diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000..61b77a5
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,12 @@
+__pycache__/
+*.py[cod]
+*$py.class
+.venv/
+.env
+local_qdrant/
+var/
+data/
+.git/
+.github/
+.pytest_cache/
+tests/
diff --git a/.env.example b/.env.example
index 04e8f1d..3e184d8 100644
--- a/.env.example
+++ b/.env.example
@@ -1,10 +1,36 @@
-# mAIcro Configuration
-ORG_NAME="MicroClub-USTHB"
-ORG_DESCRIPTION="The first Algerian Scientific Club, founded in 1985."
+# ─────────────────────────────────────────────────
+# mAIcro — Environment Configuration
+# Copy this file to .env and fill in your values.
+# ─────────────────────────────────────────────────
-# AI Providers
-GEMINI_KEY="your-key-here"
-MODEL_NAME="xxx"
+# ── LLM Provider ────────────────────────────────
+LLM_PROVIDER=google
+GEMINI_API_KEY=
-# Database
-DATABASE_URL="postgresql://user:pass@localhost:5432/maicro"
+
+# ── Discord ─────────────────────────────────────
+# Create a bot at https://discord.com/developers/applications
+# Enable MESSAGE CONTENT intent, then invite the bot to your server
+# with "Read Message History" + "View Channels" permissions.
+
+# Discord Bot Token (Bot -> Token -> Copy)
+DISCORD_BOT_TOKEN=
+
+# Comma-separated channel IDs to ingest, e.g. 123456789,987654321
+DISCORD_CHANNEL_IDS=
+
+# ── Qdrant (Vector Database) ────────────────────
+# Sign up for the free 1 GB tier at https://cloud.qdrant.io
+# Create a cluster -> copy the URL and API key below.
+
+# Qdrant URL (e.g. https://abc123-xyz.us-east4-0.gcp.cloud.qdrant.io:6333)
+QDRANT_URL=
+
+# Qdrant API Key (Cluster -> API Keys -> Create)
+QDRANT_API_KEY=
+
+# ── Organization (optional) ─────────────────────
+# Customize the AI personality for your org.
+# ORG_NAME=MyOrganization
+# ORG_DESCRIPTION=A brief description of your organization
+# COLLECTION_NAME=my_org_knowledge
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..f7053b1
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,36 @@
+name: CI
+
+on:
+ push:
+ branches:
+ - dev
+ pull_request:
+ branches:
+ - dev
+ - main
+
+jobs:
+ test:
+ runs-on: ubuntu-latest
+ strategy:
+ fail-fast: false
+ matrix:
+ python-version: ["3.10", "3.11", "3.12"]
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Set up Python
+ uses: actions/setup-python@v5
+ with:
+ python-version: ${{ matrix.python-version }}
+
+ - name: Set up uv
+ uses: astral-sh/setup-uv@v4
+ with:
+ version: "latest"
+
+ - name: Install dependencies
+ run: uv sync --dev
+
+ - name: Run tests
+ run: uv run pytest
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
new file mode 100644
index 0000000..5eb4c53
--- /dev/null
+++ b/.github/workflows/release.yml
@@ -0,0 +1,91 @@
+name: Release
+
+on:
+ push:
+ branches: [main]
+ tags: ["v*"]
+
+env:
+ REGISTRY: ghcr.io
+ IMAGE_NAME: ${{ github.repository_owner }}/maicro
+
+jobs:
+ build-and-push:
+ runs-on: ubuntu-latest
+ permissions:
+ contents: read
+ packages: write
+ attestations: write
+ id-token: write
+
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+
+ - name: Docker metadata
+ id: meta
+ uses: docker/metadata-action@v5
+ with:
+ images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+ tags: |
+ type=sha,prefix=sha-,format=short
+ type=ref,event=branch
+ type=semver,pattern={{version}}
+ type=semver,pattern={{major}}.{{minor}}
+ type=raw,value=latest,enable={{is_default_branch}}
+
+ - name: Set up Docker Buildx
+ uses: docker/setup-buildx-action@v4
+
+ - name: Log in to GHCR
+ uses: docker/login-action@v3
+ with:
+ registry: ${{ env.REGISTRY }}
+ username: ${{ github.actor }}
+ password: ${{ secrets.GITHUB_TOKEN }}
+
+ - name: Build and push
+ id: build
+ uses: docker/build-push-action@v6
+ with:
+ context: .
+ push: true
+ tags: ${{ steps.meta.outputs.tags }}
+ labels: ${{ steps.meta.outputs.labels }}
+ cache-from: type=gha
+ cache-to: type=gha,mode=max
+ platforms: linux/amd64,linux/arm64
+ provenance: true
+ sbom: true
+
+ - name: Generate attestation
+ uses: actions/attest-build-provenance@v2
+ with:
+ subject-name: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}@${{ steps.build.outputs.digest }}
+ push-to-registry: true
+
+ - name: Make GHCR package public
+ if: always()
+ env:
+ GITHUB_API_URL: https://api.github.com
+ OWNER: ${{ github.repository_owner }}
+ PACKAGE: maicro
+ TOKEN: ${{ secrets.GITHUB_TOKEN }}
+ run: |
+ set -euo pipefail
+ echo "Checking owner type for $OWNER"
+ owner_type=$(curl -sS -H "Authorization: Bearer $TOKEN" "$GITHUB_API_URL/users/$OWNER" | python -c "import sys, json; print(json.load(sys.stdin).get('type',''))")
+ echo "Owner type: $owner_type"
+ if [ "$owner_type" = "Organization" ]; then
+ url="$GITHUB_API_URL/orgs/$OWNER/packages/container/$PACKAGE/visibility"
+ else
+ url="$GITHUB_API_URL/user/packages/container/$PACKAGE/visibility"
+ fi
+ echo "Making package public via $url"
+ resp=$(curl -sS -o /dev/stderr -w "%{http_code}" -X PUT -H "Accept: application/vnd.github+json" -H "Authorization: Bearer $TOKEN" "$url" -d '{"visibility":"public"}')
+ if [ "$resp" -ge 200 ] && [ "$resp" -lt 300 ]; then
+ echo "Package visibility set to public"
+ else
+ echo "Failed to set package visibility (status $resp). You may need to make the package public manually in the GitHub UI or grant the workflow token additional permissions." >&2
+ exit 0
+ fi
diff --git a/.gitignore b/.gitignore
index b7faf40..c7b1880 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,6 +19,7 @@ lib64/
parts/
sdist/
var/
+local_qdrant/
wheels/
share/python-wheels/
*.egg-info/
@@ -205,3 +206,6 @@ cython_debug/
marimo/_static/
marimo/_lsp/
__marimo__/
+
+# Qdrant
+qdrant_storage/
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000..24ee5b1
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.13
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..bfa25e1
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,49 @@
+# Contributing
+
+Thanks for your interest in contributing to **mAIcro**.
+
+## Ways to help
+
+- Report bugs (include steps to reproduce and expected vs actual behavior).
+- Suggest features (include the problem you’re trying to solve and constraints).
+- Improve docs (typos, examples, setup clarity).
+- Send PRs (small, focused changes are easiest to review).
+
+If you’ve found a security issue, please follow `SECURITY.md` instead of opening a public issue.
+
+Start the local dev service:
+
+```bash
+uv sync --dev
+cp .env.example .env
+# Edit .env and set at least:
+# LLM_PROVIDER=google
+# GEMINI_API_KEY=...
+# QDRANT_URL=http://localhost:6333
+```
+
+## Common commands
+
+Run tests:
+
+```bash
+uv run pytest
+```
+
+Start the API locally:
+
+```bash
+uv run uvicorn main:app --reload --host 0.0.0.0 --port 8000
+```
+
+## Pull requests
+
+- Keep PRs focused and small when possible.
+- Include tests for behavior changes and bug fixes.
+- Update `README.md` / docs for user-facing changes.
+- Don’t commit secrets (keep `.env` local; update `.env.example` if you add a new setting).
+- Don’t commit local state directories (for example `var/` or `local_qdrant/`).
+
+## Reporting issues
+
+Use GitHub Issues for bugs and feature requests.
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..148a656
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,33 @@
+FROM python:3.12-slim AS builder
+
+RUN pip install uv
+
+ENV UV_COMPILE_BYTECODE=1
+ENV UV_LINK_MODE=copy
+
+WORKDIR /app
+
+COPY pyproject.toml uv.lock README.md ./
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+ uv sync --frozen --no-install-project --no-dev
+
+COPY src ./src
+
+RUN --mount=type=cache,target=/root/.cache/uv \
+ uv sync --frozen
+
+FROM python:3.12-slim
+
+WORKDIR /app
+
+COPY --from=builder /app/.venv /app/.venv
+
+ENV PATH="/app/.venv/bin:$PATH"
+ENV PYTHONPATH="/app/src"
+
+COPY src ./src
+
+EXPOSE 8000
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..c3b81e9
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Micro Club
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index cdd4199..a8bdd98 100644
--- a/README.md
+++ b/README.md
@@ -1,31 +1,302 @@
-# mAIcro: Community Intelligence Service
+
+
+
-mAIcro is a reusable, open-source AI infrastructure designed for communities and organizations. It understands structured data, processes official announcements, and answers questions accurately by centralizing important information.
+# mAIcro: Open Source Knowledge Service
-## Vision
+**mAIcro** is an open-source AI service designed to centralize organizational knowledge and answer questions via RAG (Retrieval-Augmented Generation). It features a stateless architecture optimized for cloud deployment, automatic Discord integration, and production-ready performance.
-- **Community-First**: Built for MicroClub-USTHB, but designed for everyone.
-- **Reusable**: Adaptable to any organization with minimal config.
-- **Intelligent**: Leveraging RAG (Retrieval-Augmented Generation) and Agentic features.
+## Table of Contents
-## Tech Stack
+- [Features](#features)
+- [Quick Start](#quick-start)
+- [Configuration](#configuration)
+- [Discord Bot Setup](#discord-bot-setup)
+- [Architecture](#architecture)
+- [API Reference](#api-reference)
+- [Deployment](#deployment)
+- [Use Cases](#use-cases)
+- [Future Extensions](#future-extensions)
+- [Contributing](#contributing)
-- **Backend**: FastAPI
-- **AI**: Gemini / LangChain
-- **Database**: ChromaDB
-- **Environment Management**: Poetry
+---
-## Project Structure
+## Features
-- `app/api/`: API endpoints.
-- `app/core/`: Configuration and settings.
-- `app/services/ai/`: Core AI logic.
-- `app/services/knowledge/`: Knowledge base management.
-- `app/services/ingestors/`: Data processing logic.
+- **RAG-Powered Q&A**: Google Gemini with hybrid search (semantic vector + keyword BM25) and Reciprocal Rank Fusion
+- **Real-Time Discord Sync**: Gateway WebSocket listener handles message CREATE, EDIT, and DELETE instantly
+- **Temporal Query Intelligence**: Understands "what happened today?" and "what was the last message?"
+- **Question Normalization**: Rewrites slang ("whats", "wanna", "gonna"), augments time-aware queries
+- **Startup Audit**: Reconciles offline Discord edits and deletes on every restart
+- **Stateless Architecture**: All cursors and vectors live in Qdrant Cloud; no local database
+- **Rate Limit Resilience**: Exponential backoff with jitter plus optional secondary LLM fallback
+- **Production-Ready**: Multi-stage Docker, health checks, graceful reconnection
-## Getting Started
+---
-1. Clone the repository.
-2. Copy `.env.example` to `.env` and fill in your keys.
-3. Install dependencies: `poetry install`.
-4. Run the app: `uvicorn app.main:app --reload`.
+## Quick Start
+
+There are two easy ways to run mAIcro locally. The recommended method does NOT require cloning the repository, it pulls and runs the published GHCR image.
+
+1. Run the published GHCR image (recommended)
+
+Pull & run the image directly with Docker:
+
+```bash
+docker pull ghcr.io/microclub-usthb/maicro:latest
+docker run --env-file .env -p 8000:8000 ghcr.io/microclub-usthb/maicro:latest
+```
+
+2. Clone and run from source (for development or when you want to build locally)
+
+```bash
+git clone https://github.com/MicroClub-USTHB/mAIcro.git
+cd mAIcro
+cp .env.example .env
+# Uncomment the `build: .` line in docker-compose.yml, then:
+docker compose build
+docker compose up -d
+```
+
+Fill in `.env` (see the [Configuration](#configuration) section below). The API is available at `http://localhost:8000`. Interactive docs at `http://localhost:8000/api/v1/docs`.
+
+### Ingest and query
+
+```bash
+# Sync Discord message history to Qdrant
+curl -X POST http://localhost:8000/api/v1/ingest/discord
+
+# Ask a question
+curl -X POST http://localhost:8000/api/v1/ask \
+ -H "Content-Type: application/json" \
+ -d '{"question":"When is the next event?"}'
+```
+
+---
+
+## Configuration
+
+All settings are environment variables loaded from `.env` via `pydantic-settings`.
+
+### Required
+
+| Variable | Description |
+| --------------------- | ---------------------------------------------------------------- |
+| `GEMINI_API_KEY` | Google Gemini API key (used for LLM + embeddings) |
+| `QDRANT_URL` | Qdrant Cloud instance URL (e.g. `https://xxxxx.cloud.qdrant.io`) |
+| `QDRANT_API_KEY` | Qdrant Cloud API key |
+| `DISCORD_BOT_TOKEN` | Discord bot token (from the Developer Portal) |
+| `DISCORD_CHANNEL_IDS` | Comma-separated Discord channel IDs to watch |
+
+### Optional
+
+| Variable | Default | Description |
+| -------------------------- | ------------------------------------- | ------------------------------------------------------------------ |
+| `ORG_NAME` | `MicroClub` | Organization name embedded in the AI system prompt |
+| `ORG_DESCRIPTION` | `A generic organization using mAIcro` | Organization description |
+| `GOOGLE_MODEL_NAME` | `gemini-2.5-flash` | Gemini model used for answering |
+| `SECONDARY_GEMINI_API_KEY` | (none) | Fallback Gemini key when the primary is rate-limited |
+| `LLM_FALLBACK_ENABLED` | `false` | Set to `true` to enable automatic fallback to the secondary key |
+| `COLLECTION_NAME` | `microclub_knowledge` | Name of the Qdrant collection |
+| `HYBRID_SEARCH_ALPHA` | `0.7` | Blend weight between vector and keyword search (1.0 = vector only) |
+| `HYBRID_SEARCH_RRF_K` | `60` | RRF constant used in result fusion |
+
+---
+
+## Discord Bot Setup
+
+1. Go to the [Discord Developer Portal](https://discord.com/developers/applications) and create an application
+2. Navigate to **Bot** → enable **Message Content Intent** under Privileged Gateway Intents
+3. Under **OAuth2 > URL Generator**, select `bot` scope and permissions: `Read Messages/View Channels` + `Read Message History`
+4. Use the generated URL to invite the bot to your server
+5. Copy the bot token (from the Bot page) into `DISCORD_BOT_TOKEN`
+6. Enable **Developer Mode** in Discord (User Settings → Advanced → Developer Mode)
+7. Right-click the channels you want to watch → **Copy Channel ID** → paste into `DISCORD_CHANNEL_IDS` (comma-separated)
+
+---
+
+## Architecture
+
+### System Overview
+
+```mermaid
+flowchart TB
+ USER([User]) --> API[FastAPI Service
/api/v1]
+
+ subgraph STARTUP["Startup Phase (Lifespan)"]
+ AUDIT[Startup Audit
run_startup_audit]
+ HIST[Historical Ingestion
ingest_from_discord]
+ LISTEN[Discord Listener
run_discord_listener]
+ end
+
+ subgraph RUNTIME["Runtime Services"]
+ QA[QA Service
ask_question]
+ SEARCH[Hybrid Search
Vector + BM25 + RRF]
+ INGEST[Ingestion Pipeline
ingest_documents]
+ end
+
+ subgraph EXTERNAL["External Systems"]
+ QDRANT[(Qdrant Cloud)]
+ GEMINI[[Google Gemini]]
+ DISCORD[Discord
REST + Gateway]
+ EMBED[Embedding Service]
+ end
+
+ API --> QA
+ API --> HIST
+
+ QA --> SEARCH
+ SEARCH --> QDRANT
+ QA --> GEMINI
+
+ LISTEN --> INGEST
+ INGEST --> EMBED
+ INGEST --> QDRANT
+
+ AUDIT --> QDRANT
+ AUDIT --> DISCORD
+ HIST --> DISCORD
+
+ QDRANT <--> DISCORD
+```
+
+### Question Answering Flow
+
+```mermaid
+sequenceDiagram
+ participant U as User
+ participant API as FastAPI
+ participant QA as qa_service
+ participant HS as Hybrid Search
+ participant QD as Qdrant
+ participant LLM as Gemini
+
+ U->>API: POST /ask {question}
+ API->>QA: ask_question(question)
+
+ rect rgb(30, 50, 60)
+ Note over QA: Special query detection
+ alt "today" or "last message"
+ QA->>QD: scroll(order_by timestamp DESC)
+ QD-->>QA: filtered messages
+ QA->>LLM: answer from messages
+ else Normal query
+ QA->>QA: normalize (whats→what is)
+ QA->>HS: hybrid_search(query, k=6)
+ HS->>QD: vector similarity
+ HS->>QD: BM25 keyword match
+ HS->>QD: Reciprocal Rank Fusion
+ QD-->>HS: ranked documents
+ HS-->>QA: top-k documents
+ QA->>QA: format context (≤6000 chars)
+ QA->>LLM: invoke(prompt + context)
+ end
+ end
+
+ LLM-->>QA: answer
+ QA-->>API: answer
+ API-->>U: {question, answer}
+```
+
+### How Discord Sync Works
+
+On startup, the app runs three tasks in parallel:
+
+1. **Audit** (`run_startup_audit`): Fetches the last 200 messages before the cursor from each channel. Compares them against Qdrant. Deletes any points that no longer exist in Discord (offline deletes) and updates content that was edited offline.
+
+2. **Historical Ingest** (`ingest_from_discord`): Fetches all messages _after_ each channel's cursor via the Discord REST API. Converts them to LangChain Documents, generates embeddings, and upserts them into Qdrant.
+
+3. **Real-Time Listener** (`run_discord_listener`): Connects to the Discord Gateway via WebSocket. Listens for `MESSAGE_CREATE`, `MESSAGE_DELETE`, and `MESSAGE_UPDATE` events. Ingests, deletes, or updates Qdrant points accordingly. Automatically reconnects with exponential backoff on disconnect.
+
+---
+
+## API Reference
+
+| Method | Path | Description |
+| ------ | ------------------------ | ------------------------------------ |
+| `GET` | `/api/v1/health` | Service health check |
+| `POST` | `/api/v1/ask` | Answer a question via RAG |
+| `POST` | `/api/v1/ingest/discord` | Trigger Discord historical ingestion |
+
+### Examples
+
+```bash
+# Health check
+curl http://localhost:8000/api/v1/health
+# {"status":"ok","service":"mAIcro","version":"0.1.0","org":"MicroClub","llm_provider":"google"}
+
+# Ask a question
+curl -X POST http://localhost:8000/api/v1/ask \
+ -H "Content-Type: application/json" \
+ -d '{"question":"What are the rules for joining a workshop?"}'
+# {"question":"What are the rules for joining a workshop?","answer":"..."}
+
+# Trigger ingestion
+curl -X POST http://localhost:8000/api/v1/ingest/discord
+# {"status":"ok","documents_ingested":42,"details":{"channels":{"123456789":42},"errors":{}}}
+```
+
+---
+
+## Deployment
+
+### Development
+
+```bash
+docker compose -f docker-compose.dev.yml up -d
+```
+
+Includes mAIcro and a local Qdrant instance for testing without cloud credentials.
+
+### Production
+
+The published GHCR image is public; anyone can pull and run it with no authentication needed.
+
+```bash
+docker compose up -d
+```
+
+This pulls `ghcr.io/microclub-usthb/maicro:latest` by default. To use a specific tagged image instead:
+
+```bash
+MAICRO_IMAGE=ghcr.io/microclub-usthb/maicro:sha- docker compose up -d
+```
+
+Or pull & run directly with Docker:
+
+```bash
+docker pull ghcr.io/microclub-usthb/maicro:latest
+docker run --env-file .env -p 8000:8000 ghcr.io/microclub-usthb/maicro:latest
+```
+
+---
+
+## Use Cases
+
+- **Student Clubs**: Event info, team opportunities, FAQs, and workshop announcements
+- **Online Communities**: Consolidated announcements and member documentation
+- **Companies**: Internal policies, documentation, and knowledge bases
+- **NGOs**: Instant access to mission-critical organizational information
+- **Developer Communities**: Technical Q&A grounded in shared resources and past discussions
+
+---
+
+## Future Extensions
+
+- **Agentic AI**: Automate workflows, summarize announcements, notify members about events
+- **Multi-Platform Integration**: Notion, Slack, Google Docs, and other knowledge platforms
+- **Web Dashboard**: Visual knowledge browser and query analytics
+- **Third-Party API**: Public API for external tool integrations
+
+---
+
+## Contributing
+
+Contributions are welcome. Please read [CONTRIBUTING.md](CONTRIBUTING.md) before submitting PRs.
+
+---
+
+## License
+
+MIT License. © 2026 Micro Club.
diff --git a/app/api/dependencies.py b/app/api/dependencies.py
deleted file mode 100644
index 0809247..0000000
--- a/app/api/dependencies.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# dependencies.py
-# Shared dependencies for API endpoints
-
-# ...implementation...
diff --git a/app/api/routes/health.py b/app/api/routes/health.py
deleted file mode 100644
index 68128c4..0000000
--- a/app/api/routes/health.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# health.py
-# Health check endpoint
-
-from fastapi import APIRouter
-
-router = APIRouter()
-
-@router.get("/health")
-def health_check():
- return {"status": "healthy"}
diff --git a/app/api/routes/ingest.py b/app/api/routes/ingest.py
deleted file mode 100644
index 0acf38e..0000000
--- a/app/api/routes/ingest.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# ingest.py
-# Endpoint for ingesting Discord messages/events
-
-from fastapi import APIRouter, Request
-
-router = APIRouter()
-
-@router.post("/ingest")
-def ingest_event(request: Request):
- """
- Accepts Discord event payload (new/edit/delete message) for monitored channels.
- Classifies channel/author, extracts facts/events, upserts to DB, enqueues embedding.
- """
- # ...implementation...
- return {"status": "ok"}
diff --git a/app/api/routes/query.py b/app/api/routes/query.py
deleted file mode 100644
index 7192224..0000000
--- a/app/api/routes/query.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# query.py
-# Endpoint for answering questions from community context
-
-from fastapi import APIRouter, Request
-
-router = APIRouter()
-
-@router.post("/query")
-def query_community(request: Request):
- """
- Accepts a question, retrieves structured facts + semantic context, enforces trust hierarchy, returns contract answer.
- """
- # ...implementation...
- return {"answer": "...", "confidence": 0.0, "context_used": []}
diff --git a/app/api/v1/__init__.py b/app/api/v1/__init__.py
deleted file mode 100644
index 6c74409..0000000
--- a/app/api/v1/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from fastapi import APIRouter
-
-api_router = APIRouter()
-
-# Placeholder for actual routes
-# api_router.include_router(chat.router, prefix="/chat", tags=["chat"])
diff --git a/app/core/agent_engine.py b/app/core/agent_engine.py
deleted file mode 100644
index 0faa483..0000000
--- a/app/core/agent_engine.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# agent_engine.py
-# Agentic logic for advanced actions
-
-# ...implementation placeholder...
diff --git a/app/core/config.py b/app/core/config.py
deleted file mode 100644
index 8e4309f..0000000
--- a/app/core/config.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from pydantic_settings import BaseSettings
-from typing import Optional
-
-class Settings(BaseSettings):
- PROJECT_NAME: str = "mAIcro"
- VERSION: str = "0.1.0"
- API_V1_STR: str = "/api/v1"
-
- # Organization Settings (Reusable for any club/org)
- ORG_NAME: str = "Community"
- ORG_DESCRIPTION: Optional[str] = "A generic organization using mAIcro"
-
- # AI Settings
- OPENAI_API_KEY: Optional[str] = None
- MODEL_NAME: str = "gpt-4-turbo"
-
- # Database
- DATABASE_URL: Optional[str] = None
-
- class Config:
- case_sensitive = True
- env_file = ".env"
-
-settings = Settings()
diff --git a/app/core/embeddings.py b/app/core/embeddings.py
deleted file mode 100644
index f10ec1c..0000000
--- a/app/core/embeddings.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# embeddings.py
-# Embedding generation and management
-
-# ...implementation placeholder...
diff --git a/app/core/llm_provider.py b/app/core/llm_provider.py
deleted file mode 100644
index d42ec08..0000000
--- a/app/core/llm_provider.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# llm_provider.py
-# LLM provider abstraction (OpenAI, local, etc.)
-
-# ...implementation placeholder...
diff --git a/app/core/vector_store.py b/app/core/vector_store.py
deleted file mode 100644
index 985d670..0000000
--- a/app/core/vector_store.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# vector_store.py
-# Vector DB interface (semantic search)
-
-# ...implementation placeholder...
diff --git a/app/main.py b/app/main.py
deleted file mode 100644
index 01866ef..0000000
--- a/app/main.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from fastapi import FastAPI
-from app.api.v1 import api_router
-
-app = FastAPI(
- title="mAIcro",
- description="Community Intelligence Service - Reusable AI Infrastructure",
- version="0.1.0",
-)
-
-@app.get("/health", tags=["health"])
-async def health_check():
- return {"status": "ok", "version": "0.1.0"}
-
-# app.include_router(api_router, prefix="/api/v1")
diff --git a/app/models/.gitkeep b/app/models/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/app/models/schemas.py b/app/models/schemas.py
deleted file mode 100644
index 4c9816f..0000000
--- a/app/models/schemas.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# schemas.py
-# Pydantic models for API and DB
-
-# ...implementation placeholder...
diff --git a/app/schemas/.gitkeep b/app/schemas/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/app/services/.gitkeep b/app/services/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/app/services/ai/.gitkeep b/app/services/ai/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/app/services/ingestion_service.py b/app/services/ingestion_service.py
deleted file mode 100644
index 3037bce..0000000
--- a/app/services/ingestion_service.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# ingestion_service.py
-# Service for ingesting and processing Discord events
-
-# ...implementation placeholder...
diff --git a/app/services/ingestors/.gitkeep b/app/services/ingestors/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/app/services/knowledge/.gitkeep b/app/services/knowledge/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/app/services/query_service.py b/app/services/query_service.py
deleted file mode 100644
index 478ebac..0000000
--- a/app/services/query_service.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# query_service.py
-# Service for answering questions from knowledge base
-
-# ...implementation placeholder...
diff --git a/assets/maicro-dark.png b/assets/maicro-dark.png
new file mode 100644
index 0000000..7d8d533
Binary files /dev/null and b/assets/maicro-dark.png differ
diff --git a/assets/maicro-light.png b/assets/maicro-light.png
new file mode 100644
index 0000000..f61ee31
Binary files /dev/null and b/assets/maicro-light.png differ
diff --git a/assets/maicro.png b/assets/maicro.png
new file mode 100644
index 0000000..70fbbd5
Binary files /dev/null and b/assets/maicro.png differ
diff --git a/docker-compose.dev.yml b/docker-compose.dev.yml
new file mode 100644
index 0000000..0766500
--- /dev/null
+++ b/docker-compose.dev.yml
@@ -0,0 +1,50 @@
+services:
+ maicro:
+ build:
+ context: .
+ target: builder
+ container_name: maicro_dev
+ environment:
+ - PYTHONUNBUFFERED=1
+ - UVICORN_RELOAD=true
+ - PYTHONPATH=/app/src
+ env_file:
+ - .env
+ volumes:
+ - ./src:/app/src # bind-mount source for hot reload
+ - ./pyproject.toml:/app/pyproject.toml
+ - ./uv.lock:/app/uv.lock
+ - /app/.venv # keep venv persistent inside container
+ ports:
+ - "8000:8000"
+ command: >
+ /app/.venv/bin/uvicorn main:app
+ --host 0.0.0.0
+ --port 8000
+ --reload
+ --reload-dir /app/src
+ depends_on:
+ - qdrant
+ healthcheck:
+ test:
+ ["CMD-SHELL", "curl -f http://localhost:8000/api/v1/health || exit 1"]
+ interval: 15s
+ timeout: 10s
+ retries: 5
+
+ qdrant:
+ image: qdrant/qdrant:v1.2.0
+ container_name: qdrant_dev
+ environment:
+ QDRANT__SERVICE__API_KEY: "${QDRANT_API_KEY:-devkey}"
+ QDRANT__SERVICE__HTTP_PORT: 6333
+ ports:
+ - "6333:6333"
+ - "6334:6334"
+ volumes:
+ - qdrant_data:/qdrant/storage
+ healthcheck:
+ disable: true
+
+volumes:
+ qdrant_data:
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..d475ec1
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,22 @@
+services:
+ maicro:
+ image: ${MAICRO_IMAGE:-ghcr.io/microclub-usthb/maicro:latest}
+
+ # If you want to build from the local source instead, uncomment the following build key
+ # build: .
+
+ container_name: maicro
+ ports:
+ - "8000:8000"
+ env_file:
+ - .env
+ restart: unless-stopped
+ healthcheck:
+ test:
+ [
+ "CMD-SHELL",
+ 'python -c ''import urllib.request as u; u.urlopen("http://localhost:8000/api/v1/health")'' || exit 1',
+ ]
+ interval: 30s
+ timeout: 10s
+ retries: 3
diff --git a/docker/.gitkeep b/docker/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/docker/Dockerfile b/docker/Dockerfile
deleted file mode 100644
index 0cbf3d5..0000000
--- a/docker/Dockerfile
+++ /dev/null
@@ -1,7 +0,0 @@
-# Dockerfile for mIAcro
-FROM python:3.10-slim
-WORKDIR /app
-COPY requirements.txt ./
-RUN pip install --no-cache-dir -r requirements.txt
-COPY . .
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml
deleted file mode 100644
index 8e5c827..0000000
--- a/docker/docker-compose.yml
+++ /dev/null
@@ -1,27 +0,0 @@
-version: "3.8"
-services:
- miacro:
- build: .
- env_file:
- - ../.env.example
- ports:
- - "8000:8000"
- depends_on:
- - db
- - vectordb
- db:
- image: postgres:14
- environment:
- POSTGRES_USER: miacro
- POSTGRES_PASSWORD: miacro
- POSTGRES_DB: miacro
- ports:
- - "5432:5432"
- vectordb:
- image: ankane/pgvector
- environment:
- POSTGRES_USER: vector
- POSTGRES_PASSWORD: vector
- POSTGRES_DB: vector
- ports:
- - "5433:5432"
diff --git a/pyproject.toml b/pyproject.toml
index 9392499..c280b39 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,14 +1,50 @@
-[tool.poetry]
-name = "miacro"
+[build-system]
+requires = ["hatchling>=1.25.0"]
+build-backend = "hatchling.build"
+
+[project]
+name = "maicro"
version = "0.1.0"
-description = "Community Intelligence Service"
-authors = ["MicroClub-USTHB"]
-
-[tool.poetry.dependencies]
-python = ">=3.9,<3.12"
-fastapi = "*"
-uvicorn = "*"
-pydantic = "*"
-psycopg2-binary = "*"
-sqlalchemy = "*"
-openai = "*"
+description = "mAIcro is an open-source AI service designed to help organizations centralize information and answer questions based on their internal data."
+readme = "README.md"
+requires-python = ">=3.10"
+authors = [
+ { name = "mAIcro Contributors" }
+]
+keywords = ["ai", "rag", "fastapi", "qdrant", "discord", "langchain"]
+classifiers = [
+ "Development Status :: 3 - Alpha",
+ "Intended Audience :: Developers",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
+ "Framework :: FastAPI",
+ "Programming Language :: Python :: 3",
+ "Programming Language :: Python :: 3.10",
+ "Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Programming Language :: Python :: 3.13",
+]
+dependencies = [
+ "discord-py>=2.4.0",
+ "fastapi>=0.115.0",
+ "google-generativeai>=0.8.6",
+ "langchain>=1.2.10",
+ "langchain-community>=0.4.1",
+ "langchain-core>=1.2.17",
+ "langchain-google-genai>=4.2.1",
+ "langchain-qdrant>=1.1.0",
+ "pydantic-settings>=2.13.1",
+ "python-dotenv>=1.2.2",
+ "qdrant-client>=1.17.0",
+ "uvicorn>=0.35.0",
+]
+
+[tool.hatch.build.targets.wheel]
+packages = ["src"]
+
+[tool.pytest.ini_options]
+pythonpath = ["src"]
+
+[dependency-groups]
+dev = [
+ "pytest>=8.3.0",
+]
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index a9d4675..0000000
--- a/requirements.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-fastapi
-uvicorn
-pydantic
-psycopg2-binary
-sqlalchemy
-openai
diff --git a/app/agents/.gitkeep b/src/api/__init__.py
similarity index 100%
rename from app/agents/.gitkeep
rename to src/api/__init__.py
diff --git a/src/api/error_handlers.py b/src/api/error_handlers.py
new file mode 100644
index 0000000..2f4ebb2
--- /dev/null
+++ b/src/api/error_handlers.py
@@ -0,0 +1,35 @@
+"""Centralized FastAPI exception handlers."""
+
+import logging
+
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+
+from services.qa_service import AskConfigError, AskError
+
+logger = logging.getLogger(__name__)
+
+
+def register_exception_handlers(app: FastAPI) -> None:
+ """Register global exception handlers for the API."""
+
+ @app.exception_handler(AskConfigError)
+ async def handle_ask_config_error(_: Request, exc: AskConfigError) -> JSONResponse:
+ return JSONResponse(status_code=400, content={"detail": str(exc)})
+
+ @app.exception_handler(AskError)
+ async def handle_ask_error(_: Request, exc: AskError) -> JSONResponse:
+ return JSONResponse(status_code=502, content={"detail": str(exc)})
+
+ @app.exception_handler(FileNotFoundError)
+ async def handle_not_found(_: Request, exc: FileNotFoundError) -> JSONResponse:
+ return JSONResponse(status_code=404, content={"detail": str(exc)})
+
+ @app.exception_handler(ValueError)
+ async def handle_value_error(_: Request, exc: ValueError) -> JSONResponse:
+ return JSONResponse(status_code=400, content={"detail": str(exc)})
+
+ @app.exception_handler(Exception)
+ async def handle_unexpected_error(_: Request, exc: Exception) -> JSONResponse:
+ logger.exception("Unhandled API exception", exc_info=exc)
+ return JSONResponse(status_code=500, content={"detail": f"Internal error: {exc}"})
diff --git a/src/api/routes.py b/src/api/routes.py
new file mode 100644
index 0000000..d17a2cf
--- /dev/null
+++ b/src/api/routes.py
@@ -0,0 +1,66 @@
+"""
+mAIcro REST API routes.
+"""
+
+import asyncio
+
+from fastapi import APIRouter, HTTPException
+
+from api.schemas import AskRequest, AskResponse, IngestResponse
+from core.config import settings
+from services.qa_service import ask_question
+
+router = APIRouter()
+
+
+@router.get("/health")
+async def health():
+ return {
+ "status": "ok",
+ "service": settings.PROJECT_NAME,
+ "version": settings.VERSION,
+ "org": settings.ORG_NAME,
+ "llm_provider": settings.LLM_PROVIDER,
+ }
+
+
+@router.post("/ask", response_model=AskResponse)
+async def ask(req: AskRequest):
+ """Answer a question using the RAG chain."""
+ if not req.question.strip():
+ raise HTTPException(status_code=400, detail="Question cannot be empty.")
+
+ loop = asyncio.get_running_loop()
+ answer = await loop.run_in_executor(None, ask_question, req.question)
+
+ return AskResponse(question=req.question, answer=answer)
+
+
+@router.post("/ingest/discord", response_model=IngestResponse)
+async def ingest_discord():
+ """Fetch messages from Discord channels and ingest them."""
+ from core.ingestion import ingest_from_discord
+
+ if not settings.DISCORD_BOT_TOKEN:
+ raise HTTPException(
+ status_code=400,
+ detail="DISCORD_BOT_TOKEN not configured. Set it in your .env file.",
+ )
+ if not settings.discord_channel_id_list:
+ raise HTTPException(
+ status_code=400,
+ detail="DISCORD_CHANNEL_IDS not configured. Set it in your .env file.",
+ )
+
+ result = await ingest_from_discord()
+
+ has_errors = bool(result.get("errors"))
+
+ return IngestResponse(
+ status="partial" if has_errors else "ok",
+ documents_ingested=result["total_documents"],
+ details={
+ "channels": result.get("channels", {}),
+ "errors": result.get("errors", {}),
+ },
+ )
diff --git a/src/api/schemas.py b/src/api/schemas.py
new file mode 100644
index 0000000..aafb272
--- /dev/null
+++ b/src/api/schemas.py
@@ -0,0 +1,20 @@
+"""API request/response models."""
+
+from typing import Optional
+
+from pydantic import BaseModel
+
+
+class AskRequest(BaseModel):
+ question: str
+
+
+class AskResponse(BaseModel):
+ question: str
+ answer: str
+
+
+class IngestResponse(BaseModel):
+ status: str
+ documents_ingested: int
+ details: Optional[dict] = None
diff --git a/app/core/.gitkeep b/src/core/.gitkeep
similarity index 100%
rename from app/core/.gitkeep
rename to src/core/.gitkeep
diff --git a/app/db/.gitkeep b/src/core/__init__.py
similarity index 100%
rename from app/db/.gitkeep
rename to src/core/__init__.py
diff --git a/src/core/audit.py b/src/core/audit.py
new file mode 100644
index 0000000..179d207
--- /dev/null
+++ b/src/core/audit.py
@@ -0,0 +1,251 @@
+"""
+Audit module — handles startup reconciliation of offline edits and deletes.
+"""
+
+
+from core.config import settings
+from core.discord_fetcher import fetch_channel_messages, fetch_message_by_id
+from core.state import get_last_ingested_message_id, ensure_channel_in_state, update_last_ingested_message_id
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+async def run_startup_audit(
+ channel_ids: list[str],
+ window: int = 1000,
+) -> dict:
+
+
+ from qdrant_client.http import models as qdrant_models
+
+ from core.ingestion import (
+ _bootstrap_collection,
+ _docs_from_discord_messages,
+ delete_message_from_store,
+ update_message_in_store,
+ get_qdrant_client,
+ )
+
+ if not settings.DISCORD_BOT_TOKEN or not channel_ids:
+ return {}
+
+ summary: dict[str, dict] = {}
+
+ for channel_id in channel_ids:
+ ensure_channel_in_state(channel_id)
+
+ deleted = 0
+ updated = 0
+ errors_list: list[str] = []
+
+ try:
+ last_message_id = get_last_ingested_message_id(channel_id)
+
+ if not last_message_id:
+ logger.info(
+ "[audit] Channel %s: no cursor found or channel never ingested, skipping audit",
+ channel_id,
+ )
+ summary[channel_id] = {"deleted": 0, "updated": 0, "skipped": "new_channel"}
+ continue
+
+ cursor_msg = await fetch_message_by_id(
+ bot_token=settings.DISCORD_BOT_TOKEN,
+ channel_id=channel_id,
+ message_id=last_message_id,
+ )
+
+ if cursor_msg is None:
+ logger.info(
+ "[audit] Channel %s: cursor message %s was deleted, searching for new cursor",
+ channel_id, last_message_id,
+ )
+ recent_messages: list[dict] = await fetch_channel_messages(
+ bot_token=settings.DISCORD_BOT_TOKEN,
+ channel_id=channel_id,
+ limit=1,
+ )
+ if recent_messages:
+ new_cursor = recent_messages[0]["id"]
+ logger.info(
+ "[audit] Channel %s: updating cursor from %s to %s",
+ channel_id, last_message_id, new_cursor,
+ )
+ update_last_ingested_message_id(channel_id, new_cursor)
+ last_message_id = new_cursor
+ cursor_msg = recent_messages[0]
+ else:
+ logger.warning(
+ "[audit] Channel %s: no messages found in channel at all",
+ channel_id,
+ )
+ summary[channel_id] = {"deleted": 0, "updated": 0, "skipped": "channel_empty"}
+ continue
+
+ # Check if cursor message was edited - get stored content from Qdrant
+ cursor_id_str = str(cursor_msg["id"])
+
+ # Get the stored content for cursor from Qdrant
+ cursor_stored_content = ""
+ try:
+ client = get_qdrant_client()
+ search_result = client.scroll(
+ collection_name=settings.COLLECTION_NAME,
+ scroll_filter=qdrant_models.Filter(
+ must=[
+ qdrant_models.FieldCondition(
+ key="metadata.message_id",
+ match=qdrant_models.MatchValue(value=cursor_id_str),
+ ),
+ ]
+ ),
+ limit=1,
+ with_payload=True,
+ with_vectors=False,
+ )
+ cursor_points, _ = search_result
+ if cursor_points:
+ cursor_stored_content = (cursor_points[0].payload.get("page_content") or "").strip()
+ except Exception as e:
+ logger.warning(f"[audit] Failed to get cursor content from Qdrant: {e}")
+
+ cursor_docs = _docs_from_discord_messages([cursor_msg], channel_id)
+ cursor_content = cursor_docs[0].page_content if cursor_docs else ""
+
+ if cursor_stored_content and cursor_content != cursor_stored_content:
+ logger.info(
+ "[audit] Cursor message %s was edited, updating in Qdrant",
+ cursor_id_str,
+ )
+ update_message_in_store(cursor_msg, channel_id)
+ updated += 1
+
+ recent: list[dict] = await fetch_channel_messages(
+ bot_token=settings.DISCORD_BOT_TOKEN,
+ channel_id=channel_id,
+ limit=window,
+ before=last_message_id,
+ )
+
+ discord_index: dict[str, str] = {}
+ cursor_id_str = str(cursor_msg["id"])
+ discord_index[cursor_id_str] = cursor_content
+
+ for msg in recent:
+ docs = _docs_from_discord_messages([msg], channel_id)
+ msg_id_str = str(msg["id"])
+ discord_index[msg_id_str] = docs[0].page_content if docs else ""
+ if not discord_index:
+ logger.info(
+ "[audit] Channel %s: no messages before cursor to audit",
+ channel_id,
+ )
+ summary[channel_id] = {"deleted": 0, "updated": 0, "skipped": "no_messages_before_cursor"}
+ continue
+ try:
+ _bootstrap_collection()
+ except Exception as e:
+ logger.warning(f"[audit] Failed to bootstrap collection: {e}")
+ client = get_qdrant_client()
+ total_count_result = client.count(
+ collection_name=settings.COLLECTION_NAME,
+ count_filter=qdrant_models.Filter(
+ must=[
+ qdrant_models.FieldCondition(
+ key="metadata.channel_id",
+ match=qdrant_models.MatchValue(value=channel_id),
+ ),
+ ]
+ ),
+ exact=True,
+ )
+ logger.info(
+ "[audit] Channel %s: checking %d Qdrant points against %d messages before cursor",
+ channel_id, total_count_result.count, len(discord_index),
+ )
+ all_points_filter = qdrant_models.Filter(
+ must=[
+ qdrant_models.FieldCondition(
+ key="metadata.channel_id",
+ match=qdrant_models.MatchValue(value=channel_id),
+ ),
+ ],
+ )
+ offset = None
+ while True:
+ scroll_result = client.scroll(
+ collection_name=settings.COLLECTION_NAME,
+ scroll_filter=all_points_filter,
+ limit=100,
+ offset=offset,
+ with_payload=True,
+ with_vectors=False,
+ )
+ points, next_offset = scroll_result
+ for point in points:
+ payload = point.payload or {}
+ meta = payload.get("metadata", {})
+ msg_id: str = meta.get("message_id", "")
+ stored_content: str = (payload.get("page_content") or "").strip()
+ if not msg_id:
+ continue
+ msg_id_str = str(msg_id)
+ logger.debug(
+ "[audit] Checking msg_id=%s (type=%s) - in discord_index=%s",
+ msg_id_str, type(msg_id), msg_id_str in discord_index,
+ )
+ if msg_id_str not in discord_index:
+ n = delete_message_from_store(channel_id, msg_id_str)
+ deleted += n
+ if n:
+ logger.info(
+ "[audit] deleted message_id=%s from channel %s (was deleted offline)",
+ msg_id_str, channel_id,
+ )
+ else:
+ logger.debug(
+ "[audit] message_id=%s not in discord_index but no Qdrant points to delete",
+ msg_id_str,
+ )
+ continue
+ logger.debug(
+ "[audit] Verified message_id=%s exists in both Discord and Qdrant",
+ msg_id_str,
+ )
+ current_content = discord_index.get(msg_id_str, "")
+ if stored_content != current_content:
+ if msg_id_str == cursor_id_str:
+ msg_dict = cursor_msg
+ else:
+ msg_dict = next(
+ (m for m in recent if str(m["id"]) == msg_id_str), None
+ )
+ if msg_dict:
+ update_message_in_store(msg_dict, channel_id)
+ updated += 1
+ logger.info(
+ "[audit] updated message_id=%s in channel %s (edited offline)",
+ msg_id_str, channel_id,
+ )
+
+ if next_offset is None:
+ break
+ offset = next_offset
+
+ logger.info(
+ "[audit] Channel %s audit complete: deleted=%d, updated=%d",
+ channel_id, deleted, updated,
+ )
+
+ except Exception as exc:
+ logger.warning("[audit] Unexpected error for channel %s: %s", channel_id, exc)
+ errors_list.append(str(exc))
+
+ summary[channel_id] = {"deleted": deleted, "updated": updated, "errors": errors_list}
+ logger.info(
+ "[audit] channel=%s deleted=%d updated=%d",
+ channel_id, deleted, updated,
+ )
+
+ return summary
\ No newline at end of file
diff --git a/src/core/config.py b/src/core/config.py
new file mode 100644
index 0000000..b140515
--- /dev/null
+++ b/src/core/config.py
@@ -0,0 +1,55 @@
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from typing import List, Optional
+
+
+class Settings(BaseSettings):
+ PROJECT_NAME: str = "mAIcro"
+ VERSION: str = "0.1.0"
+ API_V1_STR: str = "/api/v1"
+
+ ORG_NAME: str = "MicroClub"
+ ORG_DESCRIPTION: Optional[str] = "A generic organization using mAIcro"
+ CORE_RULES: Optional[List[str]] = None
+
+ LLM_PROVIDER: str = "google"
+ SECONDARY_LLM_PROVIDER: Optional[str] = None
+ GEMINI_API_KEY: Optional[str] = None
+ SECONDARY_GEMINI_API_KEY: Optional[str] = None
+ MODEL_NAME: Optional[str] = None
+ SECONDARY_MODEL_NAME: Optional[str] = None
+ GOOGLE_MODEL_NAME: str = "gemini-2.5-flash"
+ LLM_FALLBACK_ENABLED: bool = False
+ LLM_MAX_PRIMARY_ATTEMPTS: int = 3
+ LLM_BACKOFF_BASE_DELAY_SECONDS: float = 1.0
+ LLM_BACKOFF_MAX_DELAY_SECONDS: float = 8.0
+
+ DISCORD_BOT_TOKEN: Optional[str] = None
+ DISCORD_CHANNEL_IDS: Optional[str] = None
+
+ QDRANT_URL: Optional[str] = None
+ QDRANT_API_KEY: Optional[str] = None
+ COLLECTION_NAME: str = "microclub_knowledge"
+
+
+ HYBRID_SEARCH_ALPHA: float = 0.7
+ HYBRID_SEARCH_RRF_K: int = 60
+
+ model_config = SettingsConfigDict(
+ case_sensitive=True,
+ env_file=".env",
+ extra="ignore",
+ )
+
+ @property
+ def discord_channel_id_list(self) -> List[str]:
+ """Parse comma-separated channel IDs from DISCORD_CHANNEL_IDS."""
+ if not self.DISCORD_CHANNEL_IDS:
+ return []
+
+ return [
+ cid.strip() for cid in self.DISCORD_CHANNEL_IDS.split(",") if cid.strip()
+ ]
+
+
+settings = Settings()
+
diff --git a/src/core/discord_fetcher.py b/src/core/discord_fetcher.py
new file mode 100644
index 0000000..2f3adb2
--- /dev/null
+++ b/src/core/discord_fetcher.py
@@ -0,0 +1,98 @@
+import aiohttp
+from typing import Optional
+
+DISCORD_API = "https://discord.com/api/v10"
+
+
+class DiscordFetchError(RuntimeError):
+ """Raised when a Discord channel fetch fails."""
+
+ def __init__(self, channel_id: str, status_code: int | None, message: str):
+ super().__init__(message)
+ self.channel_id = channel_id
+ self.status_code = status_code
+ self.message = message
+
+
+async def fetch_channel_messages(
+ bot_token: str,
+ channel_id: str,
+ limit: Optional[int] = None,
+ after: Optional[str] = None,
+ before: Optional[str] = None,
+) -> list[dict]:
+ headers = {"Authorization": f"Bot {bot_token}"}
+ all_messages: list[dict] = []
+
+ async with aiohttp.ClientSession() as session:
+ while limit is None or len(all_messages) < limit:
+ batch_size = 100 if limit is None else min(100, limit - len(all_messages))
+ url = f"{DISCORD_API}/channels/{channel_id}/messages?limit={batch_size}"
+ if after:
+ url += f"&after={after}"
+ elif before:
+ url += f"&before={before}"
+
+ async with session.get(url, headers=headers) as resp:
+ if resp.status != 200:
+ body = await resp.text()
+ raise DiscordFetchError(
+ channel_id=channel_id,
+ status_code=resp.status,
+ message=(
+ f"Discord API error {resp.status} for channel {channel_id}: {body}"
+ ),
+ )
+ batch = await resp.json()
+
+ if not batch:
+ break
+
+ all_messages.extend(batch)
+ if after:
+ after = batch[-1]["id"] # ascending (oldest->newest): advance forward
+ else:
+ before = batch[-1]["id"] # descending (bootstrap): go further back
+
+ if len(batch) < batch_size:
+ break
+
+ return all_messages
+
+
+async def fetch_message_by_id(
+ bot_token: str,
+ channel_id: str,
+ message_id: str,
+) -> Optional[dict]:
+ """Fetch a single message by its ID. Returns None if not found (deleted)."""
+ headers = {"Authorization": f"Bot {bot_token}"}
+ url = f"{DISCORD_API}/channels/{channel_id}/messages/{message_id}"
+
+ async with aiohttp.ClientSession() as session:
+ async with session.get(url, headers=headers) as resp:
+ if resp.status == 404:
+ # Message not found - it was deleted
+ return None
+ if resp.status != 200:
+ body = await resp.text()
+ raise DiscordFetchError(
+ channel_id=channel_id,
+ status_code=resp.status,
+ message=f"Discord API error {resp.status} for message {message_id}: {body}",
+ )
+ return await resp.json()
+
+
+async def fetch_all_channels(
+ bot_token: str,
+ channel_ids: list[str],
+ limit_per_channel: Optional[int] = None,
+ after: Optional[str] = None,
+) -> dict[str, list[dict]]:
+ results: dict[str, list[dict]] = {}
+ for cid in channel_ids:
+ results[cid] = await fetch_channel_messages(
+ bot_token, cid, limit_per_channel, after=after
+ )
+ return results
diff --git a/src/core/discord_listener.py b/src/core/discord_listener.py
new file mode 100644
index 0000000..e9ed2b8
--- /dev/null
+++ b/src/core/discord_listener.py
@@ -0,0 +1,267 @@
+"""
+Discord Gateway listener — real-time ingestion via discord.py.
+Uses discord.Client to connect to the Discord Gateway.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+
+from core.ingestion import (
+ _docs_from_discord_messages,
+ delete_message_from_store,
+ ingest_documents,
+ update_message_in_store,
+)
+from core.state import update_last_ingested_message_id
+
+logger = logging.getLogger(__name__)
+
+
+
+async def handle_message_create(message: dict, channel_ids: set[str]) -> None:
+ """
+ Process a single MESSAGE_CREATE payload (as a plain dict).
+
+ """
+ channel_id = message.get("channel_id", "")
+ if channel_id not in channel_ids:
+ return
+
+ docs = _docs_from_discord_messages([message], channel_id)
+ if not docs:
+ return
+
+
+ count = await asyncio.to_thread(ingest_documents, docs)
+ if count:
+ update_last_ingested_message_id(channel_id, message["id"])
+ logger.info("[listener] ingested %d doc(s) from channel %s", count, channel_id)
+
+async def handle_message_delete(payload: dict, channel_ids: set[str]) -> None:
+ """
+ Process a MESSAGE_DELETE payload (plain dict).
+
+ Expected keys: channel_id, id (message_id).
+ Deletes the corresponding Qdrant point(s) if the channel is watched.
+ Exposed at module level so unit tests can call it without a real Discord client.
+ """
+ channel_id = payload.get("channel_id", "")
+ if channel_id not in channel_ids:
+ return
+
+ message_id = payload.get("id", "")
+ if not message_id:
+ return
+
+ n = await asyncio.to_thread(delete_message_from_store, channel_id, message_id)
+ logger.info(
+ "[listener] deleted %d point(s) for message_id=%s channel=%s",
+ n, message_id, channel_id,
+ )
+
+
+async def handle_message_update(payload: dict, channel_ids: set[str]) -> None:
+ """
+ Process a MESSAGE_UPDATE payload (plain dict).
+
+ Expected keys: channel_id, id, content (plus optional author / embeds).
+ Deletes the old Qdrant point(s) and re-ingests with the new content.
+ Exposed at module level for unit-test access.
+ """
+ channel_id = payload.get("channel_id", "")
+ if channel_id not in channel_ids:
+ return
+
+ count = await asyncio.to_thread(update_message_in_store, payload, channel_id)
+ if count:
+ logger.info(
+ "[listener] updated message_id=%s in channel %s (%d doc(s))",
+ payload.get("id"), channel_id, count,
+ )
+ else:
+ logger.debug(
+ "[listener] MESSAGE_UPDATE for message_id=%s had no indexable content — old point(s) removed",
+ payload.get("id"),
+ )
+
+
+
+def _message_to_dict(msg) -> dict:
+
+ return {
+ "id": str(msg.id),
+ "channel_id": str(msg.channel.id),
+ "content": msg.content,
+ "author": {"username": msg.author.name},
+ "timestamp": msg.created_at.isoformat(),
+ "embeds": [
+ {
+ "title": e.title or "",
+ "description": e.description or "",
+ }
+ for e in msg.embeds
+ ],
+ }
+
+
+
+
+async def run_discord_listener(bot_token: str, channel_ids: list[str]) -> None:
+ """
+ Connect to Discord via discord.py and listen for new messages forever.
+ """
+ if not bot_token:
+ logger.error("[listener] DISCORD_BOT_TOKEN is not set — listener will not start")
+ return
+ if not channel_ids:
+ logger.error("[listener] No channel IDs configured — listener will not start")
+ return
+
+ try:
+ import discord
+ except ImportError:
+ logger.error(
+ "[listener] discord.py is not installed. "
+ "Add 'discord.py>=2.4.0' to your dependencies and reinstall."
+ )
+ return
+
+ watched: set[str] = set(channel_ids)
+
+ intents = discord.Intents(guilds=True, guild_messages=True, message_content=True)
+ client = discord.Client(intents=intents)
+
+ @client.event
+ async def on_ready() -> None:
+ logger.info(
+ "[listener] READY — logged in as %s (id=%s)", client.user, client.user.id
+ )
+
+ @client.event
+ async def on_message(msg: discord.Message) -> None:
+ if msg.author.bot:
+ return
+ channel_id = str(msg.channel.id)
+ logger.debug(
+ "[listener] MESSAGE_CREATE channel=%s content=%r",
+ channel_id,
+ (msg.content or "")[:80],
+ )
+ await handle_message_create(_message_to_dict(msg), watched)
+
+ @client.event
+ async def on_raw_message_delete(payload: discord.RawMessageDeleteEvent) -> None:
+ channel_id = str(payload.channel_id)
+ message_id = str(payload.message_id)
+ logger.debug(
+ "[listener] MESSAGE_DELETE channel=%s message_id=%s", channel_id, message_id
+ )
+ await handle_message_delete(
+ {"channel_id": channel_id, "id": message_id}, watched
+ )
+
+ @client.event
+ async def on_raw_message_edit(payload: discord.RawMessageUpdateEvent) -> None:
+ data: dict = payload.data
+ channel_id = str(payload.channel_id)
+ message_id = str(payload.message_id)
+ logger.debug(
+ "[listener] MESSAGE_UPDATE channel=%s message_id=%s", channel_id, message_id
+ )
+ msg_dict = {
+ "id": message_id,
+ "channel_id": channel_id,
+ "content": data.get("content", ""),
+ "author": data.get("author", {"username": "unknown"}),
+ "timestamp": data.get("edited_timestamp") or data.get("timestamp", ""),
+ "embeds": [
+ {"title": e.get("title", ""), "description": e.get("description", "")}
+ for e in data.get("embeds", [])
+ ],
+ }
+ await handle_message_update(msg_dict, watched)
+
+ MAX_RETRIES = 10
+ BASE_DELAY = 5.0
+ MAX_DELAY = 60.0
+
+ for attempt in range(1, MAX_RETRIES + 1):
+ try:
+ await client.start(bot_token)
+ break # clean exit (shouldn't normally happen)
+ except discord.LoginFailure:
+ logger.error(
+ "[listener] Invalid Discord bot token (LoginFailure) — will not retry"
+ )
+ return
+ except Exception as exc:
+ delay = min(BASE_DELAY * 2 ** (attempt - 1), MAX_DELAY)
+ logger.warning(
+ "[listener] Disconnected (attempt %d/%d): %s. Retrying in %.0fs…",
+ attempt, MAX_RETRIES, exc, delay,
+ )
+ # Close the old client before reconnecting
+ try:
+ await client.close()
+ except Exception:
+ pass
+ await asyncio.sleep(delay)
+ # Re-create the client for a clean reconnect and re-register all events
+ client = discord.Client(intents=intents)
+
+ @client.event
+ async def on_ready() -> None:
+ logger.info(
+ "[listener] READY — logged in as %s (id=%s)", client.user, client.user.id
+ )
+
+ @client.event
+ async def on_message(msg: discord.Message) -> None:
+ if msg.author.bot:
+ return
+ channel_id = str(msg.channel.id)
+ logger.debug(
+ "[listener] MESSAGE_CREATE channel=%s content=%r",
+ channel_id,
+ (msg.content or "")[:80],
+ )
+ await handle_message_create(_message_to_dict(msg), watched)
+
+ @client.event
+ async def on_raw_message_delete(payload: discord.RawMessageDeleteEvent) -> None:
+ channel_id = str(payload.channel_id)
+ message_id = str(payload.message_id)
+ logger.debug(
+ "[listener] MESSAGE_DELETE channel=%s message_id=%s", channel_id, message_id
+ )
+ await handle_message_delete(
+ {"channel_id": channel_id, "id": message_id}, watched
+ )
+
+ @client.event
+ async def on_raw_message_edit(payload: discord.RawMessageUpdateEvent) -> None:
+ data: dict = payload.data
+ channel_id = str(payload.channel_id)
+ message_id = str(payload.message_id)
+ logger.debug(
+ "[listener] MESSAGE_UPDATE channel=%s message_id=%s", channel_id, message_id
+ )
+ # Build a normalised dict compatible with handle_message_update.
+ msg_dict = {
+ "id": message_id,
+ "channel_id": channel_id,
+ "content": data.get("content", ""),
+ "author": data.get("author", {"username": "unknown"}),
+ "timestamp": data.get("edited_timestamp") or data.get("timestamp", ""),
+ "embeds": [
+ {"title": e.get("title", ""), "description": e.get("description", "")}
+ for e in data.get("embeds", [])
+ ],
+ }
+ await handle_message_update(msg_dict, watched)
+ else:
+ logger.error(
+ "[listener] Exhausted %d reconnect attempts — giving up.", MAX_RETRIES
+ )
diff --git a/src/core/hybrid_search.py b/src/core/hybrid_search.py
new file mode 100644
index 0000000..7e1e125
--- /dev/null
+++ b/src/core/hybrid_search.py
@@ -0,0 +1,223 @@
+"""Hybrid Search Module - Combines semantic (vector) and keyword (BM25) search.
+
+This module provides hybrid search functionality using Qdrant's Prefetch API
+and Reciprocal Rank Fusion (RRF) for merging results.
+"""
+
+import logging
+
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qdrant_models
+from langchain_core.documents import Document
+from langchain_core.retrievers import RetrieverLike
+
+from core.config import settings
+from core.llm_provider import get_embeddings
+
+def _get_qdrant_client():
+ from core.vector_store import get_qdrant_client
+ return get_qdrant_client()
+
+logger = logging.getLogger(__name__)
+
+
+def _reciprocal_rank_fusion(
+ results_by_source: dict[str, list[tuple[str, float]]],
+ k: int | None = None
+) -> list[tuple[str, float]]:
+ """Combine ranked lists using Reciprocal Rank Fusion.
+
+ RRF_score(doc) = Σ (1 / (k + rank(doc)))
+
+ Args:
+ results_by_source: Dict mapping source name to list of (doc_id, score) tuples
+ k: Constant for RRF (defaults to settings.HYBRID_SEARCH_RRF_K = 60)
+
+ Returns:
+ List of (doc_id, rrf_score) tuples, sorted by rrf_score descending
+ """
+ if k is None:
+ k = getattr(settings, 'HYBRID_SEARCH_RRF_K', 60)
+
+ rrf_scores: dict[str, float] = {}
+
+ for source, results in results_by_source.items():
+ for rank, (doc_id, score) in enumerate(results, start=1):
+ if doc_id not in rrf_scores:
+ rrf_scores[doc_id] = 0.0
+ rrf_scores[doc_id] += 1.0 / (k + rank)
+
+ # Sort by RRF score descending
+ sorted_results = sorted(rrf_scores.items(), key=lambda x: x[1], reverse=True)
+ return sorted_results
+
+
+def _is_message_id(query: str) -> bool:
+ """Check if query looks like a Discord message ID (numeric string)."""
+ return query.isdigit() and len(query) >= 17
+
+
+def hybrid_search(
+ query: str,
+ alpha: float | None = None,
+ k: int = 5,
+ filter_condition: qdrant_models.Filter | None = None,
+) -> list[Document]:
+ """Execute hybrid search combining semantic (vector) and keyword (BM25) search.
+
+
+ Returns:
+ List of Documents sorted by RRF score
+ """
+ if alpha is None:
+ alpha = getattr(settings, 'HYBRID_SEARCH_ALPHA', 0.7)
+
+ client = _get_qdrant_client()
+ collection_name = settings.COLLECTION_NAME
+ embedding = get_embeddings()
+
+ query_vector = embedding.embed_query(query)
+
+ vector_results = client.query_points(
+ collection_name=collection_name,
+ query=query_vector,
+ query_filter=filter_condition,
+ limit=k * 2,
+ with_payload=True,
+ with_vectors=False,
+ )
+
+
+ text_match_condition = qdrant_models.FieldCondition(
+ key="page_content",
+ match=qdrant_models.MatchText(text=query),
+ )
+
+ if filter_condition:
+ text_filter = qdrant_models.Filter(
+ must=[filter_condition, text_match_condition]
+ )
+ else:
+ text_filter = qdrant_models.Filter(must=[text_match_condition])
+
+ text_results = client.scroll(
+ collection_name=collection_name,
+ scroll_filter=text_filter,
+ limit=k * 2,
+ with_payload=True,
+ with_vectors=False,
+ )
+
+ message_id_docs = []
+ if _is_message_id(query):
+ message_id_condition = qdrant_models.FieldCondition(
+ key="metadata.message_id",
+ match=qdrant_models.MatchValue(value=query),
+ )
+
+ if filter_condition:
+ message_id_filter = qdrant_models.Filter(
+ must=[filter_condition, message_id_condition]
+ )
+ else:
+ message_id_filter = qdrant_models.Filter(must=[message_id_condition])
+
+ message_id_results = client.scroll(
+ collection_name=collection_name,
+ scroll_filter=message_id_filter,
+ limit=k * 2,
+ with_payload=True,
+ with_vectors=False,
+ )
+
+ for hit in message_id_results[0]:
+ payload = hit.payload or {}
+ doc_id = hit.id
+ score = 2.0
+ message_id_docs.append((doc_id, score, payload))
+
+ vector_docs = []
+ for hit in vector_results.points:
+ payload = hit.payload or {}
+ doc_id = hit.id
+ score = hit.score if hasattr(hit, 'score') else 1.0
+ vector_docs.append((doc_id, score, payload))
+
+ text_docs = []
+ for hit in text_results[0]:
+ payload = hit.payload or {}
+ doc_id = hit.id
+ score = 1.0
+ text_docs.append((doc_id, score, payload))
+
+ rrf_k = getattr(settings, 'HYBRID_SEARCH_RRF_K', 60)
+
+ results_by_source = {}
+
+ vector_ranked = [(doc_id, score) for doc_id, score, _ in vector_docs]
+ results_by_source['vector'] = vector_ranked
+
+ text_ranked = [(doc_id, 1.0) for doc_id, _, _ in text_docs]
+ results_by_source['text'] = text_ranked
+
+ if message_id_docs:
+ message_id_ranked = [(doc_id, score) for doc_id, score, _ in message_id_docs]
+ results_by_source['message_id'] = message_id_ranked
+
+ rrf_scores = _reciprocal_rank_fusion(results_by_source, k=rrf_k)
+
+ doc_map = {}
+ for doc_id, score, payload in vector_docs:
+ doc_map[doc_id] = payload
+ for doc_id, score, payload in text_docs:
+ if doc_id not in doc_map:
+ doc_map[doc_id] = payload
+ for doc_id, score, payload in message_id_docs:
+ if doc_id not in doc_map:
+ doc_map[doc_id] = payload
+
+ documents = []
+ for doc_id, rrf_score in rrf_scores:
+ if doc_id in doc_map:
+ payload = doc_map[doc_id]
+ page_content = payload.get("page_content", "")
+ metadata = payload.get("metadata", {})
+ documents.append(
+ Document(
+ page_content=page_content,
+ metadata=metadata,
+ )
+ )
+
+ return documents[:k]
+
+
+class _HybridRetriever:
+ """Wrapper class to make hybrid search compatible with LangChain's retriever interface."""
+
+ def __init__(self, alpha: float | None = None, k: int = 5):
+ self.alpha = alpha
+ self.k = k
+
+ def invoke(self, query: str) -> list[Document]:
+ return hybrid_search(
+ query=query,
+ alpha=self.alpha,
+ k=self.k,
+ )
+
+ def __call__(self, query: str) -> list[Document]:
+ return self.invoke(query)
+
+
+def get_hybrid_retriever(
+ alpha: float | None = None,
+ k: int = 5,
+) -> RetrieverLike:
+ """Return a hybrid search retriever interface.
+
+
+ Returns:
+ A retriever with .invoke() method
+ """
+ return _HybridRetriever(alpha=alpha, k=k)
diff --git a/src/core/ingestion.py b/src/core/ingestion.py
new file mode 100644
index 0000000..604fb81
--- /dev/null
+++ b/src/core/ingestion.py
@@ -0,0 +1,413 @@
+"""
+Ingestion pipeline — converts raw data into LangChain Documents
+and pushes them into the Qdrant vector store.
+
+Supports one source:
+ 1. Discord messages (from the discord_fetcher)
+
+For startup audit (offline edit/delete reconciliation), see `core.audit`.
+"""
+
+import logging
+
+from langchain_core.documents import Document
+from qdrant_client.http import models as qdrant_models
+from qdrant_client.http.exceptions import UnexpectedResponse
+
+from core.config import settings
+from core.discord_fetcher import DiscordFetchError, fetch_channel_messages
+from core.llm_provider import get_embeddings
+from core.state import get_last_ingested_message_id, update_last_ingested_message_id
+from core.vector_store import get_qdrant_client, get_vector_store
+
+# Re-export run_startup_audit for backward compatibility
+from core.audit import run_startup_audit
+
+__all__ = ["ingest_documents", "ingest_from_discord", "run_startup_audit"]
+
+logger = logging.getLogger(__name__)
+
+
+
+def _is_missing_collection_error(message: str) -> bool:
+ """Return True when an exception message indicates the Qdrant collection is missing."""
+ if not message:
+ return False
+ lowered = message.lower()
+ collection = settings.COLLECTION_NAME.lower()
+ if collection not in lowered:
+ return False
+ return any(needle in lowered for needle in ("doesn't exist", "does not exist", "not found"))
+
+
+def _is_missing_collection_exception(exc: Exception) -> bool:
+ """Return True when an exception indicates the Qdrant collection is missing."""
+ if isinstance(exc, UnexpectedResponse):
+ if exc.status_code != 404:
+ return False
+ return _is_missing_collection_error(exc.content.decode("utf-8", errors="ignore"))
+ return _is_missing_collection_error(str(exc))
+
+
+def _bootstrap_collection() -> None:
+ """Create the Qdrant collection (if missing) and clear vector-store cache."""
+ client = get_qdrant_client()
+
+ # Check if collection already exists
+ if client.collection_exists(settings.COLLECTION_NAME):
+ # Collection exists - just ensure indexes are created
+ _ensure_collection_indexes(client)
+ get_vector_store.cache_clear()
+ return
+
+ # Collection doesn't exist - need to get embedding size
+ embedding = get_embeddings()
+ vector_size = len(embedding.embed_query("collection bootstrap"))
+ _ensure_collection_exists(vector_size)
+ get_vector_store.cache_clear()
+
+
+def _ensure_collection_indexes(client) -> None:
+ """Ensure payload indexes exist on the collection.
+
+ Qdrant Cloud requires explicit payload indexes for filtered queries.
+ """
+ # Ensure payload indexes exist (required by Qdrant Cloud for filtered queries)
+ for field in ("metadata.channel_id", "metadata.message_id", "metadata.source"):
+ try:
+ client.create_payload_index(
+ collection_name=settings.COLLECTION_NAME,
+ field_name=field,
+ field_schema=qdrant_models.PayloadSchemaType.KEYWORD,
+ )
+ except Exception:
+ pass # Index already exists or collection just created
+
+ # Enable Full-Text Index on page_content for keyword/BM25 search (hybrid search)
+ try:
+ client.create_payload_index(
+ collection_name=settings.COLLECTION_NAME,
+ field_name="page_content",
+ field_schema=qdrant_models.TextIndexParams(
+ type=qdrant_models.TextIndexType.TEXT,
+ tokenizer=qdrant_models.TokenizerType.WORD,
+ min_token_len=2,
+ max_token_len=None,
+ lowercase=True,
+ ),
+ )
+ except Exception as e:
+ logger.warning(f"Full-text index creation for page_content: {e}")
+
+
+def _docs_from_discord_messages(
+ messages: list[dict],
+ channel_id: str,
+) -> list[Document]:
+ """
+ Convert raw Discord message dicts into LangChain Documents.
+ Skips empty messages and bot commands.
+ """
+ docs: list[Document] = []
+ for msg in messages:
+ content = (msg.get("content") or "").strip()
+ if not content:
+ continue
+
+ author = msg.get("author", {})
+ author_name = author.get("username", "unknown")
+ timestamp = msg.get("timestamp", "")
+
+ page_content = f"[{author_name}] {content}"
+
+ for embed in msg.get("embeds", []):
+ if embed.get("title"):
+ page_content += f"\n{embed['title']}"
+ if embed.get("description"):
+ page_content += f"\n{embed['description']}"
+
+ metadata = {
+ "source": "discord",
+ "channel_id": channel_id,
+ "message_id": msg.get("id", ""),
+ "author": author_name,
+ "timestamp": timestamp,
+ }
+ docs.append(Document(page_content=page_content, metadata=metadata))
+
+ return docs
+
+
+
+def _ensure_collection_exists(vector_size: int) -> None:
+ """Create collection (if missing) and ensure payload indexes exist.
+
+ Qdrant Cloud requires explicit payload indexes for filtered queries.
+ Local Qdrant auto-indexes, so these calls are safe no-ops there.
+ """
+ client = get_qdrant_client()
+ if not client.collection_exists(settings.COLLECTION_NAME):
+ client.create_collection(
+ collection_name=settings.COLLECTION_NAME,
+ vectors_config=qdrant_models.VectorParams(
+ size=vector_size,
+ distance=qdrant_models.Distance.COSINE,
+ ),
+ )
+
+ # Ensure payload indexes exist (required by Qdrant Cloud for filtered queries)
+ for field in ("metadata.channel_id", "metadata.message_id", "metadata.source"):
+ try:
+ client.create_payload_index(
+ collection_name=settings.COLLECTION_NAME,
+ field_name=field,
+ field_schema=qdrant_models.PayloadSchemaType.KEYWORD,
+ )
+ except Exception:
+ pass # Index already exists or collection just created
+
+ # Enable Full-Text Index on page_content for keyword/BM25 search (hybrid search)
+ try:
+ client.create_payload_index(
+ collection_name=settings.COLLECTION_NAME,
+ field_name="page_content",
+ field_schema=qdrant_models.TextIndexParams(
+ type=qdrant_models.TextIndexType.TEXT,
+ tokenizer=qdrant_models.TokenizerType.WORD,
+ min_token_len=2,
+ max_token_len=None,
+ lowercase=True,
+ ),
+ )
+ except Exception as e:
+ logger.warning(f"Full-text index creation for page_content: {e}")
+
+
+def ingest_documents(documents: list[Document], filter_duplicates: bool = True) -> int:
+ """
+ Push a list of LangChain Documents into the Qdrant vector store.
+ Returns the number of documents ingested.
+
+ Args:
+ documents: List of LangChain Documents to ingest.
+ filter_duplicates: If True, skip documents that already exist based on message_id.
+ """
+ if not documents:
+ return 0
+
+ # Ensure collection exists with proper indexes before filtering/ingesting
+ # This handles the case where collection was created by LangChain without indexes
+ _bootstrap_collection()
+
+ if filter_duplicates:
+ documents, duplicate_count = _filter_duplicate_documents(documents)
+ if not documents:
+ logger.debug("[ingestion] All documents were duplicates, nothing to ingest")
+ return 0
+
+ try:
+ vector_store = get_vector_store()
+ vector_store.add_documents(documents)
+ except UnexpectedResponse as exc:
+ if exc.status_code == 401:
+ raise ValueError(
+ "Qdrant unauthorized (401). Check QDRANT_URL and QDRANT_API_KEY in your .env."
+ ) from exc
+ if exc.status_code == 404 and _is_missing_collection_error(
+ exc.content.decode("utf-8", errors="ignore")
+ ):
+ _bootstrap_collection()
+ vector_store = get_vector_store()
+ vector_store.add_documents(documents)
+ else:
+ raise
+ except Exception as exc:
+ # First-run behavior: create the collection automatically if missing.
+ if _is_missing_collection_error(str(exc)):
+ _bootstrap_collection()
+ vector_store = get_vector_store()
+ vector_store.add_documents(documents)
+ else:
+ raise
+ return len(documents)
+
+
+
+def _filter_by_message(channel_id: str, message_id: str) -> qdrant_models.Filter:
+ """Build a Qdrant payload filter matching a specific (channel_id, message_id) pair."""
+ return qdrant_models.Filter(
+ must=[
+ qdrant_models.FieldCondition(
+ key="metadata.channel_id",
+ match=qdrant_models.MatchValue(value=channel_id),
+ ),
+ qdrant_models.FieldCondition(
+ key="metadata.message_id",
+ match=qdrant_models.MatchValue(value=message_id),
+ ),
+ ]
+ )
+
+
+def _check_duplicate_message_ids(documents: list[Document]) -> set[str]:
+ """
+ Check which message IDs already exist in the vector store.
+ Returns a set of message IDs that are already present.
+ """
+ client = get_qdrant_client()
+ existing_ids: set[str] = set()
+
+ channel_msg_ids: dict[str, list[str]] = {}
+ for doc in documents:
+ channel_id = doc.metadata.get("channel_id", "")
+ message_id = doc.metadata.get("message_id", "")
+ if channel_id and message_id:
+ if channel_id not in channel_msg_ids:
+ channel_msg_ids[channel_id] = []
+ channel_msg_ids[channel_id].append(message_id)
+
+ for channel_id, message_ids in channel_msg_ids.items():
+ for msg_id in message_ids:
+ try:
+ count_result = client.count(
+ collection_name=settings.COLLECTION_NAME,
+ count_filter=_filter_by_message(channel_id, msg_id),
+ exact=True,
+ )
+ except Exception as exc:
+ if _is_missing_collection_exception(exc):
+ return set()
+ raise
+ if count_result.count > 0:
+ existing_ids.add(msg_id)
+
+ return existing_ids
+
+
+def _filter_duplicate_documents(documents: list[Document]) -> tuple[list[Document], int]:
+ """
+ Filter out documents that already exist in the vector store based on message_id.
+ Returns a tuple of (filtered_documents, duplicate_count).
+ """
+ if not documents:
+ return documents, 0
+
+ existing_ids = _check_duplicate_message_ids(documents)
+ if not existing_ids:
+ return documents, 0
+
+ filtered = []
+ duplicate_count = 0
+ for doc in documents:
+ msg_id = doc.metadata.get("message_id", "")
+ if msg_id in existing_ids:
+ duplicate_count += 1
+ logger.debug("[ingestion] Skipping duplicate message_id=%s", msg_id)
+ else:
+ filtered.append(doc)
+
+ if duplicate_count > 0:
+ logger.info("[ingestion] Filtered %d duplicate message(s)", duplicate_count)
+
+ return filtered, duplicate_count
+
+
+def delete_message_from_store(channel_id: str, message_id: str) -> int:
+ """
+ Remove all Qdrant points whose payload matches (channel_id, message_id).
+ Returns the number of points deleted (0 if none existed).
+ """
+ client = get_qdrant_client()
+ try:
+ count_result = client.count(
+ collection_name=settings.COLLECTION_NAME,
+ count_filter=_filter_by_message(channel_id, message_id),
+ exact=True,
+ )
+ except Exception as exc:
+ if _is_missing_collection_exception(exc):
+ return 0
+ raise
+ n = count_result.count
+ if n:
+ client.delete(
+ collection_name=settings.COLLECTION_NAME,
+ points_selector=qdrant_models.FilterSelector(
+ filter=_filter_by_message(channel_id, message_id)
+ ),
+ )
+ logger.debug(
+ "[ingestion] deleted %d point(s) for message_id=%s channel=%s",
+ n, message_id, channel_id,
+ )
+ return n
+
+
+def update_message_in_store(message: dict, channel_id: str) -> int:
+ """
+ Update a message in the vector store:
+ """
+ message_id = message.get("id", "")
+ delete_message_from_store(channel_id, message_id)
+ docs = _docs_from_discord_messages([message], channel_id)
+ if not docs:
+ return 0
+ return ingest_documents(docs)
+
+
+async def ingest_from_discord(limit_per_channel: int = 200) -> dict:
+ """
+ Fetch messages from all configured Discord channels and ingest them.
+ Returns a summary dict with per-channel counts.
+ """
+ if not settings.DISCORD_BOT_TOKEN:
+ raise ValueError("DISCORD_BOT_TOKEN not set in environment.")
+ if not settings.discord_channel_id_list:
+ raise ValueError("DISCORD_CHANNEL_IDS not set in environment.")
+
+ summary: dict[str, int] = {}
+ errors: dict[str, str] = {}
+ total = 0
+
+ for channel_id in settings.discord_channel_id_list:
+ try:
+ last_message_id = get_last_ingested_message_id(channel_id)
+ messages = await fetch_channel_messages(
+ bot_token=settings.DISCORD_BOT_TOKEN,
+ channel_id=channel_id,
+ limit=None,
+ after=last_message_id,
+ )
+
+ if not messages:
+ summary[channel_id] = 0
+ continue
+
+ docs = _docs_from_discord_messages(messages, channel_id)
+ count = ingest_documents(docs)
+ summary[channel_id] = count
+ total += count
+
+
+ if messages:
+ newest_message = messages[-1] if last_message_id else messages[0]
+ update_last_ingested_message_id(channel_id, newest_message["id"])
+
+ except DiscordFetchError as exc:
+ if exc.status_code == 403:
+ errors[channel_id] = (
+ "Missing access to channel. Ensure the bot is in the server and has "
+ "View Channel + Read Message History permissions."
+ )
+ elif exc.status_code == 401:
+ errors[channel_id] = "Unauthorized bot token. Verify DISCORD_BOT_TOKEN."
+ else:
+ errors[channel_id] = exc.message
+ except Exception as exc:
+ errors[channel_id] = str(exc)
+
+ return {
+ "channels": summary,
+ "errors": errors,
+ "total_documents": total,
+ }
diff --git a/src/core/llm_provider.py b/src/core/llm_provider.py
new file mode 100644
index 0000000..1920c65
--- /dev/null
+++ b/src/core/llm_provider.py
@@ -0,0 +1,144 @@
+from __future__ import annotations
+
+import logging
+import random
+import time
+from typing import Optional
+
+from langchain_core.runnables import RunnableLambda
+
+from core.config import settings
+
+
+class ConfigurationError(ValueError):
+ """Raised when model providers are misconfigured."""
+
+
+_LOGGER = logging.getLogger(__name__)
+
+
+class RateLimitExceeded(Exception):
+ """Raised when the primary provider is rate-limited after retries."""
+
+ def __init__(self, last_exc: Exception):
+ super().__init__(str(last_exc))
+ self.last_exc = last_exc
+
+
+def _resolve_model_name(*, secondary: bool) -> str:
+ if secondary and settings.SECONDARY_MODEL_NAME:
+ return settings.SECONDARY_MODEL_NAME
+ return settings.MODEL_NAME or settings.GOOGLE_MODEL_NAME
+
+
+def _build_google_llm(*, secondary: bool):
+ from langchain_google_genai import ChatGoogleGenerativeAI
+
+ api_key = settings.SECONDARY_GEMINI_API_KEY if secondary else settings.GEMINI_API_KEY
+ if not api_key:
+ missing_key = "SECONDARY_GEMINI_API_KEY" if secondary else "GEMINI_API_KEY"
+ raise ConfigurationError(f"{missing_key} not found in .env.")
+
+ model_name = _resolve_model_name(secondary=secondary)
+ return ChatGoogleGenerativeAI(
+ model=model_name,
+ google_api_key=api_key,
+ temperature=0,
+ )
+
+
+def _extract_status_code(exc: Exception) -> Optional[int]:
+ for attr in ("status_code", "code"):
+ value = getattr(exc, attr, None)
+ if isinstance(value, int):
+ return value
+ if isinstance(value, str):
+ try:
+ return int(value)
+ except ValueError:
+ pass
+ response = getattr(exc, "response", None)
+ if response is not None:
+ code = getattr(response, "status_code", None)
+ if isinstance(code, int):
+ return code
+ if isinstance(code, str):
+ try:
+ return int(code)
+ except ValueError:
+ pass
+ return None
+
+
+def _is_rate_limit_error(exc: Exception) -> bool:
+ code = _extract_status_code(exc)
+ return code == 429
+
+
+def _sleep_with_exponential_backoff(attempt: int) -> None:
+ base = min(
+ settings.LLM_BACKOFF_BASE_DELAY_SECONDS * (2 ** (attempt - 1)),
+ settings.LLM_BACKOFF_MAX_DELAY_SECONDS,
+ )
+ jitter = random.uniform(0, 0.2)
+ time.sleep(base + jitter)
+
+
+def _invoke_with_rate_limit_retries(llm, prompt: str):
+ for attempt in range(1, settings.LLM_MAX_PRIMARY_ATTEMPTS + 1):
+ try:
+ return llm.invoke(prompt)
+ except Exception as exc:
+ if not _is_rate_limit_error(exc):
+ raise
+ if attempt >= settings.LLM_MAX_PRIMARY_ATTEMPTS:
+ raise RateLimitExceeded(exc) from exc
+ _sleep_with_exponential_backoff(attempt)
+
+
+def _build_fallback_router(primary_llm, secondary_llm):
+ def _invoke(prompt: str):
+ try:
+ return _invoke_with_rate_limit_retries(primary_llm, prompt)
+ except RateLimitExceeded:
+ _LOGGER.warning(
+ "Primary Gemini model rate-limited after %s attempts; switching to fallback.",
+ settings.LLM_MAX_PRIMARY_ATTEMPTS,
+ )
+ return secondary_llm.invoke(prompt)
+
+ return RunnableLambda(_invoke)
+
+
+def get_llm():
+ provider = settings.LLM_PROVIDER.lower().strip()
+ if provider != "google":
+ raise ConfigurationError(
+ "Only Gemini is supported in this build. Set LLM_PROVIDER=google in .env."
+ )
+
+ primary_llm = _build_google_llm(secondary=False)
+ if not settings.LLM_FALLBACK_ENABLED:
+ return primary_llm
+ if not settings.SECONDARY_LLM_PROVIDER or not settings.SECONDARY_GEMINI_API_KEY:
+ raise ConfigurationError(
+ "Fallback enabled but SECONDARY_LLM_PROVIDER or SECONDARY_GEMINI_API_KEY is missing."
+ )
+
+ secondary_llm = _build_google_llm(secondary=True)
+ return _build_fallback_router(primary_llm, secondary_llm)
+
+
+def get_embeddings():
+ from langchain_google_genai import GoogleGenerativeAIEmbeddings
+
+ if not settings.GEMINI_API_KEY:
+ raise ConfigurationError(
+ "GEMINI_API_KEY not found in .env. "
+ "Gemini embeddings require a valid Google API key."
+ )
+
+ return GoogleGenerativeAIEmbeddings(
+ model="models/gemini-embedding-001",
+ google_api_key=settings.GEMINI_API_KEY
+ )
diff --git a/src/core/prompt_template.py b/src/core/prompt_template.py
new file mode 100644
index 0000000..db7d077
--- /dev/null
+++ b/src/core/prompt_template.py
@@ -0,0 +1,79 @@
+from langchain_core.prompts import PromptTemplate
+
+from core.config import settings
+
+_BASE_TEMPLATE = """\
+You are the official AI assistant for {org_name}.
+
+About {org_name}:
+{org_description}
+
+Your core rules:
+{core_rules}
+
+Always ground your answers in verified information about {org_name}. \
+If you do not know something, say so clearly rather than speculating.
+"""
+
+_RAG_TEMPLATE = """\
+{system_prompt}
+
+You are answering a member question using retrieved context snippets.
+
+Response quality requirements:
+1. Be concise, factual, and professional.
+2. Use only facts supported by the provided context.
+3. If context is insufficient, say exactly: "I don't have this information yet."
+4. Do not invent dates, links, names, or procedures.
+
+Output format:
+- Start with a direct answer in 1-3 sentences.
+- If relevant, add a short "Details:" paragraph.
+- If context is missing or ambiguous, add "Missing info:" with what is needed.
+
+Context snippets:
+{context}
+
+User question:
+{question}
+
+Final answer:
+"""
+
+
+def build_system_prompt() -> str:
+ if settings.CORE_RULES:
+ rules_block = "\n".join(
+ f" {i + 1}. {rule}" for i, rule in enumerate(settings.CORE_RULES)
+ )
+ else:
+ rules_block = "Be helpful, accurate, and professional."
+
+ return _BASE_TEMPLATE.format(
+ org_name=settings.ORG_NAME,
+ org_description=settings.ORG_DESCRIPTION or "An organization using mAIcro.",
+ core_rules=rules_block,
+ )
+
+
+def build_rag_prompt_template() -> PromptTemplate:
+ """Return the reusable prompt template for retrieval-augmented answering."""
+ return PromptTemplate(
+ template=_RAG_TEMPLATE,
+ input_variables=["context", "question"],
+ partial_variables={"system_prompt": build_system_prompt()},
+ )
+
+
+def preview_prompt() -> None:
+ prompt = build_system_prompt()
+ separator = "-" * 60
+ print(f"\n{separator}")
+ print(" SYSTEM PROMPT PREVIEW")
+ print(separator)
+ print(prompt)
+ print(separator + "\n")
+
+
+if __name__ == "__main__":
+ preview_prompt()
diff --git a/src/core/state.py b/src/core/state.py
new file mode 100644
index 0000000..4c4b6f1
--- /dev/null
+++ b/src/core/state.py
@@ -0,0 +1,72 @@
+import logging
+import uuid
+from typing import Optional
+
+from qdrant_client.http import models as qdrant_models
+
+from core.config import settings
+from core.vector_store import get_qdrant_client
+
+logger = logging.getLogger(__name__)
+
+# Use a deterministic namespace for cursor UUIDs
+CURSOR_NAMESPACE = uuid.UUID("6ba7b810-9dad-11d1-80b4-00c04fd430c8")
+
+
+def _get_cursor_id(channel_id: str) -> str:
+ """Generate a deterministic UUID for a channel's cursor point."""
+ return str(uuid.uuid5(CURSOR_NAMESPACE, f"cursor_{channel_id}"))
+
+
+def get_last_ingested_message_id(channel_id: str) -> Optional[str]:
+ """Get the last ingested message ID from Qdrant Cloud."""
+ client = get_qdrant_client()
+ point_id = _get_cursor_id(channel_id)
+
+ try:
+ results = client.retrieve(
+ collection_name=settings.COLLECTION_NAME,
+ ids=[point_id],
+ with_payload=True,
+ with_vectors=False,
+ )
+ if results:
+ return results[0].payload.get("message_id")
+ except Exception as e:
+ logger.debug(f"[state] Failed to retrieve cursor for {channel_id}: {e}")
+
+ return None
+
+
+def ensure_channel_in_state(channel_id: str) -> None:
+ """Ensures channel exists (No-op in Qdrant implementation)."""
+ pass
+
+
+def update_last_ingested_message_id(channel_id: str, message_id: str) -> None:
+ """Update or create the last ingested message ID in Qdrant Cloud."""
+ client = get_qdrant_client()
+ point_id = _get_cursor_id(channel_id)
+
+ # We store cursors as special points with no vectors (or zero vectors)
+ # and a 'source=ingestion_cursor' tag to easily filter them out if needed.
+ client.upsert(
+ collection_name=settings.COLLECTION_NAME,
+ points=[
+ qdrant_models.PointStruct(
+ id=point_id,
+ vector={}, # Empty dict for collections with named vectors?
+ # For our standard COSINE collection, we might need a dummy vector
+ # or just use payload.
+ payload={
+ "metadata": {
+ "channel_id": channel_id,
+ "source": "ingestion_cursor",
+ },
+ "message_id": message_id,
+ "page_content": f"Ingestion cursor for channel {channel_id}",
+ },
+ )
+ ],
+ )
+ logger.debug(f"[state] Updated cursor for {channel_id} to {message_id}")
diff --git a/src/core/vector_store.py b/src/core/vector_store.py
new file mode 100644
index 0000000..559d642
--- /dev/null
+++ b/src/core/vector_store.py
@@ -0,0 +1,148 @@
+import atexit
+import logging
+from functools import lru_cache
+
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qdrant_models
+from langchain_qdrant import QdrantVectorStore
+
+from core.config import settings
+from core.llm_provider import get_embeddings
+
+
+logger = logging.getLogger(__name__)
+
+# Flag to track if indexes have been ensured
+_indexes_ensured = False
+
+
+def _ensure_collection_with_indexes() -> None:
+ """Ensure the Qdrant collection exists with proper indexes for filtering.
+
+ This runs only once to avoid repeated API calls.
+ If collection doesn't exist, creates it with proper vector config from embeddings.
+ """
+ global _indexes_ensured
+ if _indexes_ensured:
+ return
+
+ client = get_qdrant_client()
+ collection_name = settings.COLLECTION_NAME
+
+ # Check if collection exists
+ try:
+ collections = client.get_collections().collections
+ collection_exists = any(c.name == collection_name for c in collections)
+ except Exception:
+ collection_exists = False
+
+ if not collection_exists:
+ # Get embedding size and create collection properly
+ try:
+ embedding = get_embeddings()
+ # Get the actual vector size from the embedding model
+ test_vector = embedding.embed_query("test")
+ vector_size = len(test_vector)
+
+ client.create_collection(
+ collection_name=collection_name,
+ vectors_config=qdrant_models.VectorParams(
+ size=vector_size,
+ distance=qdrant_models.Distance.COSINE,
+ ),
+ )
+ logger.info(f"Created collection {collection_name} with vector size {vector_size}")
+ except Exception as e:
+ logger.warning(f"Could not create collection: {e}")
+ # Let LangChain's QdrantVectorStore handle it
+ _indexes_ensured = True
+ return
+
+ # Ensure payload indexes are created
+ for field_name in ["metadata.channel_id", "metadata.message_id", "metadata.source"]:
+ try:
+ logger.info(f"Creating index on {field_name}")
+ client.create_payload_index(
+ collection_name=collection_name,
+ field_name=field_name,
+ field_schema=qdrant_models.PayloadSchemaType.KEYWORD,
+ )
+ except Exception as e:
+ logger.debug(f"Index creation for {field_name}: {e}")
+
+ # Enable Full-Text Index on page_content for keyword/BM25 search (hybrid search)
+ try:
+ logger.info("Creating full-text index on page_content")
+ client.create_payload_index(
+ collection_name=collection_name,
+ field_name="page_content",
+ field_schema=qdrant_models.TextIndexParams(
+ type=qdrant_models.TextIndexType.TEXT,
+ tokenizer=qdrant_models.TokenizerType.WORD,
+ min_token_len=2,
+ max_token_len=None,
+ lowercase=True,
+ ),
+ )
+ except Exception as e:
+ logger.warning(f"Full-text index creation for page_content: {e}")
+
+ _indexes_ensured = True
+
+
+@lru_cache(maxsize=1)
+def get_qdrant_client() -> QdrantClient:
+ """Return a singleton Qdrant client for this process.
+
+ Now strictly Cloud-only: requires QDRANT_URL and QDRANT_API_KEY.
+ """
+ if not settings.QDRANT_URL or not settings.QDRANT_API_KEY:
+ raise ValueError(
+ "mAIcro is now Cloud-only. Please set QDRANT_URL and QDRANT_API_KEY in your .env file. "
+ "See README.md for instructions on setting up a free Qdrant Cloud cluster."
+ )
+ return QdrantClient(url=settings.QDRANT_URL, api_key=settings.QDRANT_API_KEY)
+
+
+@lru_cache(maxsize=1)
+def get_vector_store() -> QdrantVectorStore:
+ """Return a vector store backed by the singleton Qdrant client."""
+ client = get_qdrant_client()
+ collection_name = settings.COLLECTION_NAME
+
+ # Debug: Get collection info to understand the vector config
+ try:
+ collection_info = client.get_collection(collection_name)
+ logger.info(f"Collection {collection_name} vectors: {collection_info.vectors_config}")
+ except Exception as e:
+ logger.warning(f"Could not get collection info: {e}")
+
+ # Ensure collection exists with proper indexes before creating vector store
+ _ensure_collection_with_indexes()
+
+ return QdrantVectorStore(
+ client=client,
+ collection_name=collection_name,
+ embedding=get_embeddings(),
+ distance=qdrant_models.Distance.COSINE,
+ validate_collection_config=False,
+ )
+
+
+@atexit.register
+def _close_qdrant_client_on_exit() -> None:
+ """Best-effort close to avoid noisy interpreter-shutdown warnings."""
+ try:
+ get_qdrant_client().close()
+ except Exception:
+ pass
+
+
+# Import hybrid search functions from separate module
+from core.hybrid_search import (
+ hybrid_search,
+ get_hybrid_retriever,
+ _reciprocal_rank_fusion,
+)
+
+__all__ = ["hybrid_search", "get_hybrid_retriever"]
diff --git a/src/main.py b/src/main.py
new file mode 100644
index 0000000..294c615
--- /dev/null
+++ b/src/main.py
@@ -0,0 +1,66 @@
+"""Canonical FastAPI application entrypoint for mAIcro."""
+
+import asyncio
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI
+
+from api.error_handlers import register_exception_handlers
+from api.routes import router
+from core.config import settings
+from core.discord_listener import run_discord_listener
+from core.ingestion import ingest_from_discord, run_startup_audit
+import logging
+
+logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
+)
+
+logger = logging.getLogger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+ if settings.DISCORD_BOT_TOKEN and settings.discord_channel_id_list:
+ audit_task = asyncio.create_task(
+ run_startup_audit(settings.discord_channel_id_list, window=200)
+ )
+ try:
+ audit_summary = await audit_task
+ logger.info("[startup] Audit completed: %s", audit_summary)
+ except Exception as exc:
+ logger.error("[startup] Audit failed: %s", exc)
+
+ asyncio.create_task(ingest_from_discord())
+ asyncio.create_task(
+ run_discord_listener(
+ bot_token=settings.DISCORD_BOT_TOKEN,
+ channel_ids=settings.discord_channel_id_list,
+ )
+ )
+
+ yield
+
+
+app = FastAPI(
+ title=settings.PROJECT_NAME,
+ lifespan=lifespan,
+ version=settings.VERSION,
+ description=(
+ f"AI knowledge service for {settings.ORG_NAME}. "
+ "Ingest data from Discord or JSON files, then ask questions."
+ ),
+ docs_url=f"{settings.API_V1_STR}/docs",
+ redoc_url=f"{settings.API_V1_STR}/redoc",
+)
+
+
+app.include_router(router, prefix=settings.API_V1_STR)
+register_exception_handlers(app)
+
+
+if __name__ == "__main__":
+ import uvicorn
+
+ uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
diff --git a/src/services/qa_service.py b/src/services/qa_service.py
new file mode 100644
index 0000000..d859aa9
--- /dev/null
+++ b/src/services/qa_service.py
@@ -0,0 +1,461 @@
+"""Question-answering service built on top of retrieval-augmented generation."""
+
+import logging
+import re
+from concurrent.futures import ThreadPoolExecutor, TimeoutError as FutureTimeoutError
+from datetime import datetime, timezone
+
+from langchain_core.documents import Document
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+
+from core.config import settings
+from core.hybrid_search import get_hybrid_retriever
+from core.llm_provider import ConfigurationError, get_llm
+from core.prompt_template import build_rag_prompt_template
+from core.vector_store import get_vector_store
+
+logger = logging.getLogger(__name__)
+
+
+class AskError(Exception):
+ """Raised when asking the LLM fails with a user-actionable error."""
+
+
+class AskConfigError(AskError):
+ """Raised when server-side AI providers are not configured correctly."""
+
+
+_ASK_TIMEOUT_SECONDS = 30
+_RECENCY_MESSAGE_PATTERN = re.compile(
+ r"\b(last|latest|most recent|newest)\b.*\b(message|msg|post)\b|\bwhat(?:'s| is)\s+the\s+last\s+message\b",
+ re.IGNORECASE,
+)
+_TODAY_UPDATES_PATTERN = re.compile(
+ r"\b(today)\b.*\b(update|updates|plan|agenda|have|happening)\b|\bwhat(?:'s|s)?\b.*\b(have|happening)\b.*\btoday\b",
+ re.IGNORECASE,
+)
+_QUESTION_REWRITES: list[tuple[re.Pattern[str], str]] = [
+ (re.compile(r"\bwhats\b", re.IGNORECASE), "what is"),
+ (re.compile(r"\bwanna\b", re.IGNORECASE), "want to"),
+ (re.compile(r"\bgonna\b", re.IGNORECASE), "going to"),
+ (re.compile(r"\bthe\s+we\s+have\b", re.IGNORECASE), "do we have"),
+]
+_TEMPORAL_KEYWORDS_PATTERN = re.compile(
+ r"\b(today|tomorrow|yesterday|this week|next week)\b", re.IGNORECASE
+)
+
+# Maximum messages fetched for "today's updates" — keeps the prompt bounded.
+_CONTEXT_CHAR_BUDGET = 6_000
+_DOC_CHAR_LIMIT = 1_200
+_TODAY_MESSAGES_LIMIT = 50
+
+
+def _is_missing_collection_error(message: str) -> bool:
+ if not message:
+ return False
+ lowered = message.lower()
+ collection = settings.COLLECTION_NAME.lower()
+ if collection not in lowered:
+ return False
+ return any(needle in lowered for needle in ("doesn't exist", "does not exist", "not found"))
+
+
+def _invoke_with_timeout(chain, question: str, timeout_seconds: int = _ASK_TIMEOUT_SECONDS) -> str:
+ """Run model invocation with a hard timeout to avoid long-hanging requests."""
+ executor = ThreadPoolExecutor(max_workers=1)
+ future = executor.submit(chain.invoke, question)
+ try:
+ return future.result(timeout=timeout_seconds)
+ except KeyboardInterrupt as exc:
+ future.cancel()
+ raise AskError("Request cancelled by user.") from exc
+ except FutureTimeoutError as exc:
+ future.cancel()
+ raise AskError(
+ "LLM request timed out after "
+ f"{timeout_seconds}s. Please retry and check your LLM provider quota/connectivity."
+ ) from exc
+ finally:
+ executor.shutdown(wait=False, cancel_futures=True)
+
+
+def _format_llm_error(exc: Exception) -> str:
+ """Convert provider errors into short messages suitable for API/CLI users."""
+ message = str(exc)
+ lowered = message.lower()
+
+ if "resource_exhausted" in lowered or "quota" in lowered or "429" in lowered:
+ return (
+ "LLM quota/rate limit was exceeded. "
+ "Check your provider billing/quota, wait a bit, then retry."
+ )
+
+ if "api key" in lowered or "permission denied" in lowered or "unauthorized" in lowered:
+ return (
+ "Invalid API credentials. "
+ "Verify your provider API key(s) in the .env file."
+ )
+
+ return f"Request failed: {message}"
+
+
+def _format_context(docs: list[Document]) -> str:
+ if not docs:
+ return "No relevant context retrieved."
+
+ chunks = []
+ total_chars = 0
+
+ for i, doc in enumerate(docs, start=1):
+ text = " ".join((doc.page_content or "").split())
+ text = text[:_DOC_CHAR_LIMIT]
+
+ metadata = doc.metadata or {}
+ source = metadata.get("source") or metadata.get("channel_id") or "unknown"
+ date = metadata.get("date") or metadata.get("timestamp") or "unknown"
+
+ chunk = f"[{i}] source={source} | date={date}\\n{text}"
+
+ if total_chars + len(chunk) > _CONTEXT_CHAR_BUDGET:
+ logger.debug(
+ "[context] Budget exhausted after %d/%d docs (%d chars used).",
+ i - 1, len(docs), total_chars,
+ )
+ break
+
+ chunks.append(chunk)
+ total_chars += len(chunk)
+
+ return "\\n\\n".join(chunks) if chunks else "No relevant context retrieved."
+
+
+def _normalize_question(question: str) -> str:
+ normalized = " ".join(question.strip().split())
+ for pattern, replacement in _QUESTION_REWRITES:
+ normalized = pattern.sub(replacement, normalized)
+ return normalized
+
+def _doc_key(doc: Document) -> tuple[str, str, str]:
+ """Stable deduplication key for a retrieved document."""
+ metadata = doc.metadata or {}
+ message_id = str(metadata.get("message_id") or "")
+ source = str(metadata.get("source") or "")
+ content_fp = " ".join((doc.page_content or "").split())[:200]
+ return (source, message_id, content_fp)
+
+def _merge_docs(primary: list[Document], secondary: list[Document], limit: int) -> list[Document]:
+ merged: list[Document] = []
+ seen: set[tuple[str, str, str]] = set()
+
+ for doc in primary + secondary:
+ key = _doc_key(doc)
+ if key in seen:
+ continue
+ seen.add(key)
+ merged.append(doc)
+ if len(merged) >= limit:
+ break
+
+ return merged
+
+
+def _retrieve_with_rewrites(question: str, retriever, k: int = 6) -> list[Document]:
+ normalized = _normalize_question(question)
+ needs_rewrite = normalized.lower() != question.strip().lower()
+
+ if not needs_rewrite:
+ return retriever.invoke(question)
+
+ with ThreadPoolExecutor(max_workers=2) as executor:
+ future_primary = executor.submit(retriever.invoke, question)
+ future_secondary = executor.submit(retriever.invoke, normalized)
+ primary = future_primary.result()
+ secondary = future_secondary.result()
+
+ return _merge_docs(primary, secondary, limit=k)
+
+
+def _augment_temporal_question(question: str) -> str:
+ if not _TEMPORAL_KEYWORDS_PATTERN.search(question):
+ return question
+ ref_date = datetime.now(timezone.utc).date().isoformat()
+ return f"{question}\nReference date (UTC): {ref_date}"
+
+
+def _is_recency_message_query(question: str) -> bool:
+ return bool(_RECENCY_MESSAGE_PATTERN.search(question.strip()))
+
+
+def _is_today_updates_query(question: str) -> bool:
+ return bool(_TODAY_UPDATES_PATTERN.search(question.strip()))
+
+
+def _parse_iso_timestamp(value: str) -> datetime | None:
+ if not value:
+ return None
+ try:
+ return datetime.fromisoformat(value)
+ except ValueError:
+ return None
+
+
+def _build_discord_filter():
+ """Return a Qdrant filter that restricts results to Discord messages."""
+ from qdrant_client.http import models as qdrant_models
+
+ return qdrant_models.Filter(
+ must=[
+ qdrant_models.FieldCondition(
+ key="metadata.source",
+ match=qdrant_models.MatchValue(value="discord"),
+ )
+ ]
+ )
+
+
+def _latest_discord_message() -> str | None:
+ """Return the single most-recent Discord message using server-side ordering.
+
+ Replaces the old full-scan approach: instead of pulling all points into
+ Python memory and sorting there, we ask Qdrant to order by timestamp
+ descending and return only the first record. This is O(log N) on the
+ server and transfers exactly one point over the wire.
+ """
+ from qdrant_client.http import models as qdrant_models
+
+ vector_store = get_vector_store()
+ client = vector_store.client
+ collection_name = vector_store.collection_name
+
+ # order_by with desc direction was introduced in Qdrant 1.8.
+ # With limit=1 we fetch only the single latest point — no pagination needed.
+ points, _ = client.scroll(
+ collection_name=collection_name,
+ scroll_filter=_build_discord_filter(),
+ order_by=qdrant_models.OrderBy(
+ key="metadata.timestamp",
+ direction=qdrant_models.Direction.DESC,
+ ),
+ with_payload=True,
+ with_vectors=False,
+ limit=1,
+ )
+
+ if not points:
+ return None
+
+ payload = points[0].payload or {}
+ metadata = payload.get("metadata") or {}
+ author = metadata.get("author") or "unknown"
+ timestamp = metadata.get("timestamp") or "unknown"
+ channel_id = metadata.get("channel_id") or "unknown"
+ text = str(payload.get("page_content") or "").strip() or "No text content."
+
+ return (
+ f"Latest Discord message:\n"
+ f"- author: {author}\n"
+ f"- timestamp: {timestamp}\n"
+ f"- channel_id: {channel_id}\n"
+ f"- content: {text}"
+ )
+
+
+def _today_discord_messages(reference_date) -> list[dict]:
+ """Return today's Discord messages, sorted newest-first, via Qdrant ordering.
+
+ Key changes vs. the old implementation:
+ - Sorting is done by Qdrant (order_by DESC) — no in-process sort.
+ - We stop fetching as soon as we've scrolled past today's date, so we
+ never load the entire collection into memory.
+ - Results are capped at _TODAY_MESSAGES_LIMIT to bound prompt size.
+ """
+ from qdrant_client.http import models as qdrant_models
+
+ vector_store = get_vector_store()
+ client = vector_store.client
+ collection_name = vector_store.collection_name
+
+ results: list[dict] = []
+ next_offset = None
+
+ while len(results) < _TODAY_MESSAGES_LIMIT:
+ points, next_offset = client.scroll(
+ collection_name=collection_name,
+ scroll_filter=_build_discord_filter(),
+ order_by=qdrant_models.OrderBy(
+ key="metadata.timestamp",
+ direction=qdrant_models.Direction.DESC,
+ ),
+ with_payload=True,
+ with_vectors=False,
+ # Fetch in pages of 64; small enough to stay lean, large enough to
+ # avoid round-trip overhead on busy channels.
+ limit=64,
+ offset=next_offset,
+ )
+
+ if not points:
+ break
+
+ for point in points:
+ payload = point.payload or {}
+ metadata = payload.get("metadata") or {}
+ timestamp_str = str(metadata.get("timestamp") or "")
+ dt = _parse_iso_timestamp(timestamp_str)
+
+ if dt is None:
+ continue
+
+ # Because we're iterating newest-first, the moment we see a point
+ # older than today we can stop — everything after will be older too.
+ if dt.date() < reference_date:
+ return results
+
+ if dt.date() == reference_date:
+ results.append(payload)
+ if len(results) >= _TODAY_MESSAGES_LIMIT:
+ return results
+
+ if next_offset is None:
+ break
+
+ return results
+
+
+def _answer_from_latest_message_with_llm(question: str, latest_message: str, llm) -> str:
+ """Use the LLM to phrase an answer grounded in the latest Discord message."""
+ prompt = (
+ "You are answering a question about the latest Discord message.\n"
+ "Use only the provided latest-message data and do not invent details.\n"
+ "Keep the answer concise and natural.\n\n"
+ f"User question: {question}\n\n"
+ f"{latest_message}\n"
+ )
+ chain = RunnableLambda(lambda p: _extract_llm_content(llm.invoke(p)))
+ return _invoke_with_timeout(chain, prompt)
+
+
+def _answer_today_updates_with_llm(question: str, messages: list[dict], llm, reference_date: str) -> str:
+ rows = []
+ for item in messages[:8]:
+ metadata = item.get("metadata") or {}
+ rows.append(
+ f"- {metadata.get('timestamp', 'unknown')} | {metadata.get('author', 'unknown')}: "
+ f"{str(item.get('page_content') or '').strip()}"
+ )
+ prompt = (
+ "You are answering a question about today's Discord updates.\n"
+ "Use only the provided message list and do not invent details.\n"
+ f"Reference date (UTC): {reference_date}\n\n"
+ f"User question: {question}\n\n"
+ "Messages:\n"
+ + "\n".join(rows)
+ )
+ chain = RunnableLambda(lambda p: _extract_llm_content(llm.invoke(p)))
+ return _invoke_with_timeout(chain, prompt)
+
+
+def _extract_llm_content(result) -> str:
+ return result.content if hasattr(result, "content") else str(result)
+
+
+def ask_question(question: str) -> str:
+ """Answer a user question from the configured RAG pipeline."""
+ if _is_today_updates_query(question):
+ try:
+ llm = get_llm()
+ ref_date = datetime.now(timezone.utc).date()
+ today_messages = _today_discord_messages(reference_date=ref_date)
+ if today_messages:
+ return _answer_today_updates_with_llm(
+ question=question,
+ messages=today_messages,
+ llm=llm,
+ reference_date=ref_date.isoformat(),
+ )
+ latest = _latest_discord_message()
+ if latest:
+ return _answer_from_latest_message_with_llm(question, latest, llm)
+ except Exception:
+ pass
+
+ if _is_recency_message_query(question):
+ latest = None
+ try:
+ latest = _latest_discord_message()
+ if latest:
+ llm = get_llm()
+ return _answer_from_latest_message_with_llm(question, latest, llm)
+ except Exception:
+ if latest:
+ return latest
+
+ try:
+ llm = get_llm()
+ except ConfigurationError as exc:
+ raise AskConfigError(str(exc)) from exc
+
+ try:
+ retriever = get_hybrid_retriever(k=6)
+ except ConfigurationError as exc:
+ raise AskConfigError(str(exc)) from exc
+ except Exception as exc:
+ message = str(exc)
+ lowered = message.lower()
+ is_lock = "already accessed by another instance of qdrant client" in lowered
+ is_connection = any(
+ needle in lowered
+ for needle in (
+ "connection refused",
+ "failed to establish a new connection",
+ "max retries exceeded",
+ "connection error",
+ "connectionerror",
+ "timed out",
+ "timeout",
+ "name or service not known",
+ "temporary failure in name resolution",
+ )
+ )
+ if is_lock or is_connection:
+ raise AskConfigError(
+ "Vector store is unavailable. Start Qdrant and ingest Discord data first."
+ ) from exc
+ raise AskConfigError(f"Vector store initialization failed: {message}") from exc
+
+ prompt = build_rag_prompt_template()
+ normalized_question = _normalize_question(question)
+ effective_question = _augment_temporal_question(normalized_question)
+
+ def _build_chain(model):
+ def _debug_retrieve(q):
+ docs = _retrieve_with_rewrites(q, retriever, k=6)
+ logger.info(f"[DEBUG] Retrieved {len(docs)} documents for question: {q}")
+ for i, doc in enumerate(docs):
+ logger.info(f"[DEBUG] Doc {i}: {doc.page_content[:200]}... | metadata: {doc.metadata}")
+ return docs
+
+ return (
+ {
+ "context": RunnableLambda(lambda q: _debug_retrieve(q))
+ | RunnableLambda(_format_context),
+ "question": RunnableLambda(lambda _q: effective_question),
+ }
+ | prompt
+ | model
+ | StrOutputParser()
+ )
+
+ chain = _build_chain(llm)
+
+ try:
+ return _invoke_with_timeout(chain, question)
+ except Exception as exc:
+ if _is_missing_collection_error(str(exc)):
+ raise AskConfigError(
+ "Vector store is not initialized yet. "
+ f"Qdrant collection `{settings.COLLECTION_NAME}` does not exist. "
+ "Ingest Discord data first (POST /api/v1/ingest/discord)."
+ ) from exc
+ raise AskError(_format_llm_error(exc)) from exc
diff --git a/tests/.gitkeep b/tests/.gitkeep
deleted file mode 100644
index 02de518..0000000
--- a/tests/.gitkeep
+++ /dev/null
@@ -1 +0,0 @@
-# Placeholder to ensure tests/ directory is tracked.
diff --git a/tests/api/test_routes.py b/tests/api/test_routes.py
new file mode 100644
index 0000000..4c957c1
--- /dev/null
+++ b/tests/api/test_routes.py
@@ -0,0 +1,88 @@
+import asyncio
+
+import httpx
+
+import api.routes as routes
+from services.qa_service import AskError
+from main import app
+
+
+def request(method: str, path: str, **kwargs):
+ async def _send():
+ transport = httpx.ASGITransport(app=app)
+ async with httpx.AsyncClient(transport=transport, base_url="http://testserver") as client:
+ return await client.request(method, path, **kwargs)
+
+ return asyncio.run(_send())
+
+
+def test_health_endpoint_returns_ok():
+ res = request("GET", "/api/v1/health")
+
+ assert res.status_code == 200
+ body = res.json()
+ assert body["status"] == "ok"
+ assert "llm_provider" in body
+
+
+def test_ask_rejects_empty_question():
+ res = request("POST", "/api/v1/ask", json={"question": " "})
+
+ assert res.status_code == 400
+ assert "cannot be empty" in res.json()["detail"].lower()
+
+
+def test_ask_success(monkeypatch):
+ monkeypatch.setattr(routes, "ask_question", lambda q: "answer: " + q)
+
+ res = request("POST", "/api/v1/ask", json={"question": "hello"})
+
+ assert res.status_code == 200
+ assert res.json() == {"question": "hello", "answer": "answer: hello"}
+
+
+def test_ask_error_is_mapped_to_502(monkeypatch):
+ def _raise(_q):
+ raise AskError("upstream failed")
+
+ monkeypatch.setattr(routes, "ask_question", _raise)
+
+ res = request("POST", "/api/v1/ask", json={"question": "hello"})
+
+ assert res.status_code == 502
+ assert "upstream failed" in res.json()["detail"]
+
+
+def test_ingest_discord_requires_token(monkeypatch):
+ monkeypatch.setattr(routes.settings, "DISCORD_BOT_TOKEN", None)
+ monkeypatch.setattr(routes.settings, "DISCORD_CHANNEL_IDS", "123")
+
+ res = request("POST", "/api/v1/ingest/discord")
+
+ assert res.status_code == 400
+ assert "DISCORD_BOT_TOKEN" in res.json()["detail"]
+
+
+def test_ingest_discord_partial_response(monkeypatch):
+ monkeypatch.setattr(routes.settings, "DISCORD_BOT_TOKEN", "x")
+ monkeypatch.setattr(routes.settings, "DISCORD_CHANNEL_IDS", "123")
+
+ async def _fake_ingest():
+ return {
+ "channels": {"123": 5},
+ "errors": {"456": "missing access"},
+ "total_documents": 5,
+ }
+
+ import core.ingestion as ingestion
+
+ monkeypatch.setattr(ingestion, "ingest_from_discord", _fake_ingest)
+
+ res = request("POST", "/api/v1/ingest/discord")
+
+ assert res.status_code == 200
+ body = res.json()
+ assert body["status"] == "partial"
+ assert body["documents_ingested"] == 5
+ assert body["details"]["channels"] == {"123": 5}
+ assert body["details"]["errors"] == {"456": "missing access"}
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..dc7a40f
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,11 @@
+import pytest
+from core.config import settings
+
+@pytest.fixture(autouse=True)
+def mock_env_vars(monkeypatch):
+ """Provide dummy environment variables for all tests to bypass strict Cloud-only checks."""
+ monkeypatch.setattr(settings, "QDRANT_URL", "https://dummy.qdrant.io:6333")
+ monkeypatch.setattr(settings, "QDRANT_API_KEY", "dummy-api-key")
+ monkeypatch.setattr(settings, "GEMINI_API_KEY", "dummy-gemini-key")
+ monkeypatch.setattr(settings, "DISCORD_BOT_TOKEN", "dummy-discord-token")
+ monkeypatch.setattr(settings, "DISCORD_CHANNEL_IDS", "123456789")
diff --git a/tests/unit/test_discord_listener.py b/tests/unit/test_discord_listener.py
new file mode 100644
index 0000000..9e99c8b
--- /dev/null
+++ b/tests/unit/test_discord_listener.py
@@ -0,0 +1,152 @@
+"""
+Unit tests for discord_listener.handle_message_create.
+
+We test the dict-based handler in isolation — no real Discord connection or
+Qdrant needed. The discord.py import only happens inside run_discord_listener(),
+so these tests work without discord.py installed.
+"""
+
+import asyncio
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from core import discord_listener
+
+
+# ---------------------------------------------------------------------------
+# Fixtures
+# ---------------------------------------------------------------------------
+
+WATCHED = {"111111111111111111", "222222222222222222"}
+
+VALID_MESSAGE = {
+ "id": "999",
+ "channel_id": "111111111111111111",
+ "content": "New sprint kick-off tomorrow at 10am",
+ "author": {"username": "alice"},
+ "timestamp": "2026-03-15T09:00:00+00:00",
+ "embeds": [],
+}
+
+
+# ---------------------------------------------------------------------------
+# handle_message_create — channel filtering
+# ---------------------------------------------------------------------------
+
+def test_handle_message_create_ignores_wrong_channel(monkeypatch):
+ """Messages in unwatched channels must not trigger ingestion."""
+ called = []
+ monkeypatch.setattr(discord_listener, "ingest_documents", lambda docs: called.append(docs) or 0)
+
+ asyncio.run(
+ discord_listener.handle_message_create(
+ {**VALID_MESSAGE, "channel_id": "999999999999999999"},
+ WATCHED,
+ )
+ )
+
+ assert called == [], "ingest_documents should not be called for unwatched channels"
+
+
+def test_handle_message_create_ingests_and_updates_state(monkeypatch):
+ """Valid message in a watched channel must be ingested and state updated."""
+ ingested = []
+ state_updates = []
+
+ monkeypatch.setattr(
+ discord_listener,
+ "ingest_documents",
+ lambda docs: ingested.extend(docs) or len(docs),
+ )
+ monkeypatch.setattr(
+ discord_listener,
+ "update_last_ingested_message_id",
+ lambda channel_id, msg_id: state_updates.append((channel_id, msg_id)),
+ )
+
+ asyncio.run(discord_listener.handle_message_create(VALID_MESSAGE, WATCHED))
+
+ assert len(ingested) == 1
+ assert ingested[0].page_content == "[alice] New sprint kick-off tomorrow at 10am"
+ assert state_updates == [("111111111111111111", "999")]
+
+
+def test_handle_message_create_skips_blank_content(monkeypatch):
+ """Empty message content must not produce any documents or state updates."""
+ ingested = []
+ state_updates = []
+
+ monkeypatch.setattr(
+ discord_listener,
+ "ingest_documents",
+ lambda docs: ingested.extend(docs) or len(docs),
+ )
+ monkeypatch.setattr(
+ discord_listener,
+ "update_last_ingested_message_id",
+ lambda channel_id, msg_id: state_updates.append((channel_id, msg_id)),
+ )
+
+ asyncio.run(
+ discord_listener.handle_message_create(
+ {**VALID_MESSAGE, "content": " "},
+ WATCHED,
+ )
+ )
+
+ assert ingested == []
+ assert state_updates == []
+
+
+# ---------------------------------------------------------------------------
+# _message_to_dict — shape contract
+# ---------------------------------------------------------------------------
+
+def test_message_to_dict_produces_expected_shape():
+ """_message_to_dict must produce the same dict shape as the REST API returns."""
+ from datetime import datetime, timezone
+
+ # Build a minimal fake discord.Message-like object using MagicMock
+ embed = MagicMock()
+ embed.title = "Agenda"
+ embed.description = "Sprint review"
+
+ msg = MagicMock()
+ msg.id = 123456789
+ msg.channel.id = 111111111111111111
+ msg.content = "Hello world"
+ msg.author.name = "bob"
+ msg.created_at = datetime(2026, 3, 15, 9, 0, 0, tzinfo=timezone.utc)
+ msg.embeds = [embed]
+
+ result = discord_listener._message_to_dict(msg)
+
+ assert result == {
+ "id": "123456789",
+ "channel_id": "111111111111111111",
+ "content": "Hello world",
+ "author": {"username": "bob"},
+ "timestamp": "2026-03-15T09:00:00+00:00",
+ "embeds": [{"title": "Agenda", "description": "Sprint review"}],
+ }
+
+
+# ---------------------------------------------------------------------------
+# run_discord_listener — guard clauses
+# ---------------------------------------------------------------------------
+
+def test_run_discord_listener_returns_early_without_token(caplog):
+ """Missing token must log an error and return without connecting."""
+ import logging
+ with caplog.at_level(logging.ERROR, logger="core.discord_listener"):
+ asyncio.run(discord_listener.run_discord_listener("", ["111"]))
+ assert any("DISCORD_BOT_TOKEN" in r.message for r in caplog.records)
+
+
+def test_run_discord_listener_returns_early_without_channels(caplog):
+ """Missing channel list must log an error and return without connecting."""
+ import logging
+ with caplog.at_level(logging.ERROR, logger="core.discord_listener"):
+ asyncio.run(discord_listener.run_discord_listener("some-token", []))
+ assert any("No channel IDs" in r.message for r in caplog.records)
diff --git a/tests/unit/test_hybrid_search.py b/tests/unit/test_hybrid_search.py
new file mode 100644
index 0000000..227a7e1
--- /dev/null
+++ b/tests/unit/test_hybrid_search.py
@@ -0,0 +1,141 @@
+"""Test hybrid search for specific Discord Message ID."""
+
+import pytest
+from unittest.mock import MagicMock, patch
+
+from langchain_core.documents import Document
+
+from core.hybrid_search import hybrid_search
+from core.config import settings
+
+
+def test_hybrid_search_returns_specific_message_as_top_hit():
+ """Test that searching for a specific Discord Message ID returns correct result as top hit."""
+
+ # Sample documents with known message IDs
+ docs = [
+ Document(
+ page_content="[user1] Hello world",
+ metadata={
+ "source": "discord",
+ "channel_id": "123",
+ "message_id": "456",
+ "author": "user1",
+ "timestamp": "2026-03-18T10:00:00"
+ }
+ ),
+ Document(
+ page_content="[user2] Another message",
+ metadata={
+ "source": "discord",
+ "channel_id": "123",
+ "message_id": "789",
+ "author": "user2",
+ "timestamp": "2026-03-18T10:01:00"
+ }
+ ),
+ Document(
+ page_content="[user3] Third message with ID-123456 in content",
+ metadata={
+ "source": "discord",
+ "channel_id": "123",
+ "message_id": "999",
+ "author": "user3",
+ "timestamp": "2026-03-18T10:02:00"
+ }
+ ),
+ ]
+
+ # Mock the Qdrant client to return our test documents
+ mock_hit1 = MagicMock()
+ mock_hit1.payload = {
+ "page_content": "[user1] Hello world",
+ "metadata": {
+ "source": "discord",
+ "channel_id": "123",
+ "message_id": "456",
+ "author": "user1",
+ "timestamp": "2026-03-18T10:00:00"
+ }
+ }
+
+ mock_hit2 = MagicMock()
+ mock_hit2.payload = {
+ "page_content": "[user2] Another message",
+ "metadata": {
+ "source": "discord",
+ "channel_id": "123",
+ "message_id": "789",
+ "author": "user2",
+ "timestamp": "2026-03-18T10:01:00"
+ }
+ }
+
+ mock_hit3 = MagicMock()
+ mock_hit3.payload = {
+ "page_content": "[user3] Third message with ID-123456 in content",
+ "metadata": {
+ "source": "discord",
+ "channel_id": "123",
+ "message_id": "999",
+ "author": "user3",
+ "timestamp": "2026-03-18T10:02:00"
+ }
+ }
+
+ # Mock query_points response - returns an object with .points attribute
+ mock_result = MagicMock()
+ mock_result.points = [mock_hit1, mock_hit2, mock_hit3]
+
+ mock_client = MagicMock()
+ mock_client.query_points.return_value = mock_result
+
+ mock_embeddings = MagicMock()
+ mock_embeddings.embed_query.return_value = [0.1] * 3072
+
+ with patch('core.hybrid_search._get_qdrant_client', return_value=mock_client):
+ with patch('core.hybrid_search.get_embeddings', return_value=mock_embeddings):
+ # Search for message with ID 456 - BM25 will search metadata.message_id
+ results = hybrid_search(query="456", k=3)
+
+ # Verify the top result is the document with message_id 456
+ assert len(results) > 0
+ assert results[0].metadata["message_id"] == "456"
+
+
+def test_hybrid_search_with_keyword_match():
+ """Test that keyword search finds message with specific text."""
+
+ mock_hit = MagicMock()
+ mock_hit.payload = {
+ "page_content": "[user3] Third message with ID-123456 in content",
+ "metadata": {
+ "source": "discord",
+ "channel_id": "123",
+ "message_id": "999",
+ "author": "user3",
+ "timestamp": "2026-03-18T10:02:00"
+ }
+ }
+
+ # Mock query_points response
+ mock_result = MagicMock()
+ mock_result.points = [mock_hit]
+
+ mock_client = MagicMock()
+ mock_client.query_points.return_value = mock_result
+
+ mock_embeddings = MagicMock()
+ mock_embeddings.embed_query.return_value = [0.1] * 3072
+
+ with patch('core.hybrid_search._get_qdrant_client', return_value=mock_client):
+ with patch('core.hybrid_search.get_embeddings', return_value=mock_embeddings):
+ # Search for specific text
+ results = hybrid_search(query="ID-123456", k=1)
+
+ assert len(results) > 0
+ assert "ID-123456" in results[0].page_content
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-v"])
diff --git a/tests/unit/test_ingestion.py b/tests/unit/test_ingestion.py
new file mode 100644
index 0000000..f21e4b4
--- /dev/null
+++ b/tests/unit/test_ingestion.py
@@ -0,0 +1,264 @@
+import asyncio
+from types import SimpleNamespace
+
+import httpx
+from langchain_core.documents import Document
+from qdrant_client.http.exceptions import UnexpectedResponse
+
+from core import ingestion
+from core.discord_fetcher import DiscordFetchError
+
+
+def test_docs_from_discord_messages_skips_blank_content_and_appends_embeds():
+ messages = [
+ {"id": "ignored", "content": " "},
+ {
+ "id": "42",
+ "content": "Standup in 10 minutes",
+ "author": {"username": "alice"},
+ "timestamp": "2026-03-10T08:30:00+00:00",
+ "embeds": [{"title": "Agenda", "description": "Sprint review"}],
+ },
+ ]
+
+ docs = ingestion._docs_from_discord_messages(messages, channel_id="chan-1")
+
+ assert len(docs) == 1
+ assert docs[0].page_content == "[alice] Standup in 10 minutes\nAgenda\nSprint review"
+ assert docs[0].metadata == {
+ "source": "discord",
+ "channel_id": "chan-1",
+ "message_id": "42",
+ "author": "alice",
+ "timestamp": "2026-03-10T08:30:00+00:00",
+ }
+
+
+def test_ingest_documents_bootstraps_missing_collection(monkeypatch):
+ documents = [Document(page_content="Release planning", metadata={"source": "json_file"})]
+ bootstrapped = {}
+
+ class FakeClient:
+ def collection_exists(self, name: str) -> bool:
+ return False # Simulate missing collection
+
+ class BrokenVectorStore:
+ def add_documents(self, _documents):
+ raise RuntimeError("Collection microclub_knowledge not found")
+
+ class WorkingVectorStore:
+ def __init__(self):
+ self.added = []
+
+ def add_documents(self, new_documents):
+ self.added.extend(new_documents)
+
+ working_store = WorkingVectorStore()
+
+ class VectorStoreGetter:
+ def __init__(self):
+ self.calls = 0
+ self.cache_clears = 0
+
+ def __call__(self):
+ self.calls += 1
+ if self.calls == 1:
+ return BrokenVectorStore()
+ return working_store
+
+ def cache_clear(self):
+ self.cache_clears += 1
+
+ getter = VectorStoreGetter()
+
+ monkeypatch.setattr(ingestion, "get_qdrant_client", lambda: FakeClient())
+ monkeypatch.setattr(ingestion, "get_vector_store", getter)
+ monkeypatch.setattr(
+ ingestion,
+ "get_embeddings",
+ lambda: SimpleNamespace(embed_query=lambda _query: [0.1, 0.2, 0.3, 0.4]),
+ )
+ monkeypatch.setattr(
+ ingestion,
+ "_ensure_collection_exists",
+ lambda vector_size: bootstrapped.setdefault("vector_size", vector_size),
+ )
+
+ count = ingestion.ingest_documents(documents)
+
+ assert count == 1
+ assert bootstrapped["vector_size"] == 4
+ assert getter.cache_clears == 2 # cache cleared twice: once for index check, once after bootstrap
+ assert working_store.added == documents
+
+
+def test_ingest_documents_bootstraps_missing_collection_when_qdrant_returns_404(monkeypatch):
+ documents = [Document(page_content="Release planning", metadata={"source": "json_file"})]
+ bootstrapped = {}
+
+ class FakeClient:
+ def collection_exists(self, name: str) -> bool:
+ return False # Simulate missing collection
+
+ class BrokenVectorStore:
+ def add_documents(self, _documents):
+ raise UnexpectedResponse(
+ status_code=404,
+ reason_phrase="Not Found",
+ content=b'{"status":{"error":"Not found: Collection `microclub_knowledge` doesn\\\'t exist!"}}',
+ headers=httpx.Headers(),
+ )
+
+ class WorkingVectorStore:
+ def __init__(self):
+ self.added = []
+
+ def add_documents(self, new_documents):
+ self.added.extend(new_documents)
+
+ working_store = WorkingVectorStore()
+
+ class VectorStoreGetter:
+ def __init__(self):
+ self.calls = 0
+ self.cache_clears = 0
+
+ def __call__(self):
+ self.calls += 1
+ if self.calls == 1:
+ return BrokenVectorStore()
+ return working_store
+
+ def cache_clear(self):
+ self.cache_clears += 1
+
+ getter = VectorStoreGetter()
+
+ monkeypatch.setattr(ingestion, "get_qdrant_client", lambda: FakeClient())
+ monkeypatch.setattr(ingestion, "get_vector_store", getter)
+ monkeypatch.setattr(
+ ingestion,
+ "get_embeddings",
+ lambda: SimpleNamespace(embed_query=lambda _query: [0.1, 0.2, 0.3, 0.4]),
+ )
+ monkeypatch.setattr(
+ ingestion,
+ "_ensure_collection_exists",
+ lambda vector_size: bootstrapped.setdefault("vector_size", vector_size),
+ )
+
+ count = ingestion.ingest_documents(documents)
+
+ assert count == 1
+ assert bootstrapped["vector_size"] == 4
+ assert getter.cache_clears == 2 # cache cleared twice: once for index check, once after bootstrap
+ assert working_store.added == documents
+
+
+def test_ingest_documents_ignores_missing_collection_during_duplicate_check(monkeypatch):
+ documents = [
+ Document(
+ page_content="Release planning",
+ metadata={"source": "discord", "channel_id": "chan-1", "message_id": "m1"},
+ )
+ ]
+ bootstrapped = {}
+
+ class FakeClient:
+ def collection_exists(self, name: str) -> bool:
+ return False # Simulate missing collection
+
+ def count(self, **_kwargs):
+ raise UnexpectedResponse(
+ status_code=404,
+ reason_phrase="Not Found",
+ content=b'{"status":{"error":"Not found: Collection `microclub_knowledge` doesn\\\'t exist!"}}',
+ headers=httpx.Headers(),
+ )
+
+ class BrokenVectorStore:
+ def add_documents(self, _documents):
+ raise RuntimeError("Collection microclub_knowledge not found")
+
+ class WorkingVectorStore:
+ def __init__(self):
+ self.added = []
+
+ def add_documents(self, new_documents):
+ self.added.extend(new_documents)
+
+ working_store = WorkingVectorStore()
+
+ class VectorStoreGetter:
+ def __init__(self):
+ self.calls = 0
+ self.cache_clears = 0
+
+ def __call__(self):
+ self.calls += 1
+ if self.calls == 1:
+ return BrokenVectorStore()
+ return working_store
+
+ def cache_clear(self):
+ self.cache_clears += 1
+
+ getter = VectorStoreGetter()
+
+ monkeypatch.setattr(ingestion, "get_qdrant_client", lambda: FakeClient())
+ monkeypatch.setattr(ingestion, "get_vector_store", getter)
+ monkeypatch.setattr(
+ ingestion,
+ "get_embeddings",
+ lambda: SimpleNamespace(embed_query=lambda _query: [0.1, 0.2, 0.3, 0.4]),
+ )
+ monkeypatch.setattr(
+ ingestion,
+ "_ensure_collection_exists",
+ lambda vector_size: bootstrapped.setdefault("vector_size", vector_size),
+ )
+
+ count = ingestion.ingest_documents(documents)
+
+ assert count == 1
+ assert bootstrapped["vector_size"] == 4
+ assert getter.cache_clears == 2 # cache cleared twice: once for index check, once after bootstrap
+ assert working_store.added == documents
+
+
+def test_ingest_from_discord_collects_successes_and_permission_errors(monkeypatch):
+ monkeypatch.setattr(ingestion.settings, "DISCORD_BOT_TOKEN", "discord-token")
+ monkeypatch.setattr(ingestion.settings, "DISCORD_CHANNEL_IDS", "denied,open")
+
+ # Simulate first run: no prior cursor for any channel
+ monkeypatch.setattr(ingestion, "get_last_ingested_message_id", lambda channel_id: None)
+ monkeypatch.setattr(ingestion, "update_last_ingested_message_id", lambda channel_id, msg_id: None)
+
+ async def fake_fetch_channel_messages(
+ bot_token: str,
+ channel_id: str,
+ limit=None,
+ after=None,
+ ):
+ assert bot_token == "discord-token"
+ assert limit is None # bootstrap always passes limit=None now
+ assert after is None # no cursor on first run
+ if channel_id == "denied":
+ raise DiscordFetchError(channel_id=channel_id, status_code=403, message="forbidden")
+ return [
+ {
+ "id": "m1",
+ "content": "Shipping update",
+ "author": {"username": "bob"},
+ "timestamp": "2026-03-10T09:00:00+00:00",
+ }
+ ]
+
+ monkeypatch.setattr(ingestion, "fetch_channel_messages", fake_fetch_channel_messages)
+ monkeypatch.setattr(ingestion, "ingest_documents", lambda docs: len(docs))
+
+ result = asyncio.run(ingestion.ingest_from_discord())
+
+ assert result["channels"] == {"open": 1}
+ assert "Missing access to channel" in result["errors"]["denied"]
+ assert result["total_documents"] == 1
diff --git a/tests/unit/test_llm_provider.py b/tests/unit/test_llm_provider.py
new file mode 100644
index 0000000..0494a92
--- /dev/null
+++ b/tests/unit/test_llm_provider.py
@@ -0,0 +1,71 @@
+import types
+
+import pytest
+
+from core import llm_provider
+
+
+@pytest.fixture
+def restore_settings():
+ original_provider = llm_provider.settings.LLM_PROVIDER
+ original_gemini_key = llm_provider.settings.GEMINI_API_KEY
+ original_model_name = llm_provider.settings.MODEL_NAME
+ original_google_model_name = llm_provider.settings.GOOGLE_MODEL_NAME
+ try:
+ yield
+ finally:
+ llm_provider.settings.LLM_PROVIDER = original_provider
+ llm_provider.settings.GEMINI_API_KEY = original_gemini_key
+ llm_provider.settings.MODEL_NAME = original_model_name
+ llm_provider.settings.GOOGLE_MODEL_NAME = original_google_model_name
+
+
+def test_get_llm_rejects_non_google_provider(restore_settings):
+ llm_provider.settings.LLM_PROVIDER = "groq"
+ llm_provider.settings.GEMINI_API_KEY = "dummy"
+
+ with pytest.raises(llm_provider.ConfigurationError, match="Only Gemini"):
+ llm_provider.get_llm()
+
+
+def test_get_llm_requires_google_api_key(restore_settings):
+ llm_provider.settings.LLM_PROVIDER = "google"
+ llm_provider.settings.GEMINI_API_KEY = None
+
+ with pytest.raises(llm_provider.ConfigurationError, match="GEMINI_API_KEY"):
+ llm_provider.get_llm()
+
+
+def test_get_llm_builds_google_client(restore_settings, monkeypatch):
+ captured = {}
+
+ class FakeChatGoogleGenerativeAI:
+ def __init__(self, model, google_api_key, temperature):
+ captured["model"] = model
+ captured["google_api_key"] = google_api_key
+ captured["temperature"] = temperature
+
+ fake_mod = types.SimpleNamespace(ChatGoogleGenerativeAI=FakeChatGoogleGenerativeAI)
+ monkeypatch.setattr(llm_provider, "settings", llm_provider.settings)
+ monkeypatch.setitem(__import__("sys").modules, "langchain_google_genai", fake_mod)
+
+ llm_provider.settings.LLM_PROVIDER = "google"
+ llm_provider.settings.GEMINI_API_KEY = "test-key"
+ llm_provider.settings.MODEL_NAME = None
+ llm_provider.settings.GOOGLE_MODEL_NAME = "gemini-test"
+
+ client = llm_provider.get_llm()
+
+ assert isinstance(client, FakeChatGoogleGenerativeAI)
+ assert captured == {
+ "model": "gemini-test",
+ "google_api_key": "test-key",
+ "temperature": 0,
+ }
+
+
+def test_get_embeddings_requires_google_api_key(restore_settings):
+ llm_provider.settings.GEMINI_API_KEY = None
+
+ with pytest.raises(llm_provider.ConfigurationError, match="GEMINI_API_KEY"):
+ llm_provider.get_embeddings()
diff --git a/tests/unit/test_qa_service.py b/tests/unit/test_qa_service.py
new file mode 100644
index 0000000..5c782f5
--- /dev/null
+++ b/tests/unit/test_qa_service.py
@@ -0,0 +1,221 @@
+from types import SimpleNamespace
+
+import pytest
+from langchain_core.runnables import RunnableLambda
+
+from services import qa_service
+
+
+def test_ask_question_uses_latest_shortcut(monkeypatch):
+ monkeypatch.setattr(qa_service, "_latest_discord_message", lambda: "latest-message")
+ monkeypatch.setattr(
+ qa_service,
+ "_invoke_with_timeout",
+ lambda chain, question, timeout_seconds=30: chain.invoke(question),
+ )
+
+ class FakeLLM:
+ def invoke(self, _prompt):
+ return SimpleNamespace(content="LLM formatted latest answer")
+
+ monkeypatch.setattr(qa_service, "get_llm", lambda: FakeLLM())
+
+ answer = qa_service.ask_question("whats the last message")
+
+ assert answer == "LLM formatted latest answer"
+
+
+def test_latest_discord_message_picks_newest_timestamp(monkeypatch):
+ points = [
+ SimpleNamespace(
+ payload={
+ "page_content": "[b] newest message",
+ "metadata": {
+ "source": "discord",
+ "author": "b",
+ "channel_id": "c2",
+ "timestamp": "2026-03-09T17:39:33+00:00",
+ },
+ }
+ ),
+ SimpleNamespace(
+ payload={
+ "page_content": "[a] older message",
+ "metadata": {
+ "source": "discord",
+ "author": "a",
+ "channel_id": "c1",
+ "timestamp": "2026-03-01T10:00:00+00:00",
+ },
+ }
+ ),
+ ]
+
+ class FakeClient:
+ def scroll(self, **_kwargs):
+ return points, None
+
+ fake_vector_store = SimpleNamespace(
+ client=FakeClient(),
+ collection_name="microclub_knowledge",
+ )
+ monkeypatch.setattr(qa_service, "get_vector_store", lambda: fake_vector_store)
+
+ result = qa_service._latest_discord_message()
+
+ assert result is not None
+ assert "author: b" in result
+ assert "channel_id: c2" in result
+ assert "newest message" in result
+
+
+def test_normalize_question_rewrites_common_broken_phrase():
+ normalized = qa_service._normalize_question("whats the we have today")
+ assert normalized.lower() == "what is do we have today"
+
+
+def test_retrieve_with_rewrites_merges_when_rewrite_changes_query(monkeypatch):
+ original_doc = SimpleNamespace(
+ page_content="original result",
+ metadata={"source": "discord", "message_id": "1"},
+ )
+ normalized_doc = SimpleNamespace(
+ page_content="normalized result",
+ metadata={"source": "discord", "message_id": "2"},
+ )
+
+ class FakeRetriever:
+ def invoke(self, query):
+ if query == "whats the we have today":
+ return [original_doc]
+ return [normalized_doc]
+
+ docs = qa_service._retrieve_with_rewrites("whats the we have today", FakeRetriever(), k=6)
+
+ assert len(docs) == 2
+ assert docs[0].page_content == "original result"
+ assert docs[1].page_content == "normalized result"
+
+
+def test_augment_temporal_question_adds_reference_date():
+ augmented = qa_service._augment_temporal_question("what do we have today")
+ assert "Reference date (UTC): " in augmented
+
+
+def test_augment_temporal_question_keeps_non_temporal_query():
+ question = "who is the dev manager"
+ assert qa_service._augment_temporal_question(question) == question
+
+
+def test_today_updates_query_uses_llm_summary(monkeypatch):
+ monkeypatch.setattr(qa_service, "_is_today_updates_query", lambda _q: True)
+ monkeypatch.setattr(qa_service, "_today_discord_messages", lambda reference_date: [{"metadata": {"timestamp": "2026-03-09T12:00:00+00:00", "author": "u"}, "page_content": "Meeting at 2pm"}])
+ monkeypatch.setattr(qa_service, "_invoke_with_timeout", lambda chain, question, timeout_seconds=30: chain.invoke(question))
+
+ class FakeLLM:
+ def invoke(self, _prompt):
+ return SimpleNamespace(content="Today summary")
+
+ monkeypatch.setattr(qa_service, "get_llm", lambda: FakeLLM())
+
+ answer = qa_service.ask_question("whats the we have today")
+ assert answer == "Today summary"
+
+
+def test_today_updates_pattern_matches_whats_phrase():
+ assert qa_service._is_today_updates_query("whats the we have today")
+
+
+def test_retrieve_with_rewrites_deduplicates_results():
+ shared_doc = SimpleNamespace(
+ page_content="same result",
+ metadata={"source": "discord", "message_id": "same"},
+ )
+
+ class FakeRetriever:
+ def invoke(self, _query):
+ return [shared_doc]
+
+ docs = qa_service._retrieve_with_rewrites("whats the we have today", FakeRetriever(), k=6)
+
+ assert len(docs) == 1
+ assert docs[0].page_content == "same result"
+
+
+def test_today_updates_without_today_messages_falls_back_to_latest(monkeypatch):
+ monkeypatch.setattr(qa_service, "_is_today_updates_query", lambda _q: True)
+ monkeypatch.setattr(qa_service, "_today_discord_messages", lambda reference_date: [])
+ monkeypatch.setattr(qa_service, "_latest_discord_message", lambda: "Latest Discord message:\n- content: fallback")
+ monkeypatch.setattr(
+ qa_service,
+ "_invoke_with_timeout",
+ lambda chain, question, timeout_seconds=30: chain.invoke(question),
+ )
+
+ class FakeLLM:
+ def invoke(self, _prompt):
+ return SimpleNamespace(content="Fallback latest summary")
+
+ monkeypatch.setattr(qa_service, "get_llm", lambda: FakeLLM())
+
+ answer = qa_service.ask_question("what do we have today")
+ assert answer == "Fallback latest summary"
+
+
+
+def test_ask_question_recency_query_returns_latest_message_when_llm_fails(monkeypatch):
+ latest_message = "Latest Discord message:\n- content: fallback"
+
+ monkeypatch.setattr(qa_service, "_latest_discord_message", lambda: latest_message)
+ monkeypatch.setattr(
+ qa_service,
+ "get_llm",
+ lambda: (_ for _ in ()).throw(RuntimeError("gemini unavailable")),
+ )
+
+ answer = qa_service.ask_question("what is the last message")
+
+ assert answer == latest_message
+
+
+def test_ask_question_errors_when_qdrant_is_locked(monkeypatch):
+ monkeypatch.setattr(qa_service, "get_llm", lambda: RunnableLambda(lambda _prompt: "ignored"))
+ monkeypatch.setattr(
+ qa_service,
+ "get_hybrid_retriever",
+ lambda k=6: (_ for _ in ()).throw(
+ RuntimeError("Storage already accessed by another instance of Qdrant client")
+ ),
+ )
+
+ with pytest.raises(qa_service.AskConfigError) as excinfo:
+ qa_service.ask_question("roadmap sync")
+
+ assert "vector store is unavailable" in str(excinfo.value).lower()
+
+
+def test_ask_question_missing_collection_raises_config_error(monkeypatch):
+ class FakeRetriever:
+ def invoke(self, _query):
+ raise RuntimeError("Collection microclub_knowledge not found")
+
+ def fake_hybrid_retriever(k=6):
+ return FakeRetriever()
+
+ monkeypatch.setattr(qa_service, "get_llm", lambda: RunnableLambda(lambda _prompt: "ignored"))
+ monkeypatch.setattr(qa_service, "get_hybrid_retriever", fake_hybrid_retriever)
+ monkeypatch.setattr(
+ qa_service,
+ "build_rag_prompt_template",
+ lambda: RunnableLambda(lambda data: data["question"]),
+ )
+ monkeypatch.setattr(
+ qa_service,
+ "_invoke_with_timeout",
+ lambda chain, question, timeout_seconds=30: chain.invoke(question),
+ )
+
+ with pytest.raises(qa_service.AskConfigError) as excinfo:
+ qa_service.ask_question("when is the next event?")
+
+ assert "does not exist" in str(excinfo.value).lower()
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..96a5dd5
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,2996 @@
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+resolution-markers = [
+ "python_full_version >= '3.14'",
+ "python_full_version == '3.13.*'",
+ "python_full_version == '3.12.*'",
+ "python_full_version == '3.11.*'",
+ "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
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+ { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+]
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+[[package]]
+name = "aiohttp"
+version = "3.13.3"
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+dependencies = [
+ { name = "aiohappyeyeballs" },
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