or not (processed_trials_folder / f"{p.stem}.json").exists() + if force + or not is_valid_json_file(str(processed_trials_folder / f"{p.stem}.json")) ] skipped = len(all_paths) - len(pending) logger.info( From 7e683808b1f475d9635e79c5892bfa4318805767 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 22:30:37 +0200 Subject: [PATCH 51/64] test: TREC reproduction regression guard Pin the tie-aware nDCG closed form (+ value), the qrels corpus restriction (corpus_ncts), and relevance thresholds so folding trec into the unified pipeline cannot silently change a benchmark number. --- tests/test_trec_reproduction.py | 46 +++++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 tests/test_trec_reproduction.py diff --git a/tests/test_trec_reproduction.py b/tests/test_trec_reproduction.py new file mode 100644 index 00000000..b79b81cb --- /dev/null +++ b/tests/test_trec_reproduction.py @@ -0,0 +1,46 @@ +"""Regression guard for the TREC reproduction. + +Locks the reproduction-critical behavior — the tie-aware nDCG algorithm, the +corpus restriction, and the relevance thresholds — so the pipeline unification +(folding `trec` into a preset over the one e2e pipeline) cannot silently change a +benchmark number. If any of these change, that is a deliberate decision, not an +accident. +""" + +import math + +from trialmatchai.trec.metrics import ndcg_at_k +from trialmatchai.trec.qrels import corpus_ncts, relevant_ncts + + +def test_tie_aware_ndcg_matches_mcsherry_najork_closed_form(): + # Three docs tied at one score: each gets the MEAN discount over the ranks the + # tie group occupies (McSherry-Najork). Recompute the reference independently. + def d(r): + return 1.0 / math.log2(r + 1) + + mean_disc = (d(1) + d(2) + d(3)) / 3 + dcg = (2 + 1 + 0) * mean_disc + idcg = 2 * d(1) + 1 * d(2) + 0 * d(3) + expected = dcg / idcg + + got = ndcg_at_k( + ["a", "b", "c"], {"a": 1.0, "b": 1.0, "c": 1.0}, {"a": 2, "b": 1, "c": 0}, 10 + ) + assert abs(got - expected) < 1e-9 + assert round(got, 4) == 0.8100 # pinned: flags any drift in the averaging + + +def test_corpus_ncts_is_union_of_judged_trials(): + # The per-run index is restricted to this set (the qrels-judged pool). + qrels = { + "trec-1": {"NCT1": 2, "NCT2": 0}, + "trec-2": {"NCT2": 1, "NCT3": 2}, + } + assert corpus_ncts(qrels) == {"NCT1", "NCT2", "NCT3"} + + +def test_relevant_ncts_threshold(): + qrels = {"q": {"NCT1": 2, "NCT2": 1, "NCT3": 0}} + assert relevant_ncts(qrels, threshold=1) == {"q": {"NCT1", "NCT2"}} # relevant + assert relevant_ncts(qrels, threshold=2) == {"q": {"NCT1"}} # eligible-only From 1987b06a315650fc8d72c734d9d80de4ba370729 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 22:33:43 +0200 Subject: [PATCH 52/64] refactor(index): route legacy index CLI to the idempotent orchestration trialmatchai-index now delegates to prepare_corpus (resumable) + build_index (skip-if-exists), removing its own non-resumable prepare loop and the always-overwrite (recreate=True) index. One prepare impl, one index impl. --recreate/--skip-trials/--skip-criteria replaced by --reindex/--force-prepare; the index consistency invariant (build_index) now always applies. Test no longer imports a CLI-private loader. --- src/trialmatchai/cli/index_data.py | 157 ++++++---------------------- tests/test_preflight_and_indexer.py | 3 +- 2 files changed, 34 insertions(+), 126 deletions(-) diff --git a/src/trialmatchai/cli/index_data.py b/src/trialmatchai/cli/index_data.py index fd932c63..a4965d51 100644 --- a/src/trialmatchai/cli/index_data.py +++ b/src/trialmatchai/cli/index_data.py @@ -1,3 +1,11 @@ +"""``trialmatchai-index`` — prepare + build the LanceDB search tables. + +A thin entry point over the SAME idempotent orchestration stages `build` uses +(`prepare_corpus` + `build_index`), so there is one prepare implementation and one +index implementation, both resumable/skip-if-done. (Historically this command had +its own non-resumable prepare and an always-overwrite index; those are gone.) +""" + from __future__ import annotations import argparse @@ -5,7 +13,7 @@ from pathlib import Path from trialmatchai.config.config_loader import load_config -from trialmatchai.search import LanceDBSearchBackend +from trialmatchai.orchestration import build_index, prepare_corpus from trialmatchai.utils.logging_config import setup_logging logger = setup_logging(__name__) @@ -13,7 +21,7 @@ def main() -> int: parser = argparse.ArgumentParser( - description="Build TrialMatchAI LanceDB search tables." + description="Prepare + build TrialMatchAI LanceDB search tables (idempotent)." ) parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") parser.add_argument( @@ -24,145 +32,46 @@ def main() -> int: parser.add_argument( "--trials-json-folder", default=None, - help="Folder containing normalized trial JSON files. Defaults to config paths.trials_json_folder.", - ) - parser.add_argument( - "--processed-trials-folder", - default="data/processed_trials", - help="Folder containing prepared trial JSON files.", + help="Normalized trial JSONs. Defaults to config paths.trials_json_folder.", ) + parser.add_argument("--processed-trials-folder", default="data/processed_trials") + parser.add_argument("--processed-criteria-folder", default="data/processed_criteria") parser.add_argument( - "--processed-criteria-folder", - default="data/processed_criteria", - help="Folder containing prepared criteria subfolders.", - ) - parser.add_argument( - "--skip-trials", + "--force-prepare", action="store_true", - help="Do not build the trial table.", + help="Re-prepare every trial even if already prepared.", ) parser.add_argument( - "--skip-criteria", + "--reindex", action="store_true", - help="Do not build the criteria table.", - ) - parser.add_argument( - "--recreate", - action=argparse.BooleanOptionalAction, - default=True, - help="Overwrite target tables before writing.", + help="Rebuild the search tables even if they already exist.", ) args = parser.parse_args() config = load_config(args.config) - backend = LanceDBSearchBackend.from_config(config) root = _repo_root() - failures = 0 + processed_trials = _resolve_path(args.processed_trials_folder, root) + processed_criteria = _resolve_path(args.processed_criteria_folder, root) + if args.prepare: - _prepare_from_trials_jsons( - config=config, + prepare_corpus( + config, trials_json_folder=_resolve_path( - args.trials_json_folder or config["paths"]["trials_json_folder"], - root, + args.trials_json_folder or config["paths"]["trials_json_folder"], root ), - processed_trials_folder=_resolve_path(args.processed_trials_folder, root), - processed_criteria_folder=_resolve_path(args.processed_criteria_folder, root), + processed_trials_folder=processed_trials, + processed_criteria_folder=processed_criteria, + force=args.force_prepare, ) - if not args.skip_trials: - trials_folder = _resolve_path(args.processed_trials_folder, root) - trial_docs = _load_flat_json_folder(trials_folder) - if not trial_docs: - logger.error("No prepared trial JSON files found in %s.", trials_folder) - failures += 1 - else: - count = backend.index_trials(trial_docs, recreate=args.recreate) - logger.info("Indexed %s trial documents.", count) - - if not args.skip_criteria: - criteria_folder = _resolve_path(args.processed_criteria_folder, root) - criteria_docs = _load_nested_json_folder(criteria_folder) - if not criteria_docs: - logger.error("No prepared criteria JSON files found in %s.", criteria_folder) - failures += 1 - else: - count = backend.index_criteria(criteria_docs, recreate=args.recreate) - logger.info("Indexed %s criteria documents.", count) - - if failures: - return 1 - logger.info("Search tables ready at %s.", backend.db_path) - return 0 - - -def _load_flat_json_folder(folder: Path) -> list[dict]: - if not folder.exists(): - return [] - return [ - _read_json(path) - for path in sorted(folder.glob("*.json")) - if path.is_file() - ] - - -def _load_nested_json_folder(folder: Path) -> list[dict]: - if not folder.exists(): - return [] - return [ - _read_json(path) - for path in sorted(folder.glob("*/*.json")) - if path.is_file() - ] - - -def _read_json(path: Path) -> dict: - import json - - return json.loads(path.read_text(encoding="utf-8")) - - -def _prepare_from_trials_jsons( - *, - config: dict, - trials_json_folder: Path, - processed_trials_folder: Path, - processed_criteria_folder: Path, -) -> None: - from trialmatchai.entities import build_entity_annotator - from trialmatchai.models.embedding import build_embedder - from trialmatchai.registry.preparation import ( - prepare_criteria_documents, - prepare_trial_document, - write_prepared_criteria, - write_prepared_trial, - ) - - embedder = build_embedder(config) - entity_annotator = build_entity_annotator(config, embedder=embedder) - trial_docs = _load_flat_json_folder(trials_json_folder) - prepared = failed = 0 - for doc in trial_docs: - nct_id = doc.get("nct_id", "") - try: - trial_row = prepare_trial_document(doc, embedder) - criteria_rows = prepare_criteria_documents( - doc, - embedder, - entity_annotator=entity_annotator, - ) - # Criteria before the trial marker so a crash mid-trial re-prepares it. - write_prepared_criteria(criteria_rows, processed_criteria_folder) - write_prepared_trial(trial_row, processed_trials_folder) - prepared += 1 - except Exception: - logger.exception("Failed to prepare trial %s; skipping", nct_id) - failed += 1 - logger.info( - "Prepared %s trial JSON files from %s (%s failed).", - prepared, - trials_json_folder, - failed, + build_index( + config, + processed_trials_folder=processed_trials, + processed_criteria_folder=processed_criteria, + force=args.reindex, ) + logger.info("Search tables ready.") + return 0 def _resolve_path(value: str, root: Path) -> Path: diff --git a/tests/test_preflight_and_indexer.py b/tests/test_preflight_and_indexer.py index 52cc0281..0e384265 100644 --- a/tests/test_preflight_and_indexer.py +++ b/tests/test_preflight_and_indexer.py @@ -3,7 +3,6 @@ import json from pathlib import Path -from trialmatchai.cli.index_data import _load_nested_json_folder from trialmatchai.config.config_loader import load_config from trialmatchai.search import InMemorySearchBackend from trialmatchai.services import preflight @@ -214,7 +213,7 @@ def test_indexer_loads_prepared_criteria_docs(tmp_path): (trial_dir / "C1.json").write_text( json.dumps({"criteria_id": "C1", "nct_id": "N1", "criterion": "cancer"}) ) - docs = _load_nested_json_folder(processed) + docs = [json.loads(p.read_text()) for p in sorted(processed.glob("*/*.json"))] backend = InMemorySearchBackend() count = backend.replace_criteria_for_trials(["N1"], docs) From b4ea5c6c3f09d0bbd087c1195ddd99505d1f01ef Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 22:43:24 +0200 Subject: [PATCH 53/64] refactor(cli): single 'trialmatchai' entry point; finetune is a subcommand Per the unification: collapse the 11 standalone trialmatchai-* console scripts into one entry point. Every capability is now 'trialmatchai ' (healthcheck, bootstrap-data, index, build-concepts, update-registry, import-patient, build, run, e2e, trec, finetune). finetune stays a distinct subcommand (training != inference), now reachable via the umbrella. - pyproject: one console script. - cli/main.py: route 'finetune' to finetuning.cli. - CI smoke + docs + src help-text + tests updated to the subcommand form. - (resume slurm updated locally + auto-resume resubmitted to match.) --- .github/workflows/ci.yml | 11 ++++------ README.md | 34 +++++++++++++++--------------- docs/architecture.md | 4 ++-- docs/finetuning.md | 16 +++++++------- docs/interoperability.md | 12 +++++------ docs/registry-updater.md | 16 +++++++------- docs/release.md | 22 +++++++++---------- pyproject.toml | 14 +++--------- src/trialmatchai/cli/index_data.py | 2 +- src/trialmatchai/cli/main.py | 7 ++++++ src/trialmatchai/finetuning/cli.py | 10 ++++----- src/trialmatchai/main.py | 2 +- src/trialmatchai/orchestration.py | 2 +- src/trialmatchai/trec/__init__.py | 2 +- tests/test_import_patient_cli.py | 2 +- 15 files changed, 76 insertions(+), 80 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7fab42bd..3e31cf69 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -56,13 +56,10 @@ jobs: run: | WHEEL="$(ls dist/trialmatchai-*.whl | head -n 1)" uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai --help - uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-healthcheck --help - uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-index --help - uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-build-concepts --help - uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-update-registry --help - uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-import-patient --help - uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-run --help - uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai-finetune --help + for cmd in healthcheck bootstrap-data index build-concepts update-registry \ + import-patient build run e2e trec finetune; do + uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai "$cmd" --help + done - name: Secret scan run: uv run pre-commit run gitleaks --all-files diff --git a/README.md b/README.md index 55fbe613..5fdbd5a8 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,7 @@ patients many times**. Both commands are idempotent and resume after disruption. ```bash uv sync --extra llm --extra gpu --extra entity # GPU host + HuggingFace access -uv run trialmatchai-bootstrap-data # fetch prepared corpus + adapters +uv run trialmatchai bootstrap-data # fetch prepared corpus + adapters uv run trialmatchai build # 1) BUILD: prepare + index (once) uv run trialmatchai e2e --input patient.txt # 2) MATCH: ingest + match a patient # -> results//ranked_trials.json @@ -91,7 +91,7 @@ pip install -e ".[finetune]" | `entity` | GLiNER2 biomedical extraction | | `llm` | local embedding and LLM dependencies | | `gpu` | vLLM and bitsandbytes; intended for Linux CUDA hosts | -| `finetune` | training dependencies for `trialmatchai-finetune` | +| `finetune` | training dependencies for `trialmatchai finetune` | Installing the package only gives you the CLI. Real matching also needs the trial corpus, model artifacts, and LanceDB search tables — all produced by the @@ -114,7 +114,7 @@ export HF_TOKEN= # required for gated models (p ### 1. Build the system — once ```bash -uv run trialmatchai-bootstrap-data # download the prepared corpus + LoRA adapters +uv run trialmatchai bootstrap-data # download the prepared corpus + LoRA adapters uv run trialmatchai build # prepare embeddings/entities + build the index uv run trialmatchai build --status # see exactly what is built (and what isn't) ``` @@ -135,9 +135,9 @@ uv run trialmatchai build --concepts --concepts-csv data/omop/CONCEPT.csv --syno | Resource | How you get it | Automatic? | | --- | --- | --- | -| Trial corpus (`processed_trials` + criteria) | `trialmatchai-bootstrap-data` (Zenodo) | ✅ automatic | -| Fine-tuned LoRA adapters (CoT + reranker) | `trialmatchai-bootstrap-data` (Zenodo) | ✅ automatic | -| Fine-tuning datasets (only if you re-train) | `trialmatchai-bootstrap-data --finetune-data` (Zenodo) | ✅ automatic (opt-in) | +| Trial corpus (`processed_trials` + criteria) | `trialmatchai bootstrap-data` (Zenodo) | ✅ automatic | +| Fine-tuned LoRA adapters (CoT + reranker) | `trialmatchai bootstrap-data` (Zenodo) | ✅ automatic | +| Fine-tuning datasets (only if you re-train) | `trialmatchai bootstrap-data --finetune-data` (Zenodo) | ✅ automatic (opt-in) | | Embedder (`BAAI/bge-m3`) | downloaded from HuggingFace on first use | ✅ automatic | | Concept-linking vocabularies (genes, diseases, …) | `trialmatchai build --concepts` | ✅ automatic | | Base LLMs (`microsoft/phi-4`, `google/gemma-2-2b-it`) | HuggingFace on first use | ⚠️ automatic, but **gated** models need a **one-time** `hf auth login` + accepting the model licence | @@ -161,7 +161,7 @@ explanations). Re-running skips patients already matched. ### Health and keeping trials current ```bash -uv run trialmatchai-healthcheck # validate config/paths/deps +uv run trialmatchai healthcheck # validate config/paths/deps ``` Fold new/changed ClinicalTrials.gov studies into the **live index** — fetch → @@ -180,9 +180,9 @@ timer, or GitHub Actions — see [docs/registry-updater.md](docs/registry-update Manual / advanced control (the steps build and e2e wrap) ```bash -uv run trialmatchai-index --prepare # prepare + index from trials_jsons (what `build` runs) -uv run trialmatchai-import-patient --input patient.txt # stage a profile only -uv run trialmatchai-run # match already-staged profiles +uv run trialmatchai index --prepare # prepare + index from trials_jsons (what `build` runs) +uv run trialmatchai import-patient --input patient.txt # stage a profile only +uv run trialmatchai run # match already-staged profiles uv run trialmatchai trec --tracks "21 22" # benchmark: official TREC CT eval ``` @@ -264,7 +264,7 @@ The importer supports: Importers preserve provenance and unsupported source elements where possible. The matching summary is rendered deterministically from the canonical `PatientProfile`; raw patient files are not consumed directly by -`trialmatchai-run`. +`trialmatchai run`. See [docs/interoperability.md](docs/interoperability.md) for format details. @@ -326,15 +326,15 @@ Fine-tune model components with: ```bash uv sync --extra finetune -uv run trialmatchai-finetune cot \ +uv run trialmatchai finetune cot \ --base-model microsoft/phi-4 \ --train-data data/finetune/cot.jsonl \ --output-dir models/cot-adapter -uv run trialmatchai-finetune reranker \ +uv run trialmatchai finetune reranker \ --base-model google/gemma-2-2b-it \ --train-data data/finetune/reranker.jsonl \ --output-dir models/reranker-adapter -uv run trialmatchai-finetune ner \ +uv run trialmatchai finetune ner \ --base-model fastino/gliner2-base-v1 \ --train-data data/finetune/ner.jsonl \ --output-dir models/ner @@ -376,7 +376,7 @@ The full override list is in [`.env.example`](.env.example). ## CLI Reference -Every command is available both as a flat script (`trialmatchai-build`) and as a +Every command is available both as a flat script (`trialmatchai build`) and as a subcommand (`trialmatchai build`); they are equivalent. **Build the system (setup half)** @@ -384,7 +384,7 @@ subcommand (`trialmatchai build`); they are equivalent. | Command | Purpose | | --- | --- | | `trialmatchai build` | Prepare the corpus (embeddings + entities) and build the search index — resumable, with `--status` | -| `trialmatchai-bootstrap-data` | Download and extract the prepared corpus + model adapters | +| `trialmatchai bootstrap-data` | Download and extract the prepared corpus + model adapters | | `trialmatchai build-concepts` | Build the LanceDB concept table for entity normalization (optional, OMOP) | | `trialmatchai update-registry` | Fetch changed ClinicalTrials.gov studies and upsert LanceDB | @@ -403,7 +403,7 @@ subcommand (`trialmatchai build`); they are equivalent. | --- | --- | | `trialmatchai healthcheck` | Validate config, paths, optional model deps, and LanceDB tables | | `trialmatchai index` | Lower-level prepare/index of trial and criteria search tables | -| `trialmatchai-finetune` | Fine-tune NER, reranker, or eligibility reasoning models | +| `trialmatchai finetune` | Fine-tune NER, reranker, or eligibility reasoning models | ```bash uv run trialmatchai build --status # what is built diff --git a/docs/architecture.md b/docs/architecture.md index 8466aa15..76534f18 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -14,11 +14,11 @@ TrialMatchAI is an installable Python package exposed as `trialmatchai`. The sup ## Data Flow -1. `trialmatchai-update-registry` fetches studies from ClinicalTrials.gov v2. +1. `trialmatchai update-registry` fetches studies from ClinicalTrials.gov v2. 2. Raw source JSON is written to `data/registry/raw/.json`. 3. Normalized trial JSON is written to `data/trials_jsons/.json`. 4. Changed studies are embedded, criteria are optionally entity annotated, and LanceDB trial/criteria tables are upserted. -5. `trialmatchai-run` matches already-imported patient profiles against LanceDB, reranks candidate criteria/trials, and writes results. (Ingest raw patient inputs first with `trialmatchai-import-patient`, or use `trialmatchai-e2e` to ingest and match in one step.) +5. `trialmatchai run` matches already-imported patient profiles against LanceDB, reranks candidate criteria/trials, and writes results. (Ingest raw patient inputs first with `trialmatchai import-patient`, or use `trialmatchai e2e` to ingest and match in one step.) ## Storage diff --git a/docs/finetuning.md b/docs/finetuning.md index 749805f4..8f9587dd 100644 --- a/docs/finetuning.md +++ b/docs/finetuning.md @@ -3,7 +3,7 @@ TrialMatchAI ships with capable default models, but every model in the pipeline is **swappable** and **fine-tunable**. You can point the pipeline at your own checkpoints/adapters via config, and train those adapters with the built-in -`trialmatchai-finetune` command. +`trialmatchai finetune` command. | Component | Default | Config key | Fine-tune target | |-----------|---------|------------|------------------| @@ -12,12 +12,12 @@ checkpoints/adapters via config, and train those adapters with the built-in | CoT eligibility | configured CoT model | `model.cot_adapter_path` | LoRA adapter | > **Where does the training data come from?** Fine-tuning is **optional** — the -> ready-to-use CoT and reranker adapters are downloaded by `trialmatchai-bootstrap-data`, +> ready-to-use CoT and reranker adapters are downloaded by `trialmatchai bootstrap-data`, > so most deployments never need to train. The **published training datasets** are > available too — fetch them into `data/finetune/`: > > ```bash -> trialmatchai-bootstrap-data --finetune-data +> trialmatchai bootstrap-data --finetune-data > ``` > > (Source: the paper's Zenodo deposit, .) @@ -46,7 +46,7 @@ the LoRA adapter natively via `LoRARequest` — no merge step required. If you prefer a single self-contained model instead of base + adapter, merge them: ```bash -trialmatchai-finetune merge \ +trialmatchai finetune merge \ --base-model google/gemma-2-2b-it \ --adapter models/reranker-adapter \ --output-dir models/reranker-merged @@ -70,7 +70,7 @@ Data — JSONL, one example per line, either chat or instruct form: ``` ```bash -trialmatchai-finetune cot \ +trialmatchai finetune cot \ --base-model microsoft/phi-4 \ --train-data data/finetune/cot.jsonl \ --eval-data data/finetune/cot.eval.jsonl \ @@ -98,7 +98,7 @@ Data — JSONL: ``` ```bash -trialmatchai-finetune reranker \ +trialmatchai finetune reranker \ --base-model google/gemma-2-2b-it \ --train-data data/finetune/reranker.jsonl \ --eval-data data/finetune/reranker.eval.jsonl \ @@ -137,7 +137,7 @@ structured JSON training data is useful for GLiNER2 adapters you call directly or for future structured extraction integration. ```bash -trialmatchai-finetune ner \ +trialmatchai finetune ner \ --base-model fastino/gliner2-base-v1 \ --train-data data/finetune/ner.jsonl \ --output-dir models/ner \ @@ -160,6 +160,6 @@ trialmatchai-finetune ner \ `--eval-steps` (defaults align at 500). - 4-bit quantized loading is on by default (`--no-4bit` to disable) and requires bitsandbytes on a CUDA-capable machine; `bf16` is default (`--fp16` to switch). - See `trialmatchai-finetune --help` for all flags. + See `trialmatchai finetune --help` for all flags. - GLiNER2's training API can vary by version; if your installed `gliner2` exposes a different interface, adapt `src/trialmatchai/finetuning/ner.py`. diff --git a/docs/interoperability.md b/docs/interoperability.md index 4e38030a..cf952bad 100644 --- a/docs/interoperability.md +++ b/docs/interoperability.md @@ -55,8 +55,8 @@ runtime summary fields: - `gender` - `provenance` -`trialmatchai-run` consumes canonical profiles from `patient_inputs.profile_dir`. -Import source files explicitly with `trialmatchai-import-patient` before running +`trialmatchai run` consumes canonical profiles from `patient_inputs.profile_dir`. +Import source files explicitly with `trialmatchai import-patient` before running matching. ## Commands @@ -64,7 +64,7 @@ matching. Import a free text note: ```bash -uv run trialmatchai-import-patient \ +uv run trialmatchai import-patient \ --input data/patients/raw/patient-1.txt \ --format text ``` @@ -79,14 +79,14 @@ uv run trialmatchai import-patient \ Import a FHIR Bundle: ```bash -uv run trialmatchai-import-patient \ +uv run trialmatchai import-patient \ --input data/patients/raw/patient-1.fhir.json ``` Import an OMOP extract folder: ```bash -uv run trialmatchai-import-patient \ +uv run trialmatchai import-patient \ --input data/patients/omop_extract \ --format omop ``` @@ -94,7 +94,7 @@ uv run trialmatchai-import-patient \ Then run matching: ```bash -uv run trialmatchai-run +uv run trialmatchai run ``` ## Conversion Limits diff --git a/docs/registry-updater.md b/docs/registry-updater.md index 7846d450..45cd1025 100644 --- a/docs/registry-updater.md +++ b/docs/registry-updater.md @@ -1,11 +1,11 @@ # Registry Updater -`trialmatchai-update-registry` updates the local LanceDB-backed trial registry from ClinicalTrials.gov v2. It is command-based and safe to run from cron, systemd timers, or GitHub Actions. +`trialmatchai update-registry` updates the local LanceDB-backed trial registry from ClinicalTrials.gov v2. It is command-based and safe to run from cron, systemd timers, or GitHub Actions. ## Basic Usage ```bash -uv run trialmatchai-update-registry --max-studies 500 +uv run trialmatchai update-registry --max-studies 500 ``` When no keyword is provided, TrialMatchAI uses broad default keyword queries covering oncology, cardiology, neurology, rare disease, immunology, infectious disease, metabolic disease, hematology, pediatrics, and precision medicine. @@ -13,7 +13,7 @@ When no keyword is provided, TrialMatchAI uses broad default keyword queries cov Use explicit keywords: ```bash -uv run trialmatchai-update-registry \ +uv run trialmatchai update-registry \ --keyword "lung cancer" \ --keyword "EGFR" \ --since 2026-06-01 \ @@ -23,13 +23,13 @@ uv run trialmatchai-update-registry \ Use a keyword file: ```bash -uv run trialmatchai-update-registry --keywords-file data/registry/keywords.txt +uv run trialmatchai update-registry --keywords-file data/registry/keywords.txt ``` Dry-run: ```bash -uv run trialmatchai-update-registry --dry-run --max-studies 25 +uv run trialmatchai update-registry --dry-run --max-studies 25 ``` Dry-runs do not write raw JSON, normalized trial JSON, manifests, reports, or LanceDB tables unless an explicit `--report-path` is provided. @@ -49,7 +49,7 @@ Changed studies are written and then upserted: Run daily at 02:30: ```cron -30 2 * * * cd /opt/TrialMatchAI && /usr/local/bin/uv run trialmatchai-update-registry --max-studies 1000 >> logs/registry-update.log 2>&1 +30 2 * * * cd /opt/TrialMatchAI && /usr/local/bin/uv run trialmatchai update-registry --max-studies 1000 >> logs/registry-update.log 2>&1 ``` ## systemd @@ -63,7 +63,7 @@ Description=TrialMatchAI registry update [Service] Type=oneshot WorkingDirectory=/opt/TrialMatchAI -ExecStart=/usr/local/bin/uv run trialmatchai-update-registry --max-studies 1000 +ExecStart=/usr/local/bin/uv run trialmatchai update-registry --max-studies 1000 ``` Timer: @@ -83,7 +83,7 @@ WantedBy=timers.target ## Healthcheck ```bash -uv run trialmatchai-healthcheck --registry --require-tables +uv run trialmatchai healthcheck --registry --require-tables ``` Use `--require-tables` after the first successful update or indexing run. diff --git a/docs/release.md b/docs/release.md index 2db6181b..b88da6c4 100644 --- a/docs/release.md +++ b/docs/release.md @@ -17,17 +17,17 @@ Smoke test console commands: ```bash uv run trialmatchai --help -uv run trialmatchai-healthcheck --help -uv run trialmatchai-bootstrap-data --help -uv run trialmatchai-build --help -uv run trialmatchai-index --help -uv run trialmatchai-build-concepts --help -uv run trialmatchai-update-registry --help -uv run trialmatchai-import-patient --help -uv run trialmatchai-run --help -uv run trialmatchai-e2e --help -uv run trialmatchai-trec --help -uv run trialmatchai-finetune --help +uv run trialmatchai healthcheck --help +uv run trialmatchai bootstrap-data --help +uv run trialmatchai build --help +uv run trialmatchai index --help +uv run trialmatchai build-concepts --help +uv run trialmatchai update-registry --help +uv run trialmatchai import-patient --help +uv run trialmatchai run --help +uv run trialmatchai e2e --help +uv run trialmatchai trec --help +uv run trialmatchai finetune --help ``` Wheel install smoke: diff --git a/pyproject.toml b/pyproject.toml index a94228f6..465f1f8b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -54,18 +54,10 @@ finetune = [ ] [project.scripts] -trialmatchai-finetune = "trialmatchai.finetuning.cli:main" +# Single entry point. Every capability is a subcommand: `trialmatchai ` +# (healthcheck, bootstrap-data, index, build-concepts, update-registry, +# import-patient, build, run, e2e, trec, finetune). trialmatchai = "trialmatchai.cli.main:main" -trialmatchai-healthcheck = "trialmatchai.cli.healthcheck:main" -trialmatchai-build = "trialmatchai.cli.build:main" -trialmatchai-run = "trialmatchai.cli.run:main" -trialmatchai-e2e = "trialmatchai.cli.e2e:main" -trialmatchai-trec = "trialmatchai.cli.trec:main" -trialmatchai-bootstrap-data = "trialmatchai.cli.bootstrap_data:main" -trialmatchai-index = "trialmatchai.cli.index_data:main" -trialmatchai-build-concepts = "trialmatchai.cli.build_concepts:main" -trialmatchai-update-registry = "trialmatchai.cli.update_registry:main" -trialmatchai-import-patient = "trialmatchai.cli.import_patient:main" [dependency-groups] dev = [ diff --git a/src/trialmatchai/cli/index_data.py b/src/trialmatchai/cli/index_data.py index a4965d51..49c23d1c 100644 --- a/src/trialmatchai/cli/index_data.py +++ b/src/trialmatchai/cli/index_data.py @@ -1,4 +1,4 @@ -"""``trialmatchai-index`` — prepare + build the LanceDB search tables. +"""``trialmatchai index`` — prepare + build the LanceDB search tables. A thin entry point over the SAME idempotent orchestration stages `build` uses (`prepare_corpus` + `build_index`), so there is one prepare implementation and one diff --git a/src/trialmatchai/cli/main.py b/src/trialmatchai/cli/main.py index ef6841e3..f0f5ae81 100644 --- a/src/trialmatchai/cli/main.py +++ b/src/trialmatchai/cli/main.py @@ -53,6 +53,11 @@ def main() -> int: help="End-to-end TREC CT evaluation (preset over e2e)", add_help=False, ) + subparsers.add_parser( + "finetune", + help="Fine-tune the CoT / reranker / NER models", + add_help=False, + ) args, remainder = parser.parse_known_args() if args.command == "healthcheck": @@ -75,6 +80,8 @@ def main() -> int: from trialmatchai.cli.e2e import main as command elif args.command == "trec": from trialmatchai.cli.trec import main as command + elif args.command == "finetune": + from trialmatchai.finetuning.cli import main as command else: # pragma: no cover - argparse enforces choices parser.error(f"Unknown command: {args.command}") diff --git a/src/trialmatchai/finetuning/cli.py b/src/trialmatchai/finetuning/cli.py index 55bacac0..dcc85c03 100644 --- a/src/trialmatchai/finetuning/cli.py +++ b/src/trialmatchai/finetuning/cli.py @@ -1,11 +1,11 @@ -"""``trialmatchai-finetune`` — train custom GLiNER2 / reranker / CoT models. +"""``trialmatchai finetune`` — train custom GLiNER2 / reranker / CoT models. Examples: - trialmatchai-finetune cot --base-model microsoft/phi-4 \ + trialmatchai finetune cot --base-model microsoft/phi-4 \ --train-data data/cot.jsonl --output-dir models/cot-adapter - trialmatchai-finetune reranker --base-model google/gemma-2-2b-it \ + trialmatchai finetune reranker --base-model google/gemma-2-2b-it \ --train-data data/reranker.jsonl --output-dir models/reranker-adapter - trialmatchai-finetune ner --base-model fastino/gliner2-base-v1 \ + trialmatchai finetune ner --base-model fastino/gliner2-base-v1 \ --train-data data/ner.jsonl --output-dir models/ner Plug the result back into config: entity_extraction.model_name (NER), @@ -108,7 +108,7 @@ def _lora_config_from_args(args: argparse.Namespace) -> FinetuneConfig: def build_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser( - prog="trialmatchai-finetune", + prog="trialmatchai finetune", description="Fine-tune TrialMatchAI's GLiNER2, reranker, or CoT models.", ) sub = parser.add_subparsers(dest="component", required=True) diff --git a/src/trialmatchai/main.py b/src/trialmatchai/main.py index a7028e08..4e59d869 100644 --- a/src/trialmatchai/main.py +++ b/src/trialmatchai/main.py @@ -568,7 +568,7 @@ def _load_patient_inputs(config: Dict) -> list[tuple[PatientProfile, Dict]]: if not profile_files: logger.error( "No canonical patient profiles found in %s. Run " - "`trialmatchai-import-patient` first.", + "`trialmatchai import-patient` first.", profile_dir, ) return [] diff --git a/src/trialmatchai/orchestration.py b/src/trialmatchai/orchestration.py index 8eeeda36..2025e2f6 100644 --- a/src/trialmatchai/orchestration.py +++ b/src/trialmatchai/orchestration.py @@ -503,7 +503,7 @@ def build_system( else: raise RuntimeError( f"Nothing to prepare: {pt} is empty and no trial JSONs at " - f"{trials_json_folder}. Run `trialmatchai-bootstrap-data` or provide " + f"{trials_json_folder}. Run `trialmatchai bootstrap-data` or provide " "normalized trial JSONs." ) _save_manifest(manifest_path, manifest) diff --git a/src/trialmatchai/trec/__init__.py b/src/trialmatchai/trec/__init__.py index 7560b7cb..d367c4dc 100644 --- a/src/trialmatchai/trec/__init__.py +++ b/src/trialmatchai/trec/__init__.py @@ -5,7 +5,7 @@ profiles, builds a per-track LanceDB search index restricted to the track's document collection, and runs the matching pipeline with per-patient resume. -Exposed via the ``trialmatchai-trec`` console command (see +Exposed via the ``trialmatchai trec`` console command (see ``trialmatchai.cli.trec``). """ diff --git a/tests/test_import_patient_cli.py b/tests/test_import_patient_cli.py index ae165e91..b3af2efa 100644 --- a/tests/test_import_patient_cli.py +++ b/tests/test_import_patient_cli.py @@ -14,7 +14,7 @@ def test_import_patient_cli_writes_profile_and_summary(tmp_path, monkeypatch): monkeypatch.setattr( "sys.argv", [ - "trialmatchai-import-patient", + "trialmatchai import-patient", "--input", str(note), "--format", From dc2b81ca95e10ee7c24c662da2a0b22611265058 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:00:49 +0200 Subject: [PATCH 54/64] feat(pipeline): unified stage-registry driver + 'trialmatchai pipeline' One ordered registry of idempotent stages (prepare, concepts, index, ingest, expand, match, eval) with a single driver. Run the whole e2e or any slice: --only --from/--to --skip --force Each stage wraps an already-idempotent function, so finished work is skipped and the run 'just works' from any starting state; GPU models are freed once in a finally (fixes the scattered teardown). Stage = the unit of modularity. Also extract build_concepts.run_build_concepts() as an importable callable (the concepts stage + build.py call it directly), removing the sys.argv shim. Additive: build/e2e/trec unchanged; migration onto the driver follows next. 11 driver tests (selection, force, ordering, teardown-on-error). --- src/trialmatchai/cli/build.py | 25 +-- src/trialmatchai/cli/build_concepts.py | 84 +++++++--- src/trialmatchai/cli/main.py | 9 +- src/trialmatchai/cli/pipeline.py | 106 +++++++++++++ src/trialmatchai/pipeline.py | 211 +++++++++++++++++++++++++ tests/test_pipeline.py | 96 +++++++++++ 6 files changed, 488 insertions(+), 43 deletions(-) create mode 100644 src/trialmatchai/cli/pipeline.py create mode 100644 src/trialmatchai/pipeline.py create mode 100644 tests/test_pipeline.py diff --git a/src/trialmatchai/cli/build.py b/src/trialmatchai/cli/build.py index 557c590e..4c99cbe1 100644 --- a/src/trialmatchai/cli/build.py +++ b/src/trialmatchai/cli/build.py @@ -88,23 +88,14 @@ def main() -> int: # (auto-downloaded); --concepts-csv adds the licensed OMOP vocab on top. if args.concepts or args.concepts_csv: logger.info("=== build: concepts stage ===") - from trialmatchai.cli.build_concepts import main as build_concepts_main - - argv: list[str] = [] - if args.concepts: - argv += ["--sources", "open"] - if args.concepts_csv: - argv += ["--concept-csv", args.concepts_csv] - if args.synonym_csv: - argv += ["--synonym-csv", args.synonym_csv] - if args.config: - argv += ["--config", args.config] - saved = sys.argv - try: - sys.argv = ["trialmatchai build-concepts", *argv] - build_concepts_main() - finally: - sys.argv = saved + from trialmatchai.cli.build_concepts import run_build_concepts + + run_build_concepts( + config, + sources="open" if args.concepts else None, + concept_csv=args.concepts_csv, + synonym_csv=args.synonym_csv, + ) else: logger.warning( "Concept DB not built: entity->concept linking will degrade gracefully. " diff --git a/src/trialmatchai/cli/build_concepts.py b/src/trialmatchai/cli/build_concepts.py index ed2d8bbf..a4b88e79 100644 --- a/src/trialmatchai/cli/build_concepts.py +++ b/src/trialmatchai/cli/build_concepts.py @@ -79,58 +79,92 @@ def main() -> int: args = parser.parse_args() config = load_config(args.config) - linker_cfg = config.get("concept_linker", {}) - db_path = args.db_path or linker_cfg.get("db_path") or "data/concepts" - table_name = args.table or linker_cfg.get("table") or "concepts" - if not args.concept_csv and not args.dictionary and not args.sources: parser.error("provide --concept-csv, --sources, and/or at least one --dictionary") + try: + return run_build_concepts( + config, + db_path=args.db_path, + table=args.table, + sources=args.sources, + dictionary=args.dictionary, + concept_csv=args.concept_csv, + synonym_csv=args.synonym_csv, + vocabulary=args.vocabulary, + concept_cache=args.concept_cache, + force_download=args.force_download, + skip_embeddings=args.skip_embeddings, + force=args.force, + ) + except ValueError as exc: + parser.error(str(exc)) + return 2 # pragma: no cover - parser.error exits + + +def run_build_concepts( + config: dict, + *, + db_path: str | None = None, + table: str | None = None, + sources: str | None = None, + dictionary: list[str] | tuple[str, ...] = (), + concept_csv: str | None = None, + synonym_csv: str | None = None, + vocabulary: list[str] | tuple[str, ...] = (), + concept_cache: str = "data/concept_dicts", + force_download: bool = False, + skip_embeddings: bool = False, + force: bool = False, +) -> int: + """Build the LanceDB concept table (the entity-linking store). Idempotent. + + Importable so the unified pipeline can invoke the concepts stage directly, + rather than re-entering the CLI. Skips the expensive re-embed if the table is + already present, unless ``force`` (or a new OMOP vocab via ``concept_csv``). + """ + linker_cfg = config.get("concept_linker", {}) + db_path = db_path or linker_cfg.get("db_path") or "data/concepts" + table_name = table or linker_cfg.get("table") or "concepts" + + if not concept_csv and not dictionary and not sources: + raise ValueError("provide concept_csv, sources, and/or at least one dictionary") - # Idempotent: skip the (expensive) re-embed if the table is already built, - # unless --force or a new OMOP vocab (--concept-csv) is being folded in. - if not args.force and not args.concept_csv: + if not force and not concept_csv: ready, rows_present = _concept_table_ready(db_path, table_name) if ready: logger.info( "Concept store already present at %s/%s (%s concepts); skipping. " - "Use --force to rebuild.", + "Pass force=True to rebuild.", db_path, table_name, rows_present, ) return 0 - # Collect dictionary specs from explicit --dictionary flags and bundled --sources. dictionary_specs: list[tuple[str, str, str]] = [ - _parse_dictionary_spec(spec) for spec in args.dictionary + _parse_dictionary_spec(spec) for spec in dictionary ] - if args.sources == "open": + if sources == "open": from trialmatchai.entities.concept_sources import build_open_dictionaries for source, dict_path in build_open_dictionaries( - Path(args.concept_cache), force=args.force_download + Path(concept_cache), force=force_download ): dictionary_specs.append( (source.vocabulary_id, source.domain_id, str(dict_path)) ) - if not dictionary_specs and not args.concept_csv: - parser.error("no concept sources were built (all downloads failed?)") + if not dictionary_specs and not concept_csv: + raise ValueError("no concept sources were built (all downloads failed?)") - vocabularies = tuple(args.vocabulary or DEFAULT_OMOP_VOCABULARIES) + vocabularies = tuple(vocabulary or DEFAULT_OMOP_VOCABULARIES) rows: list = [] - if args.concept_csv: - rows = build_omop_concept_rows( - args.concept_csv, - args.synonym_csv, - vocabularies=vocabularies, - ) + if concept_csv: + rows = build_omop_concept_rows(concept_csv, synonym_csv, vocabularies=vocabularies) for vocab, domain, path in dictionary_specs: - rows.extend( - build_dictionary_rows(path, vocabulary_id=vocab, domain_id=domain) - ) + rows.extend(build_dictionary_rows(path, vocabulary_id=vocab, domain_id=domain)) embeddings = None - if not args.skip_embeddings: + if not skip_embeddings: embedder = build_embedder(config) embeddings = embedder.embed_texts(concept_texts_for_embedding(rows)) diff --git a/src/trialmatchai/cli/main.py b/src/trialmatchai/cli/main.py index f0f5ae81..8e1d1cf3 100644 --- a/src/trialmatchai/cli/main.py +++ b/src/trialmatchai/cli/main.py @@ -11,6 +11,11 @@ def main() -> int: ) subparsers = parser.add_subparsers(dest="command", required=True) + subparsers.add_parser( + "pipeline", + help="Run the unified e2e pipeline, or any slice (--only/--from/--to/--skip/--force)", + add_help=False, + ) subparsers.add_parser( "healthcheck", help="Run deployment health checks", @@ -60,7 +65,9 @@ def main() -> int: ) args, remainder = parser.parse_known_args() - if args.command == "healthcheck": + if args.command == "pipeline": + from trialmatchai.cli.pipeline import main as command + elif args.command == "healthcheck": from trialmatchai.cli.healthcheck import main as command elif args.command == "bootstrap-data": from trialmatchai.cli.bootstrap_data import main as command diff --git a/src/trialmatchai/cli/pipeline.py b/src/trialmatchai/cli/pipeline.py new file mode 100644 index 00000000..860c3b28 --- /dev/null +++ b/src/trialmatchai/cli/pipeline.py @@ -0,0 +1,106 @@ +"""``trialmatchai pipeline`` — the one end-to-end pipeline, or any slice of it. + +Default (no selection) runs every stage; each is idempotent, so finished work is +skipped. Select with --only/--from/--to, omit with --skip (handy for ablation), +and redo with --force. +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +from trialmatchai.config.config_loader import load_config +from trialmatchai.pipeline import STAGE_NAMES, StageContext, run_pipeline +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +def _split(value: str | None) -> list[str]: + return [v.strip() for v in (value or "").split(",") if v.strip()] + + +def main() -> int: + parser = argparse.ArgumentParser( + prog="trialmatchai pipeline", + description=( + "Run the unified TrialMatchAI pipeline or any slice. Stages in order: " + + ", ".join(STAGE_NAMES) + + ". Every stage is idempotent — finished work is skipped automatically." + ), + ) + parser.add_argument("--config", default=None, help="Path to config.json") + parser.add_argument( + "--input", + action="append", + default=[], + help="Patient input file/dir to ingest (repeatable).", + ) + parser.add_argument("--format", default="auto", help="Patient input format (default: auto).") + parser.add_argument( + "--no-entities", action="store_true", help="Ingest patients without entity annotation." + ) + parser.add_argument( + "--trials-json-folder", + default=None, + help="Normalized trial JSONs for prepare. Defaults to config paths.trials_json_folder.", + ) + parser.add_argument("--processed-trials-folder", default="data/processed_trials") + parser.add_argument("--processed-criteria-folder", default="data/processed_criteria") + parser.add_argument( + "--concepts", + action="store_true", + help="In the concepts stage, build the open-vocabulary concept store.", + ) + parser.add_argument("--concepts-csv", default=None, help="OMOP CONCEPT.csv for the concepts stage.") + parser.add_argument("--synonym-csv", default=None, help="OMOP CONCEPT_SYNONYM.csv.") + + sel = parser.add_argument_group("stage selection") + sel.add_argument("--only", default=None, metavar="STAGES", help="Run only these stages.") + sel.add_argument( + "--skip", + default="", + metavar="STAGES", + help="Skip these stages (e.g. ablation: --skip expand).", + ) + sel.add_argument("--from", dest="from_stage", default=None, metavar="STAGE", help="First stage.") + sel.add_argument("--to", dest="to_stage", default=None, metavar="STAGE", help="Last stage.") + sel.add_argument( + "--force", + default="", + metavar="STAGES", + help="Re-run these stages even if done ('all' forces everything).", + ) + args = parser.parse_args() + + config = load_config(args.config) + ctx = StageContext( + config=config, + trials_json_folder=Path(args.trials_json_folder) if args.trials_json_folder else None, + processed_trials_folder=Path(args.processed_trials_folder), + processed_criteria_folder=Path(args.processed_criteria_folder), + inputs=list(args.input), + input_format=args.format, + with_entities=not args.no_entities, + concepts="open" if args.concepts else None, + concept_csv=args.concepts_csv, + synonym_csv=args.synonym_csv, + force=set(_split(args.force)), + ) + try: + return run_pipeline( + ctx, + only=_split(args.only) or None, + skip=_split(args.skip), + from_stage=args.from_stage, + to_stage=args.to_stage, + ) + except ValueError as exc: + parser.error(str(exc)) + return 2 # pragma: no cover - parser.error exits + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/src/trialmatchai/pipeline.py b/src/trialmatchai/pipeline.py new file mode 100644 index 00000000..d11786ad --- /dev/null +++ b/src/trialmatchai/pipeline.py @@ -0,0 +1,211 @@ +"""The single TrialMatchAI pipeline: an ordered registry of idempotent stages. + +Every command is a *slice* of this one pipeline. Each stage wraps an +already-idempotent orchestration function (it internally skips work that is done), +so the driver only decides **which** stages to run from the user's selection +(`--only` / `--skip` / `--from` / `--to`) and which to **force** (`--force`). + +Because each stage is idempotent, running the whole pipeline from any starting +state "just works": finished stages are cheap no-ops, unfinished ones run. That is +the "one e2e workflow, maximally modular, never redo finished work" contract — a +stage is the unit of modularity, and the e2e run is simply "run every stage". +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Callable, Iterable, Sequence + +from trialmatchai.utils.logging_config import setup_logging + +logger = setup_logging(__name__) + + +@dataclass +class StageContext: + """Everything the stages need, resolved once and threaded through the run.""" + + config: dict[str, Any] + trials_json_folder: Path | None = None + processed_trials_folder: Path = Path("data/processed_trials") + processed_criteria_folder: Path = Path("data/processed_criteria") + inputs: list[str] = field(default_factory=list) + input_format: str = "auto" + with_entities: bool = True + nct_filter: set[str] | None = None + concepts: str | None = None # "open" -> build the open concept store + concept_csv: str | None = None + synonym_csv: str | None = None + qrels: dict | None = None # provided by the TREC preset -> enables eval + results_dir: Path | None = None + force: set[str] = field(default_factory=set) + + def forced(self, name: str) -> bool: + return name in self.force or "all" in self.force + + +# --------------------------------------------------------------------------- # +# Stage run wrappers — each delegates to an existing idempotent function. +# --------------------------------------------------------------------------- # +def _run_prepare(ctx: StageContext) -> None: + from trialmatchai.orchestration import prepare_corpus + + trials_json = ctx.trials_json_folder or ctx.config["paths"]["trials_json_folder"] + prepare_corpus( + ctx.config, + trials_json_folder=trials_json, + processed_trials_folder=ctx.processed_trials_folder, + processed_criteria_folder=ctx.processed_criteria_folder, + force=ctx.forced("prepare"), + ) + + +def _run_concepts(ctx: StageContext) -> None: + if not ctx.concepts and not ctx.concept_csv: + logger.info( + "concepts: not requested (pass concepts='open' and/or a concept_csv); skipping." + ) + return + from trialmatchai.cli.build_concepts import run_build_concepts + + run_build_concepts( + ctx.config, + sources=ctx.concepts, + concept_csv=ctx.concept_csv, + synonym_csv=ctx.synonym_csv, + force=ctx.forced("concepts"), + ) + + +def _run_index(ctx: StageContext) -> None: + from trialmatchai.orchestration import build_index + + build_index( + ctx.config, + processed_trials_folder=ctx.processed_trials_folder, + processed_criteria_folder=ctx.processed_criteria_folder, + nct_filter=ctx.nct_filter, + force=ctx.forced("index"), + ) + + +def _run_ingest(ctx: StageContext) -> None: + if not ctx.inputs: + logger.info("ingest: no inputs given; skipping (using already-imported profiles).") + return + from trialmatchai.orchestration import ingest_inputs + + ingest_inputs( + ctx.config, + ctx.inputs, + input_format=ctx.input_format, + with_entities=ctx.with_entities, + force=ctx.forced("ingest"), + ) + + +def _run_expand(ctx: StageContext) -> None: + from trialmatchai.orchestration import expand_queries + + expand_queries(ctx.config, force=ctx.forced("expand")) + + +def _run_match(ctx: StageContext) -> None: + from trialmatchai.orchestration import run_matching + + rc = run_matching(ctx.config, resume=True, force=ctx.forced("match")) + if rc != 0: + raise RuntimeError(f"match stage returned exit code {rc}") + + +def _run_eval(ctx: StageContext) -> None: + if ctx.qrels is None: + logger.info("eval: no qrels provided; skipping (not a benchmark run).") + return + from trialmatchai.trec import qrels as qrels_mod + from trialmatchai.utils.file_utils import write_json_file + + results_dir = Path(ctx.results_dir or ctx.config["paths"]["output_dir"]) + metrics = qrels_mod.evaluate(ctx.qrels, results_dir) + write_json_file(metrics, str(results_dir / "evaluation_metrics.json")) + logger.info("eval: %s", metrics.get("mean")) + + +@dataclass(frozen=True) +class Stage: + name: str + run: Callable[[StageContext], None] + help: str + + +# The canonical ordered pipeline. (acquire/fetch live in bootstrap-data / +# update-registry today; they will join this registry as `fetch` is unified.) +STAGES: tuple[Stage, ...] = ( + Stage("prepare", _run_prepare, "embed + entity-annotate the trial corpus"), + Stage("concepts", _run_concepts, "build the entity-linking concept store"), + Stage("index", _run_index, "build the LanceDB search tables"), + Stage("ingest", _run_ingest, "import patient inputs into canonical profiles"), + Stage("expand", _run_expand, "CoT query expansion of patient summaries"), + Stage("match", _run_match, "retrieval + reranking + CoT eligibility + ranking"), + Stage("eval", _run_eval, "score results against qrels (benchmark runs)"), +) +STAGE_NAMES: tuple[str, ...] = tuple(s.name for s in STAGES) + + +def _validate(names: Iterable[str]) -> None: + bad = sorted(set(names) - set(STAGE_NAMES)) + if bad: + raise ValueError(f"unknown stage(s) {bad}; valid stages: {list(STAGE_NAMES)}") + + +def select_stages( + *, + only: Sequence[str] | None = None, + skip: Sequence[str] = (), + from_stage: str | None = None, + to_stage: str | None = None, +) -> list[Stage]: + """Resolve the user's selection into an ordered list of stages to run.""" + if only: + _validate(only) + chosen = set(only) + return [s for s in STAGES if s.name in chosen] + + for endpoint in (from_stage, to_stage): + if endpoint is not None: + _validate([endpoint]) + _validate(skip) + + start = STAGE_NAMES.index(from_stage) if from_stage else 0 + end = STAGE_NAMES.index(to_stage) + 1 if to_stage else len(STAGES) + if start > end - 1: + raise ValueError(f"--from {from_stage} is after --to {to_stage}") + skipped = set(skip) + return [s for s in STAGES[start:end] if s.name not in skipped] + + +def run_pipeline( + ctx: StageContext, + *, + only: Sequence[str] | None = None, + skip: Sequence[str] = (), + from_stage: str | None = None, + to_stage: str | None = None, +) -> int: + """Run the selected pipeline slice, freeing GPU models once at the end.""" + stages = select_stages(only=only, skip=skip, from_stage=from_stage, to_stage=to_stage) + if not stages: + logger.warning("No stages selected; nothing to do.") + return 0 + logger.info("Pipeline: %s", " -> ".join(s.name for s in stages)) + try: + for stage in stages: + logger.info("================ stage: %s ================", stage.name) + stage.run(ctx) + finally: + from trialmatchai.orchestration import free_models + + free_models() + logger.info("Pipeline complete: %s", " -> ".join(s.name for s in stages)) + return 0 diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 00000000..29ff5986 --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,96 @@ +"""The unified pipeline driver: stage selection, force semantics, teardown.""" + +import pytest + +from trialmatchai import pipeline +from trialmatchai.pipeline import StageContext, select_stages + + +def _names(stages): + return [s.name for s in stages] + + +def test_default_selection_is_every_stage_in_order(): + assert _names(select_stages()) == list(pipeline.STAGE_NAMES) + + +def test_only_selects_exactly_those_in_canonical_order(): + assert _names(select_stages(only=["match", "prepare"])) == ["prepare", "match"] + + +def test_from_to_slice(): + assert _names(select_stages(from_stage="index", to_stage="match")) == [ + "index", + "ingest", + "expand", + "match", + ] + + +def test_to_alone_runs_the_build_half(): + assert _names(select_stages(to_stage="index")) == ["prepare", "concepts", "index"] + + +def test_from_alone_runs_the_run_half(): + assert _names(select_stages(from_stage="ingest")) == ["ingest", "expand", "match", "eval"] + + +def test_skip_removes_a_stage(): + assert "expand" not in _names(select_stages(skip=["expand"])) + + +def test_unknown_stage_raises(): + with pytest.raises(ValueError): + select_stages(only=["nonsense"]) + with pytest.raises(ValueError): + select_stages(skip=["nope"]) + with pytest.raises(ValueError): + select_stages(from_stage="bogus") + + +def test_from_after_to_raises(): + with pytest.raises(ValueError): + select_stages(from_stage="match", to_stage="prepare") + + +def test_forced_semantics(): + ctx = StageContext(config={}, force={"match"}) + assert ctx.forced("match") is True + assert ctx.forced("prepare") is False + assert StageContext(config={}, force={"all"}).forced("anything") is True + + +def _fake_stages(record): + return tuple( + pipeline.Stage(n, (lambda name: lambda ctx: record.append(name))(n), "") + for n in ("a", "b", "c") + ) + + +def test_run_pipeline_runs_selected_in_order_and_frees_models(monkeypatch): + ran, freed = [], [] + fakes = _fake_stages(ran) + monkeypatch.setattr(pipeline, "STAGES", fakes) + monkeypatch.setattr(pipeline, "STAGE_NAMES", tuple(s.name for s in fakes)) + monkeypatch.setattr("trialmatchai.orchestration.free_models", lambda: freed.append(1)) + + rc = pipeline.run_pipeline(StageContext(config={}), only=["a", "c"]) + assert rc == 0 + assert ran == ["a", "c"] + assert freed == [1] # teardown ran once + + +def test_run_pipeline_frees_models_even_on_stage_error(monkeypatch): + freed = [] + + def boom(ctx): + raise RuntimeError("stage failed") + + fakes = (pipeline.Stage("a", boom, ""),) + monkeypatch.setattr(pipeline, "STAGES", fakes) + monkeypatch.setattr(pipeline, "STAGE_NAMES", ("a",)) + monkeypatch.setattr("trialmatchai.orchestration.free_models", lambda: freed.append(1)) + + with pytest.raises(RuntimeError): + pipeline.run_pipeline(StageContext(config={})) + assert freed == [1] # GPU freed despite the failure From 1f9d02b4b80a2777d59c45e5440b9fdc25ba3b81 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:06:54 +0200 Subject: [PATCH 55/64] refactor(e2e): run_e2e delegates to the unified pipeline (index..match) run_e2e is now a thin preset over run_pipeline(--from index --to match), mapping its granular force flags to per-stage forces. Same stages, same idempotency, one teardown. Removes a duplicate stage-chain; the driver is the single spine. --- src/trialmatchai/orchestration.py | 44 +++++++++++++++++-------------- 1 file changed, 24 insertions(+), 20 deletions(-) diff --git a/src/trialmatchai/orchestration.py b/src/trialmatchai/orchestration.py index 2025e2f6..3227016e 100644 --- a/src/trialmatchai/orchestration.py +++ b/src/trialmatchai/orchestration.py @@ -295,27 +295,31 @@ def run_e2e( force_reindex: bool = False, force_rematch: bool = False, ) -> int: - """Ingest -> index -> match, each stage idempotent. Returns a process code.""" - if inputs: - ingest_inputs( - config, - inputs, - input_format=input_format, - with_entities=with_entities, - force=force_reingest, - ) - expand_queries(config, force=force_rematch) - build_index( - config, - processed_trials_folder=processed_trials_folder, - processed_criteria_folder=processed_criteria_folder, - nct_filter=nct_filter, - force=force_reindex, + """Ingest -> index -> match: the run-half slice of the unified pipeline. + + A thin preset over ``run_pipeline`` (index..match): index/ingest/expand/match + are idempotent so this resumes from any state, and GPU models are freed once. + """ + from trialmatchai.pipeline import StageContext, run_pipeline + + force: set[str] = set() + if force_reingest: + force.add("ingest") + if force_reindex: + force.add("index") + if force_rematch: + force.update({"expand", "match"}) + ctx = StageContext( + config=config, + processed_trials_folder=Path(processed_trials_folder), + processed_criteria_folder=Path(processed_criteria_folder), + inputs=list(inputs), + input_format=input_format, + with_entities=with_entities, + nct_filter=set(nct_filter) if nct_filter is not None else None, + force=force, ) - try: - return run_matching(config, resume=True, force=force_rematch) - finally: - free_models() + return run_pipeline(ctx, from_stage="index", to_stage="match") # --------------------------------------------------------------------------- # From 4e57ba55c6337df9aa19ecd95b0f43a37ecd2c25 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:10:32 +0200 Subject: [PATCH 56/64] build: PyPI-ready metadata + trusted-publishing release workflow Add authors/keywords/classifiers/project-urls so the PyPI page is professional (name 'trialmatchai' is available). Add .github/workflows/release.yml: on a published GitHub Release it builds the sdist+wheel, runs twine check, and publishes to PyPI via Trusted Publishing (OIDC, no stored token). After this, 'uv pip install trialmatchai' works with no clone; data/models still come from 'trialmatchai bootstrap-data' (Zenodo). --- .github/workflows/release.yml | 44 +++++++++++++++++++++++++++++++++++ pyproject.toml | 30 ++++++++++++++++++++++-- 2 files changed, 72 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/release.yml diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 00000000..c225b296 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,44 @@ +name: Release + +# Publishes trialmatchai to PyPI when a GitHub Release is published. +# Uses PyPI Trusted Publishing (OIDC) — no API token is stored in the repo. +# One-time setup on PyPI: add a Trusted Publisher for this repo + workflow + +# the `pypi` environment (https://docs.pypi.org/trusted-publishers/). + +on: + release: + types: [published] + workflow_dispatch: {} + +permissions: + contents: read + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + - name: Build sdist + wheel + run: uv build + - name: Check the built metadata + run: uvx twine check dist/* + - uses: actions/upload-artifact@v4 + with: + name: dist + path: dist/ + + pypi-publish: + needs: build + runs-on: ubuntu-latest + environment: pypi + permissions: + id-token: write # required for PyPI Trusted Publishing (OIDC) + steps: + - uses: actions/download-artifact@v4 + with: + name: dist + path: dist/ + - uses: astral-sh/setup-uv@v5 + - name: Publish to PyPI + run: uv publish --trusted-publishing always diff --git a/pyproject.toml b/pyproject.toml index 465f1f8b..6a2f9cba 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,11 +5,31 @@ build-backend = "setuptools.build_meta" [project] name = "trialmatchai" version = "0.2.0" -description = "AI-driven clinical trial matching pipeline." +description = "AI-driven patient-to-clinical-trial matching: hybrid retrieval + LLM eligibility reasoning." readme = "README.md" requires-python = ">=3.11,<3.12" license = "MIT" license-files = ["LICENSE"] +authors = [{ name = "Majd Abdallah", email = "majd@physight.com" }] +keywords = [ + "clinical-trials", + "patient-trial-matching", + "biomedical-nlp", + "information-retrieval", + "large-language-models", + "eligibility", + "healthcare", +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Healthcare Industry", + "Intended Audience :: Science/Research", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Topic :: Scientific/Engineering :: Bio-Informatics", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] dependencies = [ "numpy>=2.0,<3", "pandas==2.2.3", @@ -55,10 +75,16 @@ finetune = [ [project.scripts] # Single entry point. Every capability is a subcommand: `trialmatchai ` -# (healthcheck, bootstrap-data, index, build-concepts, update-registry, +# (pipeline, healthcheck, bootstrap-data, index, build-concepts, update-registry, # import-patient, build, run, e2e, trec, finetune). trialmatchai = "trialmatchai.cli.main:main" +[project.urls] +Homepage = "https://github.com/cbib/TrialMatchAI" +Repository = "https://github.com/cbib/TrialMatchAI" +Issues = "https://github.com/cbib/TrialMatchAI/issues" +Paper = "https://doi.org/10.1038/s41467-026-70509-w" + [dependency-groups] dev = [ "pytest>=8.3", From f1d80d343804141e8c53e6e028365a2dd2d61f72 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:11:30 +0200 Subject: [PATCH 57/64] build: use gmail for package author attribution --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 6a2f9cba..f1ae92cb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,7 @@ readme = "README.md" requires-python = ">=3.11,<3.12" license = "MIT" license-files = ["LICENSE"] -authors = [{ name = "Majd Abdallah", email = "majd@physight.com" }] +authors = [{ name = "Majd Abdallah", email = "abdallahmajd7@gmail.com" }] keywords = [ "clinical-trials", "patient-trial-matching", From 74ac109994686641e78f1ca1ed1d2e61f41595b1 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:18:30 +0200 Subject: [PATCH 58/64] docs: professional MkDocs Material site + auto API reference + deploy Add a MkDocs Material site (mkdocs.yml) with mkdocstrings auto API reference from the docstrings, a home page, and a unified Pipeline & CLI guide (stage list + --only/--skip/--from/--to/--force + presets + ablation). Add the 'docs' dependency group and .github/workflows/docs.yml (build --strict on push/PR, gh-deploy to GitHub Pages on main). Build validated locally (mkdocs build --strict, clean). --- .github/workflows/docs.yml | 28 +++++ .gitignore | 1 + docs/api.md | 53 ++++++++ docs/index.md | 54 ++++++++ docs/pipeline.md | 85 +++++++++++++ mkdocs.yml | 65 ++++++++++ pyproject.toml | 4 + uv.lock | 248 +++++++++++++++++++++++++++++++++++++ 8 files changed, 538 insertions(+) create mode 100644 .github/workflows/docs.yml create mode 100644 docs/api.md create mode 100644 docs/index.md create mode 100644 docs/pipeline.md create mode 100644 mkdocs.yml diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 00000000..7b340991 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,28 @@ +name: Docs + +# Build the MkDocs site (strict) on every push/PR; deploy to GitHub Pages on main. +on: + push: + branches: [main, deployment-readiness-audit] + pull_request: + branches: [main] + workflow_dispatch: {} + +permissions: + contents: write # mkdocs gh-deploy pushes to the gh-pages branch + +jobs: + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: astral-sh/setup-uv@v5 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Build docs (strict) + run: uv run --group docs mkdocs build --strict + - name: Deploy to GitHub Pages (main only) + if: github.ref == 'refs/heads/main' + run: uv run --group docs mkdocs gh-deploy --force diff --git a/.gitignore b/.gitignore index e089064b..f5c27f2b 100644 --- a/.gitignore +++ b/.gitignore @@ -49,3 +49,4 @@ scripts/*.sh **/tmp/ .DS_Store Thumbs.db +site/ diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 00000000..b8db898a --- /dev/null +++ b/docs/api.md @@ -0,0 +1,53 @@ +# API reference + +Auto-generated from docstrings. The pipeline is the primary public API; the +orchestration stages and evaluation metrics are documented for programmatic use. + +## Pipeline + +::: trialmatchai.pipeline + options: + members: + - StageContext + - Stage + - STAGES + - select_stages + - run_pipeline + +## Orchestration stages + +::: trialmatchai.orchestration + options: + members: + - ingest_inputs + - expand_queries + - build_index + - run_matching + - prepare_corpus + - build_system + - build_state + +## Registry updater + +::: trialmatchai.registry.updater + options: + members: + - RegistryUpdater + - RegistryUpdateConfig + - RegistryUpdateReport + +## Evaluation metrics + +::: trialmatchai.trec.metrics + options: + members: + - ndcg_at_k + - condensed_ndcg + - precision_at_k + +::: trialmatchai.trec.qrels + options: + members: + - parse_qrels + - corpus_ncts + - evaluate diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..3f495894 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,54 @@ +# TrialMatchAI + +**AI-driven patient-to-clinical-trial matching.** Import a patient — free text, +FHIR, Phenopacket, or OMOP — and get ranked, eligible trials with criterion-level +eligibility explanations. Local LanceDB hybrid search + vLLM reasoning on a single +GPU server; no Elasticsearch or hosted vector database to run. + +!!! warning "Research and informational use only" + TrialMatchAI is not medical advice, not a medical device, and must not replace + review by qualified healthcare professionals. + +## Install + +```bash +uv pip install "trialmatchai[llm,gpu,entity]" # full model-backed runtime (GPU host) +# or, lightweight (CLI + base deps only): +uv pip install trialmatchai +``` + +## The two halves + +TrialMatchAI runs in two halves — **build the system once**, then **match patients +many times** — and both are idempotent: finished work is never redone. + +```bash +trialmatchai bootstrap-data # fetch the prepared corpus + adapters (Zenodo) +trialmatchai build --concepts # prepare + index + concept store (resumable) +trialmatchai e2e --input patient.txt # ingest + match one patient +# -> results//ranked_trials.json +``` + +## One pipeline, maximally modular + +Under the hood everything is a slice of a single, ordered pipeline of idempotent +stages. Run the whole thing, or any subset: + +```bash +trialmatchai pipeline # run every stage (skipping what's done) +trialmatchai pipeline --only match # just (re)match +trialmatchai pipeline --to index # the build half +trialmatchai pipeline --skip expand # ablation: no query expansion +trialmatchai pipeline --force match # redo a stage even if done +``` + +See **[Pipeline & CLI](pipeline.md)** for the stage list and flag scheme, +**[Architecture](architecture.md)** for how it fits together, and the +**[API reference](api.md)** for the Python API. + +## Cite + +> Abdallah, M. *et al.* TrialMatchAI: an end-to-end AI-powered clinical trial +> recommendation system to streamline patient-to-trial matching. *Nature +> Communications* **17**, 4472 (2026). +> diff --git a/docs/pipeline.md b/docs/pipeline.md new file mode 100644 index 00000000..9e671d64 --- /dev/null +++ b/docs/pipeline.md @@ -0,0 +1,85 @@ +# Pipeline & CLI + +TrialMatchAI is **one end-to-end pipeline** built from an ordered registry of +**idempotent stages**. Every command is a *slice* of this pipeline. Because each +stage detects and skips work that is already done, a run "just works" from any +starting state — finished stages are cheap no-ops, unfinished ones run. + +## The stages + +| # | Stage | What it does | Idempotency check | +|---|-------|--------------|-------------------| +| 1 | `prepare` | embed + entity-annotate the trial corpus | per-trial prepared file | +| 2 | `concepts` | build the entity-linking concept store | concept table present | +| 3 | `index` | build the LanceDB search tables | both tables present | +| 4 | `ingest` | import patient inputs into canonical profiles | per-patient profile | +| 5 | `expand` | CoT query expansion of patient summaries | `query_expanded` marker | +| 6 | `match` | retrieval + reranking + CoT eligibility + ranking | per-patient `ranked_trials.json` | +| 7 | `eval` | score results against qrels (benchmark runs) | benchmark-only | + +## The single command + +```bash +trialmatchai pipeline [selection] [options] +``` + +**Selection — run any subset** (the unit of modularity is the stage): + +| Flag | Meaning | Example | +|------|---------|---------| +| *(none)* | run every stage, skipping what's done | `trialmatchai pipeline` | +| `--only` | run exactly these stages | `--only match,eval` | +| `--from` / `--to` | run a contiguous slice | `--from index --to match` | +| `--skip` | omit stages (great for ablation) | `--skip expand` | +| `--force` | redo stages even if done (`all` = everything) | `--force match` | + +**Options:** `--input` (repeatable patient files/dirs), `--format`, +`--trials-json-folder`, `--processed-trials-folder`, `--processed-criteria-folder`, +`--concepts` / `--concepts-csv` / `--synonym-csv`, `--config`. + +```bash +trialmatchai pipeline --only prepare,index # build the search index +trialmatchai pipeline --input patient.fhir.json # ingest + match one patient +trialmatchai pipeline --skip concepts,expand # leaner run for an ablation +trialmatchai pipeline --force all # rebuild everything from scratch +``` + +## Ablation + +Stage flags double as ablation knobs — toggle a component and compare: + +```bash +trialmatchai pipeline --skip expand # matching without LLM query expansion +trialmatchai pipeline --skip concepts # without entity-concept linking +``` + +Component backends (reranker, CoT, search mode `bm25`/`vector`/`hybrid`) are set in +the config; see [Architecture](architecture.md). + +## Presets (the same pipeline, named) + +These are thin wrappers over the pipeline that add their own setup: + +| Command | Equivalent slice | Adds | +|---------|------------------|------| +| `trialmatchai build` | `--to index` | build manifest; bootstrap-aware prepare | +| `trialmatchai e2e` | `--from index --to match` | patient ingestion convenience | +| `trialmatchai trec` | `--from index --to eval` (per track) | official topics + qrels + corpus restriction | +| `trialmatchai run` | `--only match` | match already-staged profiles | +| `trialmatchai index` | `--only prepare,index` | — | + +Every command is idempotent and resumable: re-running continues from the last +completed work. + +## Python API + +```python +from trialmatchai.config.config_loader import load_config +from trialmatchai.pipeline import StageContext, run_pipeline + +ctx = StageContext(config=load_config(), inputs=["patient.txt"]) +run_pipeline(ctx, from_stage="index", to_stage="match") +``` + +See the [API reference](api.md) for `StageContext`, `Stage`, `select_stages`, and +`run_pipeline`. diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..7a566fec --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,65 @@ +site_name: TrialMatchAI +site_description: AI-driven patient-to-clinical-trial matching — hybrid retrieval + LLM eligibility reasoning +site_url: https://cbib.github.io/TrialMatchAI/ +repo_url: https://github.com/cbib/TrialMatchAI +repo_name: cbib/TrialMatchAI +copyright: "© TrialMatchAI — MIT licensed" + +theme: + name: material + features: + - navigation.sections + - navigation.top + - navigation.tracking + - content.code.copy + - content.code.annotate + - search.suggest + - search.highlight + palette: + - scheme: default + primary: teal + accent: teal + toggle: + icon: material/weather-night + name: Switch to dark mode + - scheme: slate + primary: teal + accent: teal + toggle: + icon: material/weather-sunny + name: Switch to light mode + +plugins: + - search + - mkdocstrings: + handlers: + python: + paths: [src] + options: + show_root_heading: true + show_source: true + members_order: source + separate_signature: true + docstring_section_style: spacy + +markdown_extensions: + - admonition + - pymdownx.details + - pymdownx.superfences + - pymdownx.highlight: + anchor_linenums: true + - pymdownx.tabbed: + alternate_style: true + - tables + - toc: + permalink: true + +nav: + - Home: index.md + - Architecture: architecture.md + - Pipeline & CLI: pipeline.md + - Patient interoperability: interoperability.md + - Registry updater: registry-updater.md + - Fine-tuning: 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--- a/LICENSE +++ b/LICENSE @@ -1,7 +1,7 @@ MIT License Copyright (c) 2024 -Majd Abdallah (1,2) (majd.abdallah@u-bordeaux.fr ) +Majd Abdallah (1,2) (abdallahmajd7@gmail.com) Macha Nikolski (1,2) (macha.nikolski@u-bordeaux.fr) Mikaël Georges (1,2) (mikael.georges@u-bordeaux.fr) From 2f350f491313ec0000b971d48b1bda20f79afa5e Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:21:03 +0200 Subject: [PATCH 60/64] docs(readme): lead the CLI reference with the unified pipeline command --- README.md | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5fdbd5a8..9f61b0c3 100644 --- a/README.md +++ b/README.md @@ -376,8 +376,16 @@ The full override list is in [`.env.example`](.env.example). ## CLI Reference -Every command is available both as a flat script (`trialmatchai build`) and as a -subcommand (`trialmatchai build`); they are equivalent. +There is a single entry point — `trialmatchai` — and every capability is a +subcommand. Under the hood they are all slices of **one idempotent pipeline**. + +**The unified pipeline (run any subset)** + +| Command | Purpose | +| --- | --- | +| `trialmatchai pipeline` | Run the whole pipeline, or any slice: `--only` / `--from` / `--to` / `--skip` / `--force` over the stages `prepare → concepts → index → ingest → expand → match → eval`. Idempotent — finished work is skipped. See [docs](https://cbib.github.io/TrialMatchAI/pipeline/). | + +The commands below are convenience presets over that pipeline. **Build the system (setup half)** From 71ab76abca3e630ddb50b0d11ea4aff4aa7160f6 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:35:48 +0200 Subject: [PATCH 61/64] chore: clear the merge-review minors - build-system: setuptools floor 68 -> 77 (PEP 639 SPDX license metadata). - ci: add 'pipeline' (the headline command) to the CLI help-smoke loop. - docs: correct e2e ordering text to the real slice (index -> ingest -> expand -> match); behavior was already correct, only the help/docstrings were stale. --- .github/workflows/ci.yml | 2 +- pyproject.toml | 3 ++- src/trialmatchai/cli/e2e.py | 11 ++++++----- src/trialmatchai/cli/main.py | 2 +- src/trialmatchai/orchestration.py | 2 +- 5 files changed, 11 insertions(+), 9 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3e31cf69..4ecf1987 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -56,7 +56,7 @@ jobs: run: | WHEEL="$(ls dist/trialmatchai-*.whl | head -n 1)" uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai --help - for cmd in healthcheck bootstrap-data index build-concepts update-registry \ + for cmd in pipeline healthcheck bootstrap-data index build-concepts update-registry \ import-patient build run e2e trec finetune; do uv run --python 3.11 --isolated --with "$WHEEL" trialmatchai "$cmd" --help done diff --git a/pyproject.toml b/pyproject.toml index 9acbeeaf..85133a79 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,6 @@ [build-system] -requires = ["setuptools>=68", "wheel"] +# >=77 for PEP 639 SPDX license metadata (license = "MIT" + license-files). +requires = ["setuptools>=77", "wheel"] build-backend = "setuptools.build_meta" [project] diff --git a/src/trialmatchai/cli/e2e.py b/src/trialmatchai/cli/e2e.py index 4d3b32b3..4e4c335b 100644 --- a/src/trialmatchai/cli/e2e.py +++ b/src/trialmatchai/cli/e2e.py @@ -1,8 +1,9 @@ -"""``trialmatchai e2e`` — run the whole pipeline end-to-end in one command. +"""``trialmatchai e2e`` — match a patient end-to-end in one command. -Ingest patient inputs (any supported format, auto-detected) -> build the search -index -> match. Every stage is idempotent: re-running skips already-imported -patients, an existing index, and already-matched patients. +A preset over the unified pipeline (the slice index -> ingest -> expand -> match): +imports patient inputs (any supported format, auto-detected), ensures the search +index, and matches. Every stage is idempotent: re-running skips an existing index, +already-imported patients, and already-matched patients. """ from __future__ import annotations @@ -19,7 +20,7 @@ def main() -> int: parser = argparse.ArgumentParser( - description="Run the TrialMatchAI pipeline end-to-end (ingest -> index -> match)." + description="Match a patient end-to-end (pipeline slice: index -> ingest -> expand -> match)." ) parser.add_argument("--config", default=None, help="Path to TrialMatchAI config JSON") parser.add_argument( diff --git a/src/trialmatchai/cli/main.py b/src/trialmatchai/cli/main.py index 8e1d1cf3..df526399 100644 --- a/src/trialmatchai/cli/main.py +++ b/src/trialmatchai/cli/main.py @@ -50,7 +50,7 @@ def main() -> int: subparsers.add_parser("run", help="Run the matching pipeline", add_help=False) subparsers.add_parser( "e2e", - help="Run the whole pipeline end-to-end (ingest -> index -> match), idempotent", + help="Match a patient end-to-end (pipeline slice index -> ingest -> expand -> match), idempotent", add_help=False, ) subparsers.add_parser( diff --git a/src/trialmatchai/orchestration.py b/src/trialmatchai/orchestration.py index 3227016e..f2fd4964 100644 --- a/src/trialmatchai/orchestration.py +++ b/src/trialmatchai/orchestration.py @@ -295,7 +295,7 @@ def run_e2e( force_reindex: bool = False, force_rematch: bool = False, ) -> int: - """Ingest -> index -> match: the run-half slice of the unified pipeline. + """Run-half of the unified pipeline (slice index -> ingest -> expand -> match). A thin preset over ``run_pipeline`` (index..match): index/ingest/expand/match are idempotent so this resumes from any state, and GPU models are freed once. From 7d896ce15e50d6bb3dce6ddcb4cf71d31f2d3c50 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:44:53 +0200 Subject: [PATCH 62/64] chore(logging): debug-log vLLM teardown swallows instead of silent pass --- src/trialmatchai/models/llm/vllm_loader.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/trialmatchai/models/llm/vllm_loader.py b/src/trialmatchai/models/llm/vllm_loader.py index 566b9e34..7f4a6ea4 100644 --- a/src/trialmatchai/models/llm/vllm_loader.py +++ b/src/trialmatchai/models/llm/vllm_loader.py @@ -81,13 +81,13 @@ def free_vllm_engines() -> None: try: del engine except Exception: - pass + logger.debug("vLLM teardown: engine del failed (ignored)", exc_info=True) try: import gc gc.collect() except Exception: - pass + logger.debug("vLLM teardown: gc.collect failed (ignored)", exc_info=True) try: from vllm.distributed.parallel_state import ( # type: ignore destroy_model_parallel, @@ -95,14 +95,14 @@ def free_vllm_engines() -> None: destroy_model_parallel() except Exception: - pass + logger.debug("vLLM teardown: destroy_model_parallel failed (ignored)", exc_info=True) try: import torch if torch.cuda.is_available(): torch.cuda.empty_cache() except Exception: - pass + logger.debug("vLLM teardown: cuda.empty_cache failed (ignored)", exc_info=True) def load_vllm_engine( From 8e6413432c56b7801cb56212b147969dc14eb959 Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:47:15 +0200 Subject: [PATCH 63/64] docs(readme): trim deep-internals into the docs site; condense data section MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Move the first-level-retrieval, constraint-aware-ranking, and bring-your-own-models deep dives to a concise 'Learn more' section linking the docs site; condense the data/storage layout. README ~415 lines (from 490) — front-door, not the manual. --- README.md | 116 ++++++++++-------------------------------------------- 1 file changed, 21 insertions(+), 95 deletions(-) diff --git a/README.md b/README.md index 9f61b0c3..36c43536 100644 --- a/README.md +++ b/README.md @@ -226,31 +226,16 @@ The generative LLM stages, reranker and eligibility reasoning, run on vLLM. LoRA adapters are served natively through vLLM. NER, reranker, and eligibility reasoning are configurable and fine-tunable. -## Data And Storage +## Data and storage -TrialMatchAI uses embedded LanceDB tables by default: - -- Search DB: `data/search` -- Trial table: `trials` -- Criteria table: `criteria` -- Concept-linking DB: `data/concepts` -- Concept table: `concepts` - -ClinicalTrials.gov records are normalized to JSON files under -`data/trials_jsons/.json`. During indexing, TrialMatchAI prepares: - -- one trial row per NCT ID, including text fields, metadata filters, date/age - fields, and embedding vectors -- one criteria row per eligibility criterion, including criterion text, - criterion embedding, eligibility type, entity annotations, and parsed - eligibility constraints - -The trial and criteria tables each get full-text search fields and vector -columns, so retrieval can run in `bm25`, `vector`, or `hybrid` mode. - -Patient inputs are imported before matching. Each imported patient is stored as -a canonical profile under `data/patients/profiles/.json`, with a -matching summary under `data/patients/summaries/.json`. +Everything is **embedded LanceDB** — no external services. A search DB +(`data/search`, with `trials` + `criteria` tables) and a concept-linking DB +(`data/concepts`). ClinicalTrials.gov records are normalized to +`data/trials_jsons/.json`, then prepared into one trial row and one +criteria row per eligibility criterion (text + embeddings + entity annotations + +parsed constraints). Both tables carry full-text and vector columns, so retrieval +runs in `bm25`, `vector`, or `hybrid` mode. Imported patients live under +`data/patients/{profiles,summaries}/`. ## Patient Inputs @@ -268,80 +253,21 @@ The matching summary is rendered deterministically from the canonical See [docs/interoperability.md](docs/interoperability.md) for format details. -## First-Level Retrieval - -First-level retrieval is recall-oriented. It builds a multi-channel query plan -from the canonical `PatientProfile` and matching summary, then searches each -channel separately and fuses candidates with reciprocal rank fusion. - -Channels include primary conditions, linked concept synonyms, broader disease -terms, patient narrative text, biomarkers, prior therapy or procedures, and -optional LLM-generated expansions. LLM expansion is off by default; deterministic -concept and patient-profile expansion are the default path. - -By default the first level hard-filters by age, sex/gender, and recruitment -status (`search.first_level.hard_filters`). Geographic **location** is an opt-in -hard filter: add `"location"` to `hard_filters` to keep only trials with a -recruiting site in the patient's country (country-level, site-aware, and -recall-safe — trials with unknown site countries are never dropped; patient -location is populated by the FHIR and OMOP importers). Biomarkers, phase, prior -therapy, and eligibility constraints remain soft signals for later stages. - -When enabled, first-level artifacts are written under `results//`: - -- `first_level_query_plan.json` -- `first_level_candidates.json` - -## Constraint-Aware Retrieval +## Learn more -TrialMatchAI parses common eligibility logic from criteria rows and compares it -with the canonical `PatientProfile` during second-stage retrieval. V1 supports -age, sex or gender, conditions, medications and prior therapy, procedures, labs, -biomarkers, ECOG/Karnofsky-style performance status, temporal phrases, and -inclusion/exclusion polarity. +Deeper guides live in the **[documentation site](https://cbib.github.io/TrialMatchAI/)**: -Constraints are a soft ranking signal. They can boost matching inclusion -criteria, penalize violated inclusion or exclusion criteria, and leave unknown -facts neutral. They do not hard-exclude trials and they are not medical advice; -the final vLLM eligibility reasoning remains the final judge. - -When enabled, per-patient reports are written under `results//`: - -- `constraint_evaluations.json` -- `constraint_summary.md` -- `top_trials_explained.json` - -## Bring Your Own Models - -Defaults are starting points. Point the pipeline at your own checkpoints or -adapters through config or environment variables. - -| Component | Default | Config key | -| --- | --- | --- | -| Biomedical extraction | `fastino/gliner2-base-v1` | `entity_extraction.model_name` | -| Reranker | `google/gemma-2-2b-it` | `model.reranker_adapter_path` | -| CoT eligibility | configured CoT model | `model.cot_adapter_path` | - -Fine-tune model components with: - -```bash -uv sync --extra finetune -uv run trialmatchai finetune cot \ - --base-model microsoft/phi-4 \ - --train-data data/finetune/cot.jsonl \ - --output-dir models/cot-adapter -uv run trialmatchai finetune reranker \ - --base-model google/gemma-2-2b-it \ - --train-data data/finetune/reranker.jsonl \ - --output-dir models/reranker-adapter -uv run trialmatchai finetune ner \ - --base-model fastino/gliner2-base-v1 \ - --train-data data/finetune/ner.jsonl \ - --output-dir models/ner -``` +- **[Pipeline & CLI](https://cbib.github.io/TrialMatchAI/pipeline/)** — the stage registry, `--only/--skip/--from/--to/--force`, ablation, and presets. +- **[Architecture](https://cbib.github.io/TrialMatchAI/architecture/)** — multi-channel first-level retrieval, constraint-aware ranking, and the LanceDB tables. +- **[Patient interoperability](https://cbib.github.io/TrialMatchAI/interoperability/)** — text / FHIR / Phenopacket / OMOP importers. +- **[Fine-tuning & custom models](https://cbib.github.io/TrialMatchAI/finetuning/)** — swap the NER, reranker, and CoT models; training-data formats. +- **[Registry updater](https://cbib.github.io/TrialMatchAI/registry-updater/)** — keep trials current from ClinicalTrials.gov. +- **[API reference](https://cbib.github.io/TrialMatchAI/api/)** — the Python API. -See [docs/finetuning.md](docs/finetuning.md) for accepted training formats and -adapter configuration. +To bring your own models, point `entity_extraction.model_name`, +`model.reranker_adapter_path`, and `model.cot_adapter_path` at your checkpoints / +adapters, or train them with `trialmatchai finetune {cot,reranker,ner}` — see the +[fine-tuning guide](https://cbib.github.io/TrialMatchAI/finetuning/). ## Configuration From be1252fea3d97e39b718069627468da44eb05c3d Mon Sep 17 00:00:00 2001 From: Majd Abdallah Date: Sun, 28 Jun 2026 23:49:44 +0200 Subject: [PATCH 64/64] docs: add CHANGELOG with the 0.2.0 deployment-ready release notes --- CHANGELOG.md | 69 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..d94ccf1b --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,69 @@ +# Changelog + +All notable changes to TrialMatchAI are documented here. The format follows +[Keep a Changelog](https://keepachangelog.com/) and the project adheres to +[Semantic Versioning](https://semver.org/). + +## [0.2.0] — 2026-06-28 + +The **deployment-readiness** release: TrialMatchAI becomes one installable, +idempotent, end-to-end pipeline behind a single CLI, with crash-safe resume, +faithful evaluation, professional docs, and PyPI-ready packaging. + +### Added +- **Unified pipeline.** A single ordered registry of idempotent stages + (`prepare → concepts → index → ingest → expand → match → eval`) driven by one + command — `trialmatchai pipeline` — with `--only / --skip / --from / --to / + --force`. Run the whole thing or any slice; finished work is skipped. Stage + flags double as ablation knobs. +- **Single entry point.** One `trialmatchai` console script with 12 subcommands + (`pipeline, healthcheck, bootstrap-data, index, build-concepts, + update-registry, import-patient, build, run, e2e, trec, finetune`). +- **Entity-linking concept store** from openly-licensed vocabularies + (genes, diseases, chemicals, cell lines, cell types, phenotypes), auto-downloaded + via `trialmatchai build --concepts` / `build-concepts --sources open`; OMOP + (SNOMED/LOINC/RxNorm) foldable via `--concepts-csv`. +- **Registry updater** (`update-registry`) — incremental fetch → prepare → upsert + from the ClinicalTrials.gov v2 API, with a `--watch` server mode. +- **`bootstrap-data`** for the prepared corpus + LoRA adapters, plus + `--finetune-data` for the published training sets (Zenodo). +- **Tie-aware nDCG** (McSherry–Najork) + `P@10`, with a deterministic ranking + tie-break and a TREC reproduction regression guard. +- **Multi-format patient ingestion** — text, FHIR, Phenopacket, OMOP; patient + location populated from FHIR/OMOP with an optional country-level trial filter. +- **PyPI-ready packaging** + a Trusted-Publishing (OIDC) release workflow. +- **Documentation site** (MkDocs Material + auto API reference) with a deploy + workflow; `gitleaks` pre-commit and Dependabot. + +### Changed +- **LanceDB** embedded hybrid search replaces Elasticsearch; **vLLM** is the only + LLM backend (LoRA adapters served natively) with a single shared engine. +- **GLiNER2** in-process NER replaces the BERN2 socket daemons; concept linking + moves to a LanceDB FTS+vector store. +- **Crash-safe resume everywhere** — atomic writes (`tmp + fsync + os.replace`), + completion-marker-last ordering, and parse-validated resume gates; every stage + is idempotent and re-running continues from the last completed work. +- **Two-halves CLI** (`build` once, then `e2e`/`run`/`trec` many times) with + fail-fast preflight; a Transformers CPU backend for non-GPU paths. +- **Official TREC sourcing** (NIST topics/qrels); the prepare corpus is the + judged pool, matching the published methodology. +- Faithful domain-aware criteria chunking + a hybrid genetic-variant recognizer. + +### Fixed +- Resume/ingest correctness: transiently-failed trials are retried (not locked + in); stale criteria cleared on re-prepare; final ranking scoped to the current + shortlist; eligibility-criteria source falls back `processed_trials → + trials_jsons` (fail-loud on empty); `import-patient` writes atomically, + summary-before-profile. +- The CoT/reranker vLLM engine is loaded **once** and shared (was reloaded + per patient). +- FHIR importer hardened for real-world EHR exports. +- `concepts` / `run` / `prepare` are now idempotent (skip-if-done, `--force` to + redo). + +### Removed +- Legacy `source/` (BERN2 daemon + CRF parser) and `utils/` experiment trees. +- The homegrown secret scanner (replaced by `gitleaks`). +- The standalone `trialmatchai-*` console scripts (now `trialmatchai `). + +[0.2.0]: https://github.com/cbib/TrialMatchAI/releases/tag/v0.2.0

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