From d4fbec1f0496c589185eb196a1aebc125beb2a8b Mon Sep 17 00:00:00 2001
From: ian cao <xuhongcao216@gmail.com>
Date: Mon, 20 Jul 2026 17:25:55 +0800
Subject: [PATCH] feat: integrate DataLink semantic service

---
 .env.example                                  |   18 +
 .github/workflows/ci.yml                      |   59 +
 README.md                                     |   11 +-
 README_zh.md                                  |   11 +-
 apps/api/src/builtin-datalink-server.ts       |   80 +
 apps/api/src/config-api.ts                    |   50 +-
 apps/api/src/server.ts                        |    6 +
 docs/en/architecture/overview.md              |    5 +-
 docs/en/guides/datalink.md                    |   84 +
 docs/en/guides/web-workbench.md               |    3 +-
 docs/en/quick-start.md                        |   41 +-
 docs/en/security.md                           |    2 +-
 docs/javascripts/language-switcher.js         |    1 +
 docs/zh/architecture/overview.md              |    5 +-
 docs/zh/guides/datalink.md                    |   84 +
 docs/zh/guides/web-workbench.md               |    3 +-
 docs/zh/quick-start.md                        |   41 +-
 docs/zh/security.md                           |    2 +-
 mkdocs.yml                                    |    1 +
 package.json                                  |   12 +
 scripts/datalink-stack-config.mjs             |   28 +
 scripts/datalink-stack-config.test.mjs        |   26 +
 scripts/dev.mjs                               |  119 +-
 scripts/smoke-builtin-datalink.mjs            |   45 +
 scripts/stack-runner.mjs                      |  151 +
 scripts/start-datalink.mjs                    |   47 +
 scripts/start.mjs                             |    4 +
 services/datalink/.gitignore                  |   26 +
 services/datalink/LICENSE.txt                 |  201 +
 services/datalink/README.md                   |  339 +
 services/datalink/README_zh.md                |  339 +
 .../datalink/datalink_config.example.json     |   26 +
 services/datalink/docs/DESIGN.md              |  553 ++
 services/datalink/docs/DL-en.png              |  Bin 0 -> 1324834 bytes
 services/datalink/docs/DL-zh.png              |  Bin 0 -> 1324834 bytes
 services/datalink/docs/DataMap.png            |  Bin 0 -> 2206888 bytes
 services/datalink/docs/INTERFACE.md           |  751 ++
 services/datalink/docs/README.md              |   15 +
 .../datalink/docs/pipeline/01-connector.md    |  226 +
 .../datalink/docs/pipeline/02-extractor.md    |  176 +
 .../datalink/docs/pipeline/03-profiler.md     |  209 +
 .../datalink/docs/pipeline/04-inferrer.md     |  345 +
 services/datalink/docs/pipeline/05-mapper.md  |  416 +
 .../docs/pipeline/06-storage-and-retrieval.md |  539 ++
 .../pipeline/07-pipeline-orchestration.md     |  376 +
 .../bird/cursor/eval.config.example.json      |   20 +
 .../evaluate/bird/cursor/eval_common.py       |  782 ++
 .../evaluate/bird/cursor/eval_grade.py        |  214 +
 .../datalink/evaluate/bird/cursor/eval_run.py |  202 +
 services/datalink/pyproject.toml              |   77 +
 services/datalink/src/datalink/__init__.py    |    3 +
 .../datalink/src/datalink/api/__init__.py     |    1 +
 services/datalink/src/datalink/api/deps.py    |   54 +
 services/datalink/src/datalink/api/server.py  |  415 +
 .../datalink/src/datalink/builder/__init__.py |    1 +
 .../datalink/src/datalink/builder/pipeline.py | 1053 +++
 .../datalink/src/datalink/cli/__init__.py     |    1 +
 services/datalink/src/datalink/cli/main.py    |  724 ++
 services/datalink/src/datalink/config.py      |  239 +
 .../src/datalink/connector/__init__.py        |    1 +
 .../datalink/src/datalink/connector/base.py   |   44 +
 .../src/datalink/connector/database.py        |  406 +
 .../datalink/src/datalink/connector/file.py   |  168 +
 .../src/datalink/extractor/__init__.py        |    1 +
 .../src/datalink/extractor/tabular.py         |  114 +
 .../datalink/src/datalink/graph/__init__.py   |    1 +
 .../datalink/src/datalink/graph/retrieval.py  | 2241 ++++++
 .../datalink/src/datalink/graph/schema.sql    |   86 +
 .../datalink/src/datalink/graph/storage.py    | 1010 +++
 .../src/datalink/inferrer/__init__.py         |    1 +
 .../src/datalink/inferrer/correlated.py       |  313 +
 .../src/datalink/inferrer/distribution.py     |  172 +
 .../src/datalink/inferrer/joinable.py         |  169 +
 .../datalink/src/datalink/inferrer/synonym.py |  174 +
 .../datalink/src/datalink/mapper/__init__.py  |    1 +
 .../src/datalink/mapper/llm_mapper.py         | 1205 +++
 .../src/datalink/mapper/prompts/__init__.py   |    1 +
 .../mapper/prompts/mapping_prompt.txt         |   19 +
 .../datalink/mapper/prompts/merge_prompt.txt  |   37 +
 .../mapper/prompts/table_comment_prompt.txt   |   13 +
 .../datalink/src/datalink/mcp/__init__.py     |    1 +
 services/datalink/src/datalink/mcp/server.py  |  370 +
 .../datalink/src/datalink/models/__init__.py  |   38 +
 .../src/datalink/models/datasource.py         |   79 +
 services/datalink/src/datalink/models/edge.py |   97 +
 services/datalink/src/datalink/models/node.py |   93 +
 .../datalink/src/datalink/models/profile.py   |   66 +
 .../src/datalink/profiler/__init__.py         |    1 +
 .../datalink/src/datalink/profiler/tabular.py |  354 +
 .../datalink/src/datalink/utils/__init__.py   |   26 +
 .../datalink/src/datalink/utils/credential.py |  323 +
 .../datalink/src/datalink/utils/embedding.py  |  155 +
 services/datalink/src/datalink/utils/ids.py   |   61 +
 .../datalink/src/datalink/utils/sql_ident.py  |   60 +
 services/datalink/tests/conftest.py           |   36 +
 services/datalink/tests/test_builder.py       |  245 +
 services/datalink/tests/test_cli.py           |  433 ++
 services/datalink/tests/test_connector.py     |  247 +
 services/datalink/tests/test_credential.py    |  392 +
 services/datalink/tests/test_data/orders.csv  |   11 +
 .../datalink/tests/test_data/transactions.csv |   11 +
 services/datalink/tests/test_data/users.csv   |   11 +
 .../tests/test_datafoundry_integration.py     |   43 +
 services/datalink/tests/test_extractor.py     |  117 +
 services/datalink/tests/test_ids.py           |   56 +
 services/datalink/tests/test_inferrer.py      |  219 +
 services/datalink/tests/test_mapper.py        |  993 +++
 services/datalink/tests/test_models.py        |  250 +
 services/datalink/tests/test_profiler.py      |  168 +
 services/datalink/tests/test_retrieval.py     |  216 +
 services/datalink/tests/test_sql_ident.py     |   43 +
 services/datalink/tests/test_storage.py       |  419 +
 services/datalink/uv.lock                     | 6735 +++++++++++++++++
 113 files changed, 27653 insertions(+), 155 deletions(-)
 create mode 100644 apps/api/src/builtin-datalink-server.ts
 create mode 100644 docs/en/guides/datalink.md
 create mode 100644 docs/zh/guides/datalink.md
 create mode 100644 scripts/datalink-stack-config.mjs
 create mode 100644 scripts/datalink-stack-config.test.mjs
 create mode 100644 scripts/smoke-builtin-datalink.mjs
 create mode 100644 scripts/stack-runner.mjs
 create mode 100755 scripts/start-datalink.mjs
 create mode 100755 scripts/start.mjs
 create mode 100644 services/datalink/.gitignore
 create mode 100644 services/datalink/LICENSE.txt
 create mode 100644 services/datalink/README.md
 create mode 100644 services/datalink/README_zh.md
 create mode 100644 services/datalink/datalink_config.example.json
 create mode 100644 services/datalink/docs/DESIGN.md
 create mode 100644 services/datalink/docs/DL-en.png
 create mode 100644 services/datalink/docs/DL-zh.png
 create mode 100644 services/datalink/docs/DataMap.png
 create mode 100644 services/datalink/docs/INTERFACE.md
 create mode 100644 services/datalink/docs/README.md
 create mode 100644 services/datalink/docs/pipeline/01-connector.md
 create mode 100644 services/datalink/docs/pipeline/02-extractor.md
 create mode 100644 services/datalink/docs/pipeline/03-profiler.md
 create mode 100644 services/datalink/docs/pipeline/04-inferrer.md
 create mode 100644 services/datalink/docs/pipeline/05-mapper.md
 create mode 100644 services/datalink/docs/pipeline/06-storage-and-retrieval.md
 create mode 100644 services/datalink/docs/pipeline/07-pipeline-orchestration.md
 create mode 100644 services/datalink/evaluate/bird/cursor/eval.config.example.json
 create mode 100644 services/datalink/evaluate/bird/cursor/eval_common.py
 create mode 100644 services/datalink/evaluate/bird/cursor/eval_grade.py
 create mode 100644 services/datalink/evaluate/bird/cursor/eval_run.py
 create mode 100644 services/datalink/pyproject.toml
 create mode 100644 services/datalink/src/datalink/__init__.py
 create mode 100644 services/datalink/src/datalink/api/__init__.py
 create mode 100644 services/datalink/src/datalink/api/deps.py
 create mode 100644 services/datalink/src/datalink/api/server.py
 create mode 100644 services/datalink/src/datalink/builder/__init__.py
 create mode 100644 services/datalink/src/datalink/builder/pipeline.py
 create mode 100644 services/datalink/src/datalink/cli/__init__.py
 create mode 100644 services/datalink/src/datalink/cli/main.py
 create mode 100644 services/datalink/src/datalink/config.py
 create mode 100644 services/datalink/src/datalink/connector/__init__.py
 create mode 100644 services/datalink/src/datalink/connector/base.py
 create mode 100644 services/datalink/src/datalink/connector/database.py
 create mode 100644 services/datalink/src/datalink/connector/file.py
 create mode 100644 services/datalink/src/datalink/extractor/__init__.py
 create mode 100644 services/datalink/src/datalink/extractor/tabular.py
 create mode 100644 services/datalink/src/datalink/graph/__init__.py
 create mode 100644 services/datalink/src/datalink/graph/retrieval.py
 create mode 100644 services/datalink/src/datalink/graph/schema.sql
 create mode 100644 services/datalink/src/datalink/graph/storage.py
 create mode 100644 services/datalink/src/datalink/inferrer/__init__.py
 create mode 100644 services/datalink/src/datalink/inferrer/correlated.py
 create mode 100644 services/datalink/src/datalink/inferrer/distribution.py
 create mode 100644 services/datalink/src/datalink/inferrer/joinable.py
 create mode 100644 services/datalink/src/datalink/inferrer/synonym.py
 create mode 100644 services/datalink/src/datalink/mapper/__init__.py
 create mode 100644 services/datalink/src/datalink/mapper/llm_mapper.py
 create mode 100644 services/datalink/src/datalink/mapper/prompts/__init__.py
 create mode 100644 services/datalink/src/datalink/mapper/prompts/mapping_prompt.txt
 create mode 100644 services/datalink/src/datalink/mapper/prompts/merge_prompt.txt
 create mode 100644 services/datalink/src/datalink/mapper/prompts/table_comment_prompt.txt
 create mode 100644 services/datalink/src/datalink/mcp/__init__.py
 create mode 100644 services/datalink/src/datalink/mcp/server.py
 create mode 100644 services/datalink/src/datalink/models/__init__.py
 create mode 100644 services/datalink/src/datalink/models/datasource.py
 create mode 100644 services/datalink/src/datalink/models/edge.py
 create mode 100644 services/datalink/src/datalink/models/node.py
 create mode 100644 services/datalink/src/datalink/models/profile.py
 create mode 100644 services/datalink/src/datalink/profiler/__init__.py
 create mode 100644 services/datalink/src/datalink/profiler/tabular.py
 create mode 100644 services/datalink/src/datalink/utils/__init__.py
 create mode 100644 services/datalink/src/datalink/utils/credential.py
 create mode 100644 services/datalink/src/datalink/utils/embedding.py
 create mode 100644 services/datalink/src/datalink/utils/ids.py
 create mode 100644 services/datalink/src/datalink/utils/sql_ident.py
 create mode 100644 services/datalink/tests/conftest.py
 create mode 100644 services/datalink/tests/test_builder.py
 create mode 100644 services/datalink/tests/test_cli.py
 create mode 100644 services/datalink/tests/test_connector.py
 create mode 100644 services/datalink/tests/test_credential.py
 create mode 100644 services/datalink/tests/test_data/orders.csv
 create mode 100644 services/datalink/tests/test_data/transactions.csv
 create mode 100644 services/datalink/tests/test_data/users.csv
 create mode 100644 services/datalink/tests/test_datafoundry_integration.py
 create mode 100644 services/datalink/tests/test_extractor.py
 create mode 100644 services/datalink/tests/test_ids.py
 create mode 100644 services/datalink/tests/test_inferrer.py
 create mode 100644 services/datalink/tests/test_mapper.py
 create mode 100644 services/datalink/tests/test_models.py
 create mode 100644 services/datalink/tests/test_profiler.py
 create mode 100644 services/datalink/tests/test_retrieval.py
 create mode 100644 services/datalink/tests/test_sql_ident.py
 create mode 100644 services/datalink/tests/test_storage.py
 create mode 100644 services/datalink/uv.lock

diff --git a/.env.example b/.env.example
index 593c7577..79f84ae0 100644
--- a/.env.example
+++ b/.env.example
@@ -44,6 +44,24 @@ EMBEDDING_OUTPUT_TYPE=dense
 EMBEDDING_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 EMBEDDING_API_KEY=
 
+# First-party DataLink semantic service. Disabled by default so existing
+# DataFoundry deployments do not require Python or uv. Set true to let
+# `npm run dev` / `npm run start` launch REST + MCP with the main stack.
+DATALINK_ENABLED=false
+DATALINK_CONFIG_PATH=services/datalink/datalink_config.json
+DATALINK_GRAPH_DB_PATH=storage/datalink/datalink.db
+DATALINK_API_HOST=127.0.0.1
+DATALINK_API_PORT=8081
+DATALINK_MCP_HOST=127.0.0.1
+DATALINK_MCP_PORT=8080
+# Optional overrides. Empty values fall back to LLM_* / EMBEDDING_* above.
+DATALINK_LLM_MODEL=
+DATALINK_LLM_BASE_URL=
+DATALINK_LLM_API_KEY=
+DATALINK_EMBEDDING_MODEL=
+DATALINK_EMBEDDING_BASE_URL=
+DATALINK_EMBEDDING_API_KEY=
+
 # Agent execute_command sandbox: Python venv with numpy/pandas/matplotlib/scikit-learn.
 # Default auto-detects repo `.venv` (also checks npm INIT_CWD). Override with absolute path:
 # WORKSPACE_PYTHON_VENV=/path/to/DataFoundry/.venv
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 9da75ed2..df52c81e 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -79,6 +79,12 @@ jobs:
       - name: Run configuration API regression
         run: node scripts/smoke-config-api.mjs
 
+      - name: Run built-in DataLink lifecycle regression
+        run: node scripts/smoke-builtin-datalink.mjs
+
+      - name: Run DataLink stack configuration tests
+        run: npm run test:datalink-stack
+
       - name: Run file and workspace regression
         run: node scripts/smoke-files.mjs
 
@@ -97,6 +103,59 @@ jobs:
       - name: Run built-in DTC Growth regression
         run: node --test scripts/test-builtin-dtc-growth-datasource.mjs
 
+  datalink:
+    name: DataLink Service
+    runs-on: ubuntu-latest
+    timeout-minutes: 20
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v5
+
+      - name: Setup Python
+        uses: actions/setup-python@v6
+        with:
+          python-version: "3.12"
+
+      - name: Setup uv
+        uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+
+      - name: Install DataLink dependencies
+        run: uv sync --project services/datalink --locked
+
+      - name: Lint DataLink
+        run: npm run lint:datalink
+
+      - name: Test DataLink
+        run: npm run test:datalink
+
+      - name: Build DataLink package
+        run: npm run build:datalink
+
+      - name: Smoke DataLink REST and MCP
+        shell: bash
+        run: |
+          set -euo pipefail
+          export DATALINK_CONFIG_PATH="$RUNNER_TEMP/datalink_config.json"
+          export DATALINK_GRAPH_DB_PATH="$RUNNER_TEMP/datalink.db"
+          uv run --project services/datalink datalink api --host 127.0.0.1 --port 8081 &
+          rest_pid=$!
+          uv run --project services/datalink datalink serve --host 127.0.0.1 --port 8080 --transport streamable-http --db "$DATALINK_GRAPH_DB_PATH" &
+          mcp_pid=$!
+          trap 'kill "$rest_pid" "$mcp_pid" 2>/dev/null || true' EXIT
+          for _ in $(seq 1 30); do
+            curl --fail --silent http://127.0.0.1:8081/healthz && break
+            sleep 1
+          done
+          curl --fail --silent http://127.0.0.1:8081/healthz
+          for _ in $(seq 1 30); do
+            status=$(curl --silent --output /dev/null --write-out '%{http_code}' http://127.0.0.1:8080/mcp || true)
+            if [ "$status" != "000" ]; then break; fi
+            sleep 1
+          done
+          test "$status" != "000"
+
   docs:
     name: Docs
     runs-on: ubuntu-latest
diff --git a/README.md b/README.md
index 3cd32dd4..5cafb72e 100644
--- a/README.md
+++ b/README.md
@@ -63,7 +63,7 @@ DataFoundry 0.2 turns the first usable workbench into a more complete, stateful
 
 - **Branchable, concurrent analysis** — Keep multiple sessions running, queue follow-up prompts, restore completed work, and branch from an earlier question or checkpoint without overwriting the original path.
 - **Evidence-first follow-ups** — Reference a complete output or a selected table/text region in the next question; resolved evidence is carried into the governed run context with diagnostics.
-- **Semantic trace and first-party Data Link** — Inspect checkpoint-backed run structure in a semantic Trace DAG. With our newly open-sourced [Data Link](https://github.com/datagallery-lab/datalink), connect tables and columns to business concepts, entities, joinable paths, and confidence-scored relationships for stronger agent grounding.
+- **Semantic trace and built-in DataLink** — Inspect checkpoint-backed run structure in a semantic Trace DAG. The first-party DataLink service now ships in `services/datalink`, connecting tables and columns to business concepts, entities, joinable paths, and confidence-scored relationships for stronger agent grounding.
 - **Reusable outputs and workspace assets** — Preview and export tables, charts, reports, SQL, and files; upload files into an active session, then promote supported files for reuse across sessions.
 - **Production-facing Web foundation** — Built-in password authentication, same-origin API proxying, bilingual UI, model connection tests, onboarding, and an auto-provisioned DTC growth analysis case.
 
@@ -96,6 +96,10 @@ LLM_MODEL=qwen-plus                # or deepseek-chat, gpt-4o, ...
 LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 LLM_API_KEY=replace-with-your-key
 
+# Optional: launch the built-in semantic service with the main stack.
+# Requires Python 3.10+ and uv; then run `npm run install:datalink` once.
+DATALINK_ENABLED=true
+
 DATAFOUNDRY_AUTH_MODE=password
 AUTH_SESSION_SECRET=replace-with-at-least-32-random-characters
 AUTH_PUBLIC_BASE_URL=http://127.0.0.1:3000   # real production: https://your.domain
@@ -116,10 +120,11 @@ Build and start:
 ```bash
 npm run build
 npm run build:web
-npm run start:api    # :8787  — /healthz liveness, /ready readiness
-npm run start:web    # :3000  — password mode via same-origin BFF
+npm run start        # Web :3000 + API :8787 (+ DataLink :8080/:8081 when enabled)
 ```
 
+For split-process deployment, `start:api` and `start:web` remain available; start DataLink separately with `start:datalink:mcp` and `start:datalink:api` when enabled.
+
 Open `http://127.0.0.1:3000/login`, register or sign in, go to `/data-tasks`, and ask:
 
 ```text
diff --git a/README_zh.md b/README_zh.md
index d56d7e96..fdae8c83 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -63,7 +63,7 @@ DataFoundry 0.2 在首个可用版本上，进一步补齐了有状态、可追
 
 - **可分支的并发分析** — 多个会话可同时运行，运行中可排队后续问题；恢复历史后，可从早期问题或 checkpoint 创建新分支，不覆盖原分析路径。
 - **证据驱动的追问** — 可将完整产出，或选中的表格区域、文本片段引用到下一个问题；证据解析结果和诊断信息会进入受控 run context。
-- **语义 Trace 与自研 Data Link** — 通过基于 checkpoint 的语义 Trace DAG 复盘执行结构；结合我们新近开源的 [Data Link](https://github.com/datagallery-lab/datalink)，把表和字段连接到业务概念、实体、可 JOIN 路径和带置信度的关系，为 Agent 提供更强语义支撑。
+- **语义 Trace 与内置 DataLink** — 通过基于 checkpoint 的语义 Trace DAG 复盘执行结构；自研 DataLink 服务现已随仓库内置于 `services/datalink`，把表和字段连接到业务概念、实体、可 JOIN 路径和带置信度的关系，为 Agent 提供更强语义支撑。
 - **可复用的产出与工作区资产** — 统一预览和导出表格、图表、报告、SQL 和文件；文件可先上传到当前会话，再提升为跨会话复用的工作区资产。
 - **面向正式部署的 Web 基础** — 内置密码认证、同源 API 代理、中英双语界面、模型连接测试、首次引导，并自动准备 DTC 增长分析案例。
 
@@ -96,6 +96,10 @@ LLM_MODEL=qwen-plus                # 或 deepseek-chat、gpt-4o……
 LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
 LLM_API_KEY=replace-with-your-key
 
+# 可选：随主应用启动内置语义服务。
+# 需要 Python 3.10+ 与 uv，并先执行一次 `npm run install:datalink`。
+DATALINK_ENABLED=true
+
 DATAFOUNDRY_AUTH_MODE=password
 AUTH_SESSION_SECRET=replace-with-at-least-32-random-characters
 AUTH_PUBLIC_BASE_URL=http://127.0.0.1:3000   # 真实生产改为 https://你的域名
@@ -116,10 +120,11 @@ API_PROXY_TARGET=http://127.0.0.1:8787
 ```bash
 npm run build
 npm run build:web
-npm run start:api    # :8787  — /healthz 存活，/ready 就绪
-npm run start:web    # :3000  — password 模式走同源 BFF
+npm run start        # Web :3000 + API :8787（启用后含 DataLink :8080/:8081）
 ```
 
+需要拆分进程部署时，仍可使用 `start:api` 与 `start:web`；开启 DataLink 后再分别运行 `start:datalink:mcp` 和 `start:datalink:api`。
+
 打开 `http://127.0.0.1:3000/login` 注册登录后进入 `/data-tasks`，提问：
 
 ```text
diff --git a/apps/api/src/builtin-datalink-server.ts b/apps/api/src/builtin-datalink-server.ts
new file mode 100644
index 00000000..f016f79f
--- /dev/null
+++ b/apps/api/src/builtin-datalink-server.ts
@@ -0,0 +1,80 @@
+import type { MetadataStore } from "@datafoundry/metadata";
+
+export const BUILTIN_DATALINK_SERVER_ID = "builtin-datalink";
+
+const DATALINK_TOOL_MANIFEST = [{ name: "datalink_explore" }] as const;
+
+export const isBuiltinDatalinkEnabled = (env: NodeJS.ProcessEnv = process.env): boolean =>
+  ["1", "true", "yes", "on"].includes((env.DATALINK_ENABLED ?? "").trim().toLowerCase());
+
+export const ensureBuiltinDatalinkServer = (input: {
+  env?: NodeJS.ProcessEnv;
+  metadataStore: MetadataStore;
+  userId: string;
+  workspaceId: string;
+}): "created" | "removed" | "skipped" | "updated" => {
+  const env = input.env ?? process.env;
+  const key = {
+    id: BUILTIN_DATALINK_SERVER_ID,
+    workspace_id: input.workspaceId,
+    user_id: input.userId,
+    kind: "mcp-server" as const,
+  };
+  const current = input.metadataStore.configResources.find(key);
+  if (!isBuiltinDatalinkEnabled(env)) {
+    if (current?.builtin) {
+      input.metadataStore.configResources.delete(key);
+      return "removed";
+    }
+    return "skipped";
+  }
+
+  // Never overwrite a user-owned resource that happens to use the reserved id.
+  if (current && !current.builtin) return "skipped";
+
+  const payload = builtinDatalinkPayload(env);
+  if (
+    current
+    && current.name === "DataLink"
+    && current.description === "First-party DataFoundry semantic graph service."
+    && JSON.stringify(current.payload) === JSON.stringify(payload)
+  ) {
+    return "skipped";
+  }
+
+  input.metadataStore.configResources.upsert({
+    ...key,
+    name: "DataLink",
+    description: "First-party DataFoundry semantic graph service.",
+    payload,
+    default_enabled: current?.default_enabled ?? true,
+    builtin: true,
+    status: "untested",
+    ...(current ? { expected_revision: current.revision } : {}),
+  });
+  return current ? "updated" : "created";
+};
+
+const builtinDatalinkPayload = (env: NodeJS.ProcessEnv): Record<string, unknown> => {
+  const apiHost = connectHost(env.DATALINK_API_HOST);
+  const mcpHost = connectHost(env.DATALINK_MCP_HOST);
+  const apiPort = port(env.DATALINK_API_PORT, 8081);
+  const mcpPort = port(env.DATALINK_MCP_PORT, 8080);
+  return {
+    apiUrl: `http://${apiHost}:${apiPort}`,
+    managed: true,
+    serverUrl: `http://${mcpHost}:${mcpPort}/mcp`,
+    toolManifest: DATALINK_TOOL_MANIFEST,
+    transport: "streamable-http",
+  };
+};
+
+const connectHost = (value: string | undefined): string => {
+  const host = value?.trim() || "127.0.0.1";
+  return host === "0.0.0.0" || host === "::" ? "127.0.0.1" : host;
+};
+
+const port = (value: string | undefined, fallback: number): number => {
+  const parsed = Number(value);
+  return Number.isInteger(parsed) && parsed >= 1 && parsed <= 65535 ? parsed : fallback;
+};
diff --git a/apps/api/src/config-api.ts b/apps/api/src/config-api.ts
index dc3beeeb..191b5bee 100644
--- a/apps/api/src/config-api.ts
+++ b/apps/api/src/config-api.ts
@@ -55,6 +55,7 @@ import { basename, join, resolve, sep } from "node:path";
 import writeXlsxFile, { type SheetData } from "write-excel-file/node";
 
 import { resolveEffectiveRunConfig } from "./run-input.js";
+import { BUILTIN_DATALINK_SERVER_ID } from "./builtin-datalink-server.js";
 import {
   llmEnvFingerprint,
   modelProfileConnectivityPayloadChanged,
@@ -796,7 +797,7 @@ const handleDatalinkRequest = async (
   const target = segments[0];
   const action = segments[1];
   if (target === "servers" && request.method === "GET") {
-    return ok({ servers: listDatalinkServers(context) });
+    return ok({ servers: await listDatalinkServers(context) });
   }
   if (!target) {
     return methodNotAllowed();
@@ -867,12 +868,20 @@ type DatalinkApiCallResult = {
   text: string;
 };
 
-const listDatalinkServers = (context: Required<ConfigApiContext>): Record<string, unknown>[] =>
-  context.metadataStore.configResources.list({
+const listDatalinkServers = async (
+  context: Required<ConfigApiContext>
+): Promise<Record<string, unknown>[]> => {
+  const resources = context.metadataStore.configResources.list({
     workspace_id: context.workspaceId,
     user_id: context.userId,
     kind: "mcp-server"
-  }).filter(isDatalinkServer).map(datalinkServerDto);
+  }).filter(isDatalinkServer).sort((left, right) =>
+    Number(right.id === BUILTIN_DATALINK_SERVER_ID) - Number(left.id === BUILTIN_DATALINK_SERVER_ID)
+  );
+  return Promise.all(resources.map(async (resource) =>
+    datalinkServerDto(resource, await builtinDatalinkHealthStatus(resource))
+  ));
+};
 
 const getDatalinkServer = (
   context: Required<ConfigApiContext>,
@@ -903,13 +912,17 @@ const isDatalinkServer = (resource: ConfigResourceRecord): boolean => {
 const isDatalinkManifestToolName = (toolName: string): boolean =>
   toolName.startsWith("datalink_") || toolName.startsWith("datagraph_");
 
-const datalinkServerDto = (resource: ConfigResourceRecord): Record<string, unknown> => {
+const datalinkServerDto = (
+  resource: ConfigResourceRecord,
+  healthStatus = resource.status
+): Record<string, unknown> => {
   const tools = datalinkToolManifest(resource);
   return {
     id: resource.id,
     name: datalinkServerDisplayName(resource),
     description: resource.description ?? "",
-    healthStatus: resource.status,
+    healthStatus,
+    managed: resource.builtin && resource.payload.managed === true,
     serverUrl: stringValue(resource.payload.serverUrl) ?? stringValue(resource.payload.url) ?? "",
     apiUrl: stringValue(resource.payload.apiUrl) ?? "",
     transport: stringValue(resource.payload.transport) ?? "streamable-http",
@@ -919,6 +932,21 @@ const datalinkServerDto = (resource: ConfigResourceRecord): Record<string, unkno
   };
 };
 
+const builtinDatalinkHealthStatus = async (resource: ConfigResourceRecord): Promise<string> => {
+  if (resource.id !== BUILTIN_DATALINK_SERVER_ID || resource.payload.managed !== true) {
+    return resource.status;
+  }
+  try {
+    const response = await fetch(datalinkApiEndpoint(resource, "/healthz"), {
+      headers: { Accept: "application/json" },
+      signal: AbortSignal.timeout(1_000)
+    });
+    return response.ok ? "connected" : "unavailable";
+  } catch {
+    return "unavailable";
+  }
+};
+
 const datalinkServerDisplayName = (resource: ConfigResourceRecord): string => {
   if (resource.name.toLowerCase() === "datagraph") {
     return "datalink";
@@ -1636,6 +1664,9 @@ const handleGenericResourceRequest = async (
       user_id: context.userId,
       kind
     });
+    if (current.builtin && current.id === BUILTIN_DATALINK_SERVER_ID) {
+      throw new Error(`BUILTIN_RESOURCE_READONLY:${id}`);
+    }
     if (kind === "knowledge-base") {
       context.metadataStore.db.prepare(
         "DELETE FROM knowledge_embeddings WHERE user_id = ? AND collection_id = ?"
@@ -1687,6 +1718,13 @@ const saveConfigResourceInTransaction = (
     user_id: context.userId,
     kind
   });
+  if (current?.builtin && current.id === BUILTIN_DATALINK_SERVER_ID) {
+    const mutableKeys = new Set(["defaultEnabled", "revision"]);
+    const readonlyKeys = Object.keys(body).filter((key) => !mutableKeys.has(key));
+    if (readonlyKeys.length > 0) {
+      throw new Error(`BUILTIN_RESOURCE_READONLY:${id}`);
+    }
+  }
   if (current?.builtin && kind === "skill") {
     const mutableKeys = new Set(["defaultEnabled", "revision"]);
     const readonlyKeys = Object.keys(body).filter((key) => !mutableKeys.has(key));
diff --git a/apps/api/src/server.ts b/apps/api/src/server.ts
index 395c74b1..e10ef171 100644
--- a/apps/api/src/server.ts
+++ b/apps/api/src/server.ts
@@ -40,6 +40,7 @@ import { Observable } from "rxjs";
 import { handleConfigApiRequest } from "./config-api.js";
 import { createAsyncMemoByKey, createStartupTimer } from "./async-memo.js";
 import { ensureBuiltinDtcGrowthDatasource } from "./builtin-dtc-growth-datasource.js";
+import { ensureBuiltinDatalinkServer } from "./builtin-datalink-server.js";
 import { reclaimOrphanedQueuedAndRunningRuns } from "./stale-active-runs.js";
 import { loadPasswordAuthConfig, type PasswordAuthConfig } from "./auth/config.js";
 import { AuthService, type AuthIdentity } from "./auth/service.js";
@@ -1261,6 +1262,11 @@ const ensureBuiltinConfigResources = async (
     userId,
     workspaceId
   });
+  ensureBuiltinDatalinkServer({
+    metadataStore,
+    userId,
+    workspaceId
+  });
 
   for (const source of BUILTIN_SKILL_SOURCES) {
     const content = readFileSync(source.path);
diff --git a/docs/en/architecture/overview.md b/docs/en/architecture/overview.md
index 07e16913..44969513 100644
--- a/docs/en/architecture/overview.md
+++ b/docs/en/architecture/overview.md
@@ -109,10 +109,11 @@ The default path is formal mode (`password` + `build` / `start`); do not run `np
 
 ```bash
 npm run build && npm run build:web
-npm run start:api
-npm run start:web
+npm run start
 ```
 
+`npm run start` manages Web and API with the same local-process deployment used previously. With `DATALINK_ENABLED=true`, it additionally starts the bundled Python DataLink REST and MCP processes; with the default `false`, the original two-process topology is unchanged. Separate `start:*` commands remain available for external process supervisors.
+
 | Environment | Email | Public URL |
 | --- | --- | --- |
 | Formal test | `AUTH_EMAIL_DELIVERY=test` | Local / private |
diff --git a/docs/en/guides/datalink.md b/docs/en/guides/datalink.md
new file mode 100644
index 00000000..9f0e8ce0
--- /dev/null
+++ b/docs/en/guides/datalink.md
@@ -0,0 +1,84 @@
+# DataLink semantic service
+
+DataLink is DataFoundry's first-party semantic graph service. It connects physical schemas and profiles to business concepts, entities, join paths, and confidence-scored relationships. The source lives in `services/datalink` and remains Python-based.
+
+## Runtime topology
+
+When enabled, the existing DataFoundry deployment starts four local processes:
+
+| Process | Default endpoint | Purpose |
+| --- | --- | --- |
+| Web | `http://127.0.0.1:3000` | Workbench UI |
+| DataFoundry API | `http://127.0.0.1:8787` | Agent runtime and management API |
+| DataLink MCP | `http://127.0.0.1:8080/mcp` | `datalink_explore` for agent grounding |
+| DataLink REST | `http://127.0.0.1:8081` | Graph management and visualization API |
+
+The API registers `builtin-datalink` once per user and workspace. The managed resource is preferred in the DataLink panel, cannot be deleted through the configuration API, and reports `unavailable` when its REST health probe fails. User-configured external DataLink servers remain supported.
+
+## Enable the bundled service
+
+Install Python 3.10+ and [uv](https://docs.astral.sh/uv/), then run:
+
+```bash
+npm run install:datalink
+```
+
+Set the root `.env`:
+
+```bash
+DATALINK_ENABLED=true
+```
+
+Use `npm run dev` for contributor hot reload or the formal deployment commands:
+
+```bash
+npm run build
+npm run build:web
+npm run start
+```
+
+`Ctrl+C` terminates all child processes. If `DATALINK_ENABLED=false` or is omitted, DataFoundry starts only Web and API and does not check for Python or uv.
+
+## Configuration
+
+Default managed paths and endpoints are:
+
+```bash
+DATALINK_CONFIG_PATH=services/datalink/datalink_config.json
+DATALINK_GRAPH_DB_PATH=storage/datalink/datalink.db
+DATALINK_API_HOST=127.0.0.1
+DATALINK_API_PORT=8081
+DATALINK_MCP_HOST=127.0.0.1
+DATALINK_MCP_PORT=8080
+```
+
+DataLink reuses DataFoundry's `LLM_*` and `EMBEDDING_*` settings. Define `DATALINK_LLM_MODEL`, `DATALINK_LLM_BASE_URL`, `DATALINK_LLM_API_KEY`, or their `DATALINK_EMBEDDING_*` equivalents only when the semantic service needs a separate provider.
+
+API keys do not need to be written to `datalink_config.json`. Keep them in environment variables or your deployment secret manager. The graph database is stored under the ignored `storage/` directory by default; include it in the deployment backup policy.
+
+## Split processes
+
+For an existing process supervisor, use the same deployment model with separate commands:
+
+```bash
+npm run start:api
+npm run start:web
+npm run start:datalink:mcp
+npm run start:datalink:api
+```
+
+All four commands read the root `.env`. Disabling automatic startup does not remove support for external DataLink servers configured through MCP settings.
+
+## Verification
+
+```bash
+curl http://127.0.0.1:8081/healthz
+```
+
+Expected response:
+
+```json
+{"status":"ok","service":"datalink"}
+```
+
+If startup reports that uv is missing, install uv and rerun `npm run install:datalink`. If the workbench shows the managed server as unavailable, verify ports `8080` and `8081`, inspect both DataLink process logs, and confirm the configured graph path is writable.
diff --git a/docs/en/guides/web-workbench.md b/docs/en/guides/web-workbench.md
index 2703cec3..14bb0778 100644
--- a/docs/en/guides/web-workbench.md
+++ b/docs/en/guides/web-workbench.md
@@ -8,8 +8,7 @@ Default path is the **formal** stack (`password` + `build` / `start`). Formal te
 
 ```bash
 npm run build && npm run build:web
-npm run start:api
-npm run start:web
+npm run start
 ```
 
 Open:
diff --git a/docs/en/quick-start.md b/docs/en/quick-start.md
index f838f361..f67f692d 100644
--- a/docs/en/quick-start.md
+++ b/docs/en/quick-start.md
@@ -11,7 +11,7 @@ Formal mode has two environments. **Startup commands are the same**; the main di
 
 Do **not** run `npm run dev` / `dev:api` / `dev:web` in either formal environment. Contributor hot-reload is in the appendix.
 
-You do not need a business database for the first run. You only need Node.js, npm, and a model API key compatible with the OpenAI `/chat/completions` interface.
+You do not need a business database for the first run. You only need Node.js, npm, and a model API key compatible with the OpenAI `/chat/completions` interface. The built-in DataLink semantic service additionally requires Python 3.10+ and [uv](https://docs.astral.sh/uv/), but remains opt-in.
 
 ## Requirements
 
@@ -19,6 +19,7 @@ You do not need a business database for the first run. You only need Node.js, np
 - npm
 - Linux, macOS, or Windows
 - A model API key—for example Qwen, DeepSeek, or another OpenAI-compatible service
+- Optional for DataLink: Python >= 3.10 and uv
 
 On Windows, install and run the project in the same environment. Do not share `node_modules` between Windows and WSL.
 
@@ -62,7 +63,25 @@ LLM_BASE_URL=https://api.deepseek.com
 LLM_API_KEY=your-api-key
 ```
 
-### 2.2 Formal test (recommended for first acceptance)
+### 2.2 DataLink semantic service (optional)
+
+DataLink is included under `services/datalink` but does not start by default. To launch its REST and MCP processes with DataFoundry:
+
+```bash
+npm run install:datalink
+```
+
+Then set in the root `.env`:
+
+```bash
+DATALINK_ENABLED=true
+```
+
+It reuses `LLM_*` and `EMBEDDING_*` by default. `DATALINK_LLM_*`, `DATALINK_EMBEDDING_*`, host, port, config-path, and graph-path variables in `.env.example` provide explicit overrides. Keep `false` to run the existing Web/API stack without Python or uv.
+
+See [DataLink](guides/datalink.md) for process topology and split-process commands.
+
+### 2.3 Formal test (recommended for first acceptance)
 
 Root `.env`:
 
@@ -87,7 +106,7 @@ API_PROXY_TARGET=http://127.0.0.1:8787
 
 On register / password reset, copy the verification link from the **API process console**.
 
-### 2.3 Real production
+### 2.4 Real production
 
 Start from the formal-test settings, then change to:
 
@@ -111,8 +130,7 @@ Keep the frontend on `password`, empty public API URLs, and `API_PROXY_TARGET`.
 ```bash
 npm run build
 npm run build:web
-npm run start:api    # :8787
-npm run start:web    # :3000
+npm run start        # Web :3000 + API :8787; DataLink :8080/:8081 when enabled
 ```
 
 Checks:
@@ -120,8 +138,12 @@ Checks:
 ```bash
 curl http://127.0.0.1:8787/healthz   # process up
 curl http://127.0.0.1:8787/ready     # Mastra / builtins ready (includes startup_ms)
+# When DATALINK_ENABLED=true:
+curl http://127.0.0.1:8081/healthz
 ```
 
+For a process supervisor or separate hosts, keep using `start:api` and `start:web`, plus `start:datalink:mcp` and `start:datalink:api`. The same `.env` controls all four commands.
+
 Open [http://127.0.0.1:3000/login](http://127.0.0.1:3000/login) (or your public origin in real production), register or sign in, then go to `/data-tasks`.
 
 After changing any `NEXT_PUBLIC_*` value in `apps/web/.env.local`, run `npm run build:web` again.
@@ -191,7 +213,7 @@ Symptom: Browser cannot open the workbench URL.
 
 Fix:
 
-- Confirm `npm run start:web` is still running (do not use `dev` in formal mode).
+- Confirm `npm run start` is still running (do not use `dev` in formal mode).
 - Check whether port 3000 is in use.
 - If 3000 is taken, use the frontend port shown in terminal output.
 
@@ -209,12 +231,12 @@ curl http://127.0.0.1:8787/ready
 If the health check fails:
 
 ```bash
-npm run start:api
+npm run start
 ```
 
 ### No verification email
 
-- **Formal test** (`AUTH_EMAIL_DELIVERY=test`): copy the link from the `start:api` terminal.
+- **Formal test** (`AUTH_EMAIL_DELIVERY=test`): copy the link from the `npm run start` terminal.
 - **Real production** (`smtp`): check `AUTH_SMTP_*` and that `AUTH_PUBLIC_BASE_URL` matches the public origin.
 
 ### Model unavailable
@@ -262,9 +284,12 @@ npm run dev
 # or: npm run dev:api && npm run dev:web
 ```
 
+With `DATALINK_ENABLED=true`, prefer the combined `npm run dev`; the split form also requires `dev:datalink:mcp` and `dev:datalink:api`.
+
 ## Next steps
 
 - Use the Web UI: [Web workbench guide](guides/web-workbench.md)
 - Use the terminal UI: [TUI guide](guides/tui.md)
 - Connect your own data: [Data sources guide](guides/data-sources.md)
+- Enable semantic grounding: [DataLink guide](guides/datalink.md)
 - Review capability boundaries: [Capabilities](capabilities.md)
diff --git a/docs/en/security.md b/docs/en/security.md
index c6fa624d..820af4f7 100644
--- a/docs/en/security.md
+++ b/docs/en/security.md
@@ -101,7 +101,7 @@ Two formal environments share the same start commands:
 
 Password mode adds `/api/v1/auth/*` endpoints for registration, login, email verification, password reset, logout, session listing, and password change. Unsafe requests require `X-CSRF-Token` from the `df_csrf` cookie. The session cookie is `df_session`.
 
-Also set `NEXT_PUBLIC_DATAFOUNDRY_AUTH_MODE=password` and leave `NEXT_PUBLIC_AGENT_RUNTIME_URL` / `NEXT_PUBLIC_CONFIG_API_URL` empty so the browser uses the same-origin Next BFF; point the upstream API with `API_PROXY_TARGET` in `apps/web/.env.local`. Start with `npm run build && npm run build:web && npm run start:api && npm run start:web`. Real-production reverse-proxy sample: `deploy/nginx.datafoundry.conf.example`.
+Also set `NEXT_PUBLIC_DATAFOUNDRY_AUTH_MODE=password` and leave `NEXT_PUBLIC_AGENT_RUNTIME_URL` / `NEXT_PUBLIC_CONFIG_API_URL` empty so the browser uses the same-origin Next BFF; point the upstream API with `API_PROXY_TARGET` in `apps/web/.env.local`. Build with `npm run build && npm run build:web`, then run `npm run start`. Real-production reverse-proxy sample: `deploy/nginx.datafoundry.conf.example`.
 
 Real production also needs secret management, audit export, access control, and operations monitoring.
 
diff --git a/docs/javascripts/language-switcher.js b/docs/javascripts/language-switcher.js
index 80e05c48..a81c844d 100644
--- a/docs/javascripts/language-switcher.js
+++ b/docs/javascripts/language-switcher.js
@@ -105,6 +105,7 @@
     ["Web workbench", "Web 工作台"],
     ["TUI", "TUI"],
     ["Data sources", "数据源"],
+    ["DataLink", "DataLink"],
     ["Examples", "案例"],
     ["DTC growth operating review", "DTC 增长经营复盘"],
     ["Developer Guide", "开发者指南"],
diff --git a/docs/zh/architecture/overview.md b/docs/zh/architecture/overview.md
index 4c633a70..356310be 100644
--- a/docs/zh/architecture/overview.md
+++ b/docs/zh/architecture/overview.md
@@ -113,10 +113,11 @@ password 模式在 `/api/v1/auth/*` 下提供基于 Cookie 的会话、非安全
 
 ```bash
 npm run build && npm run build:web
-npm run start:api
-npm run start:web
+npm run start
 ```
 
+`npm run start` 仍以原有本地进程方式管理 Web 与 API。设置 `DATALINK_ENABLED=true` 后，会额外启动仓库内置的 Python DataLink REST 与 MCP 进程；默认 `false` 时保持原来的双进程拓扑。已有进程守护系统仍可使用拆分的 `start:*` 命令。
+
 | 环境 | 邮箱 | 公网地址 |
 | --- | --- | --- |
 | 正式测试 | `AUTH_EMAIL_DELIVERY=test` | 本机 / 内网 |
diff --git a/docs/zh/guides/datalink.md b/docs/zh/guides/datalink.md
new file mode 100644
index 00000000..e31e0930
--- /dev/null
+++ b/docs/zh/guides/datalink.md
@@ -0,0 +1,84 @@
+# DataLink 语义服务
+
+DataLink 是 DataFoundry 的一方语义图服务，用于将物理 Schema 与数据画像连接到业务概念、实体、可 JOIN 路径和带置信度的关系。源码位于 `services/datalink`，继续使用 Python 实现。
+
+## 运行拓扑
+
+启用后，现有 DataFoundry 部署会启动四个本地进程：
+
+| 进程 | 默认地址 | 用途 |
+| --- | --- | --- |
+| Web | `http://127.0.0.1:3000` | 工作台界面 |
+| DataFoundry API | `http://127.0.0.1:8787` | Agent Runtime 与管理 API |
+| DataLink MCP | `http://127.0.0.1:8080/mcp` | 向 Agent 提供 `datalink_explore` 语义上下文 |
+| DataLink REST | `http://127.0.0.1:8081` | 图谱管理与可视化 API |
+
+API 会按用户和 workspace 幂等注册 `builtin-datalink`。该托管资源会优先显示在 DataLink 面板中，不能通过配置 API 删除；REST 健康检查失败时显示为 `unavailable`。用户自行配置的外部 DataLink 服务仍可继续使用。
+
+## 启用内置服务
+
+安装 Python 3.10+ 与 [uv](https://docs.astral.sh/uv/) 后执行：
+
+```bash
+npm run install:datalink
+```
+
+在根目录 `.env` 中设置：
+
+```bash
+DATALINK_ENABLED=true
+```
+
+贡献者热更新使用 `npm run dev`；正式部署使用：
+
+```bash
+npm run build
+npm run build:web
+npm run start
+```
+
+按 `Ctrl+C` 会统一结束所有子进程。`DATALINK_ENABLED=false` 或未配置时，只启动 Web 与 API，也不会检查 Python 或 uv。
+
+## 配置
+
+托管服务默认使用以下路径与端口：
+
+```bash
+DATALINK_CONFIG_PATH=services/datalink/datalink_config.json
+DATALINK_GRAPH_DB_PATH=storage/datalink/datalink.db
+DATALINK_API_HOST=127.0.0.1
+DATALINK_API_PORT=8081
+DATALINK_MCP_HOST=127.0.0.1
+DATALINK_MCP_PORT=8080
+```
+
+DataLink 默认复用 DataFoundry 的 `LLM_*` 与 `EMBEDDING_*`。只有需要为语义服务指定独立供应商时，才配置 `DATALINK_LLM_MODEL`、`DATALINK_LLM_BASE_URL`、`DATALINK_LLM_API_KEY` 或对应的 `DATALINK_EMBEDDING_*`。
+
+API Key 不需要写入 `datalink_config.json`，应保存在环境变量或部署 Secret 管理系统中。图数据库默认存放在已忽略的 `storage/` 目录，正式部署时需要纳入备份策略。
+
+## 拆分进程
+
+已有进程守护系统时，仍沿用当前部署方式分别启动：
+
+```bash
+npm run start:api
+npm run start:web
+npm run start:datalink:mcp
+npm run start:datalink:api
+```
+
+四个命令读取同一份根目录 `.env`。关闭自动启动不会影响通过 MCP 设置连接外部 DataLink 服务。
+
+## 验证
+
+```bash
+curl http://127.0.0.1:8081/healthz
+```
+
+预期响应：
+
+```json
+{"status":"ok","service":"datalink"}
+```
+
+如果启动提示缺少 uv，请安装后重新执行 `npm run install:datalink`。如果工作台显示托管服务不可用，请检查 `8080`、`8081` 端口与两个 DataLink 进程日志，并确认图数据库路径可写。
diff --git a/docs/zh/guides/web-workbench.md b/docs/zh/guides/web-workbench.md
index a45a3e4b..c9637303 100644
--- a/docs/zh/guides/web-workbench.md
+++ b/docs/zh/guides/web-workbench.md
@@ -8,8 +8,7 @@
 
 ```bash
 npm run build && npm run build:web
-npm run start:api
-npm run start:web
+npm run start
 ```
 
 打开：
diff --git a/docs/zh/quick-start.md b/docs/zh/quick-start.md
index 13d6f893..e764f50f 100644
--- a/docs/zh/quick-start.md
+++ b/docs/zh/quick-start.md
@@ -11,7 +11,7 @@
 
 两种正式态都**不要跑** `npm run dev` / `dev:api` / `dev:web`。贡献者本地热更新见文末附录。
 
-首次体验不需要准备业务数据库。你只需要 Node.js、npm 和一个兼容 OpenAI `/chat/completions` 接口的模型 API Key。
+首次体验不需要准备业务数据库。你只需要 Node.js、npm 和一个兼容 OpenAI `/chat/completions` 接口的模型 API Key。内置 DataLink 语义服务还需要 Python 3.10+ 与 [uv](https://docs.astral.sh/uv/)，但默认不开启。
 
 ## 环境要求
 
@@ -19,6 +19,7 @@
 - npm
 - Linux、macOS 或 Windows
 - 一个模型 API Key，例如通义千问、DeepSeek 或其他 OpenAI-compatible 服务
+- DataLink 可选依赖：Python >= 3.10 与 uv
 
 Windows 用户请在同一个系统内安装和运行项目。不要在 Windows 和 WSL 之间共用 `node_modules`。
 
@@ -62,7 +63,25 @@ LLM_BASE_URL=https://api.deepseek.com
 LLM_API_KEY=你的_API_Key
 ```
 
-### 2.2 正式测试（推荐首次验收）
+### 2.2 DataLink 语义服务（可选）
+
+DataLink 源码已内置在 `services/datalink`，默认不会随主应用启动。需要启用 REST 与 MCP 服务时先执行：
+
+```bash
+npm run install:datalink
+```
+
+然后在根目录 `.env` 中设置：
+
+```bash
+DATALINK_ENABLED=true
+```
+
+默认复用 `LLM_*` 与 `EMBEDDING_*`；如需独立配置，可使用 `.env.example` 中的 `DATALINK_LLM_*`、`DATALINK_EMBEDDING_*`、主机、端口、配置路径和图数据库路径。保持 `false` 时，原有 Web/API 部署不依赖 Python 或 uv。
+
+进程关系与拆分启动方式见 [DataLink 指南](guides/datalink.md)。
+
+### 2.3 正式测试（推荐首次验收）
 
 根目录 `.env`：
 
@@ -87,7 +106,7 @@ API_PROXY_TARGET=http://127.0.0.1:8787
 
 注册/重置密码时，验证链接会打印在 **API 进程控制台**，复制到浏览器即可。
 
-### 2.3 真实生产
+### 2.4 真实生产
 
 在正式测试配置基础上改为：
 
@@ -111,8 +130,7 @@ AUTH_SMTP_PASSWORD=
 ```bash
 npm run build
 npm run build:web
-npm run start:api    # :8787
-npm run start:web    # :3000
+npm run start        # Web :3000 + API :8787；启用时含 DataLink :8080/:8081
 ```
 
 检查：
@@ -120,8 +138,12 @@ npm run start:web    # :3000
 ```bash
 curl http://127.0.0.1:8787/healthz   # 进程存活
 curl http://127.0.0.1:8787/ready     # Mastra / builtin 就绪（含 startup_ms）
+# DATALINK_ENABLED=true 时：
+curl http://127.0.0.1:8081/healthz
 ```
 
+使用进程守护或拆分主机时，可继续分别运行 `start:api`、`start:web`、`start:datalink:mcp` 与 `start:datalink:api`；四个命令读取同一份 `.env`。
+
 打开 [http://127.0.0.1:3000/login](http://127.0.0.1:3000/login)（真实生产则打开你的公网域名）注册或登录后进入 `/data-tasks`。
 
 改过 `apps/web/.env.local` 中的 `NEXT_PUBLIC_*` 后，需要重新执行 `npm run build:web`。
@@ -191,7 +213,7 @@ node -v
 
 处理：
 
-- 确认 `npm run start:web` 还在运行（正式态不要开 `dev`）。
+- 确认 `npm run start` 还在运行（正式态不要开 `dev`）。
 - 检查 3000 端口是否被占用。
 - 如果 3000 被占用，查看终端输出中的实际前端端口。
 
@@ -209,12 +231,12 @@ curl http://127.0.0.1:8787/ready
 如果健康检查失败：
 
 ```bash
-npm run start:api
+npm run start
 ```
 
 ### 注册收不到邮件
 
-- **正式测试**（`AUTH_EMAIL_DELIVERY=test`）：到运行 `start:api` 的终端里找验证链接。
+- **正式测试**（`AUTH_EMAIL_DELIVERY=test`）：到运行 `npm run start` 的终端里找验证链接。
 - **真实生产**（`smtp`）：检查 `AUTH_SMTP_*` 与发信账号；确认 `AUTH_PUBLIC_BASE_URL` 与对外域名一致。
 
 ### 模型不可用
@@ -262,9 +284,12 @@ npm run dev
 # 或：npm run dev:api && npm run dev:web
 ```
 
+设置 `DATALINK_ENABLED=true` 后建议使用统一的 `npm run dev`；拆分启动时还需要运行 `dev:datalink:mcp` 与 `dev:datalink:api`。
+
 ## 下一步
 
 - 使用 Web 界面：[Web 工作台指南](guides/web-workbench.md)
 - 使用终端界面：[TUI 指南](guides/tui.md)
 - 连接自己的数据：[数据源指南](guides/data-sources.md)
+- 启用语义增强：[DataLink 指南](guides/datalink.md)
 - 查看能力边界：[能力全览](capabilities.md)
diff --git a/docs/zh/security.md b/docs/zh/security.md
index d5481c71..1ac7b46a 100644
--- a/docs/zh/security.md
+++ b/docs/zh/security.md
@@ -101,7 +101,7 @@ AUTH_EMAIL_FROM=DataFoundry <no-reply@example.com>
 
 密码模式提供 `/api/v1/auth/*` 接口，用于注册、登录、邮箱验证、密码重置、退出登录、会话列表和修改密码。非安全方法请求需要携带来自 `df_csrf` Cookie 的 `X-CSRF-Token`。会话 Cookie 名为 `df_session`。
 
-前端请同步设置 `NEXT_PUBLIC_DATAFOUNDRY_AUTH_MODE=password`，并留空 `NEXT_PUBLIC_AGENT_RUNTIME_URL` / `NEXT_PUBLIC_CONFIG_API_URL`，让浏览器走同源 Next BFF；上游 API 用 `API_PROXY_TARGET`（写在 `apps/web/.env.local`）。启动命令：`npm run build && npm run build:web && npm run start:api && npm run start:web`。真实生产反代样例见 `deploy/nginx.datafoundry.conf.example`。
+前端请同步设置 `NEXT_PUBLIC_DATAFOUNDRY_AUTH_MODE=password`，并留空 `NEXT_PUBLIC_AGENT_RUNTIME_URL` / `NEXT_PUBLIC_CONFIG_API_URL`，让浏览器走同源 Next BFF；上游 API 用 `API_PROXY_TARGET`（写在 `apps/web/.env.local`）。先执行 `npm run build && npm run build:web`，再运行 `npm run start`。真实生产反代样例见 `deploy/nginx.datafoundry.conf.example`。
 
 真实生产部署还需要 Secret 管理、审计导出、访问控制和运维监控。
 
diff --git a/mkdocs.yml b/mkdocs.yml
index e4aebf9d..b468d139 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -74,6 +74,7 @@ nav:
           - Web workbench: en/guides/web-workbench.md
           - TUI: en/guides/tui.md
           - Data sources: en/guides/data-sources.md
+          - DataLink: en/guides/datalink.md
       - Examples:
           - DTC growth operating review: en/examples/dtc-growth-demo.md
   - Developer Guide:
diff --git a/package.json b/package.json
index feaad081..f5089618 100644
--- a/package.json
+++ b/package.json
@@ -37,6 +37,7 @@
     "docs",
     "mkdocs.yml",
     "packages",
+    "services",
     "requirements-docs.txt",
     "requirements.txt",
     "scripts",
@@ -56,11 +57,21 @@
     "postinstall": "node scripts/postinstall.mjs",
     "predev:api": "node scripts/ensure-dev-environment.mjs",
     "dev": "node scripts/dev.mjs",
+    "start": "node scripts/start.mjs",
     "dev:api": "npm --workspace @datafoundry/api run dev",
     "start:api": "npm --prefix apps/api run start",
     "dev:web": "npm --workspace @datafoundry/web run dev",
     "build:web": "npm --workspace @datafoundry/web run build",
     "start:web": "npm --prefix apps/web run start",
+    "install:datalink": "uv sync --project services/datalink --locked",
+    "lint:datalink": "uv run --project services/datalink ruff check services/datalink/src services/datalink/tests",
+    "test:datalink": "uv run --project services/datalink pytest services/datalink/tests",
+    "build:datalink": "uv build --project services/datalink",
+    "dev:datalink:api": "node scripts/start-datalink.mjs api",
+    "dev:datalink:mcp": "node scripts/start-datalink.mjs mcp",
+    "start:datalink:api": "node scripts/start-datalink.mjs api",
+    "start:datalink:mcp": "node scripts/start-datalink.mjs mcp",
+    "test:datalink-stack": "node --test scripts/datalink-stack-config.test.mjs",
     "test:web": "npm --workspace @datafoundry/web run test",
     "dev:tui": "npm --workspace @datafoundry/tui run dev",
     "start:tui": "npm --workspace @datafoundry/tui run start --",
@@ -87,6 +98,7 @@
     "smoke:run-config-disabled": "npm run build && node scripts/smoke-run-config-disabled.mjs",
     "smoke:data-gateway": "npm run build && node scripts/smoke-data-gateway.mjs",
     "smoke:datalink-semantic": "node scripts/smoke-datalink-semantic.mjs",
+    "smoke:builtin-datalink": "npm run build && node scripts/smoke-builtin-datalink.mjs",
     "smoke:server-datasources": "npm run build && node scripts/smoke-server-datasources-e2e.mjs",
     "smoke:sql": "npm run build && node scripts/smoke-sql-readonly.mjs",
     "smoke:agent": "npm run build && node scripts/smoke-agent-runtime.mjs",
diff --git a/scripts/datalink-stack-config.mjs b/scripts/datalink-stack-config.mjs
new file mode 100644
index 00000000..c3c12374
--- /dev/null
+++ b/scripts/datalink-stack-config.mjs
@@ -0,0 +1,28 @@
+import { resolve } from "node:path";
+
+export function datalinkEnabled(env = process.env) {
+  return ["1", "true", "yes", "on"].includes((env.DATALINK_ENABLED ?? "").trim().toLowerCase());
+}
+
+export function resolveDatalinkEnv(root, env = process.env) {
+  return {
+    ...env,
+    DATALINK_CONFIG_PATH:
+      env.DATALINK_CONFIG_PATH?.trim() || resolve(root, "services/datalink/datalink_config.json"),
+    DATALINK_GRAPH_DB_PATH:
+      env.DATALINK_GRAPH_DB_PATH?.trim() || resolve(root, "storage/datalink/datalink.db"),
+    DATALINK_API_HOST: env.DATALINK_API_HOST?.trim() || "127.0.0.1",
+    DATALINK_API_PORT: String(readPort(env.DATALINK_API_PORT, 8081, "DATALINK_API_PORT")),
+    DATALINK_MCP_HOST: env.DATALINK_MCP_HOST?.trim() || "127.0.0.1",
+    DATALINK_MCP_PORT: String(readPort(env.DATALINK_MCP_PORT, 8080, "DATALINK_MCP_PORT")),
+  };
+}
+
+function readPort(value, fallback, name) {
+  if (!value?.trim()) return fallback;
+  const port = Number(value);
+  if (!Number.isInteger(port) || port < 1 || port > 65535) {
+    throw new Error(`${name} must be an integer between 1 and 65535.`);
+  }
+  return port;
+}
diff --git a/scripts/datalink-stack-config.test.mjs b/scripts/datalink-stack-config.test.mjs
new file mode 100644
index 00000000..bae5026e
--- /dev/null
+++ b/scripts/datalink-stack-config.test.mjs
@@ -0,0 +1,26 @@
+import assert from "node:assert/strict";
+import { test } from "node:test";
+
+import { datalinkEnabled, resolveDatalinkEnv } from "./datalink-stack-config.mjs";
+
+test("DataLink is opt-in", () => {
+  assert.equal(datalinkEnabled({}), false);
+  assert.equal(datalinkEnabled({ DATALINK_ENABLED: "true" }), true);
+  assert.equal(datalinkEnabled({ DATALINK_ENABLED: "1" }), true);
+  assert.equal(datalinkEnabled({ DATALINK_ENABLED: "false" }), false);
+});
+
+test("DataLink paths and ports have repository-owned defaults", () => {
+  const env = resolveDatalinkEnv("/repo", {});
+  assert.equal(env.DATALINK_CONFIG_PATH, "/repo/services/datalink/datalink_config.json");
+  assert.equal(env.DATALINK_GRAPH_DB_PATH, "/repo/storage/datalink/datalink.db");
+  assert.equal(env.DATALINK_API_PORT, "8081");
+  assert.equal(env.DATALINK_MCP_PORT, "8080");
+});
+
+test("invalid DataLink ports fail before processes start", () => {
+  assert.throws(
+    () => resolveDatalinkEnv("/repo", { DATALINK_API_PORT: "70000" }),
+    /DATALINK_API_PORT/,
+  );
+});
diff --git a/scripts/dev.mjs b/scripts/dev.mjs
index 995bccfb..4bfe109f 100755
--- a/scripts/dev.mjs
+++ b/scripts/dev.mjs
@@ -1,119 +1,4 @@
 #!/usr/bin/env node
-/**
- * Build workspace packages and start API + web dev servers (Linux, Windows, macOS).
- *
- * Usage:
- *   npm run dev          # start both
- *   npm run dev -- --api # API only
- *   npm run dev -- --web # web only
- */
-import { spawn, execSync } from "node:child_process";
-import { dirname, join } from "node:path";
-import { fileURLToPath } from "node:url";
+import { runStack } from "./stack-runner.mjs";
 
-const root = join(dirname(fileURLToPath(import.meta.url)), "..");
-const args = process.argv.slice(2);
-const apiOnly = args.includes("--api");
-const webOnly = args.includes("--web");
-const startApi = !webOnly || apiOnly;
-const startWeb = !apiOnly || webOnly;
-
-execSync("node scripts/ensure-dev-environment.mjs", {
-  cwd: root,
-  stdio: "inherit",
-  env: process.env,
-  shell: true,
-});
-
-for (const port of [8787, 3000]) {
-  try {
-    freePort(port);
-  } catch {
-    // Port may already be free; dev servers will fail loudly if it stays busy.
-  }
-}
-
-/** @type {import('node:child_process').ChildProcess[]} */
-const children = [];
-
-if (startApi) {
-  children.push(spawnNpm(["--workspace", "@datafoundry/api", "run", "dev"]));
-}
-if (startWeb) {
-  children.push(spawnNpm(["--workspace", "@datafoundry/web", "run", "dev"]));
-}
-
-if (children.length === 0) {
-  console.error("Nothing to start. Use --api and/or --web.");
-  process.exit(1);
-}
-
-console.log(
-  "\n[dev] " +
-    (startApi ? "API → http://127.0.0.1:8787  " : "") +
-    (startWeb ? "Web → http://localhost:3000/data-tasks" : "") +
-    "\n",
-);
-
-function shutdown(signal) {
-  for (const child of children) {
-    if (!child.killed) child.kill(signal);
-  }
-}
-
-process.on("SIGINT", () => shutdown("SIGINT"));
-process.on("SIGTERM", () => shutdown("SIGTERM"));
-
-for (const child of children) {
-  child.on("exit", (code, signal) => {
-    if (signal) return;
-    if (code && code !== 0) {
-      shutdown("SIGTERM");
-      process.exit(code);
-    }
-  });
-}
-
-function spawnNpm(args) {
-  return spawn("npm", args, {
-    cwd: root,
-    stdio: "inherit",
-    env: process.env,
-    shell: true,
-  });
-}
-
-function freePort(port) {
-  if (process.platform === "win32") {
-    let output = "";
-    try {
-      output = execSync(`netstat -ano | findstr :${port}`, {
-        encoding: "utf8",
-        shell: true,
-        stdio: ["ignore", "pipe", "ignore"],
-      });
-    } catch {
-      return;
-    }
-
-    const pids = new Set();
-    for (const line of output.split(/\r?\n/u)) {
-      if (!/\bLISTENING\b/u.test(line)) continue;
-      const pid = line.trim().split(/\s+/u).at(-1);
-      if (pid && /^\d+$/u.test(pid) && pid !== "0") {
-        pids.add(pid);
-      }
-    }
-
-    for (const pid of pids) {
-      execSync(`taskkill /F /PID ${pid}`, { stdio: "ignore", shell: true });
-    }
-    return;
-  }
-
-  execSync(`fuser -k ${port}/tcp 2>/dev/null || true`, {
-    cwd: root,
-    stdio: "ignore",
-    shell: true,
-  });
-}
+await runStack({ mode: "development", args: process.argv.slice(2) });
diff --git a/scripts/smoke-builtin-datalink.mjs b/scripts/smoke-builtin-datalink.mjs
new file mode 100644
index 00000000..db36794b
--- /dev/null
+++ b/scripts/smoke-builtin-datalink.mjs
@@ -0,0 +1,45 @@
+import assert from "node:assert/strict";
+import { mkdtempSync } from "node:fs";
+import { tmpdir } from "node:os";
+import { join } from "node:path";
+
+import {
+  BUILTIN_DATALINK_SERVER_ID,
+  ensureBuiltinDatalinkServer,
+} from "../apps/api/dist/builtin-datalink-server.js";
+import { createMetadataStore } from "../packages/metadata/dist/index.js";
+
+const root = mkdtempSync(join(tmpdir(), "datafoundry-builtin-datalink-"));
+const metadataStore = createMetadataStore({ database_path: join(root, "metadata.sqlite") });
+const common = { metadataStore, userId: "dev-user", workspaceId: "default" };
+const enabledEnv = {
+  DATALINK_ENABLED: "true",
+  DATALINK_API_PORT: "18081",
+  DATALINK_MCP_PORT: "18080",
+};
+
+assert.equal(ensureBuiltinDatalinkServer({ ...common, env: enabledEnv }), "created");
+assert.equal(ensureBuiltinDatalinkServer({ ...common, env: enabledEnv }), "skipped");
+
+const resource = metadataStore.configResources.get({
+  id: BUILTIN_DATALINK_SERVER_ID,
+  workspace_id: "default",
+  user_id: "dev-user",
+  kind: "mcp-server",
+});
+assert.equal(resource.builtin, true);
+assert.equal(resource.default_enabled, true);
+assert.equal(resource.payload.apiUrl, "http://127.0.0.1:18081");
+assert.equal(resource.payload.serverUrl, "http://127.0.0.1:18080/mcp");
+assert.deepEqual(resource.payload.toolManifest, [{ name: "datalink_explore" }]);
+
+assert.equal(ensureBuiltinDatalinkServer({ ...common, env: {} }), "removed");
+assert.equal(metadataStore.configResources.find({
+  id: BUILTIN_DATALINK_SERVER_ID,
+  workspace_id: "default",
+  user_id: "dev-user",
+  kind: "mcp-server",
+}), undefined);
+
+metadataStore.close();
+console.log("Built-in DataLink lifecycle smoke OK");
diff --git a/scripts/stack-runner.mjs b/scripts/stack-runner.mjs
new file mode 100644
index 00000000..40650a01
--- /dev/null
+++ b/scripts/stack-runner.mjs
@@ -0,0 +1,151 @@
+import { execFileSync, execSync, spawn } from "node:child_process";
+import { existsSync } from "node:fs";
+import { loadEnvFile } from "node:process";
+import { dirname, join } from "node:path";
+import { fileURLToPath } from "node:url";
+
+import { datalinkEnabled, resolveDatalinkEnv } from "./datalink-stack-config.mjs";
+
+const root = join(dirname(fileURLToPath(import.meta.url)), "..");
+
+export async function runStack({ mode, args = [] }) {
+  loadRootEnv();
+  const apiOnly = args.includes("--api");
+  const webOnly = args.includes("--web");
+  const startApi = !webOnly || apiOnly;
+  const startWeb = !apiOnly || webOnly;
+  const startDatalink = startApi && datalinkEnabled();
+  const datalinkEnv = resolveDatalinkEnv(root);
+
+  if (mode === "development") {
+    execSync("node scripts/ensure-dev-environment.mjs", {
+      cwd: root,
+      stdio: "inherit",
+      env: process.env,
+      shell: true,
+    });
+    const ports = [
+      ...(startApi ? [8787] : []),
+      ...(startWeb ? [3000] : []),
+      ...(startDatalink
+        ? [Number(datalinkEnv.DATALINK_MCP_PORT), Number(datalinkEnv.DATALINK_API_PORT)]
+        : []),
+    ];
+    for (const port of ports) freePort(port);
+  }
+
+  if (startDatalink) ensureUvAvailable();
+
+  const children = [];
+  if (startDatalink) {
+    children.push(spawnProcess("DataLink MCP", "uv", [
+      "run", "--project", "services/datalink", "datalink", "serve",
+      "--host", datalinkEnv.DATALINK_MCP_HOST,
+      "--port", datalinkEnv.DATALINK_MCP_PORT,
+      "--transport", "streamable-http",
+      "--db", datalinkEnv.DATALINK_GRAPH_DB_PATH,
+    ], datalinkEnv));
+    children.push(spawnProcess("DataLink REST", "uv", [
+      "run", "--project", "services/datalink", "datalink", "api",
+      "--host", datalinkEnv.DATALINK_API_HOST,
+      "--port", datalinkEnv.DATALINK_API_PORT,
+    ], datalinkEnv));
+  }
+  if (startApi) {
+    const command = mode === "development"
+      ? ["--workspace", "@datafoundry/api", "run", "dev"]
+      : ["--prefix", "apps/api", "run", "start"];
+    children.push(spawnProcess("DataFoundry API", "npm", command, datalinkEnv));
+  }
+  if (startWeb) {
+    const command = mode === "development"
+      ? ["--workspace", "@datafoundry/web", "run", "dev"]
+      : ["--prefix", "apps/web", "run", "start"];
+    children.push(spawnProcess("DataFoundry Web", "npm", command, datalinkEnv));
+  }
+
+  if (children.length === 0) {
+    throw new Error("Nothing to start. Use --api and/or --web.");
+  }
+
+  console.log(formatEndpoints({ datalinkEnv, startApi, startDatalink, startWeb }));
+  let shuttingDown = false;
+  const shutdown = (signal) => {
+    if (shuttingDown) return;
+    shuttingDown = true;
+    for (const { child } of children) {
+      if (!child.killed) child.kill(signal);
+    }
+  };
+
+  process.on("SIGINT", () => shutdown("SIGINT"));
+  process.on("SIGTERM", () => shutdown("SIGTERM"));
+  for (const { child, label } of children) {
+    child.on("exit", (code, signal) => {
+      if (shuttingDown || signal) return;
+      console.error(`[stack] ${label} exited with code ${code ?? "unknown"}.`);
+      shutdown("SIGTERM");
+      process.exitCode = code && code !== 0 ? code : 1;
+    });
+  }
+}
+
+function loadRootEnv() {
+  const envPath = join(root, ".env");
+  if (existsSync(envPath)) loadEnvFile(envPath);
+}
+
+function ensureUvAvailable() {
+  try {
+    execFileSync("uv", ["--version"], { stdio: "ignore" });
+  } catch {
+    throw new Error(
+      "DATALINK_ENABLED=true requires uv and Python 3.10+. Install uv, then run `npm run install:datalink`.",
+    );
+  }
+}
+
+function spawnProcess(label, command, args, env) {
+  const child = spawn(command, args, {
+    cwd: root,
+    stdio: "inherit",
+    env,
+    shell: process.platform === "win32",
+  });
+  child.on("error", (error) => console.error(`[stack] Unable to start ${label}: ${error.message}`));
+  return { child, label };
+}
+
+function formatEndpoints({ datalinkEnv, startApi, startDatalink, startWeb }) {
+  const endpoints = [];
+  if (startApi) endpoints.push("API http://127.0.0.1:8787");
+  if (startWeb) endpoints.push("Web http://localhost:3000/data-tasks");
+  if (startDatalink) {
+    endpoints.push(`DataLink MCP http://127.0.0.1:${datalinkEnv.DATALINK_MCP_PORT}/mcp`);
+    endpoints.push(`DataLink REST http://127.0.0.1:${datalinkEnv.DATALINK_API_PORT}`);
+  }
+  return `\n[stack] ${endpoints.join(" | ")}\n`;
+}
+
+function freePort(port) {
+  try {
+    if (process.platform === "win32") {
+      const output = execSync(`netstat -ano | findstr :${port}`, {
+        encoding: "utf8",
+        shell: true,
+        stdio: ["ignore", "pipe", "ignore"],
+      });
+      const pids = new Set();
+      for (const line of output.split(/\r?\n/u)) {
+        if (!/\bLISTENING\b/u.test(line)) continue;
+        const pid = line.trim().split(/\s+/u).at(-1);
+        if (pid && /^\d+$/u.test(pid) && pid !== "0") pids.add(pid);
+      }
+      for (const pid of pids) execSync(`taskkill /F /PID ${pid}`, { stdio: "ignore", shell: true });
+      return;
+    }
+    execSync(`fuser -k ${port}/tcp 2>/dev/null || true`, { cwd: root, stdio: "ignore", shell: true });
+  } catch {
+    // The port was already free.
+  }
+}
diff --git a/scripts/start-datalink.mjs b/scripts/start-datalink.mjs
new file mode 100755
index 00000000..e49e5153
--- /dev/null
+++ b/scripts/start-datalink.mjs
@@ -0,0 +1,47 @@
+#!/usr/bin/env node
+import { spawn } from "node:child_process";
+import { existsSync } from "node:fs";
+import { loadEnvFile } from "node:process";
+import { dirname, join } from "node:path";
+import { fileURLToPath } from "node:url";
+
+import { resolveDatalinkEnv } from "./datalink-stack-config.mjs";
+
+const root = join(dirname(fileURLToPath(import.meta.url)), "..");
+const envPath = join(root, ".env");
+if (existsSync(envPath)) loadEnvFile(envPath);
+const env = resolveDatalinkEnv(root);
+const service = process.argv[2];
+const args = service === "api"
+  ? [
+      "run", "--project", "services/datalink", "datalink", "api",
+      "--host", env.DATALINK_API_HOST,
+      "--port", env.DATALINK_API_PORT,
+    ]
+  : service === "mcp"
+    ? [
+        "run", "--project", "services/datalink", "datalink", "serve",
+        "--host", env.DATALINK_MCP_HOST,
+        "--port", env.DATALINK_MCP_PORT,
+        "--transport", "streamable-http",
+        "--db", env.DATALINK_GRAPH_DB_PATH,
+      ]
+    : undefined;
+
+if (!args) throw new Error("Usage: node scripts/start-datalink.mjs <api|mcp>");
+
+const child = spawn("uv", args, {
+  cwd: root,
+  env,
+  stdio: "inherit",
+  shell: process.platform === "win32",
+});
+child.on("error", (error) => {
+  console.error(`[datalink] Unable to start: ${error.message}`);
+  process.exitCode = 1;
+});
+child.on("exit", (code) => {
+  process.exitCode = code ?? 1;
+});
+process.on("SIGINT", () => child.kill("SIGINT"));
+process.on("SIGTERM", () => child.kill("SIGTERM"));
diff --git a/scripts/start.mjs b/scripts/start.mjs
new file mode 100755
index 00000000..f05efd57
--- /dev/null
+++ b/scripts/start.mjs
@@ -0,0 +1,4 @@
+#!/usr/bin/env node
+import { runStack } from "./stack-runner.mjs";
+
+await runStack({ mode: "production", args: process.argv.slice(2) });
diff --git a/services/datalink/.gitignore b/services/datalink/.gitignore
new file mode 100644
index 00000000..8aeff1e4
--- /dev/null
+++ b/services/datalink/.gitignore
@@ -0,0 +1,26 @@
+.venv
+
+__pycache__
+
+.cursorignore
+
+.claude
+
+.pytest_cache
+
+*.so
+
+# Config files may contain API keys — never commit
+datalink_config.json
+
+*.db
+
+scripts
+
+.cursor
+
+.DS_Store
+
+eval.config.json
+
+design/
diff --git a/services/datalink/LICENSE.txt b/services/datalink/LICENSE.txt
new file mode 100644
index 00000000..e93aca40
--- /dev/null
+++ b/services/datalink/LICENSE.txt
@@ -0,0 +1,201 @@
+                                 Apache License
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+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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diff --git a/services/datalink/README.md b/services/datalink/README.md
new file mode 100644
index 00000000..a9638241
--- /dev/null
+++ b/services/datalink/README.md
@@ -0,0 +1,339 @@
+<h1 align="center">DataLink 🧬</h1>
+
+> DataLink is maintained as DataFoundry's first-party semantic service under `services/datalink`. Run it from the repository root through DataFoundry's stack commands; the standalone CLI remains available for service development.
+
+<p align="center">
+  A unified data map for data agents — connecting data across sources and formats<br/>through queryable relationships, profiles, concepts, and confidence signals.
+</p>
+
+<p align="center">
+  English · <a href="README_zh.md">简体中文</a>
+</p>
+
+<p align="center">
+  <a href="#-quick-start"><strong>Quick Start</strong></a>
+  ·
+  <a href="docs/README.md"><strong>Docs</strong></a>
+  ·
+  <a href="docs/DESIGN.md"><strong>Design</strong></a>
+  ·
+  <a href="docs/INTERFACE.md"><strong>Interface Reference</strong></a>
+  ·
+  <a href="#-contributing"><strong>Contributing</strong></a>
+  ·
+  <a href="#-license"><strong>License</strong></a>
+</p>
+
+## ✨ Why DataLink
+
+Modern data agents can see schemas, but schemas alone rarely explain how data should be used. Which fields can be joined, which fields refer to the same business concept, which relationships are explicit constraints, and which are only candidates all require additional context.
+
+DataLink organizes structures, profiles, business concepts, and relationship signals into a **unified data map**. The design is not tied to one data format:
+
+- 🗺️ **Unified organization** — each data type keeps its own structure while sharing a common relationship model and query surface.
+- 🏗️ **Layered representation** — the structural layer records where data lives and what it looks like; the concept layer records what it represents and what it relates to.
+- 🔎 **Relationship-aware** — explicit edges such as foreign keys and lineage, plus inferred edges such as joinability, concept equivalence, correlation, and distribution similarity, carry confidence scores.
+- 🧩 **Extensible by data type** — new formats can define their own connectors, extractors, profilers, nodes, and profiling strategies while reusing graph storage, retrieval, and agent integration capabilities.
+- 📦 **Agent-ready** — retrieval APIs exposed as MCP tools and REST API. The MCP server exposes `datalink_explore` for instant agent queries; the REST API mirrors all CLI commands for management and integration.
+
+> **DataLink is a data map that continuously calibrates itself.** It first uses static analysis — schema extraction, content profiling, algorithmic inference, and concept mapping — to establish a baseline map, helping the system understand data structures, features, and possible relationships. When data agents actually use the data, their execution trajectories — for example, which tables are joined, which columns are queried together, and which analysis paths are repeatedly traversed — provide usage evidence that static analysis alone cannot capture. This evidence can further enrich and calibrate the data map while providing confidence estimates grounded in real usage. The ultimate form of relationship construction in DataLink is therefore two-directional: static governance provides the baseline map, while agent trajectories enrich and calibrate it with evidence from real usage.
+
+> For now, DataLink focuses on relational databases, CSV, and Parquet, with Table/Column as its core objects. PPT, PDF, Markdown, and image connectors will be added progressively as future extensions for unstructured data types within the unified data map.
+
+<p align="center">
+  <img src="docs/DataMap.png" alt="DataLink unified data map connecting related data points across tables, records, documents, slides, and images" width="900"/>
+</p>
+
+The illustration shows the design goal of the unified data map: DataLink organizes, labels, and exposes relationships across sources and formats. Tables and records represent the current implementation focus, while documents, slides, and images illustrate future extension directions.
+
+## 🗺️ How It Works
+
+<p align="center">
+  <img src="docs/DL-en.png" alt="DataLink Architecture" width="720"/>
+</p>
+
+The build pipeline organizes supported data sources into a queryable data map:
+
+```
+Connector → Extractor → Profiler → Inferrer → Mapper → Graph
+```
+
+The current pipeline is implemented primarily for tabular data, with responsibilities separated so additional data types can be added over time:
+
+1. **Connector** currently connects to relational databases and CSV/Parquet files; future formats can provide dedicated connectors.
+2. **Extractor** currently creates Table/Column nodes and explicit relationships such as `contains` and `foreign_key`; other formats can define their own structural nodes.
+3. **Profiler** currently computes column types, distributions, cardinality, and samples; other formats can add suitable structure and content profiles.
+4. **Inferrer** discovers implicit relationships with confidence scores — `joinable`, `semantic_synonym`, `correlated`, `distribution_similar`, `co_occurs`.
+5. **Mapper** associates structural nodes with Concept/Entity nodes using metadata or LLM inference, providing a common entry point for concept normalization across sources and formats.
+6. **Graph** stores nodes, relationships, and profiles in SQLite and exposes a unified query interface for agents.
+
+**The design idea:** a revenue column in a database, a revenue chart in a slide deck, and a revenue paragraph in a PDF can eventually connect to the same Concept node. Each format keeps its own structure while sharing business concepts, relationships, and query behavior. The current release validates this approach through its table and column implementation.
+
+## ⚡ Quick Start
+
+Install with [uv](https://docs.astral.sh/uv/):
+
+```bash
+uv pip install -e .
+```
+
+Or with pip:
+
+```bash
+pip install -e .
+```
+
+Configure your LLM provider:
+
+```bash
+cp datalink_config.example.json datalink_config.json
+```
+
+Edit `datalink_config.json` — set your LLM API key:
+
+```json
+{
+  "llm": {
+    "model": "gpt-4o",
+    "api_key": "sk-...",
+    "base_url": "https://api.openai.com/v1"
+  },
+  "embedding": {
+    "model": "text-embedding-3-small",
+    "similarity_threshold": 0.75
+  },
+  "merge_llm_temperature": 0.0,
+  "graph_db_path": "datalink.db"
+}
+```
+
+OpenAI-compatible providers (DeepSeek, Qwen, etc.) work by changing `base_url`:
+
+```json
+{
+  "llm": {
+    "model": "deepseek-chat",
+    "api_key": "...",
+    "base_url": "https://api.deepseek.com"
+  }
+}
+```
+
+Embedding pre-filtering improves merge accuracy and reduces token cost. Leave `embedding.model` empty to skip pre-filtering and use pure LLM judgment:
+
+```json
+{
+  "embedding": {
+    "model": "",
+    "similarity_threshold": 0.75
+  }
+}
+```
+
+Build a graph from your data:
+
+```bash
+# Add tables from a database (also works as initial build on empty graph)
+datalink add-table --source "postgresql://user:pass@localhost/mydb"
+
+# MySQL — use bare mysql:// or mysql+pymysql:// (driver auto-detected)
+datalink add-table --source "mysql://user:pass@localhost/mydb"
+
+# Add tables from CSV files
+datalink add-table --source "./data/*.csv"
+
+# Add a specific table
+datalink add-table --source "postgresql://..." --table orders
+
+# Rebuild the graph when underlying data has changed (old data preserved if pipeline fails)
+datalink rebuild
+
+# Remove a table and its nodes/edges
+datalink remove-table --table orders
+```
+
+> **Deduplication**: Re-adding an existing table (same source path + same table name) is automatically skipped. Use `remove-table` first to re-add.
+
+Explore the graph:
+
+```bash
+# Search for nodes
+datalink search "customer" --type column
+
+# Find paths between two nodes
+datalink path --from column:postgres:users:email --to column:postgres:orders:customer_email
+
+# One-call exploration — answers the whole question
+datalink explore "how do users connect to orders"
+
+# Graph overview stats
+datalink info
+
+# Show the full graph as JSON
+datalink show
+```
+
+## 🧩 What You Can Build With It
+
+| Use case | How DataLink helps |
+| --- | --- |
+| **NL2SQL accuracy** | `joinable` and `semantic_synonym` edges let agents find the right JOINs without trial-and-error. |
+| **Cross-format insight** | A concept like "revenue" can link a database column, a PPT chart, and a PDF paragraph — agents see the full picture. |
+| **Data lineage & discovery** | `derived_from`, `references`, and `find_paths` make lineage and hidden relationships queryable. |
+| **Schema exploration** | `search_nodes` and `extract_subgraph` let agents understand unfamiliar datasets fast. |
+| **MCP-powered agents** | Run `datalink serve` and any MCP client (Claude, Cursor, Copilot) can call retrieval tools directly. |
+| **Statistical QA** | Column profiles (distribution, cardinality, patterns) answer "what does this data look like?" without querying the source. |
+
+## 🛠️ Architecture
+
+DataLink uses decoupled modules with explicit extension points. The current implementation is tabular-first, while the module boundaries leave room for additional data types:
+
+| Module | Responsibility | Extensible? |
+| --- | --- | --- |
+| `connector` | Connect to data sources, extract schema + sample | ✅ add new source types |
+| `extractor` | Create structural nodes per data type | ✅ add new structural vocabularies |
+| `profiler` | Compute type-appropriate fingerprints | ✅ add new profiling strategies |
+| `inferrer` | Discover implicit relationships with confidence | — shared across types |
+| `mapper` | Associate structural nodes with Concept/Entity | — shared foundation |
+| `graph` | SQLite storage, CRUD, four retrieval interfaces | — shared engine |
+| `builder` | Orchestrate the build pipeline | — shared orchestration |
+| `cli` | Typer CLI — add-table, rebuild, show, search, explore, path, info, serve, api, config | — shared interface |
+| `mcp` | MCP Server exposing retrieval as agent tools | — shared interface |
+| `api` | FastAPI REST API — mirrors all CLI commands as HTTP endpoints | — shared interface |
+
+**Adding a new data type** such as PPT requires a connector, extractor, profiler, suitable node models, and applicable relationship rules. Graph storage, relationship representation, retrieval APIs, and agent integration can be reused or extended within the existing framework.
+
+## 🔌 MCP Tools
+
+Start the MCP server:
+
+```bash
+# Default SSE transport
+datalink serve --port 8080
+
+# Recommended streamable-http transport (more stable)
+datalink serve --port 8080 --transport streamable-http
+```
+
+The MCP server exposes **one core tool** for agent retrieval:
+
+| Tool | Description |
+| --- | --- |
+| `datalink_explore` | Universal retrieval — one call answers the whole data question |
+
+Write operations (add-table, rebuild, remove-table) and show are available via the **REST API** instead — they are offline/management operations that don't belong in an agent's retrieval context.
+
+```bash
+# Start the REST API server (default port 8081)
+datalink api
+
+# Example: add a table via REST API
+curl -X POST http://localhost:8081/add-table -H "Content-Type: application/json" \
+  -d '{"source": "./data/", "source_type": "csv"}'
+
+# Example: explore via REST API
+curl -X POST http://localhost:8081/explore -H "Content-Type: application/json" \
+  -d '{"query": "customer orders"}'
+```
+
+**`datalink_explore` output example:**
+
+```
+matched: 3 columns across 2 tables
+
+## orders — 3 columns, 10,000 rows, source: csv
+### customer_id (integer, identifier)
+- meaning: FK referencing customers.id
+- data: null 0%, unique 8,921, range [1–999]
+- also related: customers.id (FK, conf 1.00)
+
+## customers — 5 columns, 999 rows, source: csv
+### id (integer, identifier)
+- meaning: primary key, referenced by orders.customer_id
+- data: null 0%, unique 999, range [1–999]
+### email (varchar, email_address)
+- meaning: contact email, unique per customer
+- data: null 2%, unique 979, samples ["a@b.com", "c@d.com"]
+
+## Cross-table query patterns
+orders.customer_id → customers.id (FK, conf 1.00)
+  SQL: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id
+```
+
+**Optional** (enable via `datalink_config.json` `mcp_tools` field or `DATALINK_MCP_TOOLS` env var):
+
+| Tool | Description |
+| --- | --- |
+| `datalink_search_nodes` | Search nodes by name or field role |
+| `datalink_get_node` | Get node details with connected edges and profile |
+| `datalink_find_paths` | Find relationship paths between two nodes |
+| `datalink_extract_subgraph` | Extract a neighborhood subgraph around given nodes |
+| `datalink_list_datasets` | List all data sources with basic stats |
+| `datalink_list_pending_edges` | List dangling edges referencing missing nodes |
+
+## 🛣️ Roadmap
+
+<table>
+  <tr>
+    <td><strong>PPT connector</strong><br/>Slide, TextBox, Image, and Chart nodes; layout and text profiling; cross-slide concept links.</td>
+    <td><strong>PDF / Markdown connector</strong><br/>Section, Paragraph, Table, Figure as structural nodes; content & structure profiling.</td>
+  </tr>
+  <tr>
+    <td><strong>Image connector</strong><br/>Image nodes with vision-model profiles for captions, detected objects, and scenes; visual concept mapping.</td>
+    <td><strong>Incremental update</strong><br/>Detect source changes, re-profile affected nodes, update edges without full rebuild.</td>
+  </tr>
+  <tr>
+    <td><strong>Embedding-backed search ✅</strong><br/>Vector similarity on node names, descriptions, and concept content for hybrid retrieval (text + vector) in `search_nodes` and `explore`. Configurable and rebuildable.</td>
+    <td><strong>REST API ✅</strong><br/>FastAPI adapter mirrors all CLI commands as HTTP endpoints. Manage the graph (add-table, rebuild, remove-table) and query it (explore, search, etc.) via REST.</td>
+  </tr>
+  <tr>
+    <td><strong>Agent trajectory–driven edges</strong><br/>Extract real-world relationships from agent execution traces — which tables get joined, which columns are queried together, which paths agents walk. Trajectory evidence enriches the static governance baseline with usage-grounded edges such as `frequently_joined` and `co_occurs_in_query`, completing the two-directional relationship construction.</td>
+    <td></td>
+  </tr>
+</table>
+
+## 📚 Documentation
+
+<table>
+  <tr>
+    <td><a href="docs/README.md"><strong>Docs Index</strong></a><br/>Navigate all documentation.</td>
+    <td><a href="docs/DESIGN.md"><strong>Design Document</strong></a><br/>Layered data map, relationship types, build pipeline, and retrieval APIs.</td>
+  </tr>
+  <tr>
+    <td><a href="docs/INTERFACE.md"><strong>Interface Reference</strong></a><br/>CLI commands, REST API endpoints, MCP tools, and retrieval API specifications.</td>
+    <td><a href="docs/pipeline/"><strong>Pipeline Details</strong></a><br/>Step-by-step build pipeline documentation.</td>
+  </tr>
+</table>
+
+## 🛠️ Developer Loop
+
+```bash
+uv sync
+uv run pytest
+uv run ruff check src/ tests/
+```
+
+Run targeted tests for the module you're working on:
+
+```bash
+uv run pytest tests/test_profiler.py
+uv run pytest tests/test_inferrer.py
+```
+
+## 🤝 Contributing
+
+DataLink is under active development. Small, well-scoped contributions are easiest to review.
+
+1. Open an issue or discussion for architectural changes, new data type connectors, edge type proposals, or retrieval API extensions.
+2. Keep pull requests focused on one module boundary.
+3. Run `uv run pytest` and `uv run ruff check` for the modules you touched.
+4. Update docs when a change affects CLI commands, MCP tools, relationship definitions, or retrieval behavior.
+5. Do not commit credentials, database files, or generated graph storage.
+
+## 🧪 Status
+
+DataLink is in early development (v0.1.0). Its working capabilities currently focus on **tabular data**: relational databases, CSV, and Parquet support structural extraction, column profiling, relationship inference, concept mapping, graph storage, and agent retrieval. The unified data map is the long-term direction; PPT, PDF, Markdown, and image support still requires dedicated node models and processing components.
+
+## 📄 License
+
+Apache License 2.0. See [LICENSE.txt](LICENSE.txt).
diff --git a/services/datalink/README_zh.md b/services/datalink/README_zh.md
new file mode 100644
index 00000000..21d5cad3
--- /dev/null
+++ b/services/datalink/README_zh.md
@@ -0,0 +1,339 @@
+<h1 align="center">DataLink 🧬</h1>
+
+> DataLink 现作为 DataFoundry 的一方语义服务维护在 `services/datalink`。正式使用请从仓库根目录通过 DataFoundry 的统一命令启动；独立 CLI 继续用于服务开发与调试。
+
+<p align="center">
+  面向数据 Agent 的统一数据地图——连接不同来源、不同形态的数据，<br/>提供可查询、可扩展、带有关系与置信度的数据上下文。
+</p>
+
+<p align="center">
+  <a href="README.md">English</a> · 简体中文
+</p>
+
+<p align="center">
+  <a href="#-快速开始"><strong>快速开始</strong></a>
+  ·
+  <a href="docs/README.md"><strong>文档</strong></a>
+  ·
+  <a href="docs/DESIGN.md"><strong>设计文档</strong></a>
+  ·
+  <a href="docs/INTERFACE.md"><strong>接口参考</strong></a>
+  ·
+  <a href="#-贡献指南"><strong>贡献指南</strong></a>
+  ·
+  <a href="#-许可证"><strong>许可证</strong></a>
+</p>
+
+## ✨ 为什么需要 DataLink
+
+数据 Agent 能看到 schema，却很难仅凭 schema 判断数据之间的真实联系。表名和列头一目了然，但哪些字段可以 JOIN、哪些字段表达同一个业务概念、哪些关系来自明确约束、哪些只是候选线索，仍然需要额外的上下文。
+
+DataLink 将数据结构、内容特征、业务概念和关联线索组织成一张**统一数据地图**。它的设计不绑定某一种数据形态：
+
+- 🗺️ **统一组织** — 每种数据类型保留自己的结构特征，同时进入同一套关系模型和查询接口。
+- 🏗️ **分层表达** — 数据结构层记录数据在哪里、长什么样；业务概念层记录数据表示什么、与哪些对象有关。
+- 🔎 **关系感知** — 显式边（外键、血缘）和推断边（可 JOIN、概念同义、相关性、分布相似）都带置信度。Agent 按确定性筛选，不必靠猜。
+- 🧩 **按类型扩展** — 新数据类型可以定义自己的 Connector、Extractor、Profiler、节点和画像策略，并复用关系表达、图存储、检索与 Agent 接入能力。
+- 📦 **Agent 开箱即用** — 检索 API 通过 MCP 工具和 REST API 暴露。MCP server 提供 `datalink_explore` 供 Agent 即时查询；REST API 与 CLI 命令一一对应，方便管理和集成。
+
+> **DataLink 是一张会持续校准的数据地图。** 首先通过静态分析完成 schema 提取、内容画像、算法推断和概念映射，建立数据地图的基线，帮助系统理解数据的结构、特征，以及数据之间可能存在的关系。当数据 Agent 实际使用数据时，它的执行轨迹——例如哪些表被 JOIN、哪些列被共同查询、哪些分析路径被反复走过——会提供静态分析难以获得的使用证据。这些证据可以进一步丰富和校准数据地图，并为关系提供更贴近实际使用的置信度评估。因此 DataLink 中数据关系构建的最终形态是双向的：静态治理提供基线地图，Agent 轨迹则用真实使用证据来丰富和校准地图。
+
+> 现阶段，DataLink 主要支持关系型数据库、CSV 和 Parquet，核心处理对象仍是 Table/Column。PPT、PDF、Markdown、图片等非结构化数据类型将作为后续扩展方向，逐步接入统一的数据地图。
+
+<p align="center">
+  <img src="docs/DataMap.png" alt="DataLink 统一数据地图：连接表格、记录、文档、幻灯片和图片中的相关数据点" width="900"/>
+</p>
+
+上图展示了统一数据地图的设计目标：DataLink 组织、标注并对外提供跨数据源、跨格式的数据关系；其中表格和记录代表当前主要实现，文档、幻灯片和图片展示后续扩展方向。
+
+## 🗺️ 工作原理
+
+<p align="center">
+  <img src="docs/DL-zh.png" alt="DataLink 架构图" width="720"/>
+</p>
+
+构建管线把已接入的数据源组织成可查询的数据地图：
+
+```
+Connector → Extractor → Profiler → Inferrer → Semantic Mapper → Graph
+```
+
+当前管线以表格数据为主要实现，同时按照可扩展的数据类型处理流程划分职责：
+
+1. **Connector** — 当前连接关系型数据库和 CSV/Parquet 文件，提取 schema 与样本数据；未来可为其他类型提供专用连接器。
+2. **Extractor** — 当前生成 Table/Column 等结构节点并建立 `contains`、`foreign_key` 等显式关系；其他类型可定义自己的结构节点。
+3. **Profiler** — 当前计算字段类型、分布、基数、样本等列画像；其他类型可扩展对应的内容与结构画像。
+4. **Inferrer** — 以置信度分数发现隐式关系——`joinable`、`semantic_synonym`、`correlated`、`distribution_similar`、`co_occurs`。
+5. **Mapper** — 利用元数据或 LLM 推断，将结构节点关联到 Concept/Entity 节点，产出 `represents` 边，为跨来源、跨类型的概念归一提供统一入口。
+6. **Graph** — 将节点、关系和画像存入 SQLite（邻接表），通过统一接口供 Agent 查询。
+
+**核心设计理念**：数据库里的“营收”列、PPT 里的“营收”图表、PDF 里关于“营收”的段落，未来都可以关联到同一个 Concept 节点。不同类型保留自己的结构表达，但共享业务概念、关系模型和查询方式。当前版本已经在表和列上实现了这套流程，并以此验证统一数据地图的核心能力。
+
+## ⚡ 快速开始
+
+使用 [uv](https://docs.astral.sh/uv/) 安装：
+
+```bash
+uv pip install -e .
+```
+
+或使用 pip：
+
+```bash
+pip install -e .
+```
+
+配置 LLM 提供商：
+
+```bash
+cp datalink_config.example.json datalink_config.json
+```
+
+编辑 `datalink_config.json`，填入 LLM API Key：
+
+```json
+{
+  "llm": {
+    "model": "gpt-4o",
+    "api_key": "sk-...",
+    "base_url": "https://api.openai.com/v1"
+  },
+  "embedding": {
+    "model": "text-embedding-3-small",
+    "similarity_threshold": 0.75
+  },
+  "merge_llm_temperature": 0.0,
+  "graph_db_path": "datalink.db"
+}
+```
+
+OpenAI 兼容提供商（DeepSeek、Qwen 等）只需改 `base_url`：
+
+```json
+{
+  "llm": {
+    "model": "deepseek-chat",
+    "api_key": "...",
+    "base_url": "https://api.deepseek.com"
+  }
+}
+```
+
+Embedding 粗筛能提升合并准确性、降低 token 开销。`embedding.model` 留空则跳过粗筛，全靠 LLM 判断：
+
+```json
+{
+  "embedding": {
+    "model": "",
+    "similarity_threshold": 0.75
+  }
+}
+```
+
+从数据构建图谱：
+
+```bash
+# 从数据库添加表（空图谱上等于首次构建）
+datalink add-table --source "postgresql://user:pass@localhost/mydb"
+
+# MySQL — 直接用 mysql:// 或 mysql+pymysql://（驱动自动检测）
+datalink add-table --source "mysql://user:pass@localhost/mydb"
+
+# 从 CSV 文件添加表
+datalink add-table --source "./data/*.csv"
+
+# 添加指定表
+datalink add-table --source "postgresql://..." --table orders
+
+# 底层数据变更后重建图谱（pipeline 失败时旧数据保留）
+datalink rebuild
+
+# 移除一张表及其关联节点/边
+datalink remove-table --table orders
+```
+
+> **去重**：重复添加已存在的表（同数据源路径 + 同表名）会自动跳过。如需重新添加，先 `remove-table` 再 `add-table`。
+
+探索图谱：
+
+```bash
+# 搜索节点
+datalink search "customer" --type column
+
+# 查找两节点之间的路径
+datalink path --from column:postgres:users:email --to column:postgres:orders:customer_email
+
+# 一键探索——一次调用回答完整问题
+datalink explore "用户和订单是如何关联的"
+
+# 图谱概览统计
+datalink info
+
+# 以 JSON 输出完整图谱
+datalink show
+```
+
+## 🧩 用 DataLink 可以做什么
+
+| 应用场景 | DataLink 的作用 |
+| --- | --- |
+| **NL2SQL 准确性** | `joinable` 和 `semantic_synonym` 边让 Agent 直接找到正确的 JOIN，不用反复试错。 |
+| **跨格式洞察** | "营收"这一概念可以把数据库列、PPT 图表、PDF 段落连在一起——Agent 看到的是完整拼图。 |
+| **数据血缘与发现** | `derived_from`、`references` 和 `find_paths` 让血缘和隐含关系变得可查询。 |
+| **Schema 探索** | `search_nodes` 和 `extract_subgraph` 让 Agent 快速摸清陌生数据集的结构。 |
+| **MCP 驱动的 Agent** | 运行 `datalink serve`，任何 MCP 客户端（Claude、Cursor、Copilot）都能直接调用检索工具。 |
+| **统计问答** | 列级画像（分布、基数、模式）无需查询源数据就能回答"这数据长什么样？"。 |
+
+## 🛠️ 架构
+
+DataLink 采用分层、解耦的模块设计。当前实现以表格数据为主，但模块边界为更多数据类型预留了扩展入口：
+
+| 模块 | 职责 | 可扩展？ |
+| --- | --- | --- |
+| `connector` | 连接数据源，提取 schema 与样本 | ✅ 新增数据源类型 |
+| `extractor` | 按数据类型创建结构节点 | ✅ 新增结构词汇 |
+| `profiler` | 计算适配类型的指纹 | ✅ 新增画像策略 |
+| `inferrer` | 以置信度发现隐式关系 | — 跨类型共享 |
+| `mapper` | 将结构节点关联到 Concept/Entity | — 共享基础能力 |
+| `graph` | SQLite 存储、CRUD、四种检索接口 | — 共享引擎 |
+| `builder` | 编排构建管线 | — 共享编排 |
+| `cli` | Typer CLI——add-table、rebuild、show、search、explore、path、info、serve、api、config | — 共享界面 |
+| `mcp` | MCP Server，将检索暴露为 Agent 工具（默认只暴露 datalink_explore） | — 共享界面 |
+| `api` | FastAPI REST API——与 CLI 命令一一对应的 HTTP 端点 | — 共享界面 |
+
+**扩展新数据类型**（例如 PPT）时，需要实现相应的 Connector、Extractor、Profiler，并按该类型补充节点模型和适用的关系推断策略。图存储、关系表达、查询接口及 Agent 接入方式可以在现有框架上复用或扩展。
+
+## 🔌 MCP 工具
+
+启动 MCP 服务器：
+
+```bash
+# 默认 SSE transport
+datalink serve --port 8080
+
+# 推荐 streamable-http transport（更稳定）
+datalink serve --port 8080 --transport streamable-http
+```
+
+MCP 服务器只暴露**一个核心检索工具**：
+
+| 工具 | 描述 |
+| --- | --- |
+| `datalink_explore` | 万能检索——一次调用回答完整数据问题 |
+
+写操作（add-table、rebuild、remove-table）和 show 通过 **REST API** 提供——它们是离线/管理操作，不属于 Agent 的检索上下文。
+
+```bash
+# 启动 REST API 服务器（默认端口 8081）
+datalink api
+
+# 示例：通过 REST API 添加表
+curl -X POST http://localhost:8081/add-table -H "Content-Type: application/json" \
+  -d '{"source": "./data/", "source_type": "csv"}'
+
+# 示例：通过 REST API 检索
+curl -X POST http://localhost:8081/explore -H "Content-Type: application/json" \
+  -d '{"query": "客户 订单"}'
+```
+
+**`datalink_explore` 输出示例：**
+
+```
+matched: 3 columns across 2 tables
+
+## orders — 3 columns, 10,000 rows, source: csv
+### customer_id (integer, identifier)
+- meaning: FK 引用 customers.id
+- data: null 0%, unique 8,921, range [1–999]
+- also related: customers.id (FK, conf 1.00)
+
+## customers — 5 columns, 999 rows, source: csv
+### id (integer, identifier)
+- meaning: 主键，被 orders.customer_id 引用
+- data: null 0%, unique 999, range [1–999]
+### email (varchar, email_address)
+- meaning: 联系邮箱，每个客户唯一
+- data: null 2%, unique 979, samples ["a@b.com", "c@d.com"]
+
+## Cross-table query patterns
+orders.customer_id → customers.id (FK, conf 1.00)
+  SQL: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id
+```
+
+**辅助工具**（通过 `datalink_config.json` 的 `mcp_tools` 字段或 `DATALINK_MCP_TOOLS` 环境变量启用）：
+
+| 工具 | 描述 |
+| --- | --- |
+| `datalink_search_nodes` | 按名称或字段角色搜索节点 |
+| `datalink_get_node` | 获取节点详情、邻接边和画像 |
+| `datalink_find_paths` | 查找两节点之间的关系路径 |
+| `datalink_extract_subgraph` | 提取指定节点周围的邻域子图 |
+| `datalink_list_datasets` | 列出所有数据源及基本统计 |
+| `datalink_list_pending_edges` | 列出引用缺失节点的悬空边 |
+
+## 🛣️ 路线图
+
+<table>
+  <tr>
+    <td><strong>PPT 连接器</strong><br/>Slide、TextBox、Image、Chart 作为结构节点；布局与文本画像；跨幻灯片含义链接。</td>
+    <td><strong>PDF / Markdown 连接器</strong><br/>Section、Paragraph、Table、Figure 作为结构节点；内容与结构画像。</td>
+  </tr>
+  <tr>
+    <td><strong>图片连接器</strong><br/>Image 节点 + 视觉模型画像（描述、检测物体、场景）；视觉概念映射。</td>
+    <td><strong>增量更新</strong><br/>检测数据源变更，只对受影响节点重新画像、更新边，无需全量重建。</td>
+  </tr>
+  <tr>
+    <td><strong>向量相似检索 ✅</strong><br/>对节点名称、描述和概念内容做向量相似度检索，让 `search_nodes` 和 `explore` 成为混合检索（全文 + 向量），可配置、可重建。</td>
+    <td><strong>REST API ✅</strong><br/>FastAPI 适配器，与 CLI 命令一一对应的 HTTP 端点。通过 REST 管理图谱（add-table、rebuild、remove-table）和查询数据（explore、search 等）。</td>
+  </tr>
+  <tr>
+    <td><strong>Agent 轨迹驱动的边关系</strong><br/>从 Agent 的执行轨迹中提取真实的数据关系——哪些表被 JOIN、哪些列被一起查询、Agent 走了哪些路径。轨迹证据用真实使用行为丰富静态治理基线，产出 `frequently_joined`、`co_occurs_in_query` 等使用接地边，完成双向关系构建的闭环。</td>
+    <td></td>
+  </tr>
+</table>
+
+## 📚 文档
+
+<table>
+  <tr>
+    <td><a href="docs/README.md"><strong>文档索引</strong></a><br/>浏览全部文档。</td>
+    <td><a href="docs/DESIGN.md"><strong>设计文档</strong></a><br/>双层架构、边类型、构建管线与检索 API。</td>
+  </tr>
+  <tr>
+    <td><a href="docs/INTERFACE.md"><strong>接口参考</strong></a><br/>CLI 命令、REST API 端点、MCP 工具与检索 API 规格。</td>
+    <td><a href="docs/pipeline/"><strong>管线详情</strong></a><br/>逐步构建管线文档。</td>
+  </tr>
+</table>
+
+## 🛠️ 开发
+
+```bash
+uv sync
+uv run pytest
+uv run ruff check src/ tests/
+```
+
+针对正在开发的模块跑定向测试：
+
+```bash
+uv run pytest tests/test_profiler.py
+uv run pytest tests/test_inferrer.py
+```
+
+## 🤝 贡献指南
+
+DataLink 正在积极开发中。小而聚焦的 PR 最容易通过审查。
+
+1. 涉及架构变更、新数据类型连接器、边类型提案或检索 API 扩展，请先开 Issue 或 Discussion。
+2. PR 聚焦于一个模块边界。
+3. 对改动模块跑 `uv run pytest` 和 `uv run ruff check`。
+4. 变更影响 CLI 命令、MCP 工具、边含义或检索行为时，请同步更新文档。
+5. 不要提交凭证、数据库文件或生成的图谱存储。
+
+## 🧪 项目状态
+
+DataLink 处于早期开发阶段（v0.1.0）。当前可用能力主要面向**表格数据**：关系型数据库、CSV 和 Parquet 已支持结构提取、列画像、关系推断、概念映射、图存储与 Agent 检索。统一数据地图是项目的长期设计方向；PPT、PDF、Markdown、图片等非表格连接器仍在路线图中，需要继续补齐对应的节点模型和处理组件。
+
+## 📄 许可证
+
+Apache License 2.0。详见 [LICENSE.txt](LICENSE.txt)。
diff --git a/services/datalink/datalink_config.example.json b/services/datalink/datalink_config.example.json
new file mode 100644
index 00000000..b7b3f76a
--- /dev/null
+++ b/services/datalink/datalink_config.example.json
@@ -0,0 +1,26 @@
+{
+  "llm": {
+    "model": "gpt-4o",
+    "api_key": "",
+    "base_url": "https://api.openai.com/v1",
+    "temperature": 0.7,
+    "max_tokens": 16384,
+    "timeout": 120.0
+  },
+  "embedding": {
+    "model": "",
+    "api_key": "",
+    "base_url": "",
+    "similarity_threshold": 0.75,
+    "timeout": 60.0
+  },
+  "merge_llm_temperature": 0.0,
+  "merge_batch_interval": 10,
+  "mapping_batch_size": 15,
+  "graph_db_path": "../../storage/datalink/datalink.db",
+  "sample_size": 1000,
+  "confidence_threshold": 0.3,
+  "joinable_overlap_threshold": 0.1,
+  "correlation_threshold": 0.5,
+  "mcp_tools": ""
+}
diff --git a/services/datalink/docs/DESIGN.md b/services/datalink/docs/DESIGN.md
new file mode 100644
index 00000000..8215381c
--- /dev/null
+++ b/services/datalink/docs/DESIGN.md
@@ -0,0 +1,553 @@
+# DataLink 设计文档
+
+## 1. 项目概述
+
+**DataLink** 是一个面向数据 Agent 的**统一数据地图系统**。它将数据结构、画像、业务概念和关联线索组织为可查询的关系网络，为数据发现、分析规划和 Agent 工具调用提供上下文。
+
+项目的长期设计目标是接入表格、文档、幻灯片、图片等多种数据类型，使不同类型保留各自结构，同时共享关系表达、存储和查询能力。**当前版本的实际实现主要面向表格数据**，支持关系型数据库、CSV 和 Parquet，核心节点仍是 Table/Column；非表格类型需要继续补充连接器、节点模型和处理组件。
+
+核心能力：
+
+- 从 CSV/Parquet 文件或关系型数据库自动构建数据地图
+- 维护分层表达：数据结构层（Table/Column）+ 业务概念层（Concept/Entity）
+- 推断列之间的隐式关系（可 JOIN、同义、分布相似、统计相关）
+- 通过 CLI 和 MCP Server 供人类与 AI Agent 查询
+
+技术栈：Python 3.10+、Pydantic、Pandas、SQLAlchemy、SQLite、Typer、Rich、MCP（FastMCP）、OpenAI/Anthropic LLM。
+
+---
+
+## 2. 核心设计理念
+
+### 2.1 分层数据地图
+
+| 层级 | 节点类型 | 含义 | 示例 |
+|------|----------|------|------|
+| **数据结构层** | `TableNode` | 表/数据集 | `orders`, `customers` |
+| **数据结构层** | `ColumnNode` | 列/字段 | `customer_id`, `amount` |
+| **业务概念层** | `ConceptNode` | 可度量的概念/属性 | `revenue`, `person_identifier` |
+| **业务概念层** | `EntityNode` | 可识别的实体/集合 | `customer`, `order` |
+
+数据结构层来自 schema 与采样数据；业务概念层来自元数据注释或 LLM 推断，并通过 `represents` / `has_concept` 边与结构节点连接。这种分层方式用于统一组织数据，并不限制未来只能采用 Table/Column 节点。
+
+### 2.2 边的分类体系
+
+边按来源与用途分为四类（见 `models/edge.py`）：
+
+| 类别 | 说明 | 典型边类型 | 置信度 |
+|------|------|------------|--------|
+| **A. 结构显式** | Schema/元数据直接给出 | `contains`, `foreign_key` | 1.0 |
+| **B. 推断关系** | 算法推断 | `joinable`, `semantic_synonym`, `correlated`, `distribution_similar` | 0.0–1.0 |
+| **C. 上下文** | 使用行为——从 Agent 执行轨迹中提取的关系（规划中） | `co_occurs_in_query`, `frequently_joined` | — |
+| **D. 跨层关联** | 数据结构 ↔ 业务概念 | `represents`, `has_concept` | 0.5–1.0 |
+
+推断边会按 `confidence_threshold`（默认 0.3）过滤后入库。
+
+**边关系的双向构建：** DataLink 的边关系最终形态来自两个方向。一是*静态治理*——通过 schema 提取、内容画像、算法推断和概念映射，理解数据长什么样、*可能*存在什么关系（类别 A/B/D）。二是*轨迹驱动*——当 Agent 实际使用数据时，其执行轨迹（哪些表被 JOIN、哪些列被一起查询、走了哪些路径）揭示静态分析无法发现的真实关系（类别 C）。两者相辅相成：静态治理提供基线地图，轨迹证据用真实使用行为来丰富和校准地图。类别 C 的边类型尚未实现，将在后续版本中从 Agent 的 `datalink_explore` 调用和 SQL 生成记录中提取。
+
+**Pending edges（悬空边）：** 当边的 `source_id` 或 `target_id` 对应的节点尚未入库（典型：跨数据源 FK 的目标表还没加入），边不会丢弃而是存入 `pending_edges` 表，标记 `missing_endpoints`（如 `["target"]`）。检索时这些边作为 `suggested_edges` 展示，不参与路径发现和子图扩展。当新数据源通过 `add_table` 加入后，Pipeline 自动调用 `resolve_pending_edges()` 将两端节点都存在的 pending 边移入 `edges` 主表。
+
+### 2.3 设计原则
+
+1. **统一地图，不抹平类型差异**：不同数据类型可以使用不同的节点结构和画像方法，但通过统一的关系模型、存储和查询接口协作。
+2. **扩展点前置**：Connector、Extractor、Profiler 和关系推断按数据类型逐步扩展，公共能力尽量沉淀在 Graph、Retrieval、CLI、REST 和 MCP 层。
+3. **采样而非全量扫描**：当前表格实现默认采样 1000 行做 Profiling，平衡速度与准确性。
+4. **概念归一**：当前所有列通过 LLMMapper 推断 Concept/Entity；有 comment 的列将 comment 作为额外信号，以提高映射质量。
+5. **增量维护**：支持 `add_table` / `rebuild` / `remove_table`，不必每次全量重建。
+6. **Agent 友好**：通过 MCP、REST 和 CLI 提供搜索、路径发现和一键探索等能力。
+7. **双向关系构建**：边关系既来自静态治理（schema、画像、推断），也来自 Agent 使用轨迹（哪些表被 JOIN、哪些列被一起查询）。静态治理提供基线地图，轨迹证据丰富和校准地图——两者相辅相成。当前只实现了静态治理方向，轨迹驱动方向将在路线图中推进。
+
+---
+
+## 3. 系统架构
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                        用户 / AI Agent                           │
+└──────────────┬──────────────────────────────┬───────────────────┘
+               │ CLI (Typer)                  │ MCP Server (FastMCP)
+               ▼                              ▼
+┌──────────────────────────────────────────────────────────────────┐
+│                     builder/pipeline.py                          │
+│                   BuildPipeline (编排层)                         │
+└──┬────────┬────────┬────────┬────────┬────────┬────────┬─────────┘
+   │        │        │        │        │        │        │
+   ▼        ▼        ▼        ▼        ▼        ▼        ▼
+connector   extractor     profiler  inferrer  mapper   graph
+   │        │        │        │        │        │
+   │        │        │        │        │        ├── storage.py  (SQLite CRUD)
+   │        │        │        │        │        └── retrieval.py (查询 API)
+   ▼        ▼        ▼        ▼        ▼
+ CSV/    Table/    Column    Join/    Concept/
+ DB      Column    Profile   Synonym  Entity
+         节点      指纹       等推断   映射
+```
+
+### 目录结构
+
+```
+DataLink/
+├── src/datalink/
+│   ├── cli/           # 命令行入口
+│   ├── mcp/           # MCP Server（Agent 集成）
+│   ├── builder/       # 构建流水线编排
+│   ├── connector/     # 数据源连接（文件/数据库）
+│   ├── extractor/     # 结构层节点提取
+│   ├── profiler/      # 列统计指纹
+│   ├── inferrer/      # 隐式关系推断
+│   ├── mapper/        # 概念层映射
+│   ├── graph/         # 存储 + 检索
+│   ├── models/        # Pydantic 数据模型
+│   └── config.py      # 全局配置
+├── tests/             # 单元测试（每模块对应）
+├── datalink_config.json
+└── pyproject.toml
+```
+
+---
+
+## 4. 构建流水线（Build Pipeline）
+
+`BuildPipeline`（`builder/pipeline.py`）是核心编排器，完整构建分 6 步：
+
+以下步骤描述的是**当前表格数据实现**。未来接入其他类型时，可沿用相同职责划分，但需要为该类型实现适配组件，不能假设现有 `TabularExtractor`、`TabularProfiler` 和列级 Inferrer 可以直接复用。
+
+```mermaid
+flowchart LR
+    A[1. Connect<br/>连接数据源] --> B[2. Extract<br/>提取结构节点]
+    B --> C[3. Profile<br/>列统计指纹]
+    C --> D[4. Infer<br/>推断隐式关系]
+    D --> E[5. Map<br/>概念层映射]
+    E --> F[6. Store<br/>写入 SQLite]
+```
+
+### Step 1: Connect（连接数据源）
+
+- `FileConnector`：CSV/Parquet，每个文件视为一张表
+- `DatabaseConnector`：SQLAlchemy 内省 schema、FK、comment，并采样数据
+- 输出：`DatasourceInfo`（tables + sample_data）
+
+### Step 2: Extract（提取结构层）
+
+`TabularExtractor` 从 `DatasourceInfo` 生成：
+
+- `TableNode`：ID 格式 `table:{source}:{table_name}`
+- `ColumnNode`：ID 格式 `column:{source}:{table}:{column}`
+- 显式边：`contains`（表→列）、`foreign_key`（列→列）
+
+### Step 3: Profile（列指纹）
+
+`TabularProfiler` 对每列计算 `ColumnProfile`：
+
+- 基础统计：null_rate、cardinality、unique_rate
+- 类型检测：integer / float / string / datetime 等
+- 字段角色：email_address、identifier、monetary_value 等（规则 + 正则）
+- 数值/字符串统计、top_values、直方图、sample_values（供 LLM 使用）
+
+### Step 4: Infer（推断隐式关系）
+
+| Inferrer | 边类型 | 逻辑 |
+|----------|--------|------|
+| `JoinableInferrer` | `joinable` | 跨表列值域重叠率 ≥ 阈值 |
+| `SynonymInferrer` | `semantic_synonym` | 同 semantic_type / 列名同义词组 |
+| `DistributionInferrer` | `distribution_similar` | 数值/分类分布相似度 |
+| `CorrelationInferrer` | `correlated` | 仅对已 joinable 的数值列算 Pearson 相关 |
+
+### Step 5: Map（概念层映射）
+
+- `LLMMapper`：**所有列**统一走 LLM 推断 Concept/Entity（有 comment 的列将 comment 作为额外信号带入 prompt）
+- `add_table` 时新增 Concept/Entity 通过 `merge_with_existing()` 与已有节点做消歧合并：**Embedding 粗筛 + LLM 精判**两阶段模型推理 + 边重定向 + 去重
+  - Embedding 粗筛（可选）：计算新旧节点 cosine 相似度，≥阈值的配对进入候选列表
+  - LLM 精判（必选）：候选列表 + 仅相关已有节点发给 LLM 确认合并决策（embedding 粗筛过滤掉了不相似的已有节点，不再全量注入）
+  - 未配置 embedding 模型时全量已有节点注入 LLM 判断
+
+### Step 6: Store（持久化）
+
+`GraphStorage` 写入 SQLite（`datalink.db`），并记录 build 元数据。
+
+### 构建模式
+
+Pipeline 提供三种构建模式 + 三种重建子模式：
+
+- **`add_datasource` / `add_table`**（添加表/数据源）：向图谱添加来自一个数据源的表。不指定表名时处理该数据源的所有表；在空图谱上调用等同于首次构建。**去重校验**：添加前会检查图谱中已存在的表（通过 table ID 匹配，ID = `table:{source}:{table_name}`），已存在的表自动跳过而非重复注册。返回结果中标注 `added_tables`（新添加的）和 `skipped_tables`（跳过的）。如需重新添加已有表，先 `remove_table` 再 `add_datasource`。添加后自动为新节点构建 embedding 向量（如果配置了 embedding 模型）。CLI `datalink add-table`、MCP `add_table`。文件路径在入口处自动 resolve 为绝对路径，确保节点 ID 全局唯一。
+- **`rebuild`**（重建）：从 SQLite 中读取所有已有 TableNode 的元数据，自动重新连接数据源。支持三种模式（`--mode/-m`）：
+  - **`full`**（默认）：重走完整 pipeline（extract → profile → infer → map → embed）。安全性保证：compute-first → clear-last 模式，旧数据只在新 pipeline 成功后才清除，失败则旧图谱完好保留。CLI `datalink rebuild`、MCP `rebuild(mode="full")`。
+  - **`vec`**：只重建 embedding 向量索引。不改变图谱数据、不调用 LLM。适用场景：更换了 embedding 模型配置。CLI `datalink rebuild --mode vec`、MCP `rebuild(mode="vec")`。
+  - **`profile`**：重新统计所有 table/column 的 profile 值，并重建依赖 profile 的推断边（joinable、distribution_similar、semantic_synonym、correlated）。不涉及概念层、不调用 LLM。适用场景：数据源数据量变化但概念结构不变。CLI `datalink rebuild --mode profile`、MCP `rebuild(mode="profile")`。
+- **`remove_table`**（删除）：级联删除表/列/边/Profile/embedding，可选清理孤立 Concept/Entity；同时清理引用已移除节点的 pending edges。孤立概念节点清理分两阶段：Stage 1 删除无结构层 `represents` 锚定的 Concept；Stage 2 删除无存活 Concept 连接的 Entity。`has_concept` 边（概念层内部）不算锚定。CLI `datalink remove-table`、MCP `remove_table`。
+
+Pipeline 内部还有一个 `init_build` 方法（先 clear_all 再调 add_datasource），供需要显式清空的场景使用，但 CLI/MCP 入口不需要它——空图谱上 `add-table` 自然就是初始构建。
+
+---
+
+## 5. 模块详解
+
+### 5.1 connector — 数据源连接
+
+| 文件 | 职责 |
+|------|------|
+| `base.py` | 抽象接口：`connect`, `get_datasource_info`, `get_sample_data`, `disconnect` |
+| `file.py` | CSV/Parquet，Pandas 读入，无 FK |
+| `database.py` | SQLAlchemy 内省，支持 PostgreSQL/MySQL/SQLite 等。**Driver fallback**：裸 scheme（`mysql://`）自动尝试 `pymysql` 等替代驱动；MySQL/MariaDB/SQLite 不做 URL path dot 拆分，且默认 `schema_name` 清为 `None`（避免 SQLite 查询 `public.sqlite_master`） |
+
+### 5.2 extractor — 结构提取
+
+`tabular.py`：将 `TableInfo` / `ColumnInfo` / `ForeignKeyInfo` 转为图谱节点与显式边，ID 规则稳定可复现。
+
+### 5.3 profiler — 统计分析
+
+`tabular.py`：基于采样数据生成 `ColumnProfile`，是 inferrer 与 mapper 的共同输入。
+
+### 5.4 inferrer — 关系推断
+
+四个独立 Inferrer，可单独测试与调参（阈值在 `DataLinkConfig` 中配置）。
+
+### 5.5 mapper — 概念映射
+
+- **LLMMapper**：所有列统一走 LLM 推理，有 comment 的列将 comment 作为额外信号带入 prompt；批量分析列名、类型、统计、样本值，输出 JSON 结构化 Concept/Entity；`add_table` 时通过 `merge_with_existing` 与已有 Concept/Entity 做消歧合并（Embedding 粗筛 + LLM 精判两阶段模型推理）
+
+### 5.6 graph — 存储与检索
+
+**storage.py**（SQLite）：
+
+- 表：`nodes`, `edges`, `column_profiles`, `metadata`, `pending_edges`, `node_embeddings`
+- CRUD、批量写入、`remove_table`、孤立节点清理、`remove_edges_by_types`、embedding CRUD、`get_graph_stats`
+
+**retrieval.py**（检索 API）：
+
+1. `explore` — **万能检索入口**（query → 格式化文本，内含节点详情 + 关系链 + 数据指纹）
+   - 内部组合调用 search / get_node / find_paths / profile 等
+   - 多维度匹配 + 沿边扩展召回
+   - 自适应输出预算
+   - focus 参数调节深度
+2. `search_nodes` — 名称/semantic_type 搜索 + 向量相似检索（混合检索，embedding 未配置时退化为纯全文）（辅助方法，不默认暴露给 agent）
+3. `get_node` — 节点详情 + 邻接边 + Profile（辅助方法）
+4. `find_paths` — BFS 路径发现（辅助方法）
+5. `extract_subgraph` — 从指定节点按 hop 扩展子图（辅助方法）
+6. `list_datasets` — 列出所有表及统计（辅助方法）
+
+### 5.7 cli — 命令行
+
+CLI 命令与 MCP 工具一一对应：
+
+| 命令 | 功能 | 对应 MCP |
+|------|------|----------|
+| `datalink add-table` | 添加表/数据源 | `add_table` |
+| `datalink rebuild` | 重建图谱 | `rebuild` |
+| `datalink remove-table` | 删除表 | `remove_table` |
+| `datalink explore` | 万能检索 | `datalink_explore` |
+| `datalink search` | 搜索节点 | `datalink_search_nodes` |
+| `datalink get-node` | 节点详情 | `datalink_get_node` |
+| `datalink path` | 两节点间路径 | `datalink_find_paths` |
+| `datalink extract-subgraph` | 子图扩展 | `datalink_extract_subgraph` |
+| `datalink info` | 图谱概览（全局统计） | — |
+| `datalink list-datasets` | 表列表 | `datalink_list_datasets` |
+| `datalink pending-edges` | 悬空边列表 | `datalink_list_pending_edges` |
+| `datalink serve` | 启动 MCP Server | — |
+| `datalink config` | 写配置 | — |
+
+### 5.8 mcp — Agent 集成
+
+基于 FastMCP，支持两种 transport 协议：
+- **SSE**（传统，默认） — 长连接流式推送
+- **streamable-http**（推荐） — 更稳定，适合长耗时操作
+
+启动方式：`datalink serve --transport streamable-http`
+
+默认暴露（命名与 CLI 一致）：
+
+- `datalink_explore` — 万能检索，一个调用回答整个数据问题
+- `add_table` — 添加表/数据源（table 可选，null 则加全部；空图谱上等于首次构建；已有表自动跳过）
+- `rebuild` — 重建图谱（三种模式：full/vec/profile；pipeline 失败时旧数据保留）
+- `remove_table` — 删除表
+
+可选暴露（通过 `DATALINK_MCP_TOOLS` 环境变量，逗号分隔全名）：
+
+- `datalink_search_nodes` — 精确名称搜索
+- `datalink_get_node` — 单节点详情
+- `datalink_find_paths` — 路径发现
+- `datalink_extract_subgraph` — 子图扩展
+- `datalink_list_datasets` — 表概览
+- `datalink_list_pending_edges` — 悬空边列表
+
+示例：`DATALINK_MCP_TOOLS=datalink_search_nodes,datalink_get_node`
+
+Agent 可通过 MCP 的 `datalink_explore` 自主探索数据关系、规划 JOIN 路径，一次调用即可获得完整上下文。
+
+---
+
+## 6. 数据模型
+
+### 6.1 核心模型（`models/`）
+
+```
+Node (基类)
+├── ColumnNode   — table_id, dtype, semantic_type, profile_id, comment
+├── TableNode    — source, source_type, row_count, column_ids
+├── ConceptNode  — description, unit, dimension
+└── EntityNode   — description
+
+Edge — source_id, target_id, type, confidence, properties
+
+ColumnProfile — 列统计指纹（dtype, semantic_type, 分布, top_values 等）
+
+DatasourceConfig / DatasourceInfo — 连接配置与提取结果
+```
+
+### 6.2 存储 Schema（`graph/schema.sql`）
+
+- **nodes**：统一存四类节点，`properties` 为 JSON
+- **edges**：带 `confidence`，**无 FK 级联删除**（`INSERT OR REPLACE` on nodes 会触发内部 DELETE+INSERT，级联会破坏已有边关系；边清理由 `remove_table` / `cleanup_orphaned_semantic_nodes` 显式处理）
+- **column_profiles**：与 column 节点 1:1，同样无 FK 级联（Profile 清理由 `remove_table` 显式处理）
+- **node_embeddings**：node_id → embedding_vector (float32 BLOB) + embedding_model + searchable_text，独立于主表，`rebuild --mode vec` 时重建
+- **metadata**：构建元信息
+
+索引覆盖 type、name、source/target、confidence 等常见查询。
+
+---
+
+## 7. 配置说明
+
+配置文件：`datalink_config.json`（也可通过 `DataLinkConfig.load()` 加载）
+
+| 配置项 | 默认值 | 说明 |
+|--------|--------|------|
+| `graph_db_path` | `datalink.db` | SQLite 数据库文件名（裸文件名或相对路径自动解析到 `~/.datalink/storage/`，绝对路径原样使用） |
+| `sample_size` | 1000 | 采样行数 |
+| `confidence_threshold` | 0.3 | 推断边最低置信度 |
+| `joinable_overlap_threshold` | 0.1 | JOIN 推断重叠率阈值 |
+| `correlation_threshold` | 0.5 | 相关系数绝对值阈值 |
+| `llm.model` | `gpt-4o` | LLM 模型名 |
+| `llm.api_key` | — | 也可从 `OPENAI_API_KEY` 环境变量读取 |
+| `llm.base_url` | `https://api.openai.com/v1` | OpenAI-compatible API 地址 |
+| `llm.timeout` | `120.0` | LLM API HTTP 超时秒数（自托管网关 504 时增大此值） |
+| `embedding.model` | `""` | Embedding 模型名（空=跳过向量检索和粗筛） |
+| `embedding.api_key` | `""` | 空=回退到 `llm.api_key` |
+| `embedding.base_url` | `""` | 空=回退到 `llm.base_url` |
+| `embedding.similarity_threshold` | `0.75` | Embedding 粗筛 cosine 相似度阈值（同时用于向量检索最低相似度过滤） |
+| `embedding.timeout` | `60.0` | Embedding API HTTP 超时秒数 |
+
+**Embedding 用途**：配置后生效于两个场景：
+1. **图谱消歧**（merge_with_existing）：embedding 预筛候选合并对，降低 LLM 调用开销（原有功能）
+2. **混合检索**（search_nodes / explore）：向量相似检索 + 全文检索合并，提升召回率（新功能）
+
+配置 `embedding.model` 后需运行 `datalink rebuild --mode vec`（或在 `add-table`/`rebuild` 时自动构建）来生成向量索引。更换模型后需再次 `rebuild --mode vec` 以重建向量。
+| `merge_llm_temperature` | `0.0` | merge LLM 调用温度（低=更确定性） |
+| `merge_batch_interval` | `10` | 分批推理时每 N 个 batch 才做一次 merge（1=每个 batch 后都 merge） |
+| `mapping_batch_size` | `15` | LLM mapping 每批列数（减小如 5 可降低单次推理时间，适合网关 timeout 短的情况） |
+
+---
+
+## 8. 凭证遮蔽
+
+DataLink 的节点 ID 和 `source` 属性中会嵌入数据库连接串（如 `postgresql://admin:s3cret@host/db`）。当 DataLink 作为概念层供 Agent 使用时，这些凭证不应该出现在 Agent 的输出中。
+
+### 8.1 遵循方案
+
+采用 **方案 A**：输出层遮蔽 + 遮蔽 ID → 真实 ID 反查映射。
+
+- 不修改 ID 生成和存储方式——数据库中始终使用真实 ID
+- 所有检索接口加 `mask_credential=True` 参数，在输出时自动遮蔽连接串中的 `user:pass@` 部分
+- 支持 `dialect+driver` 格式（如 `mysql+pymysql://`、`postgresql+psycopg2://`）的 URL 遮蔽
+- 遮蔽后的 ID 可以作为后续接口的输入——系统维护 `{masked_id → real_id}` 映射并自动反查
+- CLI 命令不做输出遮蔽（人用终端），但支持接收遮蔽 ID 作为输入
+
+### 8.2 遮蔽范围
+
+`mask_result()` 递归遍历整个输出结构，遮蔽以下字段中的凭证：
+
+| 字段类型 | 具体字段 | 遮蔽方法 |
+|---|---|---|
+| ID 字段 | `id`, `source_id`, `target_id`, `other_id`, `table_id`, `profile_id` | `mask_id()` — 识别 ID 格式并精确遮蔽 source 段 |
+| 凭证字段 | `source`, `connection_string` | `mask_credentials()` — 遮蔽 userinfo 部分 |
+| ID 列表字段 | `column_ids` | 元素逐个 `mask_id()` |
+| 嵌套 dict | `properties`, `other_node` | 递归 `mask_result()` |
+| 其他字符串值 | 任意 key 的字符串值 | 兜底检测：如果包含 DB URL 模式，用 `_mask_embedded_urls()` 遮蔽 |
+
+### 8.3 遵循规则
+
+```
+postgresql://admin:s3cret@db.example.com:5432/mydb → postgresql://***:***@db.example.com:5432/mydb
+postgresql+psycopg2://admin:s3cret@host/db → postgresql+psycopg2://***:***@host/db
+mysql+pymysql://root:pass@localhost/testdb → mysql+pymysql://***:***@localhost/testdb
+postgresql://user@host/db → postgresql://***@host/db
+sqlite:///path/to/db.sqlite → 不变（无 userinfo）
+/home/user/data/orders.csv → 不变（不是 DB URL）
+```
+
+节点 ID 中对应的遮蔽效果：
+
+```
+table:postgresql://admin:s3cret@host/db:orders → table:postgresql://***:***@host/db:orders
+column:postgresql://admin:s3cret@host/db:orders:col → column:postgresql://***:***@host/db:orders:col
+table:/home/user/data:orders → 不变
+```
+
+### 8.4 实现位置
+
+| 文件 | 作用 |
+|---|---|
+| `datalink/utils/credential.py` | `mask_credentials()`, `mask_id()`, `mask_result()`, `is_masked_id()`, `build_id_mapping()`, `resolve_masked_id()` |
+| `datalink/graph/retrieval.py` | 所有公共检索方法加 `mask_credential` 参数；构造时构建 ID 映射；输入参数自动反查 |
+| `datalink/mcp/server.py` | 所有检索 MCP 工具加 `mask_credential=True` 参数，传递给 retrieval |
+
+### 8.5 默认值
+
+| 调用方 | `mask_credential` 默认值 |
+|---|---|
+| MCP 工具 | `True`（Agent 场景安全优先） |
+| CLI 命令 | 输出不做遮蔽（传 `mask_credential=False` 给 retrieval），但输入参数支持接收遮蔽 ID |
+| `GraphRetrieval` 公共方法 | `True` |
+
+---
+
+## 9. 快速上手指南
+
+### 9.1 环境准备
+
+```bash
+uv sync
+```
+
+### 9.2 构建图谱
+
+```bash
+# 从 CSV 目录构建（首次建图谱 = add-table 不传 --table）
+datalink add-table --source ./data/
+
+# 从数据库构建
+datalink add-table --source "postgresql://user:pass@localhost/mydb"
+
+datalink info
+datalink search "customer"
+```
+
+### 9.2 重建图谱（数据源内容有变化时）
+
+```bash
+# 完整重建（默认）
+datalink rebuild
+
+# 只重建向量索引（更换了 embedding 模型时）
+datalink rebuild --mode vec
+
+# 只重算统计值 + 依赖统计的推断边（数据量变化但业务含义不变时）
+datalink rebuild --mode profile
+```
+
+### 9.3 增量添加数据源
+
+```bash
+# 只添加指定表
+datalink add-table --source ./new_data/ --table new_orders
+
+# 添加整个数据源的所有表
+datalink add-table --source "postgresql://user:pass@localhost/another_db"
+```
+
+### 9.4 探索关系
+
+```bash
+# 查找两列之间的连接路径
+datalink path --from column:source:orders:customer_id --to column:source:users:id
+```
+
+### 9.5 Agent 集成
+
+```bash
+datalink serve --port 8080
+```
+
+在 Cursor 等支持 MCP 的 IDE 中配置 DataLink MCP Server，Agent 即可调用 `add_table`、`rebuild`、`explore` 等工具。
+
+### 9.6 开发调试
+
+```bash
+# 运行测试
+uv run pytest
+
+# 代码检查
+uv run ruff check src tests
+```
+
+测试覆盖：`test_connector`, `test_extractor`, `test_profiler`, `test_inferrer`, `test_mapper`, `test_builder`, `test_storage`, `test_retrieval`, `test_cli`, `test_models`。
+
+---
+
+## 10. 典型使用场景
+
+### 场景 1：Agent 找「收入」相关字段
+
+1. `search_nodes("revenue")` → 命中 Concept 或 Column
+2. `get_node(concept_id)` → 查看 `represents` 边指向哪些 Column
+3. `list_datasets()` → 确认字段所在表
+
+### 场景 2：跨表 JOIN 规划
+
+1. 定位两表的关键列 ID
+2. `find_paths(col_a, col_b, edge_types="joinable,foreign_key,semantic_synonym")`
+3. 按 confidence 选择 JOIN 路径
+
+### 场景 3：增量接入新数据源
+
+```bash
+# 添加整个数据源（不指定表名）
+datalink add-table --source "postgresql://user:pass@localhost/another_db"
+
+# 或者只添加一张表
+datalink add-table --source ./new_data/ --table new_orders
+```
+
+新表会与已有图谱做 joinable/synonym 等联合推断。
+
+### 场景 4：数据更新后刷新图谱
+
+```bash
+# 完整重建（数据大幅变化）
+datalink rebuild
+
+# 只重算统计 + 推断边（数据量变化但概念结构不变）
+datalink rebuild --mode profile
+
+# 只重建向量（更换了 embedding 模型）
+datalink rebuild --mode vec
+```
+
+系统自动从已有元数据中读取数据源信息，重新连接并重建。不需要重新告诉系统数据在哪。
+
+---
+
+## 11. 扩展点
+
+| 扩展方向 | 建议入口 |
+|----------|----------|
+| 新数据源（JSON、API） | 实现 `BaseConnector` 子类 |
+| 新节点类型（如 Dashboard） | 扩展 `NodeType` + extractor |
+| 新推断规则 | 在 `inferrer/` 新增 Inferrer，在 pipeline 中注册 |
+| 上下文边（查询共现） | 实现 `EdgeType.CO_OCCURS_IN_QUERY` 等 |
+| 向量相似检索 | 在 `GraphRetrieval.search_nodes` 中增加 embedding |
+| 非 SQLite 存储 | 实现与 `GraphStorage` 等价的存储层 |
+
+---
+
+## 11. 关键代码入口
+
+| 用途 | 文件 |
+|------|------|
+| 构建编排 | `src/datalink/builder/pipeline.py` |
+| CLI 入口 | `src/datalink/cli/main.py` |
+| MCP 入口 | `src/datalink/mcp/server.py` |
+| 图谱查询 | `src/datalink/graph/retrieval.py` |
+| 数据模型 | `src/datalink/models/` |
+| 配置 | `src/datalink/config.py` |
+
+---
+
+## 12. 已知限制
+
+1. 当前只有表格数据连接器、Table/Column 结构节点、列画像和列级关系推断；PPT、PDF、Markdown、图片等类型尚未实现。
+2. 文件源无 FK，跨文件关系依赖 inferrer；数据库源的跨数据源 FK 会存入 pending_edges 表，在新数据源加入后自动 resolve。
+3. LLM 映射需要 API Key；无 Key 时整个业务概念层为空（Concept/Entity 都不生成）。
+4. CorrelationInferrer 在 `add_table` 流程中不调用——无法获取已有表的 sample_data（未持久化）来做跨表 merge。
+5. 上下文边（C 类）尚未实现。
+6. 永远无法 resolve 的 pending edges（目标数据源永不加入数据地图）仅作为 suggested_edges 展示，不参与路径发现和子图扩展。
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diff --git a/services/datalink/docs/INTERFACE.md b/services/datalink/docs/INTERFACE.md
new file mode 100644
index 00000000..5137a938
--- /dev/null
+++ b/services/datalink/docs/INTERFACE.md
@@ -0,0 +1,751 @@
+# DataLink 对外接口使用说明
+
+本文档列出所有外界能感知的接口——CLI 命令、REST API 和 MCP 工具，包括参数、行为、配置方式和典型用法。
+
+---
+
+## 接口分层
+
+DataLink 的接口分为两层：
+
+- **在线检索**：agent 在对话中即时查询数据图谱。通过 MCP 工具（`datalink_explore`）或 REST API 读操作完成。
+- **离线管理**：建图、改图、调试等操作。通过 CLI 命令或 REST API 写操作完成。
+
+| 层 | 接口类型 | 典型用途 |
+|---|---|---|
+| **在线检索** | MCP `datalink_explore`、REST API `/explore` 等 | Agent 查询数据：什么表可以 JOIN、某列是什么意思 |
+| **离线管理** | CLI 命令、REST API `/add-table` 等 | 管理员建图、重建索引、调试图谱状态 |
+
+MCP 默认只暴露 `datalink_explore`（检索），不再包含写操作。写操作通过 REST API 或 CLI 完成。
+
+---
+
+## CLI 命令
+
+通过 `datalink` 命令调用。所有命令均支持 `--db` 指定数据库路径（默认 `datalink.db`）。
+
+### 写操作
+
+#### `datalink add-table` — 添加表/数据源
+
+向图谱添加来自一个数据源的表。这是构建图谱的核心入口。
+
+- 传 `--table` → 只添加指定的那张表
+- **不传 `--table` → 添加该数据源的所有表**
+- 在空图谱上调用时，效果等同于首次构建
+- **已有表自动跳过**：如果要添加的表已存在于图谱中（同数据源路径 + 同表名），该表会被跳过而非重复注册。返回结果中会标注 `skipped_tables`。如需重新添加，请先 `remove-table` 删除再重新添加。
+
+```bash
+# 首次建图谱：添加整个 CSV 目录的所有表
+datalink add-table --source ./data/
+
+# 首次建图谱：从数据库添加所有表
+datalink add-table --source "postgresql://user:pass@localhost/mydb"
+
+# 增量添加：只加一张表
+datalink add-table --source ./new_data/ --table new_orders
+
+# 增量添加：添加整个新数据源的所有表
+datalink add-table --source "postgresql://user:pass@localhost/another_db"
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--source` / `-s` | 是 | — | 数据源路径：CSV/Parquet 文件/目录，或数据库连接串 |
+| `--table` / `-t` | 否 | `""` | 表名（空 = 添加所有表） |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+此命令通过 `DataLinkConfig.load()` 读取配置，`datalink_config.json` 中的阈值和 LLM 设置都会生效。
+
+数据源类型自动检测：以 `postgresql://` / `postgresql+psycopg2://` / `mysql://` / `mysql+pymysql://` 等（支持 `dialect+driver://` 格式）开头 → 数据库；以 `.csv` 或目录结尾 → CSV；以 `.parquet` / `.pq` 结尾 → Parquet。
+
+数据库驱动自动回退：如果连接串使用裸 scheme（如 `mysql://`）未指定 driver，SQLAlchemy 默认使用 `mysqlclient`（C 扩展）。若该驱动未安装，系统自动尝试已安装的替代驱动（`mysql://` → `mysql+pymysql://`，`postgresql://` → `postgresql+psycopg2://`）。建议直接使用 `dialect+driver://` 格式（如 `mysql+pymysql://`）以确保确定性。
+
+数据库 schema 处理：
+- PostgreSQL：默认使用 `public` schema；如需指定其他 schema，可在连接串的数据库名后加 `.schema_name`（如 `postgresql://user:pass@host/mydb.myschema`）。
+- MySQL/MariaDB：不使用 PostgreSQL 式 schema，URL path 就是数据库名（如 `mysql://.../appdb`）。系统不会对 MySQL URL path 中的 dot 做 schema 拆分——连接串 `/appdb.codex_install_test` 不是合法的 MySQL 用法。要添加特定表，使用 `--table` 参数。
+- SQLite：使用 SQLAlchemy 连接串，例如 `sqlite:////absolute/path/to/file.sqlite`（Linux 绝对路径 4 个斜杠）或 `sqlite:///C:/path/to/file.db`（Windows）。文件名中的 `.db` / `.sqlite` 不会被当成 schema；连接后 `schema_name` 自动为 `None`。表名含连字符等特殊字符时，COUNT/采样 SQL 会自动加引号。
+
+#### `datalink rebuild` — 重建图谱
+
+从已有数据源重建数据地图。**不需要传任何数据源信息**，系统会从 SQLite 中读取所有已有表的元数据。
+
+三种重建模式：
+
+- **`full`**（默认）：清空图谱，重走完整 pipeline（extract → profile → infer → map → embed）。这是原来的 rebuild 行为，现在也包括 embedding 向量构建。适用场景：数据源内容大幅变化、需要全面刷新。
+
+- **`vec`**：只重建 embedding 向量索引。不改变图谱数据、不调用 LLM——仅用当前配置的 embedding 模型重新计算所有节点的可检索文本向量。适用场景：用户更换了 embedding 模型，需要更新向量索引。
+
+- **`profile`**：重新统计所有 table/column 的 profile 值，并重建依赖 profile 的推断边（joinable、distribution_similar、semantic_synonym、correlated）。不涉及概念层（REPRESENTS/HAS_CONCEPT）、不调用 LLM。适用场景：数据源的数据量有变化但概念结构不需要更新——profile 变化可能影响推断边（如枚举值变了 → joinable overlap 变了 → 边可能增减），所以 profile 模式会一并重建这些边。
+
+**安全性保证（mode=full）**：旧数据只在新 pipeline 成功完成后才会被清除。如果 pipeline 失败（如 LLM 超时），旧图谱数据完好保留，再次执行 rebuild 即可恢复。
+
+```bash
+# 完整重建（默认）
+datalink rebuild
+
+# 只重建向量索引
+datalink rebuild --mode vec
+
+# 只重算统计值
+datalink rebuild --mode profile
+
+# 指定数据库路径
+datalink rebuild --db /path/to/graph.db
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--mode` / `-m` | 否 | `full` | 重建模式：`full`、`vec`、`profile` |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+此命令通过 `DataLinkConfig.load()` 读取配置。如果图谱中没有任何表，会报错提示先用 `add-table` 初始化。
+
+`mode=vec` 需要配置了 embedding 模型才能使用，否则会报错提示先配置 `embedding.model`。
+
+#### `datalink remove-table` — 删除表
+
+从图谱中删除一张表及其所有列、边、Profile，可选清理孤立的 Concept/Entity。孤立清理分两阶段：先删无结构层 `represents` 锚定的 Concept，再删无存活 Concept 连接的 Entity（`has_concept` 边不算锚定）。
+
+```bash
+datalink remove-table --table orders
+datalink remove-table --table "table:csv:orders"
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--table` / `-t` | 是 | — | 表名或完整表 ID（以 `table:` 开头） |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+传表名时会自动查找对应 ID。删除后自动清理引用已移除节点的 pending edges。
+
+### 读操作
+
+#### `datalink show` — 展示完整图谱
+
+读取当前图谱数据库中的所有节点和边，以 JSON 格式输出。适合导出图谱数据、调试、或快速查看整个图谱状态。
+
+```bash
+datalink show
+datalink show --db /path/to/graph.db
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+CLI 不做凭证遮蔽（终端输出只给操作者本人看）。
+
+**注意**：`show` 是离线调试工具，不适合 agent 在对话中使用（输出量可能很大）。REST API `/show` 返回遮蔽后的 JSON。
+
+以下读操作 CLI 命令与 REST API 端点一一对应。
+
+#### `datalink explore` — 万能检索
+
+**推荐优先使用。** 给一个 query，一次返回相关节点详情 + 关系链 + 数据指纹。对应 REST API `/explore`。
+
+```bash
+datalink explore "revenue customer_id orders"
+datalink explore "email" --focus data_profile
+datalink explore "how is orders connected to customers" --focus join_paths
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `query` | 是 | — | 关键词、名称、自然语言描述 |
+| `--focus` / `-f` | 否 | 均衡 | 聚焦方向：`join_paths`、`schema`、`data_profile` |
+| `--max-nodes` / `-n` | 否 | 自动 | 详情节点上限（0 = 自动按项目大小） |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+#### `datalink search` — 搜索节点
+
+按名称子串搜索节点，返回位置列表（无详情）。`explore` 通常更好。对应 REST API `/search`。
+
+```bash
+datalink search "customer" --type column --limit 20
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `query` | 是 | — | 搜索关键词 |
+| `--type` / `-t` | 否 | 全类型 | 过滤节点类型：`column`、`table`、`concept`、`entity` |
+| `--limit` / `-l` | 否 | 10 | 最大结果数 |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+#### `datalink get-node` — 节点详情
+
+获取单个节点的详细信息，包括邻接边和完整 Profile。对应 REST API `/get-node`。
+
+节点 ID 支持短别名格式：`frpm.County Name`、`frpm`、`County Name` 等（自动解析为完整 ID）。
+
+节点不存在时命令以退出码 1 终止（而非退出码 0），便于脚本和 Agent 判断。
+
+边信息包含方向标识：出边显示为 `→`（当前节点指向对方），入边显示为 `←`（对方指向当前节点）。
+
+对于表类型节点，`contains` 边（表→列）不再展开输出——列名列表已包含在 `column_ids` 属性中，展示了完整的列名。
+
+```bash
+datalink get-node column:csv:orders:customer_id
+datalink get-node frpm.County Name
+datalink get-node column:csv:orders:customer_id --no-edges
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `node_id` | 是 | — | 节点 ID（支持完整 ID、遮蔽 ID、短别名） |
+| `--edges` / `--no-edges` | 否 | 显示 | 是否包含邻接边信息 |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+#### `datalink path` — 两节点间路径
+
+查找两个节点之间的连接路径（BFS，按置信度排序）。对应 REST API `/path`。
+
+节点 ID 支持短别名格式：`frpm.County Name`、`frpm`、`County Name` 等（自动解析为完整 ID）。
+
+```bash
+datalink path --from column:csv:orders:customer_id --to column:csv:customers:id
+datalink path --from col1 --to col2 --edge-types joinable,foreign_key
+datalink path --from frpm.CDSCode --to schools.CDSCode --limit 5
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--from` | 是 | — | 起始节点 ID（支持完整 ID、遮蔽 ID、短别名） |
+| `--to` | 是 | — | 目标节点 ID（同上） |
+| `--depth` / `-d` | 否 | 3 | 最大路径深度 |
+| `--limit` / `-l` | 否 | 3 | 最大返回路径数 |
+| `--edge-types` | 否 | 全类型 | 逗号分隔的边类型（如 `joinable,foreign_key`） |
+| `--db` | 否 | `datalink.db` | 图谱数据库路径 |
+
+#### `datalink extract-subgraph` — 子图扩展
+
+从指定节点按 hop 扩展子图，返回周围节点和边。对应 REST API `/extract-subgraph`。
+
+```bash
+datalink extract-subgraph column:csv:orders:customer_id --hops 2
+datalink extract-subgraph "node1,node2,node3" --hops 1
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `node_ids` | 是 | — | 逗号分隔的起始节点 ID |
+| `--hops` / `-h` | 否 | 2 | 扩展层数 |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+#### `datalink info` — 图谱概览
+
+显示图谱统计：节点/边/Profile 数量，按类型分布。对应 REST API `/info`。
+
+```bash
+datalink info
+datalink info --db /path/to/graph.db
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+#### `datalink list-datasets` — 表列表
+
+列出所有表/数据集及其基本统计（列数、边数）。对应 REST API `/list-datasets`。
+
+```bash
+datalink list-datasets
+datalink list-datasets --db /path/to/graph.db
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+#### `datalink pending-edges` — 悬空边列表
+
+列出引用尚未入库节点的边（典型：跨数据源 FK）。对应 REST API `/pending-edges`。
+
+```bash
+datalink pending-edges --type foreign_key
+datalink pending-edges --node column:csv:orders:region
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--node` / `-n` | 否 | 全部 | 过滤涉及某节点的 pending edges |
+| `--type` / `-t` | 否 | 全类型 | 过滤边类型（如 `foreign_key`、`joinable`） |
+| `--limit` / `-l` | 否 | 50 | 最大结果数 |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+
+### 服务与配置
+
+#### `datalink serve` — 启动 MCP Server
+
+启动 MCP Server 供 AI Agent 连接。默认只暴露 `datalink_explore`（纯检索）。
+
+```bash
+# 默认 SSE transport（传统模式）
+datalink serve --port 8080
+
+# 推荐 streamable-http transport（更稳定）
+datalink serve --port 8080 --transport streamable-http
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--port` / `-p` | 否 | 8080 | 监听端口 |
+| `--db` / `-d` | 否 | `datalink.db` | 图谱数据库路径 |
+| `--transport` / `-t` | 否 | `sse` | Transport 协议：`sse`（传统）或 `streamable-http`（推荐） |
+
+#### `datalink api` — 启动 REST API Server
+
+启动 REST API Server，提供所有 CLI 命令的 HTTP 等价接口。
+
+```bash
+# 默认端口 8081（避免和 MCP 的 8080 冲突）
+datalink api
+
+# 指定端口和绑定地址
+datalink api --port 9000 --host 127.0.0.1
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--port` / `-p` | 否 | 8081 | 监听端口 |
+| `--host` | 否 | `0.0.0.0` | 绑定地址（`127.0.0.1` = 仅本地） |
+
+#### `datalink config` — 写配置
+
+修改 `datalink_config.json`，仅传入有值的字段会被更新。
+
+```bash
+datalink config --llm-model deepseek-chat --llm-base-url https://api.deepseek.com/v1
+datalink config --mcp-tools "datalink_search_nodes,datalink_get_node"
+datalink config --db /data/my_graph.db
+```
+
+| 参数 | 必填 | 默认值 | 说明 |
+|---|---|---|---|
+| `--llm-model` | 否 | 不改 | LLM 模型名 |
+| `--llm-api-key` | 否 | 不改 | LLM API Key（也可用 `OPENAI_API_KEY` 环境变量） |
+| `--llm-base-url` | 否 | 不改 | LLM API 地址（OpenAI-compatible） |
+| `--db` / `-d` | 否 | 不改 | 图谱数据库路径 |
+| `--mcp-tools` / `-t` | 否 | 不改 | MCP 辅助工具白名单（逗号分隔全名，空字符串 = 仅核心工具） |
+
+---
+
+## REST API
+
+通过 `datalink api` 启动 REST API server。所有端点名称与 CLI 命令保持一致。
+
+### 凭证遮蔽
+
+REST API 的所有检索端点默认启用凭证遮蔽（和 MCP 一致）。数据库连接串中的用户名和密码会被替换为 `***`。
+
+`/show` 端点也默认遮蔽。`/config` 端点的 `GET` 响应中 API key 被遮蔽为 `***`。
+
+### 写操作（离线管理）
+
+#### `POST /add-table` — 添加表/数据源
+
+与 CLI `datalink add-table` 等价。
+
+```json
+{
+  "source": "./data/",
+  "table": null,
+  "source_type": "csv"
+}
+```
+
+```json
+{
+  "source": "postgresql://user:pass@localhost/mydb",
+  "table": "products",
+  "source_type": "database",
+  "schema_name": null
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `source` | string | 是 | — | 数据源路径或数据库连接串 |
+| `table` | string | 否 | null | 指定单表名，null → 添加所有表 |
+| `source_type` | string | 否 | `"csv"` | `"csv"`、`"parquet"`、`"database"` |
+| `schema_name` | string | 否 | null | 数据库 schema 名。PostgreSQL 默认 `public` |
+
+返回：JSON，含 `status`（`"success"` 或 `"skipped"`）、`added_tables`、`skipped_tables`、`stats`。
+
+#### `POST /rebuild` — 重建图谱
+
+与 CLI `datalink rebuild` 等价。
+
+```json
+{"mode": "full"}
+```
+
+```json
+{"mode": "vec"}
+```
+
+```json
+{"mode": "profile"}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `mode` | string | 否 | `"full"` | `"full"`、`"vec"`、`"profile"` |
+
+返回：JSON，含 `status` 和对应模式的统计信息。
+
+#### `POST /remove-table` — 删除表
+
+与 CLI `datalink remove-table` 等价。支持传表名（自动查找 ID）或完整表 ID。
+
+```json
+{
+  "table_id": "orders",
+  "cleanup_orphans": true
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `table_id` | string | 是 | — | 表名或完整表 ID |
+| `cleanup_orphans` | bool | 否 | true | 是否清理孤立 Concept/Entity |
+
+返回：JSON，含 `status`、`removed_columns`、`removed_orphans`、`stats`。
+
+#### `GET /show` — 展示完整图谱
+
+与 CLI `datalink show` 等价，但返回遮蔽后的 JSON。
+
+无参数。
+
+返回遮蔽后的 JSON：`{"nodes": [...], "edges": [...]}`。
+
+### 读操作（在线检索）
+
+#### `POST /explore` — 万能检索
+
+与 CLI `datalink explore` 等价。**Agent 应优先调用此端点。**
+
+```json
+{
+  "query": "revenue customer_id orders",
+  "focus": "join_paths",
+  "max_nodes": null
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `query` | string | 是 | — | 关键词、名称、自然语言描述 |
+| `max_nodes` | int | 否 | null | 详情节点上限（null → 自动） |
+| `focus` | string | 否 | null | `"join_paths"`、`"schema"`、`"data_profile"` 或 null |
+
+返回：格式化文本（不是 JSON），和 MCP `datalink_explore` 返回格式一致。
+
+#### `POST /search` — 搜索节点
+
+```json
+{
+  "query": "customer",
+  "node_type": "column",
+  "limit": 10
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `query` | string | 是 | — | 搜索关键词 |
+| `node_type` | string | 否 | null | `"column"`、`"table"`、`"concept"`、`"entity"` |
+| `limit` | int | 否 | 10 | 最大结果数 |
+
+返回：JSON 列表。
+
+#### `POST /get-node` — 节点详情
+
+```json
+{
+  "node_id": "column:csv:orders:customer_id",
+  "include_edges": true
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `node_id` | string | 是 | — | 节点 ID（完整 ID、遮蔽 ID、短别名均可） |
+| `include_edges` | bool | 否 | true | 是否包含邻接边 |
+
+返回：JSON，含节点详情 + 边列表 + profile + pending edges。对于表类型节点，`contains` 边不再返回（列名列表已包含在 `column_ids` 属性中）。响应大小超过 25000 chars 时自动裁剪低置信度边。
+
+#### `POST /path` — 两节点间路径
+
+```json
+{
+  "source_id": "column:csv:orders:customer_id",
+  "target_id": "column:csv:customers:id",
+  "max_depth": 3,
+  "limit": 3,
+  "edge_types": null
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `source_id` | string | 是 | — | 起始节点 ID（支持短别名） |
+| `target_id` | string | 是 | — | 目标节点 ID（支持短别名） |
+| `max_depth` | int | 否 | 3 | 最大路径深度 |
+| `limit` | int | 否 | 3 | 最大返回路径数 |
+| `edge_types` | string | 否 | null | 逗号分隔的边类型 |
+
+#### `POST /extract-subgraph` — 子图扩展
+
+```json
+{
+  "node_ids": "column:csv:orders:customer_id,column:csv:orders:order_id",
+  "max_hops": 2
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `node_ids` | string | 是 | — | 逗号分隔的起始节点 ID |
+| `max_hops` | int | 否 | 2 | 扩展层数 |
+
+#### `GET /list-datasets` — 表列表
+
+无参数。返回 JSON 列表。
+
+#### `POST /pending-edges` — 悬空边列表
+
+```json
+{
+  "node_id": null,
+  "edge_type": null,
+  "limit": 50
+}
+```
+
+| 参数 | 类型 | 必填 | 默认值 | 说明 |
+|---|---|---|---|---|
+| `node_id` | string | 否 | null | 过滤涉及某节点 |
+| `edge_type` | string | 否 | null | 过滤边类型 |
+| `limit` | int | 否 | 50 | 最大结果数 |
+
+#### `GET /info` — 图谱概览
+
+无参数。返回 JSON，含节点/边/Profile 统计。
+
+### 配置
+
+#### `GET /config` — 获取当前配置
+
+返回 JSON（API key 被遮蔽为 `***`）。
+
+#### `PATCH /config` — 更新配置
+
+```json
+{
+  "llm_model": "deepseek-chat",
+  "llm_base_url": "https://api.deepseek.com/v1",
+  "embedding_model": "text-embedding-3-small"
+}
+```
+
+| 参数 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| `llm_model` | string | 否 | LLM 模型名 |
+| `llm_api_key` | string | 否 | LLM API Key |
+| `llm_base_url` | string | 否 | LLM API 地址 |
+| `graph_db_path` | string | 否 | 图谱数据库路径 |
+| `mcp_tools` | string | 否 | MCP 辅助工具白名单 |
+| `embedding_model` | string | 否 | Embedding 模型名 |
+| `embedding_api_key` | string | 否 | Embedding API Key |
+| `embedding_base_url` | string | 否 | Embedding API 地址 |
+
+返回更新后的配置 JSON（API key 遮蔽）。仅传入有值的字段会被更新。
+
+---
+
+## MCP 工具
+
+MCP Server 通过 `datalink serve` 启动，供 AI Agent 通过 MCP 协议调用。
+
+**默认只暴露 `datalink_explore`**（万能检索）。写操作（add_table、rebuild、remove_table）和 show 已移至 REST API。
+
+### 凭证遮蔽
+
+所有 MCP 检索工具默认启用凭证遮蔽（`mask_credential=True`）。数据库连接串中的用户名和密码会被替换为 `***`，防止在 Agent 输出中泄露敏感信息。
+
+支持裸 scheme 和 `dialect+driver` 格式（如 `postgresql+psycopg2://`、`mysql+pymysql://`）。
+
+```
+原始：postgresql://admin:s3cret@db.example.com:5432/mydb
+遮蔽：postgresql://***:***@db.example.com:5432/mydb
+```
+
+遮蔽后的 ID 可以直接作为后续接口的输入参数使用——系统会自动将遮蔽 ID 反查为真实 ID。
+
+### 返回格式
+
+所有 MCP 工具返回**自然语言文本**（不是 JSON），以节省 token 消耗。文本中的节点引用使用短格式（如 `frpm.County Name`）而非完整 URI ID。每个返回值末尾附一个 ID 映射表，将短名映射回完整 ID，方便后续工具调用时回传 node_id 参数。
+
+### 短别名格式
+
+所有需要 node_id 参数的 MCP 工具支持短别名输入，无需使用完整 URI ID：
+
+| 格式 | 示例 | 说明 |
+|---|---|---|
+| 完整 ID | `column:sqlite:///.../db.sqlite:frpm:County Name` | 原始格式 |
+| 遮蔽 ID | `column:sqlite:///***:***@.../db.sqlite:frpm:County Name` | 凭证遮蔽后，自动反查 |
+| 短别名 | `frpm.County Name` | table.column 格式，自动解析 |
+| 裸名 | `frpm`、`County Name` | 自动搜索匹配 |
+
+无效格式：`profile:column:...`（Profile 不是独立节点）、带错拼的缩写（如 `schools.FundingType` vs `schools.Funding Type`）。
+
+解析失败时返回格式化提示，包含可用的 ID 格式和推荐的替代工具。
+
+### 默认暴露
+
+#### `datalink_explore` — 万能检索
+
+**Agent 应优先调用此工具回答所有数据问题。**
+
+```json
+{
+  "query": "revenue customer_id orders",
+  "focus": "join_paths",
+  "max_nodes": null
+}
+```
+
+| 参数 | 类型 | 必填 | 说明 |
+|---|---|---|---|
+| `query` | string | 是 | 关键词、名称或自然语言 |
+| `max_nodes` | int | 否 | 详情节点上限（null → 自动） |
+| `focus` | string | 否 | `"join_paths"`、`"schema"`、`"data_profile"` 或 null |
+| `mask_credential` | bool | 否 | 是否遮蔽凭证（默认 `true`） |
+
+返回：格式化文本字符串（不是 JSON），按表分组，每列自包含。
+
+### 可选暴露（辅助工具）
+
+以下 6 个工具默认不注册，Agent 不可见。需要通过配置启用。
+
+#### 启用方式
+
+两种途径，**环境变量优先**：
+
+1. 环境变量（临时覆盖，适合 CI）：
+   ```bash
+   DATALINK_MCP_TOOLS=datalink_search_nodes,datalink_get_node datalink serve
+   ```
+
+2. 配置文件（持久化）：
+   ```json`
+   { "mcp_tools": "datalink_search_nodes,datalink_get_node,datalink_find_paths" }
+   ```
+   或 CLI：
+   ```bash
+   datalink config --mcp-tools "datalink_search_nodes,datalink_get_node"
+   ```
+
+#### 辅助工具列表
+
+| 工具名 | 能力 | 说明 |
+|---|---|---|
+| `datalink_search_nodes` | 按名称/semantic_type 搜索节点 | 返回位置列表（无详情），`explore` 通常更好 |
+| `datalink_get_node` | 单节点详情 + 全部邻接边 + 完整 Profile | 需要看某一列完整 profile 时使用 |
+| `datalink_find_paths` | 两节点间路径（可指定 edge_type，默认 limit=3） | 需要精确 JOIN 规划时使用 |
+| `datalink_extract_subgraph` | 从指定节点按 hop 扩展子图 | 需要精确子图导出时使用 |
+| `datalink_list_datasets` | 所有表及统计概览 | 需要全局"项目里有什么数据"时使用 |
+| `datalink_list_pending_edges` | 悬空边列表 | 调试跨源 FK 状态时使用 |
+
+各辅助工具参数详见原文档。
+
+---
+
+## 接口对照表
+
+| 功能 | CLI 命令 | REST API | MCP 工具 | 默认可用 |
+|---|---|---|---|---|
+| 万能检索 | `datalink explore` | `POST /explore` | `datalink_explore` | ✅ MCP / REST |
+| 展示完整图谱 | `datalink show` | `GET /show` | — | ✅ CLI / REST |
+| 添加表/数据源 | `datalink add-table` | `POST /add-table` | — | ✅ CLI / REST |
+| 重建图谱 | `datalink rebuild` | `POST /rebuild` | — | ✅ CLI / REST |
+| 删除表 | `datalink remove-table` | `POST /remove-table` | — | ✅ CLI / REST |
+| 搜索节点 | `datalink search` | `POST /search` | `datalink_search_nodes` | ❌ MCP需启用 |
+| 节点详情 | `datalink get-node` | `POST /get-node` | `datalink_get_node` | ❌ MCP需启用 |
+| 路径发现 | `datalink path` | `POST /path` | `datalink_find_paths` | ❌ MCP需启用 |
+| 子图扩展 | `datalink extract-subgraph` | `POST /extract-subgraph` | `datalink_extract_subgraph` | ❌ MCP需启用 |
+| 图谱概览 | `datalink info` | `GET /info` | — | ✅ CLI / REST |
+| 表列表 | `datalink list-datasets` | `GET /list-datasets` | `datalink_list_datasets` | ❌ MCP需启用 |
+| 悬空边 | `datalink pending-edges` | `POST /pending-edges` | `datalink_list_pending_edges` | ❌ MCP需启用 |
+| 启动 MCP | `datalink serve` | — | — | ✅ CLI |
+| 启动 REST API | `datalink api` | — | — | ✅ CLI |
+| 配置 | `datalink config` | `GET/PATCH /config` | — | ✅ CLI / REST |
+
+---
+
+## 配置文件
+
+`datalink_config.json`（当前目录），可通过 `datalink config` 命令或手动编辑。
+
+```json
+{
+  "llm": {
+    "model": "gpt-4o",
+    "api_key": "",
+    "base_url": "https://api.openai.com/v1",
+    "temperature": 0.1,
+    "max_tokens": 16384,
+    "timeout": 120.0
+  },
+  "embedding": {
+    "model": "",
+    "api_key": "",
+    "base_url": "",
+    "timeout": 60.0,
+    "similarity_threshold": 0.75
+  },
+  "merge_llm_temperature": 0.0,
+  "merge_batch_interval": 10,
+  "mapping_batch_size": 15,
+  "graph_db_path": "datalink.db",
+  "sample_size": 1000,
+  "confidence_threshold": 0.3,
+  "joinable_overlap_threshold": 0.1,
+  "correlation_threshold": 0.5,
+  "mcp_tools": ""
+}
+```
+
+| 字段 | 默认值 | 说明 |
+|---|---|---|
+| `llm.model` | `"gpt-4o"` | LLM 模型名 |
+| `llm.api_key` | `""` | API Key（也可用 `OPENAI_API_KEY` 环境变量） |
+| `llm.base_url` | `"https://api.openai.com/v1"` | OpenAI-compatible API 地址 |
+| `llm.temperature` | `0.1` | LLM 温度 |
+| `llm.max_tokens` | `16384` | LLM 最大 token 数 |
+| `llm.timeout` | `120.0` | LLM API HTTP 超时秒数（自托管网关 504 时增大此值） |
+| `embedding.model` | `""` | Embedding 模型名（空=跳过向量检索和粗筛） |
+| `embedding.api_key` | `""` | 空=回退到 `llm.api_key` |
+| `embedding.base_url` | `""` | 空=回退到 `llm.base_url` |
+| `embedding.similarity_threshold` | `0.75` | Embedding 粗筛 cosine 相似度阈值（同时用于向量检索的最低相似度过滤） |
+| `embedding.timeout` | `60.0` | Embedding API HTTP 超时秒数 |
+
+**Embedding 用途**：配置后生效于两个场景：
+1. **图谱消歧**（merge_with_existing）：embedding 预筛候选合并对，降低 LLM 调用开销（原有功能）
+2. **混合检索**（search_nodes / explore）：向量相似检索 + 全文检索合并，提升召回率（新功能）
+
+配置 `embedding.model` 后需运行 `datalink rebuild --mode vec`（或在 `add-table`/`rebuild` 时自动构建）来生成向量索引。更换模型后需再次 `rebuild --mode vec` 以重建向量。
+| `merge_llm_temperature` | `0.0` | merge LLM 调用温度（低=更确定性） |
+| `merge_batch_interval` | `10` | 分批推理时每 N 个 batch 才做一次 merge |
+| `mapping_batch_size` | `15` | LLM mapping 每批列数（减小如 5 可降低单次推理时间和 prompt 大小，适合网关 timeout 较短的情况） |
+| `graph_db_path` | `"datalink.db"` | SQLite 数据库路径 |
+| `sample_size` | `1000` | 采样行数 |
+| `confidence_threshold` | `0.3` | 推断边最低置信度 |
+| `joinable_overlap_threshold` | `0.1` | JOIN 推断重叠率阈值 |
+| `correlation_threshold` | `0.5` | 相关系数阈值 |
+| `mcp_tools` | `""` | MCP 辅助工具白名单（逗号分隔全名，空 = 仅核心工具） |
+
+`mcp_tools` 环境变量 `DATALINK_MCP_TOOLS` 优先级高于配置文件，适合临时覆盖。
diff --git a/services/datalink/docs/README.md b/services/datalink/docs/README.md
new file mode 100644
index 00000000..c5ab9f80
--- /dev/null
+++ b/services/datalink/docs/README.md
@@ -0,0 +1,15 @@
+# DataLink 文档索引
+
+| 文档 | 说明 |
+|------|------|
+| [DESIGN.md](./DESIGN.md) | 项目整体架构与设计概览 |
+| [INTERFACE.md](INTERFACE.md) | 用户可使用的 CLI 及 MCP 接口 |
+| [pipeline/01-connector.md](./pipeline/01-connector.md) | 数据源连接：文件/数据库读取与采样 |
+| [pipeline/02-extractor.md](./pipeline/02-extractor.md) | 结构层提取：节点 ID 生成与显式边 |
+| [pipeline/03-profiler.md](./pipeline/03-profiler.md) | 列指纹：类型检测、字段角色分类、统计量 |
+| [pipeline/04-inferrer.md](./pipeline/04-inferrer.md) | 隐式关系推断：JOIN/同义/分布/相关 |
+| [pipeline/05-mapper.md](./pipeline/05-mapper.md) | 概念层映射：元数据与 LLM |
+| [pipeline/06-storage-and-retrieval.md](./pipeline/06-storage-and-retrieval.md) | 持久化与图检索算法 |
+| [pipeline/07-pipeline-orchestration.md](./pipeline/07-pipeline-orchestration.md) | 构建流水线编排与增量更新 |
+
+文档按流水线执行顺序编号，建议按序阅读。
diff --git a/services/datalink/docs/pipeline/01-connector.md b/services/datalink/docs/pipeline/01-connector.md
new file mode 100644
index 00000000..5c4442c3
--- /dev/null
+++ b/services/datalink/docs/pipeline/01-connector.md
@@ -0,0 +1,226 @@
+# Connector 模块实现文档
+
+**源码路径：** `src/datalink/connector/`<br>
+**输入：** `DatasourceConfig`<br>
+**输出：** `DatasourceInfo`（tables + sample_data）
+
+Connector 是流水线的第一步，负责从外部数据源读取 schema 元数据并采样行数据，供后续 Profiling 与推断使用。
+
+---
+
+## 1. 接口契约
+
+`BaseConnector` 定义四个生命周期方法：
+
+| 方法 | 职责 |
+|------|------|
+| `connect()` | 建立连接 / 加载文件到内存 |
+| `get_datasource_info()` | 返回完整 `DatasourceInfo` |
+| `get_sample_data(table, n)` | 按表名取 n 行样本 |
+| `disconnect()` | 释放连接 / 清空缓存 |
+
+Pipeline 通过 `_connect_datasource()` 调用：`connect()` → `get_datasource_info()` → `disconnect()`。
+
+---
+
+## 2. FileConnector（CSV / Parquet）
+
+**文件：** `connector/file.py`
+
+### 2.1 路径解析与加载
+
+```
+config.path
+    ├── 单文件 → _read_single_file(path)
+    │              table_name = path.stem（不含扩展名）
+    └── 目录   → _read_directory(path)
+                   glob("*.csv") + glob("*.parquet") + glob("*.pq")
+                   每个文件 → 独立 table，name = stem
+```
+
+加载逻辑：
+
+- `.csv` → `pd.read_csv(path)`，全量读入内存
+- `.parquet` / `.pq` → `pd.read_parquet(path)`
+- 结果存入 `self._dataframes: dict[str, pd.DataFrame]`
+
+**注意：** 文件源是**全量加载**，不是流式；大文件会占用较多内存。采样发生在 `get_datasource_info()` 阶段。
+
+### 2.2 Schema 推断（`_infer_columns`）
+
+对每个 DataFrame 的每一列：
+
+| 字段 | 算法 |
+|------|------|
+| `name` | `df.columns` 原样 |
+| `dtype` | `_classify_dtype(series)` |
+| `nullable` | `series.isna().any()` |
+| `is_primary_key` | `nunique == len(df) and not nullable`（启发式：全唯一非空列视为 PK） |
+| `comment` | 固定 `""`（文件无注释） |
+| `foreign_keys` | 固定 `[]` |
+
+### 2.3 类型分类（`_classify_dtype`）
+
+按优先级检测：
+
+1. Pandas 原生 dtype：`integer` / `float` / `boolean` / `datetime`
+2. `object` / `string` 列进一步试探：
+   - `pd.to_numeric(non_null)` 成功 → `numeric_string`
+   - `pd.to_datetime(non_null)` 成功 → `datetime`
+   - 否则 → `string`
+3. 其他 → `str(series.dtype)`
+
+### 2.4 采样策略
+
+```python
+sample_df = df.head(self.config.sample_size)  # 默认 1000
+sample_data[name] = sample_df.to_dict(orient="records")
+```
+
+**特点：** 取**前 N 行**，非随机采样，结果可能受文件排序影响。
+
+### 2.5 输出结构
+
+每个文件对应一个 `TableInfo`：
+
+```python
+TableInfo(
+    name=stem,
+    schema_name="file",
+    columns=[ColumnInfo, ...],
+    foreign_keys=[],
+    row_count=len(df),
+    source=str(config.path),
+)
+```
+
+`DatasourceInfo.sample_data` 为 `{table_name: [{col: val, ...}, ...]}`。
+
+---
+
+## 3. DatabaseConnector（关系型数据库）
+
+**文件：** `connector/database.py`<br>
+**依赖：** SQLAlchemy `create_engine` + `inspect()`
+
+### 3.1 连接与 schema 选择
+
+```python
+self.engine = create_engine(config.connection_string)
+```
+
+支持所有有 SQLAlchemy 驱动的数据库（PostgreSQL、MySQL、SQLite 等），包括 `dialect+driver` 格式（如 `mysql+pymysql://`、`postgresql+psycopg2://`）。
+
+**Schema 选择规则：**
+
+| 数据库类型 | 默认 schema | URL `.schema` 后缀 | 行为 |
+|---|---|---|---|
+| PostgreSQL | `public` | 支持：`postgresql://user:pass@host/mydb.myschema` → schema=`myschema`，连接到 `mydb` | 指定 schema 后按该 schema 内省；不指定则用 `public` |
+| MySQL/MariaDB | `None`（自动） | 不适用 | URL 中的 database 名即为 MySQL 库名，`Inspector.get_table_names(schema=None)` 列出当前库的所有表 |
+| SQLite | `None` | 不适用 | SQLite 单文件无 schema 层级 |
+
+**MySQL / SQLite 注意事项：** `DatasourceConfig.schema_name` 默认值为 `"public"`（PostgreSQL 约定），但 `DatabaseConnector.connect()` 会自动检测 dialect：对 MySQL/MariaDB/SQLite 将 `schema_name` 设为 `None`，避免查询不存在的 `public` schema 导致失败。SQLite 文件路径中的点（如 `.db`、`.sqlite`）也不会被 `_extract_schema_from_url()` 拆成 `db.schema`。
+
+URL 中的 `db.schema` 格式通过 `_extract_schema_from_url()` 解析：如果连接串 path 包含 `.`（如 `/mydb.myschema`），则拆分为 database name 和 schema name，重建连接串只含 database name。MySQL/MariaDB/SQLite 跳过此拆分。
+
+### 3.2 Schema 内省流程
+
+对 `config.schema_name` 下每张表（PostgreSQL 默认 `public`，MySQL/SQLite 为 `None`）：
+
+```
+inspector.get_table_names(schema=config.schema_name)
+    → 对每张 table_name:
+        _extract_columns()
+        _extract_foreign_keys()
+        _get_row_count()          # SELECT COUNT(*) FROM {qualified_table_name}
+        _get_table_comment()      # inspector.get_table_comment()
+        get_sample_data(table, sample_size)
+```
+
+`_qualified_table_name()` 对非 `public` schema 的表名加上 schema 前缀（如 `myschema.orders`），`public` schema 和 `None` schema 则直接使用裸表名。**所有拼进原始 SQL 的标识符（表名/schema）都会按方言加引号**，因此带连字符的表名（如 `dacomp-zh-006`）可以正确执行 `COUNT(*)` 与采样查询。
+
+### 3.3 列元数据（`_extract_columns`）
+
+1. `inspector.get_pk_constraint()` → 得到 PK 列集合
+2. `inspector.get_columns(table, schema)` → 每列：
+   - `name`, `dtype`（SQLAlchemy 类型字符串）, `nullable`, `comment`
+   - `is_primary_key = name in pk_columns`
+
+### 3.4 外键提取（`_extract_foreign_keys`）
+
+`inspector.get_foreign_keys()` 返回约束列表，每条转为：
+
+```python
+ForeignKeyInfo(
+    constraint_name=fk["name"],
+    source_table=table_name,
+    source_column=fk["constrained_columns"][0],   # 仅取第一列（复合 FK 简化）
+    target_table=fk["referred_table"],
+    target_column=fk["referred_columns"][0],
+)
+```
+
+这些 FK 会在 Extractor 阶段转为 `foreign_key` 边（confidence=1.0）。
+
+### 3.5 随机采样（`get_sample_data`）
+
+按方言选择排序子句：
+
+| 方言 | SQL |
+|------|-----|
+| postgresql | `ORDER BY RANDOM()` |
+| mysql | `ORDER BY RAND()` |
+| sqlite | `ORDER BY RANDOM()` |
+| 其他 | 无 ORDER（取前 N 行） |
+
+```sql
+SELECT * FROM {table_name} {order_clause} LIMIT {n}
+```
+
+通过 `pd.read_sql(text(query), engine)` 执行，转为 `list[dict]`。
+
+失败时记录 warning 并返回 `[]`，该表后续 Profiling 会被跳过。
+
+### 3.6 行数统计
+
+```sql
+SELECT COUNT(*) FROM {table_name}
+```
+
+注意：表名未加 schema 前缀，依赖数据库默认 search_path。
+
+---
+
+## 4. 数据流小结
+
+```
+DatasourceConfig
+       │
+       ▼
+  connect() ──► 内存 DataFrame / SQLAlchemy Engine
+       │
+       ▼
+get_datasource_info()
+       │
+       ├── tables[]: TableInfo（schema + FK + comments）
+       └── sample_data{}: 每表最多 sample_size 行
+       │
+       ▼
+  disconnect()
+       │
+       ▼
+  DatasourceInfo ──► Extractor / Profiler
+```
+
+---
+
+## 5. 已知限制与影响
+
+| 限制 | 对下游的影响 |
+|------|-------------|
+| 文件源无 FK | 跨表关系完全依赖 JoinableInferrer |
+| CSV 取 head 非随机 | Profile 统计可能有偏差 |
+| 数据库采样失败返回空 | 该表无 Profile，不参与推断 |
+| 复合 FK 只取第一列 | 多列 FK 可能不完整 |
+| 文件全量加载 | 大 CSV 内存压力大 |
+| `dialect+driver://` URL 需安装对应 Python driver | 如 `mysql+pymysql://` 需 `pip install pymysql`，否则 SQLAlchemy 连接失败 |
diff --git a/services/datalink/docs/pipeline/02-extractor.md b/services/datalink/docs/pipeline/02-extractor.md
new file mode 100644
index 00000000..3e971477
--- /dev/null
+++ b/services/datalink/docs/pipeline/02-extractor.md
@@ -0,0 +1,176 @@
+# Extractor 模块实现文档
+
+**源码路径：** `src/datalink/extractor/tabular.py`<br>
+**输入：** `DatasourceInfo`<br>
+**输出：** `(list[TableNode], list[ColumnNode], list[Edge])`
+
+Extractor 将 Connector 输出的原始元数据转换为图谱**结构层**节点和**显式边**（A 类边），不涉及数据值分析。
+
+---
+
+## 1. 核心类：TabularExtractor
+
+唯一公开方法：
+
+```python
+def extract(self, datasource_info: DatasourceInfo) -> tuple[list[TableNode], list[ColumnNode], list[Edge]]
+```
+
+---
+
+## 2. ID 生成规则
+
+`generate_id(*parts)` 用冒号拼接，保证**确定性**（同一数据源重复构建 ID 不变）：
+
+| 节点/边 | ID 格式 | 示例 |
+|---------|---------|------|
+| TableNode | `table:{source}:{table_name}` | `table:./data:orders` |
+| ColumnNode | `column:{source}:{table}:{column}` | `column:./data:orders:customer_id` |
+| contains 边 | `edge:contains:{table_id}:{col_id}` | — |
+| foreign_key 边 | `edge:fk:{src_table}:{src_col}:{tgt_table}:{tgt_col}` | — |
+
+其中 `source = table_info.source or config.path or config.connection_string or config.name or "unknown"`。`table_info.source` 由 Connector 写入（文件源为路径字符串，数据库源为连接字符串），优先使用以确保 ID 确定性。
+
+---
+
+## 3. 逐表处理流程
+
+对每个 `TableInfo`：
+
+### 3.1 创建 TableNode
+
+```python
+TableNode(
+    id=table_id,
+    name=table_info.name,
+    source=source_name,
+    row_count=table_info.row_count or 0,
+    properties={
+        "schema_name": table_info.schema_name,
+        "comment": table_info.comment,
+    },
+)
+```
+
+`column_ids` 在创建所有 Column 后回填。
+
+### 3.2 创建 ColumnNode
+
+对每个 `ColumnInfo`：
+
+```python
+ColumnNode(
+    id=col_id,
+    name=col_info.name,
+    table_id=table_id,
+    dtype=col_info.dtype,
+    comment=col_info.comment,
+    properties={
+        "nullable": col_info.nullable,
+        "is_primary_key": col_info.is_primary_key,
+        "default_value": str(col_info.default_value) or "",
+    },
+)
+```
+
+此阶段 `semantic_type`、`profile_id` 为空，由 Pipeline Step 3 在 Profiling 后回填。
+
+### 3.3 创建 contains 边
+
+每列一条，方向 **Table → Column**：
+
+```python
+Edge(
+    source_id=table_id,
+    target_id=col_id,
+    type=EdgeType.CONTAINS,
+    confidence=1.0,  # 默认值
+)
+```
+
+### 3.4 创建 foreign_key 边
+
+对 `table_info.foreign_keys` 中每条 FK：
+
+```python
+fk_source_id = generate_id("column", source, fk.source_table, fk.source_column)
+fk_target_id = generate_id("column", source, fk.target_table, fk.target_column)
+
+Edge(
+    source_id=fk_source_id,
+    target_id=fk_target_id,
+    type=EdgeType.FOREIGN_KEY,
+    confidence=1.0,
+    properties={"constraint_name": fk.constraint_name},
+)
+```
+
+**方向：** FK 列（source）→ 被引用列（target）。
+
+**悬空边处理：** FK 涉及的列若不在本次 extract 范围内（跨 schema 或跨数据源），边不会丢弃而是存入 `pending_edges` 表，标记 `missing_endpoints`（如 `["target"]`）。当目标数据源后续通过 `add_table` 加入图谱时，Pipeline 自动调用 `resolve_pending_edges()`，将两端节点都存在的 pending 边"接合"到 `edges` 主表，参与正常检索和遍历。
+
+---
+
+## 4. Pipeline 中的属性回填
+
+Extractor 本身不写 DB；Pipeline 在 Profiling 后会更新 ColumnNode / TableNode 的 `properties`，供 Storage 序列化：
+
+**ColumnNode 追加：**
+
+```python
+col.properties["dtype"] = col.dtype
+col.properties["semantic_type"] = col.semantic_type   # 来自 Profile
+col.properties["table_id"] = col.table_id
+col.properties["profile_id"] = col.profile_id
+col.properties["comment"] = col.comment
+```
+
+**TableNode 追加：**
+
+```python
+table.properties["source"] = table.source
+table.properties["row_count"] = table.row_count
+table.properties["column_ids"] = table.column_ids
+```
+
+Storage 的 `_row_to_node()` 从 `properties` JSON 反序列化回强类型字段。
+
+---
+
+## 5. 数据流
+
+```
+DatasourceInfo
+    tables[].columns[]  ──► ColumnNode[]
+    tables[]            ──► TableNode[]
+    tables[].foreign_keys[] ──► Edge[FOREIGN_KEY]
+    (implicit)          ──► Edge[CONTAINS] (每列一条)
+         │
+         ▼
+Pipeline Step 3: 用 profile_map 更新 semantic_type / profile_id
+         │
+         ▼
+GraphStorage.add_nodes_batch() + add_edges_batch()
+```
+
+---
+
+## 6. 复杂度
+
+设 T 表、C 总列数、F FK 数：
+
+- 时间：O(T × avg_cols + F)
+- 空间：O(T + C + F + C)（contains 边数 = C）
+
+无跨表比较，纯元数据映射。
+
+---
+
+## 7. 设计决策
+
+| 决策 | 理由 |
+|------|------|
+| ID 含 source 前缀 | 多数据源合并时避免列名冲突 |
+| FK 仅来自 schema | 文件源无 FK，留给 Inferrer |
+| 不在 Extractor 做 Profiling | 职责分离，Profile 需 sample_data |
+| properties 冗余存储 typed 字段 | SQLite 单表存所有节点，JSON 存变长属性 |
diff --git a/services/datalink/docs/pipeline/03-profiler.md b/services/datalink/docs/pipeline/03-profiler.md
new file mode 100644
index 00000000..4d3d16a0
--- /dev/null
+++ b/services/datalink/docs/pipeline/03-profiler.md
@@ -0,0 +1,209 @@
+# Profiler 模块实现文档
+
+**源码路径：** `src/datalink/profiler/tabular.py`<br>
+**输入：** `DatasourceInfo`（必须含 `sample_data`）<br>
+**输出：** `list[ColumnProfile]`
+
+Profiler 对每列采样数据计算**统计指纹（ColumnProfile）**，是 Inferrer 与 Mapper 的核心数据基础。
+
+---
+
+## 1. 入口：profile_datasource
+
+```
+对每个 table in datasource_info.tables:
+    sample_rows = sample_data[table_name]
+    if empty → warning + skip
+    df = pd.DataFrame(sample_rows)
+    对每个 column in table.columns:
+        if col_name not in df.columns → skip
+        profile_column(col_id, col_name, df[col_name], comment)
+```
+
+`col_id` 与 Extractor 一致：`column:{source}:{table}:{column}`<br>
+`profile.id` = `profile:{col_id}`
+
+---
+
+## 2. profile_column 计算流程
+
+对单列 `series`（Pandas Series）：
+
+### 2.1 基础统计
+
+```python
+non_null = series.dropna()
+total_count = len(series)
+null_count = series.isna().sum()
+null_rate = null_count / total_count
+cardinality = non_null.nunique()
+unique_rate = cardinality / total_count
+dtype = _classify_dtype(series)
+value_patterns = _detect_patterns(non_null)
+semantic_type = _classify_semantic_type(name, dtype, patterns, non_null)
+```
+
+### 2.2 数值列扩展统计
+
+条件：`pd.api.types.is_numeric_dtype(non_null)`
+
+```python
+numeric_values = pd.to_numeric(non_null, errors="coerce").dropna()
+min, max, mean, std, median = ...
+distribution_histogram = _numeric_histogram(numeric_values, bins=10)
+```
+
+直方图：NumPy `histogram(values, bins=10)` → `[{bin_start, bin_end, count}, ...]`
+
+### 2.3 字符串列扩展统计
+
+条件：`dtype == "string"`
+
+```python
+min_length, max_length, avg_length = str_values.str.len().agg(...)
+```
+
+### 2.4 高频值与样本
+
+```python
+top_values = _compute_top_values(non_null, top_n=10)
+# Counter → [{value, count, fraction}, ...]
+
+sample_values = non_null.sample(min(5, len), random_state=42).tolist()
+```
+
+`random_state=42` 保证可复现。
+
+---
+
+## 3. 类型检测算法（`_classify_dtype`）
+
+优先级链：
+
+| 步骤 | 条件 | 结果 |
+|------|------|------|
+| 1 | `is_bool_dtype` | `boolean` |
+| 2 | `is_integer_dtype` + distinct ≤ 2 + values ⊆ {0,1} | `integer_boolean` |
+| 3 | `is_integer_dtype`（其他） | `integer` |
+| 4 | `is_float_dtype` + distinct ≤ 2 + values ⊆ {0.0,1.0} | `float_boolean` |
+| 5 | `is_float_dtype`（其他） | `float` |
+| 6 | `is_datetime64_any_dtype` | `datetime` |
+| 7 | object/string：90%+ 可转 numeric，全整数 + distinct ≤ 2 + values ⊆ {0,1} | `integer_boolean` |
+| 8 | object/string：90%+ 可转 numeric，全整数 | `integer` |
+| 9 | object/string：90%+ 可转 numeric | `float` |
+| 10 | object/string：90%+ 可转 datetime | `datetime` |
+| 11 | object/string | `string` |
+| 12 | 其他 | `unknown` |
+
+**`integer_boolean` 和 `float_boolean` 的设计意图：**
+
+数据库中布尔列通常存为 BIGINT(0/1)，pandas 读出后 dtype 为 `integer`。如果直接标记为 `integer`，下游 JoinableInferrer 会误判这些列的值域高度重叠，产生无意义的 joinable 边（如 `Charter ↔ Charter School (Y/N)`）。
+
+通过检测整数列仅含 {0, 1}，标记为 `integer_boolean`，下游可以正确地：
+- semantic_type → `boolean_flag`（字段角色分类更准确）
+- JoinableInferrer → 跳过 boolean 类型列（避免虚假 JOIN 推断）
+
+---
+
+## 4. 值模式检测（`_detect_patterns`）
+
+仅对 string/object 列，从最多 20 个随机样本（`random_state=42`）匹配正则：
+
+| pattern_name | 正则（摘要） | 触发条件 |
+|--------------|-------------|----------|
+| email_pattern | `^[\w.+-]+@[\w-]+\.[\w.-]+$` | 匹配率 > 80% |
+| url_pattern | `^https?://` | > 80% |
+| date_pattern | `^\d{4}-\d{2}-\d{2}$` | > 80% |
+| datetime_pattern | ISO datetime 前缀 | > 80% |
+| uuid_pattern | UUID 格式 | > 80% |
+| phone_pattern | 电话格式 | > 80% |
+| zip_pattern | 美国邮编 | > 80% |
+
+返回匹配的 pattern_name 列表，供字段角色分类使用。
+
+---
+
+## 5. 字段角色分类（`_classify_semantic_type`）
+
+三级决策树：
+
+### 5.1 值模式优先（最可靠）
+
+```python
+pattern_to_semantic = {
+    "email_pattern": "email_address",
+    "url_pattern": "url",
+    "date_pattern": "date",
+    "datetime_pattern": "datetime",
+    "uuid_pattern": "uuid",
+    "phone_pattern": "phone_number",
+    "zip_pattern": "postal_code",
+}
+```
+
+### 5.2 列名规则（`SEMANTIC_TYPE_RULES`）
+
+`SEMANTIC_TYPE_RULES` 包含两类规则（约 30+ 条 `(regex, semantic_type)` 对）：
+
+**值模式规则**（前 8 条，对列值匹配）：
+
+- `^[\w.+-]+@[\w-]+\.[\w.-]+$` → `email_address`
+- `^\+?\d{1,3}[-.\s]?\(?\d{1,4}\)?[-.\s]?\d{1,4}[-.\s]?\d{1,9}$` → `phone_number`
+- `^https?://[\w./-]+$` → `url`
+- 等等
+
+**列名规则**（其余条目，对**小写列名**匹配）：
+
+- `.*_id$`, `^id$` → `identifier`
+- `.*_amount$`, `^amount$` → `monetary_value`
+- `.*_email$` → `email_address`
+- `.*_date$`, `.*_time$` → `timestamp`
+
+完整列表见源码 `SEMANTIC_TYPE_RULES`。
+
+### 5.3 Dtype 兜底
+
+- `boolean` / `integer_boolean` / `float_boolean` → `boolean_flag`
+- `datetime` → `timestamp`
+- `unique_rate > 0.9` 且 dtype 为 integer/string → `identifier`
+- 否则 → `unknown`
+
+---
+
+## 6. Pipeline 中的 Profile 回写
+
+```python
+profile_map = {p.column_id: p for p in all_profiles}
+for col in all_columns:
+    profile = profile_map.get(col.id)
+    if profile:
+        col.semantic_type = profile.semantic_type
+        col.profile_id = profile.id
+        col.properties[...] = ...  # 供 Storage 序列化
+```
+
+Profile 本身通过 `GraphStorage.add_profiles_batch()` 存入 `column_profiles` 表（properties 为除 id/column_id 外全部字段的 JSON）。
+
+---
+
+## 7. ColumnProfile 字段用途
+
+| 字段 | 使用者 |
+|------|--------|
+| `semantic_type` | SynonymInferrer, LLMMapper prompt |
+| `top_values`, `sample_values` | JoinableInferrer 值域重叠 |
+| `dtype` | Joinable/Distribution 类型过滤 |
+| `cardinality` | Joinable 跳过高基数列（>10000） |
+| `min/max/mean`, `distribution_histogram` | DistributionInferrer, CorrelationInferrer |
+| `sample_values` | LLMMapper prompt |
+
+---
+
+## 8. 复杂度与限制
+
+- 每列：O(n) 扫描采样行，n ≤ sample_size（默认 1000）
+- 全库：O(总列数 × n)
+- **限制：**
+  - 无 sample_data 的表/列被跳过
+  - 字段角色规则基于英文列名启发式，中文列名易归为 `unknown`
+  - top_values 仅反映采样分布，非全量精确统计
diff --git a/services/datalink/docs/pipeline/04-inferrer.md b/services/datalink/docs/pipeline/04-inferrer.md
new file mode 100644
index 00000000..a1755cce
--- /dev/null
+++ b/services/datalink/docs/pipeline/04-inferrer.md
@@ -0,0 +1,345 @@
+# Inferrer 模块实现文档
+
+**源码路径：** `src/datalink/inferrer/`<br>
+**输入：** `list[ColumnNode]` + `list[ColumnProfile]`（+ 部分模块需 `joinable_edges` / `DatasourceInfo`）<br>
+**输出：** `list[Edge]`（B 类隐式边，confidence < 1.0）
+
+四个 Inferrer 在 Pipeline Step 4 **顺序独立运行**，结果合并后统一按 `confidence_threshold` 过滤入库。
+
+---
+
+## 1. 总体比较策略
+
+| Inferrer | 比较范围 | 跳过条件 |
+|----------|----------|----------|
+| JoinableInferrer | 跨表列对 | 同表、高基数、dtype 不兼容 |
+| SynonymInferrer | 跨表列对 | 同表 |
+| DistributionInferrer | 跨表列对 | 同表、dtype 不同 |
+| CorrelationInferrer | join key 所在表的其他数值列对 | 非数值、无 joinable 边、无 sample_data |
+
+默认两两比较复杂度约为 O(C²)，C 为列总数。
+
+---
+
+## 2. JoinableInferrer
+
+**文件：** `joinable.py`<br>
+**边类型：** `EdgeType.JOINABLE`<br>
+**配置：** `joinable_overlap_threshold`（默认 0.1），`max_cardinality`（默认 900）
+
+### 2.1 算法流程
+
+```
+1. profile_map = {column_id → ColumnProfile}
+2. 按 table_id 分组 columns → table_groups
+3. 对每对 (table_a, table_b), table_a ≠ table_b:
+     对 col_a ∈ table_a, col_b ∈ table_b:
+       if cardinality > max_cardinality → skip
+       if _is_boolean_skip(profile) → skip
+       if not _compatible_dtypes → skip
+       overlap = _compute_overlap(profile_a, profile_b)
+       if overlap >= threshold → 创建边，confidence = overlap
+```
+
+### 2.2 Boolean 列跳过（`_is_boolean_skip`）
+
+布尔列（boolean、integer_boolean、float_boolean）的值域极小（如 {0, 1}），与任何其他布尔列 trivially overlap，产生的 joinable 边毫无查询意义。
+
+| dtype | 是否跳过 | 原因 |
+|------|---------|------|
+| `boolean` | ✅ 跳过 | pandas 真布尔类型 |
+| `integer_boolean` | ✅ 跳过 | BIGINT(0/1) 存储，实质布尔 |
+| `float_boolean` | ✅ 跳过 | FLOAT(0.0/1.0) 存储，实质布尔 |
+| `integer` | ❌ 不跳过 | 真整数列（标识符、数值） |
+| `string` | ❌ 不跳过 | 字符串列（可能有意义的状态码） |
+
+示例：`Charter (integer_boolean, values {0,1})` ↔ `Magnet (integer_boolean, values {0,1})` → overlap=100% → 但这是虚假的，因为布尔标记不会是 JOIN key。
+
+### 2.3 Dtype 兼容矩阵（`_compatible_dtypes`）
+
+| dtype_a | dtype_b | 兼容 |
+|---------|---------|------|
+| integer/float/integer_boolean/float_boolean | integer/float/integer_boolean/float_boolean | ✓ |
+| string | string | ✓ |
+| datetime/date | datetime/date | ✓ |
+| integer/float/integer_boolean/float_boolean | string | ✓（标识符跨类型） |
+| 其他组合 | | ✗ |
+
+注意：boolean 类型的列虽然在兼容矩阵中可匹配，但会在 2.2 的 `_is_boolean_skip` 中被跳过，实际不会产生边。
+
+### 2.3 重叠率公式（`_compute_overlap`）
+
+```python
+values_a = {top_values 的 value} ∪ {str(v) for v in sample_values}
+values_b = {同上}
+intersection = values_a & values_b
+overlap_rate = |intersection| / min(|values_a|, |values_b|)
+```
+
+**含义：** 较小值集合中有多少比例出现在另一列的采样域中。<br>
+**局限：** 基于 top-10 + 最多 5 个样本，非全量 Jaccard；高基数列被跳过。
+
+### 2.4 边属性
+
+```python
+properties = {
+    "overlap_rate": overlap_rate,
+    "dtype_a": profile_a.dtype,
+    "dtype_b": profile_b.dtype,
+}
+```
+
+---
+
+## 3. SynonymInferrer
+
+**文件：** `synonym.py`<br>
+**边类型：** `EdgeType.SEMANTIC_SYNONYM`
+
+### 3.1 置信度融合（`_compute_confidence`）
+
+输入信号：
+
+- `type_match`：两列 `semantic_type` 均非 `unknown` 且相等
+- `name_sim`：`_name_similarity(col_a.name, col_b.name)`
+- `group_match`：两列名同属 `SYNONYM_GROUPS` 中某一组
+
+决策表：
+
+| 条件 | confidence |
+|------|------------|
+| type_match AND (name_sim > 0.5 OR group_match) | **0.95** |
+| type_match only | **0.85** |
+| group_match only | **0.80** |
+| name_sim > 0.7 | **0.60** |
+| name_sim > 0.5 | **0.40** |
+| 其他 | **0.0**（不建边） |
+
+### 3.2 列名相似度（`_name_similarity`）
+
+```python
+norm_a = name_a.lower().replace("_", "")
+norm_b = name_b.lower().replace("_", "")
+
+if norm_a == norm_b: return 1.0
+
+if 子串包含:
+    ratio = min(len) / max(len)
+    return 0.5 + 0.5 * ratio   # [0.5, 1.0]
+
+jaccard = |set(norm_a) ∩ set(norm_b)| / |set(norm_a) ∪ set(norm_b)|  # 字符集合
+prefix_score = 公共前缀长度 / max(len)
+return max(jaccard, prefix_score)
+```
+
+**注意：** 非标准 Levenshtein，是字符 Jaccard + 前缀 + 子串启发式。
+
+### 3.3 同义词组（`SYNONYM_GROUPS`）
+
+预定义 17 组常见列名同义词，例如：
+
+```python
+{"customer_id", "user_id", "client_id", "account_id", "person_id"}
+{"amount", "value", "total", "sum", "price", "cost", "fee", "charge"}
+```
+
+匹配时列名转小写、`-` 换 `_` 后直接 membership 检查。
+
+---
+
+## 4. DistributionInferrer
+
+**文件：** `distribution.py`<br>
+**边类型：** `EdgeType.DISTRIBUTION_SIMILAR`<br>
+**配置：** `similarity_threshold`（默认 0.5，类内硬编码）
+
+### 4.1 分 dtype 路由（`_compute_similarity`）
+
+```
+if both numeric (integer/float) → _numeric_similarity
+if both string               → _categorical_similarity
+if both temporal (datetime/date) → _temporal_similarity
+else → 0.0
+```
+
+### 4.2 数值相似度（`_numeric_similarity`）
+
+**范围重叠：**
+
+```python
+range_a = (min_value, max_value)
+range_b = (min_value, max_value)
+overlap_start = max(range_a[0], range_b[0])
+overlap_end = min(range_a[1], range_b[1])
+if overlap_start > overlap_end: overlap_fraction = 0
+else:
+    overlap_fraction = (overlap_end - overlap_start) / (max(range) - min(range))
+```
+
+**均值相似度：**
+
+```python
+mean_similarity = 1.0 - |mean_a - mean_b| / max(|mean_a|, |mean_b|)
+```
+
+**最终：**
+
+```python
+similarity = overlap_fraction * 0.6 + mean_similarity * 0.4
+```
+
+### 4.3 分类相似度（`_categorical_similarity`）
+
+```python
+values_a = {value: fraction} from top_values
+values_b = {value: fraction} from top_values
+common = keys_a ∩ keys_b
+weighted_overlap = Σ min(values_a[v], values_b[v]) for v in common
+similarity = weighted_overlap / (sum(values_a) + sum(values_b)) * 2
+             # 等价于 weighted_overlap / (total_weight / 2)
+```
+
+类似**加权分类分布重叠**（与 histogram 交集相关）。
+
+### 4.4 时间相似度（`_temporal_similarity`）
+
+MVP 简化实现：
+
+```python
+bin_similarity = min(len(hist_a), len(hist_b)) / max(len(hist_a), len(hist_b))
+return bin_similarity * 0.5
+```
+
+仅比较直方图 bin 数量比例，**未**比较实际时间范围。
+
+---
+
+## 5. CorrelationInferrer
+
+**文件：** `correlated.py`<br>
+**边类型：** `EdgeType.CORRELATED`<br>
+**配置：** `correlation_threshold`（默认 0.5）
+
+### 5.1 核心设计
+
+相关性分析的关键洞察：**correlated 边不应连接 join key 自身，而应连接 JOIN 后对齐的其他数值列**。
+
+例如，若 `orders.customer_id ↔ users.id` 是 joinable 边（join key），则有意义的是 JOIN 后 `orders.amount` 和 `users.age` 是否线性相关——而不是 customer_id 和 id 之间的 trivial 相关（它们本就是同一组 ID）。
+
+因此算法逻辑为：
+
+```
+对每条 joinable_edge (key_a ↔ key_b):
+  1. 找到 key_a 所属表中其他数值列 (col_a2, col_a3, …)
+  2. 找到 key_b 所属表中其他数值列 (col_b2, col_b3, …)
+  3. 在 sample_data 上按 key_a = key_b 做 inner merge
+  4. 对每对 (col_a2, col_b2) 计算真正的 Pearson r
+  5. 若 |r| >= threshold → 创建 correlated 边 (col_a2 ↔ col_b2)
+```
+
+### 5.2 前置条件
+
+| 条件 | 说明 |
+|------|------|
+| joinable 边 | 必须已有 joinable 边才能确定 join key |
+| 数值列 dtype | 两列 dtype 均 ∈ `{integer, float}`，且非 join key 本身 |
+| sample_data | 两表的 sample_data 都在 `DatasourceInfo` 中可用 |
+| 对齐行数 ≥ 5 | merge 后至少 5 行有效数据才计算 Pearson |
+
+### 5.3 Pearson 相关算法（`_compute_pearson`）
+
+真正的样本级 Pearson 相关系数，不是范围比例代理：
+
+```python
+series_a = pd.to_numeric(merged[col_name_a], errors="coerce").dropna()
+series_b = pd.to_numeric(merged[col_name_b], errors="coerce").dropna()
+
+# Align indices after dropna
+common_idx = series_a.index ∩ series_b.index
+if len(common_idx) < MIN_ALIGNED_ROWS:  # 默认 5
+    return None
+
+corr = series_a.loc[common_idx].corr(series_b.loc[common_idx], method="pearson")
+```
+
+### 5.4 列名冲突处理（`_find_numeric_columns`）
+
+`pd.merge` 使用 `suffixes=("_a", "_b")`。列名在两表冲突时自动加 suffix（如 `score_a` / `score_b`），不冲突时保持原名。`_find_numeric_columns` 同时检查带 suffix 和不带 suffix 的名称：
+
+```python
+suffixed_name = f"{col_name}{suffix}"   # e.g., "score_a"
+if suffixed_name in merged.columns:
+    merged_name = suffixed_name
+elif col_name in merged.columns:        # 不冲突，保持原名
+    merged_name = col_name
+else:
+    continue                            # 该列不在 merged 结果中
+```
+
+### 5.5 边属性
+
+```python
+properties = {
+    "coefficient": corr,            # Pearson r 值
+    "method": "pearson",            # 与实现一致
+    "aligned_rows": len(merged),    # merge 后对齐行数
+    "joinable_edge": edge.id,       # 哪条 joinable 边提供了 join key
+    "join_key_a": key_a_id,         # 左侧 join key 列 ID
+    "join_key_b": key_b_id,         # 右侧 join key 列 ID
+}
+```
+
+### 5.6 Pipeline 调用方式
+
+`CorrelationInferrer.infer()` 接收 `list[DatasourceInfo]`（而非单个 `DatasourceInfo`），因为跨表 merge 需要所有相关数据源的 sample_data 同时可用：
+
+```python
+# build_full 中：
+correlated_edges = correlation_inferrer.infer(
+    all_columns, all_profiles, joinable_edges, all_ds_infos
+)
+```
+
+**`add_table` 中不调用 CorrelationInferrer**：增量添加新表时无法获取已有表的 sample_data（sample_data 未持久化到图数据库），因此无法做跨表 merge。这是一个已知限制——可通过将 sample_data 存入 metadata 表或重连数据源来解决。
+
+---
+
+## 6. Pipeline 中的合并与过滤
+
+```python
+all_inferred_edges = joinable + synonym + distribution + correlated
+
+filtered_edges = [e for e in all_edges
+                  if e.confidence >= config.confidence_threshold]  # 默认 0.3
+```
+
+显式边（FK、contains）confidence=1.0，始终保留。
+
+---
+
+## 7. add_table 时的增量推断
+
+新表列与**已有列**合并后重新跑四个 Inferrer，但只保留**至少一端为新列**的边：
+
+```python
+new_column_ids = {c.id for c in new_columns}
+def involves_new_column(edge):
+    return edge.source_id in new_column_ids or edge.target_id in new_column_ids
+```
+
+避免重复写入已有列对之间的边（Storage 用 INSERT OR REPLACE，重复 ID 会覆盖）。
+
+CorrelationInferrer 在 `add_table` 流程中**未**调用——因为无法获取已有表的 `sample_data`（未持久化到图数据库），无法做跨表 merge 来计算真正的 Pearson 相关性。
+
+---
+
+## 8. 边 ID 命名
+
+| 类型 | ID 格式 |
+|------|---------|
+| joinable | `edge:joinable:{col_a}:{col_b}` |
+| synonym | `edge:synonym:{col_a}:{col_b}` |
+| distribution | `edge:dist_similar:{col_a}:{col_b}` |
+| correlated | `edge:correlated:{col_a}:{col_b}` |
+
+无向关系：source/target 顺序取决于双重循环的枚举顺序。
diff --git a/services/datalink/docs/pipeline/05-mapper.md b/services/datalink/docs/pipeline/05-mapper.md
new file mode 100644
index 00000000..189b68fa
--- /dev/null
+++ b/services/datalink/docs/pipeline/05-mapper.md
@@ -0,0 +1,416 @@
+# Mapper 模块实现文档
+
+**源码路径：** `src/datalink/mapper/llm_mapper.py`<br>
+**输入：** `list[ColumnNode]` + `list[ColumnProfile]`（所有列，包括有 comment 的）<br>
+**输出：** `(list[ConceptNode], list[EntityNode], list[Edge])`（D 类跨层边）<br>
+**副输出：** `dict[str, str]`（table_id → inferred_comment，仅对缺少 comment 的表）
+
+Mapper 有两个职责：
+1. 将结构层 Column 映射到概念层 Concept / Entity。**所有列统一走 LLM 推理**——有 comment 的列会将 comment 作为额外信号带入 prompt。
+2. 为缺少 SQL metadata comment 的表生成 LLM 推理描述（`generate_table_comments`）。
+
+`add_table` 时新增的 Concept/Entity 需要与图中已有节点做消歧合并（`merge_with_existing`）。
+
+---
+
+## 1. LLMMapper
+
+**文件：** `llm_mapper.py`
+
+### 1.1 整体流程
+
+```
+map_columns(columns, profiles)
+    │
+    ├─ ≤15 列 → _map_columns_single()
+    │     ├─ _build_columns_data()  → JSON Lines 文本（含 comment 字段）
+    │     ├─ MAPPING_PROMPT_TEMPLATE.format(columns_data=...)
+    │     ├─ _call_llm(prompt)
+    │     ├─ _parse_response() → _try_parse_json() 渐进修复
+    │     └─ _build_nodes_and_edges()
+    │
+    ├─ >15 列 → 分批映射（每批 ≤15 列）
+    │     ├─ Batch 1 → _map_columns_single()
+    │     ├─ Batch 2 → _map_columns_single() → merge_with_existing(batch2, accumulated1)
+    │     ├─ Batch N → ... → merge_with_existing(batchN, accumulated_all)
+    │     └─ 各批独立成功/失败，渐进合并去重
+    │
+    └─ 返回合并后的 (concepts, entities, edges)
+
+add_table 时额外做一次 merge_with_existing 与图中已有节点消歧
+```
+
+### 1.2 Prompt 构造（`_build_columns_data`）
+
+每列一行 JSON，字段：
+
+```json
+{
+  "column_id": "column:...",
+  "column_name": "...",
+  "table_id": "table:...",
+  "dtype": "integer",
+  "semantic_type": "identifier",
+  "null_rate": 0.0,
+  "cardinality": 1000,
+  "unique_rate": 1.0,
+  "min_value": 1,          // 可选
+  "max_value": 1000,       // 可选
+  "mean_value": 500.5,     // 可选
+  "sample_values": ["..."], // 最多 5 个
+  "comment": "..."         // ← 关键：有 comment 的列也走 LLM，comment 作为额外信号
+}
+```
+
+**所有列**都出现在 prompt 中。有 comment 的列在 JSON 中包含 `"comment"` 字段，供 LLM 作为额外信号使用。
+
+### 1.3 LLM 调用参数
+
+使用 OpenAI SDK 的 Chat Completions API（支持任何 OpenAI 协议的服务）：
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key=api_key, base_url=config.base_url)
+client.chat.completions.create(
+    model=config.model,
+    messages=[
+        {"role": "system", "content": "You are a data semantic analyzer. Always respond with valid JSON."},
+        {"role": "user", "content": prompt},
+    ],
+    temperature=config.temperature,  # 默认 0.1
+    max_tokens=config.max_tokens,    # 默认 16384
+)
+```
+
+`_call_llm` 也支持 `temperature` 参数覆盖，用于 merge 判断时使用更低温度（`merge_llm_temperature`，默认 0.0）。
+
+### 1.4 期望的 LLM 输出结构
+
+```json
+{
+  "concepts": [{
+    "name": "person_identifier",
+    "description": "...",
+    "unit": "",
+    "dimension": "identity",
+    "columns": ["column:id1", "column:id2"],
+    "confidence": 0.9
+  }],
+  "entities": [{
+    "name": "customer",
+    "description": "...",
+    "concept_names": ["person_identifier", "email_address"],
+    "confidence": 0.85
+  }]
+}
+```
+
+Prompt 规则要求：多列可映射同一 Concept；Concept 名应泛化；Entity 聚合多个 Concept。
+
+### 1.5 响应解析（`_parse_response` → `_try_parse_json`）
+
+解析使用渐进式修复策略，对弱模型产生的格式错误 JSON 逐步尝试修复：
+
+1. **直接解析**：提取 `{`...`}` 子串，`json.loads()`
+2. **Markdown 代码块提取**：检测 `` ```json ... ``` `` 包裹
+3. **移除注释**：删除 JS-style 注释（`// ...` 和 `/* ... */`），保留后续结构字符（`}` `]`）
+4. **移除尾逗号**：删除 `},` 和 `],` 中的多余逗号
+5. **括号闭合修复**：统计未闭合的 `{` `[`，补上缺失的 `}` `]`
+
+每步修复后重新尝试 `json.loads`。所有步骤失败才返回 None。
+
+校验必须含 `concepts` 和 `entities` 键。
+
+### 1.6 图结构物化（`_build_nodes_and_edges`）
+
+**Concept：**
+
+```python
+ConceptNode(
+    id=f"concept:{concept_data['name']}",   # 按 name 全局唯一
+    name=concept_data["name"],
+    description, unit, dimension from JSON,
+    properties={"source": "llm_inference", "confidence": ...},
+)
+```
+
+对每个 `columns[]` 中的 col_id：
+
+```python
+Edge(REPRESENTS, col_id → concept.id, confidence=concept.confidence)
+```
+
+**Entity：**
+
+```python
+EntityNode(
+    id=f"entity:{entity_data['name']}",
+    ...
+)
+```
+
+对每个 `concept_names[]`：
+
+```python
+Edge(HAS_CONCEPT, entity.id → f"concept:{concept_name}", confidence=...)
+```
+
+**注意：** `has_concept` 边的 target Concept **必须**已在同次 LLM 响应的 concepts 列表中定义，否则边指向不存在的节点。
+
+---
+
+## 2. Concept/Entity 消歧合并（`merge_with_existing`）
+
+`add_table` 时，LLM 新产出的 Concept/Entity 可能与图中已有节点含义相同但名称不同。合并步骤确保含义一致的节点不会重复入库。
+
+### 2.1 两阶段模型推理
+
+合并算法采用 **Embedding 粗筛 + LLM 精判** 的两阶段模型推理：
+
+| 阶段 | 有 Embedding 配置 | 无 Embedding 配置 |
+|------|-------------------|-------------------|
+| **阶段 A：Embedding 粗筛** | 计算新旧节点的 cosine 相似度，≥ `similarity_threshold` 的配对进入候选列表 | 跳过，候选列表为空 |
+| **阶段 B：LLM 精判** | 候选配对 + 仅相关已有节点（出现在候选对中的）发给 LLM 确认合并 | 全量已有节点发给 LLM 判断所有合并 |
+| **失败回退** | LLM 失败 → 不合并，新节点直接保留 | 同上 |
+
+#### 阶段 A：Embedding 粗筛（`_embedding_prefilter`）
+
+当 `embedding.model` 配置了模型名时（如 `text-embedding-3-small`）：
+
+1. 对每个 Concept/Entity，用 `name | description | unit | dimension` 组合文本计算 embedding 向量
+2. 计算每个新节点 vs 每个已有节点的 cosine 相似度
+3. 相似度 ≥ `embedding.similarity_threshold`（默认 0.75）的配对进入候选列表
+4. 候选列表传给 LLM 精判阶段作为参考
+
+当 `embedding.model` 为空或 API 调用失败时，直接跳过粗筛，候选列表为空，LLM 精判阶段会直接对全量节点做合并判断。
+
+#### 阶段 B：LLM 精判（`_llm_merge_judge`）
+
+无论有没有 Embedding 粗筛，都走 LLM 精判：
+
+1. 构建 merge prompt，包含：
+   - 新 Concept/Entity 列表（id + name + description + unit + dimension）
+   - 已有 Concept/Entity 列表：
+     - **有 Embedding 候选对时**：仅注入出现在候选对中的已有节点（详细信息），其余不相似的节点完全省略，prompt 中注明只展示了部分已有节点
+     - **无 Embedding 候选对时**：全量注入所有已有节点（兜底保证正确性）
+   - 候选合并配对（来自 Embedding 粗筛，或"无候选——直接判断所有配对"）
+2. LLM 返回合并方案：`{"merges": [...], "new_kept": [...]}`
+3. 每个 merge 条目包含：`new_id`、`existing_id`、`reason`、`confidence`
+4. confidence < `confidence_threshold` 的合并被自动过滤掉
+5. LLM 判断考虑含义等价性，而非仅依赖名称相似度
+
+Prompt 模板位于 `mapper/prompts/merge_prompt.txt`。
+
+### 2.2 合并操作（`_execute_merge_plan`）
+
+根据 LLM 返回的合并方案执行实际合并：
+
+**概念合并：** 新概念 A 合并入已有概念 B
+
+- 保留 B（ID 不变），丢弃 A（不入库）
+- B 的 description 取两者中**更长者**（信息量更大）
+- B 的 properties 合并（追加 A 的 source 信息等）
+
+**实体合并：** 同理。
+
+### 2.3 边重定向
+
+合并节点后，所有引用被合并节点的边需要重定向：
+
+| 边类型 | 重定向规则 | 示例 |
+|--------|------------|------|
+| `REPRESENTS` (Column → Concept) | `target_id` 从 A 改为 B | `column:... → concept:customer_id` 改为 `→ concept:person_identifier` |
+| `HAS_CONCEPT` (Entity → Concept) | `source_id` 和 `target_id` 都可能需重定向 | `entity:user → concept:customer_id` 改为 `entity:customer → concept:person_identifier` |
+
+### 2.4 边去重
+
+重定向后可能产生 **(source_id, target_id, type)** 三元组完全相同的重复边。去重规则：**每组重复边保留 confidence 最高的一条，丢弃其余**。
+
+### 2.5 Concept 属于多个 Entity — 不合并
+
+一个 Concept 可以被多个 Entity 的 `has_concept` 边指向，这是多对多关系。只要 (source_id, target_id, type) 三元组不完全相同，边就共存，不做合并。
+
+例如：
+
+```
+entity:customer → concept:person_identifier   (边E1)
+entity:order   → concept:person_identifier   (边E2) ← 不同 source_id，不合并
+```
+
+但如果合并导致同一个三元组出现两次：
+
+```
+entity:customer → concept:person_identifier   (边E1, confidence=0.9)
+entity:customer → concept:person_identifier   (边E3, confidence=0.85) ← 合并后重复
+```
+
+去重后保留 E1（confidence 更高）。
+
+### 2.6 调用时机
+
+| 场景 | 是否调用 merge_with_existing |
+|------|------|
+| `build_full`（图空）单批（≤15 列） | **不调用**——没有已有节点，直接返回 |
+| `build_full`（图空）多批（>15 列） | **调用**——每隔 N 个 batch 合并一次（N = `merge_batch_interval`），最后一次必定合并 |
+| `add_table`（图已有数据） | **调用**——与已有图节点消歧合并 |
+
+分批映射的合并流程：不再每个 batch 都 merge，而是**累积 N 个 batch 后做一次 merge**（N 由 `merge_batch_interval` 控制，默认 10）。这样可以大幅减少 merge LLM 调用次数。最后一个 batch 后必定触发 merge，确保所有累积节点都被处理。
+
+例如（`merge_batch_interval=2`，6 个 batch）：
+
+```
+Batch 1 → 累积: [person_id]           → pending
+Batch 2 → 累积: [person_identifier]    → merge(interval=2) → person_identifier ≡ person_id → accumulated=[person_id]
+Batch 3 → 累积: [email]               → pending
+Batch 4 → 累积: [email_address]        → merge(interval=2) → email_address ≡ email → accumulated=[person_id, email]
+Batch 5 → 累积: [order_id]            → pending
+Batch 6 → 累积: [order_identifier]     → merge(最后一个batch) → order_identifier ≡ order_id → accumulated=[person_id, email, order_id]
+```
+
+当 `merge_batch_interval=1`（默认旧行为）时，每个 batch 都触发 merge。
+
+### 2.7 Embedding 配置
+
+在 `datalink_config.json` 中配置：
+
+```json
+{
+  "embedding": {
+    "model": "text-embedding-3-small",
+    "api_key": "",
+    "base_url": "",
+    "similarity_threshold": 0.75
+  },
+  "merge_llm_temperature": 0.0
+}
+```
+
+| 配置项 | 默认值 | 说明 |
+|--------|--------|------|
+| `embedding.model` | `""` | 模型名，空=跳过 Embedding 粗筛，纯 LLM 判断 |
+| `embedding.api_key` | `""` | 空=回退到 `llm.api_key` |
+| `embedding.base_url` | `""` | 空=回退到 `llm.base_url` |
+| `embedding.similarity_threshold` | `0.75` | cosine 相似度阈值，低于此的配对不会成为候选 |
+| `merge_llm_temperature` | `0.0` | merge LLM 调用温度，低温度确保判断确定性 |
+| `merge_batch_interval` | `10` | 分批推理时每 N 个 batch 才做一次 merge（1=每个 batch 都 merge） |
+
+---
+
+## 3. Pipeline 中的调用
+
+```python
+# Step 5: Concept mapping — all columns via LLM
+llm_mapper = LLMMapper(self.config)
+all_concepts, all_entities, all_semantic_edges = llm_mapper.map_columns(
+    all_columns, all_profiles
+)
+
+# add_table 时额外做合并
+if existing_concepts or existing_entities:
+    all_concepts, all_entities, all_semantic_edges = llm_mapper.merge_with_existing(
+        all_concepts, all_entities, all_semantic_edges,
+        existing_concepts, existing_entities,
+    )
+```
+
+与结构边、推断边一并写入 Storage，仍受 `confidence_threshold` 过滤。
+
+---
+
+## 4. 数据流图
+
+```
+ColumnNode + ColumnProfile (all columns, including those with comments)
+        │
+        ▼
+    LLMMapper.map_columns()
+        │
+        ├─ ≤15 列 → _map_columns_single → 直接返回
+        │
+        └─ >15 列 → 分批映射 + 渐进合并
+              │
+              Batch 1 ─► _map_columns_single ─► concepts/entities/edges
+              Batch 2 ─► _map_columns_single ─► merge_with_existing(batch2, batch1_accum)
+              Batch 3 ─► _map_columns_single ─► merge_with_existing(batch3, batch1+2_accum)
+              ...
+              │
+              ▼
+        返回合并后的 (concepts, entities, edges)
+
+    add_table → merge_with_existing(新结果, 已有图节点)
+            │
+            ├─ _embedding_prefilter (可选) → 候选合并配对
+            ├─ _llm_merge_judge → 合并方案
+            ├─ _execute_merge_plan → 消歧合并 + 重定向边
+            └─ _deduplicate_edges → 去重
+            │
+            ▼
+        GraphStorage.add_nodes_batch(concepts + entities)
+        GraphStorage.add_edges_batch(semantic_edges)
+```
+
+---
+
+## 5. 失败模式
+
+| 情况 | 行为 |
+|------|------|
+| 无 API Key | LLMMapper 返回空 → 整个概念层为空 |
+| LLM 超时/异常 | 记录 error，返回空 |
+| JSON 解析失败（单批） | 渐进修复 5 步尝试 → 仍失败则该批次丢弃，其他批次不受影响 |
+| JSON 解析失败（所有批次） | 所有概念层为空（只有全部批次失败时才空） |
+| Entity 引用未知 concept_name | 产生悬空 has_concept 边 |
+| 合并后边重复 | 自动去重，保留 confidence 最高 |
+| 跨批次概念/实体重复 | `merge_with_existing` 自动合并，保留先出现的节点 |
+| Embedding API 调用失败 | 跳过粗筛，直接走纯 LLM 判断 |
+| Embedding 未配置 | 同上——纯 LLM 判断 |
+| merge LLM 调用失败 | 不合并，所有新节点直接保留 |
+
+---
+
+## 6. 表描述生成（`generate_table_comments`）
+
+当表没有 SQL metadata comment 时，`generate_table_comments` 用 LLM 推理出一句描述。
+
+### 6.1 调用时机
+
+Pipeline Step 5（概念映射）完成后，Step 6（入库）之前。对 init_build 和 add_table 都会调用。
+
+### 6.2 输入信号
+
+Prompt 向 LLM 提供每张表的信息：
+- 表名 + 行数
+- 每列的 name、dtype、semantic_type、null_rate、cardinality
+- 每列的 top_values（频率最高的值）
+- 每列关联的 concept/entity 名称（如果 Step 5 已推理出来）
+
+### 6.3 输出格式
+
+LLM 返回 JSON：`{"tables": [{"table_id": "...", "comment": "..."}]}`
+
+每条 comment 限制为一句话（≤200 字符），自动写入 TableNode 的 `properties["comment"]`。
+
+### 6.4 跳过条件
+
+- 表已有 SQL metadata comment → 不调用 LLM
+- 无 API Key → 整批跳过
+- LLM 返回空或解析失败 → 跳过，表无 comment
+
+### 6.5 Prompt 模板
+
+位于 `mapper/prompts/table_comment_prompt.txt`，可独立编辑调整。
+
+---
+
+## 7. 调优建议
+
+- 降低 `llm.temperature`（已默认 0.1）提高映射稳定性
+- 数据库源尽量填写 column comment，LLM 据此做更准确的映射
+- 列数过多时自动分批（每批 15 列），各批独立成功/失败，跨批次合并去重——修改 `_BATCH_SIZE` 可调整批次大小
+- **merge 频率控制**：`merge_batch_interval`（默认 10）决定每隔多少个 batch 才做一次合并。6 个 batch + interval=10 → 只在最后一个 batch 做 1 次 merge（省 5 次 LLM 调用）；interval=1 → 旧行为，每个 batch 都 merge。大部分数据源的 batch 数 ≤ 10，设置 interval=10 即可确保只在最后做一次 merge
+- merge 合并判断使用 `merge_llm_temperature`（默认 0.0），确保判断确定性
+- 配置 embedding 模型可大幅减少 merge LLM 调用的 token 消耗——粗筛过滤掉不相关的配对，LLM 只需确认少量候选
+- embedding 粗筛阈值 `embedding.similarity_threshold`（默认 0.75）可根据实际效果微调：降低阈值增加候选对（更保守、更少遗漏），提高阈值减少候选对（更激进、LLM 负担更轻）
+- 弱模型 JSON 格式问题：`_try_parse_json` 自动修复尾逗号、JS 注释、括号未闭合等常见错误，5 步修复后仍失败则该批次跳过
+- Prompt 模板位于 `mapper/prompts/` 目录下（`mapping_prompt.txt`、`merge_prompt.txt`、`table_comment_prompt.txt`），均可独立编辑调整
diff --git a/services/datalink/docs/pipeline/06-storage-and-retrieval.md b/services/datalink/docs/pipeline/06-storage-and-retrieval.md
new file mode 100644
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@@ -0,0 +1,539 @@
+# Storage 与 Retrieval 模块实现文档
+
+**源码路径：** `src/datalink/graph/`<br>
+**Storage 输入：** 节点、边、Profile 的批量写入<br>
+**Retrieval 输入：** 查询参数 → JSON 可序列化结果或格式化文本
+
+---
+
+# Part A: GraphStorage
+
+**文件：** `storage.py` + `schema.sql`
+
+## A.1 数据库初始化
+
+```python
+db_path = _resolve_db_path(db_path)  # 裸文件名 → ~/.datalink/storage/xxx.db
+sqlite3.connect(db_path)
+PRAGMA foreign_keys = ON
+PRAGMA journal_mode = WAL
+executescript(schema.sql)
+```
+
+**路径解析规则：**
+
+| 配置值 | 实际路径 |
+|--------|----------|
+| `"datalink.db"`（裸文件名） | `~/.datalink/storage/datalink.db` |
+| `"project_a/main.db"`（相对路径） | `~/.datalink/storage/project_a/main.db` |
+| `"/data/graph.db"`（绝对路径） | `/data/graph.db`（原样使用） |
+
+`~/.datalink/storage/` 目录在首次运行时自动创建。
+
+| `remove_edges_by_types` | 批量删除指定类型的边（profile rebuild 时清理旧的推断边） |
+
+## A.2 表结构
+
+| 表 | 主键 | 核心列 |
+|----|------|--------|
+| `nodes` | `id TEXT` | `type`, `name`, `properties JSON` |
+| `edges` | `id TEXT` | `source_id`, `target_id`, `type`, `confidence`, `properties JSON` |
+| `column_profiles` | `id TEXT` | `column_id FK→nodes`, `properties JSON` |
+| `metadata` | `key TEXT` | `value`, `updated_at` |
+
+**外键级联：** 删 node → 自动删关联 edges、profiles。
+
+### pending_edges 表
+
+| 列 | 说明 |
+|----|------|
+| `id TEXT PK` | 边唯一标识 |
+| `source_id TEXT` | 源节点 ID（**无 FK 约束**，允许引用不存在的节点） |
+| `target_id TEXT` | 目标节点 ID（**无 FK 约束**，允许引用不存在的节点） |
+| `type TEXT` | 边类型（同 EdgeType 枚举值） |
+| `confidence REAL` | 置信度 |
+| `properties TEXT` | JSON blob |
+| `missing_endpoints TEXT` | JSON 数组：`["source"]` / `["target"]` / `["source","target"]`，标记哪端节点缺失 |
+| `created_at TIMESTAMP` | 创建时间 |
+
+**无 FK 约束**是关键设计——`source_id` / `target_id` 是纯 TEXT，允许引用尚未入库的节点 ID。
+
+## A.3 节点序列化策略
+
+写入时**仅**存四元组 `(id, type, name, properties)`；Column/Table 的 typed 字段（`dtype`, `table_id` 等）由 Pipeline 预先写入 `properties` JSON。
+
+读取时 `_row_to_node()` 按 `type` 反序列化为对应 Pydantic 子类：
+
+```python
+NodeType.COLUMN  → ColumnNode(table_id=props["table_id"], ...)
+NodeType.TABLE   → TableNode(source=props["source"], column_ids=props["column_ids"], ...)
+NodeType.CONCEPT → ConceptNode(description=props["description"], ...)
+NodeType.ENTITY  → EntityNode(...)
+```
+
+## A.4 Profile 序列化
+
+```python
+props = profile.model_dump(exclude={"id", "column_id"}, mode="json")
+INSERT INTO column_profiles (id, column_id, properties) VALUES (...)
+```
+
+读取：`ColumnProfile(id=..., column_id=..., **props)`。
+
+## A.5 批量写入
+
+Pipeline 使用：
+
+- `add_nodes_batch(nodes)` — `executemany` + 单次 commit
+- `add_edges_batch(edges)`
+- `add_profiles_batch(profiles)`
+
+策略：`INSERT OR REPLACE`，同 ID 覆盖。
+
+## A.6 Pending Edge 操作
+
+| 方法 | 说明 |
+|------|------|
+| `add_pending_edge(edge)` | 写入单条 pending edge |
+| `add_pending_edges_batch(edges)` | 批量写入（executemany + 单次 commit） |
+| `get_pending_edge(id)` | 查单条 |
+| `get_pending_edges_for_node(node_id)` | 查涉及某节点的所有 pending edges |
+| `get_all_pending_edges()` | 查全部 |
+| `resolve_pending_edges(available_node_ids)` | 核心：扫描 pending_edges，将两端均在 `available_node_ids` 中的边移入 edges 表，删除原 pending 记录 |
+| `remove_pending_edge(id)` | 删单条 |
+| `cleanup_pending_edges_for_removed_nodes(removed_ids)` | 删除引用已移除节点的 pending edges（永远无法 resolve） |
+| `count_pending_edges(edge_type)` | 统计 |
+
+**`resolve_pending_edges` 算法：**
+
+```
+1. SELECT * FROM pending_edges
+2. 对每条：if source_id ∈ available_node_ids AND target_id ∈ available_node_ids:
+   → INSERT OR REPLACE INTO edges (same columns)
+   → DELETE FROM pending_edges WHERE id = ?
+3. COMMIT; return resolved_count
+```
+
+**`cleanup_pending_edges_for_removed_nodes` 算法：**
+
+对每个被移除的节点 ID，删除 `source_id = nid OR target_id = nid` 的所有 pending edges。这些边永远无法 resolve。
+
+## A.7 remove_table 算法
+
+```
+1. contains_edges = get_edges_for_node(table_id, CONTAINS)
+2. column_ids = [e.target_id for e in contains_edges]
+3. all_ids = [table_id] + column_ids
+4. 对每个 nid: DELETE edges WHERE source_id=nid OR target_id=nid
+5. 对每个 col_id: DELETE column_profiles WHERE column_id=col_id
+6. DELETE nodes WHERE id IN column_ids + table_id
+7. return column_ids  → 供 Pipeline 决定是否 cleanup orphans
+```
+
+**不**自动删 Concept/Entity；由 `cleanup_orphaned_semantic_nodes()` 处理。
+
+## A.8 cleanup_orphaned_semantic_nodes
+
+两阶段清理，从概念层逐层向外剥离孤立节点：
+
+**Stage 1 — Concept 清理：**
+
+```python
+anchored_concept_ids = {所有有 represents 边从 Column/Table 指向的 concept ID}
+for concept in all_concepts:
+    if concept not in anchored_concept_ids:
+        DELETE concept 的所有边 + concept 节点
+```
+
+只有被结构层节点（Column/Table）通过 `represents` 边锚定的 Concept 才保留。`has_concept` 入边（来自 Entity）不算锚定——它是概念层内部连接，结构层删了之后没有意义。
+
+**Stage 2 — Entity 清理（此时孤立 Concept 已被删除）：**
+
+```python
+for entity in all_entities:
+    outgoing_to_structural = COUNT edges WHERE source=entity AND target IN (Column/Table)
+    has_concept_to_anchored = COUNT has_concept 边 WHERE target IN anchored_concept_ids
+    if both == 0:
+        DELETE entity 的所有边 + entity 节点
+```
+
+Entity 通过 `has_concept` 边间接锚定：如果它的 `has_concept` 目标 Concept 在 Stage 1 中存活（被结构层 `represents` 锚定），则 Entity 也保留。
+
+## A.9 get_graph_stats
+
+遍历所有 `NodeType` / `EdgeType` 枚举值分别 `COUNT(*)`，返回汇总 dict（含 0 计数类型）。另外包含 `pending_edge_count` 和 `pending_edge_type_counts`。
+
+---
+
+# Part B: GraphRetrieval
+
+**文件：** `retrieval.py`
+
+## B.1 search_nodes
+
+**主路径：** `storage.search_nodes_by_name(query, node_type, limit)`
+
+```sql
+SELECT ... FROM nodes WHERE name LIKE '%{query}%' [AND type=?] LIMIT ?
+```
+
+**扩展路径（Column）：** 额外 SQL：
+
+```sql
+SELECT ... FROM nodes
+WHERE type='column' AND properties LIKE '%{query}%' LIMIT ?
+```
+
+对 JSON 做子串匹配，再 `json.loads` 检查 `semantic_type` 是否含 query（小写）。去重后截断至 `limit`。
+
+**返回结构：** 每节点含 `id`, `type`, `name`, `properties`, `edge_count`, `edges_summary`（最多 5 条邻接边摘要）。
+
+## B.2 get_node
+
+加载节点 + 全部邻接边；每条边附带 `direction`（`"outgoing"` 或 `"incoming"`，表示对当前节点的方向）和 `other_node`（对端 id/name/type）。
+
+- `direction="outgoing"`：当前节点是边的 source（出边）
+- `direction="incoming"`：当前节点是边的 target（入边）
+
+CLI 显示时出边用 `→`，入边用 `←` 标注方向，并显示对方节点名称而非 target_id。
+
+Column 类型额外挂载 `profile` 子对象（dtype、semantic_type、null_rate、cardinality、sample_values 等）。
+
+**`suggested_edges` 字段：** 返回涉及此节点的所有 pending edges（两端节点缺失的边），每条附带 `missing_endpoints` 和 `note` 人类可读提示。这些边不参与路径发现和子图扩展，仅作为"建议关系"展示。
+
+## B.3 find_paths — 路径发现
+
+### 目标
+
+在 `max_depth` 跳内找从 `source_id` 到 `target_id` 的路径，按**路径置信度积**降序，最多 10 条。
+
+路径置信度：`∏ edge.confidence`（各边 confidence 连乘）。
+
+### 主算法：SQLite 递归 CTE
+
+```sql
+WITH RECURSIVE paths AS (
+    -- 基：起点，空边路径，depth=0，confidence=1.0
+    SELECT ?, '[]', 0, 1.0, ?
+
+    UNION ALL
+
+    -- 递归：沿边扩展
+    SELECT e.target_id,
+           json_array_append(p.path_edges, e.id),
+           p.depth + 1,
+           p.confidence * e.confidence,
+           p.source_id
+    FROM paths p
+    JOIN edges e ON (e.source_id = p.current_id OR e.target_id = p.current_id)
+                  AND e.source_id != p.current_id
+    WHERE p.depth < max_depth
+      AND [edge type filter]
+      AND cycle prevention (见下)
+)
+SELECT ... WHERE current_id = target_id ORDER BY confidence DESC LIMIT 10
+```
+
+**无向遍历：** 边可从 source 或 target 方向进入，下一跳取 `e.target_id`（实现上对反向边的处理较简化）。
+
+**环检测局限：** CTE 中用 `json_each(path_edges)` 存的是 **edge id**，不是 node id，环检测**不完全可靠**。
+
+### 降级：`_python_bfs_paths`
+
+CTE 失败时启用 BFS 队列 `(current_node, edge_path, confidence)`：
+
+- 扩展邻接边，过滤 `edge_types`
+- 用 edge_path 中已有 source/target 集合防环
+- 到达 target 且 path 非空则记录
+- 按 confidence 排序取 top 10
+
+Python 版环检测更准确，通常作为 fallback。
+
+### 结果组装
+
+将 path 中 edge id 序列解析为 `nodes[]` 和 `edges[]` 详情列表。
+
+## B.4 extract_subgraph — 子图扩展
+
+**层次 BFS（按 hop）：**
+
+```
+visited_nodes = seed node_ids
+visited_edges = {}
+current_layer = seed set
+
+for hop in 1..max_hops:
+    for nid in current_layer:
+        for edge in get_edges_for_node(nid):
+            if edge.id not in visited:
+                visited_edges.add(edge.id)
+                other = 对端节点
+                if other not in visited_nodes:
+                    visited_nodes.add(other)
+                    next_layer.add(other)
+    current_layer = next_layer
+```
+
+返回 `nodes[]`, `edges[]`, `stats{node_count, edge_count, hops}`。
+
+**无** confidence 过滤、无 edge type 过滤。
+
+## B.5 get_pending_edges — 悬空边查询
+
+**参数：** `node_id`（可选）、`edge_type`（可选）、`limit`
+
+**逻辑：**
+1. `node_id` → `get_pending_edges_for_node(node_id)`，否则 `get_all_pending_edges()`
+2. `edge_type` → Python 侧过滤
+3. 截断至 `limit`
+
+**返回结构：** 每条 pending edge 含 `id`, `type`, `source_id`, `target_id`, `confidence`, `missing_endpoints`, `note`, `properties`。
+
+## B.6 list_datasets
+
+对所有 `TableNode`：
+
+```python
+column_count = len(CONTAINS edges)
+inferred_edge_count = 对每列统计非 CONTAINS/FK 的邻接边数之和
+pending_fk_count = 对每列统计 pending_edges 中 type=foreign_key 的数量
+```
+
+返回 `id`, `name`, `source`, `row_count`, `column_count`, `inferred_edge_count`, `pending_fk_count`。
+
+---
+
+## C. Explore — 万能检索入口
+
+**文件：** `retrieval.py` → `GraphRetrieval.explore()`
+
+### C.1 设计理念
+
+DataLink 暴露一个万能检索工具 `datalink_explore`：
+
+- **单工具入口**：agent 给一个 query，一次调用获得完整上下文
+- **多维度召回**：名称、semantic_type、concept/entity、comment/description 多路匹配 + 沿边扩展
+- **实体自包含输出**：每个列节点自包含所有信息（业务含义 + 数据特征 + 关系 + 查询意义），不再按格式分段
+- **概念层吸收**：Concept/Entity 不作为独立输出节点，它们的描述被吸收进对应列的 meaning 行。搜索阶段仍用 concept/entity 做索引扩展，但输出时只展示 table + column
+- **自适应预算**：按 dataset 数量调整输出量
+- **focus 调深**：通过 focus 参数切换输出重心，不需要换工具
+
+辅助工具（search_nodes, get_node 等）保留为内部方法，explore 内部调用它们，不默认暴露给 agent。
+
+### C.2 方法签名
+
+```python
+def explore(
+    self,
+    query: str,
+    max_nodes: int | None = None,  # None → 自适应预算
+    focus: str | None = None,       # "join_paths" | "schema" | "data_profile" | None
+    mask_credential: bool = True,   # 是否遮蔽数据库凭证
+) -> str:  # 返回格式化文本，不是 JSON dict
+```
+
+### C.3 内部流程
+
+```
+explore(query)
+  ① _resolve_query(query, limit) → ResolvedNode 列表
+  ② get_explore_budget(dataset_count, focus) → ExploreBudget
+  ③ _build_context(resolved, budget) → NodeContext 列表
+  ④ _build_relationship_map(node_ids, budget) → RelationshipMap
+     （间接路径仅遍历 FK/joinable 边，仅保留跨表路径）
+  ⑤ _format_output(contexts, rel_map, resolved, budget) → 文本输出
+     过滤掉 concept/entity → 按表分组 → 每列自包含格式化 → 跨表查询模式段
+```
+
+### C.4 _resolve_query — 多维度匹配 + 沿边扩展
+
+**匹配维度：**
+
+| 维度 | 覆盖场景 | 实现 | 分数 |
+|---|---|---|---|
+| 名称精确 | `"orders"` → TableNode:orders | `name.lower() == token.lower()` | 1.0 |
+| 名称子串 | `"customer"` → customer_id, customer_name | `LIKE '%token%'` | 0.8 |
+| semantic_type | `"email"` → 所有 semantic_type=email 的列 | properties JSON 搜索 | 0.7 |
+| comment/description | `"订单金额"` → comment 或 description 包含此文字的节点 | properties JSON 搜索 | 0.5 |
+
+**沿边扩展（召回关键）：**
+
+| 被匹配节点类型 | 扩展边类型 | 扩展目标 | 分数 |
+|---|---|---|---|
+| ConceptNode | `represents`（入边） | 所有 ColumnNode | 0.6 × edge.confidence |
+| EntityNode | `has_concept`（出边） | 所有 ConceptNode | 0.5 × confidence |
+| EntityNode → ConceptNode | `represents` | ColumnNode | 0.4 × 双边 confidence |
+| TableNode | `contains` | 所有 ColumnNode | 0.4 |
+| ColumnNode | `foreign_key/joinable/semantic_synonym` | 关联 ColumnNode | 0.3 × confidence |
+
+合并、去重、按 relevance_score 排序，截断至 `budget.max_nodes`。
+
+### C.5 ExploreBudget — 自适应输出预算
+
+```python
+@dataclass
+class ExploreBudget:
+    max_output_chars: int
+    max_nodes: int
+    max_edges_per_node: int
+    max_edges_per_relationship_kind: int
+    max_sample_values: int
+    max_columns_per_table: int
+    max_pending_edges_per_node: int
+    include_relationships: bool
+    include_additional_nodes: bool
+    include_budget_note: bool
+    include_completeness_signal: bool
+    include_low_confidence_marker: bool
+```
+
+| datasets | max_output | max_nodes | max_edges | sample_vals | relationships? | additional? | budget_note? |
+|---|---|---|---|---|---|---|---|
+| < 3 | 8000 | 8 | 3 | 3 | False | False | False |
+| < 10 | 12000 | 12 | 5 | 5 | True | False | True |
+| < 50 | 16000 | 15 | 7 | 5 | True | True | True |
+| ≥ 50 | 20000 | 20 | 10 | 5 | True | True | True |
+
+**focus 参数调整：**
+
+| focus | 效果 |
+|---|---|
+| `data_profile` | sample_values 翻倍，edges 减半 |
+| `join_paths` | edges 翻倍，sample_values 减半，强制 include_relationships |
+| `schema` | max_nodes 翻倍，sample_values=0（只给 dtype） |
+| None | 均衡（默认） |
+
+### C.6 _build_context — 节点上下文构建
+
+对每个 ResolvedNode：
+1. `storage.get_node()` → 基本信息
+2. `storage.get_edges_for_node()` → 邻接边，按类型分组（join/semantic/statistical/contains），每组截断至 `budget.max_edges_per_node`
+3. Column 类型 → `storage.get_profile_for_column()` → 关键指标摘录（dtype, semantic_type, null_rate, cardinality, sample_values）
+4. `storage.get_pending_edges_for_node()` → 悬空边标注（cap 3 per node）
+
+### C.7 _build_relationship_map — 关系网络
+
+- **直接连接**：resolved 节点之间的边，按类型分组（join/semantic/statistical）
+- **间接路径**：resolved 节点间 `find_paths` ≤3 hop，**仅遍历 FK/joinable 边**（不遍历 contains/represents 等结构性边），**仅保留跨表路径**（同一表内的路径无查询价值）
+
+### C.8 _format_output — 输出格式
+
+**核心原则：实体自包含 + 概念层吸收**
+
+1. 过滤掉 concept/entity contexts — 概念信息通过 `_generate_meaning()` 吸收到列的 meaning 行
+2. 按表分组（`_group_by_table`），每个表一个 `##` 段
+3. 表级 meaning 行：如果表有 comment（来自 SQL metadata 或 LLM 推理），显示为一行
+4. 每列自包含格式化（`_format_column_selfcontained`）
+5. 跨表查询模式段（`_format_cross_table_paths`）
+
+**表级输出格式：**
+
+```
+## schools — 2 columns, 17,686 rows, source: postgresql://...
+- meaning: California schools with eligibility and participation data
+```
+
+表的 meaning 行只显示表的 comment。没有 comment 的表不显示 meaning 行。
+
+**表 comment 的来源：**
+- SQL metadata（数据库表注释） — 优先
+- LLM 推理生成（`LLMMapper.generate_table_comments`） — 当 SQL metadata 缺失时的后备
+
+**单列输出格式：**
+
+```
+### customer_id (integer, identifier)
+- meaning: foreign key referencing customers.id — use `JOIN orders ON orders.customer_id = customers.id` to link orders with their customer
+- data: null 2%, unique 95%, top values: [101, 205, 340]
+- also related: synonym↔users.user_id (0.72) — same entity type
+- pending foreign_key → ? (Referenced node not yet in graph)
+```
+
+**meaning 行的组装逻辑：**
+
+从多个来源组合成一句完整的描述，用 `; ` 连接：
+1. column comment（SQL metadata 或 CSV header）
+2. concept/entity 吸收（represents 边 → "represents concept 'revenue' (total sales, unit: USD)"）
+3. FK/joinable 翻译（→ 查询意义，如 "foreign key referencing customers.id — use JOIN ...")
+
+**also related 行：**
+
+meaning 行只包含 comment + concept吸收 + FK/joinable。其余关系（synonym, semantic_type_match, correlated, distribution_similar）紧凑列在 also related 行。
+
+**data 行的值展示：**
+
+使用 `top_values`（频率排序、天然不重复）代替随机 `sample_values`。避免高频率值重复出现。
+
+**跨表查询模式段：**
+
+```
+## Cross-table query patterns
+orders.customer_id → customers.id (FK, conf 1.00)
+  SQL: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id
+```
+
+只展示跨表的 FK/joinable 连接，附带 SQL JOIN 模板。间接多跳路径在此段展示。
+
+**已删除的旧段落：**
+- Node semantics 段（含义已内联到 meaning 行）
+- Budget note 段（对 agent 无操作意义）
+- Additional relevant 段（已内联到 also related）
+- 独立 concept/entity 输出段（概念层吸收）
+
+### C.9 辅助工具启用机制
+
+**默认只暴露：** `datalink_explore` + `build_graph` + `add_table` + `remove_table`
+
+**可选暴露（环境变量 `DATALINK_MCP_TOOLS`）：**
+
+```bash
+DATALINK_MCP_TOOLS=datalink_search_nodes,datalink_get_node,datalink_find_paths
+```
+
+工具名使用全名（不用缩写），避免歧义。
+
+| 工具全名 | 能力 |
+|---|---|
+| `datalink_search_nodes` | 精确名称搜索（位置列表） |
+| `datalink_get_node` | 单节点详情 + 全部邻接边 + 完整 profile |
+| `datalink_find_paths` | 两节点间路径（可指定 edge_type） |
+| `datalink_extract_subgraph` | 从指定节点按 hop 扩展子图 |
+| `datalink_list_datasets` | 所有表及统计概览 |
+| `datalink_list_pending_edges` | 悬空边列表 |
+
+---
+
+## D. Storage ↔ Retrieval 关系
+
+```
+BuildPipeline
+    → GraphStorage (write)
+CLI / MCP
+    → GraphRetrieval.explore() (万能检索，内部调用 search/get/paths 等)
+    → GraphRetrieval.search_nodes / get_node / find_paths 等 (辅助方法，供 explore 和 CLI 使用)
+```
+
+Retrieval **不**直接 SQL 复杂 analytics（除 find_paths CTE），大部分走 Storage 封装方法。
+
+---
+
+## E. 索引（schema.sql）
+
+| 索引 | 用途 |
+|------|------|
+| `idx_nodes_type`, `idx_nodes_name` | 按类型/名称查 |
+| `idx_edges_source/target`, `(source,type)`, `(target,type)` | 邻接边、路径遍历 |
+| `idx_edges_confidence` | 按置信度过滤（预留） |
+| `idx_profiles_column` | 列 → Profile |
+
+---
+
+## F. 已知限制
+
+| 项 | 说明 |
+|----|------|
+| 搜索 | LIKE 子串匹配 + embedding 向量检索（混合检索）；embedding 未配置时退化为纯全文检索 |
+| find_paths CTE | 环检测与无向边处理不完善 |
+| 单 SQLite 文件 | 无分布式、无并发写优化 |
+| properties JSON | 无法 SQL 内高效查询嵌套字段（除 LIKE） |
+| pending edges | 永远无法 resolve 的 pending 边（目标数据源永不加入）仅作为 suggested_edges 展示，不参与遍历 |
diff --git a/services/datalink/docs/pipeline/07-pipeline-orchestration.md b/services/datalink/docs/pipeline/07-pipeline-orchestration.md
new file mode 100644
index 00000000..cf96175a
--- /dev/null
+++ b/services/datalink/docs/pipeline/07-pipeline-orchestration.md
@@ -0,0 +1,376 @@
+# Pipeline 编排模块实现文档
+
+**源码路径：** `src/datalink/builder/pipeline.py`<br>
+**职责：** 串联 Connector → Extractor → Profiler → Inferrer → Mapper → Storage，提供全量构建与增量更新。
+
+---
+
+## 1. 入口函数
+
+### `_connect_datasource(config: DatasourceConfig) -> DatasourceInfo`
+
+```python
+if config.type == DATABASE:
+    connector = DatabaseConnector(config)
+elif config.type in (CSV, PARQUET):
+    connector = FileConnector(config)
+else:
+    raise ValueError(...)
+
+connector.connect()
+ds_info = connector.get_datasource_info()
+connector.disconnect()
+return ds_info
+```
+
+每次连接**独立** open/close，不在 Pipeline 实例上缓存 Connector。
+
+---
+
+## 2. init_build — 首次构建
+
+### 2.1 执行顺序
+
+```
+Step 1  对每个 datasource_config → _connect_datasource → all_ds_infos[]
+Step 2–5 _compute_pipeline(all_ds_infos) — 纯计算，返回所有 artifacts
+        (tables, columns, profiles, concepts, entities,
+         structural_edges, inferred_edges, semantic_edges)
+Step 6  storage.clear_all()
+        _store_pipeline_result(result) — 批量写入 nodes, edges, profiles
+        set_metadata("build_type", "init")
+```
+
+---
+
+## 2b. rebuild — 重建图谱
+
+从已有 TableNode 元数据重建，不需要用户提供数据源信息。支持三种模式：
+
+### 2b.1 三种重建模式
+
+| 模式 | 覆盖范围 | LLM 调用 | 适用场景 |
+|------|----------|-----------|----------|
+| `full`（默认） | 完整 pipeline + embedding 向量 | ✅ 有 | 数据大幅变化、全面刷新 |
+| `vec` | 仅 embedding 向量 | ❌ 无 | 更换了 embedding 模型 |
+| `profile` | 统计值 + 依赖统计的推断边 | ❌ 无 | 数据量变化但概念结构不变 |
+
+### 2b.2 mode=full — 安全性保证
+
+**rebuild 采用 compute-first → clear-last 模式**，确保旧数据安全：
+
+1. 先从已有 TableNode 读取元数据并连接数据源
+2. 调用 `_compute_pipeline` 纯计算所有结果（不写 DB）
+3. **只有 pipeline 成功后才 `clear_all` + `_store_pipeline_result`**
+4. 如果 pipeline 失败（LLM 超时等），旧数据完好保留，可再次 rebuild 恢复
+
+执行顺序：
+
+```
+Step 1  读取所有 TableNode → 按 source 分组（同一数据库连接的多张表只需一次连接）
+Step 2  对每个 source 组：
+        - 用 TableNode 的 source + source_type 构造 DatasourceConfig
+        - _connect_datasource(config) → DatasourceInfo
+        - 过滤 DatasourceInfo.tables 只保留已有表名的表
+Step 3–5 _compute_pipeline(all_ds_infos) — 纯计算，不写 DB
+        返回所有 artifacts（tables, columns, profiles, concepts, entities,
+        structural_edges, inferred_edges, semantic_edges）
+Step 6  仅在 Step 3–5 成功后：
+        storage.clear_all()
+        _store_pipeline_result(result) — 批量写入
+        set_metadata("build_type", "rebuild")
+Step 7  _build_embeddings_from_pipeline_result(result) — 如果 embedding 已配置
+```
+
+分组逻辑：同一 `source` 的多张表只需一次连接，避免重复连接数据库。
+
+### 2b.3 mode=vec — 向量重建
+
+```
+Step 1  检查 embedding 配置是否可用（model + api_key）
+Step 2  _collect_nodes_with_profiles() — 从 DB 读取所有节点 + 关联 profile
+Step 3  node_to_searchable_text(node, profile) — 为每个节点生成可检索文本
+Step 4  embedding_service.compute_embeddings(texts) — 批量计算向量
+Step 5  storage.clear_embeddings() — 清除旧向量
+        storage.add_embeddings_batch([(node_id, model_name, embedding, text)])
+        storage.set_metadata("embedding_model", model_name)
+```
+
+不涉及任何图谱数据修改，不调用 LLM。
+
+### 2b.4 mode=profile — 统计重建 + 推断边重建
+
+```
+Step 1  读取所有 TableNode → 按 source 分组
+Step 2  对每个 source 组：
+        - 用 TableNode 元数据构造 DatasourceConfig
+        - _connect_datasource → DatasourceInfo
+        - TabularProfiler.profile_datasource → 新 profiles
+Step 3  storage.add_profiles_batch(new_profiles) — 更新统计值
+        _update_column_properties(column_nodes, new_profiles) — 更新节点属性
+        _update_table_properties + add_nodes_batch — 更新表节点属性
+Step 4  storage.remove_edges_by_types([JOINABLE, DISTRIBUTION_SIMILAR,
+        SEMANTIC_SYNONYM, CORRELATED]) — 删除旧推断边
+Step 5  用更新后的 profiles + sample_data 重新推断：
+        - JoinableInferrer → new_joinable
+        - SynonymInferrer → new_synonym
+        - DistributionInferrer → new_distribution
+        - CorrelationInferrer → new_correlated (需要 sample_data)
+Step 6  _store_edges(new_inferred_edges, all_node_ids) — 存储新推断边
+```
+
+**为什么 profile rebuild 也重建推断边**：JOINABLE 依赖 `top_values/sample_values` 的 overlap 率；DISTRIBUTION_SIMILAR 依赖 `min/max/mean` 和 `histogram`；SEMANTIC_SYNONYM 依赖 `semantic_type`（间接来自 profile）；CORRELATED 依赖 `dtype` 和 joinable 边。数据源变化后这些统计值可能不同，推断边应随之更新。
+
+结构边（CONTAINS、FOREIGN_KEY）和概念关联边（REPRESENTS、HAS_CONCEPT）不受影响。
+
+### 2b.5 source_type 恢复
+
+`TableNode` 存有 `source_type` 字段（"csv"、"parquet"、"database"）。重建时直接使用该字段构造 `DatasourceConfig`。如果 `source_type` 为空（旧版本数据），则从 `source` 字符串自动推断（数据库连接串 → database，.parquet → parquet，其余 → csv）。
+
+### 2.2 边合并与过滤
+
+```python
+all_edges = structural_edges + inferred_edges + semantic_edges
+
+filtered_edges = [
+    e for e in all_edges
+    if e.confidence >= config.confidence_threshold  # 默认 0.3
+]
+```
+
+显式边 confidence=1.0，始终保留。低置信度推断边被丢弃。
+
+### 2.3 CorrelationInferrer — 一次性传入全部 datasource
+
+`CorrelationInferrer` 需要**跨表的 sample_data**做 merge + Pearson 计算，
+所以将 `all_ds_infos` 一次性传入，由其内部自行提取所有表的采样数据：
+
+```python
+correlated_edges = correlation_inferrer.infer(
+    all_columns, all_profiles, joinable_edges, all_ds_infos
+)
+```
+
+内部逻辑：遍历 `all_ds_infos` 构建 `sample_dfs: dict[table_name, DataFrame]`，
+再对每个 joinable edge 做 `pd.merge` + Pearson 计算。
+
+**`add_table` 中不调用 CorrelationInferrer**——因为已有表的 sample_data 未持久化，
+无法获取旧表的采样数据来做跨表 merge。
+
+### 2.4 返回值
+
+```python
+{
+    "status": "success",
+    "datasources": len(datasource_configs),
+    "stats": storage.get_graph_stats(),
+}
+```
+
+---
+
+## 3. add_datasource / add_table — 增量添加（含去重）
+
+### 3.0 去重校验
+
+`add_datasource` 在实际添加前会检查图谱中已存在的表（通过 table ID 匹配）。
+ID 格式为 `table:{source}:{table_name}`，同一 source + 同一表名 → ID 相同 → 视为重复。
+
+已存在的表会被**跳过**，只添加新表。返回结果中新增字段：
+
+```python
+{
+    "status": "success",       # 有新表添加
+    "added_tables": ["orders", "transactions"],  # 实际添加的表
+    "skipped_tables": ["users"],                  # 已存在、跳过的表
+    "stats": {...},
+}
+
+# 或者全部跳过时：
+{
+    "status": "skipped",
+    "added_tables": [],
+    "skipped_tables": ["users", "orders", "transactions"],
+    "message": "All tables already exist in the graph. Use remove_table first if you want to re-add.",
+    "stats": {...},
+}
+```
+
+如果想重新添加已有表，需先 `remove_table` 再 `add_datasource`。
+
+### 3.1 add_datasource 与 init_build 的差异
+
+| 步骤 | 行为 |
+|------|------|
+| Connect | 单 datasource，可选过滤 `ds_info.tables` 仅保留指定表（table_names=None 则保留全部） |
+| Extract/Profile | 仅新表 |
+| Infer | **合并**已有列 + 新列、已有 Profile + 新 Profile 后推断 |
+| Infer 输出 | 只保留 `involves_new_column(edge)` 的边 |
+| Correlation | **不调用** |
+| Store | **不清库**，`add_nodes_batch` / `add_edges_batch` 追加 |
+
+### 3.2 已有图数据加载
+
+```python
+existing_columns = storage.get_nodes_by_type(NodeType.COLUMN)
+existing_profiles = [
+    storage.get_profile_for_column(col.id)
+    for col in existing_columns
+    if profile exists
+]
+all_columns_combined = existing + new
+all_profiles_combined = existing_profiles + new_profiles
+```
+
+Joinable/Synonym/Distribution 在**合并集合**上 O(C²) 重算，再过滤出新边。
+
+### 3.3 过滤新边
+
+```python
+new_column_ids = {c.id for c in new_columns}
+
+def involves_new_column(edge):
+    return edge.source_id in new_column_ids or edge.target_id in new_column_ids
+```
+
+避免重复处理纯旧列对（边 ID 相同会 OR REPLACE 覆盖，但浪费计算）。
+
+---
+
+## 4. remove_table — 删除表
+
+```python
+removed_column_ids = storage.remove_table(table_id)
+orphan_count = storage.cleanup_orphaned_semantic_nodes()  # if cleanup_orphans
+return {
+    "status": "success",
+    "removed_columns": len(removed_column_ids),
+    "removed_orphans": orphan_count,
+    "stats": get_graph_stats(),
+}
+```
+
+**不**重新计算剩余边的 confidence，**不**删除仍被其他列引用的 Concept（除非 orphan cleanup 触发）。
+
+---
+
+## 5. 配置项在 Pipeline 中的绑定
+
+| 配置字段 | 使用位置 |
+|----------|----------|
+| `graph_db_path` | `GraphStorage(config.graph_db_path)` |
+| `joinable_overlap_threshold` | `JoinableInferrer(...)` |
+| `correlation_threshold` | `CorrelationInferrer(...)` |
+| `confidence_threshold` | 最终 `filtered_edges` |
+| `llm` | `LLMMapper(config.llm)` — LLM 调用（timeout 控制单次请求超时） |
+| `embedding` | `EmbeddingService(config.embedding, config.llm)` — 向量构建 + 混合检索 |
+| `mapping_batch_size` | `LLMMapper._batch_size` — 每批列数（默认 15，减小可降低推理时间） |
+| `embedding` | `EmbeddingService(config.embedding, config.llm)` — 向量构建 + 混合检索 |
+| `sample_size` | 在 `DatasourceConfig` 传入 Connector（CLI build 时未显式设置，用 DatasourceConfig 默认 1000） |
+| `mcp_tools` | MCP Server 辅助工具注册（`_get_tool_allowlist()`），Pipeline 不使用 |
+
+**注意：** 所有 CLI/MCP 写命令（`add-table`、`rebuild`、`remove-table`）均通过 `DataLinkConfig.load()` 读取配置，`datalink_config.json` 中的阈值和 LLM 设置都会生效。
+
+---
+
+## 6. 完整数据流（全量）
+
+```
+DatasourceConfig[]
+       │
+       ▼
+[DatasourceInfo[]]  ← Connector × N
+       │
+       ├──────────────────────────┐
+       ▼                          ▼
+TableNode[], ColumnNode[]    ColumnProfile[]
+Edge[CONTAINS|FK]                 │
+       │                          │
+       └──────────┬───────────────┘
+                  ▼
+         Edge[JOINABLE|SYNONYM|DIST|CORR]
+                  │
+                  ▼
+    ConceptNode[], EntityNode[], Edge[REPRESENTS|HAS_CONCEPT]
+                  │
+                  ▼
+         SQLite (clear + batch insert)
+                  │
+                  ▼
+    EmbeddingService.compute_embeddings() — 可选（embedding 配置时）
+                  │
+                  ▼
+         node_embeddings (float32 BLOB + searchable_text)
+                  │
+                  ▼
+            get_graph_stats()
+```
+
+---
+
+## 7. 资源生命周期
+
+```python
+pipeline = BuildPipeline(config)
+try:
+    pipeline.rebuild()           # 重建（默认 mode=full）
+    pipeline.rebuild(mode="vec")     # 只重建向量
+    pipeline.rebuild(mode="profile") # 只重算统计 + 推断边
+    pipeline.add_datasource(...) # 增量添加数据源（table_names=None 则加全部）
+    pipeline.add_table(...)      # 增量添加单表（便捷入口）
+finally:
+    pipeline.close()  # storage.close()
+```
+
+CLI/MCP 调用映射：
+- `datalink add-table --source X`（不传 --table） → `pipeline.add_datasource(config)`
+- `datalink add-table --source X --table T` → `pipeline.add_table(config, T)`
+- `datalink rebuild` → `pipeline.rebuild(mode="full")`
+- `datalink rebuild --mode vec` → `pipeline.rebuild(mode="vec")`
+- `datalink rebuild --mode profile` → `pipeline.rebuild(mode="profile")`
+
+MCP `add_table` / `rebuild` / `remove_table` 在成功后 reset 全局 `_storage` / `_retrieval` 单例，强制下次查询重新打开 DB。
+
+---
+
+## 8. 错误处理
+
+| 阶段 | 失败行为 |
+|------|----------|
+| 不支持的 datasource type | `ValueError` 向上抛 |
+| add_table 表不存在 | `ValueError("Table 'x' not found")` |
+| Connector 路径不存在 | `ValueError` |
+| LLM 失败 | 静默跳过概念映射，不中断 build |
+| Storage | SQLite 异常向上抛 |
+
+CLI 捕获 Exception 打印后 `typer.Exit(1)`。
+
+---
+
+## 9. 扩展 Pipeline 的检查清单
+
+新增处理阶段时建议：
+
+1. 在 `init_build` / `rebuild` Step 4/5 之间或之后插入调用
+2. `add_datasource` 中同步实现增量逻辑（是否合并已有节点）
+3. 新边类型加入 `EdgeType` 枚举
+4. 确认新边受 `confidence_threshold` 正确过滤
+5. 更新 `get_graph_stats` / CLI info 展示（EdgeType 已自动枚举）
+6. 补充 `tests/test_builder.py`
+
+---
+
+## 10. 性能特征（粗略）
+
+设 D 个数据源、T 表、C 列、sample_size = S：
+
+| 阶段 | 复杂度 |
+|------|--------|
+| Connect + 采样 | O(D × T × S) I/O |
+| Extract | O(C) |
+| Profile | O(C × S) |
+| Joinable/Synonym/Dist | O(C²) 比较 |
+| LLM Map | O(1) API 调用（列数影响 token） |
+| Storage batch | O(C + E) 写入 |
+
+多表、多列场景下 **Inferrer 平方级** 为主要 CPU 瓶颈。
diff --git a/services/datalink/evaluate/bird/cursor/eval.config.example.json b/services/datalink/evaluate/bird/cursor/eval.config.example.json
new file mode 100644
index 00000000..317b96d5
--- /dev/null
+++ b/services/datalink/evaluate/bird/cursor/eval.config.example.json
@@ -0,0 +1,20 @@
+{
+  "cursor_api_key": "",
+  "model": "composer-2.5",
+  "cwd": "evaluate/bird/sandbox",
+  "mcp_url": "http://127.0.0.1:8082/mcp",
+  "dataset": "evaluate/bird/dev_questions.json",
+  "answers": "evaluate/bird/dev_answers.json",
+  "output_dir": "evaluate/bird/cursor/eval_results",
+  "sqlite_url": "",
+  "sqlite_url_template": "sqlite:///{db_dir}/{db_id}/{db_id}.sqlite",
+  "db_dir": "C:/path/to/bird/dev_databases",
+  "sqlite_databases": {},
+  "limit": 10,
+  "offset": 0,
+  "difficulties": [],
+  "db_ids": [],
+  "modes": ["with_datalink", "without_datalink"],
+  "timeout_seconds": 600,
+  "sandbox_enabled": true
+}
diff --git a/services/datalink/evaluate/bird/cursor/eval_common.py b/services/datalink/evaluate/bird/cursor/eval_common.py
new file mode 100644
index 00000000..1e3d1625
--- /dev/null
+++ b/services/datalink/evaluate/bird/cursor/eval_common.py
@@ -0,0 +1,782 @@
+#!/usr/bin/env python3
+"""Shared helpers for Bird Cursor eval (run + grade)."""
+
+from __future__ import annotations
+
+import json
+import os
+import re
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlparse
+
+# cursor-sdk bridge uses os.get_blocking/set_blocking (Unix / newer Python).
+# On Windows these may be missing — patch before importing cursor_sdk.
+if not hasattr(os, "get_blocking"):
+    os.get_blocking = lambda fd: True  # type: ignore[attr-defined]
+if not hasattr(os, "set_blocking"):
+    os.set_blocking = lambda fd, blocking: None  # type: ignore[attr-defined]
+
+from sqlalchemy import create_engine, text
+from sqlalchemy.engine import Engine
+
+# ---------------------------------------------------------------------------
+# Data classes
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class EvalConfig:
+    cursor_api_key: str = ""
+    model: str = "composer-2.5"
+    cwd: str = "."
+    mcp_url: str = "http://127.0.0.1:8080/mcp"
+    dataset: str = "evaluate/bird/dev_questions.json"
+    answers: str = "evaluate/bird/dev_answers.json"
+    output_dir: str = "evaluate/bird/cursor/eval_results"
+    # SQLite: use ONE of these patterns (set absolute paths in your local config)
+    sqlite_url: str = ""  # single DB for all questions, e.g. sqlite:///C:/path/to/db.sqlite
+    sqlite_url_template: str = "sqlite:///{db_dir}/{db_id}/{db_id}.sqlite"
+    db_dir: str = ""
+    sqlite_databases: dict[str, str] = field(default_factory=dict)
+    limit: int = 0
+    offset: int = 0
+    difficulties: list[str] = field(default_factory=list)
+    db_ids: list[str] = field(default_factory=list)
+    modes: list[str] = field(default_factory=lambda: ["with_datalink", "without_datalink"])
+    timeout_seconds: int = 600
+    # When True, Cursor local agent is sandboxed to cwd (blocks grep/glob/read outside).
+    sandbox_enabled: bool = True
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> EvalConfig:
+        known = {f.name for f in cls.__dataclass_fields__.values()}  # type: ignore[attr-defined]
+        return cls(**{k: v for k, v in data.items() if k in known})
+
+    def resolve_api_key(self) -> str:
+        key = self.cursor_api_key or os.environ.get("CURSOR_API_KEY", "")
+        if not key:
+            raise SystemExit(
+                "Missing Cursor API key. Set CURSOR_API_KEY or pass cursor_api_key in config.\n"
+                "Get one at: https://cursor.com/dashboard → Integrations → User API Keys"
+            )
+        return key
+
+    def sqlite_url_for(self, db_id: str) -> str:
+        if self.sqlite_url:
+            return self.sqlite_url
+        if db_id in self.sqlite_databases:
+            return self.sqlite_databases[db_id]
+        if not self.db_dir:
+            raise ValueError(
+                f"No SQLite URL for db_id={db_id!r}. "
+                "Set sqlite_url, sqlite_databases[db_id], or db_dir + sqlite_url_template."
+            )
+        path = self.sqlite_url_template.format(db_dir=self.db_dir, db_id=db_id)
+        return path
+
+
+@dataclass
+class ToolCallRecord:
+    name: str
+    status: str
+    args: Any = None
+    result: Any = None
+
+
+@dataclass
+class RunMetrics:
+    duration_ms: int = 0
+    input_tokens: int = 0
+    output_tokens: int = 0
+    total_tokens: int = 0
+    tool_call_count: int = 0
+    datalink_tool_calls: int = 0
+    tool_calls: list[ToolCallRecord] = field(default_factory=list)
+
+
+@dataclass
+class RunResult:
+    """Agent run output (no golden SQL / correctness)."""
+
+    question_id: int
+    db_id: str
+    mode: str
+    question: str
+    predicted_sql: str | None
+    error: str | None
+    metrics: RunMetrics
+    answer_text: str
+    difficulty: str = ""
+    run_id: str = ""
+    agent_id: str = ""
+
+
+@dataclass
+class EvalResult:
+    """Scored result (run output + golden comparison)."""
+
+    question_id: int
+    db_id: str
+    mode: str
+    question: str
+    golden_sql: str
+    predicted_sql: str | None
+    correct: bool | None
+    error: str | None
+    golden_row_count: int | None
+    predicted_row_count: int | None
+    metrics: RunMetrics
+    answer_text: str
+    difficulty: str = ""
+    run_id: str = ""
+    agent_id: str = ""
+
+
+# ---------------------------------------------------------------------------
+# SQL helpers
+# ---------------------------------------------------------------------------
+
+_SQL_FENCE_RE = re.compile(r"```(?:sql)?\s*\n(.*?)```", re.IGNORECASE | re.DOTALL)
+_SELECT_RE = re.compile(r"\b(SELECT\b[\s\S]*?)(?:;|\n\n|$)", re.IGNORECASE)
+
+
+def extract_sql(text_body: str) -> str | None:
+    """Pull the most likely SQL statement from agent output."""
+    if not text_body:
+        return None
+
+    fences = _SQL_FENCE_RE.findall(text_body)
+    for block in reversed(fences):
+        stmt = block.strip().rstrip(";")
+        if stmt.upper().startswith("SELECT"):
+            return stmt
+
+    matches = _SELECT_RE.findall(text_body)
+    if matches:
+        return matches[-1].strip().rstrip(";")
+
+    stripped = text_body.strip()
+    if stripped.upper().startswith("SELECT"):
+        return stripped.rstrip(";")
+    return None
+
+
+def _normalize_cell(value: Any) -> Any:
+    if value is None:
+        return None
+    if isinstance(value, float):
+        return round(value, 6)
+    if isinstance(value, bytes):
+        return value.decode("utf-8", errors="replace")
+    return value
+
+
+def _normalize_rows(rows: list[tuple]) -> list[tuple]:
+    return [tuple(_normalize_cell(c) for c in row) for row in rows]
+
+
+def execute_sql(engine: Engine, sql: str) -> tuple[list[tuple], str | None]:
+    try:
+        with engine.connect() as conn:
+            result = conn.execute(text(sql))
+            rows = result.fetchall()
+            return _normalize_rows([tuple(r) for r in rows]), None
+    except Exception as exc:
+        return [], str(exc)
+
+
+def compare_results(golden_rows: list[tuple], pred_rows: list[tuple]) -> bool:
+    """Multiset comparison (order-insensitive)."""
+    from collections import Counter
+
+    return Counter(golden_rows) == Counter(pred_rows)
+
+
+def get_engine(db_id: str, config: EvalConfig, engine_cache: dict[str, Engine]) -> Engine:
+    if db_id not in engine_cache:
+        engine_cache[db_id] = create_engine(config.sqlite_url_for(db_id))
+    return engine_cache[db_id]
+
+
+# ---------------------------------------------------------------------------
+# Cursor SDK runner
+# ---------------------------------------------------------------------------
+
+PROMPT_TEMPLATE = """You are a text-to-SQL assistant. Answer the question by writing a single SQLite SQL query.
+
+Database id: {db_id}
+{sqlite_block}Question: {question}
+{evidence_block}
+
+Rules:
+{path_rule}{explore_rule}- Do not use Grep or Glob (they are disabled for this evaluation).
+- Use only tables/columns that exist in the database.
+- Output exactly one SQL query inside a ```sql code fence.
+- Do not execute the query yourself; only write the SQL.
+- The query must be valid SQLite syntax.。
+- You are only allowed to access files and data inside your working directory.
+- Do not search online.
+- Do not access git history.
+{python_rule}"""
+
+
+def sqlite_url_to_path(sqlite_url: str) -> str:
+    """Turn sqlite:///... URL into a filesystem path for the prompt."""
+    prefix = "sqlite:///"
+    if sqlite_url.startswith(prefix):
+        path = sqlite_url[len(prefix) :]
+        # SQLAlchemy: sqlite:////C:/... (4 slashes) -> /C:/...; normalize drive paths.
+        if len(path) >= 3 and path[0] == "/" and path[2] == ":":
+            path = path[1:]
+        return path
+    return sqlite_url
+
+
+def build_prompt(
+    item: dict[str, Any],
+    *,
+    sqlite_url: str = "",
+    inject_sqlite_path: bool = False,
+    with_datalink: bool = False,
+) -> str:
+    evidence = item.get("evidence") or ""
+    evidence_block = f"Hint: {evidence}" if evidence else ""
+    if inject_sqlite_path and sqlite_url:
+        sqlite_block = f"SQLite database file: {sqlite_url_to_path(sqlite_url)}\n"
+        path_rule = (
+            "- The database file path above is authoritative — do not search the "
+            "workspace for other DB files.\n"
+        )
+    else:
+        sqlite_block = ""
+        path_rule = ""
+    if with_datalink:
+        explore_rule = (
+            "- Do not use Python or bash to inspect or probe the database; "
+            "use DataLink MCP tools (e.g. datalink_explore) instead.\n"
+        )
+        python_rule = ""
+    else:
+        explore_rule = ""
+        python_rule = (
+            "- The environment does not have sqlite3 command in the terminal. "
+            "You have to use python if needed.\n"
+        )
+    return PROMPT_TEMPLATE.format(
+        db_id=item["db_id"],
+        sqlite_block=sqlite_block,
+        question=item["question"],
+        evidence_block=evidence_block,
+        path_rule=path_rule,
+        explore_rule=explore_rule,
+        python_rule=python_rule,
+    )
+
+
+def _mcp_hosts(mcp_url: str) -> list[str]:
+    """Hosts to allow in sandbox network policy for DataLink MCP."""
+    hosts = {"127.0.0.1", "localhost"}
+    if mcp_url:
+        parsed = urlparse(mcp_url)
+        if parsed.hostname:
+            hosts.add(parsed.hostname)
+    return sorted(hosts)
+
+
+_DENY_SEARCH_HOOK_PY = '''\
+#!/usr/bin/env python3
+"""Deny Grep/Glob during Bird Cursor eval (preToolUse)."""
+from __future__ import annotations
+
+import json
+import sys
+
+
+def main() -> None:
+    try:
+        payload = json.load(sys.stdin)
+    except json.JSONDecodeError:
+        print(json.dumps({"permission": "allow"}))
+        return
+
+    name = str(payload.get("tool_name") or "").strip()
+    if name.lower() in {"grep", "glob"}:
+        print(
+            json.dumps(
+                {
+                    "permission": "deny",
+                    "user_message": f"{name} is disabled for this evaluation.",
+                    "agent_message": (
+                        f"The {name} tool is disabled. Do not search the workspace "
+                        "with Grep/Glob. Use DataLink MCP tools if available."
+                    ),
+                }
+            )
+        )
+        return
+
+    print(json.dumps({"permission": "allow"}))
+
+
+if __name__ == "__main__":
+    main()
+'''
+
+_DENY_SEARCH_HOOKS_JSON = {
+    "version": 1,
+    "hooks": {
+        "preToolUse": [
+            {
+                "command": "python .cursor/hooks/deny-search.py",
+                "failClosed": True,
+            }
+        ]
+    },
+}
+
+
+def ensure_sandbox_policy(cwd: Path, *, mcp_url: str = "") -> Path:
+    """Ensure cwd/.cursor/sandbox.json allows MCP hosts when sandbox is on.
+
+    Does not remove existing allow entries; merges mcp hosts into networkPolicy.allow.
+    """
+    cursor_dir = cwd / ".cursor"
+    cursor_dir.mkdir(parents=True, exist_ok=True)
+    path = cursor_dir / "sandbox.json"
+
+    policy: dict[str, Any] = {
+        "type": "workspace_readwrite",
+        "additionalReadwritePaths": [],
+        "additionalReadonlyPaths": [],
+        "networkPolicy": {
+            "default": "deny",
+            "allow": _mcp_hosts(mcp_url),
+        },
+    }
+    if path.is_file():
+        try:
+            existing = json.loads(path.read_text(encoding="utf-8"))
+            if isinstance(existing, dict):
+                policy = {**policy, **existing}
+                net = policy.setdefault("networkPolicy", {})
+                if not isinstance(net, dict):
+                    net = {}
+                    policy["networkPolicy"] = net
+                allow = list(net.get("allow") or [])
+                for h in _mcp_hosts(mcp_url):
+                    if h not in allow:
+                        allow.append(h)
+                net["allow"] = allow
+                net.setdefault("default", "deny")
+        except (json.JSONDecodeError, OSError):
+            pass
+
+    path.write_text(json.dumps(policy, indent=2) + "\n", encoding="utf-8")
+    return path
+
+
+def ensure_eval_hooks(cwd: Path) -> Path:
+    """Install preToolUse hooks that deny Grep/Glob under cwd/.cursor/."""
+    hooks_dir = cwd / ".cursor" / "hooks"
+    hooks_dir.mkdir(parents=True, exist_ok=True)
+    script_path = hooks_dir / "deny-search.py"
+    script_path.write_text(_DENY_SEARCH_HOOK_PY, encoding="utf-8")
+    hooks_json = cwd / ".cursor" / "hooks.json"
+    hooks_json.write_text(
+        json.dumps(_DENY_SEARCH_HOOKS_JSON, indent=2) + "\n",
+        encoding="utf-8",
+    )
+    return hooks_json
+
+
+_KEEP_SUFFIXES = {".sqlite", ".db"}
+
+
+def reset_sandbox_cwd(cwd: Path) -> list[str]:
+    """Remove agent leftovers under cwd; keep DB files and `.cursor/`.
+
+    Returns relative paths that were deleted (for logging).
+    """
+    import shutil
+
+    cwd = cwd.resolve()
+    if not cwd.is_dir():
+        cwd.mkdir(parents=True, exist_ok=True)
+        return []
+
+    removed: list[str] = []
+    for entry in list(cwd.iterdir()):
+        name = entry.name
+        if name == ".cursor":
+            continue
+        if entry.is_file() and entry.suffix.lower() in _KEEP_SUFFIXES:
+            continue
+        try:
+            if entry.is_dir():
+                shutil.rmtree(entry)
+            else:
+                entry.unlink()
+            removed.append(name)
+        except OSError:
+            pass
+    return removed
+
+
+def run_cursor_agent(
+    *,
+    prompt: str,
+    config: EvalConfig,
+    with_datalink: bool,
+) -> tuple[str, RunMetrics, str, str]:
+    from cursor_sdk import (
+        Agent,
+        AgentOptions,
+        HttpMcpServerConfig,
+        LocalAgentOptions,
+        SandboxOptions,
+    )
+
+    mcp_servers: dict[str, Any] = {}
+    if with_datalink:
+        mcp_servers = {
+            "datalink": HttpMcpServerConfig(url=config.mcp_url, type="http"),
+        }
+
+    cwd_path = Path(config.cwd).resolve()
+    cwd = str(cwd_path)
+    api_key = config.resolve_api_key()
+
+    removed = reset_sandbox_cwd(cwd_path)
+    if removed:
+        print(f"  sandbox reset: removed {removed}")
+
+    sandbox_options = None
+    ensure_eval_hooks(cwd_path)
+    if config.sandbox_enabled:
+        ensure_sandbox_policy(cwd_path, mcp_url=config.mcp_url if with_datalink else "")
+        sandbox_options = SandboxOptions(enabled=True)
+
+    metrics = RunMetrics()
+    answer_text = ""
+    run_id = ""
+    agent_id = ""
+
+    with Agent.create(
+        AgentOptions(
+            api_key=api_key,
+            model=config.model,
+            local=LocalAgentOptions(
+                cwd=cwd,
+                # Need "project" so cwd/.cursor/hooks.json (deny Grep/Glob) is loaded.
+                # Do not include "user"/"plugins" — avoids ambient MCP/rules outside the sandbox.
+                setting_sources=["project"],
+                sandbox_options=sandbox_options,
+            ),
+            mcp_servers=mcp_servers or None,
+        )
+    ) as agent:
+        agent_id = agent.agent_id
+        run = agent.send(prompt)
+        run_id = run.id
+
+        for msg in run.messages():
+            if msg.type == "tool_call" and msg.status == "completed":
+                # Cursor MCP tools show as name="mcp"; real tool is in args.toolName.
+                display_name = msg.name or ""
+                args = msg.args
+                if isinstance(args, dict):
+                    tool_name = args.get("toolName") or args.get("tool_name") or ""
+                    provider = args.get("providerIdentifier") or args.get("provider") or ""
+                    if tool_name:
+                        display_name = str(tool_name)
+                else:
+                    tool_name = ""
+                    provider = ""
+
+                rec = ToolCallRecord(
+                    name=display_name,
+                    status=msg.status,
+                    args=args,
+                    result=msg.result,
+                )
+                metrics.tool_calls.append(rec)
+                metrics.tool_call_count += 1
+                blob = f"{msg.name} {display_name} {provider} {tool_name}".lower()
+                if "datalink" in blob:
+                    metrics.datalink_tool_calls += 1
+            elif msg.type == "usage" and msg.usage is not None:
+                metrics.input_tokens = msg.usage.input_tokens
+                metrics.output_tokens = msg.usage.output_tokens
+                metrics.total_tokens = msg.usage.total_tokens
+
+        final = run.wait()
+        answer_text = final.result or ""
+        metrics.duration_ms = final.duration_ms or 0
+        if final.usage is not None:
+            metrics.input_tokens = final.usage.input_tokens
+            metrics.output_tokens = final.usage.output_tokens
+            metrics.total_tokens = final.usage.total_tokens
+
+    return answer_text, metrics, run_id, agent_id
+
+
+# ---------------------------------------------------------------------------
+# Dataset / config IO
+# ---------------------------------------------------------------------------
+
+
+def load_json_list(path: str | Path) -> list[dict[str, Any]]:
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
+    if not isinstance(data, list):
+        raise ValueError(f"Expected a JSON array: {path}")
+    return data
+
+
+def load_answers(path: str | Path) -> dict[tuple[Any, str], dict[str, Any]]:
+    """Index answers by (question_id, db_id)."""
+    items = load_json_list(path)
+    out: dict[tuple[Any, str], dict[str, Any]] = {}
+    for item in items:
+        if "SQL" not in item:
+            raise ValueError(f"Answer entry missing SQL: {item!r}")
+        key = (item.get("question_id"), str(item.get("db_id", "")))
+        out[key] = item
+    return out
+
+
+def filter_dataset(items: list[dict[str, Any]], config: EvalConfig) -> list[dict[str, Any]]:
+    out = items
+    if config.difficulties:
+        allowed = {d.lower() for d in config.difficulties}
+        out = [x for x in out if x.get("difficulty", "").lower() in allowed]
+    if config.db_ids:
+        allowed = set(config.db_ids)
+        out = [x for x in out if x.get("db_id") in allowed]
+    if config.offset:
+        out = out[config.offset :]
+    if config.limit:
+        out = out[: config.limit]
+    return out
+
+
+def load_config(config_path: Path | None) -> EvalConfig:
+    if config_path is not None:
+        with open(config_path, encoding="utf-8") as f:
+            return EvalConfig.from_dict(json.load(f))
+    return EvalConfig()
+
+
+def resolve_config_path(args_config: str | None, script_file: str) -> Path | None:
+    """Return config path: --config if set, else sibling eval.config.json if it exists."""
+    if args_config:
+        return Path(args_config)
+    default = Path(script_file).resolve().parent / "eval.config.json"
+    return default if default.is_file() else None
+
+
+def repo_root_from(script_file: str) -> Path:
+    # evaluate/bird/cursor/<script>.py → repo root
+    return Path(script_file).resolve().parents[3]
+
+
+def metrics_from_dict(d: dict[str, Any] | None) -> RunMetrics:
+    m = d or {}
+    tool_calls = [
+        ToolCallRecord(
+            name=tc.get("name", ""),
+            status=tc.get("status", ""),
+            args=tc.get("args"),
+            result=tc.get("result"),
+        )
+        for tc in (m.get("tool_calls") or [])
+    ]
+    return RunMetrics(
+        duration_ms=m.get("duration_ms", 0),
+        input_tokens=m.get("input_tokens", 0),
+        output_tokens=m.get("output_tokens", 0),
+        total_tokens=m.get("total_tokens", 0),
+        tool_call_count=m.get("tool_call_count", 0),
+        datalink_tool_calls=m.get("datalink_tool_calls", 0),
+        tool_calls=tool_calls,
+    )
+
+
+def eval_result_from_dict(d: dict[str, Any]) -> EvalResult:
+    return EvalResult(
+        question_id=d["question_id"],
+        db_id=d["db_id"],
+        mode=d["mode"],
+        question=d.get("question", ""),
+        golden_sql=d.get("golden_sql", ""),
+        predicted_sql=d.get("predicted_sql"),
+        correct=d.get("correct"),
+        error=d.get("error"),
+        golden_row_count=d.get("golden_row_count"),
+        predicted_row_count=d.get("predicted_row_count"),
+        metrics=metrics_from_dict(d.get("metrics")),
+        answer_text=d.get("answer_text", ""),
+        difficulty=d.get("difficulty", ""),
+        run_id=d.get("run_id", ""),
+        agent_id=d.get("agent_id", ""),
+    )
+
+
+def result_to_dict(r: RunResult | EvalResult) -> dict[str, Any]:
+    return asdict(r)
+
+
+# ---------------------------------------------------------------------------
+# Summary
+# ---------------------------------------------------------------------------
+
+_DIFFICULTY_ORDER = ("simple", "moderate", "challenging")
+
+
+def _difficulty_sort_key(name: str) -> tuple[int, str]:
+    lowered = name.lower()
+    try:
+        return (_DIFFICULTY_ORDER.index(lowered), lowered)
+    except ValueError:
+        return (len(_DIFFICULTY_ORDER), lowered)
+
+
+def _group_avgs(rows: list[EvalResult]) -> dict[str, float]:
+    total = len(rows)
+    if not total:
+        return {"tools": 0.0, "tokens": 0.0, "duration_ms": 0.0, "datalink": 0.0}
+    return {
+        "tools": sum(r.metrics.tool_call_count for r in rows) / total,
+        "tokens": sum(r.metrics.total_tokens for r in rows) / total,
+        "duration_ms": sum(r.metrics.duration_ms for r in rows) / total,
+        "datalink": sum(r.metrics.datalink_tool_calls for r in rows) / total,
+    }
+
+
+def _format_group_stats(label: str, rows: list[EvalResult]) -> list[str]:
+    scored = [r for r in rows if r.correct is not None]
+    correct = sum(1 for r in scored if r.correct)
+    acc = correct / len(scored) if scored else 0.0
+    avgs = _group_avgs(rows)
+    return [
+        f"[{label}]",
+        f"  accuracy:     {correct}/{len(scored)} = {acc:.1%}",
+        f"  avg tools:    {avgs['tools']:.1f}",
+        f"  avg datalink: {avgs['datalink']:.1f}",
+        f"  avg tokens:   {avgs['tokens']:.0f}",
+        f"  avg duration: {avgs['duration_ms']:.0f} ms",
+    ]
+
+
+def _pct_change(with_val: float, without_val: float) -> str:
+    """Relative change of with vs without. Negative = lower cost with DataLink."""
+    if without_val == 0:
+        return "n/a" if with_val == 0 else "+inf"
+    pct = (with_val - without_val) / without_val * 100
+    return f"{pct:+.1f}%"
+
+
+def _format_datalink_improvement(
+    with_rows: list[EvalResult],
+    without_rows: list[EvalResult],
+) -> list[str]:
+    """Compare with_datalink vs without_datalink for tools / tokens / duration."""
+
+    def by_difficulty(rows: list[EvalResult]) -> dict[str, list[EvalResult]]:
+        out: dict[str, list[EvalResult]] = {}
+        for r in rows:
+            key = r.difficulty.strip() or "unknown"
+            out.setdefault(key, []).append(r)
+        return out
+
+    def section(label: str, w: list[EvalResult], wo: list[EvalResult]) -> list[str]:
+        aw, awo = _group_avgs(w), _group_avgs(wo)
+        return [
+            f"[{label}]",
+            (f"  tools:        {aw['tools']:.1f} vs {awo['tools']:.1f}  ({_pct_change(aw['tools'], awo['tools'])})"),
+            (
+                f"  tokens:       {aw['tokens']:.0f} vs {awo['tokens']:.0f}"
+                f"  ({_pct_change(aw['tokens'], awo['tokens'])})"
+            ),
+            (
+                f"  duration:     {aw['duration_ms']:.0f} vs {awo['duration_ms']:.0f} ms"
+                f"  ({_pct_change(aw['duration_ms'], awo['duration_ms'])})"
+            ),
+        ]
+
+    lines: list[str] = [
+        "",
+        "=" * 60,
+        "DATALINK IMPROVEMENT (with vs without; -% = lower cost)",
+        "=" * 60,
+        "",
+    ]
+    lines.extend(section("overall", with_rows, without_rows))
+
+    with_by_diff = by_difficulty(with_rows)
+    without_by_diff = by_difficulty(without_rows)
+    diffs = sorted(set(with_by_diff) | set(without_by_diff), key=_difficulty_sort_key)
+    for diff in diffs:
+        if diff == "unknown" and len(diffs) == 1:
+            continue
+        w = with_by_diff.get(diff, [])
+        wo = without_by_diff.get(diff, [])
+        if not w or not wo:
+            continue
+        lines.append("")
+        lines.extend(section(diff, w, wo))
+
+    return lines
+
+
+def format_summary(results: list[EvalResult]) -> str:
+    """Build the eval summary text (overall + per-difficulty), shared by terminal and .md."""
+    by_mode: dict[str, list[EvalResult]] = {}
+    for r in results:
+        by_mode.setdefault(r.mode, []).append(r)
+
+    lines: list[str] = [
+        "=" * 60,
+        "EVAL SUMMARY",
+        "=" * 60,
+    ]
+
+    for mode, rows in sorted(by_mode.items()):
+        lines.append("")
+        lines.extend(_format_group_stats(mode, rows))
+
+        by_diff: dict[str, list[EvalResult]] = {}
+        for r in rows:
+            key = r.difficulty.strip() or "unknown"
+            by_diff.setdefault(key, []).append(r)
+
+        if len(by_diff) <= 1 and next(iter(by_diff), "unknown") == "unknown":
+            continue
+
+        for diff in sorted(by_diff.keys(), key=_difficulty_sort_key):
+            lines.append("")
+            lines.extend(_format_group_stats(f"{mode} / {diff}", by_diff[diff]))
+
+    with_rows = by_mode.get("with_datalink", [])
+    without_rows = by_mode.get("without_datalink", [])
+    if with_rows and without_rows:
+        lines.extend(_format_datalink_improvement(with_rows, without_rows))
+
+    return "\n".join(lines)
+
+
+def print_summary(results: list[EvalResult]) -> None:
+    print("\n" + format_summary(results))
+
+
+def write_summary_md(results: list[EvalResult], path: Path) -> None:
+    body = format_summary(results)
+    path.write_text(f"```\n{body}\n```\n", encoding="utf-8")
+
+
+def write_json(path: Path, payload: list[dict[str, Any]]) -> None:
+    with open(path, "w", encoding="utf-8") as out_f:
+        json.dump(payload, out_f, ensure_ascii=False, indent=2)
+        out_f.write("\n")
diff --git a/services/datalink/evaluate/bird/cursor/eval_grade.py b/services/datalink/evaluate/bird/cursor/eval_grade.py
new file mode 100644
index 00000000..66c82516
--- /dev/null
+++ b/services/datalink/evaluate/bird/cursor/eval_grade.py
@@ -0,0 +1,214 @@
+#!/usr/bin/env python3
+"""Phase 2: score Cursor run JSON against a separate answers file.
+
+Loads agent run results + golden answers, executes both SQLs on SQLite,
+writes scored JSON (adds correct / golden_sql / row counts) and summary .md.
+
+Usage (from repo root):
+  uv run python evaluate/bird/cursor/eval_grade.py --results evaluate/bird/cursor/eval_results/run_....json
+  uv run python evaluate/bird/cursor/eval_grade.py \\
+      --results path/to/run.json \\
+      --answers evaluate/bird/dev_answers.json \\
+      --sqlite-url sqlite:///C:/path/to/california_schools.sqlite
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+from pathlib import Path
+from typing import Any
+
+from eval_common import (
+    EvalConfig,
+    EvalResult,
+    compare_results,
+    execute_sql,
+    get_engine,
+    load_answers,
+    load_config,
+    load_json_list,
+    metrics_from_dict,
+    print_summary,
+    repo_root_from,
+    resolve_config_path,
+    result_to_dict,
+    write_json,
+    write_summary_md,
+)
+from sqlalchemy.engine import Engine
+
+
+def grade_one(
+    row: dict[str, Any],
+    answers: dict[tuple[Any, str], dict[str, Any]],
+    config: EvalConfig,
+    engine_cache: dict[str, Engine],
+) -> EvalResult:
+    qid = row.get("question_id")
+    db_id = str(row.get("db_id", ""))
+    key = (qid, db_id)
+    if key not in answers:
+        # Fallback: match by question_id only if unique
+        matches = [a for (aq, ad), a in answers.items() if aq == qid]
+        if len(matches) == 1:
+            ans = matches[0]
+        else:
+            metrics = metrics_from_dict(row.get("metrics"))
+            return EvalResult(
+                question_id=qid if qid is not None else -1,
+                db_id=db_id,
+                mode=str(row.get("mode", "")),
+                question=str(row.get("question", "")),
+                golden_sql="",
+                predicted_sql=row.get("predicted_sql"),
+                correct=False,
+                error=f"No answer for question_id={qid!r} db_id={db_id!r}",
+                golden_row_count=None,
+                predicted_row_count=None,
+                metrics=metrics,
+                answer_text=str(row.get("answer_text", "")),
+                difficulty=str(row.get("difficulty", "") or ""),
+                run_id=str(row.get("run_id", "")),
+                agent_id=str(row.get("agent_id", "")),
+            )
+    else:
+        ans = answers[key]
+
+    golden_sql = ans["SQL"]
+    predicted_sql = row.get("predicted_sql")
+    # Preserve agent-side error; may append SQL errors below.
+    error = row.get("error")
+    correct: bool | None = None
+    golden_count: int | None = None
+    pred_count: int | None = None
+
+    try:
+        engine = get_engine(db_id, config, engine_cache)
+        golden_rows, golden_err = execute_sql(engine, golden_sql)
+        if golden_err:
+            raise RuntimeError(f"Golden SQL failed: {golden_err}")
+        golden_count = len(golden_rows)
+
+        if not predicted_sql:
+            correct = False
+            if not error:
+                error = "No SQL extracted from agent response"
+        else:
+            pred_rows, pred_err = execute_sql(engine, predicted_sql)
+            if pred_err:
+                correct = False
+                error = f"Predicted SQL failed: {pred_err}"
+            else:
+                pred_count = len(pred_rows)
+                correct = compare_results(golden_rows, pred_rows)
+    except Exception as exc:
+        correct = False
+        error = f"{type(exc).__name__}: {exc}"
+
+    return EvalResult(
+        question_id=qid if qid is not None else -1,
+        db_id=db_id,
+        mode=str(row.get("mode", "")),
+        question=str(row.get("question", "")),
+        golden_sql=golden_sql,
+        predicted_sql=predicted_sql,
+        correct=correct,
+        error=error,
+        golden_row_count=golden_count,
+        predicted_row_count=pred_count,
+        metrics=metrics_from_dict(row.get("metrics")),
+        answer_text=str(row.get("answer_text", "")),
+        difficulty=str(row.get("difficulty") or ans.get("difficulty") or ""),
+        run_id=str(row.get("run_id", "")),
+        agent_id=str(row.get("agent_id", "")),
+    )
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Grade Cursor run JSON against Bird answers")
+    p.add_argument(
+        "--config",
+        help="JSON config file (default: eval.config.json next to this script, if present)",
+    )
+    p.add_argument(
+        "--results",
+        required=True,
+        help="Path to run JSON from eval_run.py",
+    )
+    p.add_argument(
+        "--answers",
+        help="Path to answers JSON (question_id / db_id / SQL). "
+        "Default: config.answers or evaluate/bird/dev_answers.json",
+    )
+    p.add_argument(
+        "--output",
+        help="Scored JSON path (default: <results_stem>_scored.json next to results)",
+    )
+    p.add_argument("--sqlite-url", help="Single SQLite URL for all questions")
+    p.add_argument("--db-dir", help="Base dir for per-db SQLite files")
+    return p.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    config_path = resolve_config_path(args.config, __file__)
+    config = load_config(config_path)
+
+    if args.answers:
+        config.answers = args.answers
+    if args.sqlite_url:
+        config.sqlite_url = args.sqlite_url
+    if args.db_dir:
+        config.db_dir = args.db_dir
+
+    os.chdir(repo_root_from(__file__))
+
+    results_path = Path(args.results)
+    if not results_path.is_file():
+        raise SystemExit(f"Results file not found: {results_path}")
+
+    answers_path = Path(config.answers)
+    if not answers_path.is_file():
+        raise SystemExit(
+            f'Answers file not found: {answers_path}\nPass --answers PATH or set "answers" in eval.config.json'
+        )
+
+    if args.output:
+        out_path = Path(args.output)
+    else:
+        out_path = results_path.with_name(f"{results_path.stem}_scored.json")
+
+    summary_path = out_path.with_suffix(".md")
+
+    run_rows = load_json_list(results_path)
+    answers = load_answers(answers_path)
+    engine_cache: dict[str, Engine] = {}
+
+    print(f"Config:   {config_path if config_path is not None else '(defaults)'}")
+    print(f"Results:  {results_path} ({len(run_rows)} rows)")
+    print(f"Answers:  {answers_path} ({len(answers)} entries)")
+    print(f"Output:   {out_path}")
+    if config.sqlite_url:
+        print(f"SQLite:   {config.sqlite_url}")
+    else:
+        print(f"DB dir:   {config.db_dir or '(per sqlite_databases map)'}")
+
+    scored: list[EvalResult] = []
+    for row in run_rows:
+        r = grade_one(row, answers, config, engine_cache)
+        scored.append(r)
+        mark = "OK" if r.correct else "FAIL"
+        print(f"  [{mark}] q={r.question_id} mode={r.mode} correct={r.correct}")
+        if r.error:
+            print(f"         error: {r.error}")
+
+    write_json(out_path, [result_to_dict(r) for r in scored])
+    write_summary_md(scored, summary_path)
+    print_summary(scored)
+    print(f"\nScored results saved to {out_path}")
+    print(f"Summary saved to {summary_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/services/datalink/evaluate/bird/cursor/eval_run.py b/services/datalink/evaluate/bird/cursor/eval_run.py
new file mode 100644
index 00000000..2a8a124e
--- /dev/null
+++ b/services/datalink/evaluate/bird/cursor/eval_run.py
@@ -0,0 +1,202 @@
+#!/usr/bin/env python3
+"""Phase 1: run Cursor agents on Bird questions (no golden answers / scoring).
+
+Loads questions only (no SQL field). Writes run JSON with predicted SQL + trajectories.
+Grade later with eval_grade.py + a separate answers file.
+
+Usage (from repo root):
+  uv run python evaluate/bird/cursor/eval_run.py
+  uv run python evaluate/bird/cursor/eval_run.py --config evaluate/bird/cursor/eval.config.json --limit 3
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+import time
+import traceback
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from eval_common import (
+    EvalConfig,
+    RunMetrics,
+    RunResult,
+    build_prompt,
+    extract_sql,
+    filter_dataset,
+    load_config,
+    load_json_list,
+    repo_root_from,
+    resolve_config_path,
+    result_to_dict,
+    run_cursor_agent,
+    write_json,
+)
+
+
+def run_item(item: dict[str, Any], mode: str, config: EvalConfig) -> RunResult:
+    with_datalink = mode == "with_datalink"
+    db_id = item["db_id"]
+    qid = item.get("question_id", -1)
+
+    metrics = None
+    answer_text = ""
+    run_id = ""
+    agent_id = ""
+    predicted_sql: str | None = None
+    error: str | None = None
+
+    try:
+        sqlite_url = ""
+        if not with_datalink:
+            # Path hint for baseline only; answers are not loaded here.
+            sqlite_url = config.sqlite_url_for(db_id)
+
+        prompt = build_prompt(
+            item,
+            sqlite_url=sqlite_url,
+            inject_sqlite_path=not with_datalink,
+            with_datalink=with_datalink,
+        )
+        answer_text, metrics, run_id, agent_id = run_cursor_agent(
+            prompt=prompt,
+            config=config,
+            with_datalink=with_datalink,
+        )
+
+        predicted_sql = extract_sql(answer_text)
+        if not predicted_sql:
+            error = "No SQL extracted from agent response"
+
+    except Exception as exc:
+        error = f"{type(exc).__name__}: {exc}"
+        traceback.print_exc()
+        metrics = metrics or RunMetrics()
+
+    assert metrics is not None
+    return RunResult(
+        question_id=qid,
+        db_id=db_id,
+        mode=mode,
+        question=item["question"],
+        predicted_sql=predicted_sql,
+        error=error,
+        metrics=metrics,
+        answer_text=answer_text,
+        difficulty=str(item.get("difficulty", "") or ""),
+        run_id=run_id,
+        agent_id=agent_id,
+    )
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Run Cursor agents on Bird questions (no scoring)")
+    p.add_argument(
+        "--config",
+        help="JSON config file (default: eval.config.json next to this script, if present)",
+    )
+    p.add_argument("--dataset", help="Path to questions JSON (no SQL field)")
+    p.add_argument("--output-dir", help="Directory for run result JSON")
+    p.add_argument("--sqlite-url", help="SQLite URL for without_datalink path hint")
+    p.add_argument("--db-dir", help="Base dir for per-db SQLite files")
+    p.add_argument("--mcp-url", default=None, help="DataLink MCP streamable-http URL")
+    p.add_argument("--limit", type=int, default=None)
+    p.add_argument("--offset", type=int, default=None)
+    p.add_argument("--mode", choices=["with_datalink", "without_datalink", "both"], default="both")
+    p.add_argument("--model", default=None)
+    p.add_argument("--cwd", default=None)
+    return p.parse_args()
+
+
+def apply_cli_overrides(config: EvalConfig, args: argparse.Namespace) -> None:
+    if args.dataset:
+        config.dataset = args.dataset
+    if args.output_dir:
+        config.output_dir = args.output_dir
+    if args.sqlite_url:
+        config.sqlite_url = args.sqlite_url
+    if args.db_dir:
+        config.db_dir = args.db_dir
+    if args.mcp_url:
+        config.mcp_url = args.mcp_url
+    if args.limit is not None:
+        config.limit = args.limit
+    if args.offset is not None:
+        config.offset = args.offset
+    if args.model:
+        config.model = args.model
+    if args.cwd:
+        config.cwd = args.cwd
+    if args.mode == "with_datalink":
+        config.modes = ["with_datalink"]
+    elif args.mode == "without_datalink":
+        config.modes = ["without_datalink"]
+
+
+def main() -> None:
+    args = parse_args()
+    config_path = resolve_config_path(args.config, __file__)
+    config = load_config(config_path)
+    apply_cli_overrides(config, args)
+
+    os.chdir(repo_root_from(__file__))
+
+    items = load_json_list(config.dataset)
+    # Refuse combined files that still carry golden SQL (leak risk).
+    if any("SQL" in x for x in items):
+        raise SystemExit(
+            f"Dataset {config.dataset!r} contains a SQL field. "
+            "Use questions-only JSON (e.g. evaluate/bird/dev_questions.json)."
+        )
+
+    items = filter_dataset(items, config)
+    if not items:
+        print("No questions matched filters.")
+        sys.exit(0)
+
+    out_dir = Path(config.output_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+    stamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
+    out_path = out_dir / f"run_{stamp}.json"
+
+    print(f"Config:   {config_path if config_path is not None else '(defaults)'}")
+    print(f"Dataset:  {config.dataset} ({len(items)} questions)")
+    print(f"Modes:    {config.modes}")
+    print(f"Cwd:      {config.cwd}")
+    print(f"Sandbox:  {config.sandbox_enabled}")
+    print(f"MCP URL:  {config.mcp_url}")
+    print(f"Output:   {out_path}")
+    print("Note:     no answers loaded — scoring is eval_grade.py")
+
+    all_results: list[RunResult] = []
+
+    def flush() -> None:
+        write_json(out_path, [result_to_dict(r) for r in all_results])
+
+    for i, item in enumerate(items):
+        for mode in config.modes:
+            qid = item.get("question_id", i)
+            print(f"\n[{i + 1}/{len(items)}] q={qid} db={item['db_id']} mode={mode}")
+            t0 = time.time()
+            result = run_item(item, mode, config)
+            elapsed = time.time() - t0
+            all_results.append(result)
+
+            print(f"  tools={result.metrics.tool_call_count} tokens={result.metrics.total_tokens} ({elapsed:.1f}s)")
+            if result.error:
+                print(f"  error: {result.error}")
+            if result.predicted_sql:
+                preview = result.predicted_sql[:120].replace("\n", " ")
+                print(f"  sql: {preview}...")
+
+            flush()
+
+    print(f"\nRun results saved to {out_path}")
+    print(f"Grade with: uv run python evaluate/bird/cursor/eval_grade.py --results {out_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/services/datalink/pyproject.toml b/services/datalink/pyproject.toml
new file mode 100644
index 00000000..1e28f618
--- /dev/null
+++ b/services/datalink/pyproject.toml
@@ -0,0 +1,77 @@
+[project]
+name = "datalink"
+version = "0.1.0"
+description = "A data-focused knowledge graph system that accelerates agent understanding of data"
+readme = "README.md"
+requires-python = ">=3.10"
+license = "Apache-2.0"
+authors = [
+    { name = "DataLink Team" },
+]
+
+dependencies = [
+    "pydantic>=2.0",
+    "pandas>=2.0",
+    "numpy>=1.24",
+    "sqlalchemy>=2.0",
+    "typer>=0.9",
+    "rich>=13.0",
+    "mcp>=1.0",
+    "openai>=1.0",
+    "anthropic>=0.40",
+    "fastapi>=0.100",
+    "uvicorn>=0.20",
+]
+
+[project.optional-dependencies]
+dbconnect = [
+    "psycopg2-binary>=2.9.12",
+    "pymysql>=1.2.0",
+]
+vec-embedding = [
+    "modelscope>=1.38.0",
+    "sglang[all]>=0.5.2",
+]
+vec-search = [
+    "sqlite-vec>=0.1.6",
+]
+eval = [
+    "cursor-sdk>=0.1.9",
+]
+all = [
+    "datalink[dbconnect,vec-search,eval]",
+    "pytest>=7.0",
+    "pytest-cov>=4.0",
+    "ruff>=0.1",
+]
+
+[dependency-groups]
+dev = [
+    "pytest>=7.0",
+    "pytest-cov>=4.0",
+    "ruff>=0.1",
+]
+
+[project.scripts]
+datalink = "datalink.cli.main:app"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/datalink"]
+
+[tool.ruff]
+line-length = 120
+target-version = "py310"
+
+[tool.ruff.lint]
+select = ["E", "F", "I", "W"]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = ["src"]
+markers = [
+    "real_llm: CLI integration test requiring a configured LLM API (skipped when no key)",
+]
diff --git a/services/datalink/src/datalink/__init__.py b/services/datalink/src/datalink/__init__.py
new file mode 100644
index 00000000..ee839ddd
--- /dev/null
+++ b/services/datalink/src/datalink/__init__.py
@@ -0,0 +1,3 @@
+"""DataLink - A data-focused knowledge graph system."""
+
+__version__ = "0.1.0"
diff --git a/services/datalink/src/datalink/api/__init__.py b/services/datalink/src/datalink/api/__init__.py
new file mode 100644
index 00000000..cfc25f11
--- /dev/null
+++ b/services/datalink/src/datalink/api/__init__.py
@@ -0,0 +1 @@
+"""DataLink REST API — HTTP interface mirroring CLI commands."""
diff --git a/services/datalink/src/datalink/api/deps.py b/services/datalink/src/datalink/api/deps.py
new file mode 100644
index 00000000..7724c453
--- /dev/null
+++ b/services/datalink/src/datalink/api/deps.py
@@ -0,0 +1,54 @@
+"""Shared dependencies for DataLink REST API routes.
+
+Provides reusable dependency-injection helpers for getting config,
+storage, and retrieval instances — same pattern as MCP server.
+"""
+
+from datalink.config import DataLinkConfig
+from datalink.graph.retrieval import GraphRetrieval
+from datalink.graph.storage import GraphStorage
+
+# Global instances — reused across requests (SQLite is single-writer,
+# but concurrent reads are safe with WAL mode). Write operations
+# (add-table, rebuild, remove-table) close and reset these so the
+# next request gets a fresh view.
+_storage: GraphStorage | None = None
+_retrieval: GraphRetrieval | None = None
+
+
+def get_config() -> DataLinkConfig:
+    """Load and return the current DataLinkConfig."""
+    return DataLinkConfig.load()
+
+
+def get_storage(db_path: str = "") -> GraphStorage:
+    """Get or create the global storage instance."""
+    global _storage
+    if _storage is None:
+        effective_db_path = db_path or get_config().graph_db_path
+        _storage = GraphStorage(effective_db_path)
+    return _storage
+
+
+def get_retrieval(db_path: str = "") -> GraphRetrieval:
+    """Get or create the global retrieval instance."""
+    global _retrieval
+    if _retrieval is None:
+        config = get_config()
+        effective_db_path = db_path or config.graph_db_path
+        storage = get_storage(effective_db_path)
+        _retrieval = GraphRetrieval(storage, config)
+    return _retrieval
+
+
+def reset_global_instances() -> None:
+    """Reset global storage/retrieval after write operations.
+
+    Called after add-table, rebuild, remove-table so subsequent
+    read requests see the updated graph.
+    """
+    global _storage, _retrieval
+    if _storage:
+        _storage.close()
+    _storage = None
+    _retrieval = None
diff --git a/services/datalink/src/datalink/api/server.py b/services/datalink/src/datalink/api/server.py
new file mode 100644
index 00000000..700bdb7a
--- /dev/null
+++ b/services/datalink/src/datalink/api/server.py
@@ -0,0 +1,415 @@
+"""DataLink REST API server — HTTP endpoints mirroring CLI commands.
+
+All route names match CLI commands (add-table, rebuild, etc.) for
+consistency. Write operations (add-table, rebuild, remove-table, show)
+are synchronous — they return the result JSON after completion.
+
+Read operations (explore, search, etc.) are also synchronous but fast.
+"""
+
+import logging
+from pathlib import Path
+
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse, PlainTextResponse
+from pydantic import BaseModel, Field
+
+from datalink.api.deps import get_config, get_retrieval, get_storage, reset_global_instances
+from datalink.models.datasource import DatasourceConfig, DatasourceType
+from datalink.models.edge import EdgeType
+from datalink.models.node import NodeType
+from datalink.utils.credential import mask_result
+
+logger = logging.getLogger(__name__)
+
+app = FastAPI(
+    title="DataLink",
+    description="REST API for DataLink — all endpoints mirror CLI commands.",
+    version="0.1.0",
+)
+
+# Suppress INFO-level progress logs in API mode (like MCP)
+logging.getLogger("datalink").setLevel(logging.WARNING)
+
+
+@app.get("/healthz", summary="Service liveness")
+async def healthz() -> JSONResponse:
+    """Report process liveness without opening the graph database."""
+    return JSONResponse({"status": "ok", "service": "datalink"})
+
+
+# ── Request models ────────────────────────────────────────────────────
+
+
+class AddTableRequest(BaseModel):
+    source: str = Field(..., description="Data source path or connection string")
+    table: str | None = Field(None, description="Table name to add (null = add all tables)")
+    source_type: str = Field("csv", description="Source type: csv, parquet, or database")
+    schema_name: str | None = Field(None, description="Database schema name")
+
+
+class RebuildRequest(BaseModel):
+    mode: str = Field("full", description="Rebuild mode: full, vec, or profile")
+
+
+class RemoveTableRequest(BaseModel):
+    table_id: str = Field(..., description="Table ID or name to remove")
+    cleanup_orphans: bool = Field(True, description="Whether to clean up orphaned Concept/Entity nodes")
+
+
+class ExploreRequest(BaseModel):
+    query: str = Field(..., description="Keywords, names, or natural language description")
+    max_nodes: int | None = Field(None, description="Max nodes in detail (null = auto)")
+    focus: str | None = Field(None, description="Focus direction: join_paths, schema, data_profile, or null")
+
+
+class SearchRequest(BaseModel):
+    query: str = Field(..., description="Search keywords")
+    node_type: str | None = Field(None, description="Node type filter: column, table, concept, entity")
+    limit: int = Field(10, description="Maximum number of results")
+
+
+class GetNodeRequest(BaseModel):
+    node_id: str = Field(..., description="Node ID (full ID, masked ID, or short alias like table.col)")
+    include_edges: bool = Field(True, description="Whether to include adjacent edges")
+
+
+class PathRequest(BaseModel):
+    source_id: str = Field(..., description="Starting node ID (full ID, masked ID, or short alias)")
+    target_id: str = Field(..., description="Destination node ID (full ID, masked ID, or short alias)")
+    max_depth: int = Field(3, description="Maximum path depth")
+    limit: int = Field(3, description="Maximum number of paths to return")
+    edge_types: str | None = Field(None, description="Comma-separated edge types to traverse")
+
+
+class ExtractSubgraphRequest(BaseModel):
+    node_ids: str = Field(..., description="Comma-separated node IDs to start from")
+    max_hops: int = Field(2, description="Number of neighbor layers to expand")
+
+
+class PendingEdgesRequest(BaseModel):
+    node_id: str | None = Field(None, description="Filter by node ID")
+    edge_type: str | None = Field(None, description="Filter by edge type")
+    limit: int = Field(50, description="Maximum number of results")
+
+
+class ConfigUpdateRequest(BaseModel):
+    llm_model: str | None = Field(None, description="LLM model name")
+    llm_api_key: str | None = Field(None, description="LLM API key")
+    llm_base_url: str | None = Field(None, description="LLM API base URL")
+    graph_db_path: str | None = Field(None, description="Default graph database path")
+    mcp_tools: str | None = Field(None, description="Auxiliary MCP tools to expose (comma-separated)")
+    embedding_model: str | None = Field(None, description="Embedding model name")
+    embedding_api_key: str | None = Field(None, description="Embedding API key")
+    embedding_base_url: str | None = Field(None, description="Embedding API base URL")
+
+
+# ── Helper: build DatasourceConfig ───────────────────────────────────
+
+
+def _build_datasource_config(req: AddTableRequest) -> DatasourceConfig:
+    """Build a DatasourceConfig from an AddTableRequest."""
+    try:
+        ds_type = DatasourceType(req.source_type)
+    except ValueError:
+        raise ValueError(f"Invalid source_type: {req.source_type}")
+
+    resolved_source = req.source
+    if ds_type in (DatasourceType.CSV, DatasourceType.PARQUET):
+        resolved_source = str(Path(req.source).resolve())
+
+    schema = req.schema_name if req.schema_name else "public"
+
+    return DatasourceConfig(
+        type=ds_type,
+        path=resolved_source if ds_type in (DatasourceType.CSV, DatasourceType.PARQUET) else "",
+        connection_string=req.source if ds_type == DatasourceType.DATABASE else "",
+        schema_name=schema,
+    )
+
+
+# ── Write operations (offline) ────────────────────────────────────────
+
+
+@app.post("/add-table", summary="Add tables from a data source to the graph")
+async def add_table(req: AddTableRequest) -> JSONResponse:
+    """Add tables from a data source. Mirrors CLI `datalink add-table`."""
+    config = get_config()
+
+    try:
+        ds_config = _build_datasource_config(req)
+    except ValueError as e:
+        return JSONResponse({"error": str(e)}, status_code=400)
+
+    from datalink.builder.pipeline import BuildPipeline
+
+    pipeline = BuildPipeline(config)
+
+    try:
+        if req.table:
+            result = pipeline.add_table(ds_config, req.table)
+        else:
+            result = pipeline.add_datasource(ds_config)
+
+        reset_global_instances()
+        return JSONResponse(result)
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)
+    finally:
+        pipeline.close()
+
+
+@app.post("/rebuild", summary="Rebuild the graph from existing data sources")
+async def rebuild(req: RebuildRequest) -> JSONResponse:
+    """Rebuild the graph. Mirrors CLI `datalink rebuild`."""
+    valid_modes = {"full", "vec", "profile"}
+    if req.mode not in valid_modes:
+        return JSONResponse(
+            {"error": f"Invalid mode: '{req.mode}'. Valid: full, vec, profile"},
+            status_code=400,
+        )
+
+    config = get_config()
+
+    from datalink.builder.pipeline import BuildPipeline
+
+    pipeline = BuildPipeline(config)
+
+    try:
+        result = pipeline.rebuild(mode=req.mode)
+        reset_global_instances()
+
+        if result.get("status") == "error":
+            return JSONResponse(result, status_code=500)
+
+        return JSONResponse(result)
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)
+    finally:
+        pipeline.close()
+
+
+@app.post("/remove-table", summary="Remove a table and all its columns from the graph")
+async def remove_table(req: RemoveTableRequest) -> JSONResponse:
+    """Remove a table. Mirrors CLI `datalink remove-table`."""
+    config = get_config()
+
+    from datalink.builder.pipeline import BuildPipeline
+
+    pipeline = BuildPipeline(config)
+
+    try:
+        # Resolve table name → ID if needed
+        tid = req.table_id
+        if not req.table_id.startswith("table:"):
+            retrieval = get_retrieval()
+            tables = retrieval.list_datasets(mask_credential=False)
+            matching = [t for t in tables if t["name"] == req.table_id]
+            if matching:
+                tid = matching[0]["id"]
+            else:
+                return JSONResponse({"error": f"Table '{req.table_id}' not found"}, status_code=404)
+
+        result = pipeline.remove_table(tid, req.cleanup_orphans)
+        reset_global_instances()
+        return JSONResponse(result)
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)
+    finally:
+        pipeline.close()
+
+
+@app.get("/show", summary="Show the full graph contents as JSON")
+async def show() -> JSONResponse:
+    """Show all nodes and edges. Mirrors CLI `datalink show`."""
+    config = get_config()
+    storage = get_storage(config.graph_db_path)
+
+    try:
+        result = storage.show_graph()
+        masked = mask_result(result)
+        return JSONResponse(masked)
+    except Exception as e:
+        return JSONResponse({"error": str(e)}, status_code=500)
+
+
+# ── Read operations (online) ──────────────────────────────────────────
+
+
+@app.post("/explore", summary="Universal retrieval — answers data questions in one call")
+async def explore(req: ExploreRequest) -> PlainTextResponse:
+    """Explore the data graph. Mirrors CLI `datalink explore`.
+
+    Returns formatted text (same as MCP datalink_explore), not JSON.
+    """
+    config = get_config()
+    retrieval = get_retrieval(config.graph_db_path)
+
+    valid_focuses = {"join_paths", "schema", "data_profile", None}
+    if req.focus is not None and req.focus not in valid_focuses:
+        return PlainTextResponse(
+            f"Invalid focus: '{req.focus}'. Valid: join_paths, schema, data_profile, or omit for balanced.",
+            status_code=400,
+        )
+
+    result = retrieval.explore(req.query, max_nodes=req.max_nodes, focus=req.focus, mask_credential=True)
+    return PlainTextResponse(result)
+
+
+@app.post("/search", summary="Search nodes by name or semantic type")
+async def search(req: SearchRequest) -> JSONResponse:
+    """Search for nodes. Mirrors CLI `datalink search`."""
+    config = get_config()
+    retrieval = get_retrieval(config.graph_db_path)
+
+    nt = None
+    if req.node_type:
+        try:
+            nt = NodeType(req.node_type)
+        except ValueError:
+            return JSONResponse({"error": f"Invalid node_type: {req.node_type}"}, status_code=400)
+
+    results = retrieval.search_nodes(req.query, nt, req.limit, mask_credential=True)
+    return JSONResponse(results)
+
+
+@app.post("/get-node", summary="Get detailed information about a specific node")
+async def get_node(req: GetNodeRequest) -> JSONResponse:
+    """Get node details. Mirrors CLI `datalink get-node`."""
+    config = get_config()
+    retrieval = get_retrieval(config.graph_db_path)
+
+    result = retrieval.get_node(req.node_id, req.include_edges, mask_credential=True)
+    if result is None:
+        return JSONResponse({"error": f"Node '{req.node_id}' not found"}, status_code=404)
+    return JSONResponse(result)
+
+
+@app.post("/path", summary="Find paths between two nodes")
+async def path(req: PathRequest) -> JSONResponse:
+    """Find paths. Mirrors CLI `datalink path`."""
+    config = get_config()
+    retrieval = get_retrieval(config.graph_db_path)
+
+    types = None
+    if req.edge_types:
+        try:
+            type_names = [t.strip() for t in req.edge_types.split(",")]
+            types = [EdgeType(t) for t in type_names]
+        except ValueError as e:
+            return JSONResponse({"error": f"Invalid edge type: {e}"}, status_code=400)
+
+    paths = retrieval.find_paths(
+        req.source_id, req.target_id, req.max_depth, types, limit=req.limit, mask_credential=True
+    )
+    return JSONResponse(paths)
+
+
+@app.post("/extract-subgraph", summary="Extract a subgraph around specified nodes")
+async def extract_subgraph(req: ExtractSubgraphRequest) -> JSONResponse:
+    """Extract subgraph. Mirrors CLI `datalink extract-subgraph`."""
+    config = get_config()
+    retrieval = get_retrieval(config.graph_db_path)
+
+    ids = [id.strip() for id in req.node_ids.split(",")]
+    result = retrieval.extract_subgraph(ids, req.max_hops, mask_credential=True)
+    return JSONResponse(result)
+
+
+@app.get("/list-datasets", summary="List all tables/datasets with basic statistics")
+async def list_datasets() -> JSONResponse:
+    """List datasets. Mirrors CLI `datalink list-datasets`."""
+    config = get_config()
+    retrieval = get_retrieval(config.graph_db_path)
+
+    datasets = retrieval.list_datasets(mask_credential=True)
+    return JSONResponse(datasets)
+
+
+@app.post("/pending-edges", summary="List pending edges whose referenced nodes are not yet in the graph")
+async def pending_edges(req: PendingEdgesRequest) -> JSONResponse:
+    """List pending edges. Mirrors CLI `datalink pending-edges`."""
+    config = get_config()
+    retrieval = get_retrieval(config.graph_db_path)
+
+    et = None
+    if req.edge_type:
+        try:
+            et = EdgeType(req.edge_type)
+        except ValueError:
+            return JSONResponse({"error": f"Invalid edge type: {req.edge_type}"}, status_code=400)
+
+    nid = req.node_id if req.node_id else None
+    results = retrieval.get_pending_edges(nid, et, req.limit, mask_credential=True)
+    return JSONResponse(results)
+
+
+@app.get("/info", summary="Show overview information about the data graph")
+async def info() -> JSONResponse:
+    """Show graph overview. Mirrors CLI `datalink info`."""
+    config = get_config()
+    storage = get_storage(config.graph_db_path)
+
+    stats = storage.get_graph_stats()
+    return JSONResponse(stats)
+
+
+# ── Configuration ─────────────────────────────────────────────────────
+
+
+@app.get("/config", summary="Get current DataLink configuration")
+async def get_config_endpoint() -> JSONResponse:
+    """Get config. Mirrors CLI `datalink config` (read)."""
+    config = get_config()
+    # Mask API keys in the response
+    safe_config = config.model_dump()
+    if safe_config.get("llm", {}).get("api_key"):
+        safe_config["llm"]["api_key"] = "***"
+    if safe_config.get("embedding", {}).get("api_key"):
+        safe_config["embedding"]["api_key"] = "***"
+    return JSONResponse(safe_config)
+
+
+@app.patch("/config", summary="Update DataLink configuration")
+async def update_config(req: ConfigUpdateRequest) -> JSONResponse:
+    """Update config. Mirrors CLI `datalink config` (write)."""
+    config = get_config()
+
+    if req.llm_model:
+        config.llm.model = req.llm_model
+    if req.llm_api_key:
+        config.llm.api_key = req.llm_api_key
+    if req.llm_base_url:
+        config.llm.base_url = req.llm_base_url
+    if req.graph_db_path:
+        config.graph_db_path = req.graph_db_path
+    if req.mcp_tools:
+        config.mcp_tools = req.mcp_tools
+    if req.embedding_model:
+        config.embedding.model = req.embedding_model
+    if req.embedding_api_key:
+        config.embedding.api_key = req.embedding_api_key
+    if req.embedding_base_url:
+        config.embedding.base_url = req.embedding_base_url
+
+    config.save()
+
+    # Return updated config (with masked keys)
+    safe_config = config.model_dump()
+    if safe_config.get("llm", {}).get("api_key"):
+        safe_config["llm"]["api_key"] = "***"
+    if safe_config.get("embedding", {}).get("api_key"):
+        safe_config["embedding"]["api_key"] = "***"
+
+    return JSONResponse(safe_config)
+
+
+# ── Server startup ────────────────────────────────────────────────────
+
+
+def start_api_server(host: str = "0.0.0.0", port: int = 8081) -> None:
+    """Start the REST API server using uvicorn."""
+    import uvicorn
+
+    logger.info(f"Starting DataLink REST API server on {host}:{port}")
+    uvicorn.run(app, host=host, port=port)
diff --git a/services/datalink/src/datalink/builder/__init__.py b/services/datalink/src/datalink/builder/__init__.py
new file mode 100644
index 00000000..4a657cfd
--- /dev/null
+++ b/services/datalink/src/datalink/builder/__init__.py
@@ -0,0 +1 @@
+"""Build pipeline orchestration."""
diff --git a/services/datalink/src/datalink/builder/pipeline.py b/services/datalink/src/datalink/builder/pipeline.py
new file mode 100644
index 00000000..4afd7dff
--- /dev/null
+++ b/services/datalink/src/datalink/builder/pipeline.py
@@ -0,0 +1,1053 @@
+"""Build pipeline orchestration — init, rebuild, add_datasource, remove_table."""
+
+import logging
+from typing import Any
+
+from datalink.config import DataLinkConfig
+from datalink.connector.database import DatabaseConnector
+from datalink.connector.file import FileConnector
+from datalink.extractor.tabular import TabularExtractor
+from datalink.graph.storage import GraphStorage
+from datalink.inferrer.correlated import CorrelationInferrer
+from datalink.inferrer.distribution import DistributionInferrer
+from datalink.inferrer.joinable import JoinableInferrer
+from datalink.inferrer.synonym import SynonymInferrer
+from datalink.mapper.llm_mapper import LLMMapper
+from datalink.models.datasource import DatasourceConfig, DatasourceInfo, DatasourceType
+from datalink.models.edge import Edge, EdgeType, PendingEdge
+from datalink.models.node import ColumnNode, ConceptNode, EntityNode, Node, NodeType, TableNode
+from datalink.models.profile import ColumnProfile
+from datalink.profiler.tabular import TabularProfiler
+from datalink.utils.embedding import EmbeddingService, node_to_searchable_text
+
+logger = logging.getLogger(__name__)
+
+
+def _connect_datasource(config: DatasourceConfig) -> DatasourceInfo:
+    """Connect to a datasource and extract metadata + sample data."""
+    if config.type == DatasourceType.DATABASE:
+        connector = DatabaseConnector(config)
+    elif config.type in (DatasourceType.CSV, DatasourceType.PARQUET):
+        connector = FileConnector(config)
+    else:
+        raise ValueError(f"Unsupported datasource type: {config.type}")
+
+    connector.connect()
+    ds_info = connector.get_datasource_info()
+    connector.disconnect()
+    return ds_info
+
+
+def _detect_datasource_type_from_source(source: str) -> DatasourceType:
+    """Auto-detect datasource type from a source string.
+
+    Used by rebuild to infer the type of stored source paths.
+    """
+    if source.startswith(("postgresql://", "mysql://", "sqlite://", "mssql://", "oracle://")):
+        return DatasourceType.DATABASE
+    elif source.endswith(".parquet") or source.endswith(".pq"):
+        return DatasourceType.PARQUET
+    else:
+        # Default to CSV for file paths / directories
+        return DatasourceType.CSV
+
+
+def _build_datasource_config_from_table(table: TableNode) -> DatasourceConfig:
+    """Reconstruct a DatasourceConfig from a stored TableNode's metadata.
+
+    Uses source_type if stored, otherwise auto-detects from the source string.
+    """
+    source_type_str = table.source_type
+    if not source_type_str:
+        source_type_str = table.properties.get("source_type", "")
+    if not source_type_str:
+        source_type_str = _detect_datasource_type_from_source(table.source).value
+
+    ds_type = DatasourceType(source_type_str)
+
+    return DatasourceConfig(
+        type=ds_type,
+        path=table.source if ds_type in (DatasourceType.CSV, DatasourceType.PARQUET) else "",
+        connection_string=table.source if ds_type == DatasourceType.DATABASE else "",
+    )
+
+
+class BuildPipeline:
+    """Orchestrate the graph build pipeline.
+
+    Build modes:
+    - init_build: first-time build, user provides datasource configs
+    - rebuild: refresh graph from existing table metadata
+      - mode=full: complete rebuild (clear + re-pipeline + re-embed)
+      - mode=vec: re-build embedding vectors only (no data change)
+      - mode=profile: re-compute statistics only (no semantic layer)
+    - add_datasource: incremental add from a new datasource
+    """
+
+    def __init__(self, config: DataLinkConfig):
+        """Initialize the build pipeline with configuration.
+
+        Args:
+            config: DataLink configuration including LLM settings.
+        """
+        self.config = config
+        self.storage = GraphStorage(config.graph_db_path)
+
+        # Initialize embedding service (optional — disabled when model is empty)
+        self.embedding_service: EmbeddingService | None = None
+        if config.embedding.is_available(config.llm):
+            self.embedding_service = EmbeddingService(config.embedding, config.llm)
+
+    # ── Init build (first-time) ─────────────────────────────────────────
+
+    def init_build(self, datasource_configs: list[DatasourceConfig]) -> dict[str, Any]:
+        """First-time build: construct the entire graph from scratch.
+
+        Clears any existing data, then processes all provided datasources
+        through the full pipeline. Used when the graph is empty or the user
+        wants to start fresh with specific datasources.
+
+        Args:
+            datasource_configs: List of datasource configurations to build from.
+
+        Returns:
+            Dict with build results and statistics.
+        """
+        logger.info(f"Starting init build with {len(datasource_configs)} datasources")
+
+        # Clear any existing data
+        self.storage.clear_all()
+
+        # Delegate to the shared pipeline logic
+        all_ds_infos = self._connect_all(datasource_configs)
+        result = self._compute_pipeline(all_ds_infos)
+        self._store_pipeline_result(result)
+
+        # Store build metadata
+        self.storage.set_metadata("build_type", "init")
+        self.storage.set_metadata("build_time", str(len(datasource_configs)))
+
+        # Build embedding vectors (if configured)
+        self._build_embeddings_from_pipeline_result(result)
+
+        stats = self.storage.get_graph_stats()
+        logger.info(f"Init build complete: {stats}")
+
+        return {
+            "status": "success",
+            "datasources": len(datasource_configs),
+            "tables": len(result["tables"]),
+            "stats": stats,
+        }
+
+    # ── Rebuild (refresh from existing metadata) ────────────────────────
+
+    def rebuild(self, mode: str = "full") -> dict[str, Any]:
+        """Rebuild the graph from existing data sources.
+
+        Three rebuild modes:
+
+        - mode="full": Clear the graph and re-run the entire pipeline
+          (extract → profile → infer → map → embed). This is the original
+          rebuild behavior, now also includes embedding vector construction.
+
+        - mode="vec": Re-build embedding vectors only using the current
+          embedding model. No data changes, no LLM calls — just re-embeds
+          all existing nodes' searchable text. Useful when the user changes
+          the embedding model configuration.
+
+        - mode="profile": Re-compute column/table statistics from the
+          original data sources, without touching the semantic layer or
+          making any LLM calls. Useful when data has changed but the
+          semantic structure should remain stable.
+
+        **Safety guarantee (mode=full)**: old data is only cleared AFTER
+        the new pipeline succeeds. If the pipeline fails, the old graph
+        is preserved.
+
+        Args:
+            mode: Rebuild mode — "full", "vec", or "profile".
+
+        Returns:
+            Dict with rebuild results and statistics.
+        """
+        if mode == "vec":
+            return self._rebuild_vec()
+        elif mode == "profile":
+            return self._rebuild_profile()
+        elif mode == "full":
+            return self._rebuild_full()
+        else:
+            return {
+                "status": "error",
+                "error": f"Invalid rebuild mode: '{mode}'. Valid modes: full, vec, profile.",
+                "stats": self.storage.get_graph_stats(),
+            }
+
+    def _rebuild_full(self) -> dict[str, Any]:
+        """Full rebuild: clear and re-run the entire pipeline.
+
+        This is the original rebuild behavior, now also includes embedding
+        vector construction.
+        """
+        logger.info("Starting full rebuild from existing table metadata")
+
+        # Read all existing TableNodes BEFORE any modification
+        existing_tables = self.storage.get_nodes_by_type(NodeType.TABLE)
+        table_nodes = [t for t in existing_tables if isinstance(t, TableNode)]
+
+        if not table_nodes:
+            logger.warning("No tables found in the graph — nothing to rebuild")
+            return {
+                "status": "error",
+                "error": "No tables found in the graph. Use init-build or add-source first.",
+                "stats": self.storage.get_graph_stats(),
+            }
+
+        # Group tables by source (one DB connection may cover many tables)
+        source_groups: dict[str, list[TableNode]] = {}
+        for table in table_nodes:
+            source = table.source or table.properties.get("source", "")
+            if not source:
+                logger.warning(f"Table '{table.name}' (id={table.id}) has no source metadata — skipping")
+                continue
+            if source not in source_groups:
+                source_groups[source] = []
+            source_groups[source].append(table)
+
+        # Reconstruct DatasourceConfigs and connect
+        datasource_configs: list[DatasourceConfig] = []
+        all_ds_infos: list[DatasourceInfo] = []
+
+        for source, tables_in_group in source_groups.items():
+            # Use the first table's metadata to build the config
+            # (all tables in the same source share the same connection)
+            ds_config = _build_datasource_config_from_table(tables_in_group[0])
+            datasource_configs.append(ds_config)
+
+            ds_info = _connect_datasource(ds_config)
+
+            # Filter to only the tables that were in the original graph
+            existing_table_names = {t.name for t in tables_in_group}
+            ds_info.tables = [t for t in ds_info.tables if t.name in existing_table_names]
+            ds_info.sample_data = {
+                name: rows for name, rows in ds_info.sample_data.items() if name in existing_table_names
+            }
+
+            all_ds_infos.append(ds_info)
+
+        # Run the full pipeline WITHOUT storing to DB (compute-only).
+        # This collects all artifacts first. If it fails, old data is untouched.
+        try:
+            result = self._compute_pipeline(all_ds_infos)
+        except Exception as e:
+            logger.error(f"Rebuild pipeline failed: {e} — old graph preserved")
+            return {
+                "status": "error",
+                "error": f"Rebuild pipeline failed: {e}. Old graph data is preserved — retry rebuild to recover.",
+                "stats": self.storage.get_graph_stats(),
+            }
+
+        # Pipeline succeeded — clear old data and store new results
+        self.storage.clear_all()
+        self._store_pipeline_result(result)
+
+        # Store build metadata
+        self.storage.set_metadata("build_type", "rebuild")
+        self.storage.set_metadata("build_time", str(len(datasource_configs)))
+
+        # Build embedding vectors (if configured)
+        self._build_embeddings_from_pipeline_result(result)
+
+        stats = self.storage.get_graph_stats()
+        logger.info(f"Full rebuild complete: {stats}")
+
+        return {
+            "status": "success",
+            "datasources": len(datasource_configs),
+            "tables": len(result["tables"]),
+            "stats": stats,
+        }
+
+    # ── Add datasource (incremental) ────────────────────────────────────
+
+    def add_datasource(
+        self,
+        datasource_config: DatasourceConfig,
+        table_names: list[str] | None = None,
+    ) -> dict[str, Any]:
+        """Add tables from a datasource to the existing graph.
+
+        Args:
+            datasource_config: Configuration for the datasource.
+            table_names: Optional list of table names to add.
+                None → add all tables from this datasource.
+                ["orders", "products"] → add only these tables.
+
+        Returns:
+            Dict with add results and statistics.
+        """
+        ds_info = _connect_datasource(datasource_config)
+
+        # Filter to specific tables if requested
+        if table_names:
+            requested_set = set(table_names)
+            ds_info.tables = [t for t in ds_info.tables if t.name in requested_set]
+            ds_info.sample_data = {name: rows for name, rows in ds_info.sample_data.items() if name in requested_set}
+            if not ds_info.tables:
+                available = [t.name for t in _connect_datasource(datasource_config).tables]
+                raise ValueError(
+                    f"None of the requested tables {table_names} found in datasource. Available: {available}"
+                )
+
+        # Deduplicate: skip tables that already exist in the graph.
+        # Re-adding a table without first removing it would leave stale
+        # edges, profiles, and concept mappings behind (INSERT OR REPLACE
+        # only overwrites the node itself, not its associated data).
+        existing_tables = self.storage.get_nodes_by_type(NodeType.TABLE)
+        existing_table_ids = {n.id for n in existing_tables if isinstance(n, TableNode)}
+
+        # Build the expected IDs for each candidate table
+        # (ID format: "table:{source}:{table_name}")
+        new_tables_to_add = []
+        skipped_tables = []
+        for table_info in ds_info.tables:
+            source_name = (
+                table_info.source or datasource_config.path or datasource_config.connection_string or "unknown"
+            )
+            from datalink.extractor.tabular import generate_id
+
+            expected_id = generate_id("table", source_name, table_info.name)
+
+            if expected_id in existing_table_ids:
+                skipped_tables.append(table_info.name)
+                logger.info(f"Table '{table_info.name}' (id={expected_id}) already exists in graph — skipping")
+            else:
+                new_tables_to_add.append(table_info.name)
+
+        # Filter ds_info to only the new tables
+        if skipped_tables:
+            ds_info.tables = [t for t in ds_info.tables if t.name in set(new_tables_to_add)]
+            ds_info.sample_data = {
+                name: rows for name, rows in ds_info.sample_data.items() if name in set(new_tables_to_add)
+            }
+            logger.info(f"Skipped {len(skipped_tables)} existing tables: {skipped_tables}")
+
+        if not ds_info.tables:
+            logger.warning("All requested tables already exist in the graph — nothing to add")
+            stats = self.storage.get_graph_stats()
+            return {
+                "status": "skipped",
+                "added_tables": [],
+                "skipped_tables": skipped_tables,
+                "message": "All tables already exist in the graph. Use remove_table first if you want to re-add.",
+                "stats": stats,
+            }
+
+        added_table_names = [t.name for t in ds_info.tables]
+        logger.info(f"Adding {len(added_table_names)} new tables: {added_table_names}")
+
+        # Delegate to the shared pipeline logic
+        self._add_tables_pipeline(ds_info)
+
+        # Build embedding vectors for newly added nodes (if configured)
+        self._build_embeddings_incremental()
+
+        stats = self.storage.get_graph_stats()
+        logger.info(f"Datasource added: {stats}")
+
+        return {
+            "status": "success",
+            "added_tables": added_table_names,
+            "skipped_tables": skipped_tables,
+            "stats": stats,
+        }
+
+    def add_table(self, datasource_config: DatasourceConfig, table_name: str) -> dict[str, Any]:
+        """Add a single table to the existing graph.
+
+        Convenience wrapper around add_datasource for adding one table.
+
+        Args:
+            datasource_config: Configuration for the datasource.
+            table_name: Name of the single table to add.
+
+        Returns:
+            Dict with add results and statistics.
+        """
+        return self.add_datasource(datasource_config, [table_name])
+
+    # ── Remove table ────────────────────────────────────────────────────
+
+    def remove_table(self, table_id: str, cleanup_orphans: bool = True) -> dict[str, Any]:
+        """Remove a table and all its columns from the graph.
+
+        Args:
+            table_id: ID of the TableNode to remove.
+            cleanup_orphans: Whether to remove orphaned Concept/Entity nodes.
+
+        Returns:
+            Dict with removal results.
+        """
+        logger.info(f"Removing table '{table_id}'")
+
+        removed_column_ids = self.storage.remove_table(table_id)
+
+        # Clean up pending edges referencing removed nodes
+        removed_ids = set(removed_column_ids + [table_id])
+        self.storage.cleanup_pending_edges_for_removed_nodes(removed_ids)
+
+        # Optionally clean up orphaned semantic nodes
+        orphan_count = 0
+        if cleanup_orphans:
+            orphan_count = self.storage.cleanup_orphaned_semantic_nodes()
+
+        stats = self.storage.get_graph_stats()
+        logger.info(f"Table removed: {stats}")
+
+        return {
+            "status": "success",
+            "removed_columns": len(removed_column_ids),
+            "removed_orphans": orphan_count,
+            "stats": stats,
+        }
+
+    # ── Close ───────────────────────────────────────────────────────────
+
+    def close(self) -> None:
+        """Close the storage connection."""
+        self.storage.close()
+
+    # ── Shared pipeline logic ───────────────────────────────────────────
+
+    def _connect_all(self, datasource_configs: list[DatasourceConfig]) -> list[DatasourceInfo]:
+        """Connect to all datasources and extract metadata."""
+        all_ds_infos: list[DatasourceInfo] = []
+        for ds_config in datasource_configs:
+            ds_info = _connect_datasource(ds_config)
+            all_ds_infos.append(ds_info)
+        return all_ds_infos
+
+    def _compute_pipeline(self, all_ds_infos: list[DatasourceInfo]) -> dict[str, Any]:
+        """Run the full pipeline on all datasource infos (compute-only, no DB writes).
+
+        Steps 2-5: Extract → Profile → Infer → Map.
+        Returns all artifacts so the caller can decide when to store them.
+
+        Returns:
+            Dict with all pipeline artifacts:
+            - tables, columns, profiles (structural)
+            - concepts, entities, semantic_edges (semantic)
+            - structural_edges, inferred_edges (structural + inferred)
+        """
+        # Step 2: Extract structural nodes
+        extractor = TabularExtractor()
+        all_tables: list[TableNode] = []
+        all_columns: list[ColumnNode] = []
+        all_structural_edges: list[Edge] = []
+
+        for ds_info in all_ds_infos:
+            tables, columns, edges = extractor.extract(ds_info)
+            all_tables.extend(tables)
+            all_columns.extend(columns)
+            all_structural_edges.extend(edges)
+
+        logger.info(f"Extracted {len(all_tables)} tables, {len(all_columns)} columns")
+
+        # Step 3: Profile columns
+        profiler = TabularProfiler()
+        all_profiles: list[ColumnProfile] = []
+
+        for ds_info in all_ds_infos:
+            profiles = profiler.profile_datasource(ds_info)
+            all_profiles.extend(profiles)
+
+        self._update_column_properties(all_columns, all_profiles)
+        self._update_table_properties(all_tables)
+
+        logger.info(f"Profiled {len(all_profiles)} columns")
+
+        # Step 4: Infer implicit relationships
+        joinable_edges = JoinableInferrer(overlap_threshold=self.config.joinable_overlap_threshold).infer(
+            all_columns, all_profiles
+        )
+
+        synonym_edges = SynonymInferrer().infer(all_columns, all_profiles)
+
+        distribution_edges = DistributionInferrer().infer(all_columns, all_profiles)
+
+        correlated_edges = CorrelationInferrer(correlation_threshold=self.config.correlation_threshold).infer(
+            all_columns, all_profiles, joinable_edges, all_ds_infos
+        )
+
+        all_inferred_edges = joinable_edges + synonym_edges + distribution_edges + correlated_edges
+
+        logger.info(
+            f"Inferred {len(all_inferred_edges)} edges "
+            f"(joinable={len(joinable_edges)}, synonym={len(synonym_edges)}, "
+            f"distribution={len(distribution_edges)}, "
+            f"correlated={len(correlated_edges)})"
+        )
+
+        # Step 5: Map to semantic layer
+        llm_mapper = LLMMapper(self.config)
+        all_concepts, all_entities, all_semantic_edges = llm_mapper.map_columns(all_columns, all_profiles)
+
+        logger.info(
+            f"Semantic mapping: {len(all_concepts)} concepts, "
+            f"{len(all_entities)} entities, "
+            f"{len(all_semantic_edges)} edges"
+        )
+
+        # Step 5b: Generate table comments for tables lacking SQL metadata
+        table_comments = llm_mapper.generate_table_comments(
+            all_tables,
+            all_columns,
+            all_profiles,
+            all_concepts,
+            all_entities,
+            all_semantic_edges,
+        )
+        if table_comments:
+            logger.info(f"Generated LLM comments for {len(table_comments)} tables")
+            # Re-run _update_table_properties to persist the new comments
+            self._update_table_properties(all_tables)
+
+        # Return ALL artifacts — the caller handles DB storage
+        return {
+            "tables": all_tables,
+            "columns": all_columns,
+            "profiles": all_profiles,
+            "concepts": all_concepts,
+            "entities": all_entities,
+            "structural_edges": all_structural_edges,
+            "inferred_edges": all_inferred_edges,
+            "semantic_edges": all_semantic_edges,
+        }
+
+    def _store_pipeline_result(self, result: dict[str, Any]) -> None:
+        """Store pipeline artifacts into the graph database.
+
+        Separated from _compute_pipeline so callers can control
+        when data gets written (e.g. rebuild clears old data first).
+        """
+        all_nodes = result["tables"] + result["columns"] + result["concepts"] + result["entities"]
+        self.storage.add_nodes_batch(all_nodes)
+
+        all_edges = result["structural_edges"] + result["inferred_edges"] + result["semantic_edges"]
+        self._store_edges(all_edges, {n.id for n in all_nodes})
+
+        self.storage.add_profiles_batch(result["profiles"])
+
+    def _add_tables_pipeline(self, ds_info: DatasourceInfo) -> dict[str, Any]:
+        """Run the incremental pipeline for adding new tables.
+
+        Same steps as full pipeline, but:
+        - Infers on combined (existing + new) columns/profiles
+        - Only keeps edges involving new columns
+        - No CorrelationInferrer (missing old sample data)
+        - Semantic mapping with merge_with_existing
+        """
+        # Step 2: Extract
+        extractor = TabularExtractor()
+        new_tables, new_columns, structural_edges = extractor.extract(ds_info)
+
+        # Step 3: Profile
+        profiler = TabularProfiler()
+        new_profiles = profiler.profile_datasource(ds_info)
+
+        self._update_column_properties(new_columns, new_profiles)
+        self._update_table_properties(new_tables)
+
+        # Step 4: Infer on combined set
+        existing_column_nodes = [
+            n for n in self.storage.get_nodes_by_type(NodeType.COLUMN) if isinstance(n, ColumnNode)
+        ]
+        existing_profiles = [
+            p for col in existing_column_nodes if (p := self.storage.get_profile_for_column(col.id)) is not None
+        ]
+
+        all_columns_combined = existing_column_nodes + new_columns
+        all_profiles_combined = existing_profiles + new_profiles
+
+        new_joinable = JoinableInferrer(overlap_threshold=self.config.joinable_overlap_threshold).infer(
+            all_columns_combined, all_profiles_combined
+        )
+
+        new_synonym = SynonymInferrer().infer(all_columns_combined, all_profiles_combined)
+
+        new_distribution = DistributionInferrer().infer(all_columns_combined, all_profiles_combined)
+
+        # Only keep edges involving at least one new column
+        new_col_ids = {c.id for c in new_columns}
+
+        def involves_new(edge: Edge) -> bool:
+            return edge.source_id in new_col_ids or edge.target_id in new_col_ids
+
+        new_joinable = [e for e in new_joinable if involves_new(e)]
+        new_synonym = [e for e in new_synonym if involves_new(e)]
+        new_distribution = [e for e in new_distribution if involves_new(e)]
+
+        # Step 5: Semantic mapping + merge
+        llm_mapper = LLMMapper(self.config)
+        new_concepts, new_entities, new_semantic = llm_mapper.map_columns(new_columns, new_profiles)
+
+        # Step 5b: Generate table comments for new tables lacking SQL metadata
+        table_comments = llm_mapper.generate_table_comments(
+            new_tables,
+            new_columns,
+            new_profiles,
+            new_concepts,
+            new_entities,
+            new_semantic,
+        )
+        if table_comments:
+            logger.info(f"Generated LLM comments for {len(table_comments)} new tables")
+            self._update_table_properties(new_tables)
+
+        if new_concepts or new_entities:
+            existing_concepts = [
+                n for n in self.storage.get_nodes_by_type(NodeType.CONCEPT) if isinstance(n, ConceptNode)
+            ]
+            existing_entities = [
+                n for n in self.storage.get_nodes_by_type(NodeType.ENTITY) if isinstance(n, EntityNode)
+            ]
+
+            # Only merge if there are actual existing nodes to merge with.
+            # If the graph has no concepts/entities yet (first build on empty
+            # graph), map_columns already did self-merge internally, so
+            # calling merge_with_existing([], []) would trigger a redundant
+            # self-merge — wasting an LLM call for no benefit.
+            if existing_concepts or existing_entities:
+                new_concepts, new_entities, new_semantic = llm_mapper.merge_with_existing(
+                    new_concepts,
+                    new_entities,
+                    new_semantic,
+                    existing_concepts,
+                    existing_entities,
+                )
+
+        # Step 6: Store
+        # new_concepts/new_entities now include both genuinely new nodes
+        # and enriched existing nodes (from merge_with_existing).
+        # INSERT OR REPLACE ensures enriched existing nodes are updated.
+        all_nodes = new_tables + new_columns + new_concepts + new_entities
+        self.storage.add_nodes_batch(all_nodes)
+
+        new_edges = structural_edges + new_joinable + new_synonym + new_distribution + new_semantic
+
+        # Collect all node IDs that now exist in the graph
+        available_node_ids = set(self.storage.get_all_node_ids())
+        available_node_ids.update({n.id for n in all_nodes})
+
+        self._store_edges(new_edges, available_node_ids)
+
+        # Resolve previously pending edges that now have both endpoints
+        resolved = self.storage.resolve_pending_edges(available_node_ids)
+        logger.info(f"Resolved {resolved} previously pending edges")
+
+        self.storage.add_profiles_batch(new_profiles)
+
+        return {
+            "tables": new_tables,
+            "columns": new_columns,
+        }
+
+    # ── Rebuild modes: vec and profile ───────────────────────────────────
+
+    def _rebuild_vec(self) -> dict[str, Any]:
+        """Re-build embedding vectors only using the current embedding model.
+
+        No data changes, no LLM calls — just re-embeds all existing nodes'
+        searchable text. Useful when the user changes the embedding model
+        configuration.
+        """
+        logger.info("Starting vec rebuild — re-building embedding vectors")
+
+        # Check embedding availability
+        if not self.embedding_service or not self.embedding_service.is_available():
+            return {
+                "status": "error",
+                "error": "Embedding model not configured or API key unavailable. "
+                "Set embedding.model and embedding.api_key in datalink_config.json first.",
+                "stats": self.storage.get_graph_stats(),
+            }
+
+        # Collect all nodes + profiles
+        nodes_and_profiles = self._collect_nodes_with_profiles()
+        if not nodes_and_profiles:
+            logger.warning("No nodes found — nothing to embed")
+            return {
+                "status": "error",
+                "error": "No nodes in the graph. Use add-table first.",
+                "stats": self.storage.get_graph_stats(),
+            }
+
+        # Generate searchable text for each node
+        texts = []
+        for node, profile in nodes_and_profiles:
+            if isinstance(node, TableNode):
+                col_names = self._get_column_names_for_table(node)
+                texts.append(node_to_searchable_text(node, profile, column_names=col_names))
+            else:
+                texts.append(node_to_searchable_text(node, profile))
+
+        # Compute embeddings
+        model_name = self.embedding_service.get_model_name()
+        logger.info(f"Computing {len(texts)} embeddings with model {model_name}")
+
+        embeddings = self.embedding_service.compute_embeddings(texts)
+        if not embeddings or len(embeddings) != len(texts):
+            logger.error("Embedding computation failed or returned incomplete results")
+            return {
+                "status": "error",
+                "error": "Embedding computation failed. Check API connectivity.",
+                "stats": self.storage.get_graph_stats(),
+            }
+
+        # Clear old embeddings and store new ones
+        self.storage.clear_embeddings()
+        data = [(node.id, model_name, embeddings[i], texts[i]) for i, (node, profile) in enumerate(nodes_and_profiles)]
+        self.storage.add_embeddings_batch(data)
+
+        # Record the model in metadata
+        self.storage.set_metadata("embedding_model", model_name)
+
+        stats = self.storage.get_graph_stats()
+        logger.info(f"Vec rebuild complete: {len(data)} vectors built with model {model_name}")
+
+        return {
+            "status": "success",
+            "vectors_built": len(data),
+            "model": model_name,
+            "stats": stats,
+        }
+
+    def _rebuild_profile(self) -> dict[str, Any]:
+        """Re-compute column/table statistics and profile-dependent inferred edges.
+
+        Reconnects to each data source and re-runs the Profiler step, then
+        re-infers all edges that depend on profile statistics (JOINABLE,
+        DISTRIBUTION_SIMILAR, SEMANTIC_SYNONYM, CORRELATED). No LLM calls,
+        no semantic mapping — the semantic layer (REPRESENTS, HAS_CONCEPT)
+        and structural edges (CONTAINS, FOREIGN_KEY) are left untouched.
+
+        Useful when data has changed but the semantic structure should remain stable.
+        """
+        logger.info("Starting profile rebuild — re-computing statistics + inferred edges")
+
+        # Read all existing TableNodes
+        existing_tables = self.storage.get_nodes_by_type(NodeType.TABLE)
+        table_nodes = [t for t in existing_tables if isinstance(t, TableNode)]
+
+        if not table_nodes:
+            logger.warning("No tables found in the graph — nothing to rebuild")
+            return {
+                "status": "error",
+                "error": "No tables found in the graph. Use add-table first.",
+                "stats": self.storage.get_graph_stats(),
+            }
+
+        # Group tables by source
+        source_groups: dict[str, list[TableNode]] = {}
+        for table in table_nodes:
+            source = table.source or table.properties.get("source", "")
+            if not source:
+                logger.warning(f"Table '{table.name}' has no source metadata — skipping")
+                continue
+            if source not in source_groups:
+                source_groups[source] = []
+            source_groups[source].append(table)
+
+        # Re-connect and collect DatasourceInfos for all sources
+        profiler = TabularProfiler()
+        updated_profiles: list[ColumnProfile] = []
+        all_ds_infos: list[DatasourceInfo] = []
+
+        for source, tables_in_group in source_groups.items():
+            ds_config = _build_datasource_config_from_table(tables_in_group[0])
+            try:
+                ds_info = _connect_datasource(ds_config)
+                existing_table_names = {t.name for t in tables_in_group}
+                ds_info.tables = [t for t in ds_info.tables if t.name in existing_table_names]
+                ds_info.sample_data = {
+                    name: rows for name, rows in ds_info.sample_data.items() if name in existing_table_names
+                }
+
+                profiles = profiler.profile_datasource(ds_info)
+                updated_profiles.extend(profiles)
+                all_ds_infos.append(ds_info)
+                logger.info(f"Updated {len(profiles)} profiles for source '{source}'")
+            except Exception as e:
+                logger.warning(f"Failed to reconnect to source '{source}': {e} — skipping")
+
+        if not updated_profiles:
+            stats = self.storage.get_graph_stats()
+            return {
+                "status": "error",
+                "error": "No profiles could be updated. Check data source connectivity.",
+                "stats": stats,
+            }
+
+        # Update profiles in DB (INSERT OR REPLACE)
+        self.storage.add_profiles_batch(updated_profiles)
+
+        # Update column node properties (semantic_type, dtype, etc.)
+        column_nodes = [n for n in self.storage.get_nodes_by_type(NodeType.COLUMN) if isinstance(n, ColumnNode)]
+        self._update_column_properties(column_nodes, updated_profiles)
+
+        # Update table properties (row_count, column_ids)
+        self._update_table_properties(table_nodes)
+        # Re-store updated nodes
+        all_nodes_to_update = column_nodes + table_nodes
+        self.storage.add_nodes_batch(all_nodes_to_update)
+
+        # ── Re-infer profile-dependent edges ────────────────────────────
+        # These edges depend on profile statistics (top_values, distribution,
+        # dtype, cardinality, etc.) and must be rebuilt when profiles change.
+
+        # Remove old inferred edges (JOINABLE, DISTRIBUTION_SIMILAR,
+        # SEMANTIC_SYNONYM, CORRELATED). Structural edges (CONTAINS,
+        # FOREIGN_KEY) and semantic edges (REPRESENTS, HAS_CONCEPT) are kept.
+        profile_dependent_types = [
+            EdgeType.JOINABLE,
+            EdgeType.DISTRIBUTION_SIMILAR,
+            EdgeType.SEMANTIC_SYNONYM,
+            EdgeType.CORRELATED,
+        ]
+        old_count = self.storage.remove_edges_by_types(profile_dependent_types)
+        logger.info(f"Removed {old_count} old inferred edges for re-inference")
+
+        # Re-compute inferred edges with updated profiles
+        new_joinable = JoinableInferrer(overlap_threshold=self.config.joinable_overlap_threshold).infer(
+            column_nodes, updated_profiles
+        )
+
+        new_synonym = SynonymInferrer().infer(column_nodes, updated_profiles)
+
+        new_distribution = DistributionInferrer().infer(column_nodes, updated_profiles)
+
+        new_correlated = CorrelationInferrer(correlation_threshold=self.config.correlation_threshold).infer(
+            column_nodes, updated_profiles, new_joinable, all_ds_infos
+        )
+
+        new_inferred_edges = new_joinable + new_synonym + new_distribution + new_correlated
+        logger.info(
+            f"Re-inferred {len(new_inferred_edges)} edges "
+            f"(joinable={len(new_joinable)}, synonym={len(new_synonym)}, "
+            f"distribution={len(new_distribution)}, correlated={len(new_correlated)})"
+        )
+
+        # Store new inferred edges
+        all_node_ids = set(self.storage.get_all_node_ids())
+        self._store_edges(new_inferred_edges, all_node_ids)
+
+        stats = self.storage.get_graph_stats()
+        logger.info(f"Profile rebuild complete: {len(updated_profiles)} profiles, {len(new_inferred_edges)} edges")
+
+        return {
+            "status": "success",
+            "profiles_updated": len(updated_profiles),
+            "edges_removed": old_count,
+            "edges_rebuilt": len(new_inferred_edges),
+            "stats": stats,
+        }
+
+    # ── Embedding construction helpers ───────────────────────────────────
+
+    def _build_embeddings_from_pipeline_result(self, result: dict[str, Any]) -> None:
+        """Build embedding vectors for all nodes in a pipeline result.
+
+        Called after _store_pipeline_result in init_build and rebuild_full.
+        Only active when embedding service is configured and available.
+        """
+        if not self.embedding_service or not self.embedding_service.is_available():
+            logger.debug("Embedding service not available — skipping vector construction")
+            return
+
+        # Collect nodes + profiles from pipeline result
+        nodes_and_profiles = self._collect_nodes_with_profiles_from_result(result)
+        if not nodes_and_profiles:
+            return
+
+        # Generate searchable text
+        texts = []
+        for node, profile in nodes_and_profiles:
+            if isinstance(node, TableNode):
+                col_names = [c.name for c in result["columns"] if c.table_id == node.id][:10]
+                texts.append(node_to_searchable_text(node, profile, column_names=col_names))
+            else:
+                texts.append(node_to_searchable_text(node, profile))
+
+        # Compute embeddings
+        model_name = self.embedding_service.get_model_name()
+        embeddings = self.embedding_service.compute_embeddings(texts)
+
+        if not embeddings or len(embeddings) != len(texts):
+            logger.warning("Embedding computation failed — vectors not built")
+            return
+
+        # Store embeddings
+        data = [(node.id, model_name, embeddings[i], texts[i]) for i, (node, profile) in enumerate(nodes_and_profiles)]
+        self.storage.add_embeddings_batch(data)
+        self.storage.set_metadata("embedding_model", model_name)
+        logger.info(f"Built {len(data)} embedding vectors with model {model_name}")
+
+    def _build_embeddings_incremental(self) -> None:
+        """Build embedding vectors for nodes that don't yet have embeddings.
+
+        Called after add_datasource to embed only the new nodes.
+        Only active when embedding service is configured and available.
+        """
+        if not self.embedding_service or not self.embedding_service.is_available():
+            logger.debug("Embedding service not available — skipping incremental vector construction")
+            return
+
+        # Check if the stored embedding model matches current config
+        stored_model = self.storage.get_embedding_model()
+        current_model = self.embedding_service.get_model_name()
+
+        if stored_model and stored_model != current_model:
+            logger.warning(
+                f"Stored embedding model '{stored_model}' differs from current '{current_model}'. "
+                f"Run rebuild --mode vec to update all vectors."
+            )
+            return
+
+        # Find nodes without embeddings
+        all_embeddings = self.storage.get_all_embeddings()
+        embedded_ids = {e["node_id"] for e in all_embeddings}
+
+        nodes_and_profiles = self._collect_nodes_with_profiles()
+        unembedded = [(n, p) for n, p in nodes_and_profiles if n.id not in embedded_ids]
+
+        if not unembedded:
+            return
+
+        # Generate searchable text + compute embeddings
+        texts = []
+        for node, profile in unembedded:
+            if isinstance(node, TableNode):
+                col_names = self._get_column_names_for_table(node)
+                texts.append(node_to_searchable_text(node, profile, column_names=col_names))
+            else:
+                texts.append(node_to_searchable_text(node, profile))
+
+        embeddings = self.embedding_service.compute_embeddings(texts)
+
+        if not embeddings or len(embeddings) != len(texts):
+            logger.warning("Incremental embedding computation failed")
+            return
+
+        # Store embeddings
+        data = [(node.id, current_model, embeddings[i], texts[i]) for i, (node, profile) in enumerate(unembedded)]
+        self.storage.add_embeddings_batch(data)
+
+        # Set metadata if this is the first batch of embeddings
+        if not stored_model:
+            self.storage.set_metadata("embedding_model", current_model)
+
+        logger.info(f"Built {len(data)} incremental embedding vectors")
+
+    def _collect_nodes_with_profiles(self) -> list[tuple[Node, ColumnProfile | None]]:
+        """Collect all nodes from DB with their associated profiles (for Column nodes)."""
+        result: list[tuple[Node, ColumnProfile | None]] = []
+
+        for node_type in (NodeType.TABLE, NodeType.COLUMN, NodeType.CONCEPT, NodeType.ENTITY):
+            nodes = self.storage.get_nodes_by_type(node_type)
+            for node in nodes:
+                profile = None
+                if isinstance(node, ColumnNode):
+                    profile = self.storage.get_profile_for_column(node.id)
+                result.append((node, profile))
+
+        return result
+
+    def _collect_nodes_with_profiles_from_result(
+        self, result: dict[str, Any]
+    ) -> list[tuple[Node, ColumnProfile | None]]:
+        """Collect nodes with profiles from a pipeline result dict."""
+        profile_map = {p.column_id: p for p in result["profiles"]}
+
+        items: list[tuple[Node, ColumnProfile | None]] = []
+
+        for table in result["tables"]:
+            items.append((table, None))
+        for col in result["columns"]:
+            items.append((col, profile_map.get(col.id)))
+        for concept in result["concepts"]:
+            items.append((concept, None))
+        for entity in result["entities"]:
+            items.append((entity, None))
+
+        return items
+
+    def _get_column_names_for_table(self, table: TableNode) -> list[str]:
+        """Get column names for a table from stored data."""
+        col_names = []
+        for cid in table.column_ids[:10]:
+            col = self.storage.get_node(cid)
+            if col:
+                col_names.append(col.name)
+        return col_names
+
+    # ── Helpers ──────────────────────────────────────────────────────────
+
+    def _update_column_properties(self, columns: list[ColumnNode], profiles: list[ColumnProfile]) -> None:
+        """Update column nodes with profile references and semantic types."""
+        profile_map = {p.column_id: p for p in profiles}
+        for col in columns:
+            profile = profile_map.get(col.id)
+            if profile:
+                col.semantic_type = profile.semantic_type
+                col.profile_id = profile.id
+                col.properties["dtype"] = col.dtype
+                col.properties["semantic_type"] = col.semantic_type
+                col.properties["table_id"] = col.table_id
+                col.properties["profile_id"] = col.profile_id
+                col.properties["comment"] = col.comment
+
+    def _update_table_properties(self, tables: list[TableNode]) -> None:
+        """Update table nodes with properties for DB storage."""
+        for table in tables:
+            table.properties["source"] = table.source
+            table.properties["source_type"] = table.source_type
+            table.properties["row_count"] = table.row_count
+            table.properties["column_ids"] = table.column_ids
+
+    def _store_edges(self, edges: list[Edge], available_node_ids: set[str]) -> tuple[int, int]:
+        """Store edges, splitting into resolved and pending.
+
+        Returns (resolved_count, pending_count).
+        """
+        resolved: list[Edge] = []
+        pending: list[PendingEdge] = []
+
+        for edge in edges:
+            if edge.confidence < self.config.confidence_threshold:
+                continue
+            src_exists = edge.source_id in available_node_ids
+            tgt_exists = edge.target_id in available_node_ids
+            if src_exists and tgt_exists:
+                resolved.append(edge)
+            else:
+                missing = []
+                if not src_exists:
+                    missing.append("source")
+                if not tgt_exists:
+                    missing.append("target")
+                pending.append(
+                    PendingEdge(
+                        id=edge.id,
+                        source_id=edge.source_id,
+                        target_id=edge.target_id,
+                        type=edge.type,
+                        confidence=edge.confidence,
+                        properties=edge.properties,
+                        missing_endpoints=missing,
+                    )
+                )
+
+        self.storage.add_edges_batch(resolved)
+        self.storage.add_pending_edges_batch(pending)
+        return len(resolved), len(pending)
diff --git a/services/datalink/src/datalink/cli/__init__.py b/services/datalink/src/datalink/cli/__init__.py
new file mode 100644
index 00000000..67e2476d
--- /dev/null
+++ b/services/datalink/src/datalink/cli/__init__.py
@@ -0,0 +1 @@
+"""CLI entry point."""
diff --git a/services/datalink/src/datalink/cli/main.py b/services/datalink/src/datalink/cli/main.py
new file mode 100644
index 00000000..7acaba06
--- /dev/null
+++ b/services/datalink/src/datalink/cli/main.py
@@ -0,0 +1,724 @@
+"""DataLink CLI — command-line interface for building and querying the data graph."""
+
+import logging
+import os
+import re
+import sys
+
+import typer
+from rich.console import Console
+from rich.panel import Panel
+from rich.table import Table
+
+from datalink.config import DataLinkConfig
+from datalink.models.datasource import DatasourceConfig, DatasourceType
+from datalink.models.node import NodeType
+
+app = typer.Typer(
+    name="datalink",
+    help="DataLink — Build and query a data-focused knowledge graph.",
+    add_completion=False,
+)
+
+# Force UTF-8 output on Windows to avoid UnicodeEncodeError with characters
+# like → ← ↔ ⚠️ that the default GBK codec cannot encode.
+if sys.platform == "win32":
+    os.environ.setdefault("PYTHONIOENCODING", "utf-8")
+    # Reconfigure stdout/stderr to UTF-8 if they are still using the default encoding
+    for stream in (sys.stdout, sys.stderr):
+        if hasattr(stream, "reconfigure"):
+            try:
+                stream.reconfigure(encoding="utf-8")
+            except Exception:
+                pass
+
+_console_base = Console()
+# Use a wider fallback width when output is piped (Rich defaults to 80
+# which over-compresses tables).  Real terminals use their actual width.
+console = Console(width=160 if not _console_base.is_terminal else None)
+
+# Configure logging — INFO level to stderr for CLI progress visibility.
+# MCP server overrides this to WARNING at startup (see mcp/server.py).
+logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s", stream=sys.stderr)
+
+
+def _detect_datasource_type(source: str) -> DatasourceType:
+    """Auto-detect datasource type from the source string."""
+    # Match both bare scheme (mysql://) and dialect+driver (mysql+pymysql://)
+    if re.match(r"^(?:postgresql|mysql|sqlite|mssql|oracle|clickhouse|mariadb)(?:\+\w+)?://", source, re.IGNORECASE):
+        return DatasourceType.DATABASE
+    elif source.endswith(".csv") or source.endswith("/") or source.endswith("\\"):
+        return DatasourceType.CSV
+    elif source.endswith(".parquet") or source.endswith(".pq"):
+        return DatasourceType.PARQUET
+    else:
+        # Check if it's a directory with CSV/Parquet files
+        from pathlib import Path
+
+        path = Path(source)
+        if path.is_dir():
+            if list(path.glob("*.csv")):
+                return DatasourceType.CSV
+            if list(path.glob("*.parquet")):
+                return DatasourceType.PARQUET
+        return DatasourceType.CSV  # Default fallback
+
+
+def _make_datasource_config(source: str) -> DatasourceConfig:
+    """Build a DatasourceConfig from a source string.
+
+    For file-type sources (CSV/Parquet), relative paths are resolved to
+    absolute paths so that node IDs and TableNode.source are unique even
+    when the same filename exists in different directories.
+    """
+    ds_type = _detect_datasource_type(source)
+
+    # Resolve file paths to absolute
+    resolved_source = source
+    if ds_type in (DatasourceType.CSV, DatasourceType.PARQUET):
+        from pathlib import Path
+
+        resolved_source = str(Path(source).resolve())
+
+    return DatasourceConfig(
+        type=ds_type,
+        path=resolved_source if ds_type in (DatasourceType.CSV, DatasourceType.PARQUET) else "",
+        connection_string=source if ds_type == DatasourceType.DATABASE else "",
+    )
+
+
+# ── Write commands ─────────────────────────────────────────────────────
+
+
+@app.command()
+def add_table(
+    source: str = typer.Option(..., "--source", "-s", help="Data source path or connection string"),
+    table: str = typer.Option("", "--table", "-t", help="Table name to add (empty = add all tables from source)"),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Add tables from a data source to the graph.
+
+    If --table is specified, adds only that table.
+    If --table is omitted, adds all tables from the data source.
+    On an empty graph this serves as the initial build.
+    """
+    config = DataLinkConfig.load()
+    config.graph_db_path = db_path
+
+    ds_config = _make_datasource_config(source)
+
+    from datalink.builder.pipeline import BuildPipeline
+
+    pipeline = BuildPipeline(config)
+
+    try:
+        if table:
+            console.print(f"[bold blue]Adding table '{table}'...[/bold blue]")
+            result = pipeline.add_table(ds_config, table)
+            if result["status"] == "skipped":
+                console.print(f"[bold yellow]⚠ Table '{table}' already exists — skipped[/bold yellow]")
+                console.print(f"  Use 'datalink remove-table --table {table}' first to re-add it.")
+            else:
+                console.print(f"[bold green]✓ Added table '{table}'[/bold green]")
+        else:
+            console.print("[bold blue]Adding all tables from source...[/bold blue]")
+            result = pipeline.add_datasource(ds_config)
+            added = result.get("added_tables", [])
+            skipped = result.get("skipped_tables", [])
+            if added:
+                console.print(f"[bold green]✓ Added {len(added)} tables: {added}[/bold green]")
+            if skipped:
+                console.print(f"[bold yellow]⚠ Skipped {len(skipped)} existing tables: {skipped}[/bold yellow]")
+                console.print("  Use 'datalink remove-table' first to re-add them.")
+            if not added and skipped:
+                console.print("[bold yellow]All tables already exist — nothing to add.[/bold yellow]")
+
+        stats = result["stats"]
+        console.print(f"  Tables: {stats['node_type_counts']['table']}")
+        console.print(f"  Columns: {stats['node_type_counts']['column']}")
+        console.print(f"  Total Edges: {stats['total_edges']}")
+    except Exception as e:
+        console.print(f"[bold red]Add table failed:[/bold red] {e}")
+        raise typer.Exit(code=1)
+    finally:
+        pipeline.close()
+
+
+@app.command()
+def rebuild(
+    mode: str = typer.Option(
+        "full",
+        "--mode",
+        "-m",
+        help="Rebuild mode: full, vec, profile",
+    ),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Rebuild the graph from existing data sources.
+
+    Three rebuild modes:
+
+    - full: Clear the graph and re-run the entire pipeline (original behavior),
+      now also includes embedding vector construction.
+
+    - vec: Re-build embedding vectors only using the current embedding model.
+      No data changes, no LLM calls. Use this when you change the embedding
+      model configuration and need to update all vectors.
+
+    - profile: Re-compute column/table statistics and profile-dependent inferred
+      edges (joinable, distribution_similar, synonym, correlated). No semantic
+      layer changes, no LLM calls. Use this when data has changed but the
+      semantic structure should remain stable.
+
+    **Safety guarantee (mode=full)**: old data is only cleared AFTER the new
+    pipeline succeeds. If the pipeline fails, the old graph data is preserved.
+    """
+    valid_modes = {"full", "vec", "profile"}
+    if mode not in valid_modes:
+        console.print(f"[bold red]Invalid mode: '{mode}'. Valid: full, vec, profile[/bold red]")
+        raise typer.Exit(code=1)
+
+    console.print(f"[bold blue]Rebuilding DataLink (mode={mode})...[/bold blue]")
+
+    config = DataLinkConfig.load()
+    config.graph_db_path = db_path
+
+    from datalink.builder.pipeline import BuildPipeline
+
+    pipeline = BuildPipeline(config)
+
+    try:
+        result = pipeline.rebuild(mode=mode)
+
+        if result["status"] == "error":
+            console.print(f"[bold red]Rebuild failed:[/bold red] {result['error']}")
+            raise typer.Exit(code=1)
+
+        stats = result["stats"]
+
+        if mode == "vec":
+            table = Table(title="Vec Rebuild Results")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green")
+            table.add_row("Vectors Built", str(result.get("vectors_built", 0)))
+            table.add_row("Model", result.get("model", ""))
+            table.add_row("Total Nodes", str(stats["total_nodes"]))
+            console.print(table)
+            console.print("[bold green]Vector rebuild complete![/bold green]")
+
+        elif mode == "profile":
+            table = Table(title="Profile Rebuild Results")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green")
+            table.add_row("Profiles Updated", str(result.get("profiles_updated", 0)))
+            table.add_row("Edges Removed (old)", str(result.get("edges_removed", 0)))
+            table.add_row("Edges Rebuilt (new)", str(result.get("edges_rebuilt", 0)))
+            table.add_row("Tables", str(stats["node_type_counts"]["table"]))
+            console.print(table)
+            console.print("[bold green]Profile rebuild complete![/bold green]")
+
+        else:  # mode == "full"
+            table = Table(title="Full Rebuild Results")
+            table.add_column("Metric", style="cyan")
+            table.add_column("Value", style="green")
+
+            table.add_row("Datasources", str(result["datasources"]))
+            table.add_row("Tables", str(result["tables"]))
+            table.add_row("Columns", str(stats["node_type_counts"]["column"]))
+            table.add_row("Concepts", str(stats["node_type_counts"]["concept"]))
+            table.add_row("Entities", str(stats["node_type_counts"]["entity"]))
+            table.add_row("Total Edges", str(stats["total_edges"]))
+            table.add_row("Contains Edges", str(stats["edge_type_counts"]["contains"]))
+            table.add_row("FK Edges", str(stats["edge_type_counts"]["foreign_key"]))
+            table.add_row("Joinable Edges", str(stats["edge_type_counts"]["joinable"]))
+            table.add_row("Synonym Edges", str(stats["edge_type_counts"]["semantic_synonym"]))
+            table.add_row("Distribution Edges", str(stats["edge_type_counts"]["distribution_similar"]))
+            table.add_row("Correlated Edges", str(stats["edge_type_counts"]["correlated"]))
+            console.print(table)
+            console.print("[bold green]Full rebuild complete![/bold green]")
+    except Exception as e:
+        console.print(f"[bold red]Rebuild failed:[/bold red] {e}")
+        raise typer.Exit(code=1)
+    finally:
+        pipeline.close()
+
+
+@app.command()
+def show(
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Show the full graph contents (nodes and edges) as JSON."""
+    import json as json_mod
+
+    from datalink.graph.storage import GraphStorage
+
+    storage = GraphStorage(db_path)
+
+    try:
+        result = storage.show_graph()
+        console.print(json_mod.dumps(result, indent=2, ensure_ascii=False))
+    except Exception as e:
+        console.print(f"[bold red]Show failed:[/bold red] {e}")
+        raise typer.Exit(code=1)
+    finally:
+        storage.close()
+
+
+@app.command()
+def remove_table(
+    table: str = typer.Option(..., "--table", "-t", help="Table ID or name to remove"),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Remove a table and all its columns from the graph."""
+    console.print(f"[bold blue]Removing table '{table}'...[/bold blue]")
+
+    config = DataLinkConfig.load()
+    config.graph_db_path = db_path
+
+    from datalink.builder.pipeline import BuildPipeline
+
+    pipeline = BuildPipeline(config)
+
+    try:
+        # If table is a name, find its ID
+        table_id = table
+        if not table.startswith("table:"):
+            # Search for the table by name
+            from datalink.graph.retrieval import GraphRetrieval
+
+            retrieval = GraphRetrieval(pipeline.storage)
+            tables = retrieval.list_datasets(mask_credential=False)
+            matching = [t for t in tables if t["name"] == table]
+            if matching:
+                table_id = matching[0]["id"]
+            else:
+                console.print(f"[bold red]Table '{table}' not found[/bold red]")
+                raise typer.Exit(code=1)
+
+        result = pipeline.remove_table(table_id)
+        console.print("[bold green]Removed table[/bold green]")
+        console.print(f"  Removed columns: {result['removed_columns']}")
+        console.print(f"  Removed orphaned concepts/entities: {result['removed_orphans']}")
+    except Exception as e:
+        console.print(f"[bold red]Remove table failed:[/bold red] {e}")
+        raise typer.Exit(code=1)
+    finally:
+        pipeline.close()
+
+
+# ── Read commands ──────────────────────────────────────────────────────
+
+
+@app.command()
+def explore(
+    query: str = typer.Argument(..., help="Search query (keywords, names, natural language)"),
+    focus: str = typer.Option("", "--focus", "-f", help="Focus: join_paths, schema, data_profile"),
+    max_nodes: int = typer.Option(0, "--max-nodes", "-n", help="Max nodes (0=auto)"),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Explore the data graph — one call answers the whole question."""
+    from datalink.graph.retrieval import GraphRetrieval
+    from datalink.graph.storage import GraphStorage
+
+    storage = GraphStorage(db_path)
+    retrieval = GraphRetrieval(storage)
+
+    focus_val = focus if focus else None
+    max_nodes_val = max_nodes if max_nodes > 0 else None
+
+    if focus_val and focus_val not in ("join_paths", "schema", "data_profile"):
+        console.print(f"[bold red]Invalid focus: {focus}. Valid: join_paths, schema, data_profile[/bold red]")
+        raise typer.Exit(code=1)
+
+    result = retrieval.explore(query, max_nodes=max_nodes_val, focus=focus_val, mask_credential=False)
+    console.print(Panel(result, title=f"Explore: {query}", border_style="blue"))
+    storage.close()
+
+
+@app.command()
+def search(
+    query: str = typer.Argument(..., help="Search query"),
+    type: str = typer.Option("", "--type", "-t", help="Node type filter (column, table, concept, entity)"),
+    limit: int = typer.Option(10, "--limit", "-l", help="Maximum number of results"),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Search for nodes in the data graph."""
+    from datalink.graph.retrieval import GraphRetrieval
+    from datalink.graph.storage import GraphStorage
+
+    storage = GraphStorage(db_path)
+    retrieval = GraphRetrieval(storage)
+
+    node_type = None
+    if type:
+        try:
+            node_type = NodeType(type)
+        except ValueError:
+            console.print(f"[bold red]Invalid node type: {type}[/bold red]")
+            raise typer.Exit(code=1)
+
+    results = retrieval.search_nodes(query, node_type, limit, mask_credential=False)
+
+    if not results:
+        console.print(f"[yellow]No results found for '{query}'[/yellow]")
+        return
+
+    table = Table(title=f"Search Results for '{query}'", show_lines=True)
+    table.add_column("Name", style="green")
+    table.add_column("Type", style="blue")
+    table.add_column("Edges", style="yellow")
+    table.add_column("ID", style="cyan", max_width=60, overflow="ellipsis", no_wrap=True)
+
+    for r in results:
+        table.add_row(r["name"], r["type"], str(r["edge_count"]), r["id"])
+
+    console.print(table)
+    storage.close()
+
+
+@app.command()
+def info(
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Show overview information about the data graph."""
+    from datalink.graph.storage import GraphStorage
+
+    storage = GraphStorage(db_path)
+
+    stats = storage.get_graph_stats()
+
+    panel = Panel(
+        f"[bold]DataLink Overview[/bold]\n\n"
+        f"Total Nodes: {stats['total_nodes']}\n"
+        f"  Tables: {stats['node_type_counts']['table']}\n"
+        f"  Columns: {stats['node_type_counts']['column']}\n"
+        f"  Concepts: {stats['node_type_counts']['concept']}\n"
+        f"  Entities: {stats['node_type_counts']['entity']}\n\n"
+        f"Total Edges: {stats['total_edges']}\n"
+        f"  Contains: {stats['edge_type_counts']['contains']}\n"
+        f"  FK: {stats['edge_type_counts']['foreign_key']}\n"
+        f"  Joinable: {stats['edge_type_counts']['joinable']}\n"
+        f"  Synonyms: {stats['edge_type_counts']['semantic_synonym']}\n"
+        f"  Distribution: {stats['edge_type_counts']['distribution_similar']}\n"
+        f"  Correlated: {stats['edge_type_counts']['correlated']}\n"
+        f"  Represents: {stats['edge_type_counts']['represents']}\n\n"
+        f"Pending Edges: {stats['pending_edge_count']}\n"
+        f"  Pending FK: {stats['pending_edge_type_counts']['foreign_key']}\n"
+        f"  Pending Joinable: {stats['pending_edge_type_counts']['joinable']}\n\n"
+        f"Profiles: {stats['total_profiles']}\n"
+        f"DB Path: {db_path}",
+        title="DataLink",
+        border_style="blue",
+    )
+    console.print(panel)
+    storage.close()
+
+
+@app.command()
+def path(
+    from_node: str = typer.Option(..., "--from", help="Source node ID"),
+    to_node: str = typer.Option(..., "--to", help="Target node ID"),
+    max_depth: int = typer.Option(3, "--depth", "-d", help="Maximum path depth"),
+    limit: int = typer.Option(3, "--limit", "-l", help="Maximum number of paths to return"),
+    edge_types: str = typer.Option(
+        "", "--edge-types", "-e", help="Comma-separated edge types to traverse (e.g., joinable,foreign_key)"
+    ),
+    db_path: str = typer.Option("datalink.db", "--db", help="Path to graph database file"),
+) -> None:
+    """Find paths between two nodes in the data graph."""
+    from datalink.graph.retrieval import GraphRetrieval
+    from datalink.graph.storage import GraphStorage
+    from datalink.models.edge import EdgeType
+
+    storage = GraphStorage(db_path)
+    retrieval = GraphRetrieval(storage)
+
+    et = None
+    if edge_types:
+        try:
+            type_names = [t.strip() for t in edge_types.split(",")]
+            et = [EdgeType(t) for t in type_names]
+        except ValueError as exc:
+            console.print(f"[bold red]Invalid edge type: {exc}[/bold red]")
+            raise typer.Exit(code=1)
+
+    paths = retrieval.find_paths(from_node, to_node, max_depth, et, limit=limit, mask_credential=False)
+
+    if not paths:
+        console.print(f"[yellow]No paths found between '{from_node}' and '{to_node}'[/yellow]")
+        return
+
+    for i, p in enumerate(paths):
+        console.print(f"\n[bold]Path {i + 1}[/bold] (confidence: {p['confidence']:.3f}, length: {p['length']})")
+
+        for j, node_id in enumerate(p["nodes"]):
+            node = storage.get_node(node_id)
+            if node:
+                prefix = "  → " if j > 0 else "  "
+                console.print(f"{prefix}[cyan]{node.type.value}[/cyan]: {node.name} ({node_id})")
+
+        console.print("  Edges:")
+        for e in p["edges"]:
+            console.print(f"    [yellow]{e['type']}[/yellow] (confidence: {e['confidence']:.2f})")
+
+    storage.close()
+
+
+@app.command()
+def pending_edges(
+    node_id: str = typer.Option("", "--node", "-n", help="Filter by node ID"),
+    edge_type: str = typer.Option("", "--type", "-t", help="Filter by edge type (e.g., foreign_key, joinable)"),
+    limit: int = typer.Option(50, "--limit", "-l", help="Maximum number of results"),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """List pending (dangling) edges that reference nodes not yet in the graph."""
+    from datalink.graph.retrieval import GraphRetrieval
+    from datalink.graph.storage import GraphStorage
+    from datalink.models.edge import EdgeType
+
+    storage = GraphStorage(db_path)
+    retrieval = GraphRetrieval(storage)
+
+    et = None
+    if edge_type:
+        try:
+            et = EdgeType(edge_type)
+        except ValueError:
+            console.print(f"[bold red]Invalid edge type: {edge_type}[/bold red]")
+            raise typer.Exit(code=1)
+
+    nid = node_id if node_id else None
+    results = retrieval.get_pending_edges(nid, et, limit, mask_credential=False)
+
+    if not results:
+        console.print("[green]No pending edges found — all edges are resolved.[/green]")
+        storage.close()
+        return
+
+    table = Table(title="Pending (Dangling) Edges", show_lines=True)
+    table.add_column("Type", style="blue")
+    table.add_column("Missing", style="red")
+    table.add_column("Note", style="magenta", max_width=40)
+    table.add_column("Source", style="yellow", max_width=50, overflow="ellipsis", no_wrap=True)
+    table.add_column("Target", style="yellow", max_width=50, overflow="ellipsis", no_wrap=True)
+    table.add_column("ID", style="cyan", max_width=40, overflow="ellipsis", no_wrap=True)
+
+    for r in results:
+        table.add_row(
+            r["type"],
+            ",".join(r["missing_endpoints"]),
+            r["note"],
+            r["source_id"],
+            r["target_id"],
+            r["id"],
+        )
+
+    console.print(table)
+    storage.close()
+
+
+@app.command()
+def list_datasets(
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """List all tables/datasets in the data graph with basic statistics."""
+    from datalink.graph.retrieval import GraphRetrieval
+    from datalink.graph.storage import GraphStorage
+
+    storage = GraphStorage(db_path)
+    retrieval = GraphRetrieval(storage)
+
+    datasets = retrieval.list_datasets(mask_credential=False)
+
+    if not datasets:
+        console.print("[yellow]No datasets found[/yellow]")
+        storage.close()
+        return
+
+    table = Table(title="Datasets", show_lines=True)
+    table.add_column("Name", style="green")
+    table.add_column("Columns", style="blue")
+    table.add_column("Edges", style="yellow")
+    table.add_column("ID", style="cyan", max_width=60, overflow="ellipsis", no_wrap=True)
+
+    for ds in datasets:
+        table.add_row(ds["name"], str(ds["column_count"]), str(ds["inferred_edge_count"]), ds["id"])
+
+    console.print(table)
+    storage.close()
+
+
+@app.command()
+def get_node(
+    node_id: str = typer.Argument(..., help="Node ID to retrieve"),
+    include_edges: bool = typer.Option(True, "--edges/--no-edges", help="Include adjacent edges"),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Get detailed information about a specific node."""
+    from datalink.graph.retrieval import GraphRetrieval
+    from datalink.graph.storage import GraphStorage
+
+    storage = GraphStorage(db_path)
+    retrieval = GraphRetrieval(storage)
+
+    result = retrieval.get_node(node_id, include_edges, mask_credential=False)
+
+    if result is None:
+        console.print(f"[yellow]Node '{node_id}' not found[/yellow]")
+        storage.close()
+        raise typer.Exit(code=1)
+
+    # Display node info
+    console.print(f"[bold cyan]{result['type']}[/bold cyan]: {result['name']} ({result['id']})")
+
+    if "profile" in result:
+        console.print("\n[bold]Profile:[/bold]")
+        profile = result["profile"]
+        for key, value in profile.items():
+            if key in ("id", "column_id"):
+                continue
+            console.print(f"  {key}: {value}")
+
+    if include_edges and "edges" in result:
+        console.print(f"\n[bold]Edges ({len(result['edges'])}):[/bold]")
+        for e in result["edges"]:
+            # Show direction and the "other" node, not just target_id.
+            other = e.get("other_node")
+            if other:
+                other_label = f"{other['type']}: {other['name']}"
+            else:
+                other_label = e["target_id"] if e["direction"] == "outgoing" else e["source_id"]
+            arrow = "→" if e["direction"] == "outgoing" else "←"
+            console.print(f"  [yellow]{e['type']}[/yellow] {arrow} {other_label} (confidence: {e['confidence']:.2f})")
+
+    storage.close()
+
+
+@app.command()
+def extract_subgraph(
+    node_ids: str = typer.Argument(..., help="Comma-separated node IDs to start from"),
+    max_hops: int = typer.Option(2, "--hops", "-h", help="Number of neighbor layers to expand"),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+) -> None:
+    """Extract a subgraph around specified nodes, expanding by neighbor hops."""
+    from datalink.graph.retrieval import GraphRetrieval
+    from datalink.graph.storage import GraphStorage
+
+    storage = GraphStorage(db_path)
+    retrieval = GraphRetrieval(storage)
+
+    ids = [id.strip() for id in node_ids.split(",")]
+    result = retrieval.extract_subgraph(ids, max_hops, mask_credential=False)
+
+    stats = result["stats"]
+    console.print(f"[bold]Subgraph:[/bold] {stats['node_count']} nodes, {stats['edge_count']} edges")
+
+    # Display nodes
+    table = Table(title="Subgraph Nodes", show_lines=True)
+    table.add_column("Name", style="green")
+    table.add_column("Type", style="blue")
+    table.add_column("ID", style="cyan", max_width=60, overflow="ellipsis", no_wrap=True)
+
+    for n in result["nodes"]:
+        table.add_row(n["name"], n["type"], n["id"])
+
+    console.print(table)
+
+    # Display edges
+    if result["edges"]:
+        edge_table = Table(title="Subgraph Edges", show_lines=True)
+        edge_table.add_column("Type", style="blue")
+        edge_table.add_column("Conf", style="magenta")
+        edge_table.add_column("Source", style="yellow", max_width=50, overflow="ellipsis", no_wrap=True)
+        edge_table.add_column("Target", style="yellow", max_width=50, overflow="ellipsis", no_wrap=True)
+
+        for e in result["edges"]:
+            edge_table.add_row(e["type"], f"{e['confidence']:.2f}", e["source_id"], e["target_id"])
+
+        console.print(edge_table)
+
+    storage.close()
+
+
+# ── Service & config ───────────────────────────────────────────────────
+
+
+@app.command()
+def serve(
+    port: int = typer.Option(8080, "--port", "-p", help="Port for MCP server"),
+    host: str = typer.Option(
+        "", "--host", help="Bind address (empty=0.0.0.0 for all interfaces, 127.0.0.1 for localhost-only)"
+    ),
+    db_path: str = typer.Option("datalink.db", "--db", "-d", help="Path to graph database file"),
+    transport: str = typer.Option(
+        "sse", "--transport", "-t", help="Transport protocol: 'sse' (legacy) or 'streamable-http' (recommended)"
+    ),
+) -> None:
+    """Start the DataLink MCP server for agent integration."""
+    if transport not in ("sse", "streamable-http"):
+        console.print(f"[bold red]Invalid transport: {transport}. Use 'sse' or 'streamable-http'.[/bold red]")
+        raise typer.Exit(code=1)
+
+    effective_host = host or "0.0.0.0"
+    console.print(
+        f"[bold blue]Starting DataLink MCP server on {effective_host}:{port} (transport={transport})...[/bold blue]"
+    )
+
+    from datalink.mcp.server import start_mcp_server
+
+    start_mcp_server(db_path=db_path, port=port, transport=transport, host=host)
+
+
+@app.command()
+def api(
+    port: int = typer.Option(8081, "--port", "-p", help="Port for REST API server"),
+    host: str = typer.Option(
+        "0.0.0.0", "--host", help="Bind address (default 0.0.0.0 for all interfaces, 127.0.0.1 for localhost-only)"
+    ),
+) -> None:
+    """Start the DataLink REST API server (mirrors all CLI commands as HTTP endpoints)."""
+    console.print(f"[bold blue]Starting DataLink REST API server on {host}:{port}...[/bold blue]")
+
+    from datalink.api.server import start_api_server
+
+    start_api_server(host=host, port=port)
+
+
+@app.command()
+def config(
+    llm_model: str = typer.Option("", "--llm-model", help="LLM model name"),
+    llm_api_key: str = typer.Option("", "--llm-api-key", help="LLM API key"),
+    llm_base_url: str = typer.Option("", "--llm-base-url", help="LLM API base URL (OpenAI-compatible)"),
+    db_path: str = typer.Option("", "--db", "-d", help="Default graph database path"),
+    mcp_tools: str = typer.Option(
+        "",
+        "--mcp-tools",
+        "-t",
+        help="Auxiliary MCP tools to expose (comma-separated, e.g. datalink_search_nodes,datalink_get_node)",
+    ),
+) -> None:
+    """Configure DataLink settings."""
+    current = DataLinkConfig.load()
+
+    if llm_model:
+        current.llm.model = llm_model
+    if llm_api_key:
+        current.llm.api_key = llm_api_key
+    if llm_base_url:
+        current.llm.base_url = llm_base_url
+    if db_path:
+        current.graph_db_path = db_path
+    if mcp_tools:
+        current.mcp_tools = mcp_tools
+
+    current.save()
+
+    console.print("[bold green]Configuration saved![/bold green]")
+    console.print(f"  LLM Model: {current.llm.model}")
+    console.print(f"  LLM Base URL: {current.llm.base_url}")
+    console.print(f"  DB Path: {current.graph_db_path}")
+    console.print(f"  MCP Tools: {current.mcp_tools or '(none — only core tools)'}")
+
+
+if __name__ == "__main__":
+    app()
diff --git a/services/datalink/src/datalink/config.py b/services/datalink/src/datalink/config.py
new file mode 100644
index 00000000..dd15232b
--- /dev/null
+++ b/services/datalink/src/datalink/config.py
@@ -0,0 +1,239 @@
+"""Global configuration for DataLink."""
+
+import json
+import os
+from pathlib import Path
+
+from pydantic import BaseModel, Field
+
+
+def _first_env(*names: str) -> str:
+    for name in names:
+        value = os.environ.get(name, "").strip()
+        if value:
+            return value
+    return ""
+
+
+class LLMConfig(BaseModel):
+    """Configuration for LLM calls via OpenAI-compatible Chat Completions API.
+
+    Three required parameters:
+    - api_key: Authentication key
+    - model: Model name (e.g., "gpt-4o", "deepseek-chat")
+    - base_url: API endpoint URL (e.g., "https://api.openai.com/v1",
+                "https://api.deepseek.com/v1", "http://localhost:11434/v1")
+
+    Any service implementing the OpenAI Chat Completions protocol works.
+    """
+
+    model: str = Field(
+        default="gpt-4o",
+        description="Model name to use (e.g., gpt-4o, deepseek-chat, qwen-plus)",
+    )
+    api_key: str = Field(
+        default="",
+        description="API key (can also be set via OPENAI_API_KEY env var)",
+    )
+    base_url: str = Field(
+        default="https://api.openai.com/v1",
+        description="API endpoint base URL for OpenAI-compatible service",
+    )
+    temperature: float = Field(
+        default=0.1,
+        ge=0.0,
+        le=1.0,
+        description="Temperature for LLM calls (low for deterministic mapping)",
+    )
+    max_tokens: int = Field(
+        default=16384,
+        description="Maximum tokens per LLM response",
+    )
+    timeout: float = Field(
+        default=120.0,
+        description="HTTP timeout in seconds for LLM API calls (includes retries). "
+        "Increase this if your API gateway returns 504 before the SDK finishes. "
+        "OpenAI SDK default is 600s, but self-hosted gateways often timeout sooner.",
+    )
+
+    def get_api_key(self) -> str:
+        """Get API key from config or environment variable."""
+        if self.api_key:
+            return self.api_key
+        return os.environ.get("OPENAI_API_KEY", "")
+
+
+class EmbeddingConfig(BaseModel):
+    """Configuration for embedding model calls via OpenAI-compatible API.
+
+    Used by merge_with_existing for pre-filtering candidate matches.
+    If model is empty, embedding pre-filtering is skipped and merge
+    falls back to pure LLM judgment.
+    """
+
+    model: str = Field(
+        default="",
+        description="Embedding model name (e.g., text-embedding-3-small). Empty = skip embedding pre-filter.",
+    )
+    api_key: str = Field(
+        default="",
+        description="API key for embedding service. Falls back to LLMConfig.api_key if empty.",
+    )
+    base_url: str = Field(
+        default="",
+        description="API endpoint base URL for embedding service. Falls back to LLMConfig.base_url if empty.",
+    )
+    similarity_threshold: float = Field(
+        default=0.75,
+        ge=0.0,
+        le=1.0,
+        description="Minimum cosine similarity to consider as a merge candidate (pre-filter threshold).",
+    )
+    timeout: float = Field(
+        default=60.0,
+        description="HTTP timeout in seconds for embedding API calls. "
+        "Embedding calls are typically faster than LLM calls, so the default is lower.",
+    )
+
+    def get_api_key(self, llm_config: LLMConfig | None = None) -> str:
+        """Get API key from config or fall back to LLM config / env var."""
+        if self.api_key:
+            return self.api_key
+        if llm_config and llm_config.api_key:
+            return llm_config.api_key
+        return os.environ.get("OPENAI_API_KEY", "")
+
+    def get_base_url(self, llm_config: LLMConfig | None = None) -> str:
+        """Get base URL from config or fall back to LLM config."""
+        if self.base_url:
+            return self.base_url
+        if llm_config and llm_config.base_url:
+            return llm_config.base_url
+        return "https://api.openai.com/v1"
+
+    def is_available(self, llm_config: LLMConfig | None = None) -> bool:
+        """Check if embedding service is usable.
+
+        Returns True when model is configured and API key is available.
+        Used to decide whether to enable vector retrieval features.
+        """
+        if not self.model:
+            return False
+        return bool(self.get_api_key(llm_config))
+
+
+class DataLinkConfig(BaseModel):
+    """Top-level DataLink configuration."""
+
+    llm: LLMConfig = Field(default_factory=LLMConfig, description="LLM configuration")
+    embedding: EmbeddingConfig = Field(
+        default_factory=EmbeddingConfig, description="Embedding model configuration for merge pre-filtering"
+    )
+    merge_llm_temperature: float = Field(
+        default=0.0,
+        ge=0.0,
+        le=1.0,
+        description="Temperature for LLM merge judgment calls (low for deterministic matching)",
+    )
+    merge_batch_interval: int = Field(
+        default=10,
+        ge=1,
+        description="Number of mapping batches to accumulate before running a merge. "
+        "Higher values reduce merge LLM calls (1 = merge after every batch).",
+    )
+    mapping_batch_size: int = Field(
+        default=15,
+        ge=1,
+        description="Number of columns per LLM mapping batch. "
+        "Smaller values (e.g. 5) reduce prompt size and LLM response time, "
+        "helpful when the API gateway has a short timeout (e.g. 60s). "
+        "Default 15 works well for most OpenAI-compatible endpoints.",
+    )
+    graph_db_path: str = Field(
+        default="datalink.db",
+        description="SQLite database filename (resolved under ~/.datalink/storage/ unless an absolute path is given)",
+    )
+    sample_size: int = Field(
+        default=1000,
+        description="Default sample size for profiling",
+    )
+    confidence_threshold: float = Field(
+        default=0.3,
+        ge=0.0,
+        le=1.0,
+        description="Minimum confidence threshold for inferred edges",
+    )
+
+    # Inferrer-specific thresholds
+    joinable_overlap_threshold: float = Field(
+        default=0.1,
+        description="Minimum value overlap rate to consider columns joinable",
+    )
+    correlation_threshold: float = Field(
+        default=0.5,
+        description="Minimum absolute correlation coefficient to create an edge",
+    )
+
+    # MCP auxiliary tools — comma-separated full tool names to expose alongside
+    # the default datalink_explore + write tools. Empty string (default) means
+    # only core tools are visible to agents.
+    # Example: "datalink_search_nodes,datalink_get_node,datalink_find_paths"
+    # Can also be set via DATALINK_MCP_TOOLS env var (env var takes precedence).
+    mcp_tools: str = Field(
+        default="",
+        description="Comma-separated auxiliary MCP tool names to expose (empty = only core tools)",
+    )
+
+    @classmethod
+    def load(cls, path: str | Path | None = None) -> "DataLinkConfig":
+        """Load configuration from a JSON file.
+
+        If path is None, looks for datalink_config.json in current directory,
+        then falls back to defaults.
+        """
+        if path is None:
+            path = Path(os.environ.get("DATALINK_CONFIG_PATH", Path.cwd() / "datalink_config.json"))
+        else:
+            path = Path(path)
+
+        if path.exists():
+            with open(path) as f:
+                data = json.load(f)
+            config = cls(**data)
+        else:
+            config = cls()
+
+        llm_model = _first_env("DATALINK_LLM_MODEL", "LLM_MODEL")
+        llm_base_url = _first_env("DATALINK_LLM_BASE_URL", "LLM_BASE_URL")
+        llm_api_key = _first_env("DATALINK_LLM_API_KEY", "LLM_API_KEY", "OPENAI_API_KEY")
+        embedding_model = _first_env("DATALINK_EMBEDDING_MODEL", "EMBEDDING_MODEL")
+        embedding_base_url = _first_env("DATALINK_EMBEDDING_BASE_URL", "EMBEDDING_BASE_URL")
+        embedding_api_key = _first_env("DATALINK_EMBEDDING_API_KEY", "EMBEDDING_API_KEY")
+        graph_db_path = _first_env("DATALINK_GRAPH_DB_PATH")
+
+        if llm_model:
+            config.llm.model = llm_model
+        if llm_base_url:
+            config.llm.base_url = llm_base_url
+        if llm_api_key:
+            config.llm.api_key = llm_api_key
+        if embedding_model:
+            config.embedding.model = embedding_model
+        if embedding_base_url:
+            config.embedding.base_url = embedding_base_url
+        if embedding_api_key:
+            config.embedding.api_key = embedding_api_key
+        if graph_db_path:
+            config.graph_db_path = str(Path(graph_db_path).expanduser().resolve())
+        return config
+
+    def save(self, path: str | Path | None = None) -> None:
+        """Save configuration to a JSON file."""
+        if path is None:
+            path = Path(os.environ.get("DATALINK_CONFIG_PATH", Path.cwd() / "datalink_config.json"))
+        else:
+            path = Path(path)
+
+        path.parent.mkdir(parents=True, exist_ok=True)
+        with open(path, "w") as f:
+            json.dump(self.model_dump(), f, indent=2)
diff --git a/services/datalink/src/datalink/connector/__init__.py b/services/datalink/src/datalink/connector/__init__.py
new file mode 100644
index 00000000..af6cb749
--- /dev/null
+++ b/services/datalink/src/datalink/connector/__init__.py
@@ -0,0 +1 @@
+"""Data source connectors."""
diff --git a/services/datalink/src/datalink/connector/base.py b/services/datalink/src/datalink/connector/base.py
new file mode 100644
index 00000000..f81fd6ad
--- /dev/null
+++ b/services/datalink/src/datalink/connector/base.py
@@ -0,0 +1,44 @@
+"""Base connector interface for data sources."""
+
+from abc import ABC, abstractmethod
+
+from datalink.models.datasource import DatasourceConfig, DatasourceInfo
+
+
+class BaseConnector(ABC):
+    """Abstract base class for data source connectors.
+
+    Each connector type (database, file) implements this interface to
+    extract schema metadata and sample data from a data source.
+    """
+
+    def __init__(self, config: DatasourceConfig):
+        self.config = config
+
+    @abstractmethod
+    def connect(self) -> None:
+        """Establish connection to the data source."""
+        ...
+
+    @abstractmethod
+    def get_datasource_info(self) -> DatasourceInfo:
+        """Extract complete datasource info: tables, columns, FKs, comments, sample data."""
+        ...
+
+    @abstractmethod
+    def get_sample_data(self, table_name: str, n: int = 1000) -> list[dict]:
+        """Get sample rows from a specific table.
+
+        Args:
+            table_name: Name of the table to sample.
+            n: Number of rows to sample.
+
+        Returns:
+            List of dicts, each representing a row.
+        """
+        ...
+
+    @abstractmethod
+    def disconnect(self) -> None:
+        """Close the connection to the data source."""
+        ...
diff --git a/services/datalink/src/datalink/connector/database.py b/services/datalink/src/datalink/connector/database.py
new file mode 100644
index 00000000..75de48a5
--- /dev/null
+++ b/services/datalink/src/datalink/connector/database.py
@@ -0,0 +1,406 @@
+"""Database connector using SQLAlchemy for schema introspection."""
+
+import logging
+from typing import Any
+from urllib.parse import urlparse, urlunparse
+
+import pandas as pd
+from sqlalchemy import create_engine, inspect, text
+from sqlalchemy.engine import Engine
+
+from datalink.connector.base import BaseConnector
+from datalink.models.datasource import (
+    ColumnInfo,
+    DatasourceConfig,
+    DatasourceInfo,
+    ForeignKeyInfo,
+    TableInfo,
+)
+from datalink.utils.sql_ident import dialect_from_source, quote_identifier
+
+logger = logging.getLogger(__name__)
+
+
+class DatabaseConnector(BaseConnector):
+    """Connector for relational databases via SQLAlchemy.
+
+    Supports PostgreSQL, MySQL, SQLite, and any database with a SQLAlchemy driver.
+    Extracts tables, columns, foreign keys, and comments from the database schema.
+
+    Schema selection: if the database name in the connection string contains a dot
+    (e.g. ``mydb.myschema``), the part after the dot is extracted as the schema name
+    and the connection string is cleaned to only contain the database name for
+    SQLAlchemy.  For PostgreSQL this means:
+
+        postgresql://user:pass@host/mydb.myschema  →  schema "myschema", connect to "mydb"
+        postgresql://user:pass@host/mydb            →  schema "public" (default), connect to "mydb"
+
+    Other databases (MySQL, SQLite) don't use PostgreSQL-style schemas, so
+    ``db.schema`` path splitting is skipped for them.  SQLite file paths
+    commonly contain dots (``.db``, ``.sqlite``) that must not be treated
+    as schema separators.
+    """
+
+    # Dialects without PostgreSQL-style schemas / db.schema URL notation.
+    _NO_PG_SCHEMA_DIALECTS = frozenset({"mysql", "mariadb", "sqlite"})
+
+    def __init__(self, config: DatasourceConfig):
+        super().__init__(config)
+        self.engine: Engine | None = None
+        # Preserve the original connection string (with db.schema notation)
+        # so that TableInfo.source stores it for rebuild recovery.
+        self._original_connection_string = config.connection_string
+
+    def connect(self) -> None:
+        """Create a SQLAlchemy engine from the connection string.
+
+        Extracts the schema from ``db.schema`` notation in the connection
+        string path, cleans the URL for SQLAlchemy, and stores the schema
+        in ``config.schema_name``.
+
+        For MySQL/MariaDB/SQLite (dialects that don't use PostgreSQL-style
+        schemas), the default schema_name "public" is replaced with None
+        so that the Inspector lists tables from the connected database
+        rather than searching for a non-existent "public" schema.
+
+        Driver fallback: if the connection string uses a bare scheme
+        (e.g. ``mysql://`` or ``mariadb://``) without specifying a driver,
+        SQLAlchemy defaults to ``mysqlclient``/``MySQLdb`` which is often
+        not installed.  This method automatically retries with ``+pymysql``
+        (or ``+psycopg2`` for PostgreSQL) if the default driver is missing.
+        """
+        if not self.config.connection_string:
+            raise ValueError("Database connector requires a connection_string in config")
+
+        clean_url, schema_from_url = self._extract_schema_from_url(self.config.connection_string)
+
+        # Apply schema from URL if provided; otherwise keep the config default ("public")
+        if schema_from_url:
+            self.config.schema_name = schema_from_url
+
+        # Try creating the engine; if the default driver is missing,
+        # automatically retry with common alternative drivers.
+        self.engine = self._create_engine_with_driver_fallback(clean_url)
+
+        # MySQL/MariaDB/SQLite don't have PostgreSQL-style schemas.
+        # Inspector.get_table_names(schema=None) lists tables in the connected db/file.
+        dialect = self.engine.dialect.name
+        if dialect in self._NO_PG_SCHEMA_DIALECTS and self.config.schema_name == "public":
+            self.config.schema_name = None
+
+        logger.info(f"Connected to database (schema={self.config.schema_name}): {clean_url}")
+
+    # Driver fallback mapping: bare scheme → alternative drivers to try
+    _DRIVER_FALLBACKS: dict[str, list[str]] = {
+        "mysql": ["pymysql"],
+        "mariadb": ["pymysql"],
+        "postgresql": ["psycopg2"],
+    }
+
+    def _create_engine_with_driver_fallback(self, url: str) -> Engine:
+        """Create a SQLAlchemy engine, retrying with alternative drivers
+        if the default dialect driver is not installed.
+
+        For URLs like ``mysql://user:pass@host/db`` (bare scheme, no
+        ``+driver``), SQLAlchemy uses ``mysqlclient`` (MySQLdb) which is
+        a C extension often missing.  This method detects the failure and
+        retries with ``mysql+pymysql://`` etc.
+        """
+        # Check if the URL already specifies a driver (contains + in scheme)
+        parsed = urlparse(url)
+        scheme = parsed.scheme
+        if "+" in scheme:
+            # Driver already specified — try directly, no fallback
+            return create_engine(url)
+
+        base_dialect = scheme
+        if base_dialect not in self._DRIVER_FALLBACKS:
+            # No known fallback — try directly
+            return create_engine(url)
+
+        # Try the bare scheme first
+        try:
+            engine = create_engine(url)
+            # Force a lightweight connection test to catch missing-driver
+            # errors early (create_engine itself doesn't connect)
+            with engine.connect():
+                pass
+            return engine
+        except (ImportError, ModuleNotFoundError) as exc:
+            logger.info(
+                f"Default driver for '{base_dialect}' not available ({exc}), "
+                f"trying fallbacks: {self._DRIVER_FALLBACKS[base_dialect]}"
+            )
+
+        # Try each fallback driver
+        for driver in self._DRIVER_FALLBACKS[base_dialect]:
+            fallback_url = url.replace(f"{base_dialect}://", f"{base_dialect}+{driver}://", 1)
+            try:
+                engine = create_engine(fallback_url)
+                with engine.connect():
+                    pass
+                logger.info(f"Successfully connected using fallback driver '{driver}'")
+                return engine
+            except (ImportError, ModuleNotFoundError):
+                # Driver module not installed — try next fallback
+                continue
+            except Exception:
+                # Driver IS installed but connection failed (auth, network, etc.)
+                # This is a real DB error, not a driver issue — return the
+                # engine so the real error propagates to the caller.
+                logger.info(
+                    f"Fallback driver '{driver}' loaded but connection test failed — "
+                    f"returning engine for real error to propagate"
+                )
+                return engine
+
+        # All fallbacks exhausted (no driver module found for any variant)
+        available = [f"{base_dialect}+{d}" for d in self._DRIVER_FALLBACKS[base_dialect]]
+        raise ImportError(
+            f"No usable driver found for '{base_dialect}'. "
+            f" Tried '{base_dialect}' (default) and fallbacks {available}. "
+            f"Install one of: pip install {base_dialect}client pymysql psycopg2-binary"
+        )
+
+    def get_datasource_info(self) -> DatasourceInfo:
+        """Introspect the database schema and extract all metadata."""
+        if self.engine is None:
+            self.connect()
+
+        inspector = inspect(self.engine)
+        schema_name = self.config.schema_name
+
+        # For MySQL/MariaDB, schema_name is None internally (Inspector uses
+        # schema=None to list tables in the connected database).  For TableInfo
+        # we store the database name from the URL so the field is meaningful
+        # (e.g. "appdb") rather than None.
+        table_info_schema_name = schema_name
+        if schema_name is None and self.engine is not None:
+            dialect = self.engine.dialect.name
+            if dialect in ("mysql", "mariadb"):
+                # Extract the database name from the connection URL
+                parsed = urlparse(self._original_connection_string)
+                db_path = parsed.path.lstrip("/")
+                # Strip any .schema suffix that was extracted earlier
+                if "." in db_path:
+                    db_path = db_path.split(".", 1)[0]
+                table_info_schema_name = db_path if db_path else None
+
+        tables: list[TableInfo] = []
+        sample_data: dict[str, list[dict[str, Any]]] = {}
+
+        table_names = inspector.get_table_names(schema=schema_name)
+        logger.info(f"Found {len(table_names)} tables in schema '{schema_name}'")
+
+        for table_name in table_names:
+            # Extract columns
+            columns = self._extract_columns(inspector, table_name, schema_name)
+
+            # Extract foreign keys
+            foreign_keys = self._extract_foreign_keys(inspector, table_name, schema_name)
+
+            # Get row count
+            row_count = self._get_row_count(table_name)
+
+            # Get table comment
+            table_comment = self._get_table_comment(inspector, table_name, schema_name)
+
+            table_info = TableInfo(
+                name=table_name,
+                schema_name=table_info_schema_name,
+                columns=columns,
+                foreign_keys=foreign_keys,
+                row_count=row_count,
+                comment=table_comment,
+                # Use original connection string (with db.schema) so rebuild can recover schema
+                source=self._original_connection_string,
+            )
+            tables.append(table_info)
+
+            # Get sample data
+            sample_data[table_name] = self.get_sample_data(table_name, self.config.sample_size)
+
+        return DatasourceInfo(
+            config=self.config,
+            tables=tables,
+            sample_data=sample_data,
+        )
+
+    def get_sample_data(self, table_name: str, n: int = 1000) -> list[dict[str, Any]]:
+        """Sample rows from a table using random ordering."""
+        if self.engine is None:
+            self.connect()
+
+        qualified_name = self._qualified_table_name(table_name)
+
+        # Use RANDOM() for PostgreSQL, RAND() for MySQL, or ROWID for SQLite
+        dialect = self.engine.dialect.name
+        if dialect == "postgresql":
+            order_clause = "ORDER BY RANDOM()"
+        elif dialect == "mysql":
+            order_clause = "ORDER BY RAND()"
+        elif dialect == "sqlite":
+            order_clause = "ORDER BY RANDOM()"
+        else:
+            # Fallback: no random ordering, just take first N
+            order_clause = ""
+
+        query = f"SELECT * FROM {qualified_name} {order_clause} LIMIT {n}"
+        try:
+            df = pd.read_sql(text(query), self.engine)
+            return df.to_dict(orient="records")
+        except Exception as e:
+            logger.warning(f"Failed to sample table '{table_name}': {e}")
+            return []
+
+    def disconnect(self) -> None:
+        """Dispose the SQLAlchemy engine."""
+        if self.engine is not None:
+            self.engine.dispose()
+            self.engine = None
+            logger.info("Disconnected from database")
+
+    # ── Schema / URL helpers ──────────────────────────────────────────────
+
+    def _extract_schema_from_url(self, connection_string: str) -> tuple[str, str | None]:
+        """Extract schema from ``db.schema`` notation in the connection string path.
+
+        For PostgreSQL, a dot in the path (e.g. ``/mydb.myschema``) splits into
+        database name and schema name.  Returns:
+
+            (clean_url_with_only_dbname, schema_value_or_None)
+
+        For MySQL/MariaDB/SQLite, there is no PostgreSQL-style schema — dots
+        in the path are NOT interpreted as schema separators.  This is
+        essential for SQLite, where file paths commonly contain extensions
+        like ``.db`` / ``.sqlite``.
+
+        Query parameters, netloc, etc. are preserved — only the path is modified.
+        """
+        parsed = urlparse(connection_string)
+        path = parsed.path
+        scheme = parsed.scheme
+
+        # Determine the base dialect (strip +driver suffix)
+        base_dialect = scheme.split("+")[0] if "+" in scheme else scheme
+
+        # Dialects without PostgreSQL-style schemas — no dot splitting.
+        if base_dialect in self._NO_PG_SCHEMA_DIALECTS:
+            return connection_string, None
+
+        # PostgreSQL and others: interpret dot as db.schema
+        db_path = path.lstrip("/")
+
+        if "." in db_path:
+            dbname, schema = db_path.split(".", 1)
+            # Rebuild path with only the database name
+            new_path = f"/{dbname}" if dbname else path
+            clean_url = urlunparse(
+                (
+                    parsed.scheme,
+                    parsed.netloc,
+                    new_path,
+                    parsed.params,
+                    parsed.query,
+                    parsed.fragment,
+                )
+            )
+            logger.info(f"Extracted schema '{schema}' from connection string path")
+            return clean_url, schema
+
+        # No dot found — no schema specification, keep URL unchanged
+        return connection_string, None
+
+    def _sql_dialect(self) -> str | None:
+        """Best-effort dialect name for identifier quoting."""
+        if self.engine is not None:
+            return self.engine.dialect.name
+        return dialect_from_source(self.config.connection_string or self._original_connection_string)
+
+    def _quote_ident(self, name: str) -> str:
+        """Quote a SQL identifier using the connected dialect when available."""
+        if self.engine is not None:
+            return self.engine.dialect.identifier_preparer.quote(name)
+        return quote_identifier(name, self._sql_dialect())
+
+    def _qualified_table_name(self, table_name: str) -> str:
+        """Return a schema-qualified, quoted table name for raw SQL queries.
+
+        Identifiers are always quoted so names with hyphens (e.g. ``dacomp-zh-006``)
+        or reserved words work on SQLite / PostgreSQL / MySQL.
+
+        For PostgreSQL's default "public" schema, a bare table name is enough
+        because public is on the default search_path.  For any other schema,
+        we prefix with the quoted schema name.
+        """
+        quoted_table = self._quote_ident(table_name)
+        if self.config.schema_name and self.config.schema_name != "public":
+            return f"{self._quote_ident(self.config.schema_name)}.{quoted_table}"
+        return quoted_table
+
+    # ── Inspector helpers ─────────────────────────────────────────────────
+
+    def _extract_columns(self, inspector: Any, table_name: str, schema_name: str) -> list[ColumnInfo]:
+        """Extract column metadata from the database inspector."""
+        columns = []
+        pk_columns = set()
+
+        # Get primary key columns
+        try:
+            pk_info = inspector.get_pk_constraint(table_name, schema=schema_name)
+            pk_columns = set(pk_info.get("constrained_columns", []))
+        except Exception:
+            logger.warning(f"Could not get PK constraint for '{table_name}'")
+
+        for col in inspector.get_columns(table_name, schema=schema_name):
+            columns.append(
+                ColumnInfo(
+                    name=col["name"],
+                    dtype=str(col.get("type", "")),
+                    nullable=col.get("nullable", True),
+                    is_primary_key=col["name"] in pk_columns,
+                    comment=col.get("comment", "") or "",
+                )
+            )
+        return columns
+
+    def _extract_foreign_keys(self, inspector: Any, table_name: str, schema_name: str) -> list[ForeignKeyInfo]:
+        """Extract foreign key constraints from the database inspector."""
+        fks = []
+        try:
+            fk_info = inspector.get_foreign_keys(table_name, schema=schema_name)
+            for fk in fk_info:
+                fks.append(
+                    ForeignKeyInfo(
+                        constraint_name=fk.get("name", "") or "",
+                        source_table=table_name,
+                        source_column=fk["constrained_columns"][0] if fk["constrained_columns"] else "",
+                        target_table=fk["referred_table"],
+                        target_column=fk["referred_columns"][0] if fk["referred_columns"] else "",
+                    )
+                )
+        except Exception as e:
+            logger.warning(f"Could not get FK constraints for '{table_name}': {e}")
+        return fks
+
+    def _get_row_count(self, table_name: str) -> int | None:
+        """Get the row count for a table."""
+        if self.engine is None:
+            return None
+        qualified_name = self._qualified_table_name(table_name)
+        try:
+            with self.engine.connect() as conn:
+                result = conn.execute(text(f"SELECT COUNT(*) FROM {qualified_name}"))
+                return result.scalar()
+        except Exception as e:
+            logger.warning(f"Could not get row count for '{table_name}': {e}")
+            return None
+
+    def _get_table_comment(self, inspector: Any, table_name: str, schema_name: str) -> str:
+        """Get the table-level comment from metadata."""
+        try:
+            table_info = inspector.get_table_comment(table_name, schema=schema_name)
+            return table_info.get("text", "") or ""
+        except Exception:
+            # Some dialects don't support table comments
+            return ""
diff --git a/services/datalink/src/datalink/connector/file.py b/services/datalink/src/datalink/connector/file.py
new file mode 100644
index 00000000..3090a546
--- /dev/null
+++ b/services/datalink/src/datalink/connector/file.py
@@ -0,0 +1,168 @@
+"""File connector for CSV and Parquet data sources."""
+
+import logging
+from pathlib import Path
+from typing import Any
+
+import pandas as pd
+
+from datalink.connector.base import BaseConnector
+from datalink.models.datasource import (
+    ColumnInfo,
+    DatasourceConfig,
+    DatasourceInfo,
+    TableInfo,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class FileConnector(BaseConnector):
+    """Connector for local CSV and Parquet files.
+
+    Treats each file as a table. Extracts schema by reading the file
+    and inferring types from the data. No foreign key information
+    is available from flat files — that must be inferred later.
+    """
+
+    def __init__(self, config: DatasourceConfig):
+        super().__init__(config)
+        self._dataframes: dict[str, pd.DataFrame] = {}
+
+    def connect(self) -> None:
+        """Read all files from the specified path."""
+        path = Path(self.config.path)
+        if not path.exists():
+            raise ValueError(f"Path does not exist: {path}")
+
+        if path.is_file():
+            self._read_single_file(path)
+        elif path.is_dir():
+            self._read_directory(path)
+        else:
+            logger.warning(f"Path is neither file nor directory: {path}")
+            return
+
+    def get_datasource_info(self) -> DatasourceInfo:
+        """Extract schema metadata and sample data from loaded files."""
+        if not self._dataframes:
+            self.connect()
+
+        tables: list[TableInfo] = []
+        sample_data: dict[str, list[dict[str, Any]]] = {}
+
+        for name, df in self._dataframes.items():
+            columns = self._infer_columns(df)
+            table_info = TableInfo(
+                name=name,
+                schema_name="file",
+                columns=columns,
+                foreign_keys=[],  # No FK info from flat files
+                row_count=len(df),
+                source=str(self.config.path),
+            )
+            tables.append(table_info)
+
+            # Convert sample data to list of dicts
+            sample_df = df.head(self.config.sample_size)
+            sample_data[name] = sample_df.to_dict(orient="records")
+
+        return DatasourceInfo(
+            config=self.config,
+            tables=tables,
+            sample_data=sample_data,
+        )
+
+    def get_sample_data(self, table_name: str, n: int = 1000) -> list[dict[str, Any]]:
+        """Get sample rows from a loaded DataFrame."""
+        if table_name not in self._dataframes:
+            logger.warning(f"Table '{table_name}' not found in loaded data")
+            return []
+        df = self._dataframes[table_name]
+        sample_df = df.head(n)
+        return sample_df.to_dict(orient="records")
+
+    def disconnect(self) -> None:
+        """Clear loaded dataframes."""
+        self._dataframes.clear()
+
+    def _read_single_file(self, path: Path) -> None:
+        """Read a single CSV or Parquet file."""
+        name = path.stem  # filename without extension
+        df = self._load_file(path)
+        self._dataframes[name] = df
+        logger.info(f"Loaded file '{path.name}' as table '{name}' ({len(df)} rows)")
+
+    def _read_directory(self, path: Path) -> None:
+        """Read all CSV and Parquet files in a directory."""
+        csv_files = list(path.glob("*.csv"))
+        parquet_files = list(path.glob("*.parquet")) + list(path.glob("*.pq"))
+
+        if not csv_files and not parquet_files:
+            logger.warning(f"No CSV or Parquet files found in: {path}")
+            return
+
+        for f in csv_files + parquet_files:
+            name = f.stem
+            df = self._load_file(f)
+            self._dataframes[name] = df
+            logger.info(f"Loaded '{f.name}' as table '{name}' ({len(df)} rows)")
+
+    def _load_file(self, path: Path) -> pd.DataFrame:
+        """Load a file into a DataFrame based on its extension."""
+        ext = path.suffix.lower()
+        if ext == ".csv":
+            return pd.read_csv(path)
+        elif ext in (".parquet", ".pq"):
+            return pd.read_parquet(path)
+        else:
+            raise ValueError(f"Unsupported file format: {ext}")
+
+    def _infer_columns(self, df: pd.DataFrame) -> list[ColumnInfo]:
+        """Infer column metadata from a DataFrame."""
+        columns = []
+        for col_name in df.columns:
+            dtype_str = self._classify_dtype(df[col_name])
+            nullable = df[col_name].isna().any()
+            unique_count = df[col_name].nunique()
+            total_count = len(df)
+
+            columns.append(
+                ColumnInfo(
+                    name=col_name,
+                    dtype=dtype_str,
+                    nullable=nullable,
+                    is_primary_key=unique_count == total_count and not nullable,
+                    comment="",  # No comments from flat files
+                )
+            )
+        return columns
+
+    def _classify_dtype(self, series: pd.Series) -> str:
+        """Classify a pandas Series dtype into a more descriptive type string."""
+        if pd.api.types.is_integer_dtype(series):
+            return "integer"
+        elif pd.api.types.is_float_dtype(series):
+            return "float"
+        elif pd.api.types.is_bool_dtype(series):
+            return "boolean"
+        elif pd.api.types.is_datetime64_any_dtype(series):
+            return "datetime"
+        elif pd.api.types.is_string_dtype(series) or pd.api.types.is_object_dtype(series):
+            # Try to detect if it's actually numeric strings or datetime strings
+            non_null = series.dropna()
+            if len(non_null) > 0:
+                try:
+                    pd.to_numeric(non_null)
+                    return "numeric_string"
+                except (ValueError, TypeError):
+                    pass
+                try:
+                    dt = pd.to_datetime(non_null, errors="coerce", format="mixed")
+                    if dt.notna().sum() / len(non_null) > 0.9:
+                        return "datetime_string"
+                except (ValueError, TypeError):
+                    pass
+            return "string"
+        else:
+            return str(series.dtype)
diff --git a/services/datalink/src/datalink/extractor/__init__.py b/services/datalink/src/datalink/extractor/__init__.py
new file mode 100644
index 00000000..50fe4b8c
--- /dev/null
+++ b/services/datalink/src/datalink/extractor/__init__.py
@@ -0,0 +1 @@
+"""Structural node extractors."""
diff --git a/services/datalink/src/datalink/extractor/tabular.py b/services/datalink/src/datalink/extractor/tabular.py
new file mode 100644
index 00000000..f074caea
--- /dev/null
+++ b/services/datalink/src/datalink/extractor/tabular.py
@@ -0,0 +1,114 @@
+"""Extract structural nodes from tabular datasource info."""
+
+import logging
+
+from datalink.models.datasource import DatasourceInfo
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode, TableNode
+
+logger = logging.getLogger(__name__)
+
+
+def generate_id(*parts: str) -> str:
+    """Generate a deterministic node/edge ID from parts.
+
+    Format: type:source:table:column (for columns) or type:source:table (for tables).
+    """
+    return ":".join(parts)
+
+
+class TabularExtractor:
+    """Extract TableNode, ColumnNode, and explicit edges from tabular datasource info."""
+
+    def extract(self, datasource_info: DatasourceInfo) -> tuple[list[TableNode], list[ColumnNode], list[Edge]]:
+        """Extract all structural nodes and explicit edges.
+
+        Returns:
+            Tuple of (table_nodes, column_nodes, edges) where edges include
+            both 'contains' and 'foreign_key' edges.
+        """
+        # Each table_info.source contains the real path/connection_string
+        # (set by FileConnector as str(config.path) and DatabaseConnector as
+        # config.connection_string). Use that as the stable identifier for
+        # node IDs and rebuild reconnection.
+        tables: list[TableNode] = []
+        columns: list[ColumnNode] = []
+        edges: list[Edge] = []
+
+        for table_info in datasource_info.tables:
+            source_name = (
+                table_info.source
+                or datasource_info.config.path
+                or datasource_info.config.connection_string
+                or "unknown"
+            )
+
+            # Create TableNode
+            table_id = generate_id("table", source_name, table_info.name)
+            table_node = TableNode(
+                id=table_id,
+                name=table_info.name,
+                source=source_name,
+                row_count=table_info.row_count or 0,
+                properties={
+                    "schema_name": table_info.schema_name,
+                    "comment": table_info.comment,
+                },
+            )
+            tables.append(table_node)
+
+            # Create ColumnNodes and contains edges
+            column_ids = []
+            for col_info in table_info.columns:
+                col_id = generate_id("column", source_name, table_info.name, col_info.name)
+                col_node = ColumnNode(
+                    id=col_id,
+                    name=col_info.name,
+                    table_id=table_id,
+                    dtype=col_info.dtype,
+                    comment=col_info.comment,
+                    properties={
+                        "nullable": col_info.nullable,
+                        "is_primary_key": col_info.is_primary_key,
+                        "default_value": (str(col_info.default_value) if col_info.default_value else ""),
+                    },
+                )
+                columns.append(col_node)
+                column_ids.append(col_id)
+
+                # contains edge: Table → Column
+                contains_edge = Edge(
+                    id=generate_id("edge", "contains", table_id, col_id),
+                    source_id=table_id,
+                    target_id=col_id,
+                    type=EdgeType.CONTAINS,
+                )
+                edges.append(contains_edge)
+
+            # Update table's column_ids
+            table_node.column_ids = column_ids
+
+            # Create foreign_key edges
+            for fk in table_info.foreign_keys:
+                fk_source_id = generate_id("column", source_name, fk.source_table, fk.source_column)
+                fk_target_id = generate_id("column", source_name, fk.target_table, fk.target_column)
+                fk_edge = Edge(
+                    id=generate_id(
+                        "edge",
+                        "fk",
+                        fk.source_table,
+                        fk.source_column,
+                        fk.target_table,
+                        fk.target_column,
+                    ),
+                    source_id=fk_source_id,
+                    target_id=fk_target_id,
+                    type=EdgeType.FOREIGN_KEY,
+                    properties={
+                        "constraint_name": fk.constraint_name,
+                    },
+                )
+                edges.append(fk_edge)
+
+        logger.info(f"Extracted {len(tables)} tables, {len(columns)} columns, {len(edges)} edges")
+        return tables, columns, edges
diff --git a/services/datalink/src/datalink/graph/__init__.py b/services/datalink/src/datalink/graph/__init__.py
new file mode 100644
index 00000000..4ce3280a
--- /dev/null
+++ b/services/datalink/src/datalink/graph/__init__.py
@@ -0,0 +1 @@
+"""Graph storage and retrieval."""
diff --git a/services/datalink/src/datalink/graph/retrieval.py b/services/datalink/src/datalink/graph/retrieval.py
new file mode 100644
index 00000000..9449e1da
--- /dev/null
+++ b/services/datalink/src/datalink/graph/retrieval.py
@@ -0,0 +1,2241 @@
+"""Retrieval interfaces for DataLink — explore, search, get, paths, subgraph."""
+
+import json
+import logging
+from dataclasses import dataclass, field
+from typing import Any
+
+from datalink.config import DataLinkConfig
+from datalink.graph.storage import GraphStorage
+from datalink.models.edge import Edge, EdgeType, PendingEdge
+from datalink.models.node import ColumnNode, ConceptNode, EntityNode, Node, NodeType, TableNode
+from datalink.models.profile import ColumnProfile
+from datalink.utils.credential import (
+    build_id_mapping,
+    mask_credentials,
+    mask_result,
+    resolve_masked_id,
+)
+from datalink.utils.embedding import EmbeddingService
+from datalink.utils.ids import parse_column_id
+from datalink.utils.sql_ident import dialect_from_source, quote_identifier, quote_qualified
+
+logger = logging.getLogger(__name__)
+
+# ── Explore dataclasses ──────────────────────────────────────────────
+
+
+@dataclass
+class ResolvedNode:
+    """A node resolved from query tokens, with match metadata."""
+
+    node_id: str
+    name: str
+    node_type: NodeType
+    match_reason: str  # "name_exact", "name_substring", "semantic_type", "concept_expand", etc.
+    relevance_score: float  # 0.0–1.0
+
+
+@dataclass
+class NodeContext:
+    """Context built for one resolved node."""
+
+    node: Node
+    edges_by_group: dict[str, list[dict[str, Any]]]  # group_key → edge summary list
+    profile_summary: dict[str, Any] | None = None  # Column nodes only
+    pending_edges: list[dict[str, Any]] = field(default_factory=list)
+
+
+@dataclass
+class RelationshipMap:
+    """Relationships among resolved nodes."""
+
+    join_relations: list[dict[str, Any]]  # foreign_key + joinable edges
+    semantic_relations: list[dict[str, Any]]  # represents/has_concept/semantic_synonym
+    statistical_relations: list[dict[str, Any]]  # correlated/distribution_similar
+    indirect_paths: list[dict[str, Any]]  # multi-hop paths among resolved nodes
+
+
+@dataclass
+class ExploreBudget:
+    """Adaptive output budget, scaled to project size.
+
+    Multi-dimensional budget controlling explore output size and content,
+    inspired by codegraph's ExploreOutputBudget design.
+    """
+
+    max_output_chars: int
+    max_nodes: int
+    max_edges_per_node: int
+    max_edges_per_relationship_kind: int  # max edges shown per relationship category
+    max_sample_values: int
+    max_columns_per_table: int  # max columns to show per table group
+    max_pending_edges_per_node: int  # max pending edges per node
+    include_relationships: bool
+    include_additional_nodes: bool
+    include_budget_note: bool
+    include_completeness_signal: bool  # tell agent "all data included, no further calls needed"
+    include_low_confidence_marker: bool  # show ⚠️ for low-confidence matches
+
+
+@dataclass
+class TableGroup:
+    """Nodes grouped under one table."""
+
+    table_node: Node
+    column_nodes: list[NodeContext]
+    ungrouped: bool = False  # for Concept/Entity not under any table
+
+
+# ── Helper functions ─────────────────────────────────────────────────
+
+# Edge type groupings for relationship map
+JOIN_EDGE_TYPES = {EdgeType.FOREIGN_KEY, EdgeType.JOINABLE}
+SEMANTIC_EDGE_TYPES = {
+    EdgeType.REPRESENTS,
+    EdgeType.HAS_CONCEPT,
+    EdgeType.SEMANTIC_SYNONYM,
+    EdgeType.SEMANTIC_TYPE_MATCH,
+}
+STATISTICAL_EDGE_TYPES = {EdgeType.CORRELATED, EdgeType.DISTRIBUTION_SIMILAR}
+
+
+def _describe_missing(pe: PendingEdge) -> str:
+    """Generate a human-readable note about missing endpoints of a pending edge."""
+    missing = pe.missing_endpoints
+    if "source" in missing and "target" in missing:
+        return f"Both endpoints not yet in graph (source: {pe.source_id}, target: {pe.target_id})"
+    elif "target" in missing:
+        return f"Referenced node '{pe.target_id}' not yet in graph"
+    elif "source" in missing:
+        return f"Source node '{pe.source_id}' not yet in graph"
+    else:
+        return "Endpoints status unknown"
+
+
+def get_explore_budget(dataset_count: int, focus: str | None = None) -> ExploreBudget:
+    """Calculate adaptive output budget based on project size and focus direction.
+
+    Multi-tier budget scaled to dataset count, with focus-specific adjustments.
+    Inspired by codegraph's tiered ExploreOutputBudget design.
+    """
+
+    # Base budget by project size — 4 tiers
+    if dataset_count < 3:
+        budget = ExploreBudget(
+            max_output_chars=8000,
+            max_nodes=8,
+            max_edges_per_node=3,
+            max_edges_per_relationship_kind=4,
+            max_sample_values=3,
+            max_columns_per_table=5,
+            max_pending_edges_per_node=2,
+            include_relationships=False,
+            include_additional_nodes=False,
+            include_budget_note=False,
+            include_completeness_signal=True,
+            include_low_confidence_marker=True,
+        )
+    elif dataset_count < 10:
+        budget = ExploreBudget(
+            max_output_chars=12000,
+            max_nodes=12,
+            max_edges_per_node=5,
+            max_edges_per_relationship_kind=6,
+            max_sample_values=5,
+            max_columns_per_table=8,
+            max_pending_edges_per_node=2,
+            include_relationships=True,
+            include_additional_nodes=False,
+            include_budget_note=True,
+            include_completeness_signal=True,
+            include_low_confidence_marker=True,
+        )
+    elif dataset_count < 50:
+        budget = ExploreBudget(
+            max_output_chars=16000,
+            max_nodes=15,
+            max_edges_per_node=7,
+            max_edges_per_relationship_kind=10,
+            max_sample_values=5,
+            max_columns_per_table=10,
+            max_pending_edges_per_node=3,
+            include_relationships=True,
+            include_additional_nodes=True,
+            include_budget_note=True,
+            include_completeness_signal=False,
+            include_low_confidence_marker=True,
+        )
+    else:
+        budget = ExploreBudget(
+            max_output_chars=20000,
+            max_nodes=20,
+            max_edges_per_node=10,
+            max_edges_per_relationship_kind=15,
+            max_sample_values=5,
+            max_columns_per_table=12,
+            max_pending_edges_per_node=3,
+            include_relationships=True,
+            include_additional_nodes=True,
+            include_budget_note=True,
+            include_completeness_signal=False,
+            include_low_confidence_marker=True,
+        )
+
+    # Focus adjustments: redistribute budget without changing total cap
+    if focus == "data_profile":
+        budget.max_sample_values = min(budget.max_sample_values * 2, 20)
+        budget.max_edges_per_node = max(budget.max_edges_per_node // 2, 3)
+        budget.max_edges_per_relationship_kind = max(budget.max_edges_per_relationship_kind // 2, 3)
+    elif focus == "join_paths":
+        budget.max_edges_per_node = min(budget.max_edges_per_node * 2, 20)
+        budget.max_edges_per_relationship_kind = min(budget.max_edges_per_relationship_kind * 2, 20)
+        budget.max_sample_values = max(budget.max_sample_values // 2, 2)
+        budget.include_relationships = True  # always show paths with this focus
+    elif focus == "schema":
+        budget.max_nodes = min(budget.max_nodes * 2, 40)
+        budget.max_columns_per_table = min(budget.max_columns_per_table * 2, 30)
+        budget.max_sample_values = 0  # schema focus: just dtype, no samples
+        budget.max_edges_per_node = max(budget.max_edges_per_node // 2, 2)
+
+    return budget
+
+
+def _edge_direction_label(edge: Edge, from_node_id: str) -> str:
+    """How an edge reads from the perspective of `from_node_id`."""
+    if edge.source_id == from_node_id:
+        return "→"
+    return "←"
+
+
+class GraphRetrieval:
+    """Four retrieval APIs on top of the GraphStorage layer.
+
+    1. search_nodes — full-text/semantic search
+    2. get_node — node detail + adjacent edges
+    3. find_paths — graph traversal between two nodes
+    4. extract_subgraph — expand a set of nodes by hops
+    """
+
+    def __init__(self, storage: GraphStorage, config: DataLinkConfig | None = None):
+        """Initialize retrieval with an existing storage instance.
+
+        Args:
+            storage: GraphStorage instance to query against.
+            config: DataLinkConfig for embedding service initialization.
+                    If None, loads from default config file.
+                    When embedding.model is empty, vector retrieval is disabled.
+        """
+        self.storage = storage
+        self.config = config or DataLinkConfig.load()
+        self._id_mapping: dict[str, str] | None = None
+
+        # Initialize embedding service (optional — disabled when model is empty)
+        self.embedding_service: EmbeddingService | None = None
+        if self.config.embedding.is_available(self.config.llm):
+            self.embedding_service = EmbeddingService(self.config.embedding, self.config.llm)
+            logger.info(f"Vector retrieval enabled (model: {self.config.embedding.model})")
+
+    def _ensure_id_mapping(self) -> dict[str, str]:
+        """Build or return the masked→real ID mapping from all stored node IDs."""
+        if self._id_mapping is None:
+            self._id_mapping = build_id_mapping(self.storage.get_all_node_ids())
+        return self._id_mapping
+
+    def _resolve_input_id(self, node_id: str) -> str:
+        """Resolve a possibly-masked or short-alias input ID to the real ID.
+
+        Resolution order:
+        1. Masked ID (contains ***:***@ or ://***) → resolve via mapping
+        2. Full ID (starts with table: column: concept: entity: edge:) → pass through
+        3. Short alias "table_name.column_name" → find matching column node
+        4. Bare name → exact name match across all node types
+        5. Not resolved → return as-is (caller will handle "not found")
+        """
+        # 1. Masked ID resolution
+        resolved = resolve_masked_id(node_id, self._ensure_id_mapping())
+        if resolved != node_id:
+            return resolved
+
+        # 2. Already a valid full-format ID — pass through
+        first_segment = node_id.split(":")[0] if ":" in node_id else ""
+        if first_segment in ("table", "column", "concept", "entity", "edge"):
+            return node_id
+
+        # 3. Short alias: "table_name.column_name"
+        if "." in node_id:
+            table_name, col_name = node_id.split(".", 1)
+            # Search for columns matching the column name
+            candidates = self.storage.search_nodes_by_name(col_name, NodeType.COLUMN, 10)
+            for c in candidates:
+                if isinstance(c, ColumnNode) and c.name == col_name:
+                    # Check that the parent table name matches
+                    parent_table = self.storage.get_node(c.table_id)
+                    if parent_table and parent_table.name == table_name:
+                        return c.id
+
+        # 4. Bare name — exact match across all types
+        for nt in (NodeType.TABLE, NodeType.COLUMN, NodeType.CONCEPT, NodeType.ENTITY):
+            candidates = self.storage.search_nodes_by_name(node_id, nt, 5)
+            for c in candidates:
+                if c.name == node_id:
+                    return c.id
+
+        # 5. Not resolved
+        return node_id
+
+    def _resolve_input_ids(self, node_ids: list[str]) -> list[str]:
+        """Resolve a list of possibly-masked or short-alias input IDs to real IDs."""
+        return [self._resolve_input_id(nid) for nid in node_ids]
+
+    def search_nodes(
+        self,
+        query: str,
+        node_type: NodeType | None = None,
+        limit: int = 10,
+        mask_credential: bool = True,
+    ) -> list[dict[str, Any]]:
+        """Search nodes by name (substring match) and optionally by type.
+
+        Also searches properties JSON for semantic_type and other indexed fields.
+        When embedding service is available, additionally performs vector
+        similarity search and merges results (hybrid retrieval).
+
+        Args:
+            query: Search string (substring match on node name).
+            node_type: Optional filter by node type (column, table, concept, entity).
+            limit: Maximum number of results.
+
+        Returns:
+            List of dicts with node info and brief edge summary.
+        """
+        # ── Text-based search (existing logic) ──
+        nodes = self.storage.search_nodes_by_name(query, node_type, limit)
+
+        # Also search by semantic_type in properties
+        if not node_type or node_type == NodeType.COLUMN:
+            conn = self.storage.conn
+            prop_rows = conn.execute(
+                "SELECT id, type, name, properties FROM nodes WHERE type = 'column' AND properties LIKE ? LIMIT ?",
+                (f"%{query}%", limit),
+            ).fetchall()
+
+            # Convert rows and add to results (avoid duplicates)
+            existing_ids = {n.id for n in nodes}
+            for row in prop_rows:
+                id_str, type_str, name, properties_json = row
+                if id_str not in existing_ids:
+                    existing_ids.add(id_str)
+                    # Parse properties to check if semantic_type matches
+                    try:
+                        props = json.loads(properties_json) if properties_json else {}
+                        if query.lower() in props.get("semantic_type", "").lower():
+                            nodes.append(self.storage._row_to_node(row))
+                    except Exception:
+                        pass
+
+        # ── Vector-based search (hybrid retrieval) ──
+        vec_nodes: dict[str, tuple[Node, float]] = {}  # node_id → (node, similarity)
+        if self.embedding_service and self.embedding_service.is_available():
+            # Check if embedding vectors exist in the DB
+            stored_model = self.storage.get_embedding_model()
+            if stored_model and stored_model == self.embedding_service.get_model_name():
+                query_embedding = self.embedding_service.compute_embedding(query)
+                if query_embedding:
+                    # Use a lower threshold for retrieval than for merge pre-filter.
+                    # The config similarity_threshold (default 0.75) is designed for
+                    # merge candidate filtering, not for retrieval where cross-language
+                    # and short-query matches naturally have lower similarity scores.
+                    vec_results = self.storage.search_nodes_by_embedding(
+                        query_embedding, node_type, limit * 2, threshold=0.35
+                    )
+                    for node, sim in vec_results:
+                        if node.id not in vec_nodes or sim > vec_nodes[node.id][1]:
+                            vec_nodes[node.id] = (node, sim)
+
+        # ── Merge text + vector results ──
+        # Track text-based node IDs for dedup
+        text_node_ids = {n.id for n in nodes}
+        has_text_matches = bool(text_node_ids)
+
+        # Add vector-only results (nodes not found by text search)
+        # Weight logic:
+        # - When text search also found results: vector is supplementary → downweight
+        # - When text search found nothing: vector is the ONLY dimension → full weight
+        merged_nodes: list[tuple[Node, float]] = []  # (node, score)
+        # Text match scores: exact=1.0, substring=0.8
+        for node in nodes:
+            score = 1.0 if node.name.lower() == query.lower() else 0.8
+            merged_nodes.append((node, score))
+
+        # Vector results: only add nodes NOT already found by text search,
+        # since text matches are more reliable for exact/substring queries.
+        # Vector similarity is a supplementary dimension.
+        # When text search found matches, vector results are supplementary → 0.6 weight.
+        # When text search found nothing, vector is the only effective dimension → 0.8 weight.
+        # (Not 1.0 because vector similarity is inherently less precise than text matches.)
+        vec_weight = 0.6 if has_text_matches else 0.8
+        if vec_nodes:
+            for nid, (node, sim) in vec_nodes.items():
+                if nid not in text_node_ids:
+                    merged_nodes.append((node, sim * vec_weight))
+
+        # Sort by score descending, truncate to limit
+        merged_nodes.sort(key=lambda x: x[1], reverse=True)
+        merged_nodes = merged_nodes[:limit]
+
+        # Build result dicts
+        results = []
+        for node, score in merged_nodes:
+            edges = self.storage.get_edges_for_node(node.id)
+            result = {
+                "id": node.id,
+                "type": node.type.value,
+                "name": node.name,
+                "properties": node.properties,
+                "edge_count": len(edges),
+                "edges_summary": [
+                    {
+                        "type": e.type.value,
+                        "target_id": e.target_id if e.source_id == node.id else e.source_id,
+                        "confidence": e.confidence,
+                    }
+                    for e in edges[:5]  # Show top 5 edges
+                ],
+            }
+            results.append(result)
+
+        if mask_credential:
+            results = mask_result(results)
+
+        return results
+
+    def get_node(
+        self,
+        node_id: str,
+        include_edges: bool = True,
+        mask_credential: bool = True,
+        max_response_chars: int = 25000,
+    ) -> dict[str, Any] | None:
+        """Get a node's details and optionally all adjacent edges.
+
+        Args:
+            node_id: ID of the node to retrieve (masked IDs and short aliases are auto-resolved).
+            include_edges: Whether to include adjacent edge information.
+            mask_credential: Whether to mask credentials in output (default True).
+            max_response_chars: Maximum response size in chars (default 25000).
+                When exceeded, edges are trimmed (low-confidence edges removed first).
+                Prevents oversized responses that may be truncated by MCP protocol.
+
+        Returns:
+            Dict with node details and edges, or None if node doesn't exist.
+        """
+        node_id = self._resolve_input_id(node_id)
+        node = self.storage.get_node(node_id)
+        if node is None:
+            return None
+
+        result = {
+            "id": node.id,
+            "type": node.type.value,
+            "name": node.name,
+            "properties": node.properties,
+        }
+
+        if include_edges:
+            edges = self.storage.get_edges_for_node(node_id)
+
+            # For table nodes, skip "contains" edges — they're redundant with column_ids
+            if node.type == NodeType.TABLE:
+                edges = [e for e in edges if e.type != EdgeType.CONTAINS]
+
+            result["edges"] = []
+            for edge in edges:
+                # Determine the "other" node in this edge
+                is_outgoing = edge.source_id == node_id
+                other_id = edge.target_id if is_outgoing else edge.source_id
+                other_node = self.storage.get_node(other_id)
+                result["edges"].append(
+                    {
+                        "id": edge.id,
+                        "type": edge.type.value,
+                        "direction": "outgoing" if is_outgoing else "incoming",
+                        "source_id": edge.source_id,
+                        "target_id": edge.target_id,
+                        "confidence": edge.confidence,
+                        "properties": edge.properties,
+                        "other_node": {
+                            "id": other_id,
+                            "name": other_node.name if other_node else "unknown",
+                            "type": other_node.type.value if other_node else "unknown",
+                        }
+                        if other_node
+                        else None,
+                    }
+                )
+
+            # Response size cap: if edges list makes result too large,
+            # trim low-confidence edges first
+            result_size = len(json.dumps(result, ensure_ascii=False))
+            if result_size > max_response_chars and result["edges"]:
+                # Sort edges by confidence descending, keep only high-confidence ones
+                result["edges"].sort(key=lambda e: e.get("confidence", 0), reverse=True)
+                while len(result["edges"]) > 3 and len(json.dumps(result, ensure_ascii=False)) > max_response_chars:
+                    result["edges"].pop()
+
+            # Include profile if this is a column node
+            if node.type == NodeType.COLUMN:
+                profile = self.storage.get_profile_for_column(node_id)
+                if profile:
+                    result["profile"] = {
+                        "id": profile.id,
+                        "dtype": profile.dtype,
+                        "semantic_type": profile.semantic_type,
+                        "null_rate": profile.null_rate,
+                        "cardinality": profile.cardinality,
+                        "unique_rate": profile.unique_rate,
+                        "sample_values": profile.sample_values[:5],
+                    }
+
+            # Include pending (dangling) edges as suggested_edges
+            pending = self.storage.get_pending_edges_for_node(node_id)
+            result["suggested_edges"] = [
+                {
+                    "id": pe.id,
+                    "type": pe.type.value,
+                    "source_id": pe.source_id,
+                    "target_id": pe.target_id,
+                    "confidence": pe.confidence,
+                    "missing_endpoints": pe.missing_endpoints,
+                    "note": _describe_missing(pe),
+                }
+                for pe in pending
+            ]
+
+        if mask_credential:
+            result = mask_result(result)
+
+        return result
+
+    def find_paths(
+        self,
+        source_id: str,
+        target_id: str,
+        max_depth: int = 3,
+        edge_types: list[EdgeType] | None = None,
+        limit: int = 3,
+        mask_credential: bool = True,
+    ) -> list[dict[str, Any]]:
+        """Find paths between two nodes using BFS graph traversal.
+
+        Uses SQLite WITH RECURSIVE CTE for efficient traversal.
+
+        Args:
+            source_id: Starting node ID (masked IDs and short aliases are auto-resolved).
+            target_id: Destination node ID (masked IDs and short aliases are auto-resolved).
+            max_depth: Maximum path length (number of edges).
+            edge_types: Optional filter — only traverse edges of these types.
+            limit: Maximum number of paths to return (default 3).
+            mask_credential: Whether to mask credentials in output (default True).
+
+        Returns:
+            List of paths, each containing nodes and edges along the path,
+            sorted by total path confidence (descending).
+        """
+        source_id = self._resolve_input_id(source_id)
+        target_id = self._resolve_input_id(target_id)
+
+        # Validate both nodes exist
+        source = self.storage.get_node(source_id)
+        target = self.storage.get_node(target_id)
+        if source is None or target is None:
+            return []
+
+        # Always use Python BFS for path finding.
+        # SQLite's recursive CTE with json_each for cycle prevention causes
+        # "datatype mismatch" errors with long node/edge IDs, and alternative
+        # CTE-based cycle prevention (comma-separated strings, nested CTEs)
+        # is either unreliable or unsupported inside a recursive CTE. Python
+        # BFS is simpler, correct, and fast enough for DataLink's graph size.
+        return self._python_bfs_paths(source_id, target_id, max_depth, edge_types, limit)
+
+    def _python_bfs_paths(
+        self,
+        source_id: str,
+        target_id: str,
+        max_depth: int,
+        edge_types: list[EdgeType] | None = None,
+        limit: int = 3,
+    ) -> list[dict[str, Any]]:
+        """Fallback BFS path finder in pure Python.
+
+        Used when the SQL CTE approach fails.
+        """
+        # BFS to find shortest paths
+        visited_paths = []
+        queue = [(source_id, [], 1.0)]  # (current_node, edge_path, confidence)
+
+        type_set = {et.value for et in edge_types} if edge_types else None
+
+        while queue:
+            current, edge_path, confidence = queue.pop(0)
+
+            if len(edge_path) > max_depth:
+                continue
+
+            if current == target_id and edge_path:
+                visited_paths.append((edge_path, confidence))
+                continue
+
+            # Get edges from current node
+            edges = self.storage.get_edges_for_node(current)
+            for edge in edges:
+                if type_set and edge.type.value not in type_set:
+                    continue
+
+                # Determine next node
+                next_node = edge.target_id if edge.source_id == current else edge.source_id
+
+                # Avoid cycles
+                visited_nodes = {current}
+                for ep in edge_path:
+                    visited_nodes.add(ep.source_id)
+                    visited_nodes.add(ep.target_id)
+
+                if next_node in visited_nodes:
+                    continue
+
+                new_path = edge_path + [edge]
+                new_confidence = confidence * edge.confidence
+                queue.append((next_node, new_path, new_confidence))
+
+        # Sort by confidence (descending)
+        visited_paths.sort(key=lambda x: x[1], reverse=True)
+
+        # Convert to result format
+        results = []
+        for edge_path, confidence in visited_paths[:limit]:
+            path_nodes = [source_id]
+            path_edges_detail = []
+            for edge in edge_path:
+                path_edges_detail.append(
+                    {
+                        "id": edge.id,
+                        "type": edge.type.value,
+                        "source_id": edge.source_id,
+                        "target_id": edge.target_id,
+                        "confidence": edge.confidence,
+                    }
+                )
+                if edge.source_id == path_nodes[-1]:
+                    path_nodes.append(edge.target_id)
+                else:
+                    path_nodes.append(edge.source_id)
+
+            results.append(
+                {
+                    "nodes": path_nodes,
+                    "edges": path_edges_detail,
+                    "length": len(edge_path),
+                    "confidence": confidence,
+                }
+            )
+
+        return results
+
+    def extract_subgraph(
+        self,
+        node_ids: list[str],
+        max_hops: int = 2,
+        mask_credential: bool = True,
+    ) -> dict[str, Any]:
+        """Extract a subgraph around the specified nodes.
+
+        Starting from the given node IDs, expand max_hops layers
+        of neighbors and return all nodes and edges in the expanded subgraph.
+
+        Args:
+            node_ids: Starting node IDs (masked IDs are auto-resolved).
+            max_hops: Number of neighbor layers to expand.
+            mask_credential: Whether to mask credentials in output (default True).
+
+        Returns:
+            Dict with 'nodes' and 'edges' lists representing the subgraph.
+        """
+        node_ids = self._resolve_input_ids(node_ids)
+        visited_nodes = set(node_ids)
+        visited_edges = set()
+        current_layer = set(node_ids)
+
+        for hop in range(max_hops):
+            next_layer = set()
+            for nid in current_layer:
+                edges = self.storage.get_edges_for_node(nid)
+                for edge in edges:
+                    if edge.id not in visited_edges:
+                        visited_edges.add(edge.id)
+                        # Add the connected node
+                        other_id = edge.target_id if edge.source_id == nid else edge.source_id
+                        if other_id not in visited_nodes:
+                            visited_nodes.add(other_id)
+                            next_layer.add(other_id)
+
+            current_layer = next_layer
+            if not current_layer:
+                break
+
+        # Collect all nodes and edges
+        nodes = []
+        for nid in visited_nodes:
+            node = self.storage.get_node(nid)
+            if node:
+                nodes.append(
+                    {
+                        "id": node.id,
+                        "type": node.type.value,
+                        "name": node.name,
+                        "properties": node.properties,
+                    }
+                )
+
+        edges = []
+        for eid in visited_edges:
+            edge = self.storage.get_edge(eid)
+            if edge:
+                edges.append(
+                    {
+                        "id": edge.id,
+                        "source_id": edge.source_id,
+                        "target_id": edge.target_id,
+                        "type": edge.type.value,
+                        "confidence": edge.confidence,
+                        "properties": edge.properties,
+                    }
+                )
+
+        result = {
+            "nodes": nodes,
+            "edges": edges,
+            "stats": {
+                "node_count": len(nodes),
+                "edge_count": len(edges),
+                "hops": max_hops,
+            },
+        }
+
+        if mask_credential:
+            result = mask_result(result)
+
+        return result
+
+    def get_pending_edges(
+        self,
+        node_id: str | None = None,
+        edge_type: EdgeType | None = None,
+        limit: int = 50,
+        mask_credential: bool = True,
+    ) -> list[dict[str, Any]]:
+        """List pending (dangling) edges, optionally filtered by node or type.
+
+        Pending edges reference nodes that don't yet exist in the graph —
+        typically FK relationships pointing to tables/columns from datasources
+        that haven't been added yet.
+
+        Args:
+            node_id: Optional — filter to pending edges involving this node (masked IDs auto-resolved).
+            edge_type: Optional — filter by edge type.
+            limit: Maximum number of results.
+            mask_credential: Whether to mask credentials in output (default True).
+
+        Returns:
+            List of dicts with pending edge details and missing endpoint info.
+        """
+        if node_id:
+            node_id = self._resolve_input_id(node_id)
+        if node_id:
+            pending_edges = self.storage.get_pending_edges_for_node(node_id)
+        else:
+            pending_edges = self.storage.get_all_pending_edges()
+
+        if edge_type:
+            pending_edges = [pe for pe in pending_edges if pe.type == edge_type]
+
+        pending_edges = pending_edges[:limit]
+
+        result = [
+            {
+                "id": pe.id,
+                "type": pe.type.value,
+                "source_id": pe.source_id,
+                "target_id": pe.target_id,
+                "confidence": pe.confidence,
+                "missing_endpoints": pe.missing_endpoints,
+                "note": _describe_missing(pe),
+                "properties": pe.properties,
+            }
+            for pe in pending_edges
+        ]
+
+        if mask_credential:
+            return mask_result(result)
+
+        return result
+
+    def list_datasets(self, mask_credential: bool = True) -> list[dict[str, Any]]:
+        """List all tables/datasets in the graph with basic stats.
+
+        Returns:
+            List of dicts with table info.
+        """
+        tables = self.storage.get_nodes_by_type(NodeType.TABLE)
+        results = []
+
+        for table in tables:
+            # Get column count via contains edges
+            contains_edges = self.storage.get_edges_for_node(table.id, EdgeType.CONTAINS)
+            column_count = len(contains_edges)
+
+            # Count inferred edges involving this table's columns
+            column_ids = [e.target_id for e in contains_edges]
+            inferred_edge_count = 0
+            for col_id in column_ids:
+                col_edges = self.storage.get_edges_for_node(col_id)
+                for ce in col_edges:
+                    if ce.type not in (EdgeType.CONTAINS, EdgeType.FOREIGN_KEY):
+                        inferred_edge_count += 1
+
+            # Count pending FK edges involving this table's columns
+            pending_fk_count = 0
+            for col_id in column_ids:
+                pending = self.storage.get_pending_edges_for_node(col_id)
+                for pe in pending:
+                    if pe.type == EdgeType.FOREIGN_KEY:
+                        pending_fk_count += 1
+
+            results.append(
+                {
+                    "id": table.id,
+                    "name": table.name,
+                    "source": table.properties.get("source", ""),
+                    "row_count": table.properties.get("row_count", 0),
+                    "column_count": column_count,
+                    "inferred_edge_count": inferred_edge_count,
+                    "pending_fk_count": pending_fk_count,
+                }
+            )
+
+        if mask_credential:
+            return mask_result(results)
+
+        return results
+
+    # ── Explore: universal retrieval entry point ─────────────────────
+
+    def explore(
+        self,
+        query: str,
+        max_nodes: int | None = None,
+        focus: str | None = None,
+        mask_credential: bool = True,
+    ) -> str:
+        """Universal retrieval entry point — answers data questions in one call.
+
+        Takes a natural-language-ish query (keywords, names, short descriptions)
+        and returns organized context about relevant nodes, their relationships,
+        and data profiles. Internally combines search, get_node, find_paths,
+        and profile data into a single formatted text response.
+
+        Args:
+            query: Keywords, names, or short descriptions, e.g.
+                   "revenue customer_id orders" or "email address fields".
+            max_nodes: Maximum nodes to include in detail. None → auto-scaled.
+            focus: Optional focus direction:
+                   "join_paths" — relationship chains and JOIN paths prioritized,
+                   "schema" — table structure overview prioritized,
+                   "data_profile" — column fingerprints and quality prioritized,
+                   None — balanced output (default).
+            mask_credential: Whether to mask credentials in output (default True).
+                   When True, database connection strings in source lines are masked.
+
+        Returns:
+            Formatted text string — organized by table, with inline
+            relationships, profiles, and pending-edge notes.
+        """
+        dataset_count = self.storage.count_nodes(NodeType.TABLE)
+        budget = get_explore_budget(dataset_count, focus)
+
+        # Override max_nodes if caller specified
+        if max_nodes is not None:
+            budget.max_nodes = min(max_nodes, budget.max_nodes * 2)
+
+        # Step 1: resolve query to node set
+        resolved = self._resolve_query(query, budget.max_nodes)
+        if not resolved:
+            return (
+                f'No results found for "{query}". '
+                "Try different keywords or check that the graph has data "
+                "(run `datalink info`)."
+            )
+
+        # Step 2: build context for each resolved node
+        contexts = self._build_context(resolved, budget)
+
+        # Step 3: build relationship map among resolved nodes
+        node_ids = {rn.node_id for rn in resolved}
+        rel_map = self._build_relationship_map(node_ids, budget)
+
+        # Step 4: format output
+        output = self._format_output(contexts, rel_map, resolved, budget, mask_credential)
+
+        return output
+
+    # ── Explore internals ────────────────────────────────────────────
+
+    def _resolve_query(self, query: str, limit: int) -> list[ResolvedNode]:
+        """Parse query tokens and match against graph nodes via multiple dimensions.
+
+        Multi-dimension matching:
+        - Name substring match
+        - semantic_type match (in column properties)
+        - Concept/Entity name match → edge expansion to columns
+        - comment / description match
+        - Vector semantic match (if embedding service is available)
+        - Table name match → contains expansion to all columns
+
+        Then expand matched nodes along key edge types for higher recall.
+        """
+        tokens = [t.strip() for t in query.replace(",", " ").split() if t.strip()]
+        if not tokens:
+            return []
+
+        resolved: dict[str, ResolvedNode] = {}  # node_id → ResolvedNode (dedup)
+
+        for token in tokens:
+            token_lower = token.lower()
+
+            # 1. Name substring match — all types
+            name_matches = self.storage.search_nodes_by_name(token, None, limit * 2)
+            for node in name_matches:
+                score = 1.0 if node.name.lower() == token_lower else 0.8
+                reason = "name_exact" if score == 1.0 else "name_substring"
+                self._add_resolved(resolved, node, reason, score)
+
+            # 2. semantic_type match — column nodes only
+            conn = self.storage.conn
+            prop_rows = conn.execute(
+                "SELECT id, type, name, properties FROM nodes WHERE type = 'column' AND properties LIKE ? LIMIT ?",
+                (f"%{token}%", limit * 2),
+            ).fetchall()
+            for row in prop_rows:
+                node = self.storage._row_to_node(row)
+                if node.id in resolved:
+                    continue
+                props = json.loads(row[3]) if row[3] else {}
+                st = props.get("semantic_type", "").lower()
+                if token_lower in st:
+                    self._add_resolved(resolved, node, "semantic_type", 0.7)
+
+            # 3. comment / description match
+            for nt_val in ("column", "concept", "entity"):
+                desc_rows = conn.execute(
+                    "SELECT id, type, name, properties FROM nodes WHERE type = ? AND properties LIKE ? LIMIT ?",
+                    (nt_val, f"%{token}%", limit),
+                ).fetchall()
+                for row in desc_rows:
+                    node = self.storage._row_to_node(row)
+                    if node.id in resolved:
+                        continue
+                    props = json.loads(row[3]) if row[3] else {}
+                    if nt_val == "column":
+                        text_field = props.get("comment", "")
+                    else:
+                        text_field = props.get("description", "")
+                    if token_lower in text_field.lower():
+                        self._add_resolved(resolved, node, "comment_description", 0.5)
+
+        # 4. Vector semantic match — embedding similarity search
+        # Only active when embedding service is configured and vectors exist in DB.
+        # Serves as a supplementary dimension: finds semantically related nodes
+        # that text search may miss (e.g., "revenue" ≈ "income", "amount",
+        # or "学校" ≈ "schools" for cross-language matching).
+        #
+        # Use a lower threshold (0.35) than the config similarity_threshold
+        # (default 0.75) because: (1) the config threshold is designed for
+        # merge candidate filtering, not retrieval; (2) cross-language and
+        # short-query vs long-document matches naturally produce lower cosine
+        # similarity scores that would be filtered at 0.5 or above.
+        if self.embedding_service and self.embedding_service.is_available():
+            stored_model = self.storage.get_embedding_model()
+            if stored_model and stored_model == self.embedding_service.get_model_name():
+                query_embedding = self.embedding_service.compute_embedding(query)
+                if query_embedding:
+                    vec_results = self.storage.search_nodes_by_embedding(
+                        query_embedding, None, limit * 3, threshold=0.35
+                    )
+                    # When text search found matches, vector results are supplementary.
+                    # When text search found nothing, vector is the only effective dimension
+                    # and deserves higher weight (especially for cross-language queries).
+                    has_text_hits = bool(resolved)
+                    base_vec_weight = 0.6 if has_text_hits else 0.8
+                    for node, sim in vec_results:
+                        if node.id not in resolved:
+                            # Prioritize column/table nodes over concept/entity in
+                            # vector results: columns are the final output of explore,
+                            # while concept/entity only serve as intermediaries for
+                            # edge expansion. Without this, concept/entity nodes
+                            # (which tend to have higher cosine similarity to the query
+                            # because their names are more abstract) can monopolize
+                            # the budget and push out the more useful column matches.
+                            node_weight = base_vec_weight
+                            if node.type in (NodeType.COLUMN, NodeType.TABLE):
+                                node_weight = base_vec_weight * 1.2
+                            elif node.type in (NodeType.CONCEPT, NodeType.ENTITY):
+                                node_weight = base_vec_weight * 0.7
+                            self._add_resolved(resolved, node, "vector_semantic", sim * node_weight)
+
+        # 5. Edge expansion — expand matched nodes along key edge types
+        expanded_ids: dict[str, ResolvedNode] = {}
+        for rn in list(resolved.values()):
+            node = self.storage.get_node(rn.node_id)
+            if node is None:
+                continue
+
+            if node.type == NodeType.CONCEPT:
+                # Concept → all columns it represents
+                edges = self.storage.get_edges_for_node(node.id, EdgeType.REPRESENTS)
+                for e in edges:
+                    col_id = e.source_id if e.target_id == node.id else e.target_id
+                    col = self.storage.get_node(col_id)
+                    if col and col.id not in resolved:
+                        self._add_resolved(expanded_ids, col, "concept_expand", 0.6 * e.confidence)
+
+            elif node.type == NodeType.ENTITY:
+                # Entity → concepts → columns
+                entity_edges = self.storage.get_edges_for_node(node.id, EdgeType.HAS_CONCEPT)
+                for e in entity_edges:
+                    concept_id = e.target_id if e.source_id == node.id else e.source_id
+                    concept = self.storage.get_node(concept_id)
+                    if concept and concept.id not in resolved:
+                        self._add_resolved(
+                            expanded_ids,
+                            concept,
+                            "entity_expand",
+                            0.5 * e.confidence,
+                        )
+                    if concept:
+                        rep_edges = self.storage.get_edges_for_node(concept_id, EdgeType.REPRESENTS)
+                        for re_ in rep_edges:
+                            col_id = re_.source_id if re_.target_id == concept_id else re_.target_id
+                            col = self.storage.get_node(col_id)
+                            if col and col.id not in resolved:
+                                self._add_resolved(
+                                    expanded_ids,
+                                    col,
+                                    "entity_concept_expand",
+                                    0.4 * e.confidence * re_.confidence,
+                                )
+
+            elif node.type == NodeType.TABLE:
+                # Table → all contained columns
+                contains_edges = self.storage.get_edges_for_node(node.id, EdgeType.CONTAINS)
+                for e in contains_edges:
+                    col_id = e.target_id  # contains: table → column
+                    col = self.storage.get_node(col_id)
+                    if col and col.id not in resolved:
+                        self._add_resolved(expanded_ids, col, "table_expand", 0.4)
+
+            elif node.type == NodeType.COLUMN:
+                # Column → related columns via FK/joinable/semantic_synonym
+                for et in (EdgeType.FOREIGN_KEY, EdgeType.JOINABLE, EdgeType.SEMANTIC_SYNONYM):
+                    col_edges = self.storage.get_edges_for_node(node.id, et)
+                    for e_ in col_edges:
+                        other_id = e_.target_id if e_.source_id == node.id else e_.source_id
+                        other = self.storage.get_node(other_id)
+                        if other and other.id not in resolved:
+                            self._add_resolved(
+                                expanded_ids,
+                                other,
+                                "column_expand",
+                                0.3 * e_.confidence,
+                            )
+
+        # Merge expanded into resolved (expanded nodes are lower priority)
+        for nid, rn in expanded_ids.items():
+            if nid not in resolved:
+                resolved[nid] = rn
+
+        # Sort by relevance_score (descending), truncate to limit
+        sorted_resolved = sorted(resolved.values(), key=lambda r: r.relevance_score, reverse=True)
+        return sorted_resolved[:limit]
+
+    def _add_resolved(self, resolved: dict[str, ResolvedNode], node: Node, reason: str, score: float) -> None:
+        """Add a node to the resolved dict, keeping the best score if already present."""
+        if node.id in resolved:
+            existing = resolved[node.id]
+            if score > existing.relevance_score:
+                resolved[node.id] = ResolvedNode(
+                    node_id=node.id,
+                    name=node.name,
+                    node_type=node.type,
+                    match_reason=reason,
+                    relevance_score=score,
+                )
+        else:
+            resolved[node.id] = ResolvedNode(
+                node_id=node.id,
+                name=node.name,
+                node_type=node.type,
+                match_reason=reason,
+                relevance_score=score,
+            )
+
+    def _build_context(self, resolved: list[ResolvedNode], budget: ExploreBudget) -> list[NodeContext]:
+        """Build context for each resolved node: edges, profile, pending edges."""
+        contexts = []
+        for rn in resolved:
+            node = self.storage.get_node(rn.node_id)
+            if node is None:
+                continue
+
+            # Gather all edges, group by type category
+            all_edges = self.storage.get_edges_for_node(node.id)
+            edges_by_group: dict[str, list[dict[str, Any]]] = {}
+
+            for edge in all_edges:
+                other_id = edge.target_id if edge.source_id == node.id else edge.source_id
+                other_node = self.storage.get_node(other_id)
+
+                group_key = self._edge_group_key(edge.type)
+                summary = {
+                    "id": edge.id,
+                    "type": edge.type.value,
+                    "direction": "→" if edge.source_id == node.id else "←",
+                    "other_id": other_id,
+                    "other_name": other_node.name if other_node else "unknown",
+                    "other_type": other_node.type.value if other_node else "unknown",
+                    "confidence": edge.confidence,
+                }
+
+                if group_key not in edges_by_group:
+                    edges_by_group[group_key] = []
+                edges_by_group[group_key].append(summary)
+
+            # Truncate each group to budget
+            for key in edges_by_group:
+                edges_by_group[key] = edges_by_group[key][: budget.max_edges_per_node]
+
+            # Profile summary for column nodes
+            profile_summary = None
+            if node.type == NodeType.COLUMN:
+                profile = self.storage.get_profile_for_column(node.id)
+                if profile:
+                    profile_summary = self._profile_summary(profile, budget)
+
+            # Pending edges
+            pending_edges_list = self.storage.get_pending_edges_for_node(node.id)
+            pending_summaries = [
+                {
+                    "id": pe.id,
+                    "type": pe.type.value,
+                    "source_id": pe.source_id,
+                    "target_id": pe.target_id,
+                    "missing_endpoints": pe.missing_endpoints,
+                    "note": _describe_missing(pe),
+                    "confidence": pe.confidence,
+                }
+                for pe in pending_edges_list[:3]  # cap at 3 per node
+            ]
+
+            contexts.append(
+                NodeContext(
+                    node=node,
+                    edges_by_group=edges_by_group,
+                    profile_summary=profile_summary,
+                    pending_edges=pending_summaries,
+                )
+            )
+
+        return contexts
+
+    def _edge_group_key(self, edge_type: EdgeType) -> str:
+        """Map an EdgeType to a display group key."""
+        if edge_type in JOIN_EDGE_TYPES or edge_type == EdgeType.FOREIGN_KEY:
+            return "join"
+        if edge_type in SEMANTIC_EDGE_TYPES:
+            return "semantic"
+        if edge_type in STATISTICAL_EDGE_TYPES:
+            return "statistical"
+        if edge_type == EdgeType.CONTAINS:
+            return "contains"
+        return "other"
+
+    def _profile_summary(self, profile: ColumnProfile, budget: ExploreBudget) -> dict[str, Any]:
+        """Extract key profile metrics, respecting budget for sample_values."""
+        summary: dict[str, Any] = {
+            "dtype": profile.dtype,
+            "semantic_type": profile.semantic_type,
+            "null_rate": profile.null_rate,
+            "cardinality": profile.cardinality,
+            "unique_rate": profile.unique_rate,
+        }
+
+        if budget.max_sample_values > 0:
+            # Use top_values (distinct, frequency-ranked) instead of random
+            # sample_values. Random samples often repeat the most frequent
+            # value, producing output like "samples: [A, A, A, A, A]"
+            # which is useless for agents.
+            # Fallback: if top_values is empty, use sample_values.
+            if profile.top_values:
+                summary["representative_values"] = [
+                    tv["value"] for tv in profile.top_values[: budget.max_sample_values]
+                ]
+            else:
+                summary["representative_values"] = [str(v) for v in profile.sample_values[: budget.max_sample_values]]
+
+        # Extra metrics for data_profile focus
+        if budget.max_sample_values >= 10:
+            summary["top_values"] = profile.top_values[:5]
+            if profile.min_value is not None:
+                summary["min_value"] = profile.min_value
+                summary["max_value"] = profile.max_value
+                summary["mean_value"] = profile.mean_value
+            if profile.min_length is not None:
+                summary["min_length"] = profile.min_length
+                summary["max_length"] = profile.max_length
+
+        return summary
+
+    def _build_relationship_map(self, node_ids: set[str], budget: ExploreBudget) -> RelationshipMap:
+        """Build the relationship network among resolved nodes.
+
+        Direct connections: edges where both endpoints are in node_ids.
+        Indirect connections: multi-hop paths between resolved nodes.
+        """
+        join_relations: list[dict[str, Any]] = []
+        semantic_relations: list[dict[str, Any]] = []
+        statistical_relations: list[dict[str, Any]] = []
+
+        seen_edge_ids: set[str] = set()
+
+        for nid in node_ids:
+            edges = self.storage.get_edges_for_node(nid)
+            for edge in edges:
+                other_id = edge.target_id if edge.source_id == nid else edge.source_id
+                if other_id not in node_ids:
+                    continue
+                # Dedup
+                if edge.id in seen_edge_ids:
+                    continue
+                seen_edge_ids.add(edge.id)
+
+                src_node = self.storage.get_node(edge.source_id)
+                tgt_node = self.storage.get_node(edge.target_id)
+
+                relation = {
+                    "edge_id": edge.id,
+                    "source_id": edge.source_id,
+                    "source_name": src_node.name if src_node else "unknown",
+                    "target_id": edge.target_id,
+                    "target_name": tgt_node.name if tgt_node else "unknown",
+                    "type": edge.type.value,
+                    "confidence": edge.confidence,
+                }
+
+                if edge.type in JOIN_EDGE_TYPES or edge.type == EdgeType.FOREIGN_KEY:
+                    join_relations.append(relation)
+                elif edge.type in SEMANTIC_EDGE_TYPES:
+                    semantic_relations.append(relation)
+                elif edge.type in STATISTICAL_EDGE_TYPES:
+                    statistical_relations.append(relation)
+
+        # Indirect paths: only traverse JOIN-relevant edges (FK/joinable),
+        # not structural edges (contains, represents, etc.) which produce
+        # meaningless "column → table → column" chains.
+        indirect_paths: list[dict[str, Any]] = []
+        if budget.include_relationships:
+            join_edge_types = [EdgeType.FOREIGN_KEY, EdgeType.JOINABLE]
+            node_list = list(node_ids)
+            max_pairs = min(len(node_list) * (len(node_list) - 1) // 2, 20)
+            pair_count = 0
+            for i, src_id in enumerate(node_list):
+                for tgt_id in node_list[i + 1 :]:
+                    if pair_count >= max_pairs:
+                        break
+                    # Use mask_credential=False here — masking is handled at
+                    # the _format_output layer where we have full control.
+                    paths = self.find_paths(
+                        src_id,
+                        tgt_id,
+                        max_depth=3,
+                        edge_types=join_edge_types,
+                        mask_credential=False,
+                    )
+                    for p in paths[:2]:
+                        # Only keep paths that cross table boundaries
+                        src_node = self.storage.get_node(src_id)
+                        tgt_node = self.storage.get_node(tgt_id)
+                        if src_node and tgt_node:
+                            src_table = self._table_name_for_col(src_id) if isinstance(src_node, ColumnNode) else None
+                            tgt_table = self._table_name_for_col(tgt_id) if isinstance(tgt_node, ColumnNode) else None
+                            if src_table and tgt_table and src_table != tgt_table:
+                                indirect_paths.append(p)
+                    pair_count += 1
+
+        return RelationshipMap(
+            join_relations=join_relations,
+            semantic_relations=semantic_relations,
+            statistical_relations=statistical_relations,
+            indirect_paths=indirect_paths,
+        )
+
+    # ── New format helpers: meaning, also_related, data_line ───────────
+
+    def _generate_meaning(self, ctx: NodeContext, budget: ExploreBudget) -> str | None:
+        """Generate a meaning line for a column — combining comment, concept/entity
+        absorption, and FK/joinable translation into one coherent description.
+
+        Meaning parts are assembled in order:
+        1. column comment (from SQL metadata or CSV header)
+        2. concept absorption (represents/has_concept edges)
+        3. FK/joinable translation (query-oriented)
+        Parts are joined with "; ".
+        """
+        node = ctx.node
+        if node.type != NodeType.COLUMN or not isinstance(node, ColumnNode):
+            return None
+
+        parts: list[str] = []
+
+        # 1. Column comment
+        comment = node.comment or node.properties.get("comment", "")
+        if comment:
+            parts.append(comment)
+
+        # 2. Concept/entity absorption from semantic edges
+        for e in ctx.edges_by_group.get("semantic", []):
+            etype = e["type"]
+            other_id = e["other_id"]
+            other_node = self.storage.get_node(other_id)
+
+            if etype == "represents" and other_node and isinstance(other_node, ConceptNode):
+                # Column represents this concept — absorb its description/unit/dimension
+                concept_parts = [f'represents concept "{other_node.name}"']
+                desc_detail = []
+                if other_node.description:
+                    desc_detail.append(other_node.description)
+                if other_node.unit:
+                    desc_detail.append(f"unit: {other_node.unit}")
+                if other_node.dimension:
+                    desc_detail.append(f"dimension: {other_node.dimension}")
+                if desc_detail:
+                    concept_parts.append(f"({', '.join(desc_detail)})")
+                parts.append(" ".join(concept_parts))
+
+            elif etype == "has_concept" and other_node and isinstance(other_node, ConceptNode):
+                # Entity → concept chain — absorb concept description
+                concept_parts = [f'has concept "{other_node.name}"']
+                desc_detail = []
+                if other_node.description:
+                    desc_detail.append(other_node.description)
+                if other_node.unit:
+                    desc_detail.append(f"unit: {other_node.unit}")
+                if desc_detail:
+                    concept_parts.append(f"({', '.join(desc_detail)})")
+                parts.append(" ".join(concept_parts))
+
+        # Also check: entity nodes connected via has_concept
+        # An entity's has_concept edge: entity→concept. If this column is connected
+        # to an entity, absorb entity description.
+        for e in ctx.edges_by_group.get("semantic", []):
+            etype = e["type"]
+            other_id = e["other_id"]
+            other_node = self.storage.get_node(other_id)
+
+            if etype == "has_concept" and other_node and isinstance(other_node, EntityNode):
+                entity_parts = [f'part of entity "{other_node.name}"']
+                if other_node.description:
+                    entity_parts.append(f"({other_node.description})")
+                parts.append(" ".join(entity_parts))
+
+        # 3. FK/joinable translation — query-oriented meaning
+        for e in ctx.edges_by_group.get("join", []):
+            etype = e["type"]
+            direction = e["direction"]
+            other_name = e["other_name"]
+            other_id = e["other_id"]
+            other_table = self._table_name_for_col(other_id) if e["other_type"] == "column" else None
+
+            if etype == "foreign_key" and direction == "→":
+                # Outbound FK: this column references another table's column
+                dialect = self._dialect_for_node(node)
+                src_table = self._table_name_for_col(node.id) or ""
+                q_src = quote_identifier(src_table, dialect) if src_table else ""
+                q_src_col = quote_identifier(node.name, dialect)
+                if other_table:
+                    q_tgt = quote_qualified(other_table, other_name, dialect=dialect)
+                    tgt_label = f"{other_table}.{other_name}"
+                else:
+                    q_tgt = quote_identifier(other_name, dialect)
+                    tgt_label = other_name
+                parts.append(
+                    f"foreign key referencing {tgt_label} — "
+                    f"use `JOIN {q_src} ON {q_src}.{q_src_col} = {q_tgt}` "
+                    f"to link {src_table} with {other_table or other_name}"
+                )
+            elif etype == "foreign_key" and direction == "←":
+                # Inbound FK: another table's column references this one
+                src_label = f"{other_table}.{other_name}" if other_table else other_name
+                parts.append(
+                    f"primary key, referenced by {src_label} — target of JOIN from {other_table or other_name}"
+                )
+            elif etype == "joinable":
+                tgt_label = f"{other_table}.{other_name}" if other_table else other_name
+                parts.append(f"joinable with {tgt_label} — can JOIN on these columns")
+
+        if not parts:
+            return None
+
+        return "; ".join(parts)
+
+    def _generate_also_related(self, ctx: NodeContext, budget: ExploreBudget) -> str | None:
+        """Generate the 'also related' line — synonym/statistical relationships
+        not in meaning, translated into compact human-readable descriptions."""
+        items: list[str] = []
+
+        for e in ctx.edges_by_group.get("semantic", []):
+            etype = e["type"]
+            other_name = e["other_name"]
+            other_id = e["other_id"]
+            other_type = e["other_type"]
+            conf = e["confidence"]
+
+            # Skip represents and has_concept — those are in meaning
+            if etype in ("represents", "has_concept"):
+                continue
+
+            other_table = self._table_name_for_col(other_id) if other_type == "column" else None
+            other_label = f"{other_table}.{other_name}" if other_table else other_name
+
+            if etype == "semantic_synonym":
+                items.append(f"synonym↔{other_label} ({conf:.2f}) — same semantic meaning")
+            elif etype == "semantic_type_match":
+                items.append(f"semantic_match↔{other_label} ({conf:.2f})")
+
+        for e in ctx.edges_by_group.get("statistical", []):
+            etype = e["type"]
+            other_name = e["other_name"]
+            other_id = e["other_id"]
+            other_type = e["other_type"]
+            conf = e["confidence"]
+
+            other_table = self._table_name_for_col(other_id) if other_type == "column" else None
+            other_label = f"{other_table}.{other_name}" if other_table else other_name
+
+            if etype == "correlated":
+                items.append(f"correlated↔{other_label} ({conf:.2f}) — values tend to move together")
+            elif etype == "distribution_similar":
+                items.append(f"distribution_similar↔{other_label} ({conf:.2f})")
+
+        if not items:
+            return None
+
+        return "; ".join(items[: budget.max_edges_per_node])
+
+    def _format_data_line(self, ctx: NodeContext, budget: ExploreBudget) -> str | None:
+        """Format the data profile as a compact single line."""
+        if ctx.profile_summary is None:
+            return None
+
+        p = ctx.profile_summary
+        bits: list[str] = []
+
+        null_pct = f"{p['null_rate'] * 100:.0f}%"
+        bits.append(f"null {null_pct}")
+        bits.append(f"unique {p['unique_rate'] * 100:.0f}%")
+
+        # Numeric range
+        if "min_value" in p and p.get("min_value") is not None:
+            bits.append(f"range {p['min_value']}–{p['max_value']}")
+
+        # Representative values (distinct, from top_values)
+        if budget.max_sample_values > 0 and "representative_values" in p and p["representative_values"]:
+            vals = [str(v) for v in p["representative_values"][: budget.max_sample_values]]
+            bits.append(f"top values: [{', '.join(vals)}]")
+
+        return ", ".join(bits)
+
+    def _format_cross_table_paths(
+        self,
+        contexts: list[NodeContext],
+        rel_map: RelationshipMap,
+        budget: ExploreBudget,
+        mask_credential: bool = True,
+    ) -> list[str]:
+        """Format cross-table query patterns — FK/joinable connections between
+        different tables, with SQL JOIN templates.
+
+        This replaces the old Relationship section's join/semantic/statistical
+        details (now inlined into meaning + also related). We only show
+        cross-table connections here as a navigable summary.
+        """
+        lines: list[str] = []
+
+        # Collect cross-table FK/joinable edges from contexts
+        # (direct FK/joinable where the two columns belong to different tables)
+        cross_table_edges: list[dict[str, Any]] = []
+        seen_edge_ids: set[str] = set()
+
+        for ctx in contexts:
+            if ctx.node.type != NodeType.COLUMN:
+                continue
+            for e in ctx.edges_by_group.get("join", []):
+                if e["id"] in seen_edge_ids:
+                    continue
+                seen_edge_ids.add(e["id"])
+
+                etype = e["type"]
+                other_id = e["other_id"]
+
+                # Only cross-table connections
+                src_table = self._table_name_for_col(ctx.node.id)
+                tgt_table = self._table_name_for_col(other_id) if e["other_type"] == "column" else None
+
+                if src_table and tgt_table and src_table != tgt_table:
+                    src_label = f"{src_table}.{ctx.node.name}"
+                    tgt_label = f"{tgt_table}.{e['other_name']}"
+                    label = "FK" if etype == "foreign_key" else "joinable"
+                    arrow = "→" if etype == "foreign_key" else "↔"
+                    direction = e["direction"]
+                    dialect = self._dialect_for_node(ctx.node)
+
+                    if direction == "←":
+                        # Flip presentation: other → this
+                        src_label, tgt_label = tgt_label, src_label
+
+                    cross_table_edges.append(
+                        {
+                            "src_label": src_label,
+                            "tgt_label": tgt_label,
+                            "arrow": arrow,
+                            "label": label,
+                            "confidence": e["confidence"],
+                            "src_table": src_table if direction == "→" else tgt_table,
+                            "tgt_table": tgt_table if direction == "→" else src_table,
+                            "src_col": ctx.node.name if direction == "→" else e["other_name"],
+                            "tgt_col": e["other_name"] if direction == "→" else ctx.node.name,
+                            "dialect": dialect,
+                        }
+                    )
+
+        # Deduplicate (both directions of same edge)
+        deduped: list[dict[str, Any]] = []
+        seen_pairs: set[str] = set()
+        for edge in cross_table_edges:
+            pair_key = f"{edge['src_label']}|{edge['tgt_label']}"
+            reverse_key = f"{edge['tgt_label']}|{edge['src_label']}"
+            if pair_key not in seen_pairs and reverse_key not in seen_pairs:
+                seen_pairs.add(pair_key)
+                deduped.append(edge)
+
+        if not deduped:
+            return lines
+
+        lines.append("")
+        lines.append("## Cross-table query patterns")
+
+        for edge in deduped[: budget.max_edges_per_relationship_kind]:
+            conf = edge["confidence"]
+            conf_note = " (low conf)" if conf < 0.5 else ""
+            lines.append(
+                f"{edge['src_label']} {edge['arrow']} {edge['tgt_label']} ({edge['label']}, conf {conf:.2f}){conf_note}"
+            )
+            # Generate SQL template for FK/joinable (identifiers quoted for
+            # hyphenated / reserved names — dialect inferred from source URL).
+            dialect = edge.get("dialect")
+            q_src_t = quote_identifier(edge["src_table"], dialect)
+            q_tgt_t = quote_identifier(edge["tgt_table"], dialect)
+            q_src_c = quote_identifier(edge["src_col"], dialect)
+            q_tgt_c = quote_identifier(edge["tgt_col"], dialect)
+            sql = f"SELECT * FROM {q_src_t} JOIN {q_tgt_t} ON {q_src_t}.{q_src_c} = {q_tgt_t}.{q_tgt_c}"
+            lines.append(f"  SQL: {sql}")
+
+        # Multi-hop indirect paths (only cross-table JOIN paths)
+        if rel_map.indirect_paths:
+            lines.append("")
+            lines.append("Indirect paths:")
+            for p in rel_map.indirect_paths[:5]:
+                path_parts = []
+                for nid in p["nodes"]:
+                    node = self.storage.get_node(nid)
+                    if node and isinstance(node, ColumnNode):
+                        tname = self._table_name_for_col(node.id)
+                        path_parts.append(f"{tname}.{node.name}" if tname else node.name)
+                    else:
+                        # Skip non-column nodes in path display (tables, concepts)
+                        # They don't have meaningful table.column format
+                        path_parts.append(node.name if node else nid)
+                path_desc = " → ".join(path_parts)
+                conf_note = " (low conf)" if p["confidence"] < 0.5 else ""
+                lines.append(f"  {path_desc}  [indirect, {p['length']} hops, conf {p['confidence']:.2f}]{conf_note}")
+
+        return lines
+
+    # Low-confidence sentinel (shared between emitter and detector)
+    LOW_CONFIDENCE_MARKER = "### ⚠️ Low-confidence match"
+
+    def _format_output(
+        self,
+        contexts: list[NodeContext],
+        rel_map: RelationshipMap,
+        resolved: list[ResolvedNode],
+        budget: ExploreBudget,
+        mask_credential: bool = True,
+    ) -> str:
+        """Format the explore response as self-contained, entity-aggregated text.
+
+        Each column gets its own complete context (meaning + data + also related).
+        Concept/Entity nodes are absorbed into column meaning lines, not shown
+        independently. Cross-table query patterns are shown as a summary section.
+        """
+        # Filter out concept/entity contexts — their info is absorbed into
+        # column meaning lines via _generate_meaning()
+        filtered_contexts = [ctx for ctx in contexts if ctx.node.type in (NodeType.TABLE, NodeType.COLUMN)]
+
+        # Low-confidence check
+        best_score = max(rn.relevance_score for rn in resolved) if resolved else 0.0
+        if budget.include_low_confidence_marker and best_score < 0.5:
+            lines = [self.LOW_CONFIDENCE_MARKER]
+            lines.append("Results are approximate — try different keywords.")
+        else:
+            lines = []
+
+        # Summary header — only show scope info (what was found),
+        # not a fake "query" reconstructed from node names
+        dataset_count = self.storage.count_nodes(NodeType.TABLE)
+        col_count = len([c for c in filtered_contexts if c.node.type == NodeType.COLUMN])
+        lines.append(f"matched: {col_count} columns across {dataset_count} tables")
+
+        # Group by table (concept/entity already filtered out)
+        table_groups = self._group_by_table(filtered_contexts)
+
+        for group in table_groups:
+            table = group.table_node
+            table_name = table.name
+            if isinstance(table, TableNode):
+                row_count = table.properties.get("row_count", 0)
+                source = table.properties.get("source", "")
+                table_comment = table.properties.get("comment", "")
+            else:
+                row_count = 0
+                source = ""
+                table_comment = ""
+
+            # Truncate columns to budget
+            shown_columns = group.column_nodes[: budget.max_columns_per_table]
+            matched_columns = len(group.column_nodes)  # columns matched by this query
+            # Total columns in the table (from TableNode metadata, not just matched ones)
+            table_total_columns = 0
+            if isinstance(table, TableNode) and table.column_ids:
+                table_total_columns = len(table.column_ids)
+            elif isinstance(table, TableNode):
+                # Fallback: count from storage if column_ids not populated
+                table_total_columns = len(self.storage.get_edges_for_node(table.id, EdgeType.CONTAINS))
+
+            suffix_parts = []
+            if table_total_columns > 0 and matched_columns != table_total_columns:
+                # Not all columns matched — show both matched count and total
+                shown_count = len(shown_columns)
+                if shown_count < matched_columns:
+                    # Budget truncated even the matched set
+                    suffix_parts.append(
+                        f"{shown_count}/{matched_columns} matched columns (of {table_total_columns} total)"
+                    )
+                else:
+                    suffix_parts.append(f"{matched_columns} matched columns (of {table_total_columns} total)")
+            else:
+                # All columns matched (or only table-level info without column breakdown)
+                shown_count = len(shown_columns)
+                if table_total_columns > 0:
+                    if shown_count < table_total_columns:
+                        suffix_parts.append(f"{shown_count}/{table_total_columns} columns")
+                    else:
+                        suffix_parts.append(f"{table_total_columns} columns")
+                else:
+                    suffix_parts.append(f"{matched_columns} columns")
+            if row_count:
+                suffix_parts.append(f"{row_count:,} rows")
+            if source:
+                display_source = mask_credentials(source) if mask_credential else source
+                suffix_parts.append(f"source: {display_source}")
+            suffix = ", ".join(suffix_parts)
+
+            lines.append("")
+            lines.append(f"## {table_name} — {suffix}")
+
+            # Table meaning line: just the table comment (if available)
+            if table_comment:
+                lines.append(f"- meaning: {table_comment}")
+
+            for ctx in shown_columns:
+                lines.extend(self._format_column_selfcontained(ctx, budget))
+
+            if matched_columns > budget.max_columns_per_table:
+                omitted = matched_columns - budget.max_columns_per_table
+                lines.append(f"  …and {omitted} more columns")
+
+        # Cross-table query patterns
+        cross_paths = self._format_cross_table_paths(filtered_contexts, rel_map, budget, mask_credential)
+        lines.extend(cross_paths)
+
+        # Completeness signal — simple one-liner
+        if budget.include_completeness_signal and dataset_count < 3:
+            lines.append("")
+            lines.append("All indexed data included — no further explore calls needed.")
+
+        # Final truncation
+        text = "\n".join(lines)
+        if len(text) > budget.max_output_chars:
+            cut_text = text[: budget.max_output_chars]
+            last_section = cut_text.rfind("\n## ")
+            if last_section > budget.max_output_chars * 0.5:
+                text = cut_text[:last_section]
+            else:
+                text = cut_text
+
+        return text
+
+    def _table_name_for_col(self, col_id: str) -> str | None:
+        """Get the parent table name for a column node ID."""
+        col = self.storage.get_node(col_id)
+        if col and isinstance(col, ColumnNode):
+            table = self.storage.get_node(col.table_id)
+            if table:
+                return table.name
+        # Fallback: parse from column ID (source may contain colons)
+        parsed = parse_column_id(col_id)
+        return parsed.table_name if parsed else None
+
+    def _dialect_for_node(self, node: Node) -> str | None:
+        """Infer SQL dialect from a node's ``source`` field when present."""
+        source = getattr(node, "source", None) or ""
+        if not source and isinstance(node, ColumnNode):
+            table = self.storage.get_node(node.table_id)
+            if table:
+                source = getattr(table, "source", "") or ""
+        return dialect_from_source(source)
+
+    def _group_by_table(self, contexts: list[NodeContext]) -> list[TableGroup]:
+        """Group NodeContexts by their parent table.
+
+        Column nodes → grouped under their TableNode.
+        Table nodes → their own group.
+        Concept/Entity should be filtered out before calling this method
+        (their info is absorbed into column meaning lines).
+        """
+        table_map: dict[str, TableGroup] = {}
+        ungrouped_nodes: list[NodeContext] = []
+
+        for ctx in contexts:
+            node = ctx.node
+
+            if node.type == NodeType.TABLE:
+                if node.id not in table_map:
+                    table_map[node.id] = TableGroup(table_node=node, column_nodes=[])
+
+            elif node.type == NodeType.COLUMN and isinstance(node, ColumnNode):
+                tid = node.table_id
+                if tid not in table_map:
+                    table_node = self.storage.get_node(tid)
+                    if table_node:
+                        table_map[tid] = TableGroup(table_node=table_node, column_nodes=[])
+                    else:
+                        ungrouped_nodes.append(ctx)
+                        continue
+                table_map[tid].column_nodes.append(ctx)
+
+            elif node.type in (NodeType.CONCEPT, NodeType.ENTITY):
+                ungrouped_nodes.append(ctx)
+
+            else:
+                ungrouped_nodes.append(ctx)
+
+        groups = list(table_map.values())
+
+        # Sort columns within each group: FK outbound first, then alphabetically
+        for g in groups:
+            g.column_nodes.sort(
+                key=lambda c: (
+                    0
+                    if any(
+                        e["type"] == "foreign_key" and e["direction"] == "→" for e in c.edges_by_group.get("join", [])
+                    )
+                    else 1,
+                    c.node.name,
+                )
+            )
+
+        if ungrouped_nodes:
+            dummy = Node(id="", type=NodeType.TABLE, name="semantic_nodes")
+            groups.append(
+                TableGroup(
+                    table_node=dummy,
+                    column_nodes=ungrouped_nodes,
+                    ungrouped=True,
+                )
+            )
+
+        return groups
+
+    def _format_column_selfcontained(self, ctx: NodeContext, budget: ExploreBudget) -> list[str]:
+        """Format one column as self-contained: meaning + data + also related.
+
+        Each column gets its own complete semantic context:
+        - Line 1: ### col_name (dtype, semantic_type)
+        - Line 2: - meaning: {comment + concept absorption + FK/joinable translation}
+        - Line 3: - data: {null%, unique%, samples, range}
+        - Line 4: - also related: {synonym/statistical relationships}
+        - Line 5+: - pending edges (FK/joinable pointing to unresolved tables)
+        """
+        node = ctx.node
+        lines: list[str] = []
+
+        # Line 1: Column name + type tag (### heading)
+        name = node.name
+        if isinstance(node, ColumnNode):
+            dtype = node.dtype or ""
+            sem_type = node.semantic_type or ""
+            type_tag = dtype
+            if sem_type and sem_type != "unknown":
+                type_tag = f"{dtype}, {sem_type}"
+            lines.append(f"### {name} ({type_tag})")
+        else:
+            lines.append(f"### {name}")
+
+        # Line 2: meaning
+        meaning = self._generate_meaning(ctx, budget)
+        if meaning:
+            lines.append(f"- meaning: {meaning}")
+
+        # Line 3: data profile
+        data_line = self._format_data_line(ctx, budget)
+        if data_line:
+            lines.append(f"- data: {data_line}")
+
+        # Line 4: also related
+        also_related = self._generate_also_related(ctx, budget)
+        if also_related:
+            lines.append(f"- also related: {also_related}")
+
+        # Line 5+: pending edges
+        if ctx.pending_edges:
+            for pe in ctx.pending_edges[: budget.max_pending_edges_per_node]:
+                if pe["type"] in ("foreign_key", "joinable"):
+                    lines.append(f"- pending {pe['type']} → ? ({pe['note']})")
+
+        return lines
+
+    def _node_name(self, node_id: str) -> str:
+        """Get a node's name, or its ID if not found."""
+        node = self.storage.get_node(node_id)
+        return node.name if node else node_id
+
+    def _short_id(self, node_id: str) -> str:
+        """Convert a full node ID to a short alias for display.
+
+        Returns:
+          - Table: "frpm" (just the table name)
+          - Column: "frpm.County Name" (table.column_name)
+          - Concept: "free_meal_count" (just the concept name)
+          - Entity: "school" (just the entity name)
+          - Other: the name portion of the ID
+        """
+        node = self.storage.get_node(node_id)
+        if node is None:
+            # Fallback: parse from ID structure
+            if node_id.startswith("column:"):
+                parsed = parse_column_id(node_id)
+                if parsed:
+                    return f"{parsed.table_name}.{parsed.column_name}"
+            return node_id
+
+        if node.type == NodeType.COLUMN and isinstance(node, ColumnNode):
+            table_name = self._table_name_for_col(node_id) or node.table_id.rsplit(":", 1)[-1]
+            return f"{table_name}.{node.name}"
+        elif node.type == NodeType.TABLE:
+            return node.name
+        else:
+            return node.name
+
+    # ── MCP text format methods ──────────────────────────────────────
+
+    def format_search_nodes_text(self, results: list[dict[str, Any]], query: str, mask_credential: bool = True) -> str:
+        """Format search_nodes results as compact natural language text for MCP."""
+        if not results:
+            return f'No results found for "{query}". Try different keywords or use datalink_explore.'
+
+        lines = [f'Found {len(results)} nodes matching "{query}":']
+
+        for node in results:
+            ntype = node.get("type", "unknown")
+            name = node.get("name", "")
+            props = node.get("properties", {})
+            full_id = node.get("id", "")
+
+            if ntype == "column":
+                dtype = props.get("dtype", "")
+                sem_type = props.get("semantic_type", "")
+                type_tag = dtype
+                if sem_type and sem_type != "unknown":
+                    type_tag = f"{dtype}, {sem_type}"
+                table_name = self._table_name_for_col(full_id) or "?"
+                desc = props.get("comment", "") or ""
+                header = f"[column] {table_name}.{name} ({type_tag})"
+                if desc:
+                    header += f" — {desc}"
+
+                # Edges summary as inline Related line
+                edges_summary = node.get("edges_summary", [])
+                related_parts = []
+                for es in edges_summary[:3]:
+                    es_type = es.get("type", "")
+                    es_conf = es.get("confidence", 0)
+                    if es_conf < 0.3:
+                        continue
+                    es_target_id = es.get("target_id", "")
+                    target_short = self._short_id(es_target_id)
+                    related_parts.append(f"{es_type}↔{target_short} ({es_conf:.2f})")
+
+                lines.append(header)
+                if related_parts:
+                    lines.append(f"  Related: {', '.join(related_parts)}")
+
+            elif ntype == "table":
+                row_count = props.get("row_count", 0)
+                comment = props.get("comment", "")
+                header = f"[table] {name} ({row_count:,} rows)"
+                if comment:
+                    header += f" — {comment}"
+                lines.append(header)
+
+            elif ntype == "concept":
+                desc = props.get("description", "")
+                unit = props.get("unit", "")
+                dim = props.get("dimension", "")
+                header = f"[concept] {name}"
+                detail_parts = []
+                if desc:
+                    detail_parts.append(desc)
+                if unit:
+                    detail_parts.append(f"unit: {unit}")
+                if dim:
+                    detail_parts.append(f"dim: {dim}")
+                if detail_parts:
+                    header += f" — {', '.join(detail_parts)}"
+                lines.append(header)
+
+            elif ntype == "entity":
+                desc = props.get("description", "")
+                header = f"[entity] {name}"
+                if desc:
+                    header += f" — {desc}"
+                lines.append(header)
+
+            else:
+                lines.append(f"[{ntype}] {name}")
+
+        return "\n".join(lines)
+
+    def format_get_node_text(self, result: dict[str, Any], mask_credential: bool = True) -> str:
+        """Format get_node result as compact natural language text for MCP."""
+        ntype = result.get("type", "unknown")
+        name = result.get("name", "")
+        full_id = result.get("id", "")
+        props = result.get("properties", {})
+
+        lines = []
+
+        if ntype == "table":
+            row_count = props.get("row_count", 0)
+            source = props.get("source", "")
+            comment = props.get("comment", "")
+            column_ids = props.get("column_ids", [])
+
+            # Mask source if needed
+            display_source = mask_credentials(source) if mask_credential else source
+
+            header = f"table: {name} ({row_count:,} rows, {len(column_ids)} columns)"
+            if display_source:
+                header += f", source: {display_source}"
+            lines.append(header)
+            if comment:
+                lines.append(f"  description: {comment}")
+
+            # Column names (from properties.column_ids, extract just names)
+            col_names = []
+            for col_id in column_ids:
+                col_node = self.storage.get_node(col_id)
+                if col_node:
+                    col_names.append(col_node.name)
+                else:
+                    # Parse from ID
+                    parsed = parse_column_id(col_id)
+                    col_names.append(parsed.column_name if parsed else col_id.rsplit(":", 1)[-1])
+
+            lines.append(f"  Columns: {', '.join(col_names)}")
+
+            # Non-structural relationships (edges, excluding contains which we already removed)
+            edges = result.get("edges", [])
+            if edges:
+                lines.append("")
+                lines.append("Relationships:")
+                for e in edges[:10]:
+                    e_type = e.get("type", "")
+                    e_conf = e.get("confidence", 0)
+                    direction = e.get("direction", "")
+                    other_node = e.get("other_node") or {}
+                    other_name = other_node.get("name", "?")
+                    other_type = other_node.get("type", "")
+                    other_id = other_node.get("id", "")
+
+                    # Build short label
+                    if other_type == "column":
+                        other_short = self._short_id(other_id) if other_id else other_name
+                    elif other_type == "table":
+                        other_short = other_name
+                    else:
+                        other_short = other_name
+
+                    arrow = "→" if direction == "outgoing" else "←"
+                    label = "FK" if e_type == "foreign_key" else e_type
+
+                    src_short = self._short_id(full_id) if ntype != "table" else ""
+                    lines.append(f"  {name}.{src_short} {label}{arrow} {other_short} (conf {e_conf:.2f})")
+
+            # Suggested/pending edges
+            suggested = result.get("suggested_edges", [])
+            if suggested:
+                lines.append("")
+                lines.append("Pending relationships:")
+                for se in suggested[:5]:
+                    se_type = se.get("type", "")
+                    se_note = se.get("note", "")
+                    se_conf = se.get("confidence", 0)
+                    lines.append(f"  {se_type} → ? ({se_note}, conf {se_conf:.2f})")
+
+        elif ntype == "column":
+            dtype = props.get("dtype", "")
+            sem_type = props.get("semantic_type", "")
+            comment = props.get("comment", "")
+            type_tag = dtype
+            if sem_type and sem_type != "unknown":
+                type_tag = f"{dtype}, {sem_type}"
+            table_name = self._table_name_for_col(full_id) or "?"
+
+            lines.append(f"column: {table_name}.{name} ({type_tag})")
+            if comment:
+                lines.append(f"  description: {comment}")
+
+            # Profile info
+            profile = result.get("profile")
+            if profile:
+                null_pct = f"{profile.get('null_rate', 0) * 100:.0f}%"
+                unique_pct = f"{profile.get('unique_rate', 0) * 100:.0f}%"
+                cardinality = profile.get("cardinality", "?")
+                data_parts = [f"null {null_pct}", f"unique {unique_pct}", f"{cardinality} distinct values"]
+                sample_vals = profile.get("sample_values", [])
+                if sample_vals:
+                    vals_str = ", ".join(str(v) for v in sample_vals[:5])
+                    data_parts.append(f"sample values: [{vals_str}]")
+                lines.append(f"  data: {', '.join(data_parts)}")
+
+            # Relationship edges
+            edges = result.get("edges", [])
+            related_parts = []
+            for e in edges[:8]:
+                e_type = e.get("type", "")
+                e_conf = e.get("confidence", 0)
+                direction = e.get("direction", "")
+                other_node = e.get("other_node") or {}
+                other_name = other_node.get("name", "?")
+                other_type = other_node.get("type", "")
+                other_id = other_node.get("id", "")
+
+                other_short = self._short_id(other_id) if other_id else other_name
+
+                arrow = "→" if direction == "outgoing" else "←"
+                label = "FK" if e_type == "foreign_key" else e_type
+
+                related_parts.append(f"{label}{arrow}{other_short} ({e_conf:.2f})")
+
+            if related_parts:
+                lines.append(f"  Related: {', '.join(related_parts)}")
+
+            # Pending edges
+            suggested = result.get("suggested_edges", [])
+            if suggested:
+                for se in suggested[:3]:
+                    se_type = se.get("type", "")
+                    se_note = se.get("note", "")
+                    se_conf = se.get("confidence", 0)
+                    lines.append(f"  pending {se_type} → ? ({se_note}, conf {se_conf:.2f})")
+
+        elif ntype in ("concept", "entity"):
+            desc = props.get("description", "")
+            lines.append(f"{ntype}: {name}")
+            if desc:
+                lines.append(f"  description: {desc}")
+
+            edges = result.get("edges", [])
+            if edges:
+                related_parts = []
+                for e in edges[:5]:
+                    e_type = e.get("type", "")
+                    e_conf = e.get("confidence", 0)
+                    other_node = e.get("other_node") or {}
+                    other_name = other_node.get("name", "?")
+                    other_id = other_node.get("id", "")
+                    other_short = self._short_id(other_id)
+                    related_parts.append(f"{e_type}↔{other_short} ({e_conf:.2f})")
+                if related_parts:
+                    lines.append(f"  Related: {', '.join(related_parts)}")
+
+        else:
+            lines.append(f"{ntype}: {name}")
+
+        return "\n".join(lines)
+
+    def format_find_paths_text(
+        self, paths: list[dict[str, Any]], source_id: str, target_id: str, mask_credential: bool = True
+    ) -> str:
+        """Format find_paths results as compact natural language text for MCP."""
+
+        if not paths:
+            short_src = self._short_id(source_id)
+            short_tgt = self._short_id(target_id)
+            return f"No paths found between {short_src} and {short_tgt}."
+
+        lines = [f"Found {len(paths)} paths:"]
+
+        for i, p in enumerate(paths):
+            confidence = p.get("confidence", 0)
+            length = p.get("length", 0)
+            path_nodes = p.get("nodes", [])
+            path_edges = p.get("edges", [])
+
+            # Build short path description
+            path_parts = []
+            for nid in path_nodes:
+                node = self.storage.get_node(nid)
+                if node:
+                    path_parts.append(self._short_id(nid))
+                else:
+                    path_parts.append(nid)
+
+            # Interleave nodes with edge types
+            desc_parts = []
+            for j, node_label in enumerate(path_parts):
+                desc_parts.append(node_label)
+                if j < len(path_edges):
+                    edge_type = path_edges[j].get("type", "?")
+                    desc_parts.append(f"→ {edge_type} →")
+
+            path_desc = " ".join(desc_parts)
+            conf_note = " (low conf)" if confidence < 0.5 else ""
+            lines.append(f"Path {i + 1} ({length} hops, conf {confidence:.2f}){conf_note}:")
+            lines.append(f"  {path_desc}")
+
+        return "\n".join(lines)
+
+    def format_list_datasets_text(self, datasets: list[dict[str, Any]], mask_credential: bool = True) -> str:
+        """Format list_datasets results as compact natural language text for MCP."""
+
+        if not datasets:
+            return "No datasets found. Use datalink add-table to add data sources."
+
+        lines = [f"Datasets ({len(datasets)} tables):"]
+
+        for ds in datasets:
+            name = ds.get("name", "")
+            row_count = ds.get("row_count", 0)
+            col_count = ds.get("column_count", 0)
+            inferred_edges = ds.get("inferred_edge_count", 0)
+            pending_fk = ds.get("pending_fk_count", 0)
+
+            line = f"  {name}: {row_count:,} rows, {col_count} cols, {inferred_edges} edges, {pending_fk} pending FK"
+            lines.append(line)
+
+        return "\n".join(lines)
+
+    def format_list_pending_edges_text(self, results: list[dict[str, Any]], mask_credential: bool = True) -> str:
+        """Format list_pending_edges results as compact natural language text for MCP."""
+
+        if not results:
+            return "No pending edges found — all edges are resolved."
+
+        lines = [f"Pending edges ({len(results)} total):"]
+
+        for r in results:
+            pe_type = r.get("type", "")
+            source_id = r.get("source_id", "")
+            target_id = r.get("target_id", "")
+            note = r.get("note", "")
+            conf = r.get("confidence", 0)
+            missing = r.get("missing_endpoints", [])
+
+            src_short = self._short_id(source_id)
+            tgt_short = self._short_id(target_id)
+
+            # Show target as "?" if it's a missing endpoint
+            if "target" in missing:
+                tgt_display = "? (not yet in graph)"
+            else:
+                tgt_display = tgt_short
+
+            lines.append(f"  [{pe_type}] {src_short} → {tgt_display} (conf {conf:.2f}, {note})")
+
+        return "\n".join(lines)
+
+    def format_extract_subgraph_text(self, result: dict[str, Any], mask_credential: bool = True) -> str:
+        """Format extract_subgraph results as compact natural language text for MCP."""
+        stats = result.get("stats", {})
+        nodes = result.get("nodes", [])
+        edges = result.get("edges", [])
+
+        node_count = stats.get("node_count", len(nodes))
+        edge_count = stats.get("edge_count", len(edges))
+        hops = stats.get("hops", "?")
+
+        lines = [f"Subgraph: {node_count} nodes, {edge_count} edges ({hops} hops from seed)"]
+
+        # Nodes listing
+        node_parts = []
+        for n in nodes:
+            ntype = n.get("type", "")
+            name = n.get("name", "")
+            full_id = n.get("id", "")
+            props = n.get("properties", {})
+
+            if ntype == "column":
+                dtype = props.get("dtype", "")
+                short = self._short_id(full_id)
+                node_parts.append(f"[column] {short} ({dtype})")
+            elif ntype == "table":
+                node_parts.append(f"[table] {name}")
+            else:
+                node_parts.append(f"[{ntype}] {name}")
+
+        lines.append(f"Nodes: {', '.join(node_parts)}")
+
+        # Edges listing
+        if edges:
+            edge_parts = []
+            for e in edges[:15]:
+                e_type = e.get("type", "")
+                src_id = e.get("source_id", "")
+                tgt_id = e.get("target_id", "")
+                src_short = self._short_id(src_id)
+                tgt_short = self._short_id(tgt_id)
+                edge_parts.append(f"{src_short} → {e_type} → {tgt_short}")
+            lines.append(f"Edges: {', '.join(edge_parts)}")
+
+        return "\n".join(lines)
diff --git a/services/datalink/src/datalink/graph/schema.sql b/services/datalink/src/datalink/graph/schema.sql
new file mode 100644
index 00000000..3126b24a
--- /dev/null
+++ b/services/datalink/src/datalink/graph/schema.sql
@@ -0,0 +1,86 @@
+-- DataLink SQLite schema for dual-layer graph storage
+
+-- Nodes table: all node types in one table, differentiated by type field
+CREATE TABLE IF NOT EXISTS nodes (
+    id TEXT PRIMARY KEY,
+    type TEXT NOT NULL,           -- 'column', 'table', 'concept', 'entity'
+    name TEXT NOT NULL,
+    properties TEXT,              -- JSON blob
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Edges table: all edge types, with confidence scores
+-- NOTE: NO ON DELETE CASCADE on source_id/target_id!
+-- INSERT OR REPLACE on nodes triggers an internal DELETE+INSERT,
+-- which would cascade-delete all referencing edges — destroying
+-- existing graph relationships on every node property update.
+-- Edge cleanup is handled explicitly by remove_table /
+-- cleanup_orphaned_semantic_nodes instead.
+CREATE TABLE IF NOT EXISTS edges (
+    id TEXT PRIMARY KEY,
+    source_id TEXT NOT NULL,
+    target_id TEXT NOT NULL,
+    type TEXT NOT NULL,           -- edge type enum values
+    confidence REAL DEFAULT 1.0,
+    properties TEXT,              -- JSON blob
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Column profiles table
+-- NOTE: NO ON DELETE CASCADE — same reason as edges.
+-- Profile cleanup is handled explicitly by remove_table.
+CREATE TABLE IF NOT EXISTS column_profiles (
+    id TEXT PRIMARY KEY,
+    column_id TEXT NOT NULL,
+    properties TEXT,              -- JSON blob with all profile stats
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Metadata table: stores build info and config
+CREATE TABLE IF NOT EXISTS metadata (
+    key TEXT PRIMARY KEY,
+    value TEXT NOT NULL,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Pending edges: edges whose source/target nodes don't yet exist in the graph
+-- (e.g., FK referencing a table from a datasource not yet added)
+-- No FK constraints on source_id/target_id — they are allowed to reference non-existent nodes.
+CREATE TABLE IF NOT EXISTS pending_edges (
+    id TEXT PRIMARY KEY,
+    source_id TEXT NOT NULL,     -- may not exist in nodes table
+    target_id TEXT NOT NULL,     -- may not exist in nodes table
+    type TEXT NOT NULL,
+    confidence REAL DEFAULT 1.0,
+    properties TEXT,             -- JSON blob
+    missing_endpoints TEXT,      -- JSON array: ["source"] / ["target"] / ["source","target"]
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Indexes for common queries
+CREATE INDEX IF NOT EXISTS idx_nodes_type ON nodes(type);
+CREATE INDEX IF NOT EXISTS idx_nodes_name ON nodes(name);
+CREATE INDEX IF NOT EXISTS idx_edges_source ON edges(source_id);
+CREATE INDEX IF NOT EXISTS idx_edges_target ON edges(target_id);
+CREATE INDEX IF NOT EXISTS idx_edges_type ON edges(type);
+CREATE INDEX IF NOT EXISTS idx_edges_source_type ON edges(source_id, type);
+CREATE INDEX IF NOT EXISTS idx_edges_target_type ON edges(target_id, type);
+CREATE INDEX IF NOT EXISTS idx_edges_confidence ON edges(confidence);
+CREATE INDEX IF NOT EXISTS idx_profiles_column ON column_profiles(column_id);
+-- Node embeddings: vector index for semantic search
+-- Stores embedding vectors generated by an embedding model (e.g., Qwen3-Embedding-4B).
+-- When the user changes the embedding model, all rows must be rebuilt (mode=vec rebuild).
+CREATE TABLE IF NOT EXISTS node_embeddings (
+    node_id TEXT PRIMARY KEY,           -- references nodes.id (no FK constraint — embeddings are rebuilt independently)
+    embedding_model TEXT NOT NULL,      -- model name that produced this vector (e.g., "Qwen/Qwen3-Embedding-4B")
+    embedding_vector BLOB NOT NULL,     -- float32 array serialized via struct.pack
+    searchable_text TEXT NOT NULL,      -- the text used to generate the embedding (for validation/debugging)
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+CREATE INDEX IF NOT EXISTS idx_pending_source ON pending_edges(source_id);
+CREATE INDEX IF NOT EXISTS idx_pending_target ON pending_edges(target_id);
+CREATE INDEX IF NOT EXISTS idx_pending_type ON pending_edges(type);
+
+-- Index for filtering embeddings by model (used during rebuild to check consistency)
+CREATE INDEX IF NOT EXISTS idx_embeddings_model ON node_embeddings(embedding_model);
diff --git a/services/datalink/src/datalink/graph/storage.py b/services/datalink/src/datalink/graph/storage.py
new file mode 100644
index 00000000..90703928
--- /dev/null
+++ b/services/datalink/src/datalink/graph/storage.py
@@ -0,0 +1,1010 @@
+"""SQLite-based graph storage for DataLink."""
+
+import json
+import logging
+import math
+import sqlite3
+import struct
+from pathlib import Path
+from typing import Any
+
+from datalink.models.edge import Edge, EdgeType, PendingEdge
+from datalink.models.node import ColumnNode, ConceptNode, EntityNode, Node, NodeType, TableNode
+from datalink.models.profile import ColumnProfile
+
+logger = logging.getLogger(__name__)
+
+SCHEMA_SQL_PATH = Path(__file__).parent / "schema.sql"
+
+
+class GraphStorage:
+    """SQLite storage layer for the DataLink dual-layer graph.
+
+    Provides CRUD operations for nodes, edges, and profiles,
+    plus add_table/remove_table for incremental graph modifications.
+    """
+
+    def __init__(self, db_path: str = "datalink.db"):
+        """Initialize or open the graph database.
+
+        If db_path is a relative path or bare filename, it is resolved under
+        ~/.datalink/storage/ (the directory is created automatically).
+        If db_path is an absolute path, it is used as-is.
+
+        Args:
+            db_path: SQLite database path — filename, relative path, or absolute path.
+        """
+        self.db_path = self._resolve_db_path(db_path)
+        self.conn: sqlite3.Connection | None = None
+        self._open_and_init()
+
+    @staticmethod
+    def _resolve_db_path(db_path: str) -> str:
+        """Resolve the database path.
+
+        Absolute paths are used as-is. Relative paths and bare filenames
+        are placed under ~/.datalink/storage/ (created if missing).
+        """
+        p = Path(db_path)
+        if p.is_absolute():
+            # Ensure parent directory exists
+            p.parent.mkdir(parents=True, exist_ok=True)
+            return str(p)
+        # Relative or bare filename → ~/.datalink/storage/
+        storage_dir = Path.home() / ".datalink" / "storage"
+        storage_dir.mkdir(parents=True, exist_ok=True)
+        return str(storage_dir / p)
+
+    def _open_and_init(self) -> None:
+        """Open the database connection and initialize the schema."""
+        self.conn = sqlite3.connect(self.db_path)
+        self.conn.execute("PRAGMA foreign_keys = ON")
+        self.conn.execute("PRAGMA journal_mode = WAL")
+
+        # Read and execute schema SQL
+        with open(SCHEMA_SQL_PATH, encoding="utf-8") as f:
+            schema_sql = f.read()
+        self.conn.executescript(schema_sql)
+        self.conn.commit()
+
+        # Migrate old schema: remove ON DELETE CASCADE foreign keys from
+        # edges and column_profiles tables. INSERT OR REPLACE on nodes
+        # triggers an internal DELETE+INSERT that cascades and destroys
+        # all referencing edges/profiles — which is catastrophic when
+        # updating node properties. The new schema omits these FK
+        # constraints, but IF NOT EXISTS skips the CREATE TABLE, so
+        # we must explicitly migrate existing databases.
+        self._migrate_remove_cascade_fks()
+
+        logger.info(f"Graph storage initialized at '{self.db_path}'")
+
+    def close(self) -> None:
+        """Close the database connection."""
+        if self.conn is not None:
+            self.conn.close()
+            self.conn = None
+            logger.info("Graph storage connection closed")
+
+    # --- Node operations ---
+
+    def add_node(self, node: Node) -> None:
+        """Add a node to the graph."""
+        props = node.to_storage_properties()
+        self.conn.execute(
+            "INSERT OR REPLACE INTO nodes (id, type, name, properties) VALUES (?, ?, ?, ?)",
+            (node.id, node.type.value, node.name, json.dumps(props)),
+        )
+        self.conn.commit()
+
+    def get_node(self, node_id: str) -> Node | None:
+        """Get a node by ID. Returns None if not found."""
+        row = self.conn.execute(
+            "SELECT id, type, name, properties FROM nodes WHERE id = ?",
+            (node_id,),
+        ).fetchone()
+        if row is None:
+            return None
+        return self._row_to_node(row)
+
+    def get_nodes_by_type(self, node_type: NodeType) -> list[Node]:
+        """Get all nodes of a specific type."""
+        rows = self.conn.execute(
+            "SELECT id, type, name, properties FROM nodes WHERE type = ?",
+            (node_type.value,),
+        ).fetchall()
+        return [self._row_to_node(row) for row in rows]
+
+    def search_nodes_by_name(self, query: str, node_type: NodeType | None = None, limit: int = 10) -> list[Node]:
+        """Search nodes by name (substring match)."""
+        if node_type:
+            rows = self.conn.execute(
+                "SELECT id, type, name, properties FROM nodes WHERE name LIKE ? AND type = ? LIMIT ?",
+                (f"%{query}%", node_type.value, limit),
+            ).fetchall()
+        else:
+            rows = self.conn.execute(
+                "SELECT id, type, name, properties FROM nodes WHERE name LIKE ? LIMIT ?",
+                (f"%{query}%", limit),
+            ).fetchall()
+        return [self._row_to_node(row) for row in rows]
+
+    def remove_node(self, node_id: str) -> None:
+        """Remove a node and all edges connected to it (cascade delete)."""
+        # Count edges that will be removed
+        edge_count = self.conn.execute(
+            "SELECT COUNT(*) FROM edges WHERE source_id = ? OR target_id = ?",
+            (node_id, node_id),
+        ).fetchone()[0]
+
+        # Remove edges and profiles explicitly (no FK cascade — see schema.sql notes)
+        self.conn.execute("DELETE FROM edges WHERE source_id = ? OR target_id = ?", (node_id, node_id))
+        self.conn.execute("DELETE FROM column_profiles WHERE column_id = ?", (node_id,))
+        self.conn.execute("DELETE FROM node_embeddings WHERE node_id = ?", (node_id,))
+        self.conn.execute("DELETE FROM nodes WHERE id = ?", (node_id,))
+        self.conn.commit()
+        logger.info(f"Removed node '{node_id}' and {edge_count} connected edges")
+
+    def get_all_node_ids(self) -> list[str]:
+        """Get all node IDs in the graph."""
+        rows = self.conn.execute("SELECT id FROM nodes").fetchall()
+        return [row[0] for row in rows]
+
+    def count_nodes(self, node_type: NodeType | None = None) -> int:
+        """Count nodes, optionally filtered by type."""
+        if node_type:
+            return self.conn.execute(
+                "SELECT COUNT(*) FROM nodes WHERE type = ?",
+                (node_type.value,),
+            ).fetchone()[0]
+        return self.conn.execute("SELECT COUNT(*) FROM nodes").fetchone()[0]
+
+    # --- Edge operations ---
+
+    def add_edge(self, edge: Edge) -> None:
+        """Add an edge to the graph."""
+        self.conn.execute(
+            "INSERT OR REPLACE INTO edges (id, source_id, target_id, type, confidence, properties) "
+            "VALUES (?, ?, ?, ?, ?, ?)",
+            (edge.id, edge.source_id, edge.target_id, edge.type.value, edge.confidence, json.dumps(edge.properties)),
+        )
+        self.conn.commit()
+
+    def add_edges_batch(self, edges: list[Edge]) -> None:
+        """Add multiple edges in a single transaction."""
+        self.conn.executemany(
+            "INSERT OR REPLACE INTO edges (id, source_id, target_id, type, confidence, properties) "
+            "VALUES (?, ?, ?, ?, ?, ?)",
+            [(e.id, e.source_id, e.target_id, e.type.value, e.confidence, json.dumps(e.properties)) for e in edges],
+        )
+        self.conn.commit()
+
+    def get_edge(self, edge_id: str) -> Edge | None:
+        """Get an edge by ID."""
+        row = self.conn.execute(
+            "SELECT id, source_id, target_id, type, confidence, properties FROM edges WHERE id = ?",
+            (edge_id,),
+        ).fetchone()
+        if row is None:
+            return None
+        return self._row_to_edge(row)
+
+    def get_edges_for_node(self, node_id: str, edge_type: EdgeType | None = None) -> list[Edge]:
+        """Get all edges connected to a node, optionally filtered by type."""
+        if edge_type:
+            rows = self.conn.execute(
+                "SELECT id, source_id, target_id, type, confidence, properties FROM edges "
+                "WHERE (source_id = ? OR target_id = ?) AND type = ?",
+                (node_id, node_id, edge_type.value),
+            ).fetchall()
+        else:
+            rows = self.conn.execute(
+                "SELECT id, source_id, target_id, type, confidence, properties FROM edges "
+                "WHERE source_id = ? OR target_id = ?",
+                (node_id, node_id),
+            ).fetchall()
+        return [self._row_to_edge(row) for row in rows]
+
+    def remove_edge(self, edge_id: str) -> None:
+        """Remove a single edge."""
+        self.conn.execute("DELETE FROM edges WHERE id = ?", (edge_id,))
+        self.conn.commit()
+
+    def remove_edges_by_types(self, edge_types: list[EdgeType]) -> int:
+        """Remove all edges matching any of the given types.
+
+        Used during profile rebuild to clear inferred edges that depend
+        on profile statistics (joinable, distribution_similar, synonym, correlated)
+        before re-computing them from updated profiles.
+
+        Returns:
+            Number of edges removed.
+        """
+        type_values = [t.value for t in edge_types]
+        count = self.conn.execute(
+            f"SELECT COUNT(*) FROM edges WHERE type IN ({','.join('?' for _ in type_values)})",
+            type_values,
+        ).fetchone()[0]
+        self.conn.execute(
+            f"DELETE FROM edges WHERE type IN ({','.join('?' for _ in type_values)})",
+            type_values,
+        )
+        self.conn.commit()
+        return count
+
+    def count_edges(self, edge_type: EdgeType | None = None) -> int:
+        """Count edges, optionally filtered by type."""
+        if edge_type:
+            return self.conn.execute(
+                "SELECT COUNT(*) FROM edges WHERE type = ?",
+                (edge_type.value,),
+            ).fetchone()[0]
+        return self.conn.execute("SELECT COUNT(*) FROM edges").fetchone()[0]
+
+    # --- Pending edge operations ---
+    # Pending edges: edges whose source/target node(s) don't yet exist in the graph.
+    # They are stored separately from the main edges table and do not participate
+    # in path traversal or subgraph expansion. When new nodes are added via add_table,
+    # the Pipeline calls resolve_pending_edges() to move eligible pending edges into
+    # the edges table.
+
+    def add_pending_edge(self, edge: PendingEdge) -> None:
+        """Add a pending edge to the graph."""
+        self.conn.execute(
+            "INSERT OR REPLACE INTO pending_edges "
+            "(id, source_id, target_id, type, confidence, properties, missing_endpoints) "
+            "VALUES (?, ?, ?, ?, ?, ?, ?)",
+            (
+                edge.id,
+                edge.source_id,
+                edge.target_id,
+                edge.type.value,
+                edge.confidence,
+                json.dumps(edge.properties),
+                json.dumps(edge.missing_endpoints),
+            ),
+        )
+        self.conn.commit()
+
+    def add_pending_edges_batch(self, edges: list[PendingEdge]) -> None:
+        """Add multiple pending edges in a single transaction."""
+        self.conn.executemany(
+            "INSERT OR REPLACE INTO pending_edges "
+            "(id, source_id, target_id, type, confidence, properties, missing_endpoints) "
+            "VALUES (?, ?, ?, ?, ?, ?, ?)",
+            [
+                (
+                    e.id,
+                    e.source_id,
+                    e.target_id,
+                    e.type.value,
+                    e.confidence,
+                    json.dumps(e.properties),
+                    json.dumps(e.missing_endpoints),
+                )
+                for e in edges
+            ],
+        )
+        self.conn.commit()
+
+    def get_pending_edge(self, edge_id: str) -> PendingEdge | None:
+        """Get a pending edge by ID."""
+        row = self.conn.execute(
+            "SELECT id, source_id, target_id, type, confidence, properties, missing_endpoints "
+            "FROM pending_edges WHERE id = ?",
+            (edge_id,),
+        ).fetchone()
+        if row is None:
+            return None
+        return self._row_to_pending_edge(row)
+
+    def get_pending_edges_for_node(self, node_id: str) -> list[PendingEdge]:
+        """Get all pending edges involving a specific node."""
+        rows = self.conn.execute(
+            "SELECT id, source_id, target_id, type, confidence, properties, missing_endpoints "
+            "FROM pending_edges WHERE source_id = ? OR target_id = ?",
+            (node_id, node_id),
+        ).fetchall()
+        return [self._row_to_pending_edge(row) for row in rows]
+
+    def get_all_pending_edges(self) -> list[PendingEdge]:
+        """Get all pending edges."""
+        rows = self.conn.execute(
+            "SELECT id, source_id, target_id, type, confidence, properties, missing_endpoints FROM pending_edges"
+        ).fetchall()
+        return [self._row_to_pending_edge(row) for row in rows]
+
+    def resolve_pending_edges(self, available_node_ids: set[str]) -> int:
+        """Move pending edges whose both endpoints now exist into the edges table.
+
+        Scans the pending_edges table and checks whether both source_id and
+        target_id are present in available_node_ids. Eligible edges are moved
+        to the edges table and removed from pending_edges.
+
+        Args:
+            available_node_ids: Set of node IDs that currently exist in the graph.
+
+        Returns:
+            Number of edges resolved and moved to the edges table.
+        """
+        rows = self.conn.execute(
+            "SELECT id, source_id, target_id, type, confidence, properties, missing_endpoints FROM pending_edges"
+        ).fetchall()
+
+        resolved_count = 0
+        for row in rows:
+            id_, src, tgt, type_str, conf, props_json, missing_json = row
+            if src in available_node_ids and tgt in available_node_ids:
+                # Both endpoints exist → move to edges table
+                self.conn.execute(
+                    "INSERT OR REPLACE INTO edges "
+                    "(id, source_id, target_id, type, confidence, properties) "
+                    "VALUES (?, ?, ?, ?, ?, ?)",
+                    (id_, src, tgt, type_str, conf, props_json),
+                )
+                self.conn.execute("DELETE FROM pending_edges WHERE id = ?", (id_,))
+                resolved_count += 1
+
+        self.conn.commit()
+        if resolved_count:
+            logger.info(f"Resolved {resolved_count} pending edges into the edges table")
+        return resolved_count
+
+    def remove_pending_edge(self, edge_id: str) -> None:
+        """Remove a single pending edge."""
+        self.conn.execute("DELETE FROM pending_edges WHERE id = ?", (edge_id,))
+        self.conn.commit()
+
+    def cleanup_pending_edges_for_removed_nodes(self, removed_node_ids: set[str]) -> int:
+        """Remove pending edges that reference nodes that have been removed.
+
+        If a referenced node no longer exists, the pending edge can never be
+        resolved, so it should be cleaned up.
+
+        Args:
+            removed_node_ids: Set of node IDs that were removed from the graph.
+
+        Returns:
+            Number of pending edges removed.
+        """
+        removed_count = 0
+        for nid in removed_node_ids:
+            count = self.conn.execute(
+                "SELECT COUNT(*) FROM pending_edges WHERE source_id = ? OR target_id = ?",
+                (nid, nid),
+            ).fetchone()[0]
+            removed_count += count
+            self.conn.execute(
+                "DELETE FROM pending_edges WHERE source_id = ? OR target_id = ?",
+                (nid, nid),
+            )
+
+        self.conn.commit()
+        if removed_count:
+            logger.info(f"Cleaned up {removed_count} pending edges referencing removed nodes")
+        return removed_count
+
+    def count_pending_edges(self, edge_type: EdgeType | None = None) -> int:
+        """Count pending edges, optionally filtered by type."""
+        if edge_type:
+            return self.conn.execute(
+                "SELECT COUNT(*) FROM pending_edges WHERE type = ?",
+                (edge_type.value,),
+            ).fetchone()[0]
+        return self.conn.execute("SELECT COUNT(*) FROM pending_edges").fetchone()[0]
+
+    # --- Profile operations ---
+
+    def add_profile(self, profile: ColumnProfile) -> None:
+        """Add a column profile."""
+        props = profile.model_dump(
+            exclude={"id", "column_id"},
+            mode="json",
+        )
+        self.conn.execute(
+            "INSERT OR REPLACE INTO column_profiles (id, column_id, properties) VALUES (?, ?, ?)",
+            (profile.id, profile.column_id, json.dumps(props)),
+        )
+        self.conn.commit()
+
+    def add_profiles_batch(self, profiles: list[ColumnProfile]) -> None:
+        """Add multiple profiles in a single transaction."""
+        data = [
+            (
+                p.id,
+                p.column_id,
+                json.dumps(p.model_dump(exclude={"id", "column_id"}, mode="json")),
+            )
+            for p in profiles
+        ]
+        self.conn.executemany(
+            "INSERT OR REPLACE INTO column_profiles (id, column_id, properties) VALUES (?, ?, ?)",
+            data,
+        )
+        self.conn.commit()
+
+    def get_profile(self, profile_id: str) -> ColumnProfile | None:
+        """Get a column profile by ID."""
+        row = self.conn.execute(
+            "SELECT id, column_id, properties FROM column_profiles WHERE id = ?",
+            (profile_id,),
+        ).fetchone()
+        if row is None:
+            return None
+        return self._row_to_profile(row)
+
+    def get_profile_for_column(self, column_id: str) -> ColumnProfile | None:
+        """Get the profile for a specific column."""
+        row = self.conn.execute(
+            "SELECT id, column_id, properties FROM column_profiles WHERE column_id = ?",
+            (column_id,),
+        ).fetchone()
+        if row is None:
+            return None
+        return self._row_to_profile(row)
+
+    # --- Bulk operations for add_table/remove_table ---
+
+    def add_nodes_batch(self, nodes: list[Node]) -> None:
+        """Add multiple nodes in a single transaction."""
+        self.conn.executemany(
+            "INSERT OR REPLACE INTO nodes (id, type, name, properties) VALUES (?, ?, ?, ?)",
+            [(n.id, n.type.value, n.name, json.dumps(n.to_storage_properties())) for n in nodes],
+        )
+        self.conn.commit()
+
+    def remove_table(self, table_id: str) -> list[str]:
+        """Remove a table and all its columns, edges, profiles.
+
+        Returns list of removed column IDs for cleanup of semantic nodes.
+        """
+        # Find all columns in this table via contains edges
+        contains_edges = self.get_edges_for_node(table_id, EdgeType.CONTAINS)
+        column_ids = [e.target_id for e in contains_edges]
+
+        # Remove all edges touching the table or its columns
+        all_ids = [table_id] + column_ids
+        for nid in all_ids:
+            self.conn.execute("DELETE FROM edges WHERE source_id = ? OR target_id = ?", (nid, nid))
+
+        # Remove all profiles for these columns
+        for col_id in column_ids:
+            self.conn.execute("DELETE FROM column_profiles WHERE column_id = ?", (col_id,))
+
+        # Remove all embeddings for removed nodes
+        for nid in all_ids:
+            self.conn.execute("DELETE FROM node_embeddings WHERE node_id = ?", (nid,))
+
+        # Remove column nodes and table node
+        for col_id in column_ids:
+            self.conn.execute("DELETE FROM nodes WHERE id = ?", (col_id,))
+        self.conn.execute("DELETE FROM nodes WHERE id = ?", (table_id,))
+
+        self.conn.commit()
+        logger.info(f"Removed table '{table_id}' with {len(column_ids)} columns")
+        return column_ids
+
+    def cleanup_orphaned_semantic_nodes(self) -> int:
+        """Remove Concept/Entity nodes that have no remaining connection
+        to the structural layer, in two stages:
+
+        Stage 1 — Concept cleanup:
+            A Concept is anchored when at least one structural node
+            (Column/Table) has a ``represents`` edge targeting it.
+            Unanchored Concepts are deleted along with all edges
+            touching them (including ``has_concept`` edges from Entities).
+
+        Stage 2 — Entity cleanup:
+            An Entity is anchored when it has ``has_concept`` edges
+            targeting **surviving** Concepts (those kept in Stage 1),
+            OR when it has outgoing edges to structural nodes.
+            Unanchored Entities are deleted along with all edges
+            touching them.
+
+        ``has_concept`` edges between two semantic-layer nodes (Entity → Concept)
+        do NOT directly anchor a Concept — they are semantic-layer-internal.
+        But they DO anchor an Entity indirectly: if the target Concept survives
+        Stage 1 (is anchored by the structural layer), the Entity is also anchored.
+
+        Returns the count of removed nodes.
+        """
+        structural_types = (NodeType.COLUMN.value, NodeType.TABLE.value)
+        orphan_count = 0
+
+        # ── Stage 1: Concept cleanup ──────────────────────────────────
+        # Find Concepts anchored by structural represents edges
+        anchored_concept_ids: set[str] = set()
+        anchored_rows = self.conn.execute(
+            "SELECT target_id FROM edges WHERE type = ? AND source_id IN (SELECT id FROM nodes WHERE type IN (?, ?))",
+            (EdgeType.REPRESENTS.value, *structural_types),
+        ).fetchall()
+        anchored_concept_ids = {row[0] for row in anchored_rows}
+
+        # Remove unanchored Concepts and all their edges
+        concept_rows = self.conn.execute(
+            "SELECT id FROM nodes WHERE type = ?",
+            (NodeType.CONCEPT.value,),
+        ).fetchall()
+        for row in concept_rows:
+            node_id = row[0]
+            if node_id not in anchored_concept_ids:
+                self.conn.execute(
+                    "DELETE FROM edges WHERE source_id = ? OR target_id = ?",
+                    (node_id, node_id),
+                )
+                self.conn.execute("DELETE FROM nodes WHERE id = ?", (node_id,))
+                orphan_count += 1
+
+        # ── Stage 2: Entity cleanup ───────────────────────────────────
+        # Re-query anchored concepts (unchanged set — we only deleted
+        # unanchored ones, anchored ones are still in the DB)
+        # An Entity is anchored if it has has_concept edges to surviving
+        # Concepts, OR outgoing edges to structural nodes.
+        entity_rows = self.conn.execute(
+            "SELECT id FROM nodes WHERE type = ?",
+            (NodeType.ENTITY.value,),
+        ).fetchall()
+        for row in entity_rows:
+            node_id = row[0]
+
+            # Direct structural anchor
+            outgoing_to_structural = self.conn.execute(
+                "SELECT COUNT(*) FROM edges "
+                "WHERE source_id = ? "
+                "AND target_id IN (SELECT id FROM nodes WHERE type IN (?, ?))",
+                (node_id, *structural_types),
+            ).fetchone()[0]
+
+            # Indirect anchor through surviving Concepts
+            has_concept_to_anchored = 0
+            if anchored_concept_ids:
+                placeholders = ",".join("?" for _ in anchored_concept_ids)
+                has_concept_to_anchored = self.conn.execute(
+                    "SELECT COUNT(*) FROM edges "
+                    "WHERE source_id = ? AND type = ? "
+                    "AND target_id IN (" + placeholders + ")",
+                    (node_id, EdgeType.HAS_CONCEPT.value, *anchored_concept_ids),
+                ).fetchone()[0]
+
+            if outgoing_to_structural == 0 and has_concept_to_anchored == 0:
+                self.conn.execute(
+                    "DELETE FROM edges WHERE source_id = ? OR target_id = ?",
+                    (node_id, node_id),
+                )
+                self.conn.execute("DELETE FROM nodes WHERE id = ?", (node_id,))
+                orphan_count += 1
+
+        self.conn.commit()
+        logger.info(f"Cleaned up {orphan_count} orphaned semantic nodes")
+        return orphan_count
+
+    # --- Metadata operations ---
+
+    def set_metadata(self, key: str, value: str) -> None:
+        """Set a metadata key-value pair."""
+        self.conn.execute(
+            "INSERT OR REPLACE INTO metadata (key, value, updated_at) VALUES (?, ?, CURRENT_TIMESTAMP)",
+            (key, value),
+        )
+        self.conn.commit()
+
+    def get_metadata(self, key: str) -> str | None:
+        """Get a metadata value by key."""
+        row = self.conn.execute("SELECT value FROM metadata WHERE key = ?", (key,)).fetchone()
+        return row[0] if row else None
+
+    # --- Embedding operations ---
+
+    def add_embedding(
+        self,
+        node_id: str,
+        embedding_model: str,
+        embedding_vector: list[float],
+        searchable_text: str,
+    ) -> None:
+        """Store an embedding vector for a node.
+
+        The float32 vector is serialized as a BLOB for compact storage
+        (4 bytes per dimension, vs ~20 bytes per dimension as JSON text).
+        """
+        vector_blob = struct.pack(f"<{len(embedding_vector)}f", *embedding_vector)
+        self.conn.execute(
+            "INSERT OR REPLACE INTO node_embeddings (node_id, embedding_model, embedding_vector, searchable_text) "
+            "VALUES (?, ?, ?, ?)",
+            (node_id, embedding_model, vector_blob, searchable_text),
+        )
+        self.conn.commit()
+
+    def add_embeddings_batch(
+        self,
+        data: list[tuple[str, str, list[float], str]],
+    ) -> None:
+        """Add multiple embeddings in a single transaction.
+
+        Args:
+            data: List of (node_id, embedding_model, embedding_vector, searchable_text) tuples.
+        """
+        rows = [
+            (
+                node_id,
+                embedding_model,
+                struct.pack(f"<{len(vec)}f", *vec),
+                searchable_text,
+            )
+            for node_id, embedding_model, vec, searchable_text in data
+        ]
+        self.conn.executemany(
+            "INSERT OR REPLACE INTO node_embeddings (node_id, embedding_model, embedding_vector, searchable_text) "
+            "VALUES (?, ?, ?, ?)",
+            rows,
+        )
+        self.conn.commit()
+
+    def get_embedding(self, node_id: str) -> dict[str, Any] | None:
+        """Get an embedding for a node.
+
+        Returns dict with node_id, embedding_model, embedding_vector (list[float]),
+        searchable_text, or None if not found.
+        """
+        row = self.conn.execute(
+            "SELECT node_id, embedding_model, embedding_vector, searchable_text FROM node_embeddings WHERE node_id = ?",
+            (node_id,),
+        ).fetchone()
+        if row is None:
+            return None
+        node_id, model, blob, text = row
+        dim = len(blob) // 4
+        vector = list(struct.unpack(f"<{dim}f", blob))
+        return {
+            "node_id": node_id,
+            "embedding_model": model,
+            "embedding_vector": vector,
+            "searchable_text": text,
+        }
+
+    def get_all_embeddings(self, node_type: NodeType | None = None) -> list[dict[str, Any]]:
+        """Get all embeddings, optionally filtered by node type.
+
+        Returns list of dicts with node_id, embedding_model, embedding_vector (list[float]),
+        searchable_text, and node_type (from the nodes table).
+        """
+        if node_type:
+            rows = self.conn.execute(
+                "SELECT ne.node_id, ne.embedding_model, ne.embedding_vector, ne.searchable_text, n.type "
+                "FROM node_embeddings ne JOIN nodes n ON ne.node_id = n.id "
+                "WHERE n.type = ?",
+                (node_type.value,),
+            ).fetchall()
+        else:
+            rows = self.conn.execute(
+                "SELECT ne.node_id, ne.embedding_model, ne.embedding_vector, ne.searchable_text, n.type "
+                "FROM node_embeddings ne JOIN nodes n ON ne.node_id = n.id"
+            ).fetchall()
+
+        result = []
+        for row in rows:
+            nid, model, blob, text, nt = row
+            dim = len(blob) // 4
+            vector = list(struct.unpack(f"<{dim}f", blob))
+            result.append(
+                {
+                    "node_id": nid,
+                    "embedding_model": model,
+                    "embedding_vector": vector,
+                    "searchable_text": text,
+                    "node_type": nt,
+                }
+            )
+        return result
+
+    def clear_embeddings(self) -> None:
+        """Delete all embedding vectors (keep schema)."""
+        self.conn.execute("DELETE FROM node_embeddings")
+        self.conn.commit()
+        logger.info("All embedding vectors cleared")
+
+    def get_embedding_model(self) -> str | None:
+        """Get the stored embedding model name from metadata.
+
+        Returns None if no embedding model has been recorded.
+        """
+        return self.get_metadata("embedding_model")
+
+    def search_nodes_by_embedding(
+        self,
+        query_vector: list[float],
+        node_type: NodeType | None = None,
+        limit: int = 10,
+        threshold: float = 0.5,
+    ) -> list[tuple[Node, float]]:
+        """Search nodes by embedding vector similarity (cosine similarity).
+
+        Loads all embeddings from DB, computes cosine similarity in Python,
+        filters by threshold, and returns (node, similarity) pairs sorted
+        by similarity descending.
+
+        Args:
+            query_vector: The query embedding vector.
+            node_type: Optional filter by node type.
+            limit: Maximum number of results.
+            threshold: Minimum cosine similarity to include.
+
+        Returns:
+            List of (Node, similarity_score) tuples, sorted by similarity descending.
+        """
+        embeddings = self.get_all_embeddings(node_type)
+
+        if not embeddings:
+            return []
+
+        results: list[tuple[Node, float]] = []
+        query_norm = math.sqrt(sum(v * v for v in query_vector))
+        if query_norm == 0.0:
+            return []
+
+        for emb in embeddings:
+            vec = emb["embedding_vector"]
+            vec_norm = math.sqrt(sum(v * v for v in vec))
+            if vec_norm == 0.0:
+                continue
+
+            dot = sum(a * b for a, b in zip(query_vector, vec))
+            sim = dot / (query_norm * vec_norm)
+
+            if sim >= threshold:
+                node = self.get_node(emb["node_id"])
+                if node:
+                    results.append((node, sim))
+
+        # Sort by similarity descending
+        results.sort(key=lambda x: x[1], reverse=True)
+        return results[:limit]
+
+    # --- Utility ---
+
+    def get_graph_stats(self) -> dict[str, Any]:
+        """Get overview statistics of the graph."""
+        node_counts = {}
+        for nt in NodeType:
+            node_counts[nt.value] = self.count_nodes(nt)
+
+        edge_counts = {}
+        for et in EdgeType:
+            edge_counts[et.value] = self.count_edges(et)
+
+        pending_counts = {}
+        for et in EdgeType:
+            pending_counts[et.value] = self.count_pending_edges(et)
+
+        return {
+            "total_nodes": self.count_nodes(),
+            "node_type_counts": node_counts,
+            "total_edges": self.count_edges(),
+            "edge_type_counts": edge_counts,
+            "total_profiles": self.conn.execute("SELECT COUNT(*) FROM column_profiles").fetchone()[0],
+            "pending_edge_count": self.count_pending_edges(),
+            "pending_edge_type_counts": pending_counts,
+        }
+
+    def show_graph(self) -> dict[str, Any]:
+        """Return all nodes and edges as a structured dict for the show command.
+
+        Returns:
+            {"nodes": [{"id", "type", "name", "properties"}, ...],
+             "edges": [{"id", "source_id", "target_id", "type", "confidence", "properties"}, ...]}
+        """
+        node_rows = self.conn.execute("SELECT id, type, name, properties FROM nodes").fetchall()
+        nodes = []
+        for row in node_rows:
+            id_str, type_str, name, properties_json = row
+            nodes.append(
+                {
+                    "id": id_str,
+                    "type": type_str,
+                    "name": name,
+                    "properties": json.loads(properties_json) if properties_json else {},
+                }
+            )
+
+        edge_rows = self.conn.execute(
+            "SELECT id, source_id, target_id, type, confidence, properties FROM edges"
+        ).fetchall()
+        edges = []
+        for row in edge_rows:
+            id_str, source_id, target_id, type_str, confidence, properties_json = row
+            edges.append(
+                {
+                    "id": id_str,
+                    "source_id": source_id,
+                    "target_id": target_id,
+                    "type": type_str,
+                    "confidence": confidence,
+                    "properties": json.loads(properties_json) if properties_json else {},
+                }
+            )
+
+        return {"nodes": nodes, "edges": edges}
+
+    def clear_all(self) -> None:
+        """Delete all data from the graph (keep schema)."""
+        self.conn.execute("DELETE FROM edges")
+        self.conn.execute("DELETE FROM pending_edges")
+        self.conn.execute("DELETE FROM column_profiles")
+        self.conn.execute("DELETE FROM node_embeddings")
+        self.conn.execute("DELETE FROM nodes")
+        self.conn.execute("DELETE FROM metadata")
+        self.conn.commit()
+        logger.info("All graph data cleared")
+
+    # --- Schema migration helpers ---
+
+    def _migrate_remove_cascade_fks(self) -> None:
+        """Remove ON DELETE CASCADE foreign keys from edges & column_profiles.
+
+        SQLite does not support ALTER TABLE DROP CONSTRAINT, so we must
+        recreate the tables. The strategy:
+
+        1. Check if the old CASCADE FKs exist by probing pragma.
+        2. If found, create temp tables → copy data → drop old → recreate
+           without CASCADE → copy back → drop temp.
+        3. This is safe for existing data and preserves all rows.
+        """
+        # Check edges table for cascade FKs on source_id / target_id
+        needs_edges_migration = self._table_has_cascade_fk("edges", ["source_id", "target_id"])
+        needs_profiles_migration = self._table_has_cascade_fk("column_profiles", ["column_id"])
+
+        if not needs_edges_migration and not needs_profiles_migration:
+            # Already migrated or fresh database — nothing to do
+            return
+
+        logger.info("Migrating schema: removing ON DELETE CASCADE foreign keys from edges/column_profiles")
+
+        if needs_edges_migration:
+            self._rebuild_table_without_cascade("edges")
+            # Re-create indexes that were lost when the old table was dropped
+            self.conn.executescript(
+                "CREATE INDEX IF NOT EXISTS idx_edges_source ON edges(source_id);"
+                "CREATE INDEX IF NOT EXISTS idx_edges_target ON edges(target_id);"
+                "CREATE INDEX IF NOT EXISTS idx_edges_type ON edges(type);"
+                "CREATE INDEX IF NOT EXISTS idx_edges_source_type ON edges(source_id, type);"
+                "CREATE INDEX IF NOT EXISTS idx_edges_target_type ON edges(target_id, type);"
+                "CREATE INDEX IF NOT EXISTS idx_edges_confidence ON edges(confidence);"
+            )
+
+        if needs_profiles_migration:
+            self._rebuild_table_without_cascade("column_profiles")
+            self.conn.executescript("CREATE INDEX IF NOT EXISTS idx_profiles_column ON column_profiles(column_id);")
+
+        self.conn.commit()
+        logger.info("Schema migration complete: CASCADE FKs removed")
+
+    def _table_has_cascade_fk(self, table: str, columns: list[str]) -> bool:
+        """Check whether a table has ON DELETE CASCADE FKs on given columns."""
+        fk_list = self.conn.execute(f"PRAGMA foreign_key_list({table})").fetchall()
+        # PRAGMA foreign_key_list columns: id, seq, table, from, to, on_update, on_delete, match
+        for fk in fk_list:
+            # fk[3] = "from" column, fk[6] = "on_delete" action
+            if fk[3] in columns and fk[6] == "CASCADE":
+                return True
+        return False
+
+    def _rebuild_table_without_cascade(self, table: str) -> None:
+        """Rebuild a table to drop its ON DELETE CASCADE foreign keys.
+
+        Steps:
+        1. CREATE TABLE _migrate_{table} with the new schema (no CASCADE FKs)
+        2. INSERT INTO _migrate_{table} SELECT * FROM {table}
+        3. DROP TABLE {table}
+        4. ALTER TABLE _migrate_{table} RENAME TO {table}
+        """
+        new_schema_sql = {
+            "edges": (
+                "CREATE TABLE _migrate_edges ("
+                "id TEXT PRIMARY KEY,"
+                "source_id TEXT NOT NULL,"
+                "target_id TEXT NOT NULL,"
+                "type TEXT NOT NULL,"
+                "confidence REAL DEFAULT 1.0,"
+                "properties TEXT,"
+                "created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP)"
+            ),
+            "column_profiles": (
+                "CREATE TABLE _migrate_column_profiles ("
+                "id TEXT PRIMARY KEY,"
+                "column_id TEXT NOT NULL,"
+                "properties TEXT,"
+                "created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP)"
+            ),
+        }
+
+        if table not in new_schema_sql:
+            return
+
+        # Disable FK checks during migration so we can drop the old table
+        # without cascading (the data is preserved via the temp table)
+        self.conn.execute("PRAGMA foreign_keys = OFF")
+
+        self.conn.execute(new_schema_sql[table])
+        self.conn.execute(f"INSERT INTO _migrate_{table} SELECT * FROM {table}")
+        self.conn.execute(f"DROP TABLE {table}")
+        self.conn.execute(f"ALTER TABLE _migrate_{table} RENAME TO {table}")
+
+        # Re-enable FK checks
+        self.conn.execute("PRAGMA foreign_keys = ON")
+
+    # --- Internal helpers ---
+
+    def _row_to_node(self, row: tuple) -> Node:
+        """Convert a database row to a Node model."""
+        id_str, type_str, name, properties_json = row
+        node_type = NodeType(type_str)
+        properties = json.loads(properties_json) if properties_json else {}
+
+        if node_type == NodeType.COLUMN:
+            return ColumnNode(
+                id=id_str,
+                name=name,
+                table_id=properties.get("table_id", ""),
+                dtype=properties.get("dtype", ""),
+                semantic_type=properties.get("semantic_type", ""),
+                comment=properties.get("comment", ""),
+                profile_id=properties.get("profile_id", ""),
+                properties=properties,
+            )
+        elif node_type == NodeType.TABLE:
+            return TableNode(
+                id=id_str,
+                name=name,
+                source=properties.get("source", ""),
+                row_count=properties.get("row_count", 0),
+                column_ids=properties.get("column_ids", []),
+                properties=properties,
+            )
+        elif node_type == NodeType.CONCEPT:
+            return ConceptNode(
+                id=id_str,
+                name=name,
+                description=properties.get("description", ""),
+                unit=properties.get("unit", ""),
+                dimension=properties.get("dimension", ""),
+                properties=properties,
+            )
+        elif node_type == NodeType.ENTITY:
+            return EntityNode(
+                id=id_str,
+                name=name,
+                description=properties.get("description", ""),
+                properties=properties,
+            )
+        else:
+            return Node(id=id_str, type=node_type, name=name, properties=properties)
+
+    def _row_to_edge(self, row: tuple) -> Edge:
+        """Convert a database row to an Edge model."""
+        id_str, source_id, target_id, type_str, confidence, properties_json = row
+        return Edge(
+            id=id_str,
+            source_id=source_id,
+            target_id=target_id,
+            type=EdgeType(type_str),
+            confidence=confidence,
+            properties=json.loads(properties_json) if properties_json else {},
+        )
+
+    def _row_to_profile(self, row: tuple) -> ColumnProfile:
+        """Convert a database row to a ColumnProfile model."""
+        id_str, column_id, properties_json = row
+        props = json.loads(properties_json) if properties_json else {}
+        return ColumnProfile(id=id_str, column_id=column_id, **props)
+
+    def _row_to_pending_edge(self, row: tuple) -> PendingEdge:
+        """Convert a database row to a PendingEdge model."""
+        id_str, source_id, target_id, type_str, confidence, properties_json, missing_json = row
+        return PendingEdge(
+            id=id_str,
+            source_id=source_id,
+            target_id=target_id,
+            type=EdgeType(type_str),
+            confidence=confidence,
+            properties=json.loads(properties_json) if properties_json else {},
+            missing_endpoints=json.loads(missing_json) if missing_json else [],
+        )
diff --git a/services/datalink/src/datalink/inferrer/__init__.py b/services/datalink/src/datalink/inferrer/__init__.py
new file mode 100644
index 00000000..112924ff
--- /dev/null
+++ b/services/datalink/src/datalink/inferrer/__init__.py
@@ -0,0 +1 @@
+"""Relationship inferrers — discover implicit edges with confidence scores."""
diff --git a/services/datalink/src/datalink/inferrer/correlated.py b/services/datalink/src/datalink/inferrer/correlated.py
new file mode 100644
index 00000000..7b6769ed
--- /dev/null
+++ b/services/datalink/src/datalink/inferrer/correlated.py
@@ -0,0 +1,313 @@
+"""CorrelationInferrer — detect statistical correlations between numeric columns.
+
+The key insight: correlation only makes sense between columns from *different*
+tables, after aligning rows via a join key. So for each joinable edge (a1 ↔ b1),
+we find other numeric columns in the same tables (a2, a3, … and b2, b3, …),
+merge the sample data on a1 = b1, then compute Pearson correlation on the
+aligned (a2, b2) pairs.
+"""
+
+import logging
+
+import pandas as pd
+
+from datalink.models.datasource import DatasourceInfo
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode
+from datalink.models.profile import ColumnProfile
+from datalink.utils.ids import parse_column_id
+
+logger = logging.getLogger(__name__)
+
+NUMERIC_DTYPES = {"integer", "float"}
+
+# Minimum number of aligned rows required to compute a meaningful Pearson r
+MIN_ALIGNED_ROWS = 5
+
+
+class CorrelationInferrer:
+    """Detect statistical correlations between numeric columns across tables.
+
+    For each pair of tables connected by a joinable edge (join key), find
+    other numeric columns in each table, merge sample data on the join key,
+    and compute Pearson correlation on the aligned values.
+
+    The resulting correlated edges connect the non-key numeric columns
+    (e.g., orders.amount ↔ users.age), not the join keys themselves.
+    """
+
+    def __init__(self, correlation_threshold: float = 0.5):
+        """Initialize the correlation inferrer.
+
+        Args:
+            correlation_threshold: Minimum absolute Pearson correlation
+                                  coefficient to create an edge.
+        """
+        self.correlation_threshold = correlation_threshold
+
+    def infer(
+        self,
+        columns: list[ColumnNode],
+        profiles: list[ColumnProfile],
+        joinable_edges: list[Edge],
+        datasource_infos: list[DatasourceInfo],
+    ) -> list[Edge]:
+        """Find correlated relationships between numeric columns across tables.
+
+        For each joinable edge (key_a ↔ key_b), we:
+        1. Find all numeric columns in key_a's table (excluding key_a itself)
+        2. Find all numeric columns in key_b's table (excluding key_b itself)
+        3. Merge sample data from both tables on key_a = key_b
+        4. Compute Pearson correlation for each (numeric_a, numeric_b) pair
+        5. If |r| >= threshold, create a correlated edge between those columns
+
+        Args:
+            columns: All column nodes.
+            profiles: Corresponding column profiles.
+            joinable_edges: Pre-computed joinable edges (these define the join keys).
+            datasource_infos: Source data containing sample_data for merging.
+
+        Returns:
+            List of correlated edges (connecting non-key numeric columns).
+        """
+        edges: list[Edge] = []
+
+        # Build lookups
+        profile_map = {p.column_id: p for p in profiles}
+        col_map = {c.id: c for c in columns}
+
+        # Group columns by table_id for fast lookup
+        table_columns: dict[str, list[ColumnNode]] = {}
+        for col in columns:
+            if col.table_id not in table_columns:
+                table_columns[col.table_id] = []
+            table_columns[col.table_id].append(col)
+
+        # Build sample data lookup: table_name -> DataFrame
+        # Combine sample_data from all datasource_infos
+        sample_dfs: dict[str, pd.DataFrame] = {}
+        for ds_info in datasource_infos:
+            for table_name, rows in ds_info.sample_data.items():
+                if rows:
+                    sample_dfs[table_name] = pd.DataFrame(rows)
+
+        # For each joinable edge, find correlated non-key numeric columns
+        for joinable_edge in joinable_edges:
+            key_a_id = joinable_edge.source_id
+            key_b_id = joinable_edge.target_id
+
+            key_a = col_map.get(key_a_id)
+            key_b = col_map.get(key_b_id)
+
+            if key_a is None or key_b is None:
+                continue
+
+            # Resolve table/column names without naive split(":") —
+            # source URLs (sqlite:///..., postgresql://...) contain colons.
+            parsed_a = parse_column_id(key_a_id)
+            parsed_b = parse_column_id(key_b_id)
+            if parsed_a is None or parsed_b is None:
+                continue
+
+            table_name_a = parsed_a.table_name
+            table_name_b = parsed_b.table_name
+            col_name_a = key_a.name
+            col_name_b = key_b.name
+
+            df_a = sample_dfs.get(table_name_a)
+            df_b = sample_dfs.get(table_name_b)
+
+            if df_a is None or df_b is None:
+                logger.debug(
+                    f"Skipping correlation for {key_a_id} ↔ {key_b_id}: "
+                    f"no sample data for {table_name_a} or {table_name_b}"
+                )
+                continue
+
+            # Ensure join key columns exist in the DataFrames
+            if col_name_a not in df_a.columns or col_name_b not in df_b.columns:
+                continue
+
+            # Coerce join key columns to the same dtype before merging.
+            # Cross-table join keys may have mismatched types (e.g., int vs str)
+            # which causes pd.merge to raise a TypeError. Convert both to str
+            # so the merge always succeeds regardless of source dtype.
+            df_a = df_a.copy()
+            df_b = df_b.copy()
+            df_a[col_name_a] = df_a[col_name_a].astype(str)
+            df_b[col_name_b] = df_b[col_name_b].astype(str)
+
+            # Merge sample data on the join key
+            merged = pd.merge(
+                df_a,
+                df_b,
+                left_on=col_name_a,
+                right_on=col_name_b,
+                how="inner",
+                suffixes=("_a", "_b"),
+            )
+
+            if len(merged) < MIN_ALIGNED_ROWS:
+                logger.debug(
+                    f"Skipping correlation for {key_a_id} ↔ {key_b_id}: "
+                    f"only {len(merged)} aligned rows (need {MIN_ALIGNED_ROWS})"
+                )
+                continue
+
+            # Find other numeric columns in each table (excluding the join key)
+            numeric_cols_a = self._find_numeric_columns(
+                key_a.table_id,
+                col_name_a,
+                profile_map,
+                table_columns,
+                merged,
+                suffix="_a",
+            )
+            numeric_cols_b = self._find_numeric_columns(
+                key_b.table_id,
+                col_name_b,
+                profile_map,
+                table_columns,
+                merged,
+                suffix="_b",
+            )
+
+            if not numeric_cols_a or not numeric_cols_b:
+                continue
+
+            # Compute Pearson correlation for each pair
+            for (col_a_id, col_a_name_df), (col_b_id, col_b_name_df) in self._pair_numeric_columns(
+                numeric_cols_a, numeric_cols_b, key_a.table_id, key_b.table_id
+            ):
+                corr = self._compute_pearson(merged, col_a_name_df, col_b_name_df)
+                if corr is not None and abs(corr) >= self.correlation_threshold:
+                    edge = Edge(
+                        id=f"edge:correlated:{col_a_id}:{col_b_id}",
+                        source_id=col_a_id,
+                        target_id=col_b_id,
+                        type=EdgeType.CORRELATED,
+                        confidence=abs(corr),
+                        properties={
+                            "coefficient": corr,
+                            "method": "pearson",
+                            "aligned_rows": len(merged),
+                            "joinable_edge": joinable_edge.id,
+                            "join_key_a": key_a_id,
+                            "join_key_b": key_b_id,
+                        },
+                    )
+                    edges.append(edge)
+                    logger.debug(
+                        f"Found correlation: {col_a_id} ↔ {col_b_id} "
+                        f"(r={corr:.3f}, aligned_rows={len(merged)}, "
+                        f"join_key={key_a_id}↔{key_b_id})"
+                    )
+
+        logger.info(f"Found {len(edges)} correlated edges")
+        return edges
+
+    def _find_numeric_columns(
+        self,
+        table_id: str,
+        exclude_col_name: str,
+        profile_map: dict[str, ColumnProfile],
+        table_columns: dict[str, list[ColumnNode]],
+        merged: pd.DataFrame,
+        suffix: str,
+    ) -> list[tuple[str, str]]:
+        """Find numeric columns in a table that exist in the merged DataFrame.
+
+        After a pd.merge with suffixes=("_a", "_b"), column names that clash
+        between the two input DataFrames get a suffix appended (e.g., "col_a").
+        Column names that are unique keep their original name. So we need to
+        check both the suffixed name and the original name.
+
+        Returns list of (column_id, merged_df_column_name) tuples.
+        Excludes the join key column.
+        """
+        result = []
+        cols_in_table = table_columns.get(table_id, [])
+
+        for col in cols_in_table:
+            col_name = col.name
+
+            # Skip the join key
+            if col_name == exclude_col_name:
+                continue
+
+            # Check dtype is numeric
+            profile = profile_map.get(col.id)
+            if profile is None or profile.dtype not in NUMERIC_DTYPES:
+                continue
+
+            # Find the column in the merged DataFrame.
+            # pandas merge applies suffix only to clashing column names;
+            # unique names keep their original name.
+            suffixed_name = f"{col_name}{suffix}"
+            if suffixed_name in merged.columns:
+                merged_name = suffixed_name
+            elif col_name in merged.columns:
+                merged_name = col_name
+            else:
+                continue
+
+            # Verify it's actually numeric in the merged DataFrame
+            if pd.api.types.is_numeric_dtype(merged[merged_name]):
+                result.append((col.id, merged_name))
+
+        return result
+
+    def _pair_numeric_columns(
+        self,
+        cols_a: list[tuple[str, str]],
+        cols_b: list[tuple[str, str]],
+        table_id_a: str,
+        table_id_b: str,
+    ) -> list[tuple[tuple[str, str], tuple[str, str]]]:
+        """Generate pairs of numeric columns from different tables.
+
+        Only pairs columns from different tables (which is guaranteed here
+        since cols_a and cols_b come from different tables).
+        """
+        pairs = []
+        for col_a in cols_a:
+            for col_b in cols_b:
+                pairs.append((col_a, col_b))
+        return pairs
+
+    def _compute_pearson(
+        self,
+        merged: pd.DataFrame,
+        col_name_a: str,
+        col_name_b: str,
+    ) -> float | None:
+        """Compute Pearson correlation between two columns in the merged DataFrame.
+
+        Args:
+            merged: DataFrame with aligned rows from both tables.
+            col_name_a: Column name in merged DataFrame (with suffix).
+            col_name_b: Column name in merged DataFrame (with suffix).
+
+        Returns:
+            Pearson r value, or None if computation fails or insufficient data.
+        """
+        try:
+            series_a = pd.to_numeric(merged[col_name_a], errors="coerce").dropna()
+            series_b = pd.to_numeric(merged[col_name_b], errors="coerce").dropna()
+
+            # Align indices after dropna
+            common_idx = series_a.index.intersection(series_b.index)
+            if len(common_idx) < MIN_ALIGNED_ROWS:
+                return None
+
+            series_a = series_a.loc[common_idx]
+            series_b = series_b.loc[common_idx]
+
+            corr = series_a.corr(series_b, method="pearson")
+
+            if pd.isna(corr):
+                return None
+
+            return float(corr)
+        except Exception:
+            return None
diff --git a/services/datalink/src/datalink/inferrer/distribution.py b/services/datalink/src/datalink/inferrer/distribution.py
new file mode 100644
index 00000000..aa3f6915
--- /dev/null
+++ b/services/datalink/src/datalink/inferrer/distribution.py
@@ -0,0 +1,172 @@
+"""DistributionInferrer — detect columns with similar data distributions."""
+
+import logging
+
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode
+from datalink.models.profile import ColumnProfile
+
+logger = logging.getLogger(__name__)
+
+
+class DistributionInferrer:
+    """Detect distribution similarity between columns.
+
+    Useful for detecting columns with similar time ranges (e.g., order_date
+    and tx_timestamp) or similar categorical distributions.
+    """
+
+    def __init__(self, similarity_threshold: float = 0.5):
+        """Initialize the distribution inferrer.
+
+        Args:
+            similarity_threshold: Minimum similarity score to create an edge.
+        """
+        self.similarity_threshold = similarity_threshold
+
+    def infer(
+        self,
+        columns: list[ColumnNode],
+        profiles: list[ColumnProfile],
+    ) -> list[Edge]:
+        """Find distribution similarity relationships.
+
+        Args:
+            columns: All column nodes to compare.
+            profiles: Corresponding column profiles.
+
+        Returns:
+            List of distribution_similar edges.
+        """
+        edges: list[Edge] = []
+        profile_map = {p.column_id: p for p in profiles}
+
+        for i, col_a in enumerate(columns):
+            for col_b in columns[i + 1 :]:
+                # Skip same-table comparisons
+                if col_a.table_id == col_b.table_id:
+                    continue
+
+                profile_a = profile_map.get(col_a.id)
+                profile_b = profile_map.get(col_b.id)
+                if profile_a is None or profile_b is None:
+                    continue
+
+                # Only compare columns of similar types
+                if profile_a.dtype != profile_b.dtype:
+                    continue
+
+                similarity = self._compute_similarity(profile_a, profile_b)
+                if similarity >= self.similarity_threshold:
+                    edge = Edge(
+                        id=f"edge:dist_similar:{col_a.id}:{col_b.id}",
+                        source_id=col_a.id,
+                        target_id=col_b.id,
+                        type=EdgeType.DISTRIBUTION_SIMILAR,
+                        confidence=similarity,
+                        properties={
+                            "similarity_score": similarity,
+                            "dtype": profile_a.dtype,
+                        },
+                    )
+                    edges.append(edge)
+                    logger.debug(
+                        f"Found distribution similarity: {col_a.name} <-> {col_b.name} (score={similarity:.3f})"
+                    )
+
+        logger.info(f"Found {len(edges)} distribution similar edges")
+        return edges
+
+    def _compute_similarity(self, profile_a: ColumnProfile, profile_b: ColumnProfile) -> float:
+        """Compute distribution similarity between two column profiles.
+
+        For numeric columns: compare value ranges and histogram shapes.
+        For string columns: compare top value overlap.
+        """
+        # Numeric comparison
+        numeric_types = {"integer", "float"}
+        if profile_a.dtype in numeric_types and profile_b.dtype in numeric_types:
+            return self._numeric_similarity(profile_a, profile_b)
+
+        # String/categorical comparison
+        if profile_a.dtype == "string" and profile_b.dtype == "string":
+            return self._categorical_similarity(profile_a, profile_b)
+
+        # Temporal comparison
+        temporal_types = {"datetime", "date"}
+        if profile_a.dtype in temporal_types and profile_b.dtype in temporal_types:
+            return self._temporal_similarity(profile_a, profile_b)
+
+        return 0.0
+
+    def _numeric_similarity(self, profile_a: ColumnProfile, profile_b: ColumnProfile) -> float:
+        """Compare numeric distributions using range overlap and histogram similarity."""
+        # Range overlap: how much of the value ranges overlap
+        if profile_a.min_value is None or profile_a.max_value is None:
+            return 0.0
+        if profile_b.min_value is None or profile_b.max_value is None:
+            return 0.0
+
+        range_a = (profile_a.min_value, profile_a.max_value)
+        range_b = (profile_b.min_value, profile_b.max_value)
+
+        # Compute overlap fraction
+        overlap_start = max(range_a[0], range_b[0])
+        overlap_end = min(range_a[1], range_b[1])
+
+        if overlap_start > overlap_end:
+            return 0.0  # No overlap at all
+
+        overlap_range = overlap_end - overlap_start
+        total_range = max(range_a[1], range_b[1]) - min(range_a[0], range_b[0])
+
+        if total_range == 0:
+            return 1.0  # Both are constant values at the same point
+
+        overlap_fraction = overlap_range / total_range
+
+        # Compare mean/std similarity
+        mean_similarity = 0.0
+        if profile_a.mean_value is not None and profile_b.mean_value is not None:
+            mean_diff = abs(profile_a.mean_value - profile_b.mean_value)
+            max_mean = max(abs(profile_a.mean_value), abs(profile_b.mean_value))
+            if max_mean > 0:
+                mean_similarity = 1.0 - mean_diff / max_mean
+
+        return overlap_fraction * 0.6 + mean_similarity * 0.4
+
+    def _categorical_similarity(self, profile_a: ColumnProfile, profile_b: ColumnProfile) -> float:
+        """Compare categorical distributions using top value overlap."""
+        values_a = {item["value"]: item["fraction"] for item in profile_a.top_values}
+        values_b = {item["value"]: item["fraction"] for item in profile_b.top_values}
+
+        common_values = set(values_a.keys()) & set(values_b.keys())
+        if not common_values:
+            return 0.0
+
+        # Compute weighted overlap (sum of min fractions for common values)
+        weighted_overlap = sum(min(values_a[v], values_b[v]) for v in common_values)
+        total_weight = sum(values_a.values()) + sum(values_b.values())
+
+        if total_weight == 0:
+            return 0.0
+
+        return weighted_overlap / (total_weight / 2)
+
+    def _temporal_similarity(self, profile_a: ColumnProfile, profile_b: ColumnProfile) -> float:
+        """Compare temporal distributions using histogram overlap."""
+        if not profile_a.distribution_histogram or not profile_b.distribution_histogram:
+            return 0.0
+
+        # Simple comparison: both have temporal data
+        # For MVP, just check if they have similar number of bins and data density
+        len_a = len(profile_a.distribution_histogram)
+        len_b = len(profile_b.distribution_histogram)
+
+        if len_a == 0 or len_b == 0:
+            return 0.0
+
+        # Similar bin count suggests similar time range coverage
+        bin_similarity = min(len_a, len_b) / max(len_a, len_b)
+
+        return bin_similarity * 0.5  # Conservative estimate
diff --git a/services/datalink/src/datalink/inferrer/joinable.py b/services/datalink/src/datalink/inferrer/joinable.py
new file mode 100644
index 00000000..c6e1d0c0
--- /dev/null
+++ b/services/datalink/src/datalink/inferrer/joinable.py
@@ -0,0 +1,169 @@
+"""JoinableInferrer — detect columns that can be joined by value domain overlap."""
+
+import logging
+
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode
+from datalink.models.profile import ColumnProfile
+
+logger = logging.getLogger(__name__)
+
+
+class JoinableInferrer:
+    """Detect joinable relationships between columns based on value overlap.
+
+    Two columns are joinable if their value domains have significant overlap.
+    This is computed by comparing the intersection of top-N frequent values
+    and sample intersection rates.
+
+    Boolean-type columns (boolean, integer_boolean, float_boolean) are excluded
+    because their tiny value domains (e.g. {0, 1}) trivially overlap with any
+    other boolean column, producing meaningless joinable edges.
+    """
+
+    def __init__(self, overlap_threshold: float = 0.1, max_cardinality: int = 900):
+        """Initialize the joinable inferrer.
+
+        Args:
+            overlap_threshold: Minimum overlap rate to consider columns joinable.
+            max_cardinality: Skip columns with cardinality above this threshold
+                            (too many unique values make sampling unreliable).
+        """
+        self.overlap_threshold = overlap_threshold
+        self.max_cardinality = max_cardinality
+
+    def infer(
+        self,
+        columns: list[ColumnNode],
+        profiles: list[ColumnProfile],
+    ) -> list[Edge]:
+        """Find joinable relationships between columns across different tables.
+
+        Args:
+            columns: All column nodes to compare.
+            profiles: Corresponding column profiles with top_values and samples.
+
+        Returns:
+            List of joinable edges with confidence scores.
+        """
+        edges: list[Edge] = []
+
+        # Build lookup: column_id -> profile
+        profile_map = {p.column_id: p for p in profiles}
+
+        # Only consider columns from different tables and compatible types
+        # Group columns by table
+        table_groups: dict[str, list[ColumnNode]] = {}
+        for col in columns:
+            if col.table_id not in table_groups:
+                table_groups[col.table_id] = []
+            table_groups[col.table_id].append(col)
+
+        tables = list(table_groups.keys())
+
+        # Compare columns across different table pairs
+        for i, table_a in enumerate(tables):
+            for table_b in tables[i + 1 :]:
+                for col_a in table_groups[table_a]:
+                    profile_a = profile_map.get(col_a.id)
+                    if profile_a is None or profile_a.cardinality > self.max_cardinality:
+                        continue
+                    # Skip boolean-type columns — their tiny value domains
+                    # trivially overlap, producing meaningless joinable edges
+                    if self._is_boolean_skip(profile_a):
+                        continue
+
+                    for col_b in table_groups[table_b]:
+                        profile_b = profile_map.get(col_b.id)
+                        if profile_b is None or profile_b.cardinality > self.max_cardinality:
+                            continue
+                        if self._is_boolean_skip(profile_b):
+                            continue
+
+                        # Skip incompatible dtype pairs (string vs integer is allowed
+                        # since identifiers can be stored as either)
+                        if not self._compatible_dtypes(profile_a.dtype, profile_b.dtype):
+                            continue
+
+                        # Compute overlap rate
+                        overlap_rate = self._compute_overlap(profile_a, profile_b)
+                        if overlap_rate >= self.overlap_threshold:
+                            edge = Edge(
+                                id=f"edge:joinable:{col_a.id}:{col_b.id}",
+                                source_id=col_a.id,
+                                target_id=col_b.id,
+                                type=EdgeType.JOINABLE,
+                                confidence=overlap_rate,
+                                properties={
+                                    "overlap_rate": overlap_rate,
+                                    "dtype_a": profile_a.dtype,
+                                    "dtype_b": profile_b.dtype,
+                                },
+                            )
+                            edges.append(edge)
+                            logger.debug(f"Found joinable: {col_a.name} <-> {col_b.name} (overlap={overlap_rate:.3f})")
+
+        logger.info(f"Found {len(edges)} joinable edges")
+        return edges
+
+    def _is_boolean_skip(self, profile: ColumnProfile) -> bool:
+        """Check if a column is a boolean-type column that should be skipped.
+
+        Boolean columns (including integer_boolean and float_boolean detected
+        by the profiler) have tiny value domains that trivially overlap with
+        any other boolean column, producing spurious joinable edges.
+
+        Examples of skipped columns:
+        - boolean (pandas bool dtype)
+        - integer_boolean (BIGINT/INTEGER storing only {0, 1})
+        - float_boolean (FLOAT storing only {0.0, 1.0})
+        """
+        return profile.dtype in ("boolean", "integer_boolean", "float_boolean")
+
+    def _compatible_dtypes(self, dtype_a: str, dtype_b: str) -> bool:
+        """Check if two dtypes can potentially be compared for joinability."""
+        numeric_types = {"integer", "float", "integer_boolean", "float_boolean"}
+        string_types = {"string"}
+        temporal_types = {"datetime", "date"}
+
+        # Numeric types are compatible with each other
+        if dtype_a in numeric_types and dtype_b in numeric_types:
+            return True
+        # String types are compatible with each other
+        if dtype_a in string_types and dtype_b in string_types:
+            return True
+        # Temporal types are compatible with each other
+        if dtype_a in temporal_types and dtype_b in temporal_types:
+            return True
+        # Identifiers can be stored as integer or string
+        if (dtype_a in numeric_types and dtype_b in string_types) or (
+            dtype_a in string_types and dtype_b in numeric_types
+        ):
+            return True
+
+        return False
+
+    def _compute_overlap(self, profile_a: ColumnProfile, profile_b: ColumnProfile) -> float:
+        """Compute value overlap rate between two column profiles.
+
+        Uses top values and sample values to estimate the fraction of values
+        in column A that appear in column B's domain.
+        """
+        # Extract value sets from top_values
+        values_a = {item["value"] for item in profile_a.top_values}
+        values_b = {item["value"] for item in profile_b.top_values}
+
+        # Also include sample values
+        if profile_a.sample_values:
+            values_a.update(str(v) for v in profile_a.sample_values)
+        if profile_b.sample_values:
+            values_b.update(str(v) for v in profile_b.sample_values)
+
+        if not values_a or not values_b:
+            return 0.0
+
+        # Compute overlap: fraction of A's values that appear in B
+        intersection = values_a & values_b
+        overlap_rate = len(intersection) / min(len(values_a), len(values_b))
+
+        return overlap_rate
diff --git a/services/datalink/src/datalink/inferrer/synonym.py b/services/datalink/src/datalink/inferrer/synonym.py
new file mode 100644
index 00000000..50c2848a
--- /dev/null
+++ b/services/datalink/src/datalink/inferrer/synonym.py
@@ -0,0 +1,174 @@
+"""SynonymInferrer — detect columns with similar semantic meaning."""
+
+import logging
+
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode
+from datalink.models.profile import ColumnProfile
+
+logger = logging.getLogger(__name__)
+
+
+class SynonymInferrer:
+    """Detect semantic synonym relationships between columns.
+
+    Columns are semantic synonyms when they represent the same concept
+    but may have different names (e.g., customer_id ≡ user_id ≡ client_id).
+
+    Detection strategies:
+    1. Same semantic_type from profiling → high confidence (0.8-0.9)
+    2. Name similarity (string similarity) → medium confidence (0.5-0.7)
+    3. Both conditions → very high confidence (0.95)
+    """
+
+    # Known synonym groups for common data concepts
+    SYNONYM_GROUPS = [
+        {"id", "identifier", "uid", "uuid", "guid"},
+        {"customer_id", "user_id", "client_id", "account_id", "person_id"},
+        {"name", "full_name", "person_name", "display_name"},
+        {"email", "email_address", "mail", "email_address"},
+        {"phone", "phone_number", "telephone", "tel", "mobile"},
+        {"address", "street_address", "location", "physical_address"},
+        {"amount", "value", "total", "sum", "price", "cost", "fee", "charge"},
+        {"date", "timestamp", "created_at", "updated_at", "time", "datetime"},
+        {"status", "state", "condition", "phase"},
+        {"type", "category", "class", "kind", "group"},
+        {"city", "city_name", "town", "municipality"},
+        {"country", "country_name", "nation", "region"},
+        {"age", "years", "year_old"},
+        {"gender", "sex"},
+        {"latitude", "lat", "y"},
+        {"longitude", "lng", "lon", "x"},
+        {"description", "desc", "notes", "comment", "details", "remarks"},
+    ]
+
+    def infer(
+        self,
+        columns: list[ColumnNode],
+        profiles: list[ColumnProfile],
+    ) -> list[Edge]:
+        """Find semantic synonym relationships between columns.
+
+        Args:
+            columns: All column nodes to compare.
+            profiles: Corresponding column profiles.
+
+        Returns:
+            List of semantic_synonym edges with confidence scores.
+        """
+        edges: list[Edge] = []
+        profile_map = {p.column_id: p for p in profiles}
+
+        # Only compare columns from different tables
+        for i, col_a in enumerate(columns):
+            for col_b in columns[i + 1 :]:
+                # Skip same-table comparisons
+                if col_a.table_id == col_b.table_id:
+                    continue
+
+                profile_a = profile_map.get(col_a.id)
+                profile_b = profile_map.get(col_b.id)
+                if profile_a is None or profile_b is None:
+                    continue
+
+                confidence = self._compute_confidence(col_a, col_b, profile_a, profile_b)
+                if confidence > 0.0:
+                    edge = Edge(
+                        id=f"edge:synonym:{col_a.id}:{col_b.id}",
+                        source_id=col_a.id,
+                        target_id=col_b.id,
+                        type=EdgeType.SEMANTIC_SYNONYM,
+                        confidence=confidence,
+                        properties={
+                            "name_similarity": self._name_similarity(col_a.name, col_b.name),
+                            "semantic_type_match": profile_a.semantic_type == profile_b.semantic_type,
+                            "semantic_type_a": profile_a.semantic_type,
+                            "semantic_type_b": profile_b.semantic_type,
+                        },
+                    )
+                    edges.append(edge)
+                    logger.debug(f"Found synonym: {col_a.name} ≡ {col_b.name} (confidence={confidence:.2f})")
+
+        logger.info(f"Found {len(edges)} semantic synonym edges")
+        return edges
+
+    def _compute_confidence(
+        self,
+        col_a: ColumnNode,
+        col_b: ColumnNode,
+        profile_a: ColumnProfile,
+        profile_b: ColumnProfile,
+    ) -> float:
+        """Compute synonym confidence between two columns."""
+        # Check semantic type match
+        type_match = False
+        if profile_a.semantic_type != "unknown" and profile_b.semantic_type != "unknown":
+            if profile_a.semantic_type == profile_b.semantic_type:
+                type_match = True
+
+        # Check name similarity
+        name_sim = self._name_similarity(col_a.name, col_b.name)
+
+        # Check synonym group membership
+        group_match = self._check_synonym_group(col_a.name, col_b.name)
+
+        # Compute final confidence
+        if type_match and (name_sim > 0.5 or group_match):
+            return 0.95  # Very high confidence
+        elif type_match:
+            return 0.85  # High confidence
+        elif group_match:
+            return 0.80  # High confidence
+        elif name_sim > 0.7:
+            return 0.6  # Medium confidence
+        elif name_sim > 0.5:
+            return 0.4  # Low confidence
+
+        return 0.0  # Not a synonym
+
+    def _name_similarity(self, name_a: str, name_b: str) -> float:
+        """Compute name similarity between two column names.
+
+        Uses normalized Levenshtein-like distance and substring matching.
+        """
+        # Normalize names: remove underscores, lowercase
+        norm_a = name_a.lower().replace("_", "")
+        norm_b = name_b.lower().replace("_", "")
+
+        if norm_a == norm_b:
+            return 1.0
+
+        # Check substring containment
+        if norm_a in norm_b or norm_b in norm_a:
+            # Weight by length ratio (shorter name contained in longer)
+            ratio = min(len(norm_a), len(norm_b)) / max(len(norm_a), len(norm_b))
+            return 0.5 + 0.5 * ratio  # 0.5-1.0 range
+
+        # Compute character-level overlap
+        set_a = set(norm_a)
+        set_b = set(norm_b)
+        if not set_a or not set_b:
+            return 0.0
+
+        jaccard = len(set_a & set_b) / len(set_a | set_b)
+
+        # Also check prefix match
+        common_prefix_len = 0
+        for i in range(min(len(norm_a), len(norm_b))):
+            if norm_a[i] == norm_b[i]:
+                common_prefix_len += 1
+            else:
+                break
+        prefix_score = common_prefix_len / max(len(norm_a), len(norm_b))
+
+        return max(jaccard, prefix_score)
+
+    def _check_synonym_group(self, name_a: str, name_b: str) -> bool:
+        """Check if two column names belong to the same known synonym group."""
+        norm_a = name_a.lower().replace("-", "_")
+        norm_b = name_b.lower().replace("-", "_")
+
+        for group in self.SYNONYM_GROUPS:
+            if norm_a in group and norm_b in group:
+                return True
+        return False
diff --git a/services/datalink/src/datalink/mapper/__init__.py b/services/datalink/src/datalink/mapper/__init__.py
new file mode 100644
index 00000000..70c3820f
--- /dev/null
+++ b/services/datalink/src/datalink/mapper/__init__.py
@@ -0,0 +1 @@
+"""Semantic mappers — structural node → semantic layer (Concept/Entity)."""
diff --git a/services/datalink/src/datalink/mapper/llm_mapper.py b/services/datalink/src/datalink/mapper/llm_mapper.py
new file mode 100644
index 00000000..8cf42a37
--- /dev/null
+++ b/services/datalink/src/datalink/mapper/llm_mapper.py
@@ -0,0 +1,1205 @@
+"""LLMMapper — map structural nodes to semantic layer using LLM inference.
+
+All columns are mapped via LLM (columns with comments include them as
+extra signal in the prompt). When adding new data to an existing graph
+(add_table), new Concepts and Entities are merged with existing ones
+via a two-stage approach:
+  1. Embedding pre-filter (optional): compute cosine similarity between
+     new/existing nodes to narrow down candidate merge pairs.
+  2. LLM merge judgment (required): send candidates + full node lists
+     to LLM for semantic confirmation of merge decisions.
+
+Also generates table comments (descriptions) for tables that lack
+SQL metadata comments, using column names, profiles, and inferred
+concept/entity info as input.
+"""
+
+import json
+import logging
+import math
+from pathlib import Path
+from typing import Any
+
+from datalink.config import DataLinkConfig
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode, ConceptNode, EntityNode, Node, TableNode
+from datalink.models.profile import ColumnProfile
+
+logger = logging.getLogger(__name__)
+
+_PROMPTS_DIR = Path(__file__).parent / "prompts"
+
+# Prompt templates — loaded from external files so they can be
+# edited/tuned without touching Python code.
+MAPPING_PROMPT_TEMPLATE = (_PROMPTS_DIR / "mapping_prompt.txt").read_text(encoding="utf-8")
+TABLE_COMMENT_PROMPT_TEMPLATE = (_PROMPTS_DIR / "table_comment_prompt.txt").read_text(encoding="utf-8")
+MERGE_PROMPT_TEMPLATE = (_PROMPTS_DIR / "merge_prompt.txt").read_text(encoding="utf-8")
+
+
+class LLMMapper:
+    """Map structural nodes to Concept/Entity using LLM inference.
+
+    All columns (including those with comments) are mapped via LLM.
+    Columns with comments include them as extra signal in the prompt.
+
+    When adding new data to an existing graph (add_table), new Concepts
+    and Entities are merged with existing ones via:
+    - Embedding pre-filter (if configured) for candidate pair narrowing
+    - LLM judgment for semantic merge confirmation
+    - Edge redirection and deduplication
+    """
+
+    # Batch size for LLM calls — smaller batches reduce JSON output
+    # complexity and improve success rate for weaker models.
+    # Can be overridden via config.mapping_batch_size.
+    _DEFAULT_BATCH_SIZE = 15
+
+    def __init__(self, config: DataLinkConfig):
+        """Initialize with full DataLink configuration.
+
+        Args:
+            config: DataLink configuration including LLM and embedding settings.
+        """
+        self.config = config
+        self.llm_config = config.llm
+        self.embedding_config = config.embedding
+        self._batch_size = config.mapping_batch_size or self._DEFAULT_BATCH_SIZE
+        self._client = None
+
+    def map_columns(
+        self,
+        columns: list[ColumnNode],
+        profiles: list[ColumnProfile],
+    ) -> tuple[list[ConceptNode], list[EntityNode], list[Edge]]:
+        """Map columns to semantic Concept/Entity nodes using LLM inference.
+
+        Columns are processed in batches to reduce JSON output complexity,
+        which significantly improves success rate for weaker LLM models.
+        Each batch is independently parsed — if one batch fails, the others
+        still contribute their results.
+
+        Between batches, results are merged using the same merge_with_existing
+        logic to deduplicate concepts/entities that different batches may
+        have named differently (e.g., batch 1 says "person_id", batch 2
+        says "person_identifier").
+
+        Args:
+            columns: All column nodes to map.
+            profiles: Corresponding column profiles.
+
+        Returns:
+            Tuple of (concept_nodes, entity_nodes, represents_edges + has_concept_edges).
+        """
+        if not columns:
+            return [], [], []
+
+        profile_map = {p.column_id: p for p in profiles}
+
+        # Small column counts — single call is fine
+        if len(columns) <= self._batch_size:
+            return self._map_columns_single(columns, profile_map)
+
+        # Larger sets — batch to reduce JSON complexity, accumulate
+        # multiple batches before merging to reduce LLM merge call frequency.
+        all_concepts: list[ConceptNode] = []
+        all_entities: list[EntityNode] = []
+        all_edges: list[Edge] = []
+
+        # Unmerged batch accumulations — collected between merge intervals
+        pending_concepts: list[ConceptNode] = []
+        pending_entities: list[EntityNode] = []
+        pending_edges: list[Edge] = []
+
+        total_batches = (len(columns) + self._batch_size - 1) // self._batch_size
+        success_batches = 0
+        merge_interval = self.config.merge_batch_interval
+
+        for i in range(total_batches):
+            start = i * self._batch_size
+            end = min(start + self._batch_size, len(columns))
+            batch = columns[start:end]
+
+            logger.info(f"Mapping batch {i + 1}/{total_batches} ({len(batch)} columns)")
+            batch_concepts, batch_entities, batch_edges = self._map_columns_single(batch, profile_map)
+
+            if batch_concepts or batch_entities:
+                success_batches += 1
+                # Accumulate into pending pile (no merge yet)
+                pending_concepts.extend(batch_concepts)
+                pending_entities.extend(batch_entities)
+                pending_edges.extend(batch_edges)
+            else:
+                logger.warning(f"Batch {i + 1}/{total_batches} failed — skipping {len(batch)} columns")
+
+            # Merge every N batches, or on the last batch.
+            # The first merge point establishes the "accumulated" base.
+            # Subsequent merge points merge pending into accumulated.
+            is_merge_point = (i + 1) % merge_interval == 0 or (i + 1) == total_batches
+
+            if is_merge_point and (pending_concepts or pending_entities):
+                if not all_concepts and not all_entities:
+                    # First merge point: no accumulated base yet.
+                    # merge_with_existing handles the "no existing nodes" case
+                    # by doing a self-merge to deduplicate within the group.
+                    logger.info(
+                        f"First merge point: merging {len(pending_concepts)} concepts, "
+                        f"{len(pending_entities)} entities from batches 1-{i + 1}"
+                    )
+                    all_concepts, all_entities, all_edges = self.merge_with_existing(
+                        pending_concepts,
+                        pending_entities,
+                        pending_edges,
+                        [],
+                        [],
+                    )
+                    pending_concepts = []
+                    pending_entities = []
+                    pending_edges = []
+                else:
+                    # Subsequent merge point: merge pending into accumulated.
+                    logger.info(
+                        f"Merging accumulated {len(pending_concepts)} concepts, "
+                        f"{len(pending_entities)} entities from recent batches "
+                        f"into {len(all_concepts)} existing concepts, {len(all_entities)} entities"
+                    )
+                    all_concepts, all_entities, all_edges = self.merge_with_existing(
+                        pending_concepts,
+                        pending_entities,
+                        pending_edges,
+                        all_concepts,
+                        all_entities,
+                        all_edges,
+                    )
+                    pending_concepts = []
+                    pending_entities = []
+                    pending_edges = []
+
+        # If there are still pending nodes that haven't been merged
+        # (e.g., all batches failed or were empty), just append them.
+        if pending_concepts or pending_entities:
+            if all_concepts or all_entities:
+                all_concepts, all_entities, all_edges = self.merge_with_existing(
+                    pending_concepts,
+                    pending_entities,
+                    pending_edges,
+                    all_concepts,
+                    all_entities,
+                    all_edges,
+                )
+            else:
+                # No accumulated results yet — just use pending as the base
+                all_concepts = pending_concepts
+                all_entities = pending_entities
+                all_edges = pending_edges
+
+        logger.info(
+            f"LLMMapper: {success_batches}/{total_batches} batches succeeded, "
+            f"created {len(all_concepts)} concepts, {len(all_entities)} entities, "
+            f"{len(all_edges)} edges for {len(columns)} columns"
+        )
+        return all_concepts, all_entities, all_edges
+
+    def _map_columns_single(
+        self,
+        columns: list[ColumnNode],
+        profile_map: dict[str, ColumnProfile],
+    ) -> tuple[list[ConceptNode], list[EntityNode], list[Edge]]:
+        """Map a single batch of columns via one LLM call."""
+        columns_data = self._build_columns_data(columns, profile_map)
+
+        # Call LLM
+        prompt = MAPPING_PROMPT_TEMPLATE.format(columns_data=columns_data)
+        response = self._call_llm(prompt)
+
+        if not response:
+            logger.warning("LLM returned no response, skipping semantic mapping")
+            return [], [], []
+
+        # Parse response
+        parsed = self._parse_response(response)
+
+        if not parsed:
+            logger.warning("Failed to parse LLM response, skipping semantic mapping")
+            return [], [], []
+
+        # Convert to nodes and edges
+        return self._build_nodes_and_edges(parsed, columns, profile_map)
+
+    def _build_columns_data(self, columns: list[ColumnNode], profile_map: dict[str, ColumnProfile]) -> str:
+        """Build the column data section for the LLM prompt."""
+        lines = []
+        for col in columns:
+            profile = profile_map.get(col.id)
+            if profile is None:
+                continue
+
+            data = {
+                "column_id": col.id,
+                "column_name": col.name,
+                "table_id": col.table_id,
+                "dtype": profile.dtype,
+                "semantic_type": profile.semantic_type,
+                "null_rate": profile.null_rate,
+                "cardinality": profile.cardinality,
+                "unique_rate": profile.unique_rate,
+            }
+
+            # Add numeric stats
+            if profile.min_value is not None:
+                data["min_value"] = profile.min_value
+            if profile.max_value is not None:
+                data["max_value"] = profile.max_value
+            if profile.mean_value is not None:
+                data["mean_value"] = round(profile.mean_value, 2)
+
+            # Add sample values
+            if profile.sample_values:
+                data["sample_values"] = [str(v) for v in profile.sample_values[:5]]
+
+            # Add comment if available
+            if col.comment:
+                data["comment"] = col.comment
+
+            lines.append(json.dumps(data))
+
+        return "\n".join(lines)
+
+    def _call_llm(self, prompt: str, temperature: float | None = None) -> str | None:
+        """Call the LLM via OpenAI-compatible Chat Completions API.
+
+        Uses the OpenAI SDK with configurable base_url, supporting any
+        service that implements the OpenAI Chat Completions protocol
+        (OpenAI, Azure, vLLM, Ollama, DeepSeek, etc.).
+
+        Args:
+            prompt: The user prompt to send.
+            temperature: Override temperature for this call. None = use self.llm_config.temperature.
+        """
+        api_key = self.llm_config.get_api_key()
+        if not api_key:
+            logger.warning("No API key configured for LLM, skipping call")
+            return None
+
+        try:
+            from openai import OpenAI
+
+            client = OpenAI(
+                api_key=api_key,
+                base_url=self.llm_config.base_url,
+                timeout=self.llm_config.timeout,
+            )
+
+            temp = temperature if temperature is not None else self.llm_config.temperature
+
+            response = client.chat.completions.create(
+                model=self.llm_config.model,
+                messages=[
+                    {"role": "system", "content": "You are a data semantic analyzer. Always respond with valid JSON."},
+                    {"role": "user", "content": prompt},
+                ],
+                temperature=temp,
+                max_tokens=self.llm_config.max_tokens,
+                response_format={"type": "json_object"},
+            )
+
+            return response.choices[0].message.content
+        except Exception as e:
+            logger.error(f"LLM call failed: {e}")
+            return None
+
+    def _parse_response(self, response: str) -> dict[str, Any] | None:
+        """Parse the LLM response into a structured dict.
+
+        The response should be JSON, but LLMs sometimes add extra text
+        before/after the JSON block or produce slightly invalid JSON.
+        Uses progressive repair attempts before giving up.
+        """
+        parsed = self._try_parse_json(response)
+
+        if parsed is None:
+            logger.warning(f"Failed to parse LLM response after all repair attempts: {response[:200]}")
+            return None
+
+        # Validate structure
+        if "concepts" not in parsed or "entities" not in parsed:
+            logger.warning("LLM response missing 'concepts' or 'entities' keys")
+            return None
+
+        return parsed
+
+    def _try_parse_json(self, text: str) -> dict[str, Any] | None:
+        """Progressively attempt to parse JSON from LLM output.
+
+        Weak models often produce slightly invalid JSON. This method
+        tries progressively more aggressive repairs:
+
+        1. Direct json.loads
+        2. Extract markdown code block (```json ... ```)
+        3. Remove inline comments (// and /* */)
+        4. Remove trailing commas before } and ]
+        5. Fix unclosed brackets by appending missing } and ]
+        """
+        import re
+
+        # Step 1: Direct parse
+        result = self._extract_and_parse(text)
+        if result is not None:
+            return result
+
+        # Step 2: Extract markdown code block
+        code_block_match = re.search(r"```(?:json)?\s*\n?(.*?)\n?```", text, re.DOTALL)
+        if code_block_match:
+            result = self._extract_and_parse(code_block_match.group(1))
+            if result is not None:
+                logger.debug("Parsed JSON from markdown code block")
+                return result
+
+        # Steps 3-5 build on each other, applying repairs cumulatively
+        working = text
+
+        # Step 3: Remove comments
+        working = re.sub(r"/\*.*?\*/", "", working, flags=re.DOTALL)
+        working = re.sub(r",\s*//[^\n]*?(?=\s*})", "", working)
+        working = re.sub(r",\s*//[^\n]*?(?=\s*\])", "", working)
+        working = re.sub(r"\s*//[^\n]*?(?=\s*})", "", working)
+        working = re.sub(r"\s*//[^\n]*?(?=\s*\])", "", working)
+        working = re.sub(r"\s*//.*$", "", working, flags=re.MULTILINE)
+
+        result = self._extract_and_parse(working)
+        if result is not None:
+            logger.debug("Parsed JSON after removing comments")
+            return result
+
+        # Step 4: Remove trailing commas (after comment removal)
+        working = re.sub(r",\s*}", "}", working)
+        working = re.sub(r",\s*\]", "]", working)
+
+        result = self._extract_and_parse(working)
+        if result is not None:
+            logger.debug("Parsed JSON after removing trailing commas")
+            return result
+
+        # Step 5: Fix unclosed brackets
+        bracket_fixed = self._fix_brackets(working)
+
+        result = self._extract_and_parse(bracket_fixed)
+        if result is not None:
+            logger.debug("Parsed JSON after fixing brackets")
+            return result
+
+        return None
+
+    def _extract_and_parse(self, text: str) -> dict[str, Any] | None:
+        """Extract the outermost JSON object from text and parse it."""
+        json_start = text.find("{")
+        json_end = text.rfind("}") + 1
+
+        if json_start == -1 or json_end == 0:
+            return None
+
+        json_str = text[json_start:json_end]
+
+        try:
+            return json.loads(json_str)
+        except json.JSONDecodeError:
+            return None
+
+    def _fix_brackets(self, text: str) -> str:
+        """Attempt to fix unclosed brackets by appending missing } and ]."""
+        json_start = text.find("{")
+        if json_start == -1:
+            return text
+
+        json_str = text[json_start:]
+
+        # Count unclosed brackets
+        open_curly = 0
+        open_square = 0
+        in_string = False
+        escape_next = False
+
+        for char in json_str:
+            if escape_next:
+                escape_next = False
+                continue
+            if char == "\\":
+                escape_next = True
+                continue
+            if char == '"' and not escape_next:
+                in_string = not in_string
+                continue
+            if in_string:
+                continue
+            if char == "{":
+                open_curly += 1
+            elif char == "}":
+                open_curly -= 1
+            elif char == "[":
+                open_square += 1
+            elif char == "]":
+                open_square -= 1
+
+        # Append missing closing brackets
+        suffix = "]" * max(0, open_square) + "}" * max(0, open_curly)
+        if suffix:
+            return json_str + suffix
+        return text
+
+    def _build_nodes_and_edges(
+        self,
+        parsed: dict[str, Any],
+        columns: list[ColumnNode],
+        profile_map: dict[str, ColumnProfile],
+    ) -> tuple[list[ConceptNode], list[EntityNode], list[Edge]]:
+        """Convert parsed LLM response into Concept/Entity nodes and edges."""
+        concepts: list[ConceptNode] = []
+        entities: list[EntityNode] = []
+        edges: list[Edge] = []
+
+        # Build Concept nodes
+        for concept_data in parsed.get("concepts", []):
+            concept = ConceptNode(
+                id=f"concept:{concept_data['name']}",
+                name=concept_data["name"],
+                description=concept_data.get("description") or "",
+                unit=concept_data.get("unit") or "",
+                dimension=concept_data.get("dimension") or "",
+                properties={
+                    "source": "llm_inference",
+                    "confidence": concept_data.get("confidence") or 0.7,
+                },
+            )
+            concepts.append(concept)
+
+            # Create represents edges: Column → Concept
+            for col_id in concept_data.get("columns", []):
+                edge = Edge(
+                    id=f"edge:represents:{col_id}:{concept.id}",
+                    source_id=col_id,
+                    target_id=concept.id,
+                    type=EdgeType.REPRESENTS,
+                    confidence=concept_data.get("confidence", 0.7),
+                    properties={
+                        "source": "llm_inference",
+                    },
+                )
+                edges.append(edge)
+
+        # Build Entity nodes
+        for entity_data in parsed.get("entities", []):
+            entity = EntityNode(
+                id=f"entity:{entity_data['name']}",
+                name=entity_data["name"],
+                description=entity_data.get("description") or "",
+                properties={
+                    "source": "llm_inference",
+                    "confidence": entity_data.get("confidence") or 0.7,
+                },
+            )
+            entities.append(entity)
+
+            # Create has_concept edges: Entity → Concept
+            for concept_name in entity_data.get("concept_names", []):
+                concept_id = f"concept:{concept_name}"
+                edge = Edge(
+                    id=f"edge:has_concept:{entity.id}:{concept_id}",
+                    source_id=entity.id,
+                    target_id=concept_id,
+                    type=EdgeType.HAS_CONCEPT,
+                    confidence=entity_data.get("confidence", 0.7),
+                    properties={
+                        "source": "llm_inference",
+                    },
+                )
+                edges.append(edge)
+
+        return concepts, entities, edges
+
+    # --- Concept/Entity merge with existing graph (LLM + Embedding) ---
+
+    def merge_with_existing(
+        self,
+        new_concepts: list[ConceptNode],
+        new_entities: list[EntityNode],
+        new_edges: list[Edge],
+        existing_concepts: list[ConceptNode],
+        existing_entities: list[EntityNode],
+        existing_edges: list[Edge] | None = None,
+    ) -> tuple[list[ConceptNode], list[EntityNode], list[Edge]]:
+        """Merge new Concepts/Entities with existing ones in the graph.
+
+        Two-stage approach:
+        1. Embedding pre-filter (optional): compute cosine similarity
+           between new/existing nodes to identify candidate merge pairs.
+        2. LLM merge judgment (required): send candidates + node lists
+           to LLM for semantic confirmation.
+
+        After LLM returns a merge plan:
+        - Merged nodes are absorbed (description enriched, edges redirected)
+        - Genuinely new nodes are kept
+        - Both existing and new edges are redirected if they reference
+          merged-out nodes, then deduplicated by (source, target, type)
+          keeping highest confidence
+
+        Args:
+            new_concepts: Concepts from the latest LLM call.
+            new_entities: Entities from the latest LLM call.
+            new_edges: Edges from the latest LLM call.
+            existing_concepts: Concepts already in the graph.
+            existing_entities: Entities already in the graph.
+            existing_edges: Edges already accumulated from prior merges.
+                These are redirected and included alongside new edges.
+
+        Returns:
+            Tuple of (final_concepts, final_entities, final_edges) after
+            merging and deduplication.
+        """
+        if not existing_concepts and not existing_entities:
+            # No existing nodes — but we still need to deduplicate within
+            # the new nodes themselves (e.g., different batches may produce
+            # concept:person_id and concept:person_identifier for the same thing).
+            # Pass all new nodes as both new and existing to force a self-merge,
+            # but use a clean split to avoid duplicate entries in the result.
+            all_edges_in = list(existing_edges or []) + list(new_edges)
+            if len(new_concepts) > 1 or len(new_entities) > 1:
+                logger.info(
+                    f"Self-merging {len(new_concepts)} concepts, {len(new_entities)} entities (no existing nodes)"
+                )
+                # Split: first node becomes "existing base", rest are "new"
+                # This forces LLM to evaluate whether any "new" nodes duplicate
+                # the first, and whether any "new" nodes duplicate each other
+                # (LLM can compare across all items in the prompt).
+                base_c = new_concepts[:1]
+                base_e = new_entities[:1] if new_entities else []
+                rest_c = new_concepts[1:]
+                rest_e = new_entities[1:] if new_entities else []
+
+                result_c, result_e, result_edges = self.merge_with_existing(
+                    rest_c,
+                    rest_e,
+                    all_edges_in,
+                    base_c,
+                    base_e,
+                )
+                return result_c, result_e, result_edges
+            else:
+                # Only 1 concept/entity — no duplicates possible
+                return new_concepts, new_entities, all_edges_in
+
+        if not new_concepts and not new_entities:
+            # No new nodes to merge — return existing + existing_edges
+            all_edges_in = list(existing_edges or []) + list(new_edges)
+            return existing_concepts, existing_entities, all_edges_in
+
+        # Step 1: Embedding pre-filter (optional)
+        candidates = self._embedding_prefilter(
+            new_concepts,
+            new_entities,
+            existing_concepts,
+            existing_entities,
+        )
+
+        # Step 2: LLM merge judgment
+        merge_plan = self._llm_merge_judge(
+            new_concepts,
+            new_entities,
+            existing_concepts,
+            existing_entities,
+            candidates,
+        )
+
+        if merge_plan is None:
+            # LLM call failed — skip merge, keep all new nodes as-is,
+            # and preserve existing edges alongside new edges.
+            logger.warning("LLM merge judgment failed — keeping all new nodes without merging")
+            combined_edges = list(existing_edges or []) + list(new_edges)
+            return new_concepts, new_entities, combined_edges
+
+        # Step 3: Execute merge plan
+        final_concepts, final_entities, final_edges = self._execute_merge_plan(
+            merge_plan,
+            new_concepts,
+            new_entities,
+            new_edges,
+            existing_concepts,
+            existing_entities,
+            existing_edges=existing_edges,
+        )
+
+        # Step 4: Deduplicate edges by (source_id, target_id, type)
+        deduped_edges = self._deduplicate_edges(final_edges)
+
+        merged_count = len(merge_plan.get("merges", []))
+        if merged_count:
+            new_concept_count = len(final_concepts) - len(existing_concepts)
+            new_entity_count = len(final_entities) - len(existing_entities)
+            logger.info(
+                f"Concept/Entity merge: {merged_count} merges, "
+                f"{new_concept_count} new concepts kept (total {len(final_concepts)}), "
+                f"{new_entity_count} new entities kept (total {len(final_entities)}), "
+                f"{len(deduped_edges)} edges after dedup"
+            )
+
+        return final_concepts, final_entities, deduped_edges
+
+    def _embedding_prefilter(
+        self,
+        new_concepts: list[ConceptNode],
+        new_entities: list[EntityNode],
+        existing_concepts: list[ConceptNode],
+        existing_entities: list[EntityNode],
+    ) -> list[dict[str, Any]]:
+        """Pre-filter candidate merge pairs using embedding similarity.
+
+        Returns a list of candidate pairs: {"new_id", "existing_id",
+        "new_name", "existing_name", "similarity"}.
+
+        If embedding is not configured (model is empty) or the API call
+        fails, returns an empty list and the LLM judge will work on the
+        full set without pre-filtering.
+        """
+        if not self.embedding_config.model:
+            logger.debug("Embedding model not configured — skipping pre-filter")
+            return []
+
+        api_key = self.embedding_config.get_api_key(self.llm_config)
+        if not api_key:
+            logger.debug("No embedding API key — skipping pre-filter")
+            return []
+
+        try:
+            from openai import OpenAI
+
+            client = OpenAI(
+                api_key=api_key,
+                base_url=self.embedding_config.get_base_url(self.llm_config),
+                timeout=self.embedding_config.timeout,
+            )
+
+            # Build text representations for all nodes
+            new_nodes = new_concepts + new_entities
+            existing_nodes = existing_concepts + existing_entities
+
+            new_texts = [self._node_to_embedding_text(n) for n in new_nodes]
+            existing_texts = [self._node_to_embedding_text(n) for n in existing_nodes]
+
+            if not new_texts or not existing_texts:
+                return []
+
+            # Compute embeddings — batch call
+            new_response = client.embeddings.create(
+                model=self.embedding_config.model,
+                input=new_texts,
+            )
+            existing_response = client.embeddings.create(
+                model=self.embedding_config.model,
+                input=existing_texts,
+            )
+
+            new_embeddings = [item.embedding for item in new_response.data]
+            existing_embeddings = [item.embedding for item in existing_response.data]
+
+            # Compute cosine similarity matrix
+            candidates = []
+            threshold = self.embedding_config.similarity_threshold
+
+            for i, new_node in enumerate(new_nodes):
+                for j, existing_node in enumerate(existing_nodes):
+                    sim = self._cosine_similarity(new_embeddings[i], existing_embeddings[j])
+                    if sim >= threshold:
+                        candidates.append(
+                            {
+                                "new_id": new_node.id,
+                                "existing_id": existing_node.id,
+                                "new_name": new_node.name,
+                                "existing_name": existing_node.name,
+                                "similarity": round(sim, 4),
+                            }
+                        )
+
+            logger.info(
+                f"Embedding pre-filter: {len(candidates)} candidate pairs "
+                f"(threshold={threshold}, {len(new_nodes)} new vs {len(existing_nodes)} existing)"
+            )
+            return candidates
+
+        except Exception as e:
+            logger.warning(f"Embedding pre-filter failed: {e} — falling back to full LLM judgment")
+            return []
+
+    def _node_to_embedding_text(self, node: Node) -> str:
+        """Convert a Concept/Entity node to text for embedding computation.
+
+        Combines name, description, and type-specific fields to produce
+        a rich text representation that captures semantic meaning.
+        """
+        parts = [node.name]
+
+        if isinstance(node, ConceptNode):
+            if node.description:
+                parts.append(node.description)
+            if node.unit:
+                parts.append(f"unit: {node.unit}")
+            if node.dimension:
+                parts.append(f"dimension: {node.dimension}")
+        elif isinstance(node, EntityNode):
+            if node.description:
+                parts.append(node.description)
+
+        return " | ".join(parts)
+
+    def _cosine_similarity(self, vec_a: list[float], vec_b: list[float]) -> float:
+        """Compute cosine similarity between two vectors."""
+        dot = sum(a * b for a, b in zip(vec_a, vec_b))
+        norm_a = math.sqrt(sum(a * a for a in vec_a))
+        norm_b = math.sqrt(sum(b * b for b in vec_b))
+
+        if norm_a == 0.0 or norm_b == 0.0:
+            return 0.0
+
+        return dot / (norm_a * norm_b)
+
+    def _llm_merge_judge(
+        self,
+        new_concepts: list[ConceptNode],
+        new_entities: list[EntityNode],
+        existing_concepts: list[ConceptNode],
+        existing_entities: list[EntityNode],
+        candidates: list[dict[str, Any]],
+    ) -> dict[str, Any] | None:
+        """Send new/existing nodes and candidate pairs to LLM for merge judgment.
+
+        When embedding pre-filter produced candidates, only inject existing
+        nodes that appear in those candidate pairs — nodes below the similarity
+        threshold are excluded entirely since embedding already considered
+        name + description + unit + dimension, making them extremely unlikely
+        to need merging. This dramatically reduces prompt size for large graphs.
+
+        Returns the merge plan dict:
+        {"merges": [{"new_id", "existing_id", "reason", "confidence"}],
+         "new_kept": [id, ...]}
+
+        If the LLM call fails, returns None.
+        """
+        # Determine which existing nodes are relevant for merging.
+        # When candidates exist (embedding pre-filter succeeded), only
+        # include existing nodes that appear in candidate pairs — nodes
+        # below the similarity threshold are excluded entirely.
+        if candidates:
+            related_existing_ids = {c["existing_id"] for c in candidates}
+            filtered_existing_concepts = [c for c in existing_concepts if c.id in related_existing_ids]
+            filtered_existing_entities = [e for e in existing_entities if e.id in related_existing_ids]
+            existing_note = (
+                f"Only {len(filtered_existing_concepts)} concepts and "
+                f"{len(filtered_existing_entities)} entities are shown out of "
+                f"{len(existing_concepts)} total concepts and {len(existing_entities)} total entities "
+                f"— the rest were filtered out by embedding similarity as too dissimilar to any new node."
+            )
+        else:
+            # No embedding pre-filter — include all existing nodes so LLM
+            # can evaluate every pair (fallback for correctness).
+            filtered_existing_concepts = existing_concepts
+            filtered_existing_entities = existing_entities
+            existing_note = "All existing nodes are shown (no embedding pre-filter was applied)."
+
+        # Build data for prompt
+        new_concepts_data = self._build_merge_node_data(new_concepts, "concept")
+        new_entities_data = self._build_merge_node_data(new_entities, "entity")
+        existing_concepts_data = self._build_merge_node_data(filtered_existing_concepts, "concept")
+        existing_entities_data = self._build_merge_node_data(filtered_existing_entities, "entity")
+
+        candidates_data = ""
+        if candidates:
+            candidates_lines = [json.dumps(c) for c in candidates]
+            candidates_data = "\n".join(candidates_lines)
+        else:
+            candidates_data = "No pre-filter candidates — evaluate all pairs directly."
+
+        prompt = MERGE_PROMPT_TEMPLATE.format(
+            new_concepts_data=new_concepts_data,
+            new_entities_data=new_entities_data,
+            existing_concepts_data=existing_concepts_data,
+            existing_entities_data=existing_entities_data,
+            candidates_data=candidates_data,
+            existing_note=existing_note,
+        )
+
+        response = self._call_llm(prompt, temperature=self.config.merge_llm_temperature)
+
+        if not response:
+            return None
+
+        # Parse the merge response
+        parsed = self._try_parse_json(response)
+
+        if parsed is None:
+            logger.warning(f"Failed to parse merge response: {response[:200]}")
+            return None
+
+        # Validate structure
+        if "merges" not in parsed:
+            logger.warning("Merge response missing 'merges' key")
+            return None
+
+        # Filter merges by confidence threshold (from config)
+        min_confidence = self.config.confidence_threshold
+        parsed["merges"] = [m for m in parsed["merges"] if m.get("confidence", 0) >= min_confidence]
+
+        # Ensure new_kept exists
+        if "new_kept" not in parsed:
+            parsed["new_kept"] = []
+
+        return parsed
+
+    def _build_merge_node_data(self, nodes: list[Node], kind: str) -> str:
+        """Build the node data section for the merge prompt.
+
+        Args:
+            nodes: List of Concept or Entity nodes.
+            kind: "concept" or "entity" — used for labeling.
+        """
+        if not nodes:
+            return f"No {kind}s."
+
+        lines = []
+        for node in nodes:
+            data = {"id": node.id, "name": node.name}
+
+            if isinstance(node, ConceptNode):
+                data["description"] = node.description
+                data["unit"] = node.unit
+                data["dimension"] = node.dimension
+            elif isinstance(node, EntityNode):
+                data["description"] = node.description
+
+            lines.append(json.dumps(data))
+
+        return "\n".join(lines)
+
+    def _execute_merge_plan(
+        self,
+        merge_plan: dict[str, Any],
+        new_concepts: list[ConceptNode],
+        new_entities: list[EntityNode],
+        new_edges: list[Edge],
+        existing_concepts: list[ConceptNode],
+        existing_entities: list[EntityNode],
+        existing_edges: list[Edge] | None = None,
+    ) -> tuple[list[ConceptNode], list[EntityNode], list[Edge]]:
+        """Execute the merge plan returned by LLM.
+
+        For each merge decision:
+        - The new node is absorbed into the existing node
+        - Description is enriched if the new one has more info
+        - Properties from the new node are merged into the existing one
+        - All edges (both existing and new) referencing merged-out nodes
+          are redirected
+
+        Nodes listed in new_kept are preserved as genuinely new nodes.
+
+        Args:
+            merge_plan: LLM merge response with "merges" and "new_kept" lists.
+            new_concepts: New concepts to process.
+            new_entities: New entities to process.
+            new_edges: New edges to process.
+            existing_concepts: Existing concepts (may be enriched).
+            existing_entities: Existing entities (may be enriched).
+            existing_edges: Edges accumulated from prior merges; these
+                are also redirected if they reference merged-out nodes.
+
+        Returns:
+            Tuple of (kept_new_concepts, kept_new_entities, final_edges)
+            where final_edges includes both redirected existing edges and
+            redirected new edges.
+        """
+        # Build merge maps from the plan
+        concept_merge_map: dict[str, str] = {}  # new_concept_id → existing_concept_id
+        entity_merge_map: dict[str, str] = {}  # new_entity_id → existing_entity_id
+
+        for merge in merge_plan.get("merges", []):
+            new_id = merge.get("new_id", "")
+            existing_id = merge.get("existing_id", "")
+            if not new_id or not existing_id:
+                continue
+
+            if new_id.startswith("concept:"):
+                concept_merge_map[new_id] = existing_id
+            elif new_id.startswith("entity:"):
+                entity_merge_map[new_id] = existing_id
+
+        # Build lookups for existing nodes (for enrichment)
+        existing_concept_map = {c.id: c for c in existing_concepts}
+        existing_entity_map = {e.id: e for e in existing_entities}
+
+        # Build lookups for new nodes (for enrichment and filtering)
+        new_concept_map = {c.id: c for c in new_concepts}
+        new_entity_map = {e.id: e for e in new_entities}
+
+        # Enrich existing nodes with merged descriptions and properties
+        for new_id, existing_id in concept_merge_map.items():
+            new_c = new_concept_map.get(new_id)
+            existing_c = existing_concept_map.get(existing_id)
+            if new_c and existing_c:
+                if len(new_c.description) > len(existing_c.description):
+                    existing_c.description = new_c.description
+                for key, value in new_c.properties.items():
+                    if key not in existing_c.properties:
+                        existing_c.properties[key] = value
+                logger.debug(f"Merged concept '{new_c.name}' into existing '{existing_c.name}'")
+
+        for new_id, existing_id in entity_merge_map.items():
+            new_e = new_entity_map.get(new_id)
+            existing_e = existing_entity_map.get(existing_id)
+            if new_e and existing_e:
+                if len(new_e.description) > len(existing_e.description):
+                    existing_e.description = new_e.description
+                for key, value in new_e.properties.items():
+                    if key not in existing_e.properties:
+                        existing_e.properties[key] = value
+                logger.debug(f"Merged entity '{new_e.name}' into existing '{existing_e.name}'")
+
+        # Kept new nodes (not merged) are tracked below via final_concepts
+
+        final_concepts: list[ConceptNode] = list(existing_concepts)  # Start with existing (enriched)
+        for new_c in new_concepts:
+            if new_c.id not in concept_merge_map:
+                final_concepts.append(new_c)
+
+        final_entities: list[EntityNode] = list(existing_entities)  # Start with existing (enriched)
+        for new_e in new_entities:
+            if new_e.id not in entity_merge_map:
+                final_entities.append(new_e)
+
+        # Redirect edges that reference merged-out nodes
+        # Process both existing (accumulated) edges and new edges.
+        final_edges: list[Edge] = []
+
+        def _redirect_edge(edge: Edge) -> Edge:
+            """Redirect an edge if it references a merged-out node."""
+            if edge.type == EdgeType.REPRESENTS:
+                # Column → Concept: only target_id may need redirecting
+                redirected_target = concept_merge_map.get(edge.target_id, edge.target_id)
+                if redirected_target != edge.target_id:
+                    return Edge(
+                        id=f"edge:represents:{edge.source_id}:{redirected_target}",
+                        source_id=edge.source_id,
+                        target_id=redirected_target,
+                        type=edge.type,
+                        confidence=edge.confidence,
+                        properties=edge.properties,
+                    )
+            elif edge.type == EdgeType.HAS_CONCEPT:
+                # Entity → Concept: both may need redirecting
+                redirected_source = entity_merge_map.get(edge.source_id, edge.source_id)
+                redirected_target = concept_merge_map.get(edge.target_id, edge.target_id)
+                if redirected_source != edge.source_id or redirected_target != edge.target_id:
+                    return Edge(
+                        id=f"edge:has_concept:{redirected_source}:{redirected_target}",
+                        source_id=redirected_source,
+                        target_id=redirected_target,
+                        type=edge.type,
+                        confidence=edge.confidence,
+                        properties=edge.properties,
+                    )
+            return edge
+
+        # First: redirect existing (accumulated) edges
+        for edge in existing_edges or []:
+            final_edges.append(_redirect_edge(edge))
+
+        # Then: redirect new edges
+        for edge in new_edges:
+            final_edges.append(_redirect_edge(edge))
+
+        return final_concepts, final_entities, final_edges
+
+    def _deduplicate_edges(self, edges: list[Edge]) -> list[Edge]:
+        """Remove duplicate edges, keeping the one with highest confidence.
+
+        Two edges are duplicates if they have the same (source_id, target_id, type).
+        Among duplicates, keep the edge with the highest confidence score.
+
+        Args:
+            edges: List of edges (may contain duplicates after merge redirection).
+
+        Returns:
+            Deduplicated list of edges.
+        """
+        # Group by (source_id, target_id, type)
+        groups: dict[tuple[str, str, str], list[Edge]] = {}
+        for edge in edges:
+            key = (edge.source_id, edge.target_id, edge.type.value)
+            if key not in groups:
+                groups[key] = []
+            groups[key].append(edge)
+
+        # For each group, keep the edge with highest confidence
+        result = []
+        for key, group_edges in groups.items():
+            best = max(group_edges, key=lambda e: e.confidence)
+            result.append(best)
+
+        return result
+
+    # ── Table comment generation ──────────────────────────────────────────
+
+    def generate_table_comments(
+        self,
+        tables: list[TableNode],
+        columns: list[ColumnNode],
+        profiles: list[ColumnProfile],
+        concepts: list[ConceptNode] | None = None,
+        entities: list[EntityNode] | None = None,
+        semantic_edges: list[Edge] | None = None,
+    ) -> dict[str, str]:
+        """Generate LLM-inferred comments for tables that lack SQL metadata comments.
+
+        When a table has no comment from its source (SQL metadata, CSV header),
+        this method uses LLM to infer a one-line description based on:
+        - Table name
+        - Column names, types, semantic_types
+        - Column profiles (null_rate, cardinality, sample values)
+        - Inferred concept/entity names (if available from Step 5)
+
+        Args:
+            tables: Table nodes to generate comments for.
+            columns: All column nodes (will be grouped by table).
+            profiles: Corresponding column profiles.
+            concepts: Inferred concept nodes (optional, from Step 5).
+            entities: Inferred entity nodes (optional, from Step 5).
+            semantic_edges: Represents/has_concept edges (optional, from Step 5).
+
+        Returns:
+            Dict mapping table_id → inferred_comment string.
+            Only includes tables that had no existing comment.
+        """
+        # Find tables without comments
+        tables_without_comment = []
+        for table in tables:
+            existing_comment = table.properties.get("comment", "") or getattr(table, "comment", "")
+            if not existing_comment.strip():
+                tables_without_comment.append(table)
+
+        if not tables_without_comment:
+            logger.info("All tables have existing comments — skipping LLM comment generation")
+            return {}
+
+        logger.info(f"Generating comments for {len(tables_without_comment)} tables without SQL metadata")
+
+        # Build column lookup: table_id → list of (ColumnNode, ColumnProfile)
+        profile_map = {p.column_id: p for p in profiles}
+        column_by_table: dict[str, list[tuple[ColumnNode, ColumnProfile | None]]] = {}
+        for col in columns:
+            tid = col.table_id
+            if tid not in column_by_table:
+                column_by_table[tid] = []
+            column_by_table[tid].append((col, profile_map.get(col.id)))
+
+        # Build concept/entity summary per table (from semantic edges)
+        concept_by_column: dict[str, str] = {}
+        entity_by_column: dict[str, str] = {}
+        if concepts and semantic_edges:
+            concept_name_map = {c.id: c.name for c in concepts}
+            entity_name_map = {e.id: e.name for e in (entities or [])}
+            for edge in semantic_edges:
+                if edge.type == EdgeType.REPRESENTS:
+                    cname = concept_name_map.get(edge.target_id, "")
+                    if cname:
+                        concept_by_column[edge.source_id] = cname
+                elif edge.type == EdgeType.HAS_CONCEPT:
+                    cname = concept_name_map.get(edge.target_id, "")
+                    ename = entity_name_map.get(edge.source_id, "")
+                    if cname and ename:
+                        for e2 in semantic_edges:
+                            if e2.type == EdgeType.REPRESENTS and e2.target_id == edge.target_id:
+                                entity_by_column[e2.source_id] = ename
+
+        # Build prompt data for each table
+        tables_data_lines = []
+        for table in tables_without_comment:
+            col_list = column_by_table.get(table.id, [])
+            col_summaries = []
+            for col, profile in col_list:
+                summary = {
+                    "name": col.name,
+                    "dtype": col.dtype or (profile.dtype if profile else ""),
+                    "semantic_type": col.semantic_type or (profile.semantic_type if profile else ""),
+                }
+                if profile:
+                    summary["null_rate"] = round(profile.null_rate, 2)
+                    summary["cardinality"] = profile.cardinality
+                    if profile.top_values:
+                        summary["top_values"] = [tv["value"] for tv in profile.top_values[:3]]
+
+                concept = concept_by_column.get(col.id, "")
+                if concept:
+                    summary["concept"] = concept
+                entity = entity_by_column.get(col.id, "")
+                if entity:
+                    summary["entity"] = entity
+
+                col_summaries.append(summary)
+
+            table_data = {
+                "table_id": table.id,
+                "table_name": table.name,
+                "row_count": table.row_count,
+                "columns": col_summaries,
+            }
+            tables_data_lines.append(json.dumps(table_data))
+
+        tables_data = "\n".join(tables_data_lines)
+
+        # Call LLM
+        prompt = TABLE_COMMENT_PROMPT_TEMPLATE.format(tables_data=tables_data)
+        response = self._call_llm(prompt)
+
+        if not response:
+            logger.warning("LLM returned no response for table comment generation")
+            return {}
+
+        # Parse response
+        parsed = self._parse_table_comment_response(response)
+        if not parsed:
+            logger.warning("Failed to parse LLM table comment response")
+            return {}
+
+        # Build result: table_id → comment
+        result: dict[str, str] = {}
+        for table in tables_without_comment:
+            comment = parsed.get(table.id, "")
+            if comment and len(comment) <= 200:
+                result[table.id] = comment
+                table.properties["comment"] = comment
+            elif comment:
+                result[table.id] = comment[:200].rstrip()
+                table.properties["comment"] = comment[:200].rstrip()
+
+        logger.info(f"Generated comments for {len(result)} tables")
+        return result
+
+    def _parse_table_comment_response(self, response: str) -> dict[str, str]:
+        """Parse the LLM response for table comments.
+
+        Expected format: {"tables": [{"table_id": "...", "comment": "..."}]}
+        Uses the same progressive JSON repair as _parse_response.
+        Returns dict mapping table_id → comment.
+        """
+        parsed = self._try_parse_json(response)
+
+        if parsed is None:
+            logger.warning(f"Failed to parse table comment response: {response[:200]}")
+            return {}
+
+        if "tables" not in parsed:
+            logger.warning("Table comment response missing 'tables' key")
+            return {}
+
+        result: dict[str, str] = {}
+        for item in parsed["tables"]:
+            table_id = item.get("table_id", "")
+            comment = item.get("comment", "")
+            if table_id and comment:
+                result[table_id] = comment.strip()
+
+        return result
diff --git a/services/datalink/src/datalink/mapper/prompts/__init__.py b/services/datalink/src/datalink/mapper/prompts/__init__.py
new file mode 100644
index 00000000..a3e22921
--- /dev/null
+++ b/services/datalink/src/datalink/mapper/prompts/__init__.py
@@ -0,0 +1 @@
+# Prompt templates directory — loaded at runtime by LLMMapper.
diff --git a/services/datalink/src/datalink/mapper/prompts/mapping_prompt.txt b/services/datalink/src/datalink/mapper/prompts/mapping_prompt.txt
new file mode 100644
index 00000000..5302a692
--- /dev/null
+++ b/services/datalink/src/datalink/mapper/prompts/mapping_prompt.txt
@@ -0,0 +1,19 @@
+You are a data semantic analyzer. Given a list of data columns with their metadata, identify the semantic concepts and entities they represent.
+
+## Output Format
+
+Return a single JSON object with exactly this structure:
+
+{{"concepts": [{{"name": "snake_case_name", "description": "short one-line definition", "unit": "unit or none", "dimension": "category like identifier/monetary/temporal/name/location/classification/quantity", "columns": ["column_id_1", "column_id_2"], "confidence": 0.9}}], "entities": [{{"name": "snake_case_name", "description": "short one-line definition", "concept_names": ["concept_name_1", "concept_name_2"], "confidence": 0.9}}]}}
+
+## Rules
+
+1. **Merge same-meaning columns into ONE concept**: e.g. customer_id and user_id both map to concept "person_identifier"
+2. **Generic concept names**: use "monetary_value" not "order_amount", "person_identifier" not "customer_id"
+3. **Entity groups related concepts**: entity "customer" includes concepts like "person_identifier", "person_name", "email_address"
+4. **Confidence**: use 0.9 for obvious mappings, 0.7 for reasonable guesses, 0.5 for uncertain ones
+5. **Output ONLY valid JSON** — no comments, no extra text, no trailing commas
+
+## Columns to analyze
+
+{columns_data}
diff --git a/services/datalink/src/datalink/mapper/prompts/merge_prompt.txt b/services/datalink/src/datalink/mapper/prompts/merge_prompt.txt
new file mode 100644
index 00000000..ac5e9a84
--- /dev/null
+++ b/services/datalink/src/datalink/mapper/prompts/merge_prompt.txt
@@ -0,0 +1,37 @@
+You are a data ontology merger. Given a list of NEW concepts/entities and EXISTING concepts/entities already in the knowledge graph, determine which new ones are semantically the same as existing ones (should be merged) and which are genuinely new (should be kept as separate nodes).
+
+## Input
+
+NEW concepts:
+{new_concepts_data}
+
+NEW entities:
+{new_entities_data}
+
+EXISTING concepts:
+{existing_concepts_data}
+
+EXISTING entities:
+{existing_entities_data}
+
+NOTE: {existing_note}
+
+## Candidate pairs (from embedding pre-filter)
+These pairs have high embedding similarity and are likely the same thing, but you must confirm or reject each one:
+{candidates_data}
+
+## Output Format
+
+Return a single JSON object with exactly this structure:
+
+{{"merges": [{{"new_id": "concept:xxx", "existing_id": "concept:yyy", "reason": "both represent person identifier", "confidence": 0.95}}], "new_kept": ["concept:zzz", "entity:www"]}}
+
+## Rules
+
+1. **Merge**: Two concepts/entities should be merged if they represent the SAME real-world thing, even with different names. For example, "person_identifier" and "customer_id" both represent a person's unique ID — merge them.
+2. **Keep separate**: If they represent genuinely different things, do NOT merge. For example, "order_id" and "customer_id" are both identifiers but refer to different entities — keep them separate.
+3. **Use existing ID as the target**: When merging, always map the new_id to the existing_id (the existing node stays, the new one is absorbed).
+4. **Confidence**: Use 0.95 for very obvious merges (same thing, different name), 0.8 for reasonable merges (likely same thing), 0.6 for uncertain — anything below 0.6 should NOT be in the merges list, it means they're probably different things.
+5. **new_kept**: List the IDs of new concepts/entities that should NOT be merged (genuinely new things).
+6. **Review ALL candidates**: Even if embedding similarity is high, you must evaluate semantic meaning. High similarity does NOT guarantee they should be merged — confirm or reject based on actual semantics.
+7. **Output ONLY valid JSON** — no comments, no extra text, no trailing commas
\ No newline at end of file
diff --git a/services/datalink/src/datalink/mapper/prompts/table_comment_prompt.txt b/services/datalink/src/datalink/mapper/prompts/table_comment_prompt.txt
new file mode 100644
index 00000000..b99fecf9
--- /dev/null
+++ b/services/datalink/src/datalink/mapper/prompts/table_comment_prompt.txt
@@ -0,0 +1,13 @@
+You are a data catalog analyst. Given a table's column metadata and statistics, write a ONE-LINE description of what this table contains or represents.
+
+The description should be:
+- Concise (one sentence, max 80 characters)
+- Written as a factual statement about the data (e.g., "California schools with eligibility and participation data")
+- NOT a repeat of the table name (e.g., don't just say "Orders table")
+- Informative enough for someone who has never seen this data to understand what it contains
+
+Output format: JSON with this structure:
+{{"tables": [{{"table_id": "the_table_id", "comment": "one-line description"}}]}}
+
+Table data to analyze:
+{tables_data}
\ No newline at end of file
diff --git a/services/datalink/src/datalink/mcp/__init__.py b/services/datalink/src/datalink/mcp/__init__.py
new file mode 100644
index 00000000..e038baa4
--- /dev/null
+++ b/services/datalink/src/datalink/mcp/__init__.py
@@ -0,0 +1 @@
+"""MCP Server adapter layer."""
diff --git a/services/datalink/src/datalink/mcp/server.py b/services/datalink/src/datalink/mcp/server.py
new file mode 100644
index 00000000..f9657d3e
--- /dev/null
+++ b/services/datalink/src/datalink/mcp/server.py
@@ -0,0 +1,370 @@
+"""DataLink MCP Server — expose retrieval interfaces as MCP tools.
+
+Default surface: datalink_explore (universal retrieval) only.
+
+Write operations (add_table, rebuild, remove_table) and show are
+available via the REST API (datalink api) — they are offline/management
+operations that don't belong in an agent's retrieval context.
+
+Auxiliary tools (search, get_node, etc.) are available via DATALINK_MCP_TOOLS
+environment variable but not registered by default.
+"""
+
+import logging
+import os
+
+from mcp.server.fastmcp import FastMCP
+
+# MCP server: suppress INFO-level progress logs (CLI shows them, MCP doesn't need them)
+logging.getLogger("datalink").setLevel(logging.WARNING)
+
+from datalink.config import DataLinkConfig  # noqa: E402
+from datalink.graph.retrieval import GraphRetrieval  # noqa: E402
+from datalink.graph.storage import GraphStorage  # noqa: E402
+from datalink.models.node import NodeType  # noqa: E402
+
+logger = logging.getLogger(__name__)
+
+# Create the MCP server
+# Default host="0.0.0.0" so the server listens on all interfaces, not just localhost.
+# This allows remote clients (other machines) to connect — essential when the MCP
+# server runs on a shared / cloud host.  Local-only use still works fine on 0.0.0.0.
+_DEFAULT_HOST = os.environ.get("DATALINK_MCP_HOST", "0.0.0.0")
+mcp = FastMCP("DataLink", host=_DEFAULT_HOST)
+
+# Global storage and retrieval instances (initialized on startup)
+_storage: GraphStorage | None = None
+_retrieval: GraphRetrieval | None = None
+
+
+def _get_storage(db_path: str) -> GraphStorage:
+    """Get or create the storage instance."""
+    global _storage
+    if _storage is None:
+        _storage = GraphStorage(db_path)
+    return _storage
+
+
+def _get_retrieval(db_path: str) -> GraphRetrieval:
+    """Get or create the retrieval instance."""
+    global _retrieval
+    if _retrieval is None:
+        storage = _get_storage(db_path)
+        _retrieval = GraphRetrieval(storage, DataLinkConfig.load())
+    return _retrieval
+
+
+def _get_tool_allowlist() -> set[str] | None:
+    """Get auxiliary MCP tool allowlist — env var takes precedence over config.
+
+    Sources (in priority order):
+    1. DATALINK_MCP_TOOLS env var — comma-separated full tool names
+    2. mcp_tools field in datalink_config.json
+
+    Returns None if both are unset/empty (meaning: only default tools exposed).
+    Returns a set of tool names if either source provides one, e.g.
+      {"datalink_search_nodes", "datalink_get_node"}
+    """
+    # Env var (highest priority)
+    env_raw = os.environ.get("DATALINK_MCP_TOOLS", "")
+    if env_raw.strip():
+        return set(t.strip() for t in env_raw.split(",") if t.strip())
+
+    # Config file (fallback)
+    config = DataLinkConfig.load()
+    if config.mcp_tools.strip():
+        return set(t.strip() for t in config.mcp_tools.split(",") if t.strip())
+
+    return None
+
+
+# ── Core tool: always registered ────────────────────────────────────
+
+
+@mcp.tool()
+def datalink_explore(
+    query: str,
+    max_nodes: int | None = None,
+    focus: str | None = None,
+    mask_credential: bool = True,
+) -> str:
+    """PRIMARY TOOL — call FIRST for data questions: how is X related to Y,
+    what columns can I JOIN, what does 'revenue' mean in this data,
+    which tables share similar fields. Returns organized context about
+    relevant nodes (column details, profiles, relationships) in ONE call,
+    plus the relationship map between them. ONE call to explore is usually
+    sufficient — no need to follow up with search + get_node + find_paths
+    to get the same information piece by piece.
+
+    Args:
+        query: Keywords, names, or short descriptions (e.g., "revenue customer_id orders",
+               "how is orders connected to customers", "email address fields").
+               Use space-separated keywords, not SQL expressions.
+        max_nodes: Max nodes in detail (default: auto-scaled by project size).
+        focus: Optional focus direction — 'join_paths' (relationship chains),
+               'schema' (table structure), 'data_profile' (column fingerprints and quality),
+               or None for balanced output.
+        mask_credential: Whether to mask database credentials in output (default True).
+               When True, usernames and passwords in connection strings are replaced with ***.
+               Set to False only if the agent needs the actual connection string to access the database.
+    """
+    config = DataLinkConfig.load()
+    retrieval = _get_retrieval(config.graph_db_path)
+
+    # Validate focus
+    valid_focuses = {"join_paths", "schema", "data_profile", None}
+    if focus is not None and focus not in valid_focuses:
+        return f"Invalid focus: '{focus}'. Valid: join_paths, schema, data_profile, or omit for balanced."
+
+    return retrieval.explore(query, max_nodes=max_nodes, focus=focus, mask_credential=mask_credential)
+
+
+# ── Auxiliary tools: only registered if DATALINK_MCP_TOOLS enables them ──
+# These are plain functions — they get registered as MCP tools via mcp.tool()
+# only when start_mcp_server() decides to include them based on the allowlist.
+
+
+def _datalink_search_nodes(
+    query: str,
+    node_type: str | None = None,
+    limit: int = 10,
+    mask_credential: bool = True,
+) -> str:
+    """Search for nodes in the data graph by name or semantic type.
+    Use space-separated keywords, not SQL expressions.
+    Returns a list of matching nodes with brief relationship summaries.
+
+    Args:
+        query: Search string — keywords or short descriptions (e.g., "customer email address",
+               "revenue amount", "free meal count"). Not a SQL WHERE clause.
+        node_type: Optional filter: 'column', 'table', 'concept', 'entity'.
+        limit: Maximum number of results (default 10).
+        mask_credential: Whether to mask database credentials in output (default True).
+    """
+    config = DataLinkConfig.load()
+    retrieval = _get_retrieval(config.graph_db_path)
+
+    nt = None
+    if node_type:
+        try:
+            nt = NodeType(node_type)
+        except ValueError:
+            return f"Invalid node_type: {node_type}. Valid types: column, table, concept, entity."
+
+    results = retrieval.search_nodes(query, nt, limit, mask_credential=mask_credential)
+    return retrieval.format_search_nodes_text(results, query, mask_credential=mask_credential)
+
+
+def _datalink_get_node(
+    node_id: str,
+    include_edges: bool = True,
+    mask_credential: bool = True,
+) -> str:
+    """Get detailed information about a specific node, including its adjacent edges.
+
+    Args:
+        node_id: Node ID to retrieve. Valid formats:
+          • Full ID: type:source:table[:column] — e.g. column:sqlite:///.../db.sqlite:frpm:County Name
+          • Masked ID: same format with credentials masked (***:***@host) — auto-resolved internally
+          • Short alias: table_name.column_name or bare name — e.g. "frpm.County Name", "frpm", "County Name"
+          Supported type prefixes: table, column, concept, entity.
+          "profile:column:..." is NOT a valid node_id — profiles are attached to column nodes, not standalone.
+        include_edges: Whether to include adjacent edge information (default true).
+        mask_credential: Whether to mask database credentials in output (default True).
+    """
+    config = DataLinkConfig.load()
+    retrieval = _get_retrieval(config.graph_db_path)
+
+    result = retrieval.get_node(node_id, include_edges, mask_credential=mask_credential)
+    if result is None:
+        return (
+            f'Node "{node_id}" not found. This may be due to:\n'
+            f"  1. Wrong ID format — valid formats: column:source:table:col_name, "
+            f"or short alias like table_name.col_name\n"
+            f"  2. Column name mismatch — the actual column name may differ "
+            f"(spaces, hyphens, special chars)\n"
+            f"  3. Node does not exist in this graph\n"
+            f"Use datalink_search_nodes or datalink_explore to discover correct node IDs."
+        )
+    return retrieval.format_get_node_text(result, mask_credential=mask_credential)
+
+
+def _datalink_find_paths(
+    source_id: str,
+    target_id: str,
+    max_depth: int = 3,
+    limit: int = 3,
+    edge_types: str | None = None,
+    mask_credential: bool = True,
+) -> str:
+    """Find paths between two nodes in the data graph.
+
+    Args:
+        source_id: Starting node ID. Valid formats: full ID (type:source:table[:column]),
+                   masked ID (auto-resolved), or short alias (table_name.col_name).
+        target_id: Destination node ID. Same format rules as source_id.
+        max_depth: Maximum path length (default 3).
+        limit: Maximum number of paths to return (default 3).
+        edge_types: Optional comma-separated list of edge types to traverse
+                    (e.g., 'joinable,semantic_synonym,foreign_key').
+        mask_credential: Whether to mask database credentials in output (default True).
+    """
+    config = DataLinkConfig.load()
+    retrieval = _get_retrieval(config.graph_db_path)
+
+    from datalink.models.edge import EdgeType
+
+    types = None
+    if edge_types:
+        try:
+            type_names = [t.strip() for t in edge_types.split(",")]
+            types = [EdgeType(t) for t in type_names]
+        except ValueError as e:
+            valid = "foreign_key, joinable, semantic_synonym, semantic_type_match, "
+            valid += "distribution_similar, correlated, represents, has_concept, contains"
+            return f"Invalid edge type: {e}. Valid types: {valid}."
+
+    paths = retrieval.find_paths(source_id, target_id, max_depth, types, limit=limit, mask_credential=mask_credential)
+    # Resolve source/target IDs for display (they may have been alias-resolved internally)
+    resolved_source = retrieval._resolve_input_id(source_id)
+    resolved_target = retrieval._resolve_input_id(target_id)
+    return retrieval.format_find_paths_text(paths, resolved_source, resolved_target, mask_credential=mask_credential)
+
+
+def _datalink_extract_subgraph(
+    node_ids: str,
+    max_hops: int = 2,
+    mask_credential: bool = True,
+) -> str:
+    """Extract a subgraph around specified nodes, expanding by neighbor hops.
+
+    Args:
+        node_ids: Comma-separated list of node IDs to start from. Valid formats:
+                  full ID (type:source:table[:column]), masked ID (auto-resolved),
+                  or short alias (table_name.col_name).
+        max_hops: Number of neighbor layers to expand (default 2).
+        mask_credential: Whether to mask database credentials in output (default True).
+    """
+    config = DataLinkConfig.load()
+    retrieval = _get_retrieval(config.graph_db_path)
+
+    ids = [id.strip() for id in node_ids.split(",")]
+    result = retrieval.extract_subgraph(ids, max_hops, mask_credential=mask_credential)
+    return retrieval.format_extract_subgraph_text(result, mask_credential=mask_credential)
+
+
+def _datalink_list_datasets(mask_credential: bool = True) -> str:
+    """List all tables/datasets in the data graph with basic statistics.
+
+    Args:
+        mask_credential: Whether to mask database credentials in output (default True).
+    """
+    config = DataLinkConfig.load()
+    retrieval = _get_retrieval(config.graph_db_path)
+
+    datasets = retrieval.list_datasets(mask_credential=mask_credential)
+    return retrieval.format_list_datasets_text(datasets, mask_credential=mask_credential)
+
+
+def _datalink_list_pending_edges(
+    node_id: str = "",
+    edge_type: str = "",
+    limit: int = 50,
+    mask_credential: bool = True,
+) -> str:
+    """List pending edges whose referenced nodes are not yet in the graph.
+
+    Args:
+        node_id: Optional — filter to pending edges involving this node ID.
+                  Valid formats: full ID, masked ID, or short alias (table_name.col_name).
+        edge_type: Optional — filter by edge type (e.g., 'foreign_key', 'joinable').
+        limit: Maximum number of results (default 50).
+        mask_credential: Whether to mask database credentials in output (default True).
+    """
+    config = DataLinkConfig.load()
+    retrieval = _get_retrieval(config.graph_db_path)
+
+    from datalink.models.edge import EdgeType
+
+    et = None
+    if edge_type:
+        try:
+            et = EdgeType(edge_type)
+        except ValueError:
+            valid = "foreign_key, joinable, semantic_synonym, semantic_type_match, "
+            valid += "distribution_similar, correlated, represents, has_concept, contains"
+            return f"Invalid edge type: {edge_type}. Valid types: {valid}."
+
+    nid = node_id if node_id else None
+    results = retrieval.get_pending_edges(nid, et, limit, mask_credential=mask_credential)
+    return retrieval.format_list_pending_edges_text(results, mask_credential=mask_credential)
+
+
+# ── Auxiliary tool registry ──────────────────────────────────────────
+
+# Map of auxiliary tool name → (function, description for MCP registration)
+AUXILIARY_TOOLS: dict[str, tuple] = {
+    "datalink_search_nodes": (_datalink_search_nodes, "Search nodes by name or semantic type."),
+    "datalink_get_node": (_datalink_get_node, "Detailed node info with adjacent edges."),
+    "datalink_find_paths": (_datalink_find_paths, "Find paths between two nodes."),
+    "datalink_extract_subgraph": (_datalink_extract_subgraph, "Extract subgraph around nodes."),
+    "datalink_list_datasets": (_datalink_list_datasets, "List datasets with statistics."),
+    "datalink_list_pending_edges": (_datalink_list_pending_edges, "List pending edges with missing endpoints."),
+}
+
+
+def _register_auxiliary_tools(allowlist: set[str]) -> None:
+    """Register auxiliary tools that appear in the allowlist."""
+    for tool_name, (func, desc) in AUXILIARY_TOOLS.items():
+        if tool_name in allowlist:
+            mcp.tool(name=tool_name, description=desc)(func)
+            logger.info(f"Registered auxiliary MCP tool: {tool_name}")
+
+
+# ── Server startup ───────────────────────────────────────────────────
+
+
+def start_mcp_server(
+    db_path: str = "datalink.db",
+    port: int = 8080,
+    transport: str = "sse",
+    host: str = "",
+) -> None:
+    """Start the MCP server.
+
+    Always registers: datalink_explore (universal retrieval).
+    Optionally registers auxiliary tools based on DATALINK_MCP_TOOLS env var.
+
+    Write operations (add_table, rebuild, remove_table) and show are
+    available via the REST API (datalink api command) instead.
+
+    Args:
+        db_path: Path to the graph database.
+        port: Port to listen on.
+        transport: Transport protocol — 'sse' (legacy) or 'streamable-http' (recommended).
+        host: Bind address. Empty string = use the default (0.0.0.0, or
+              DATALINK_MCP_HOST env var). Pass '127.0.0.1' for localhost-only.
+    """
+    # Initialize storage
+    _get_storage(db_path)
+
+    # Register auxiliary tools if allowlist is set
+    allowlist = _get_tool_allowlist()
+    if allowlist:
+        _register_auxiliary_tools(allowlist)
+
+    effective_host = host or mcp.settings.host
+    logger.info(f"Starting DataLink MCP server on {effective_host}:{port}")
+    if allowlist:
+        logger.info(f"Auxiliary tools enabled: {sorted(allowlist)}")
+    else:
+        logger.info(
+            "Only core tool exposed (datalink_explore). "
+            "Set DATALINK_MCP_TOOLS to enable auxiliary tools. "
+            "Write operations (add-table, rebuild, remove-table) and show are available via REST API."
+        )
+
+    if host:
+        mcp.settings.host = host
+    mcp.settings.port = port
+    mcp.run(transport=transport)
diff --git a/services/datalink/src/datalink/models/__init__.py b/services/datalink/src/datalink/models/__init__.py
new file mode 100644
index 00000000..fc444d8d
--- /dev/null
+++ b/services/datalink/src/datalink/models/__init__.py
@@ -0,0 +1,38 @@
+"""Data models for DataLink."""
+
+from datalink.models.datasource import (
+    ColumnInfo,
+    DatasourceConfig,
+    DatasourceInfo,
+    DatasourceType,
+    ForeignKeyInfo,
+    TableInfo,
+)
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import (
+    ColumnNode,
+    ConceptNode,
+    EntityNode,
+    Node,
+    NodeType,
+    TableNode,
+)
+from datalink.models.profile import ColumnProfile
+
+__all__ = [
+    "Node",
+    "ColumnNode",
+    "TableNode",
+    "ConceptNode",
+    "EntityNode",
+    "NodeType",
+    "Edge",
+    "EdgeType",
+    "ColumnProfile",
+    "DatasourceConfig",
+    "DatasourceInfo",
+    "DatasourceType",
+    "ColumnInfo",
+    "TableInfo",
+    "ForeignKeyInfo",
+]
diff --git a/services/datalink/src/datalink/models/datasource.py b/services/datalink/src/datalink/models/datasource.py
new file mode 100644
index 00000000..666277c6
--- /dev/null
+++ b/services/datalink/src/datalink/models/datasource.py
@@ -0,0 +1,79 @@
+"""Datasource configuration and extraction result models."""
+
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class DatasourceType(str, Enum):
+    """Types of supported data sources."""
+
+    DATABASE = "database"  # PostgreSQL, MySQL, SQLite, etc.
+    CSV = "csv"  # Local CSV files
+    PARQUET = "parquet"  # Local Parquet files
+
+
+class DatasourceConfig(BaseModel):
+    """Configuration for connecting to a data source."""
+
+    type: DatasourceType = Field(description="Type of the data source")
+    # For database sources
+    connection_string: str = Field(
+        default="", description="SQLAlchemy connection string (e.g., postgresql://user:pass@host/db)"
+    )
+    # For file sources
+    path: str = Field(default="", description="Path to file or directory (for CSV/Parquet)")
+    # Schema name for database sources (optional)
+    schema_name: str = Field(default="public", description="Database schema to inspect")
+
+    # Sampling configuration
+    sample_size: int = Field(default=1000, description="Number of rows to sample for profiling")
+
+
+class ForeignKeyInfo(BaseModel):
+    """Foreign key constraint between two columns."""
+
+    constraint_name: str = Field(default="", description="Name of the FK constraint")
+    source_table: str = Field(description="Table containing the FK column")
+    source_column: str = Field(description="FK column name")
+    target_table: str = Field(description="Referenced table")
+    target_column: str = Field(description="Referenced column")
+
+
+class ColumnInfo(BaseModel):
+    """Raw column metadata extracted from a data source."""
+
+    name: str = Field(description="Column name")
+    dtype: str = Field(description="Data type as reported by the source")
+    nullable: bool = Field(default=True, description="Whether the column allows nulls")
+    is_primary_key: bool = Field(default=False, description="Whether this column is a primary key")
+    comment: str = Field(default="", description="Comment/description from metadata")
+    default_value: Any = Field(default=None, description="Default value for the column")
+
+
+class TableInfo(BaseModel):
+    """Raw table metadata extracted from a data source."""
+
+    name: str = Field(description="Table name")
+    schema_name: str | None = Field(
+        default="public",
+        description="Schema/database name. None for MySQL/MariaDB where the connected database IS the schema.",
+    )
+    columns: list[ColumnInfo] = Field(default_factory=list, description="All columns in this table")
+    foreign_keys: list[ForeignKeyInfo] = Field(default_factory=list, description="FK constraints involving this table")
+    row_count: int | None = Field(default=None, description="Number of rows (None if not available from metadata)")
+    comment: str = Field(default="", description="Table-level comment/description")
+    source: str = Field(default="", description="Source identifier this table came from")
+
+
+class DatasourceInfo(BaseModel):
+    """Complete extraction result from a data source."""
+
+    config: DatasourceConfig = Field(description="The config used to connect")
+    tables: list[TableInfo] = Field(default_factory=list, description="All tables found in this datasource")
+    # Sample data keyed by table name
+    sample_data: dict[str, list[dict[str, Any]]] = Field(
+        default_factory=dict,
+        description="Sample rows for each table: {table_name: [{col1: val1, ...}, ...]}",
+    )
diff --git a/services/datalink/src/datalink/models/edge.py b/services/datalink/src/datalink/models/edge.py
new file mode 100644
index 00000000..48c4a175
--- /dev/null
+++ b/services/datalink/src/datalink/models/edge.py
@@ -0,0 +1,97 @@
+"""Edge models for DataLink relationships."""
+
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class EdgeType(str, Enum):
+    """Types of edges in the graph.
+
+    A: Structural explicit (from schema/metadata)
+    B: Semantic implicit (inferred, with confidence)
+    C: Contextual (from usage)
+    D: Cross-layer (structural ↔ semantic)
+    """
+
+    # A: Structural explicit
+    CONTAINS = "contains"  # Table → Column
+    FOREIGN_KEY = "foreign_key"  # Column → Column (FK reference)
+    DERIVED_FROM = "derived_from"  # Table/Column → Table/Column (ETL lineage)
+    PARTITION_OF = "partition_of"  # Table → Table (time partition)
+    SCHEMA_SAME = "schema_same"  # Table → Table (identical structure)
+
+    # B: Semantic implicit (inferred)
+    SEMANTIC_SYNONYM = "semantic_synonym"  # Column → Column (name semantics ≡)
+    SEMANTIC_TYPE_MATCH = "semantic_type_match"  # Column → Column (same semantic type)
+    JOINABLE = "joinable"  # Column → Column (value domain overlap)
+    CORRELATED = "correlated"  # Column → Column (statistical correlation)
+    DISTRIBUTION_SIMILAR = "distribution_similar"  # Column → Column (similar distribution)
+
+    # C: Contextual (from usage — future)
+    CO_OCCURS_IN_QUERY = "co_occurs_in_query"  # Column → Column (same SQL)
+    FREQUENTLY_JOINED = "frequently_joined"  # Table → Table (frequent JOIN)
+    USED_IN_DASHBOARD = "used_in_dashboard"  # Column → Dashboard
+
+    # D: Cross-layer
+    REPRESENTS = "represents"  # Structural node → Semantic node (Column → Concept)
+    HAS_CONCEPT = "has_concept"  # Entity → Concept (entity has this concept)
+
+
+class Edge(BaseModel):
+    """An edge connecting two nodes with a relationship type and confidence."""
+
+    id: str = Field(description="Unique identifier for the edge")
+    source_id: str = Field(description="ID of the source node")
+    target_id: str = Field(description="ID of the target node")
+    type: EdgeType = Field(description="Type of the relationship")
+    confidence: float = Field(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="Confidence score for inferred edges (0.0-1.0), 1.0 for explicit edges",
+    )
+    properties: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional properties (e.g., FK constraint name, correlation coefficient)",
+    )
+
+    @property
+    def is_explicit(self) -> bool:
+        """Whether this edge comes from explicit schema/metadata (confidence=1.0)."""
+        return self.confidence == 1.0
+
+    @property
+    def is_inferred(self) -> bool:
+        """Whether this edge was inferred (confidence < 1.0)."""
+        return self.confidence < 1.0
+
+
+class PendingEdge(BaseModel):
+    """An edge whose source/target node(s) don't yet exist in the graph.
+
+    Typically created when a FK references a table/column from a datasource
+    that hasn't been added yet. When that datasource is later added via
+    add_table, the Pipeline resolves pending edges — moving them into the
+    main edges table so they participate in retrieval and traversal.
+    """
+
+    id: str = Field(description="Unique identifier for the pending edge")
+    source_id: str = Field(description="ID of the source node (may not exist yet)")
+    target_id: str = Field(description="ID of the target node (may not exist yet)")
+    type: EdgeType = Field(description="Type of the relationship")
+    confidence: float = Field(
+        default=1.0,
+        ge=0.0,
+        le=1.0,
+        description="Confidence score (0.0-1.0), 1.0 for explicit edges like FK",
+    )
+    properties: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional properties (e.g., FK constraint name)",
+    )
+    missing_endpoints: list[str] = Field(
+        default_factory=list,
+        description="Which endpoint IDs are missing: 'source', 'target', or both",
+    )
diff --git a/services/datalink/src/datalink/models/node.py b/services/datalink/src/datalink/models/node.py
new file mode 100644
index 00000000..dca10dfb
--- /dev/null
+++ b/services/datalink/src/datalink/models/node.py
@@ -0,0 +1,93 @@
+"""Node models for DataLink dual-layer architecture."""
+
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class NodeType(str, Enum):
+    """Types of nodes in the graph."""
+
+    COLUMN = "column"
+    TABLE = "table"
+    CONCEPT = "concept"
+    ENTITY = "entity"
+
+
+class Node(BaseModel):
+    """Base node model. All nodes share these fields."""
+
+    id: str = Field(description="Unique identifier for the node")
+    type: NodeType = Field(description="Type of the node")
+    name: str = Field(description="Human-readable name")
+    properties: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional properties as a flexible JSON dict",
+    )
+
+    def to_storage_properties(self) -> dict[str, Any]:
+        """Merge typed fields into properties dict for DB storage.
+
+        The DB stores only (id, type, name, properties). Typed fields like
+        description, dtype, etc. must be copied into properties so they
+        survive serialization/deserialization. This method does that merge
+        without mutating the model's own properties field.
+        """
+        merged = dict(self.properties)
+        for field_name in self.__class__.model_fields:
+            if field_name in ("id", "type", "name", "properties"):
+                continue
+            value = getattr(self, field_name)
+            merged[field_name] = value
+        return merged
+
+
+class ColumnNode(Node):
+    """A column in a table — the primary structural node for tabular data."""
+
+    type: NodeType = NodeType.COLUMN
+    table_id: str = Field(description="ID of the parent TableNode")
+    dtype: str = Field(default="", description="Data type (e.g., integer, varchar, float)")
+    semantic_type: str = Field(
+        default="", description="Pre-classified semantic type (e.g., email_address, monetary_value)"
+    )
+    profile_id: str = Field(default="", description="ID of the associated ColumnProfile")
+    comment: str = Field(default="", description="Comment/description from metadata (e.g., SQL column comment)")
+
+
+class TableNode(Node):
+    """A table/dataset — container of ColumnNodes."""
+
+    type: NodeType = NodeType.TABLE
+    source: str = Field(default="", description="Data source identifier (e.g., postgres://mydb, csv:./data/)")
+    source_type: str = Field(
+        default="",
+        description="Datasource type: 'csv', 'parquet', or 'database'",
+    )
+    row_count: int = Field(default=0, description="Number of rows in the table")
+    column_ids: list[str] = Field(default_factory=list, description="IDs of all ColumnNodes in this table")
+
+
+class ConceptNode(Node):
+    """A measurable concept/attribute in the semantic layer.
+
+    Examples: revenue, market_share, conversion_rate, age.
+    Concepts have units/dimensions and can be quantified.
+    """
+
+    type: NodeType = NodeType.CONCEPT
+    description: str = Field(default="", description="Description of what this concept means")
+    unit: str = Field(default="", description="Unit of measurement (e.g., USD, %, count)")
+    dimension: str = Field(default="", description="Dimension/category (e.g., monetary, temporal, demographic)")
+
+
+class EntityNode(Node):
+    """An identifiable thing/collection in the semantic layer.
+
+    Examples: Q3_performance, a_customer, east_china_region.
+    Entities have identity/name and group multiple Concepts.
+    """
+
+    type: NodeType = NodeType.ENTITY
+    description: str = Field(default="", description="Description of what this entity represents")
diff --git a/services/datalink/src/datalink/models/profile.py b/services/datalink/src/datalink/models/profile.py
new file mode 100644
index 00000000..26c4f08c
--- /dev/null
+++ b/services/datalink/src/datalink/models/profile.py
@@ -0,0 +1,66 @@
+"""Column profile model — statistical fingerprint of a data column."""
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class ColumnProfile(BaseModel):
+    """Statistical fingerprint of a column, computed from sampled data.
+
+    This profile captures the essential characteristics of a column's data
+    distribution, enabling semantic type classification and relationship inference.
+    """
+
+    id: str = Field(description="Unique identifier for this profile")
+    column_id: str = Field(description="ID of the ColumnNode this profile belongs to")
+
+    # Type information
+    dtype: str = Field(description="Detected data type (e.g., integer, float, string, datetime)")
+    semantic_type: str = Field(
+        default="unknown",
+        description="Pre-classified semantic type (e.g., email_address, monetary_value, identifier)",
+    )
+
+    # Basic statistics
+    null_rate: float = Field(default=0.0, ge=0.0, le=1.0, description="Fraction of null values")
+    cardinality: int = Field(default=0, description="Number of distinct values")
+    unique_rate: float = Field(
+        default=0.0, ge=0.0, le=1.0, description="Fraction of unique values (cardinality / total)"
+    )
+    total_count: int = Field(default=0, description="Total number of values (including nulls)")
+
+    # Numeric statistics (only populated for numeric columns)
+    min_value: float | None = Field(default=None, description="Minimum value")
+    max_value: float | None = Field(default=None, description="Maximum value")
+    mean_value: float | None = Field(default=None, description="Mean value")
+    std_value: float | None = Field(default=None, description="Standard deviation")
+    median_value: float | None = Field(default=None, description="Median value")
+
+    # String statistics (only populated for string columns)
+    min_length: int | None = Field(default=None, description="Minimum string length")
+    max_length: int | None = Field(default=None, description="Maximum string length")
+    avg_length: float | None = Field(default=None, description="Average string length")
+
+    # Value patterns and frequencies
+    top_values: list[dict[str, Any]] = Field(
+        default_factory=list,
+        description="Top-N most frequent values: [{'value': x, 'count': n, 'fraction': f}]",
+    )
+    value_patterns: list[str] = Field(
+        default_factory=list,
+        description="Detected regex patterns (e.g., email pattern, phone pattern)",
+    )
+
+    # Distribution
+    distribution_histogram: list[dict[str, Any]] = Field(
+        default_factory=list,
+        description="Distribution histogram: numeric → binned [{'bin': x, 'count': n}], "
+        "categorical → frequency [{'value': x, 'count': n}]",
+    )
+
+    # Sample values for LLM context
+    sample_values: list[Any] = Field(
+        default_factory=list,
+        description="A small set of representative sample values for semantic mapping",
+    )
diff --git a/services/datalink/src/datalink/profiler/__init__.py b/services/datalink/src/datalink/profiler/__init__.py
new file mode 100644
index 00000000..23c6d7ad
--- /dev/null
+++ b/services/datalink/src/datalink/profiler/__init__.py
@@ -0,0 +1 @@
+"""Column profilers — statistical fingerprint computation."""
diff --git a/services/datalink/src/datalink/profiler/tabular.py b/services/datalink/src/datalink/profiler/tabular.py
new file mode 100644
index 00000000..1359bae0
--- /dev/null
+++ b/services/datalink/src/datalink/profiler/tabular.py
@@ -0,0 +1,354 @@
+"""Profile tabular data columns — compute statistical fingerprints."""
+
+import logging
+import re
+from collections import Counter
+from typing import Any
+
+import numpy as np
+import pandas as pd
+
+from datalink.models.datasource import DatasourceInfo
+from datalink.models.profile import ColumnProfile
+
+logger = logging.getLogger(__name__)
+
+# Semantic type classification rules: pattern -> semantic_type
+SEMANTIC_TYPE_RULES = [
+    (r"^[\w.+-]+@[\w-]+\.[\w.-]+$", "email_address"),
+    (r"^\+?\d{1,3}[-.\s]?\(?\d{1,4}\)?[-.\s]?\d{1,4}[-.\s]?\d{1,9}$", "phone_number"),
+    (r"^https?://[\w./-]+$", "url"),
+    (r"^\d{4}-\d{2}-\d{2}$", "date_iso"),
+    (r"^\d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2}", "datetime_iso"),
+    (r"^[A-Z]{2,3}[-\d]+$", "identifier_code"),
+    (r"^\d+\.\d{2}$", "monetary_value"),
+    (r"^[\w-]+_id$", "identifier"),
+    (r"^id$", "identifier"),
+    (r".*_id$", "identifier"),
+    (r".*_name$", "person_name"),
+    (r"^name$", "person_name"),
+    (r".*_date$", "timestamp"),
+    (r".*_time$", "timestamp"),
+    (r".*_amount$", "monetary_value"),
+    (r"^amount$", "monetary_value"),
+    (r"^value$", "monetary_value"),
+    (r".*_price$", "monetary_value"),
+    (r".*_cost$", "monetary_value"),
+    (r".*_rate$", "rate_percentage"),
+    (r".*_count$", "count"),
+    (r".*_percent", "rate_percentage"),
+    (r".*_status$", "status_enum"),
+    (r"^status$", "status_enum"),
+    (r".*_type$", "type_enum"),
+    (r".*_category$", "category_enum"),
+    (r".*_email$", "email_address"),
+    (r".*_phone$", "phone_number"),
+    (r".*_address$", "physical_address"),
+    (r".*_city$", "city_name"),
+    (r".*_country$", "country_name"),
+    (r".*_zip$", "postal_code"),
+    (r".*_url$", "url"),
+]
+
+
+class TabularProfiler:
+    """Compute ColumnProfile for each column in tabular data."""
+
+    def profile_datasource(self, datasource_info: DatasourceInfo) -> list[ColumnProfile]:
+        """Profile all columns across all tables in a datasource.
+
+        Args:
+            datasource_info: Must have sample_data populated.
+
+        Returns:
+            List of ColumnProfile objects for all columns.
+        """
+        profiles = []
+
+        for table_info in datasource_info.tables:
+            # Use the real path/connection_string from table_info.source
+            # (same as TabularExtractor) for consistent node IDs
+            source_name = (
+                table_info.source
+                or datasource_info.config.path
+                or datasource_info.config.connection_string
+                or "unknown"
+            )
+            table_name = table_info.name
+            sample_rows = datasource_info.sample_data.get(table_name, [])
+
+            if not sample_rows:
+                logger.warning(f"No sample data for table '{table_name}', skipping profiling")
+                continue
+
+            # Convert to DataFrame for easier computation
+            df = pd.DataFrame(sample_rows)
+
+            for col_info in table_info.columns:
+                col_name = col_info.name
+                if col_name not in df.columns:
+                    continue
+
+                col_id = f"column:{source_name}:{table_name}:{col_name}"
+                series = df[col_name]
+
+                profile = self.profile_column(
+                    column_id=col_id,
+                    column_name=col_name,
+                    series=series,
+                    comment=col_info.comment,
+                )
+                profiles.append(profile)
+
+        logger.info(f"Profiled {len(profiles)} columns")
+        return profiles
+
+    def profile_column(
+        self,
+        column_id: str,
+        column_name: str,
+        series: pd.Series,
+        comment: str = "",
+    ) -> ColumnProfile:
+        """Compute a ColumnProfile for a single pandas Series.
+
+        Args:
+            column_id: ID of the ColumnNode this profile belongs to.
+            column_name: Name of the column.
+            series: The pandas Series containing the data.
+            comment: Optional metadata comment about the column.
+
+        Returns:
+            A ColumnProfile with all statistical fingerprints.
+        """
+        non_null = series.dropna()
+        total_count = len(series)
+        null_count = series.isna().sum()
+        null_rate = null_count / total_count if total_count > 0 else 0.0
+        cardinality = non_null.nunique()
+        unique_rate = cardinality / total_count if total_count > 0 else 0.0
+
+        # Detect dtype
+        dtype = self._classify_dtype(series)
+
+        # Compute value patterns
+        value_patterns = self._detect_patterns(non_null)
+
+        # Classify semantic type
+        semantic_type = self._classify_semantic_type(column_name, dtype, value_patterns, non_null)
+
+        # Basic stats
+        profile = ColumnProfile(
+            id=f"profile:{column_id}",
+            column_id=column_id,
+            dtype=dtype,
+            semantic_type=semantic_type,
+            null_rate=null_rate,
+            cardinality=cardinality,
+            unique_rate=unique_rate,
+            total_count=total_count,
+            value_patterns=value_patterns,
+        )
+
+        # Numeric stats
+        if pd.api.types.is_numeric_dtype(non_null):
+            numeric_values = pd.to_numeric(non_null, errors="coerce").dropna()
+            if len(numeric_values) > 0:
+                profile.min_value = float(numeric_values.min())
+                profile.max_value = float(numeric_values.max())
+                profile.mean_value = float(numeric_values.mean())
+                profile.std_value = float(numeric_values.std()) if len(numeric_values) > 1 else 0.0
+                profile.median_value = float(numeric_values.median())
+                profile.distribution_histogram = self._numeric_histogram(numeric_values)
+
+        # String stats
+        if dtype == "string" and len(non_null) > 0:
+            str_values = non_null.astype(str)
+            profile.min_length = int(str_values.str.len().min())
+            profile.max_length = int(str_values.str.len().max())
+            profile.avg_length = float(str_values.str.len().mean())
+
+        # Top values
+        profile.top_values = self._compute_top_values(non_null, top_n=10)
+
+        # Sample values (for LLM context)
+        if len(non_null) > 0:
+            sample_size = min(5, len(non_null))
+            profile.sample_values = non_null.sample(sample_size, random_state=42).tolist()
+
+        return profile
+
+    def _classify_dtype(self, series: pd.Series) -> str:
+        """Classify the data type of a column.
+
+        Detects boolean-like integer columns (BIGINT storing 0/1) by checking
+        if an integer column's distinct values are only {0, 1} — these are
+        classified as "integer_boolean" to distinguish them from genuine
+        integer identifiers and enable semantic type detection downstream.
+        """
+        non_null = series.dropna()
+        if len(non_null) == 0:
+            return "unknown"
+
+        if pd.api.types.is_bool_dtype(series):
+            return "boolean"
+        if pd.api.types.is_integer_dtype(series):
+            # Check for boolean-like integers: BIGINT/INTEGER storing only 0/1
+            distinct = non_null.nunique()
+            if distinct <= 2:
+                unique_vals = set(non_null.unique())
+                if unique_vals <= {0, 1} or unique_vals <= {0} or unique_vals <= {1}:
+                    return "integer_boolean"
+            return "integer"
+        if pd.api.types.is_float_dtype(series):
+            # Check for boolean-like floats: FLOAT storing only 0.0/1.0
+            distinct = non_null.nunique()
+            if distinct <= 2:
+                unique_vals = set(non_null.unique())
+                if unique_vals <= {0.0, 1.0} or unique_vals <= {0.0} or unique_vals <= {1.0}:
+                    return "float_boolean"
+            return "float"
+        if pd.api.types.is_datetime64_any_dtype(series):
+            return "datetime"
+
+        # Try numeric conversion for object dtype
+        if pd.api.types.is_object_dtype(series) or pd.api.types.is_string_dtype(series):
+            try:
+                numeric = pd.to_numeric(non_null, errors="coerce")
+                if numeric.notna().sum() / len(non_null) > 0.9:
+                    if numeric.mod(1).eq(0).all():
+                        # Check for boolean-like integer stored as string
+                        distinct = numeric.nunique()
+                        if distinct <= 2:
+                            unique_vals = set(numeric.dropna().unique())
+                            if unique_vals <= {0, 1}:
+                                return "integer_boolean"
+                        return "integer"
+                    return "float"
+            except Exception:
+                pass
+
+            # Try datetime
+            try:
+                dt = pd.to_datetime(non_null, errors="coerce", format="mixed")
+                if dt.notna().sum() / len(non_null) > 0.9:
+                    return "datetime"
+            except Exception:
+                pass
+
+            return "string"
+
+        return "unknown"
+
+    def _detect_patterns(self, series: pd.Series) -> list[str]:
+        """Detect common regex patterns in string values."""
+        if not (pd.api.types.is_object_dtype(series) or pd.api.types.is_string_dtype(series)):
+            return []
+
+        str_values = series.astype(str)
+        if len(str_values) == 0:
+            return []
+
+        patterns_found = []
+        sample_size = min(20, len(str_values))
+        sample = str_values.sample(sample_size, random_state=42) if len(str_values) > sample_size else str_values
+
+        # Check common patterns
+        pattern_checks = [
+            ("email_pattern", r"^[\w.+-]+@[\w-]+\.[\w.-]+$"),
+            ("url_pattern", r"^https?://"),
+            ("date_pattern", r"^\d{4}-\d{2}-\d{2}$"),
+            ("datetime_pattern", r"^\d{4}-\d{2}-\d{2}[T ]\d{2}:\d{2}"),
+            ("uuid_pattern", r"^[\da-f]{8}-[\da-f]{4}-[\da-f]{4}-[\da-f]{4}-[\da-f]{12}$"),
+            ("phone_pattern", r"^\+?\d[\d\s\-().]{7,}$"),
+            ("zip_pattern", r"^\d{5}(-\d{4})?$"),
+        ]
+
+        for pattern_name, regex in pattern_checks:
+            match_count = sum(1 for v in sample if re.match(regex, v))
+            match_rate = match_count / len(sample)
+            if match_rate > 0.8:
+                patterns_found.append(pattern_name)
+
+        return patterns_found
+
+    def _classify_semantic_type(
+        self,
+        column_name: str,
+        dtype: str,
+        value_patterns: list[str],
+        series: pd.Series,
+    ) -> str:
+        """Classify the semantic type of a column.
+
+        Uses a combination of:
+        1. Name-based rules (column name patterns)
+        2. Value pattern detection (email, URL, etc.)
+        3. Dtype heuristics
+        """
+        # Check value patterns first (most reliable)
+        for pattern_name in value_patterns:
+            pattern_to_semantic = {
+                "email_pattern": "email_address",
+                "url_pattern": "url",
+                "date_pattern": "date",
+                "datetime_pattern": "datetime",
+                "uuid_pattern": "uuid",
+                "phone_pattern": "phone_number",
+                "zip_pattern": "postal_code",
+            }
+            if pattern_name in pattern_to_semantic:
+                return pattern_to_semantic[pattern_name]
+
+        # Check name-based rules
+        col_lower = column_name.lower()
+        for pattern, semantic_type in SEMANTIC_TYPE_RULES:
+            if re.match(pattern, col_lower):
+                return semantic_type
+
+        # Dtype-based fallback
+        if dtype in ("boolean", "integer_boolean", "float_boolean"):
+            return "boolean_flag"
+        if dtype == "datetime":
+            return "timestamp"
+
+        # Check if it looks like an identifier (high unique rate, integer-ish)
+        if len(series) > 0:
+            non_null = series.dropna()
+            unique_rate = non_null.nunique() / len(series)
+            if unique_rate > 0.9 and dtype in ("integer", "string"):
+                return "identifier"
+
+        return "unknown"
+
+    def _compute_top_values(self, series: pd.Series, top_n: int = 10) -> list[dict[str, Any]]:
+        """Compute the top-N most frequent values."""
+        non_null = series.dropna()
+        if len(non_null) == 0:
+            return []
+
+        counts = Counter(non_null.tolist())
+        top = counts.most_common(top_n)
+        total = len(non_null)
+
+        return [{"value": str(val), "count": cnt, "fraction": cnt / total} for val, cnt in top]
+
+    def _numeric_histogram(self, values: pd.Series, bins: int = 10) -> list[dict[str, Any]]:
+        """Compute a binned histogram for numeric values."""
+        if len(values) < 2:
+            return []
+
+        try:
+            counts, bin_edges = np.histogram(values, bins=bins)
+            result = []
+            for i in range(len(counts)):
+                result.append(
+                    {
+                        "bin_start": float(bin_edges[i]),
+                        "bin_end": float(bin_edges[i + 1]),
+                        "count": int(counts[i]),
+                    }
+                )
+            return result
+        except Exception:
+            return []
diff --git a/services/datalink/src/datalink/utils/__init__.py b/services/datalink/src/datalink/utils/__init__.py
new file mode 100644
index 00000000..57c238de
--- /dev/null
+++ b/services/datalink/src/datalink/utils/__init__.py
@@ -0,0 +1,26 @@
+"""Utility modules for DataLink."""
+
+from datalink.utils.credential import (
+    build_id_mapping,
+    mask_credentials,
+    mask_id,
+    mask_result,
+    resolve_masked_id,
+    resolve_masked_ids,
+)
+from datalink.utils.ids import parse_column_id, parse_table_id
+from datalink.utils.sql_ident import dialect_from_source, quote_identifier, quote_qualified
+
+__all__ = [
+    "build_id_mapping",
+    "dialect_from_source",
+    "mask_credentials",
+    "mask_id",
+    "mask_result",
+    "parse_column_id",
+    "parse_table_id",
+    "quote_identifier",
+    "quote_qualified",
+    "resolve_masked_id",
+    "resolve_masked_ids",
+]
diff --git a/services/datalink/src/datalink/utils/credential.py b/services/datalink/src/datalink/utils/credential.py
new file mode 100644
index 00000000..ff1ca15e
--- /dev/null
+++ b/services/datalink/src/datalink/utils/credential.py
@@ -0,0 +1,323 @@
+"""Credential masking utilities — hide username/password in database connection strings.
+
+When DataLink is used as a semantic layer (especially via MCP), node IDs and
+source strings embed database connection strings like
+postgresql://admin:s3cret@host/db.  These helpers mask the credential parts
+so that agent-facing output never leaks usernames or passwords, while keeping
+scheme/host/port/db_name intact for context.
+
+Two modes:
+- mask_credential=True  (default for MCP / public retrieval) — output masked
+- mask_credential=False — output raw (for trusted contexts or when the agent
+  needs the actual connection string to connect to the database itself)
+"""
+
+import re
+from typing import Any
+from urllib.parse import urlparse
+
+# ── URL credential masking ──────────────────────────────────────────────
+
+# Pattern: anything that looks like a database URL scheme
+# Supports both bare scheme (postgresql://) and dialect+driver (postgresql+psycopg2://).
+_DB_URL_SCHEME = r"(?:postgresql|mysql|sqlite|mssql|oracle|clickhouse|mariadb)(?:\+\w+)?"
+_DB_URL_RE = re.compile(rf"^{_DB_URL_SCHEME}://", re.IGNORECASE)
+
+
+def mask_credentials(value: str) -> str:
+    """Mask username and password in a database connection string.
+
+    Preserves scheme, host, port, database name, and query params.
+    Replaces userinfo (user:pass@) with ***:***@.
+
+    Non-URL strings (file paths, plain names) are returned unchanged.
+
+    Examples:
+        postgresql://admin:secret123@db.example.com:5432/mydb
+        → postgresql://***:***@db.example.com:5432/mydb
+
+        postgresql://user@host/db
+        → postgresql://***@host/db
+
+        sqlite:///path/to/db.sqlite
+        → sqlite:///path/to/db.sqlite  (no userinfo to mask)
+
+        /home/user/data/orders.csv
+        → /home/user/data/orders.csv  (not a DB URL)
+    """
+    if not value or not _DB_URL_RE.match(value):
+        return value
+
+    # Use regex to mask the userinfo part (anything between scheme:// and @host)
+    # This handles all edge cases: user:pass@, user@, :pass@, etc.
+    # Supports dialect+driver schemes like mysql+pymysql://.
+    masked = re.sub(
+        rf"({_DB_URL_SCHEME})://([^@]+)@",
+        r"\1://***:***@",
+        value,
+        count=1,
+        flags=re.IGNORECASE,
+    )
+
+    # If the regex didn't match (e.g. sqlite:///path with no @), return original
+    if masked == value:
+        return value
+
+    # For URLs like "postgresql://user@host" (no password), the regex
+    # replaced "user@" with "***:***@", but the original had no colon.
+    # Fix: if original had no password (no second colon before @), use single ***
+    # Check by seeing if the original userinfo had a colon
+    try:
+        parsed = urlparse(value)
+        if not parsed.password and parsed.username:
+            # Replace the "***:***@" with "***@" for user-only URLs
+            masked = masked.replace("***:***@", "***@")
+    except Exception:
+        pass
+
+    return masked
+
+
+# ── Node ID masking ──────────────────────────────────────────────────────
+
+# Node ID format: type:source_name:table_name[:col_name]
+# Only the source_name part (2nd segment) may contain credentials.
+# We must be careful: source_name for file paths can contain colons
+# (e.g. C:\Users\...) or be a long URL.  The format is:
+#   prefix:type_prefix:rest_of_id
+# where type_prefix is "table" or "column" or "edge" or "profile".
+# The source_name is everything between the type_prefix and the
+# LAST segment(s): for tables it's "table:<source>:<table_name>",
+# for columns "column:<source>:<table_name>:<col_name>",
+# for edges it varies.
+#
+# Strategy: split on the type prefix to find boundaries, then
+# identify the source segment position based on node type.
+
+# Known ID prefixes and the position of source_name in the colon-split
+# "table"  → ["table", source_name, table_name]           — source at index 1
+# "column" → ["column", source_name, table_name, col_name] — source at index 1
+# "edge"   → varies, but source may appear at index 1 or deeper
+# "profile" → ["profile", "column:<source>:...:col_name"]  — embedded column ID
+# "pending" → similar structure
+
+# Simpler approach: since source_name is always at position 1 after
+# splitting on the first colon, and it ends where the next "known"
+# delimiter begins, we use a regex approach that finds the source_name
+# segment in the ID.
+
+
+def mask_id(node_id: str) -> str:
+    """Mask credentials in a node/edge/profile ID string.
+
+    IDs are formatted as type:source_name:... where source_name may
+    be a database connection string.  This function masks only the
+    credential parts (user:pass@), leaving the rest intact.
+
+    For simple IDs (table, column), the source_name is identified by
+    position and masked precisely.  For complex IDs (edge, profile),
+    we fall back to a regex that masks all embedded connection strings.
+
+    Examples:
+        table:postgresql://admin:secret@host/db:orders
+        → table:postgresql://***:***@host/db:orders
+
+        column:postgresql://admin:secret@host/db:orders:customer_id
+        → column:postgresql://***:***@host/db:orders:customer_id
+
+        edge:contains:table:postgresql://admin:pass@host/db:orders:...
+        → edge:contains:table:postgresql://***:***@host/db:orders:...
+
+        table:/home/user/data:orders
+        → table:/home/user/data:orders  (no credentials)
+    """
+    if not node_id:
+        return node_id
+
+    # Split into type prefix and the rest
+    first_colon = node_id.find(":")
+    if first_colon == -1:
+        return node_id
+
+    type_prefix = node_id[:first_colon]
+
+    # For "profile" IDs: "profile:column:source:..." — double-nested
+    # Mask the inner column ID recursively
+    if type_prefix == "profile":
+        inner_id = node_id[first_colon + 1 :]
+        masked_inner = mask_id(inner_id)
+        return f"profile:{masked_inner}"
+
+    # For "table" and "column" IDs — source_name is at a known position:
+    #   table:<source_name>:<table_name>
+    #   column:<source_name>:<table_name>:<col_name>
+    # The source_name can contain colons (URL scheme), so we find the
+    # boundary by looking at the last or second-last colon.
+    if type_prefix in ("table", "column"):
+        rest = node_id[first_colon + 1 :]
+        last_colon = rest.rfind(":")
+        if last_colon == -1:
+            # Only one segment — nothing to mask
+            return node_id
+
+        source_name = rest[:last_colon]
+        tail = rest[last_colon + 1 :]
+
+        # For column IDs, need second-last colon to separate source from table_name
+        if type_prefix == "column":
+            second_last_colon = rest.rfind(":", 0, last_colon)
+            if second_last_colon > 0:
+                source_name = rest[:second_last_colon]
+                tail = rest[second_last_colon + 1 :]
+
+        masked_source = mask_credentials(source_name)
+        return f"{type_prefix}:{masked_source}:{tail}"
+
+    # For all other IDs (edge, concept, entity, etc.) — the ID format
+    # is more complex.  Use regex to find and mask all embedded
+    # connection strings within the ID string.
+    return _mask_embedded_urls(node_id)
+
+
+# Regex to find DB URL patterns embedded in a string and mask their userinfo
+_EMBEDDED_URL_RE = re.compile(
+    rf"({_DB_URL_SCHEME})://([^@]+)@",
+    re.IGNORECASE,
+)
+
+
+def _mask_embedded_urls(text: str) -> str:
+    """Mask all embedded database URL userinfo in a string.
+
+    Used as a fallback for complex IDs where source_name position
+    is not deterministic (e.g. edge IDs with embedded table/column IDs).
+    """
+
+    def _replace(match):
+        scheme = match.group(1)
+        userinfo = match.group(2)
+        # Check if userinfo has a colon (user:pass) or just user
+        if ":" in userinfo:
+            return f"{scheme}://***:***@"
+        else:
+            return f"{scheme}://***@"
+
+    return _EMBEDDED_URL_RE.sub(_replace, text)
+
+
+# ── Recursive result masking ────────────────────────────────────────────
+
+# Keys whose values should be masked as node IDs
+_ID_KEYS = {"id", "source_id", "target_id", "other_id", "table_id", "profile_id"}
+
+# Keys whose values should be masked as credential-bearing strings
+_CREDENTIAL_KEYS = {"source", "connection_string"}
+
+# Keys whose values are lists of node IDs (each element masked via mask_id)
+_ID_LIST_KEYS = {"column_ids"}
+
+# Keys whose values are dicts that may contain credential-bearing entries
+_PROPERTIES_KEYS = {"properties", "other_node"}
+
+
+def mask_result(data: Any) -> Any:
+    """Recursively mask credentials in a retrieval result structure.
+
+    Walks through dicts and lists, masking:
+    - ID-like fields (id, source_id, target_id, table_id, profile_id) via mask_id()
+    - Credential fields (source, connection_string) via mask_credentials()
+    - ID list fields (column_ids) element-wise via mask_id()
+    - Nested dicts (properties, other_node) recursively
+    - Any other string containing a DB URL pattern via _mask_embedded_urls
+
+    Returns a new structure — does not mutate the input.
+    """
+    if isinstance(data, str):
+        # Standalone strings may be node IDs or source strings.
+        # _DB_URL_RE has a ^ anchor so won't match embedded URLs.
+        # Use _EMBEDDED_URL_RE (no anchor) for detection, then mask.
+        if _EMBEDDED_URL_RE.search(data):
+            return _mask_embedded_urls(data)
+        return data
+
+    if isinstance(data, list):
+        return [mask_result(item) for item in data]
+
+    if isinstance(data, dict):
+        masked = {}
+        for key, value in data.items():
+            if key in _ID_KEYS and isinstance(value, str):
+                masked[key] = mask_id(value)
+            elif key in _CREDENTIAL_KEYS and isinstance(value, str):
+                masked[key] = mask_credentials(value)
+            elif key in _ID_LIST_KEYS and isinstance(value, list):
+                masked[key] = [mask_id(item) if isinstance(item, str) else mask_result(item) for item in value]
+            elif key in _PROPERTIES_KEYS and isinstance(value, dict):
+                masked[key] = mask_result(value)
+            elif isinstance(value, (dict, list)):
+                masked[key] = mask_result(value)
+            elif isinstance(value, str):
+                # Catch-all: any string value that contains a DB URL pattern
+                # must be masked.  This prevents leaks in arbitrary properties
+                # fields that aren't in _ID_KEYS / _CREDENTIAL_KEYS.
+                # _mask_embedded_urls handles both standalone and embedded URLs.
+                if _EMBEDDED_URL_RE.search(value):
+                    masked[key] = _mask_embedded_urls(value)
+                else:
+                    masked[key] = value
+            else:
+                masked[key] = value
+        return masked
+
+    # int, float, bool, None, etc. — pass through
+    return data
+
+
+# ── Masked ID → real ID resolution ──────────────────────────────────────
+
+
+def is_masked_id(node_id: str) -> bool:
+    """Check if a node ID contains masked credentials.
+
+    Detects patterns like "***:***@" or "***@" that are produced by
+    mask_credentials / mask_id.
+    """
+    if not node_id:
+        return False
+    # Check for masked userinfo patterns: "***:***@" or "***@"
+    # "***@" could be part of "***@host" — look for "***@" preceded by ://
+    return "***:***@" in node_id or "://***@" in node_id
+
+
+def build_id_mapping(all_node_ids: set[str]) -> dict[str, str]:
+    """Build a bidirectional mapping: masked_id → real_id.
+
+    For each real node ID, compute its masked version and store the pair.
+    Overwrites are safe because mask_id is deterministic — the same real ID
+    always produces the same masked ID.
+    """
+    mapping: dict[str, str] = {}
+    for real_id in all_node_ids:
+        masked = mask_id(real_id)
+        if masked != real_id:  # only store if masking actually changed something
+            mapping[masked] = real_id
+    return mapping
+
+
+def resolve_masked_id(node_id: str, mapping: dict[str, str]) -> str:
+    """Resolve a possibly-masked node ID back to the real ID.
+
+    If the ID is not masked (doesn't contain ***), returns it unchanged.
+    If the ID is masked and found in the mapping, returns the real ID.
+    If the ID is masked but NOT in the mapping, returns it as-is
+    (the caller should handle this as a "not found" case).
+    """
+    if not is_masked_id(node_id):
+        return node_id
+
+    return mapping.get(node_id, node_id)
+
+
+def resolve_masked_ids(node_ids: list[str], mapping: dict[str, str]) -> list[str]:
+    """Resolve a list of possibly-masked node IDs."""
+    return [resolve_masked_id(nid, mapping) for nid in node_ids]
diff --git a/services/datalink/src/datalink/utils/embedding.py b/services/datalink/src/datalink/utils/embedding.py
new file mode 100644
index 00000000..c4716744
--- /dev/null
+++ b/services/datalink/src/datalink/utils/embedding.py
@@ -0,0 +1,155 @@
+"""Embedding service for DataLink — compute and manage text embedding vectors.
+
+Provides a reusable EmbeddingService that wraps OpenAI-compatible embedding API calls.
+Used by:
+- GraphRetrieval: hybrid search (text + vector)
+- BuildPipeline: pre-build embedding vectors during rebuild/add_table
+- LLMMapper: embedding pre-filter for merge_with_existing (existing usage)
+"""
+
+import logging
+
+from datalink.config import EmbeddingConfig, LLMConfig
+from datalink.models.node import ColumnNode, ConceptNode, EntityNode, Node, TableNode
+from datalink.models.profile import ColumnProfile
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingService:
+    """Embedding computation service using OpenAI-compatible API.
+
+    If the embedding model is not configured (model field is empty),
+    all operations gracefully return empty results — vector retrieval
+    is skipped and full-text search works as before.
+    """
+
+    def __init__(self, embedding_config: EmbeddingConfig, llm_config: LLMConfig | None = None):
+        """Initialize with embedding and LLM configuration.
+
+        Args:
+            embedding_config: Embedding model configuration.
+            llm_config: LLM configuration (for API key/base_url fallback).
+        """
+        self.embedding_config = embedding_config
+        self.llm_config = llm_config or LLMConfig()
+
+    def is_available(self) -> bool:
+        """Check if embedding service is usable.
+
+        Returns True when embedding model is configured and API key is available.
+        """
+        if not self.embedding_config.model:
+            return False
+        api_key = self.embedding_config.get_api_key(self.llm_config)
+        return bool(api_key)
+
+    def compute_embeddings(self, texts: list[str]) -> list[list[float]]:
+        """Compute embedding vectors for a batch of texts.
+
+        Args:
+            texts: List of text strings to embed.
+
+        Returns:
+            List of embedding vectors (list[float] each).
+            Returns empty list if service is not available or call fails.
+        """
+        if not self.is_available() or not texts:
+            return []
+
+        try:
+            from openai import OpenAI
+
+            client = OpenAI(
+                api_key=self.embedding_config.get_api_key(self.llm_config),
+                base_url=self.embedding_config.get_base_url(self.llm_config),
+                timeout=self.embedding_config.timeout,
+            )
+
+            response = client.embeddings.create(
+                model=self.embedding_config.model,
+                input=texts,
+            )
+
+            vectors = [item.embedding for item in response.data]
+            logger.info(f"Computed {len(vectors)} embeddings via {self.embedding_config.model}")
+            return vectors
+
+        except Exception as e:
+            logger.warning(f"Embedding computation failed: {e}")
+            return []
+
+    def compute_embedding(self, text: str) -> list[float]:
+        """Compute embedding vector for a single text.
+
+        Args:
+            text: Text string to embed.
+
+        Returns:
+            Embedding vector (list[float]), or empty list if unavailable.
+        """
+        result = self.compute_embeddings([text])
+        return result[0] if result else []
+
+    def get_model_name(self) -> str:
+        """Get the configured embedding model name."""
+        return self.embedding_config.model
+
+
+def node_to_searchable_text(
+    node: Node,
+    profile: ColumnProfile | None = None,
+    column_names: list[str] | None = None,
+) -> str:
+    """Convert a node to searchable text for embedding generation.
+
+    This produces a rich text representation that captures the node's
+    semantic meaning — combining name, description, type info, and
+    representative values. Used both for embedding generation and
+    for debugging/validation in the node_embeddings table.
+
+    Args:
+        node: The node to convert.
+        profile: Optional ColumnProfile (for Column nodes).
+        column_names: Optional list of column names (for Table nodes).
+
+    Returns:
+        Pipe-delimited text string suitable for embedding.
+    """
+    parts: list[str] = [node.name]
+
+    if isinstance(node, ColumnNode):
+        if node.comment:
+            parts.append(node.comment)
+        if node.semantic_type and node.semantic_type != "unknown":
+            parts.append(node.semantic_type)
+        if node.dtype:
+            parts.append(f"type: {node.dtype}")
+        if profile:
+            if profile.top_values:
+                vals = [str(tv["value"]) for tv in profile.top_values[:5]]
+                parts.append("values: " + ", ".join(vals))
+            elif profile.sample_values:
+                vals = [str(v) for v in profile.sample_values[:5]]
+                parts.append("samples: " + ", ".join(vals))
+
+    elif isinstance(node, ConceptNode):
+        if node.description:
+            parts.append(node.description)
+        if node.unit:
+            parts.append(f"unit: {node.unit}")
+        if node.dimension:
+            parts.append(f"dimension: {node.dimension}")
+
+    elif isinstance(node, EntityNode):
+        if node.description:
+            parts.append(node.description)
+
+    elif isinstance(node, TableNode):
+        comment = node.properties.get("comment", "")
+        if comment:
+            parts.append(comment)
+        if column_names:
+            parts.append("columns: " + ", ".join(column_names[:10]))
+
+    return " | ".join(parts)
diff --git a/services/datalink/src/datalink/utils/ids.py b/services/datalink/src/datalink/utils/ids.py
new file mode 100644
index 00000000..9c55ea75
--- /dev/null
+++ b/services/datalink/src/datalink/utils/ids.py
@@ -0,0 +1,61 @@
+"""Parse DataLink node IDs whose source segment may contain colons.
+
+ID formats:
+  table:<source>:<table_name>
+  column:<source>:<table_name>:<column_name>
+
+``source`` is often a SQLAlchemy URL (e.g. ``sqlite:////path/file.db`` or
+``postgresql://user:pass@host/db``), so naive ``str.split(":")`` is incorrect.
+Parse from the right: last segment(s) are table/column names.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True)
+class ParsedTableId:
+    source: str
+    table_name: str
+
+
+@dataclass(frozen=True)
+class ParsedColumnId:
+    source: str
+    table_name: str
+    column_name: str
+
+
+def parse_table_id(table_id: str) -> ParsedTableId | None:
+    """Parse ``table:<source>:<table_name>``."""
+    if not table_id.startswith("table:"):
+        return None
+    rest = table_id[len("table:") :]
+    last = rest.rfind(":")
+    if last <= 0:
+        return None
+    source = rest[:last]
+    table_name = rest[last + 1 :]
+    if not source or not table_name:
+        return None
+    return ParsedTableId(source=source, table_name=table_name)
+
+
+def parse_column_id(column_id: str) -> ParsedColumnId | None:
+    """Parse ``column:<source>:<table_name>:<column_name>``."""
+    if not column_id.startswith("column:"):
+        return None
+    rest = column_id[len("column:") :]
+    last = rest.rfind(":")
+    if last <= 0:
+        return None
+    second_last = rest.rfind(":", 0, last)
+    if second_last <= 0:
+        return None
+    source = rest[:second_last]
+    table_name = rest[second_last + 1 : last]
+    column_name = rest[last + 1 :]
+    if not source or not table_name or not column_name:
+        return None
+    return ParsedColumnId(source=source, table_name=table_name, column_name=column_name)
diff --git a/services/datalink/src/datalink/utils/sql_ident.py b/services/datalink/src/datalink/utils/sql_ident.py
new file mode 100644
index 00000000..764396fc
--- /dev/null
+++ b/services/datalink/src/datalink/utils/sql_ident.py
@@ -0,0 +1,60 @@
+"""Dialect-aware SQL identifier quoting for raw SQL strings."""
+
+from __future__ import annotations
+
+import re
+
+# Map connection-string / source prefixes to SQLAlchemy-ish dialect names.
+_DIALECT_PREFIXES: tuple[tuple[str, str], ...] = (
+    ("postgresql", "postgresql"),
+    ("postgres", "postgresql"),
+    ("mysql", "mysql"),
+    ("mariadb", "mariadb"),
+    ("sqlite", "sqlite"),
+    ("mssql", "mssql"),
+    ("oracle", "oracle"),
+    ("clickhouse", "clickhouse"),
+)
+
+
+def dialect_from_source(source: str) -> str | None:
+    """Infer SQL dialect from a connection string or node ``source`` field."""
+    if not source:
+        return None
+    lower = source.lower()
+    for prefix, dialect in _DIALECT_PREFIXES:
+        if lower.startswith(prefix):
+            return dialect
+    return None
+
+
+def quote_identifier(name: str, dialect: str | None = None) -> str:
+    """Quote a single SQL identifier (table, column, or schema name).
+
+    Always quotes so names with hyphens, spaces, or reserved words are safe.
+    """
+    if dialect in ("mysql", "mariadb"):
+        return "`" + name.replace("`", "``") + "`"
+    if dialect == "mssql":
+        return "[" + name.replace("]", "]]") + "]"
+    # PostgreSQL, SQLite, Oracle, ClickHouse, unknown → ANSI double quotes
+    return '"' + name.replace('"', '""') + '"'
+
+
+def quote_qualified(*parts: str | None, dialect: str | None = None) -> str:
+    """Join and quote non-empty identifier parts with dots.
+
+    Example::
+
+        quote_qualified("public", "orders", dialect="postgresql")
+        → '"public"."orders"'
+    """
+    return ".".join(quote_identifier(p, dialect) for p in parts if p)
+
+
+_SAFE_IDENT = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
+
+
+def needs_quoting(name: str) -> bool:
+    """Return True if ``name`` is not a plain unquoted SQL identifier."""
+    return not bool(_SAFE_IDENT.match(name))
diff --git a/services/datalink/tests/conftest.py b/services/datalink/tests/conftest.py
new file mode 100644
index 00000000..7952f0c5
--- /dev/null
+++ b/services/datalink/tests/conftest.py
@@ -0,0 +1,36 @@
+"""Shared test fixtures for DataLink tests."""
+
+from pathlib import Path
+
+import pytest
+
+# Path to test data directory
+TEST_DATA_DIR = Path(__file__).parent / "test_data"
+
+
+@pytest.fixture
+def users_csv_path() -> Path:
+    """Path to the test users.csv file."""
+    return TEST_DATA_DIR / "users.csv"
+
+
+@pytest.fixture
+def orders_csv_path() -> Path:
+    """Path to the test orders.csv file."""
+    return TEST_DATA_DIR / "orders.csv"
+
+
+@pytest.fixture
+def transactions_csv_path() -> Path:
+    """Path to the test transactions.csv file."""
+    return TEST_DATA_DIR / "transactions.csv"
+
+
+@pytest.fixture
+def all_csv_paths() -> list[Path]:
+    """Paths to all test CSV files."""
+    return [
+        TEST_DATA_DIR / "users.csv",
+        TEST_DATA_DIR / "orders.csv",
+        TEST_DATA_DIR / "transactions.csv",
+    ]
diff --git a/services/datalink/tests/test_builder.py b/services/datalink/tests/test_builder.py
new file mode 100644
index 00000000..786bb3d6
--- /dev/null
+++ b/services/datalink/tests/test_builder.py
@@ -0,0 +1,245 @@
+"""Tests for the build pipeline."""
+
+import os
+import tempfile
+
+import pytest
+
+from datalink.builder.pipeline import BuildPipeline
+from datalink.config import DataLinkConfig, LLMConfig
+from datalink.models.datasource import DatasourceConfig, DatasourceType
+from datalink.models.node import NodeType
+
+
+@pytest.fixture
+def pipeline():
+    """Create a build pipeline with a temporary database."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        db_path = f.name
+    config = DataLinkConfig(
+        graph_db_path=db_path,
+        llm=LLMConfig(model="gpt-4o", api_key=""),  # No API key for tests
+    )
+    pipeline = BuildPipeline(config)
+    yield pipeline
+    pipeline.close()
+    if os.path.exists(db_path):
+        os.unlink(db_path)
+
+
+@pytest.fixture
+def csv_datasource_config(all_csv_paths):
+    """Datasource config pointing to test CSV files."""
+    return DatasourceConfig(
+        type=DatasourceType.CSV,
+        path=str(all_csv_paths[0].parent),
+    )
+
+
+class TestInitBuild:
+    """Test initial build from data source configs."""
+
+    def test_build_from_csv_files(self, pipeline, csv_datasource_config):
+        result = pipeline.init_build([csv_datasource_config])
+
+        assert result["status"] == "success"
+        stats = result["stats"]
+        assert stats["total_nodes"] > 0
+        assert stats["total_edges"] > 0
+
+        # Should have tables and columns
+        assert stats["node_type_counts"]["table"] == 3
+        assert stats["node_type_counts"]["column"] == 15
+
+        # Should have contains edges (15 columns)
+        assert stats["edge_type_counts"]["contains"] == 15
+
+        # Should have inferred edges
+        assert stats["edge_type_counts"]["joinable"] > 0
+        assert stats["edge_type_counts"]["semantic_synonym"] > 0
+
+    def test_build_clears_previous_data(self, pipeline, csv_datasource_config):
+        # Build twice — second build should replace all data
+        pipeline.init_build([csv_datasource_config])
+        stats_after_first = pipeline.storage.get_graph_stats()
+
+        pipeline.init_build([csv_datasource_config])
+        stats_after_second = pipeline.storage.get_graph_stats()
+
+        # Same number of nodes (full rebuild, not accumulation)
+        assert stats_after_first["total_nodes"] == stats_after_second["total_nodes"]
+
+    def test_build_without_llm_skips_semantic_mapping(self, pipeline, csv_datasource_config):
+        # No API key configured, so LLM mapping should be skipped
+        result = pipeline.init_build([csv_datasource_config])
+
+        # CSV files have no comments, so no metadata mapping either
+        # → Should have 0 concepts and 0 entities
+        stats = result["stats"]
+        assert stats["node_type_counts"]["concept"] == 0
+        assert stats["node_type_counts"]["entity"] == 0
+
+
+class TestRebuild:
+    """Test rebuild from existing table metadata."""
+
+    def test_rebuild_from_existing_graph(self, pipeline, csv_datasource_config):
+        # First, init build
+        result = pipeline.init_build([csv_datasource_config])
+        assert result["status"] == "success"
+        initial_stats = pipeline.storage.get_graph_stats()
+
+        # Now rebuild
+        result = pipeline.rebuild()
+        assert result["status"] == "success"
+        rebuild_stats = pipeline.storage.get_graph_stats()
+
+        # Should produce the same number of nodes
+        assert initial_stats["total_nodes"] == rebuild_stats["total_nodes"]
+        assert initial_stats["node_type_counts"]["table"] == rebuild_stats["node_type_counts"]["table"]
+        assert initial_stats["node_type_counts"]["column"] == rebuild_stats["node_type_counts"]["column"]
+
+    def test_rebuild_empty_graph(self, pipeline):
+        # Rebuild on an empty graph should return error
+        result = pipeline.rebuild()
+        assert result["status"] == "error"
+        assert "No tables found" in result["error"]
+
+
+class TestAddDatasource:
+    """Test incrementally adding tables via add_datasource."""
+
+    def test_add_datasource_all_tables(self, pipeline, all_csv_paths):
+        # First build with just users.csv
+        users_config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(all_csv_paths[0]),  # just users.csv
+        )
+        pipeline.init_build([users_config])
+        initial_stats = pipeline.storage.get_graph_stats()
+        assert initial_stats["node_type_counts"]["table"] == 1
+
+        # Now add orders.csv and transactions.csv via add_datasource (all tables)
+        orders_config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(all_csv_paths[0].parent),  # directory with all 3 CSVs
+        )
+        result = pipeline.add_datasource(orders_config)
+        assert result["status"] == "success"
+        assert "orders" in result["added_tables"]
+        assert "transactions" in result["added_tables"]
+
+    def test_add_datasource_specific_tables(self, pipeline, all_csv_paths):
+        # First build with users.csv
+        users_config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(all_csv_paths[0]),
+        )
+        pipeline.init_build([users_config])
+        initial_stats = pipeline.storage.get_graph_stats()
+        assert initial_stats["node_type_counts"]["table"] == 1
+
+        # Now add only orders via add_datasource with table_names
+        orders_config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(all_csv_paths[0].parent),
+        )
+        result = pipeline.add_datasource(orders_config, ["orders"])
+        assert result["status"] == "success"
+        assert result["added_tables"] == ["orders"]
+
+        updated_stats = pipeline.storage.get_graph_stats()
+        assert updated_stats["node_type_counts"]["table"] == 2
+
+    def test_add_datasource_skips_existing_tables(self, pipeline, csv_datasource_config):
+        """Adding the same datasource twice should skip existing tables."""
+        # First, add all 3 tables
+        pipeline.init_build([csv_datasource_config])
+        initial_stats = pipeline.storage.get_graph_stats()
+        assert initial_stats["node_type_counts"]["table"] == 3
+
+        # Try to add the same datasource again
+        result = pipeline.add_datasource(csv_datasource_config)
+
+        # All tables should be skipped
+        assert result["status"] == "skipped"
+        assert result["added_tables"] == []
+        assert len(result["skipped_tables"]) == 3
+
+        # Graph should remain unchanged
+        updated_stats = pipeline.storage.get_graph_stats()
+        assert updated_stats["total_nodes"] == initial_stats["total_nodes"]
+
+    def test_add_datasource_mixed_new_and_existing(self, pipeline, all_csv_paths):
+        """Adding a datasource with some existing tables should skip those and add new ones."""
+        # First, add all 3 CSVs from the directory
+        directory_config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(all_csv_paths[0].parent),
+        )
+        pipeline.init_build([directory_config])
+
+        # Now try to add 2 of the same tables individually — they should be skipped
+        result = pipeline.add_datasource(directory_config, ["users", "orders"])
+
+        assert result["status"] == "skipped"
+        assert "users" in result["skipped_tables"]
+        assert "orders" in result["skipped_tables"]
+        assert result["added_tables"] == []
+
+        # Graph should remain unchanged
+        updated_stats = pipeline.storage.get_graph_stats()
+        assert updated_stats["node_type_counts"]["table"] == 3
+
+
+class TestAddTable:
+    """Test incrementally adding a single table."""
+
+    def test_add_table_to_existing_graph(self, pipeline, all_csv_paths):
+        # First build with just users.csv
+        users_config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(all_csv_paths[0]),  # just users.csv
+        )
+        pipeline.init_build([users_config])
+        initial_stats = pipeline.storage.get_graph_stats()
+        assert initial_stats["node_type_counts"]["table"] == 1
+        assert initial_stats["node_type_counts"]["column"] == 5
+
+        # Now add orders.csv as a separate table
+        orders_config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(all_csv_paths[1]),  # orders.csv
+        )
+        result = pipeline.add_table(orders_config, "orders")
+
+        assert result["status"] == "success"
+        updated_stats = pipeline.storage.get_graph_stats()
+        assert updated_stats["node_type_counts"]["table"] == 2
+        assert updated_stats["node_type_counts"]["column"] == 10  # 5 + 5
+
+        # Should now have cross-table inferred edges
+        assert updated_stats["edge_type_counts"]["joinable"] > 0
+
+
+class TestRemoveTable:
+    """Test removing a table from the graph."""
+
+    def test_remove_table(self, pipeline, csv_datasource_config):
+        # Build full graph first
+        pipeline.init_build([csv_datasource_config])
+        initial_stats = pipeline.storage.get_graph_stats()
+        assert initial_stats["node_type_counts"]["table"] == 3
+
+        # Remove the transactions table
+        # Find its ID
+        tables = pipeline.storage.get_nodes_by_type(NodeType.TABLE)
+        transactions_table = [t for t in tables if t.name == "transactions"][0]
+
+        result = pipeline.remove_table(transactions_table.id)
+        assert result["status"] == "success"
+        assert result["removed_columns"] == 5  # transactions has 5 columns
+
+        updated_stats = pipeline.storage.get_graph_stats()
+        assert updated_stats["node_type_counts"]["table"] == 2
+        assert updated_stats["node_type_counts"]["column"] == 10  # 15 - 5
diff --git a/services/datalink/tests/test_cli.py b/services/datalink/tests/test_cli.py
new file mode 100644
index 00000000..6c278e55
--- /dev/null
+++ b/services/datalink/tests/test_cli.py
@@ -0,0 +1,433 @@
+"""Tests for the CLI commands."""
+
+import json
+import os
+import tempfile
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+from typer.testing import CliRunner
+
+from datalink.cli.main import app
+from datalink.config import DataLinkConfig, LLMConfig
+from datalink.graph.storage import GraphStorage
+from datalink.mapper.llm_mapper import LLMMapper
+
+runner = CliRunner()
+
+TEST_DATA_DIR = Path(__file__).parent / "test_data"
+
+# Heuristic column-name → (concept_name, description, unit, dimension)
+_COLUMN_CONCEPTS: dict[str, tuple[str, str, str, str]] = {
+    "id": ("record_identifier", "Unique record identifier", "", "identifier"),
+    "customer_id": ("person_identifier", "Person identifier", "", "identifier"),
+    "user_id": ("person_identifier", "Person identifier", "", "identifier"),
+    "order_id": ("order_identifier", "Order identifier", "", "identifier"),
+    "tx_id": ("transaction_identifier", "Transaction identifier", "", "identifier"),
+    "name": ("person_name", "Person name", "", "text"),
+    "email": ("email_address", "Email address", "", "contact"),
+    "amount": ("monetary_value", "Monetary amount", "USD", "monetary"),
+    "value": ("monetary_value", "Monetary amount", "USD", "monetary"),
+    "signup_date": ("signup_date", "Account signup date", "", "temporal"),
+    "order_date": ("order_date", "Order date", "", "temporal"),
+    "tx_timestamp": ("transaction_timestamp", "Transaction timestamp", "", "temporal"),
+    "age": ("person_age", "Person age", "years", "demographic"),
+    "status": ("order_status", "Order status", "", "categorical"),
+    "tx_type": ("transaction_type", "Transaction type", "", "categorical"),
+}
+
+_TABLE_ENTITIES: dict[str, tuple[str, str]] = {
+    "users": ("user", "A registered user account"),
+    "orders": ("order", "A customer order"),
+    "transactions": ("transaction", "A financial transaction"),
+}
+
+
+def _make_mock_llm_response(prompt: str) -> str:
+    """Build a valid LLM JSON response from column metadata embedded in the prompt."""
+    columns: list[dict] = []
+    for line in prompt.splitlines():
+        line = line.strip()
+        if not line.startswith("{"):
+            continue
+        try:
+            data = json.loads(line)
+        except json.JSONDecodeError:
+            continue
+        if "column_id" in data:
+            columns.append(data)
+
+    if not columns:
+        return json.dumps({"concepts": [], "entities": []})
+
+    # Group columns by concept name (merge synonyms like customer_id + user_id)
+    concept_map: dict[str, dict] = {}
+    for col in columns:
+        col_name = col["column_name"].lower()
+        concept_name, desc, unit, dimension = _COLUMN_CONCEPTS.get(
+            col_name, (f"{col_name}_attribute", f"Attribute {col_name}", "", "other")
+        )
+        if concept_name not in concept_map:
+            concept_map[concept_name] = {
+                "name": concept_name,
+                "description": desc,
+                "unit": unit,
+                "dimension": dimension,
+                "columns": [],
+                "confidence": 0.85,
+            }
+        concept_map[concept_name]["columns"].append(col["column_id"])
+
+    # Build entities grouped by table
+    table_concepts: dict[str, set[str]] = {}
+    for col in columns:
+        table_id = col.get("table_id", "")
+        table_name = table_id.rsplit(":", 1)[-1] if table_id else "unknown"
+        col_name = col["column_name"].lower()
+        concept_name = _COLUMN_CONCEPTS.get(col_name, (f"{col_name}_attribute",))[0]
+        table_concepts.setdefault(table_name, set()).add(concept_name)
+
+    entities = []
+    for table_name, concept_names in table_concepts.items():
+        entity_name, entity_desc = _TABLE_ENTITIES.get(table_name, (table_name, f"Entity {table_name}"))
+        entities.append(
+            {
+                "name": entity_name,
+                "description": entity_desc,
+                "concept_names": sorted(concept_names),
+                "confidence": 0.8,
+            }
+        )
+
+    return json.dumps({"concepts": list(concept_map.values()), "entities": entities})
+
+
+def _mock_call_llm(_self, prompt: str, temperature: float | None = None) -> str:
+    """Mock LLM call that handles mapping, merge, and table comment prompts."""
+    # Detect prompt type by content
+    if "NEW concepts" in prompt or "EXISTING concepts" in prompt:
+        # Merge prompt — return no merges (keep all new nodes)
+        return json.dumps({"merges": [], "new_kept": []})
+    elif "Table data to analyze" in prompt or "tables_data" in prompt:
+        # Table comment prompt — return comments for each table_id found in prompt
+        table_ids = []
+        for line in prompt.splitlines():
+            line = line.strip()
+            if not line.startswith("{"):
+                continue
+            try:
+                data = json.loads(line)
+                if "table_id" in data:
+                    table_ids.append(data["table_id"])
+            except json.JSONDecodeError:
+                continue
+        return json.dumps(
+            {
+                "tables": [
+                    {"table_id": tid, "comment": f"Table containing {tid.split(':')[-1]} data"} for tid in table_ids
+                ]
+            }
+        )
+    else:
+        # Mapping prompt
+        return _make_mock_llm_response(prompt)
+
+
+def _llm_api_configured() -> bool:
+    """Return True when a real LLM API key is available."""
+    return bool(DataLinkConfig.load().llm.get_api_key())
+
+
+def _require_semantic_mapping(stats: dict) -> None:
+    """Skip when the pipeline ran but LLM produced no semantic nodes."""
+    if stats["node_type_counts"].get("concept", 0) == 0:
+        pytest.skip("LLM produced no semantic nodes (check API key, base_url, and model)")
+
+
+@pytest.fixture
+def require_real_llm():
+    """Skip the test when no LLM API key is configured."""
+    if not _llm_api_configured():
+        pytest.skip("No LLM API key configured (set datalink_config.json or OPENAI_API_KEY)")
+
+
+@pytest.fixture(autouse=True)
+def mock_llm_and_config(request, monkeypatch):
+    """Avoid real LLM calls and real datalink_config.json during CLI tests."""
+    if request.node.get_closest_marker("real_llm"):
+        yield
+        return
+
+    test_config = DataLinkConfig(
+        llm=LLMConfig(model="gpt-4o", api_key="test-key"),
+    )
+    monkeypatch.setattr("datalink.cli.main.DataLinkConfig.load", lambda path=None: test_config)
+
+    with patch.object(LLMMapper, "_call_llm", _mock_call_llm):
+        yield
+
+
+@pytest.fixture
+def temp_db():
+    """Create a temporary database path for CLI tests."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        db_path = f.name
+    yield db_path
+    # On Windows, SQLite files may still be locked; try multiple times
+    for _ in range(5):
+        try:
+            if os.path.exists(db_path):
+                os.unlink(db_path)
+            break
+        except PermissionError:
+            import time
+
+            time.sleep(0.5)
+
+
+class TestCLIAddTable:
+    """Test add-table command."""
+
+    def test_add_table_all_tables_initial_build(self, temp_db):
+        """add-table without --table on empty graph = initial build."""
+        result = runner.invoke(
+            app,
+            [
+                "add-table",
+                "--source",
+                str(TEST_DATA_DIR),
+                "--db",
+                temp_db,
+            ],
+        )
+        assert result.exit_code == 0
+        assert "Added" in result.output
+
+    def test_add_single_table(self, temp_db):
+        """add-table with --table adds one specific table."""
+        # First add all from users.csv (initial build)
+        runner.invoke(
+            app,
+            [
+                "add-table",
+                "--source",
+                str(TEST_DATA_DIR / "users.csv"),
+                "--db",
+                temp_db,
+            ],
+        )
+
+        # Then add orders table
+        result = runner.invoke(
+            app,
+            [
+                "add-table",
+                "--source",
+                str(TEST_DATA_DIR / "orders.csv"),
+                "--table",
+                "orders",
+                "--db",
+                temp_db,
+            ],
+        )
+        assert result.exit_code == 0
+        assert "Added table" in result.output
+
+    def test_add_table_incremental_all(self, temp_db):
+        """add-table without --table adds all tables from source incrementally."""
+        # Initial build with users
+        runner.invoke(
+            app,
+            [
+                "add-table",
+                "--source",
+                str(TEST_DATA_DIR / "users.csv"),
+                "--db",
+                temp_db,
+            ],
+        )
+
+        # Add remaining tables from the directory
+        result = runner.invoke(
+            app,
+            [
+                "add-table",
+                "--source",
+                str(TEST_DATA_DIR),
+                "--db",
+                temp_db,
+            ],
+        )
+        assert result.exit_code == 0
+        assert "Added" in result.output
+
+
+class TestCLIRebuild:
+    """Test the rebuild command."""
+
+    def test_rebuild_after_add_table(self, temp_db):
+        """rebuild works after add-table creates a graph."""
+        runner.invoke(
+            app,
+            [
+                "add-table",
+                "--source",
+                str(TEST_DATA_DIR),
+                "--db",
+                temp_db,
+            ],
+        )
+
+        result = runner.invoke(app, ["rebuild", "--db", temp_db])
+        assert result.exit_code == 0
+        assert "Full rebuild complete" in result.output
+
+    def test_rebuild_empty_graph(self, temp_db):
+        """rebuild on empty graph should fail."""
+        result = runner.invoke(app, ["rebuild", "--db", temp_db])
+        assert result.exit_code == 1
+
+
+class TestCLIRemoveTable:
+    """Test remove-table command."""
+
+    def test_remove_table(self, temp_db):
+        # Build graph first
+        runner.invoke(app, ["add-table", "--source", str(TEST_DATA_DIR), "--db", temp_db])
+
+        # Remove a table by name
+        result = runner.invoke(
+            app,
+            [
+                "remove-table",
+                "--table",
+                "transactions",
+                "--db",
+                temp_db,
+            ],
+        )
+        assert result.exit_code == 0
+        assert "Removed table" in result.output
+
+
+class TestCLISearch:
+    """Test the search command."""
+
+    def test_search_after_build(self, temp_db):
+        runner.invoke(app, ["add-table", "--source", str(TEST_DATA_DIR), "--db", temp_db])
+
+        result = runner.invoke(app, ["search", "customer_id", "--db", temp_db])
+        assert result.exit_code == 0
+        assert "customer_id" in result.output
+
+    def test_search_with_type_filter(self, temp_db):
+        runner.invoke(app, ["add-table", "--source", str(TEST_DATA_DIR), "--db", temp_db])
+
+        result = runner.invoke(app, ["search", "id", "--type", "column", "--db", temp_db])
+        assert result.exit_code == 0
+
+    def test_search_nonexistent(self, temp_db):
+        runner.invoke(app, ["add-table", "--source", str(TEST_DATA_DIR), "--db", temp_db])
+
+        result = runner.invoke(app, ["search", "xyz_nonexistent", "--db", temp_db])
+        assert result.exit_code == 0
+        assert "No results" in result.output
+
+
+class TestCLIInfo:
+    """Test the info command."""
+
+    def test_info_after_build(self, temp_db):
+        runner.invoke(app, ["add-table", "--source", str(TEST_DATA_DIR), "--db", temp_db])
+
+        result = runner.invoke(app, ["info", "--db", temp_db])
+        assert result.exit_code == 0
+        assert "DataLink Overview" in result.output
+        assert "Total Nodes" in result.output
+
+
+class TestCLIConfig:
+    """Test the config command."""
+
+    def test_config_set_llm(self, tmp_path, monkeypatch):
+        monkeypatch.chdir(tmp_path)
+        result = runner.invoke(
+            app,
+            [
+                "config",
+                "--llm-model",
+                "claude-sonnet-4-6",
+                "--llm-base-url",
+                "https://api.anthropic.com/v1",
+            ],
+        )
+        assert result.exit_code == 0
+        assert "Configuration saved" in result.output
+
+        saved = json.loads((tmp_path / "datalink_config.json").read_text())
+        assert saved["llm"]["model"] == "claude-sonnet-4-6"
+        assert saved["llm"]["base_url"] == "https://api.anthropic.com/v1"
+
+    def test_config_show_values(self, tmp_path, monkeypatch):
+        monkeypatch.chdir(tmp_path)
+        result = runner.invoke(app, ["config"])
+        assert result.exit_code == 0
+
+
+@pytest.mark.real_llm
+class TestCLIRealLLM:
+    """Optional integration tests that call a real LLM (skipped without API key)."""
+
+    def test_add_table_single_small_csv(self, temp_db, require_real_llm):
+        """add-table on users.csv (5 columns) succeeds with real semantic mapping."""
+        result = runner.invoke(
+            app,
+            [
+                "add-table",
+                "--source",
+                str(TEST_DATA_DIR / "users.csv"),
+                "--db",
+                temp_db,
+            ],
+        )
+        assert result.exit_code == 0
+        assert "Added" in result.output
+
+        storage = GraphStorage(temp_db)
+        try:
+            stats = storage.get_graph_stats()
+            assert stats["node_type_counts"]["table"] == 1
+            assert stats["node_type_counts"]["column"] == 5
+            _require_semantic_mapping(stats)
+            assert stats["node_type_counts"]["entity"] > 0
+        finally:
+            storage.close()
+
+    def test_add_two_tables_info_command(self, temp_db, require_real_llm):
+        """add-table on users + orders, then info reports semantic nodes."""
+        for csv_name in ("users.csv", "orders.csv"):
+            result = runner.invoke(
+                app,
+                [
+                    "add-table",
+                    "--source",
+                    str(TEST_DATA_DIR / csv_name),
+                    "--db",
+                    temp_db,
+                ],
+            )
+            assert result.exit_code == 0, result.output
+
+        storage = GraphStorage(temp_db)
+        try:
+            stats = storage.get_graph_stats()
+            _require_semantic_mapping(stats)
+        finally:
+            storage.close()
+
+        result = runner.invoke(app, ["info", "--db", temp_db])
+        assert result.exit_code == 0
+        assert "DataLink Overview" in result.output
+        assert "Concepts" in result.output
+        assert "Entities" in result.output
diff --git a/services/datalink/tests/test_connector.py b/services/datalink/tests/test_connector.py
new file mode 100644
index 00000000..de1a03de
--- /dev/null
+++ b/services/datalink/tests/test_connector.py
@@ -0,0 +1,247 @@
+"""Tests for data source connectors."""
+
+import sqlite3
+from pathlib import Path
+
+import pytest
+
+from datalink.connector.database import DatabaseConnector
+from datalink.connector.file import FileConnector
+from datalink.models.datasource import (
+    DatasourceConfig,
+    DatasourceType,
+)
+
+
+class TestFileConnector:
+    """Test the CSV/Parquet file connector."""
+
+    def test_connect_single_csv(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test_users",
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        connector.connect()
+        assert "users" in connector._dataframes
+
+    def test_connect_directory(self, all_csv_paths):
+        # all_csv_paths is under test_data directory
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test_all",
+            path=str(all_csv_paths[0].parent),
+        )
+        connector = FileConnector(config)
+        connector.connect()
+        assert "users" in connector._dataframes
+        assert "orders" in connector._dataframes
+        assert "transactions" in connector._dataframes
+
+    def test_get_datasource_info(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test_users",
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        info = connector.get_datasource_info()
+
+        assert len(info.tables) == 1
+        table = info.tables[0]
+        assert table.name == "users"
+        assert table.row_count == 10
+        assert len(table.columns) == 5  # id, name, email, signup_date, age
+
+        # Check specific columns
+        col_names = [c.name for c in table.columns]
+        assert "id" in col_names
+        assert "email" in col_names
+
+        # Check sample data exists
+        assert "users" in info.sample_data
+        assert len(info.sample_data["users"]) == 10
+
+    def test_get_datasource_info_all_tables(self, all_csv_paths):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test_all",
+            path=str(all_csv_paths[0].parent),
+        )
+        connector = FileConnector(config)
+        info = connector.get_datasource_info()
+
+        assert len(info.tables) == 3
+        table_names = [t.name for t in info.tables]
+        assert "users" in table_names
+        assert "orders" in table_names
+        assert "transactions" in table_names
+
+    def test_get_sample_data(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        connector.connect()
+        sample = connector.get_sample_data("users", n=5)
+        assert len(sample) == 5
+        assert "id" in sample[0]
+        assert "name" in sample[0]
+
+    def test_dtype_inference(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        info = connector.get_datasource_info()
+        table = info.tables[0]
+
+        # Check dtype classifications
+        col_map = {c.name: c for c in table.columns}
+        assert col_map["id"].dtype == "integer"
+        assert col_map["name"].dtype == "string"
+        assert col_map["email"].dtype == "string"
+        assert col_map["age"].dtype == "integer"
+
+    def test_disconnect(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        connector.connect()
+        assert len(connector._dataframes) > 0
+        connector.disconnect()
+        assert len(connector._dataframes) == 0
+
+    def test_nonexistent_path(self):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path="/nonexistent/path.csv",
+        )
+        connector = FileConnector(config)
+        with pytest.raises(ValueError, match="does not exist"):
+            connector.connect()
+
+    def test_id_as_primary_key(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        info = connector.get_datasource_info()
+        table = info.tables[0]
+        col_map = {c.name: c for c in table.columns}
+        # id column is unique and not nullable → should be detected as PK
+        assert col_map["id"].is_primary_key
+
+
+def _make_sqlite_db(path: Path, *, hyphenated: bool = False) -> None:
+    """Create a tiny SQLite DB with one table for connector tests."""
+    conn = sqlite3.connect(path)
+    try:
+        if hyphenated:
+            conn.execute('CREATE TABLE "dacomp-zh-006" (id INTEGER PRIMARY KEY, name TEXT, age INTEGER)')
+            conn.execute("INSERT INTO \"dacomp-zh-006\" (id, name, age) VALUES (1, 'Alice', 30), (2, 'Bob', 25)")
+        else:
+            conn.execute("CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT, age INTEGER)")
+            conn.execute("INSERT INTO users (id, name, age) VALUES (1, 'Alice', 30), (2, 'Bob', 25)")
+        conn.commit()
+    finally:
+        conn.close()
+
+
+class TestDatabaseConnectorSqlite:
+    """SQLite-specific DatabaseConnector behavior."""
+
+    def test_extract_schema_preserves_sqlite_extension(self, tmp_path):
+        """Dots in SQLite file paths must not be treated as db.schema."""
+        db_path = tmp_path / "dacomp-zh-006.sqlite"
+        _make_sqlite_db(db_path)
+        url = f"sqlite:///{db_path.as_posix()}"
+
+        config = DatasourceConfig(
+            type=DatasourceType.DATABASE,
+            connection_string=url,
+        )
+        connector = DatabaseConnector(config)
+        clean_url, schema = connector._extract_schema_from_url(url)
+
+        assert schema is None
+        assert clean_url == url
+        assert clean_url.endswith(".sqlite")
+
+    def test_connect_clears_public_schema(self, tmp_path):
+        db_path = tmp_path / "app.db"
+        _make_sqlite_db(db_path)
+        url = f"sqlite:///{db_path.as_posix()}"
+
+        config = DatasourceConfig(
+            type=DatasourceType.DATABASE,
+            connection_string=url,
+            schema_name="public",
+        )
+        connector = DatabaseConnector(config)
+        connector.connect()
+        try:
+            assert connector.config.schema_name is None
+        finally:
+            connector.disconnect()
+
+    def test_get_datasource_info_with_sqlite_extension(self, tmp_path):
+        """End-to-end: .sqlite path must introspect tables without public.sqlite_master."""
+        db_path = tmp_path / "dacomp-zh-006.sqlite"
+        _make_sqlite_db(db_path)
+        url = f"sqlite:///{db_path.as_posix()}"
+
+        config = DatasourceConfig(
+            type=DatasourceType.DATABASE,
+            connection_string=url,
+        )
+        connector = DatabaseConnector(config)
+        info = connector.get_datasource_info()
+        connector.disconnect()
+
+        assert len(info.tables) == 1
+        assert info.tables[0].name == "users"
+        assert info.tables[0].row_count == 2
+        col_names = [c.name for c in info.tables[0].columns]
+        assert "id" in col_names
+        assert "name" in col_names
+        assert "users" in info.sample_data
+        assert len(info.sample_data["users"]) == 2
+
+    def test_hyphenated_table_name_row_count_and_sample(self, tmp_path):
+        """Table names with hyphens must be quoted in COUNT/sample SQL."""
+        db_path = tmp_path / "dacomp-zh-006.sqlite"
+        _make_sqlite_db(db_path, hyphenated=True)
+        url = f"sqlite:///{db_path.as_posix()}"
+
+        config = DatasourceConfig(
+            type=DatasourceType.DATABASE,
+            connection_string=url,
+        )
+        connector = DatabaseConnector(config)
+        info = connector.get_datasource_info()
+        connector.disconnect()
+
+        assert len(info.tables) == 1
+        assert info.tables[0].name == "dacomp-zh-006"
+        assert info.tables[0].row_count == 2
+        assert "dacomp-zh-006" in info.sample_data
+        assert len(info.sample_data["dacomp-zh-006"]) == 2
+
+    def test_qualified_table_name_quotes_hyphens(self, tmp_path):
+        db_path = tmp_path / "app.db"
+        _make_sqlite_db(db_path)
+        url = f"sqlite:///{db_path.as_posix()}"
+        config = DatasourceConfig(type=DatasourceType.DATABASE, connection_string=url)
+        connector = DatabaseConnector(config)
+        connector.connect()
+        try:
+            assert connector._qualified_table_name("dacomp-zh-006") == '"dacomp-zh-006"'
+        finally:
+            connector.disconnect()
diff --git a/services/datalink/tests/test_credential.py b/services/datalink/tests/test_credential.py
new file mode 100644
index 00000000..74a0de61
--- /dev/null
+++ b/services/datalink/tests/test_credential.py
@@ -0,0 +1,392 @@
+"""Tests for credential masking utilities."""
+
+from datalink.utils.credential import (
+    build_id_mapping,
+    is_masked_id,
+    mask_credentials,
+    mask_id,
+    mask_result,
+    resolve_masked_id,
+    resolve_masked_ids,
+)
+
+
+class TestMaskCredentials:
+    """Test mask_credentials function."""
+
+    def test_postgresql_with_user_and_password(self):
+        url = "postgresql://admin:secret123@db.example.com:5432/mydb"
+        assert mask_credentials(url) == "postgresql://***:***@db.example.com:5432/mydb"
+
+    def test_mysql_with_user_and_password(self):
+        url = "mysql://root:pass@localhost:3306/sales"
+        assert mask_credentials(url) == "mysql://***:***@localhost:3306/sales"
+
+    def test_url_with_user_no_password(self):
+        url = "postgresql://user@host/db"
+        assert mask_credentials(url) == "postgresql://***@host/db"
+
+    def test_url_with_password_only(self):
+        # URL with user:pass but user is empty — still masks both
+        url = "postgresql://:pass@host/db"
+        result = mask_credentials(url)
+        assert "pass" not in result
+        assert "***" in result
+
+    def test_sqlite_no_credentials(self):
+        url = "sqlite:///path/to/db.sqlite"
+        assert mask_credentials(url) == "sqlite:///path/to/db.sqlite"
+
+    def test_sqlite_memory(self):
+        url = "sqlite://"
+        assert mask_credentials(url) == "sqlite://"
+
+    def test_file_path_not_masked(self):
+        path = "/home/user/data/orders.csv"
+        assert mask_credentials(path) == "/home/user/data/orders.csv"
+
+    def test_windows_path_not_masked(self):
+        path = "C:\\Users\\admin\\data\\orders.csv"
+        assert mask_credentials(path) == "C:\\Users\\admin\\data\\orders.csv"
+
+    def test_empty_string(self):
+        assert mask_credentials("") == ""
+
+    def test_simple_name_not_masked(self):
+        assert mask_credentials("unknown") == "unknown"
+
+    def test_url_with_query_params(self):
+        url = "postgresql://admin:pass@host/db?sslmode=require"
+        result = mask_credentials(url)
+        assert "admin" not in result
+        assert "pass" not in result
+        assert "sslmode=require" in result
+
+    def test_url_without_port(self):
+        url = "postgresql://admin:pass@host/mydb"
+        assert mask_credentials(url) == "postgresql://***:***@host/mydb"
+
+
+class TestMaskId:
+    """Test mask_id function for node IDs."""
+
+    def test_table_id_with_db_connection(self):
+        id = "table:postgresql://admin:secret@host/db:orders"
+        assert mask_id(id) == "table:postgresql://***:***@host/db:orders"
+
+    def test_column_id_with_db_connection(self):
+        id = "column:postgresql://admin:secret@host/db:orders:customer_id"
+        assert mask_id(id) == "column:postgresql://***:***@host/db:orders:customer_id"
+
+    def test_table_id_with_file_path(self):
+        id = "table:/home/user/data:orders"
+        assert mask_id(id) == "table:/home/user/data:orders"
+
+    def test_column_id_with_file_path(self):
+        id = "column:/home/user/data:orders:amount"
+        assert mask_id(id) == "column:/home/user/data:orders:amount"
+
+    def test_empty_id(self):
+        assert mask_id("") == ""
+
+    def test_simple_id_no_colons(self):
+        assert mask_id("concept") == "concept"
+
+    def test_profile_id_with_db_connection(self):
+        id = "profile:column:postgresql://admin:pass@host/db:orders:col"
+        result = mask_id(id)
+        assert "admin" not in result
+        assert "pass" not in result
+        assert "***" in result
+
+    def test_edge_id_with_db_connection(self):
+        id = "edge:contains:table:postgresql://admin:pass@host/db:orders:column:postgresql://admin:pass@host/db:orders:col"
+        result = mask_id(id)
+        assert "admin" not in result
+        assert "pass" not in result
+
+
+class TestMaskResult:
+    """Test mask_result for full result dicts."""
+
+    def test_mask_search_nodes_result(self):
+        result = [
+            {
+                "id": "column:postgresql://admin:pass@host/db:orders:customer_id",
+                "type": "column",
+                "name": "customer_id",
+                "properties": {
+                    "source": "postgresql://admin:pass@host/db",
+                    "dtype": "integer",
+                },
+                "edge_count": 5,
+                "edges_summary": [
+                    {
+                        "type": "joinable",
+                        "target_id": "column:postgresql://admin:pass@host/db:users:id",
+                        "confidence": 0.8,
+                    }
+                ],
+            }
+        ]
+        masked = mask_result(result)
+        assert "admin" not in masked[0]["id"]
+        assert "pass" not in masked[0]["id"]
+        assert "***" in masked[0]["id"]
+        assert "admin" not in masked[0]["properties"]["source"]
+        assert "pass" not in masked[0]["properties"]["source"]
+        assert "admin" not in masked[0]["edges_summary"][0]["target_id"]
+        assert masked[0]["name"] == "customer_id"  # names not masked
+        assert masked[0]["edge_count"] == 5  # ints not masked
+
+    def test_mask_get_node_result(self):
+        result = {
+            "id": "table:postgresql://admin:pass@host/db:orders",
+            "type": "table",
+            "name": "orders",
+            "properties": {"source": "postgresql://admin:pass@host/db"},
+            "edges": [
+                {
+                    "id": "edge:contains:...",
+                    "source_id": "table:postgresql://admin:pass@host/db:orders",
+                    "target_id": "column:postgresql://admin:pass@host/db:orders:col",
+                    "other_node": {
+                        "id": "column:postgresql://admin:pass@host/db:orders:col",
+                        "name": "col",
+                        "type": "column",
+                    },
+                }
+            ],
+        }
+        masked = mask_result(result)
+        assert "admin" not in masked["id"]
+        assert "admin" not in masked["edges"][0]["source_id"]
+        assert "admin" not in masked["edges"][0]["target_id"]
+        assert "admin" not in masked["edges"][0]["other_node"]["id"]
+        assert "admin" not in masked["properties"]["source"]
+
+    def test_mask_find_paths_result(self):
+        result = [
+            {
+                "nodes": [
+                    "table:postgresql://admin:pass@host/db:orders",
+                    "column:postgresql://admin:pass@host/db:orders:col",
+                ],
+                "edges": [
+                    {
+                        "source_id": "table:postgresql://admin:pass@host/db:orders",
+                        "target_id": "column:postgresql://admin:pass@host/db:orders:col",
+                    }
+                ],
+            }
+        ]
+        masked = mask_result(result)
+        assert "admin" not in masked[0]["nodes"][0]
+        assert "admin" not in masked[0]["edges"][0]["source_id"]
+
+    def test_mask_list_datasets_result(self):
+        result = [
+            {
+                "id": "table:postgresql://admin:pass@host/db:orders",
+                "name": "orders",
+                "source": "postgresql://admin:pass@host/db",
+                "column_count": 5,
+            }
+        ]
+        masked = mask_result(result)
+        assert "admin" not in masked[0]["id"]
+        assert "admin" not in masked[0]["source"]
+        assert "***" in masked[0]["source"]
+
+    def test_mask_file_path_result_not_masked(self):
+        result = [
+            {
+                "id": "table:/home/user/data:orders",
+                "name": "orders",
+                "source": "/home/user/data",
+            }
+        ]
+        masked = mask_result(result)
+        # File paths are not masked — they don't have credentials
+        assert masked[0]["id"] == "table:/home/user/data:orders"
+        assert masked[0]["source"] == "/home/user/data"
+
+    def test_mask_subgraph_result_nested_ids(self):
+        """P0 regression: mask_result must mask table_id, profile_id,
+        column_ids inside node properties — these embed DB URLs with
+        credentials that leaked in extract_subgraph(mask_credential=True).
+        """
+        result = {
+            "nodes": [
+                {
+                    "id": "column:postgresql://admin:s3cret@host/db:orders:customer_id",
+                    "type": "column",
+                    "name": "customer_id",
+                    "properties": {
+                        "table_id": "table:postgresql://admin:s3cret@host/db:orders",
+                        "profile_id": "profile:column:postgresql://admin:s3cret@host/db:orders:customer_id",
+                        "column_ids": [
+                            "column:postgresql://admin:s3cret@host/db:orders:id",
+                            "column:postgresql://admin:s3cret@host/db:orders:amount",
+                        ],
+                        "source": "postgresql://admin:s3cret@host/db",
+                        "dtype": "integer",
+                    },
+                },
+                {
+                    "id": "table:postgresql://admin:s3cret@host/db:orders",
+                    "type": "table",
+                    "name": "orders",
+                    "properties": {
+                        "table_id": "table:postgresql://admin:s3cret@host/db:orders",
+                        "source": "postgresql://admin:s3cret@host/db",
+                        "row_count": 500,
+                    },
+                },
+            ],
+            "edges": [
+                {
+                    "id": "edge:contains:table:postgresql://admin:s3cret@host/db:orders:column:postgresql://admin:s3cret@host/db:orders:customer_id",
+                    "source_id": "table:postgresql://admin:s3cret@host/db:orders",
+                    "target_id": "column:postgresql://admin:s3cret@host/db:orders:customer_id",
+                    "type": "contains",
+                }
+            ],
+            "stats": {"node_count": 2, "edge_count": 1},
+        }
+
+        masked = mask_result(result)
+
+        # Node IDs must be masked
+        assert "s3cret" not in masked["nodes"][0]["id"]
+        assert "s3cret" not in masked["nodes"][1]["id"]
+
+        # properties.table_id must be masked (P0 leak point)
+        assert "s3cret" not in masked["nodes"][0]["properties"]["table_id"]
+        assert "s3cret" not in masked["nodes"][1]["properties"]["table_id"]
+
+        # properties.profile_id must be masked (P0 leak point)
+        assert "s3cret" not in masked["nodes"][0]["properties"]["profile_id"]
+
+        # properties.column_ids must be masked element-wise (P0 leak point)
+        for col_id in masked["nodes"][0]["properties"]["column_ids"]:
+            assert "s3cret" not in col_id
+
+        # properties.source must be masked
+        assert "s3cret" not in masked["nodes"][0]["properties"]["source"]
+
+        # Edge IDs and source/target must be masked
+        assert "s3cret" not in masked["edges"][0]["id"]
+        assert "s3cret" not in masked["edges"][0]["source_id"]
+        assert "s3cret" not in masked["edges"][0]["target_id"]
+
+        # Non-credential fields must be preserved
+        assert masked["nodes"][0]["name"] == "customer_id"
+        assert masked["nodes"][0]["properties"]["dtype"] == "integer"
+        assert masked["stats"]["node_count"] == 2
+
+    def test_mask_arbitrary_string_with_embedded_url(self):
+        """Catch-all: any string value containing a DB URL must be masked,
+        even if the key is not in the known ID/CREDENTIAL key sets.
+        """
+        result = {
+            "nodes": [
+                {
+                    "id": "table:postgresql://admin:s3cret@host/db:orders",
+                    "type": "table",
+                    "name": "orders",
+                    "properties": {
+                        "some_random_key": "postgresql://admin:s3cret@host/db",
+                        "another_field": "just a normal string",
+                    },
+                }
+            ]
+        }
+
+        masked = mask_result(result)
+        # Catch-all masking of strings containing DB URLs
+        assert "s3cret" not in masked["nodes"][0]["properties"]["some_random_key"]
+        assert "***" in masked["nodes"][0]["properties"]["some_random_key"]
+        # Normal strings are not modified
+        assert masked["nodes"][0]["properties"]["another_field"] == "just a normal string"
+
+    def test_mask_nested_empty(self):
+        assert mask_result([]) == []
+        assert mask_result({}) == {}
+        assert mask_result(None) is None
+
+
+class TestIsMaskedId:
+    """Test is_masked_id detection."""
+
+    def test_masked_id_detected(self):
+        assert is_masked_id("table:postgresql://***:***@host/db:orders")
+
+    def test_masked_id_with_just_user(self):
+        assert is_masked_id("table:postgresql://***@host/db:orders")
+
+    def test_unmasked_id_not_detected(self):
+        assert not is_masked_id("table:/home/user/data:orders")
+
+    def test_empty_string(self):
+        assert not is_masked_id("")
+
+    def test_real_credential_not_detected_as_masked(self):
+        # A real credential string contains :pass@, not :***@
+        assert not is_masked_id("table:postgresql://admin:pass@host/db:orders")
+
+
+class TestBuildIdMapping:
+    """Test build_id_mapping."""
+
+    def test_mapping_for_db_ids(self):
+        ids = {
+            "table:postgresql://admin:pass@host/db:orders",
+            "column:postgresql://admin:pass@host/db:orders:col",
+        }
+        mapping = build_id_mapping(ids)
+        assert "table:postgresql://***:***@host/db:orders" in mapping
+        assert mapping["table:postgresql://***:***@host/db:orders"] == "table:postgresql://admin:pass@host/db:orders"
+
+    def test_mapping_skips_non_maskable_ids(self):
+        ids = {"table:/home/data:orders"}
+        mapping = build_id_mapping(ids)
+        assert len(mapping) == 0  # no mapping needed for file paths
+
+    def test_mapping_is_deterministic(self):
+        ids = {"table:postgresql://admin:pass@host/db:orders"}
+        m1 = build_id_mapping(ids)
+        m2 = build_id_mapping(ids)
+        assert m1 == m2
+
+
+class TestResolveMaskedId:
+    """Test resolve_masked_id."""
+
+    def test_resolve_masked_to_real(self):
+        mapping = {"table:postgresql://***:***@host/db:orders": "table:postgresql://admin:pass@host/db:orders"}
+        masked = "table:postgresql://***:***@host/db:orders"
+        assert resolve_masked_id(masked, mapping) == "table:postgresql://admin:pass@host/db:orders"
+
+    def test_unmasked_id_returns_unchanged(self):
+        mapping = {"table:postgresql://***:***@host/db:orders": "table:postgresql://admin:pass@host/db:orders"}
+        real = "table:/home/data:orders"
+        assert resolve_masked_id(real, mapping) == "table:/home/data:orders"
+
+    def test_unknown_masked_id_returns_as_is(self):
+        mapping = {}
+        masked = "table:postgresql://***:***@host/db:unknown"
+        # Not in mapping — return as-is (will likely fail downstream)
+        assert resolve_masked_id(masked, mapping) == masked
+
+    def test_resolve_list(self):
+        mapping = {
+            "table:postgresql://***:***@host/db:orders": "table:postgresql://admin:pass@host/db:orders",
+            "column:postgresql://***:***@host/db:orders:col": "column:postgresql://admin:pass@host/db:orders:col",
+        }
+        result = resolve_masked_ids(
+            ["table:postgresql://***:***@host/db:orders", "table:/home/data:users"],
+            mapping,
+        )
+        assert result[0] == "table:postgresql://admin:pass@host/db:orders"
+        assert result[1] == "table:/home/data:users"
diff --git a/services/datalink/tests/test_data/orders.csv b/services/datalink/tests/test_data/orders.csv
new file mode 100644
index 00000000..4fd7ddcc
--- /dev/null
+++ b/services/datalink/tests/test_data/orders.csv
@@ -0,0 +1,11 @@
+order_id,customer_id,amount,order_date,status
+101,1,85.50,2024-01-20,completed
+102,2,120.00,2024-01-25,completed
+103,1,45.30,2024-02-10,completed
+104,3,200.00,2024-02-15,completed
+105,5,78.90,2024-03-01,completed
+106,2,156.40,2024-03-05,completed
+107,4,320.00,2024-03-10,completed
+108,1,55.00,2024-04-01,pending
+109,6,410.00,2024-04-15,completed
+110,3,92.50,2024-05-01,completed
diff --git a/services/datalink/tests/test_data/transactions.csv b/services/datalink/tests/test_data/transactions.csv
new file mode 100644
index 00000000..c95d14a3
--- /dev/null
+++ b/services/datalink/tests/test_data/transactions.csv
@@ -0,0 +1,11 @@
+tx_id,user_id,value,tx_timestamp,tx_type
+1001,1,85.50,2024-01-20T10:30:00,purchase
+1002,2,120.00,2024-01-25T14:00:00,purchase
+1003,1,45.30,2024-02-10T09:15:00,purchase
+1004,3,200.00,2024-02-15T16:45:00,purchase
+1005,5,78.90,2024-03-01T11:30:00,purchase
+1006,2,156.40,2024-03-05T13:20:00,purchase
+1007,4,320.00,2024-03-10T08:00:00,purchase
+1008,7,25.00,2024-03-20T17:30:00,refund
+1009,6,410.00,2024-04-15T12:00:00,purchase
+1010,8,150.00,2024-04-20T15:45:00,purchase
diff --git a/services/datalink/tests/test_data/users.csv b/services/datalink/tests/test_data/users.csv
new file mode 100644
index 00000000..e2d5a476
--- /dev/null
+++ b/services/datalink/tests/test_data/users.csv
@@ -0,0 +1,11 @@
+id,name,email,signup_date,age
+1,Alice,alice@example.com,2023-01-15,28
+2,Bob,bob@example.com,2023-03-20,35
+3,Charlie,charlie@example.com,2023-05-10,42
+4,Diana,diana@example.com,2023-07-01,19
+5,Eve,eve@example.com,2023-09-25,31
+6,Frank,frank@example.com,2023-11-30,55
+7,Grace,grace@example.com,2024-01-20,27
+8,Henry,henry@example.com,2024-03-15,38
+9,Ivy,ivy@example.com,2024-05-01,24
+10,Jack,jack@example.com,2024-07-10,46
diff --git a/services/datalink/tests/test_datafoundry_integration.py b/services/datalink/tests/test_datafoundry_integration.py
new file mode 100644
index 00000000..29f9a162
--- /dev/null
+++ b/services/datalink/tests/test_datafoundry_integration.py
@@ -0,0 +1,43 @@
+import asyncio
+import json
+
+from datalink.api.server import healthz
+from datalink.config import DataLinkConfig
+
+
+def test_datafoundry_environment_overrides_config(monkeypatch, tmp_path):
+    config_path = tmp_path / "datalink.json"
+    config_path.write_text(json.dumps({"llm": {"model": "file-model"}, "graph_db_path": "file.db"}))
+    graph_path = tmp_path / "storage" / "datalink.db"
+
+    monkeypatch.setenv("DATALINK_CONFIG_PATH", str(config_path))
+    monkeypatch.setenv("DATALINK_GRAPH_DB_PATH", str(graph_path))
+    monkeypatch.setenv("DATALINK_LLM_MODEL", "managed-model")
+    monkeypatch.setenv("DATALINK_LLM_API_KEY", "managed-key")
+    monkeypatch.setenv("DATALINK_EMBEDDING_MODEL", "managed-embedding")
+
+    config = DataLinkConfig.load()
+
+    assert config.graph_db_path == str(graph_path)
+    assert config.llm.model == "managed-model"
+    assert config.llm.api_key == "managed-key"
+    assert config.embedding.model == "managed-embedding"
+
+
+def test_datafoundry_model_environment_is_fallback(monkeypatch):
+    monkeypatch.setenv("LLM_MODEL", "datafoundry-model")
+    monkeypatch.setenv("LLM_BASE_URL", "https://model.example/v1")
+    monkeypatch.setenv("LLM_API_KEY", "datafoundry-key")
+
+    config = DataLinkConfig.load("/does/not/exist.json")
+
+    assert config.llm.model == "datafoundry-model"
+    assert config.llm.base_url == "https://model.example/v1"
+    assert config.llm.api_key == "datafoundry-key"
+
+
+def test_healthz_does_not_require_graph_configuration():
+    response = asyncio.run(healthz())
+
+    assert response.status_code == 200
+    assert json.loads(response.body) == {"status": "ok", "service": "datalink"}
diff --git a/services/datalink/tests/test_extractor.py b/services/datalink/tests/test_extractor.py
new file mode 100644
index 00000000..2279a1fd
--- /dev/null
+++ b/services/datalink/tests/test_extractor.py
@@ -0,0 +1,117 @@
+"""Tests for the tabular extractor."""
+
+from datalink.connector.file import FileConnector
+from datalink.extractor.tabular import TabularExtractor, generate_id
+from datalink.models.datasource import DatasourceConfig, DatasourceType
+from datalink.models.edge import EdgeType
+from datalink.models.node import NodeType
+
+
+class TestGenerateId:
+    """Test the ID generation function."""
+
+    def test_simple_id(self):
+        id_str = generate_id("table", "mydb", "orders")
+        assert id_str == "table:mydb:orders"
+
+    def test_column_id(self):
+        id_str = generate_id("column", "mydb", "orders", "customer_id")
+        assert id_str == "column:mydb:orders:customer_id"
+
+
+class TestTabularExtractor:
+    """Test the tabular data extractor."""
+
+    def test_extract_single_table(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test",
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        ds_info = connector.get_datasource_info()
+
+        extractor = TabularExtractor()
+        tables, columns, edges = extractor.extract(ds_info)
+
+        assert len(tables) == 1
+        assert tables[0].name == "users"
+        assert tables[0].type == NodeType.TABLE
+        assert tables[0].row_count == 10
+
+        # Should have 5 columns (id, name, email, signup_date, age)
+        assert len(columns) == 5
+        col_names = [c.name for c in columns]
+        assert "id" in col_names
+        assert "email" in col_names
+
+        # All columns should reference the table
+        for col in columns:
+            assert col.table_id == tables[0].id
+            assert col.type == NodeType.COLUMN
+
+        # Should have 5 contains edges + 0 FK edges
+        contains_edges = [e for e in edges if e.type == EdgeType.CONTAINS]
+        fk_edges = [e for e in edges if e.type == EdgeType.FOREIGN_KEY]
+        assert len(contains_edges) == 5
+        assert len(fk_edges) == 0
+
+    def test_extract_multiple_tables(self, all_csv_paths):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test",
+            path=str(all_csv_paths[0].parent),
+        )
+        connector = FileConnector(config)
+        ds_info = connector.get_datasource_info()
+
+        extractor = TabularExtractor()
+        tables, columns, edges = extractor.extract(ds_info)
+
+        assert len(tables) == 3
+        table_names = [t.name for t in tables]
+        assert "users" in table_names
+        assert "orders" in table_names
+        assert "transactions" in table_names
+
+        # Total columns: 5 (users) + 5 (orders) + 5 (transactions) = 15
+        assert len(columns) == 15
+
+        # Total contains edges = 15
+        contains_edges = [e for e in edges if e.type == EdgeType.CONTAINS]
+        assert len(contains_edges) == 15
+
+    def test_table_column_ids_consistency(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test",
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        ds_info = connector.get_datasource_info()
+
+        extractor = TabularExtractor()
+        tables, columns, _ = extractor.extract(ds_info)
+
+        # Table's column_ids should match the extracted columns
+        table = tables[0]
+        table_column_ids = set(table.column_ids)
+        extracted_column_ids = {c.id for c in columns}
+        assert table_column_ids == extracted_column_ids
+
+    def test_column_properties(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test",
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        ds_info = connector.get_datasource_info()
+
+        extractor = TabularExtractor()
+        _, columns, _ = extractor.extract(ds_info)
+
+        # Check column properties
+        id_col = [c for c in columns if c.name == "id"][0]
+        assert id_col.dtype == "integer"
+        assert id_col.comment == ""  # CSV has no comments
diff --git a/services/datalink/tests/test_ids.py b/services/datalink/tests/test_ids.py
new file mode 100644
index 00000000..55d1b9d0
--- /dev/null
+++ b/services/datalink/tests/test_ids.py
@@ -0,0 +1,56 @@
+"""Tests for node ID parsing with colon-rich sources."""
+
+from datalink.utils.ids import parse_column_id, parse_table_id
+
+
+class TestParseTableId:
+    def test_simple_path(self):
+        parsed = parse_table_id("table:/data/orders.csv:orders")
+        assert parsed is not None
+        assert parsed.source == "/data/orders.csv"
+        assert parsed.table_name == "orders"
+
+    def test_sqlite_url_with_hyphenated_table(self):
+        url = "sqlite:////home/datalink/datalink/dacomp-zh-006.sqlite"
+        parsed = parse_table_id(f"table:{url}:dacomp-zh-006")
+        assert parsed is not None
+        assert parsed.source == url
+        assert parsed.table_name == "dacomp-zh-006"
+
+    def test_postgres_url_with_credentials(self):
+        url = "postgresql://admin:s3cret@host:5432/mydb"
+        parsed = parse_table_id(f"table:{url}:orders")
+        assert parsed is not None
+        assert parsed.source == url
+        assert parsed.table_name == "orders"
+
+    def test_invalid(self):
+        assert parse_table_id("column:x:y:z") is None
+        assert parse_table_id("table:nosegments") is None
+
+
+class TestParseColumnId:
+    def test_simple(self):
+        parsed = parse_column_id("column:/data/file.csv:users:email")
+        assert parsed is not None
+        assert parsed.source == "/data/file.csv"
+        assert parsed.table_name == "users"
+        assert parsed.column_name == "email"
+
+    def test_sqlite_url(self):
+        url = "sqlite:////home/datalink/datalink/dacomp-zh-006.sqlite"
+        parsed = parse_column_id(f"column:{url}:dacomp-zh-006:amount")
+        assert parsed is not None
+        assert parsed.source == url
+        assert parsed.table_name == "dacomp-zh-006"
+        assert parsed.column_name == "amount"
+
+    def test_naive_split_would_fail(self):
+        """Document why split(':')[2] is wrong for sqlite URLs."""
+        col_id = "column:sqlite:////home/datalink/x.sqlite:dacomp-zh-006:amount"
+        naive = col_id.split(":")
+        assert naive[2] == "////home/datalink/x.sqlite"  # path fragment — not table name
+        parsed = parse_column_id(col_id)
+        assert parsed is not None
+        assert parsed.table_name == "dacomp-zh-006"
+        assert parsed.column_name == "amount"
diff --git a/services/datalink/tests/test_inferrer.py b/services/datalink/tests/test_inferrer.py
new file mode 100644
index 00000000..ea706a00
--- /dev/null
+++ b/services/datalink/tests/test_inferrer.py
@@ -0,0 +1,219 @@
+"""Tests for relationship inferrers."""
+
+from datalink.connector.file import FileConnector
+from datalink.extractor.tabular import TabularExtractor
+from datalink.inferrer.correlated import CorrelationInferrer
+from datalink.inferrer.distribution import DistributionInferrer
+from datalink.inferrer.joinable import JoinableInferrer
+from datalink.inferrer.synonym import SynonymInferrer
+from datalink.models.datasource import DatasourceConfig, DatasourceType
+from datalink.models.edge import EdgeType
+from datalink.profiler.tabular import TabularProfiler
+
+
+def _build_test_graph(all_csv_paths):
+    """Helper to build nodes, profiles from test data."""
+    config = DatasourceConfig(
+        type=DatasourceType.CSV,
+        name="test",
+        path=str(all_csv_paths[0].parent),
+    )
+    connector = FileConnector(config)
+    ds_info = connector.get_datasource_info()
+
+    extractor = TabularExtractor()
+    tables, columns, edges = extractor.extract(ds_info)
+
+    profiler = TabularProfiler()
+    profiles = profiler.profile_datasource(ds_info)
+
+    return tables, columns, edges, profiles, ds_info
+
+
+class TestJoinableInferrer:
+    """Test the joinable relationship inference."""
+
+    def test_find_joinable_columns(self, all_csv_paths):
+        _, columns, _, profiles, _ = _build_test_graph(all_csv_paths)
+
+        inferrer = JoinableInferrer(overlap_threshold=0.1)
+        joinable_edges = inferrer.infer(columns, profiles)
+
+        # Should find at least some joinable columns
+        # (users.id ↔ orders.customer_id ↔ transactions.user_id share values)
+        assert len(joinable_edges) > 0
+
+        # Check edge type
+        for edge in joinable_edges:
+            assert edge.type == EdgeType.JOINABLE
+            assert edge.confidence > 0.0
+
+    def test_joinable_confidence_is_overlap_rate(self, all_csv_paths):
+        _, columns, _, profiles, _ = _build_test_graph(all_csv_paths)
+
+        inferrer = JoinableInferrer(overlap_threshold=0.1)
+        joinable_edges = inferrer.infer(columns, profiles)
+
+        # Confidence should equal the overlap rate
+        for edge in joinable_edges:
+            assert edge.confidence == edge.properties["overlap_rate"]
+
+    def test_no_joinable_for_high_cardinality(self):
+        """Columns with very high cardinality should be skipped."""
+        from datalink.models.node import ColumnNode
+        from datalink.models.profile import ColumnProfile
+
+        col_a = ColumnNode(id="col:t1:uuid_a", name="uuid_a", table_id="t1", dtype="string")
+        col_b = ColumnNode(id="col:t2:uuid_b", name="uuid_b", table_id="t2", dtype="string")
+
+        # Very high cardinality profiles
+        profile_a = ColumnProfile(
+            id="p_a",
+            column_id=col_a.id,
+            dtype="string",
+            cardinality=50000,
+            top_values=[],
+            sample_values=["abc1", "abc2"],
+        )
+        profile_b = ColumnProfile(
+            id="p_b",
+            column_id=col_b.id,
+            dtype="string",
+            cardinality=50000,
+            top_values=[],
+            sample_values=["def1", "def2"],
+        )
+
+        inferrer = JoinableInferrer(overlap_threshold=0.1, max_cardinality=1000)
+        edges = inferrer.infer([col_a, col_b], [profile_a, profile_b])
+
+        assert len(edges) == 0  # Should skip high-cardinality columns
+
+    def test_incompatible_dtypes_skip(self):
+        """Incompatible dtype pairs should be skipped."""
+        from datalink.models.node import ColumnNode
+        from datalink.models.profile import ColumnProfile
+
+        col_int = ColumnNode(id="col:t1:age", name="age", table_id="t1", dtype="integer")
+        col_str = ColumnNode(id="col:t2:name", name="name", table_id="t2", dtype="string")
+
+        profile_int = ColumnProfile(
+            id="p_int",
+            column_id=col_int.id,
+            dtype="integer",
+            cardinality=5,
+            top_values=[{"value": "25", "count": 2, "fraction": 0.2}],
+            sample_values=[25, 30, 35],
+        )
+        profile_str = ColumnProfile(
+            id="p_str",
+            column_id=col_str.id,
+            dtype="string",
+            cardinality=5,
+            top_values=[{"value": "Alice", "count": 1, "fraction": 0.1}],
+            sample_values=["Alice", "Bob"],
+        )
+
+        # integer and string are considered compatible (identifiers)
+        # but name (person_name semantic type) vs age is clearly not
+        # The compatibility check only checks dtypes, not semantic types
+        # So integer and string are considered compatible for joinability
+        inferrer = JoinableInferrer(overlap_threshold=0.1)
+        # This would depend on overlap rate, which is 0 here
+        edges = inferrer.infer([col_int, col_str], [profile_int, profile_str])
+        assert len(edges) == 0  # No overlap
+
+
+class TestSynonymInferrer:
+    """Test the semantic synonym inference."""
+
+    def test_find_synonym_columns(self, all_csv_paths):
+        _, columns, _, profiles, _ = _build_test_graph(all_csv_paths)
+
+        inferrer = SynonymInferrer()
+        synonym_edges = inferrer.infer(columns, profiles)
+
+        # Should find synonyms (customer_id ↔ user_id are in synonym groups)
+        assert len(synonym_edges) > 0
+
+    def test_synonym_group_match(self):
+        """Columns in the same synonym group should get high confidence."""
+        from datalink.models.node import ColumnNode
+        from datalink.models.profile import ColumnProfile
+
+        col_a = ColumnNode(id="col:t1:customer_id", name="customer_id", table_id="t1", dtype="integer")
+        col_b = ColumnNode(id="col:t2:user_id", name="user_id", table_id="t2", dtype="integer")
+
+        profile_a = ColumnProfile(
+            id="p_a",
+            column_id=col_a.id,
+            dtype="integer",
+            semantic_type="identifier",
+        )
+        profile_b = ColumnProfile(
+            id="p_b",
+            column_id=col_b.id,
+            dtype="integer",
+            semantic_type="identifier",
+        )
+
+        inferrer = SynonymInferrer()
+        edges = inferrer.infer([col_a, col_b], [profile_a, profile_b])
+
+        assert len(edges) == 1
+        assert edges[0].confidence >= 0.80  # High confidence (group match + type match)
+
+    def test_same_table_not_compared(self):
+        """Columns in the same table should not be compared for synonyms."""
+        from datalink.models.node import ColumnNode
+        from datalink.models.profile import ColumnProfile
+
+        col_a = ColumnNode(id="col:t1:id", name="id", table_id="t1", dtype="integer")
+        col_b = ColumnNode(id="col:t1:name", name="name", table_id="t1", dtype="string")
+
+        profile_a = ColumnProfile(id="p_a", column_id=col_a.id, dtype="integer")
+        profile_b = ColumnProfile(id="p_b", column_id=col_b.id, dtype="string")
+
+        inferrer = SynonymInferrer()
+        edges = inferrer.infer([col_a, col_b], [profile_a, profile_b])
+
+        assert len(edges) == 0  # Same table, no synonym comparison
+
+
+class TestDistributionInferrer:
+    """Test distribution similarity inference."""
+
+    def test_find_similar_distributions(self, all_csv_paths):
+        _, columns, _, profiles, _ = _build_test_graph(all_csv_paths)
+
+        inferrer = DistributionInferrer(similarity_threshold=0.3)
+        dist_edges = inferrer.infer(columns, profiles)
+
+        # Should find some distribution similarities
+        # (order_date and tx_timestamp might have similar temporal ranges)
+        for edge in dist_edges:
+            assert edge.type == EdgeType.DISTRIBUTION_SIMILAR
+            assert edge.confidence > 0.3
+
+
+class TestCorrelationInferrer:
+    """Test correlation inference."""
+
+    def test_correlation_requires_joinable(self, all_csv_paths):
+        _, columns, _, profiles, ds_info = _build_test_graph(all_csv_paths)
+
+        # First find joinable edges
+        joinable_inferrer = JoinableInferrer(overlap_threshold=0.1)
+        joinable_edges = joinable_inferrer.infer(columns, profiles)
+
+        # Then find correlations (requires joinable edges and datasource infos as input)
+        corr_inferrer = CorrelationInferrer(correlation_threshold=0.3)
+        corr_edges = corr_inferrer.infer(columns, profiles, joinable_edges, [ds_info])
+
+        # Correlated edges connect non-key numeric columns, not join keys themselves
+        # They should reference a joinable edge in their properties
+        for edge in corr_edges:
+            assert "joinable_edge" in edge.properties
+            assert "join_key_a" in edge.properties
+            assert "join_key_b" in edge.properties
+            assert edge.properties["method"] == "pearson"
diff --git a/services/datalink/tests/test_mapper.py b/services/datalink/tests/test_mapper.py
new file mode 100644
index 00000000..60409b94
--- /dev/null
+++ b/services/datalink/tests/test_mapper.py
@@ -0,0 +1,993 @@
+"""Tests for semantic mapper modules."""
+
+import json
+from unittest.mock import MagicMock, patch
+
+from datalink.config import DataLinkConfig, EmbeddingConfig, LLMConfig
+from datalink.mapper.llm_mapper import LLMMapper
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode, ConceptNode, EntityNode, NodeType
+from datalink.models.profile import ColumnProfile
+
+
+def _make_config(
+    llm_model="gpt-4o",
+    embedding_model="",
+    similarity_threshold=0.75,
+    confidence_threshold=0.3,
+    merge_llm_temperature=0.0,
+    merge_batch_interval=1,
+) -> DataLinkConfig:
+    """Helper to build a DataLinkConfig for tests."""
+    return DataLinkConfig(
+        llm=LLMConfig(model=llm_model),
+        embedding=EmbeddingConfig(
+            model=embedding_model,
+            similarity_threshold=similarity_threshold,
+        ),
+        confidence_threshold=confidence_threshold,
+        merge_llm_temperature=merge_llm_temperature,
+        merge_batch_interval=merge_batch_interval,
+    )
+
+
+class TestLLMMapperParsing:
+    """Test the LLM mapper's response parsing logic (without real LLM calls)."""
+
+    def test_parse_valid_json_response(self):
+        """Valid JSON response should be parsed correctly."""
+        mapper = LLMMapper(_make_config())
+
+        response = """
+Here is the analysis:
+
+{
+  "concepts": [
+    {
+      "name": "person_identifier",
+      "description": "A unique identifier for a person",
+      "unit": "",
+      "dimension": "identifier",
+      "columns": ["col:test:orders:customer_id", "col:test:transactions:user_id"],
+      "confidence": 0.9
+    },
+    {
+      "name": "monetary_value",
+      "description": "A monetary amount",
+      "unit": "USD",
+      "dimension": "monetary",
+      "columns": ["col:test:orders:amount", "col:test:transactions:value"],
+      "confidence": 0.85
+    }
+  ],
+  "entities": [
+    {
+      "name": "customer",
+      "description": "A person who makes purchases",
+      "concept_names": ["person_identifier", "person_name", "email_address"],
+      "confidence": 0.8
+    }
+  ]
+}
+"""
+
+        parsed = mapper._parse_response(response)
+        assert parsed is not None
+        assert len(parsed["concepts"]) == 2
+        assert len(parsed["entities"]) == 1
+        assert parsed["concepts"][0]["name"] == "person_identifier"
+
+    def test_parse_response_without_json(self):
+        """Response without JSON should return None."""
+        mapper = LLMMapper(_make_config())
+        parsed = mapper._parse_response("This is just text, no JSON here.")
+        assert parsed is None
+
+    def test_parse_response_with_missing_keys(self):
+        """Response with missing required keys should return None."""
+        mapper = LLMMapper(_make_config())
+        parsed = mapper._parse_response('{"concepts": []}')
+        assert parsed is None  # Missing "entities" key
+
+    def test_build_nodes_and_edges(self):
+        """Parsed response should be correctly converted to nodes and edges."""
+        mapper = LLMMapper(_make_config())
+
+        parsed = {
+            "concepts": [
+                {
+                    "name": "person_identifier",
+                    "description": "Unique person ID",
+                    "unit": "",
+                    "dimension": "identifier",
+                    "columns": ["col:test:orders:customer_id"],
+                    "confidence": 0.9,
+                }
+            ],
+            "entities": [
+                {
+                    "name": "customer",
+                    "description": "A person",
+                    "concept_names": ["person_identifier"],
+                    "confidence": 0.8,
+                }
+            ],
+        }
+
+        columns = [
+            ColumnNode(id="col:test:orders:customer_id", name="customer_id", table_id="t1", dtype="int"),
+        ]
+        profile_map = {}
+
+        concepts, entities, edges = mapper._build_nodes_and_edges(parsed, columns, profile_map)
+
+        assert len(concepts) == 1
+        assert concepts[0].name == "person_identifier"
+        assert concepts[0].type == NodeType.CONCEPT
+
+        assert len(entities) == 1
+        assert entities[0].name == "customer"
+
+        # Should have 2 edges: represents + has_concept
+        assert len(edges) == 2
+        represents_edges = [e for e in edges if e.type == EdgeType.REPRESENTS]
+        has_concept_edges = [e for e in edges if e.type == EdgeType.HAS_CONCEPT]
+        assert len(represents_edges) == 1
+        assert len(has_concept_edges) == 1
+
+    def test_build_columns_data(self):
+        """Column data should be formatted correctly for the LLM prompt."""
+        mapper = LLMMapper(_make_config())
+
+        columns = [
+            ColumnNode(id="col:test:orders:amount", name="amount", table_id="t1", dtype="float"),
+        ]
+        profiles = [
+            ColumnProfile(
+                id="p:test:orders:amount",
+                column_id="col:test:orders:amount",
+                dtype="float",
+                semantic_type="monetary_value",
+                sample_values=[85.5, 120.0],
+            ),
+        ]
+        profile_map = {p.column_id: p for p in profiles}
+
+        data = mapper._build_columns_data(columns, profile_map)
+        assert "amount" in data
+        assert "monetary_value" in data
+
+    def test_no_api_key_returns_none(self):
+        """LLM call without API key should return None."""
+        mapper = LLMMapper(_make_config(llm_model="gpt-4o"))
+        # Default LLMConfig has empty api_key
+        result = mapper._call_llm("test prompt")
+        assert result is None
+
+
+class TestMergeWithExisting:
+    """Test the merge_with_existing logic with LLM + Embedding approach."""
+
+    def test_merge_empty_existing(self):
+        """When no existing nodes, merge should return all new nodes unchanged."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        new_concepts = [
+            ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+        ]
+        new_entities = [
+            EntityNode(id="entity:customer", name="customer", description="A customer"),
+        ]
+        new_edges = [
+            Edge(
+                id="edge:represents:col1:concept:person_id",
+                source_id="col1",
+                target_id="concept:person_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            ),
+        ]
+
+        result_concepts, result_entities, result_edges = mapper.merge_with_existing(
+            new_concepts,
+            new_entities,
+            new_edges,
+            [],
+            [],
+        )
+
+        assert len(result_concepts) == 1
+        assert len(result_entities) == 1
+        assert len(result_edges) == 1
+
+    def test_merge_empty_new_preserves_existing(self):
+        """When no new nodes, merge should preserve the accumulated graph."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        existing_concepts = [
+            ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+        ]
+
+        result_concepts, result_entities, result_edges = mapper.merge_with_existing(
+            [],
+            [],
+            [],
+            existing_concepts,
+            [],
+        )
+
+        assert len(result_concepts) == 1
+        assert result_concepts[0].id == "concept:person_id"
+        assert len(result_entities) == 0
+        assert len(result_edges) == 0
+
+    def test_execute_merge_plan_concept_merge(self):
+        """Execute a merge plan that merges one concept into an existing one."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        existing_concepts = [
+            ConceptNode(id="concept:person_identifier", name="person_identifier", description="Person ID"),
+        ]
+        existing_entities = []
+
+        new_concepts = [
+            ConceptNode(
+                id="concept:customer_id",
+                name="customer_id",
+                description="A unique identifier for a customer or person",
+            ),
+        ]
+        new_entities = []
+
+        new_edges = [
+            Edge(
+                id="edge:represents:col1:concept:customer_id",
+                source_id="col1",
+                target_id="concept:customer_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            ),
+        ]
+
+        merge_plan = {
+            "merges": [
+                {
+                    "new_id": "concept:customer_id",
+                    "existing_id": "concept:person_identifier",
+                    "reason": "both represent person identifier",
+                    "confidence": 0.95,
+                }
+            ],
+            "new_kept": [],
+        }
+
+        final_concepts, final_entities, final_edges = mapper._execute_merge_plan(
+            merge_plan,
+            new_concepts,
+            new_entities,
+            new_edges,
+            existing_concepts,
+            existing_entities,
+        )
+
+        # New concept should be absorbed; existing concept remains
+        assert len(final_concepts) == 1
+        assert final_concepts[0].id == "concept:person_identifier"
+
+        # Existing concept should have enriched description
+        assert "customer or person" in final_concepts[0].description
+
+        # Edge should be redirected to existing concept
+        assert len(final_edges) == 1
+        assert final_edges[0].target_id == "concept:person_identifier"
+        assert final_edges[0].id == "edge:represents:col1:concept:person_identifier"
+
+    def test_execute_merge_plan_entity_and_concept(self):
+        """Execute a merge plan that merges both entity and concept."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        existing_concepts = [
+            ConceptNode(id="concept:person_name", name="person_name", description="Name of a person"),
+        ]
+        existing_entities = [
+            EntityNode(id="entity:customer", name="customer", description="A person who purchases"),
+        ]
+
+        new_concepts = [
+            ConceptNode(id="concept:full_name", name="full_name", description="Full legal name of a person"),
+        ]
+        new_entities = [
+            EntityNode(id="entity:user", name="user", description="A person who uses the system and makes purchases"),
+        ]
+
+        new_edges = [
+            Edge(
+                id="edge:represents:col1:concept:full_name",
+                source_id="col1",
+                target_id="concept:full_name",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            ),
+            Edge(
+                id="edge:has_concept:entity:user:concept:full_name",
+                source_id="entity:user",
+                target_id="concept:full_name",
+                type=EdgeType.HAS_CONCEPT,
+                confidence=0.8,
+            ),
+        ]
+
+        merge_plan = {
+            "merges": [
+                {
+                    "new_id": "concept:full_name",
+                    "existing_id": "concept:person_name",
+                    "reason": "both represent a person's name",
+                    "confidence": 0.95,
+                },
+                {
+                    "new_id": "entity:user",
+                    "existing_id": "entity:customer",
+                    "reason": "both represent a person who makes purchases",
+                    "confidence": 0.85,
+                },
+            ],
+            "new_kept": [],
+        }
+
+        final_concepts, final_entities, final_edges = mapper._execute_merge_plan(
+            merge_plan,
+            new_concepts,
+            new_entities,
+            new_edges,
+            existing_concepts,
+            existing_entities,
+        )
+
+        # Both new nodes merged into existing → existing nodes remain
+        assert len(final_concepts) == 1
+        assert final_concepts[0].id == "concept:person_name"
+        assert len(final_entities) == 1
+        assert final_entities[0].id == "entity:customer"
+
+        # Both edges should be redirected
+        represents = [e for e in final_edges if e.type == EdgeType.REPRESENTS]
+        has_concept = [e for e in final_edges if e.type == EdgeType.HAS_CONCEPT]
+
+        assert len(represents) == 1
+        assert represents[0].target_id == "concept:person_name"
+
+        assert len(has_concept) == 1
+        assert has_concept[0].source_id == "entity:customer"
+        assert has_concept[0].target_id == "concept:person_name"
+
+    def test_execute_merge_plan_partial_keep(self):
+        """Some new nodes merged, some kept as genuinely new."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        existing_concepts = [
+            ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+        ]
+        existing_entities = []
+
+        new_concepts = [
+            ConceptNode(id="concept:customer_id", name="customer_id", description="Customer ID"),
+            ConceptNode(id="concept:order_id", name="order_id", description="Order identifier"),
+        ]
+        new_entities = []
+
+        new_edges = [
+            Edge(
+                id="edge:represents:col1:concept:customer_id",
+                source_id="col1",
+                target_id="concept:customer_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            ),
+            Edge(
+                id="edge:represents:col2:concept:order_id",
+                source_id="col2",
+                target_id="concept:order_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.85,
+            ),
+        ]
+
+        merge_plan = {
+            "merges": [
+                {
+                    "new_id": "concept:customer_id",
+                    "existing_id": "concept:person_id",
+                    "reason": "both represent person identifier",
+                    "confidence": 0.95,
+                },
+            ],
+            "new_kept": ["concept:order_id"],
+        }
+
+        final_concepts, final_entities, final_edges = mapper._execute_merge_plan(
+            merge_plan,
+            new_concepts,
+            new_entities,
+            new_edges,
+            existing_concepts,
+            existing_entities,
+        )
+
+        # customer_id merged → person_id remains; order_id kept as new
+        assert len(final_concepts) == 2
+        concept_ids = [c.id for c in final_concepts]
+        assert "concept:person_id" in concept_ids
+        assert "concept:order_id" in concept_ids
+
+        # Two edges: one redirected, one unchanged
+        assert len(final_edges) == 2
+        redirected = [e for e in final_edges if e.target_id == "concept:person_id"]
+        unchanged = [e for e in final_edges if e.target_id == "concept:order_id"]
+        assert len(redirected) == 1
+        assert len(unchanged) == 1
+
+    def test_llm_merge_judge_prompt_building(self):
+        """Test that the merge prompt is built correctly from node data."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        new_concepts = [
+            ConceptNode(
+                id="concept:customer_id", name="customer_id", description="Customer ID", unit="", dimension="identifier"
+            ),
+        ]
+
+        data = mapper._build_merge_node_data(new_concepts, "concept")
+        parsed = json.loads(data.split("\n")[0])
+
+        assert parsed["id"] == "concept:customer_id"
+        assert parsed["name"] == "customer_id"
+        assert parsed["dimension"] == "identifier"
+
+    def test_llm_merge_judge_with_mocked_llm(self):
+        """Test merge_with_existing with a mocked LLM response."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        # Mock _call_llm to return a merge response
+        merge_response = json.dumps(
+            {
+                "merges": [
+                    {
+                        "new_id": "concept:customer_id",
+                        "existing_id": "concept:person_id",
+                        "reason": "both represent person identifier",
+                        "confidence": 0.95,
+                    },
+                ],
+                "new_kept": ["concept:order_id"],
+            }
+        )
+
+        mapper._call_llm = MagicMock(return_value=merge_response)
+
+        new_concepts = [
+            ConceptNode(id="concept:customer_id", name="customer_id", description="Customer ID"),
+            ConceptNode(id="concept:order_id", name="order_id", description="Order identifier"),
+        ]
+        existing_concepts = [
+            ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+        ]
+
+        result_concepts, result_entities, result_edges = mapper.merge_with_existing(
+            new_concepts,
+            [],
+            [],
+            existing_concepts,
+            [],
+        )
+
+        # customer_id should be merged (absorbed into person_id), order_id kept
+        # result includes existing + new kept nodes
+        assert len(result_concepts) == 2
+        concept_ids = [c.id for c in result_concepts]
+        assert "concept:person_id" in concept_ids
+        assert "concept:order_id" in concept_ids
+
+    def test_merge_llm_failure_fallback(self):
+        """When LLM call fails, all new nodes should be kept as-is."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        # Mock _call_llm to return None (failure)
+        mapper._call_llm = MagicMock(return_value=None)
+
+        new_concepts = [
+            ConceptNode(id="concept:customer_id", name="customer_id", description="Customer ID"),
+        ]
+        existing_concepts = [
+            ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+        ]
+
+        result_concepts, result_entities, result_edges = mapper.merge_with_existing(
+            new_concepts,
+            [],
+            [],
+            existing_concepts,
+            [],
+        )
+
+        # LLM failed → no merge, all new nodes kept
+        assert len(result_concepts) == 1
+        assert result_concepts[0].id == "concept:customer_id"
+
+
+class TestEmbeddingPreFilter:
+    """Test the embedding pre-filter logic."""
+
+    def test_embedding_prefilter_no_config(self):
+        """Without embedding model configured, pre-filter returns empty list."""
+        config = _make_config(embedding_model="")
+        mapper = LLMMapper(config)
+
+        new_concepts = [
+            ConceptNode(id="concept:customer_id", name="customer_id", description="Customer ID"),
+        ]
+        existing_concepts = [
+            ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+        ]
+
+        candidates = mapper._embedding_prefilter(new_concepts, [], existing_concepts, [])
+        assert len(candidates) == 0
+
+    def test_embedding_prefilter_with_mocked_api(self):
+        """Test embedding pre-filter with mocked OpenAI embeddings API."""
+        config = _make_config(
+            llm_model="gpt-4o",
+            embedding_model="text-embedding-3-small",
+            similarity_threshold=0.75,
+        )
+        # Provide a fake API key so _embedding_prefilter doesn't bail out
+        config.llm.api_key = "fake-key-for-test"
+        mapper = LLMMapper(config)
+        mapper = LLMMapper(config)
+
+        # Create mock embeddings: make customer_id and person_id similar,
+        # order_id dissimilar
+        mock_new_embeddings = [
+            [0.9, 0.1, 0.1],  # customer_id — similar to person_id
+            [0.1, 0.9, 0.1],  # order_id — dissimilar
+        ]
+        mock_existing_embeddings = [
+            [0.85, 0.15, 0.1],  # person_id — similar to customer_id
+        ]
+
+        mock_new_response = MagicMock()
+        mock_new_response.data = [
+            MagicMock(embedding=mock_new_embeddings[0]),
+            MagicMock(embedding=mock_new_embeddings[1]),
+        ]
+        mock_existing_response = MagicMock()
+        mock_existing_response.data = [
+            MagicMock(embedding=mock_existing_embeddings[0]),
+        ]
+
+        mock_client = MagicMock()
+        mock_client.embeddings.create = MagicMock(side_effect=[mock_new_response, mock_existing_response])
+
+        with patch("openai.OpenAI", return_value=mock_client):
+            new_concepts = [
+                ConceptNode(id="concept:customer_id", name="customer_id", description="Customer ID"),
+                ConceptNode(id="concept:order_id", name="order_id", description="Order identifier"),
+            ]
+            existing_concepts = [
+                ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+            ]
+
+            candidates = mapper._embedding_prefilter(
+                new_concepts,
+                [],
+                existing_concepts,
+                [],
+            )
+
+            # Only customer_id ↔ person_id should be a candidate
+            assert len(candidates) == 1
+            assert candidates[0]["new_id"] == "concept:customer_id"
+            assert candidates[0]["existing_id"] == "concept:person_id"
+            assert candidates[0]["similarity"] >= 0.75
+
+    def test_embedding_prefilter_api_failure(self):
+        """When embedding API fails, pre-filter returns empty list."""
+        config = _make_config(embedding_model="text-embedding-3-small")
+        config.llm.api_key = "fake-key-for-test"
+        mapper = LLMMapper(config)
+
+        with patch("openai.OpenAI", side_effect=Exception("API error")):
+            new_concepts = [
+                ConceptNode(id="concept:customer_id", name="customer_id", description="Customer ID"),
+            ]
+            existing_concepts = [
+                ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+            ]
+
+            candidates = mapper._embedding_prefilter(new_concepts, [], existing_concepts, [])
+            assert len(candidates) == 0
+
+    def test_node_to_embedding_text_concept(self):
+        """Concept node should produce rich embedding text."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        concept = ConceptNode(
+            id="concept:revenue",
+            name="revenue",
+            description="Total income from sales",
+            unit="USD",
+            dimension="monetary",
+        )
+        text = mapper._node_to_embedding_text(concept)
+        assert "revenue" in text
+        assert "Total income" in text
+        assert "USD" in text
+        assert "monetary" in text
+
+    def test_node_to_embedding_text_entity(self):
+        """Entity node should produce embedding text with description."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        entity = EntityNode(
+            id="entity:customer",
+            name="customer",
+            description="A person who purchases products",
+        )
+        text = mapper._node_to_embedding_text(entity)
+        assert "customer" in text
+        assert "purchases" in text
+
+    def test_cosine_similarity_identical(self):
+        """Cosine similarity of identical vectors should be 1.0."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        vec = [1.0, 2.0, 3.0]
+        sim = mapper._cosine_similarity(vec, vec)
+        assert abs(sim - 1.0) < 0.001
+
+    def test_cosine_similarity_orthogonal(self):
+        """Cosine similarity of orthogonal vectors should be 0.0."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        sim = mapper._cosine_similarity([1.0, 0.0], [0.0, 1.0])
+        assert abs(sim - 0.0) < 0.001
+
+    def test_cosine_similarity_zero_vector(self):
+        """Cosine similarity with zero vector should be 0.0."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        sim = mapper._cosine_similarity([0.0, 0.0], [1.0, 2.0])
+        assert sim == 0.0
+
+
+class TestEdgeDeduplication:
+    """Test edge deduplication after merge redirection."""
+
+    def test_deduplicate_keeps_highest_confidence(self):
+        """Among duplicate edges, keep the one with highest confidence."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        edges = [
+            Edge(
+                id="edge:represents:col1:concept:person_id",
+                source_id="col1",
+                target_id="concept:person_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.7,
+            ),
+            Edge(
+                id="edge:represents:col1:concept:person_id_v2",
+                source_id="col1",
+                target_id="concept:person_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.95,
+            ),
+        ]
+
+        deduped = mapper._deduplicate_edges(edges)
+        assert len(deduped) == 1
+        assert deduped[0].confidence == 0.95
+
+    def test_deduplicate_different_types_not_merged(self):
+        """Edges with different types should not be deduplicated."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        edges = [
+            Edge(
+                id="edge:represents:col1:concept:person_id",
+                source_id="col1",
+                target_id="concept:person_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            ),
+            Edge(
+                id="edge:has_concept:entity:customer:concept:person_id",
+                source_id="entity:customer",
+                target_id="concept:person_id",
+                type=EdgeType.HAS_CONCEPT,
+                confidence=0.8,
+            ),
+        ]
+
+        deduped = mapper._deduplicate_edges(edges)
+        assert len(deduped) == 2
+
+    def test_full_merge_with_deduplication(self):
+        """After merge + redirection, edges should be deduplicated."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        merge_response = json.dumps(
+            {
+                "merges": [
+                    {
+                        "new_id": "concept:full_name",
+                        "existing_id": "concept:person_name",
+                        "reason": "same concept",
+                        "confidence": 0.95,
+                    },
+                ],
+                "new_kept": [],
+            }
+        )
+        mapper._call_llm = MagicMock(return_value=merge_response)
+
+        existing_concepts = [
+            ConceptNode(id="concept:person_name", name="person_name", description="Name of person"),
+        ]
+        new_concepts = [
+            ConceptNode(id="concept:full_name", name="full_name", description="Full name"),
+        ]
+        # Two edges both targeting concept:full_name → after redirect both point to concept:person_name
+        new_edges = [
+            Edge(
+                id="edge:represents:col1:concept:full_name",
+                source_id="col1",
+                target_id="concept:full_name",
+                type=EdgeType.REPRESENTS,
+                confidence=0.7,
+            ),
+            Edge(
+                id="edge:represents:col2:concept:full_name",
+                source_id="col2",
+                target_id="concept:full_name",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            ),
+        ]
+
+        result_concepts, result_entities, result_edges = mapper.merge_with_existing(
+            new_concepts,
+            [],
+            new_edges,
+            existing_concepts,
+            [],
+        )
+
+        # Both edges now point to concept:person_name but have different source_ids
+        # → NOT duplicates, both should be kept
+        assert len(result_edges) == 2
+        for e in result_edges:
+            assert e.target_id == "concept:person_name"
+
+
+class TestMergeExistingNodesNotLost:
+    """Verify that existing nodes are NOT lost after merge_with_existing.
+
+    This tests the bug where _execute_merge_plan only returned new nodes,
+    causing previously accumulated concepts/entities to disappear.
+    """
+
+    def test_existing_concepts_preserved_after_merge(self):
+        """Existing concepts should appear in the merge result."""
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        merge_response = json.dumps(
+            {
+                "merges": [
+                    {
+                        "new_id": "concept:customer_id",
+                        "existing_id": "concept:person_id",
+                        "reason": "same",
+                        "confidence": 0.95,
+                    },
+                    {"new_id": "entity:user", "existing_id": "entity:customer", "reason": "same", "confidence": 0.9},
+                ],
+                "new_kept": ["concept:email"],
+            }
+        )
+        mapper._call_llm = MagicMock(return_value=merge_response)
+
+        existing_concepts = [
+            ConceptNode(id="concept:person_id", name="person_id", description="Person ID"),
+        ]
+        existing_entities = [
+            EntityNode(id="entity:customer", name="customer", description="A customer"),
+        ]
+        new_concepts = [
+            ConceptNode(id="concept:customer_id", name="customer_id", description="Customer ID"),
+            ConceptNode(id="concept:email", name="email", description="Email address"),
+        ]
+        new_entities = [
+            EntityNode(id="entity:user", name="user", description="A user"),
+        ]
+
+        result_c, result_e, result_edges = mapper.merge_with_existing(
+            new_concepts,
+            new_entities,
+            [],
+            existing_concepts,
+            existing_entities,
+        )
+
+        # person_id (existing) + email (new kept) = 2 concepts
+        assert len(result_c) == 2
+        assert any(c.id == "concept:person_id" for c in result_c)
+        assert any(c.id == "concept:email" for c in result_c)
+
+        # customer (existing, enriched) = 1 entity
+        assert len(result_e) == 1
+        assert result_e[0].id == "entity:customer"
+
+    def test_existing_entities_not_lost_in_batch_accumulation(self):
+        """Simulating the multi-batch scenario from the real logs:
+        batch 1 produces entity, batch 2 merges a similar entity into it.
+        The existing entity should survive in the accumulated result.
+        """
+        config = _make_config()
+        mapper = LLMMapper(config)
+
+        # Batch 1 on empty graph: no merge needed
+        batch1_c = [ConceptNode(id="concept:person_id", name="person_id", description="Person ID")]
+        batch1_e = [EntityNode(id="entity:customer", name="customer", description="A customer")]
+        batch1_edges = [
+            Edge(
+                id="edge:represents:col1:concept:person_id",
+                source_id="col1",
+                target_id="concept:person_id",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            ),
+        ]
+
+        accumulated_c, accumulated_e, accumulated_edges = mapper.merge_with_existing(
+            batch1_c,
+            batch1_e,
+            batch1_edges,
+            [],
+            [],
+        )
+
+        assert len(accumulated_e) == 1
+        assert accumulated_e[0].id == "entity:customer"
+
+        # Batch 2: entity:user gets merged into entity:customer
+        merge_response = json.dumps(
+            {
+                "merges": [
+                    {
+                        "new_id": "entity:user",
+                        "existing_id": "entity:customer",
+                        "reason": "same entity",
+                        "confidence": 0.95,
+                    },
+                ],
+                "new_kept": ["concept:email"],
+            }
+        )
+        mapper._call_llm = MagicMock(return_value=merge_response)
+
+        batch2_c = [ConceptNode(id="concept:email", name="email", description="Email")]
+        batch2_e = [EntityNode(id="entity:user", name="user", description="A user")]
+        batch2_edges = []
+
+        result_c, result_e, result_edges = mapper.merge_with_existing(
+            batch2_c,
+            batch2_e,
+            batch2_edges,
+            accumulated_c,
+            accumulated_e,
+        )
+
+        # CRITICAL: entity:customer MUST survive after merge
+        assert len(result_e) == 1
+        assert result_e[0].id == "entity:customer"
+
+        # All concepts should survive
+        assert len(result_c) == 2
+        assert any(c.id == "concept:person_id" for c in result_c)
+        assert any(c.id == "concept:email" for c in result_c)
+
+
+class TestMergeBatchInterval:
+    """Test the merge_batch_interval configuration."""
+
+    def test_merge_every_batch_with_interval_1(self):
+        """With merge_batch_interval=1, merge happens after every batch."""
+        config = _make_config(merge_batch_interval=1)
+        config.llm.api_key = "test-key"
+        mapper = LLMMapper(config)
+
+        # Mock mapping to return simple results
+        def mock_map_single(columns, profile_map):
+            c = ConceptNode(id=f"concept:batch_{columns[0].id}", name=f"batch_{columns[0].name}", description="test")
+            e = EntityNode(id=f"entity:batch_{columns[0].id}", name=f"batch_{columns[0].name}", description="test")
+            edge = Edge(
+                id=f"edge:{columns[0].id}",
+                source_id=columns[0].id,
+                target_id=c.id,
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            )
+            return [c], [e], [edge]
+
+        mapper._map_columns_single = mock_map_single
+        # Mock merge: no merges, keep all
+        merge_response = json.dumps({"merges": [], "new_kept": []})
+        mapper._call_llm = MagicMock(return_value=merge_response)
+
+        columns = [ColumnNode(id=f"col{i}", name=f"col{i}", table_id="t1", dtype="int") for i in range(45)]
+        profiles = [ColumnProfile(id=f"p{i}", column_id=f"col{i}", dtype="int") for i in range(45)]
+
+        result_c, result_e, result_edges = mapper.map_columns(columns, profiles)
+
+        # With interval=1 and 3 batches (45/15=3), 2 merges should happen
+        # (batch 1 accumulates, batch 2 merges, batch 3 merges)
+        assert len(result_c) > 0
+
+    def test_merge_less_frequent_with_interval_10(self):
+        """With merge_batch_interval=10, 6 batches should trigger only 1 merge."""
+        config = _make_config(merge_batch_interval=10)
+        config.llm.api_key = "test-key"
+        mapper = LLMMapper(config)
+
+        def mock_map_single(columns, profile_map):
+            c = ConceptNode(id=f"concept:batch_{len(columns)}", name=f"batch_{len(columns)}", description="test")
+            e = EntityNode(id=f"entity:batch_{len(columns)}", name=f"batch_{len(columns)}", description="test")
+            edge = Edge(
+                id=f"edge:batch_{len(columns)}",
+                source_id=columns[0].id,
+                target_id=c.id,
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            )
+            return [c], [e], [edge]
+
+        mapper._map_columns_single = mock_map_single
+
+        # Just check that the result has accumulated all batches' nodes
+        columns = [ColumnNode(id=f"col{i}", name=f"col{i}", table_id="t1", dtype="int") for i in range(90)]
+        profiles = [ColumnProfile(id=f"p{i}", column_id=f"col{i}", dtype="int") for i in range(90)]
+
+        # Override _call_llm to count merge calls
+        merge_llm_calls = []
+
+        def counting_call_llm(prompt, temperature=None):
+            merge_llm_calls.append(1)
+            return json.dumps({"merges": [], "new_kept": []})
+
+        mapper._call_llm = counting_call_llm
+
+        result_c, result_e, result_edges = mapper.map_columns(columns, profiles)
+
+        # With 6 batches and interval=10:
+        # Only 1 merge at the final batch (since 6 < 10)
+        assert len(merge_llm_calls) <= 2  # At most 1-2 merge calls (final + possibly 1 at end)
+        # All 6 batches' results should be accumulated
+        assert len(result_c) >= 6  # Should have concepts from all batches
diff --git a/services/datalink/tests/test_models.py b/services/datalink/tests/test_models.py
new file mode 100644
index 00000000..7097ff92
--- /dev/null
+++ b/services/datalink/tests/test_models.py
@@ -0,0 +1,250 @@
+"""Tests for DataLink data models."""
+
+from datalink.models import (
+    ColumnInfo,
+    ColumnNode,
+    ColumnProfile,
+    ConceptNode,
+    DatasourceConfig,
+    DatasourceInfo,
+    DatasourceType,
+    Edge,
+    EdgeType,
+    EntityNode,
+    ForeignKeyInfo,
+    NodeType,
+    TableInfo,
+    TableNode,
+)
+
+
+class TestNodeModels:
+    """Test node model creation and validation."""
+
+    def test_column_node_defaults(self):
+        node = ColumnNode(
+            id="column:postgres:orders:customer_id",
+            name="customer_id",
+            table_id="table:postgres:orders",
+            dtype="integer",
+        )
+        assert node.type == NodeType.COLUMN
+        assert node.semantic_type == ""
+        assert node.comment == ""
+        assert node.profile_id == ""
+
+    def test_column_node_with_all_fields(self):
+        node = ColumnNode(
+            id="column:postgres:orders:customer_id",
+            name="customer_id",
+            table_id="table:postgres:orders",
+            dtype="integer",
+            semantic_type="person_identifier",
+            comment="Foreign key to users table",
+            profile_id="profile:postgres:orders:customer_id",
+            properties={"is_indexed": True},
+        )
+        assert node.semantic_type == "person_identifier"
+        assert node.comment == "Foreign key to users table"
+
+    def test_table_node(self):
+        node = TableNode(
+            id="table:postgres:orders",
+            name="orders",
+            source="postgresql://localhost/mydb",
+            row_count=10000,
+            column_ids=[
+                "column:postgres:orders:order_id",
+                "column:postgres:orders:customer_id",
+                "column:postgres:orders:amount",
+            ],
+        )
+        assert node.type == NodeType.TABLE
+        assert node.row_count == 10000
+        assert len(node.column_ids) == 3
+
+    def test_concept_node(self):
+        node = ConceptNode(
+            id="concept:revenue",
+            name="revenue",
+            description="Total monetary income from sales",
+            unit="USD",
+            dimension="monetary",
+        )
+        assert node.type == NodeType.CONCEPT
+        assert node.unit == "USD"
+
+    def test_entity_node(self):
+        node = EntityNode(
+            id="entity:customer",
+            name="customer",
+            description="A person who purchases products",
+        )
+        assert node.type == NodeType.ENTITY
+        assert node.description == "A person who purchases products"
+
+
+class TestEdgeModels:
+    """Test edge model creation and validation."""
+
+    def test_explicit_edge(self):
+        edge = Edge(
+            id="edge:fk:orders_customer_id_users_id",
+            source_id="column:postgres:orders:customer_id",
+            target_id="column:postgres:users:id",
+            type=EdgeType.FOREIGN_KEY,
+        )
+        assert edge.confidence == 1.0
+        assert edge.is_explicit
+        assert not edge.is_inferred
+
+    def test_inferred_edge_with_confidence(self):
+        edge = Edge(
+            id="edge:joinable:orders_customer_id_transactions_user_id",
+            source_id="column:postgres:orders:customer_id",
+            target_id="column:postgres:transactions:user_id",
+            type=EdgeType.JOINABLE,
+            confidence=0.85,
+        )
+        assert edge.confidence == 0.85
+        assert edge.is_inferred
+        assert not edge.is_explicit
+
+    def test_confidence_bounds(self):
+        # Valid confidence
+        Edge(id="e:1", source_id="a", target_id="b", type=EdgeType.JOINABLE, confidence=0.0)
+        Edge(id="e:2", source_id="a", target_id="b", type=EdgeType.JOINABLE, confidence=1.0)
+
+    def test_contains_edge(self):
+        edge = Edge(
+            id="edge:contains:orders_customer_id",
+            source_id="table:postgres:orders",
+            target_id="column:postgres:orders:customer_id",
+            type=EdgeType.CONTAINS,
+        )
+        assert edge.type == EdgeType.CONTAINS
+
+    def test_represents_edge(self):
+        edge = Edge(
+            id="edge:represents:orders_amount_revenue",
+            source_id="column:postgres:orders:amount",
+            target_id="concept:revenue",
+            type=EdgeType.REPRESENTS,
+            confidence=0.9,
+        )
+        assert edge.type == EdgeType.REPRESENTS
+
+    def test_edge_properties(self):
+        edge = Edge(
+            id="edge:corr:1",
+            source_id="col:a",
+            target_id="col:b",
+            type=EdgeType.CORRELATED,
+            confidence=0.78,
+            properties={"coefficient": 0.78, "method": "pearson"},
+        )
+        assert edge.properties["coefficient"] == 0.78
+
+
+class TestColumnProfile:
+    """Test column profile model."""
+
+    def test_basic_profile(self):
+        profile = ColumnProfile(
+            id="profile:orders:customer_id",
+            column_id="column:postgres:orders:customer_id",
+            dtype="integer",
+            null_rate=0.02,
+            cardinality=8,
+            unique_rate=0.8,
+            total_count=10,
+            sample_values=[1, 2, 3, 5],
+        )
+        assert profile.dtype == "integer"
+        assert profile.null_rate == 0.02
+        assert profile.semantic_type == "unknown"
+
+    def test_numeric_profile(self):
+        profile = ColumnProfile(
+            id="profile:orders:amount",
+            column_id="column:postgres:orders:amount",
+            dtype="float",
+            min_value=45.3,
+            max_value=410.0,
+            mean_value=156.37,
+            std_value=112.5,
+            semantic_type="monetary_value",
+        )
+        assert profile.min_value == 45.3
+        assert profile.semantic_type == "monetary_value"
+
+    def test_string_profile(self):
+        profile = ColumnProfile(
+            id="profile:users:email",
+            column_id="column:postgres:users:email",
+            dtype="string",
+            min_length=15,
+            max_length=20,
+            avg_length=17.5,
+            value_patterns=["email_pattern"],
+            sample_values=["alice@example.com", "bob@example.com"],
+        )
+        assert profile.min_length == 15
+        assert "email_pattern" in profile.value_patterns
+
+
+class TestDatasourceModels:
+    """Test datasource configuration and info models."""
+
+    def test_datasource_config_csv(self):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test_csv",
+            path="/data/users.csv",
+        )
+        assert config.type == DatasourceType.CSV
+        assert config.sample_size == 1000
+
+    def test_datasource_config_database(self):
+        config = DatasourceConfig(
+            type=DatasourceType.DATABASE,
+            name="mydb",
+            connection_string="postgresql://user:pass@localhost/mydb",
+        )
+        assert config.type == DatasourceType.DATABASE
+
+    def test_column_info(self):
+        col = ColumnInfo(
+            name="customer_id",
+            dtype="integer",
+            nullable=False,
+            is_primary_key=False,
+            comment="FK to users.id",
+        )
+        assert col.comment == "FK to users.id"
+
+    def test_table_info_with_fk(self):
+        fk = ForeignKeyInfo(
+            constraint_name="fk_orders_customer",
+            source_table="orders",
+            source_column="customer_id",
+            target_table="users",
+            target_column="id",
+        )
+        table = TableInfo(
+            name="orders",
+            columns=[
+                ColumnInfo(name="order_id", dtype="integer", is_primary_key=True),
+                ColumnInfo(name="customer_id", dtype="integer"),
+            ],
+            foreign_keys=[fk],
+        )
+        assert len(table.foreign_keys) == 1
+        assert table.foreign_keys[0].target_table == "users"
+
+    def test_datasource_info(self):
+        info = DatasourceInfo(
+            config=DatasourceConfig(type=DatasourceType.CSV, path="/data"),
+            tables=[TableInfo(name="users", columns=[ColumnInfo(name="id", dtype="integer")])],
+        )
+        assert len(info.tables) == 1
diff --git a/services/datalink/tests/test_profiler.py b/services/datalink/tests/test_profiler.py
new file mode 100644
index 00000000..3cf19a95
--- /dev/null
+++ b/services/datalink/tests/test_profiler.py
@@ -0,0 +1,168 @@
+"""Tests for the tabular profiler."""
+
+import pandas as pd
+
+from datalink.connector.file import FileConnector
+from datalink.models.datasource import DatasourceConfig, DatasourceType
+from datalink.profiler.tabular import TabularProfiler
+
+
+class TestTabularProfiler:
+    """Test column profiling."""
+
+    def test_profile_datasource(self, all_csv_paths):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test",
+            path=str(all_csv_paths[0].parent),
+        )
+        connector = FileConnector(config)
+        ds_info = connector.get_datasource_info()
+
+        profiler = TabularProfiler()
+        profiles = profiler.profile_datasource(ds_info)
+
+        # 3 tables, total 15 columns
+        assert len(profiles) == 15
+
+    def test_profile_integer_column(self):
+        series = pd.Series([1, 2, 3, 5, 8, 2, 3, 1, 9, 10])
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:id",
+            column_name="id",
+            series=series,
+        )
+        assert profile.dtype == "integer"
+        assert profile.semantic_type == "identifier"  # high unique rate + name "id"
+        assert profile.null_rate == 0.0
+        assert profile.min_value == 1.0
+        assert profile.max_value == 10.0
+
+    def test_profile_float_column(self):
+        series = pd.Series([85.5, 120.0, 45.3, 200.0, 78.9])
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:amount",
+            column_name="amount",
+            series=series,
+        )
+        assert profile.dtype == "float"
+        assert profile.semantic_type == "monetary_value"  # name matches *_amount
+        assert profile.min_value == 45.3
+
+    def test_profile_string_column(self):
+        series = pd.Series(["alice@example.com", "bob@example.com", "charlie@example.com"])
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:email",
+            column_name="email",
+            series=series,
+        )
+        assert profile.dtype == "string"
+        assert profile.semantic_type == "email_address"  # value pattern + name
+        assert "email_pattern" in profile.value_patterns
+        assert profile.min_length is not None
+        assert profile.max_length is not None
+
+    def test_profile_datetime_column(self):
+        series = pd.Series(pd.to_datetime(["2024-01-20", "2024-03-01", "2024-05-01"]))
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:order_date",
+            column_name="order_date",
+            series=series,
+        )
+        assert profile.dtype == "datetime"
+        assert profile.semantic_type == "timestamp"  # name matches *_date
+
+    def test_profile_null_column(self):
+        series = pd.Series([1, None, 3, None, 5, 6, 7, None, 9, 10])
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:value",
+            column_name="value",
+            series=series,
+        )
+        assert profile.null_rate == 0.3  # 3 nulls out of 10
+        assert profile.total_count == 10
+
+    def test_profile_identifier_column(self):
+        series = pd.Series(range(1, 101))  # 100 unique integer values
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:user_id",
+            column_name="user_id",
+            series=series,
+        )
+        assert profile.unique_rate == 1.0
+        assert profile.semantic_type == "identifier"
+
+    def test_profile_low_cardinality_column(self):
+        series = pd.Series(["completed", "pending", "completed", "completed", "cancelled"] * 20)
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:status",
+            column_name="status",
+            series=series,
+        )
+        assert profile.semantic_type == "status_enum"  # name matches *_status
+        assert profile.cardinality == 3
+        assert len(profile.top_values) > 0
+        # Top value should be "completed" (60 occurrences out of 100)
+        top = profile.top_values[0]
+        assert top["value"] == "completed"
+
+    def test_sample_values_populated(self):
+        series = pd.Series(range(100))
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:id",
+            column_name="id",
+            series=series,
+        )
+        assert len(profile.sample_values) == 5
+
+    def test_distribution_histogram(self):
+        series = pd.Series(range(100))
+        profiler = TabularProfiler()
+        profile = profiler.profile_column(
+            column_id="col:test:t:value",
+            column_name="value",
+            series=series,
+        )
+        assert len(profile.distribution_histogram) > 0
+        # Each histogram entry should have bin_start, bin_end, count
+        entry = profile.distribution_histogram[0]
+        assert "bin_start" in entry
+        assert "bin_end" in entry
+        assert "count" in entry
+
+    def test_profile_real_users_table(self, users_csv_path):
+        config = DatasourceConfig(
+            type=DatasourceType.CSV,
+            name="test",
+            path=str(users_csv_path),
+        )
+        connector = FileConnector(config)
+        ds_info = connector.get_datasource_info()
+
+        profiler = TabularProfiler()
+        profiles = profiler.profile_datasource(ds_info)
+
+        # Should have 5 profiles for 5 columns
+        assert len(profiles) == 5
+
+        # id column → identifier
+        id_profile = [p for p in profiles if "id" in p.column_id][0]
+        assert id_profile.dtype == "integer"
+        assert id_profile.semantic_type == "identifier"
+
+        # email column → email_address
+        email_profile = [p for p in profiles if "email" in p.column_id][0]
+        assert email_profile.dtype == "string"
+        assert email_profile.semantic_type == "email_address"
+
+        # age column → unknown or integer
+        age_profile = [p for p in profiles if "age" in p.column_id][0]
+        assert age_profile.dtype == "integer"
diff --git a/services/datalink/tests/test_retrieval.py b/services/datalink/tests/test_retrieval.py
new file mode 100644
index 00000000..bc2b5c31
--- /dev/null
+++ b/services/datalink/tests/test_retrieval.py
@@ -0,0 +1,216 @@
+"""Tests for the retrieval interfaces."""
+
+import os
+import tempfile
+from pathlib import Path
+
+import pytest
+
+from datalink.builder.pipeline import BuildPipeline
+from datalink.config import DataLinkConfig, LLMConfig
+from datalink.graph.retrieval import GraphRetrieval
+from datalink.models.datasource import DatasourceConfig, DatasourceType
+from datalink.models.edge import EdgeType
+from datalink.models.node import NodeType
+
+TEST_DATA_DIR = Path(__file__).parent / "test_data"
+
+
+@pytest.fixture
+def populated_graph():
+    """Create a populated graph for retrieval testing."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        db_path = f.name
+    config = DataLinkConfig(
+        graph_db_path=db_path,
+        llm=LLMConfig(model="gpt-4o", api_key=""),
+    )
+    pipeline = BuildPipeline(config)
+
+    # Build from test data
+    ds_config = DatasourceConfig(
+        type=DatasourceType.CSV,
+        name="test",
+        path=str(TEST_DATA_DIR),
+    )
+    pipeline.init_build([ds_config])
+
+    retrieval = GraphRetrieval(pipeline.storage)
+    yield retrieval, pipeline
+
+    pipeline.close()
+    if os.path.exists(db_path):
+        os.unlink(db_path)
+
+
+class TestSearchNodes:
+    """Test node search."""
+
+    def test_search_by_name(self, populated_graph):
+        retrieval, _ = populated_graph
+        results = retrieval.search_nodes("customer_id")
+        assert len(results) > 0
+        assert any(r["name"] == "customer_id" for r in results)
+
+    def test_search_by_type(self, populated_graph):
+        retrieval, _ = populated_graph
+        results = retrieval.search_nodes("id", node_type=NodeType.COLUMN)
+        # Should find columns with "id" in name
+        for r in results:
+            assert r["type"] == "column"
+
+    def test_search_with_edge_summary(self, populated_graph):
+        retrieval, _ = populated_graph
+        results = retrieval.search_nodes("customer_id")
+        assert len(results) > 0
+        result = results[0]
+        assert result["edge_count"] > 0
+        assert len(result["edges_summary"]) > 0
+
+    def test_search_empty_query(self, populated_graph):
+        retrieval, _ = populated_graph
+        results = retrieval.search_nodes("")
+        # Empty query should still return something (all names match)
+        # Actually substring match on "" matches everything
+        # But we limit to 10
+        assert len(results) <= 10
+
+    def test_search_nonexistent(self, populated_graph):
+        retrieval, _ = populated_graph
+        results = retrieval.search_nodes("xyz_nonexistent")
+        assert len(results) == 0
+
+
+class TestGetNode:
+    """Test node detail retrieval."""
+
+    def test_get_column_node(self, populated_graph):
+        retrieval, _ = populated_graph
+        # Find a known column node ID
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        col = [c for c in columns if c.name == "customer_id"][0]
+
+        result = retrieval.get_node(col.id)
+        assert result is not None
+        assert result["type"] == "column"
+        assert result["name"] == "customer_id"
+        assert len(result["edges"]) > 0
+
+    def test_get_node_with_profile(self, populated_graph):
+        retrieval, _ = populated_graph
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        col = [c for c in columns if c.name == "customer_id"][0]
+
+        result = retrieval.get_node(col.id, include_edges=True)
+        assert "profile" in result
+        assert result["profile"]["dtype"] == "integer"
+
+    def test_get_node_without_edges(self, populated_graph):
+        retrieval, _ = populated_graph
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        col = columns[0]
+
+        result = retrieval.get_node(col.id, include_edges=False)
+        assert "edges" not in result
+
+    def test_get_nonexistent_node(self, populated_graph):
+        retrieval, _ = populated_graph
+        result = retrieval.get_node("nonexistent_id")
+        assert result is None
+
+
+class TestFindPaths:
+    """Test path finding between nodes."""
+
+    def test_find_path_between_columns(self, populated_graph):
+        retrieval, _ = populated_graph
+        # Find path from users.id to orders.customer_id
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        users_id = [c for c in columns if c.name == "id" and "users" in c.id][0]
+        orders_customer_id = [c for c in columns if c.name == "customer_id"][0]
+
+        paths = retrieval.find_paths(users_id.id, orders_customer_id.id)
+        assert len(paths) > 0
+
+        # Check path structure
+        path = paths[0]
+        assert "nodes" in path
+        assert "edges" in path
+        assert "confidence" in path
+        assert path["nodes"][0] == users_id.id
+        assert path["nodes"][-1] == orders_customer_id.id
+
+    def test_find_path_with_type_filter(self, populated_graph):
+        retrieval, _ = populated_graph
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        col_a = columns[0]
+        col_b = columns[1]
+
+        # Only traverse via joinable edges
+        paths = retrieval.find_paths(col_a.id, col_b.id, edge_types=[EdgeType.JOINABLE, EdgeType.SEMANTIC_SYNONYM])
+        # Results depend on whether such paths exist
+        # At minimum, it should not error
+        assert isinstance(paths, list)
+
+    def test_find_path_max_depth(self, populated_graph):
+        retrieval, _ = populated_graph
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        col_a = columns[0]
+        col_b = columns[-1]
+
+        # Depth 1 should find only direct connections
+        paths_d1 = retrieval.find_paths(col_a.id, col_b.id, max_depth=1)
+        # Depth 3 should find more
+        paths_d3 = retrieval.find_paths(col_a.id, col_b.id, max_depth=3)
+        # Not necessarily more paths, but should be at least as many
+        assert len(paths_d3) >= len(paths_d1)
+
+
+class TestExtractSubgraph:
+    """Test subgraph extraction."""
+
+    def test_extract_subgraph_from_column(self, populated_graph):
+        retrieval, _ = populated_graph
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        col = [c for c in columns if c.name == "customer_id"][0]
+
+        result = retrieval.extract_subgraph([col.id], max_hops=2)
+        assert result["stats"]["node_count"] > 1
+        assert result["stats"]["edge_count"] > 0
+
+        # The subgraph should contain the starting column
+        assert any(n["id"] == col.id for n in result["nodes"])
+
+    def test_extract_subgraph_limited_hops(self, populated_graph):
+        retrieval, _ = populated_graph
+        columns = retrieval.storage.get_nodes_by_type(NodeType.COLUMN)
+        col = columns[0]
+
+        # 1 hop should give fewer nodes than 2 hops
+        result_1 = retrieval.extract_subgraph([col.id], max_hops=1)
+        result_2 = retrieval.extract_subgraph([col.id], max_hops=2)
+        assert result_2["stats"]["node_count"] >= result_1["stats"]["node_count"]
+
+
+class TestListDatasets:
+    """Test dataset listing."""
+
+    def test_list_datasets(self, populated_graph):
+        retrieval, _ = populated_graph
+        datasets = retrieval.list_datasets()
+        assert len(datasets) == 3  # 3 tables from test data
+
+        # Check structure
+        for ds in datasets:
+            assert "id" in ds
+            assert "name" in ds
+            assert "column_count" in ds
+            assert ds["column_count"] == 5  # All test tables have 5 columns
+
+    def test_list_datasets_contains_table_names(self, populated_graph):
+        retrieval, _ = populated_graph
+        datasets = retrieval.list_datasets()
+        names = [ds["name"] for ds in datasets]
+        assert "users" in names
+        assert "orders" in names
+        assert "transactions" in names
diff --git a/services/datalink/tests/test_sql_ident.py b/services/datalink/tests/test_sql_ident.py
new file mode 100644
index 00000000..8d75731f
--- /dev/null
+++ b/services/datalink/tests/test_sql_ident.py
@@ -0,0 +1,43 @@
+"""Tests for SQL identifier quoting helpers."""
+
+from datalink.utils.sql_ident import (
+    dialect_from_source,
+    needs_quoting,
+    quote_identifier,
+    quote_qualified,
+)
+
+
+class TestDialectFromSource:
+    def test_sqlite(self):
+        assert dialect_from_source("sqlite:////tmp/x.db") == "sqlite"
+
+    def test_postgres(self):
+        assert dialect_from_source("postgresql://u:p@h/db") == "postgresql"
+
+    def test_mysql(self):
+        assert dialect_from_source("mysql+pymysql://u:p@h/db") == "mysql"
+
+    def test_unknown(self):
+        assert dialect_from_source("/path/to/file.csv") is None
+
+
+class TestQuoteIdentifier:
+    def test_sqlite_hyphen(self):
+        assert quote_identifier("dacomp-zh-006", "sqlite") == '"dacomp-zh-006"'
+
+    def test_mysql_hyphen(self):
+        assert quote_identifier("dacomp-zh-006", "mysql") == "`dacomp-zh-006`"
+
+    def test_mssql(self):
+        assert quote_identifier("order-items", "mssql") == "[order-items]"
+
+    def test_embedded_quote_escaped(self):
+        assert quote_identifier('a"b', "sqlite") == '"a""b"'
+
+    def test_qualified(self):
+        assert quote_qualified("myschema", "dacomp-zh-006", dialect="postgresql") == ('"myschema"."dacomp-zh-006"')
+
+    def test_needs_quoting(self):
+        assert needs_quoting("dacomp-zh-006")
+        assert not needs_quoting("orders")
diff --git a/services/datalink/tests/test_storage.py b/services/datalink/tests/test_storage.py
new file mode 100644
index 00000000..7fad40a3
--- /dev/null
+++ b/services/datalink/tests/test_storage.py
@@ -0,0 +1,419 @@
+"""Tests for the graph storage layer."""
+
+import os
+import tempfile
+
+import pytest
+
+from datalink.graph.storage import GraphStorage
+from datalink.models.edge import Edge, EdgeType
+from datalink.models.node import ColumnNode, ConceptNode, EntityNode, NodeType, TableNode
+from datalink.models.profile import ColumnProfile
+
+
+@pytest.fixture
+def storage():
+    """Create a temporary graph storage for testing."""
+    with tempfile.NamedTemporaryFile(suffix=".db", delete=False) as f:
+        db_path = f.name
+    try:
+        s = GraphStorage(db_path)
+        yield s
+        s.close()
+    finally:
+        if os.path.exists(db_path):
+            os.unlink(db_path)
+
+
+class TestNodeOperations:
+    """Test node CRUD operations."""
+
+    def test_add_and_get_table_node(self, storage):
+        table = TableNode(
+            id="table:test:users",
+            name="users",
+            source="test_csv",
+            row_count=100,
+            column_ids=["col:test:users:id"],
+        )
+        # Store column_ids and other table-specific fields in properties
+        # since the DB schema stores everything in a generic properties JSON
+        table.properties["source"] = table.source
+        table.properties["row_count"] = table.row_count
+        table.properties["column_ids"] = table.column_ids
+
+        storage.add_node(table)
+        retrieved = storage.get_node("table:test:users")
+        assert retrieved is not None
+        assert retrieved.name == "users"
+        assert retrieved.type == NodeType.TABLE
+
+    def test_add_and_get_column_node(self, storage):
+        col = ColumnNode(
+            id="column:test:users:id",
+            name="id",
+            table_id="table:test:users",
+            dtype="integer",
+            semantic_type="identifier",
+        )
+        col.properties["table_id"] = col.table_id
+        col.properties["dtype"] = col.dtype
+        col.properties["semantic_type"] = col.semantic_type
+
+        storage.add_node(col)
+        retrieved = storage.get_node("column:test:users:id")
+        assert retrieved is not None
+        assert retrieved.name == "id"
+        assert retrieved.type == NodeType.COLUMN
+
+    def test_add_concept_node(self, storage):
+        concept = ConceptNode(
+            id="concept:revenue",
+            name="revenue",
+            description="Total monetary income",
+            unit="USD",
+        )
+        concept.properties["description"] = concept.description
+        concept.properties["unit"] = concept.unit
+
+        storage.add_node(concept)
+        retrieved = storage.get_node("concept:revenue")
+        assert retrieved is not None
+        assert retrieved.name == "revenue"
+        assert retrieved.type == NodeType.CONCEPT
+
+    def test_add_entity_node(self, storage):
+        entity = EntityNode(
+            id="entity:customer",
+            name="customer",
+            description="A person who purchases products",
+        )
+        entity.properties["description"] = entity.description
+
+        storage.add_node(entity)
+        retrieved = storage.get_node("entity:customer")
+        assert retrieved is not None
+        assert retrieved.name == "customer"
+        assert retrieved.type == NodeType.ENTITY
+
+    def test_remove_node_cascades_edges(self, storage):
+        # Add nodes and an edge
+        storage.add_node(ColumnNode(id="col:a", name="a", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="col:b", name="b", table_id="t2", dtype="int"))
+        storage.add_edge(
+            Edge(
+                id="edge:1",
+                source_id="col:a",
+                target_id="col:b",
+                type=EdgeType.JOINABLE,
+                confidence=0.8,
+            )
+        )
+
+        # Remove col:a — should cascade remove the edge
+        storage.remove_node("col:a")
+
+        # Edge should be gone
+        edge = storage.get_edge("edge:1")
+        assert edge is None
+        # col:b should still exist
+        node_b = storage.get_node("col:b")
+        assert node_b is not None
+
+    def test_get_nodes_by_type(self, storage):
+        storage.add_node(TableNode(id="table:t1", name="t1", source="test"))
+        storage.add_node(TableNode(id="table:t2", name="t2", source="test"))
+        storage.add_node(ColumnNode(id="col:c1", name="c1", table_id="t1", dtype="int"))
+
+        tables = storage.get_nodes_by_type(NodeType.TABLE)
+        assert len(tables) == 2
+
+        columns = storage.get_nodes_by_type(NodeType.COLUMN)
+        assert len(columns) == 1
+
+    def test_search_nodes_by_name(self, storage):
+        storage.add_node(ColumnNode(id="col:users:id", name="id", table_id="t", dtype="int"))
+        storage.add_node(ColumnNode(id="col:users:customer_id", name="customer_id", table_id="t", dtype="int"))
+        storage.add_node(ColumnNode(id="col:orders:amount", name="amount", table_id="t", dtype="float"))
+
+        # Search for "id" in column names
+        results = storage.search_nodes_by_name("id", NodeType.COLUMN)
+        assert len(results) == 2  # "id" and "customer_id"
+
+        # Search for "amount" in all types
+        results = storage.search_nodes_by_name("amount")
+        assert len(results) == 1
+
+    def test_count_nodes(self, storage):
+        storage.add_node(TableNode(id="t:1", name="t1", source="s"))
+        storage.add_node(ColumnNode(id="c:1", name="c1", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="c:2", name="c2", table_id="t1", dtype="str"))
+
+        assert storage.count_nodes() == 3
+        assert storage.count_nodes(NodeType.TABLE) == 1
+        assert storage.count_nodes(NodeType.COLUMN) == 2
+
+
+class TestEdgeOperations:
+    """Test edge CRUD operations."""
+
+    def test_add_and_get_edge(self, storage):
+        storage.add_node(ColumnNode(id="col:a", name="a", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="col:b", name="b", table_id="t2", dtype="int"))
+
+        edge = Edge(
+            id="edge:join1",
+            source_id="col:a",
+            target_id="col:b",
+            type=EdgeType.JOINABLE,
+            confidence=0.85,
+        )
+        storage.add_edge(edge)
+
+        retrieved = storage.get_edge("edge:join1")
+        assert retrieved is not None
+        assert retrieved.type == EdgeType.JOINABLE
+        assert retrieved.confidence == 0.85
+
+    def test_add_edges_batch(self, storage):
+        storage.add_node(TableNode(id="table:t1", name="t1", source="s"))
+        storage.add_node(ColumnNode(id="col:c1", name="c1", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="col:c2", name="c2", table_id="t1", dtype="str"))
+
+        edges = [
+            Edge(id="e:1", source_id="table:t1", target_id="col:c1", type=EdgeType.CONTAINS),
+            Edge(id="e:2", source_id="table:t1", target_id="col:c2", type=EdgeType.CONTAINS),
+        ]
+        storage.add_edges_batch(edges)
+
+        assert storage.count_edges(EdgeType.CONTAINS) == 2
+
+    def test_get_edges_for_node(self, storage):
+        storage.add_node(TableNode(id="table:t1", name="t1", source="s"))
+        storage.add_node(ColumnNode(id="col:c1", name="c1", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="col:c2", name="c2", table_id="t2", dtype="int"))
+
+        storage.add_edge(Edge(id="e:1", source_id="table:t1", target_id="col:c1", type=EdgeType.CONTAINS))
+        storage.add_edge(Edge(id="e:2", source_id="col:c1", target_id="col:c2", type=EdgeType.JOINABLE))
+
+        edges = storage.get_edges_for_node("table:t1")
+        assert len(edges) == 1  # only the contains edge
+
+        edges = storage.get_edges_for_node("col:c1")
+        assert len(edges) == 2  # both contains (incoming) and joinable (outgoing)
+
+    def test_remove_edge(self, storage):
+        storage.add_node(ColumnNode(id="col:a", name="a", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="col:b", name="b", table_id="t2", dtype="int"))
+        storage.add_edge(Edge(id="e:1", source_id="col:a", target_id="col:b", type=EdgeType.JOINABLE))
+
+        storage.remove_edge("e:1")
+        assert storage.get_edge("e:1") is None
+
+    def test_count_edges(self, storage):
+        storage.add_node(TableNode(id="t1", name="t1", source="s"))
+        storage.add_node(ColumnNode(id="c1", name="c1", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="c2", name="c2", table_id="t2", dtype="int"))
+
+        storage.add_edge(Edge(id="e:1", source_id="t1", target_id="c1", type=EdgeType.CONTAINS))
+        storage.add_edge(Edge(id="e:2", source_id="c1", target_id="c2", type=EdgeType.JOINABLE))
+
+        assert storage.count_edges() == 2
+        assert storage.count_edges(EdgeType.CONTAINS) == 1
+
+
+class TestProfileOperations:
+    """Test column profile CRUD."""
+
+    def test_add_and_get_profile(self, storage):
+        storage.add_node(ColumnNode(id="col:t1:amount", name="amount", table_id="t1", dtype="float"))
+
+        profile = ColumnProfile(
+            id="profile:col:t1:amount",
+            column_id="col:t1:amount",
+            dtype="float",
+            semantic_type="monetary_value",
+            null_rate=0.05,
+            min_value=10.0,
+            max_value=500.0,
+        )
+        storage.add_profile(profile)
+
+        retrieved = storage.get_profile("profile:col:t1:amount")
+        assert retrieved is not None
+        assert retrieved.dtype == "float"
+        assert retrieved.semantic_type == "monetary_value"
+
+    def test_get_profile_for_column(self, storage):
+        storage.add_node(ColumnNode(id="col:t1:amount", name="amount", table_id="t1", dtype="float"))
+
+        profile = ColumnProfile(
+            id="profile:col:t1:amount",
+            column_id="col:t1:amount",
+            dtype="float",
+        )
+        storage.add_profile(profile)
+
+        retrieved = storage.get_profile_for_column("col:t1:amount")
+        assert retrieved is not None
+        assert retrieved.column_id == "col:t1:amount"
+
+    def test_add_profiles_batch(self, storage):
+        storage.add_node(ColumnNode(id="c1", name="c1", table_id="t1", dtype="int"))
+        storage.add_node(ColumnNode(id="c2", name="c2", table_id="t2", dtype="str"))
+
+        profiles = [
+            ColumnProfile(id="p1", column_id="c1", dtype="int"),
+            ColumnProfile(id="p2", column_id="c2", dtype="str"),
+        ]
+        storage.add_profiles_batch(profiles)
+        assert storage.get_profile("p1") is not None
+        assert storage.get_profile("p2") is not None
+
+
+class TestRemoveTable:
+    """Test the remove_table operation with cascading deletes."""
+
+    def test_remove_table_removes_columns_and_edges(self, storage):
+        # Add a table with two columns
+        storage.add_node(TableNode(id="table:test:orders", name="orders", source="test"))
+        storage.add_node(
+            ColumnNode(id="col:test:orders:order_id", name="order_id", table_id="table:test:orders", dtype="int")
+        )
+        storage.add_node(
+            ColumnNode(id="col:test:orders:amount", name="amount", table_id="table:test:orders", dtype="float")
+        )
+
+        # Add contains edges
+        storage.add_edge(
+            Edge(id="e:1", source_id="table:test:orders", target_id="col:test:orders:order_id", type=EdgeType.CONTAINS)
+        )
+        storage.add_edge(
+            Edge(id="e:2", source_id="table:test:orders", target_id="col:test:orders:amount", type=EdgeType.CONTAINS)
+        )
+
+        # Add a profile
+        storage.add_profile(ColumnProfile(id="p:1", column_id="col:test:orders:order_id", dtype="int"))
+
+        # Remove the table
+        removed_cols = storage.remove_table("table:test:orders")
+
+        assert len(removed_cols) == 2
+        assert storage.get_node("table:test:orders") is None
+        assert storage.get_node("col:test:orders:order_id") is None
+        assert storage.get_node("col:test:orders:amount") is None
+        assert storage.get_edge("e:1") is None
+        assert storage.get_edge("e:2") is None
+        assert storage.get_profile("p:1") is None
+
+
+class TestCleanupOrphanedSemanticNodes:
+    """Test cleanup of orphaned Concept/Entity nodes."""
+
+    def test_cleanup_removes_orphaned_concepts(self, storage):
+        # Add a concept with a represents edge
+        storage.add_node(ConceptNode(id="concept:revenue", name="revenue", description="Income"))
+        storage.add_node(ColumnNode(id="col:orders:amount", name="amount", table_id="t1", dtype="float"))
+        storage.add_edge(
+            Edge(
+                id="e:repr:1",
+                source_id="col:orders:amount",
+                target_id="concept:revenue",
+                type=EdgeType.REPRESENTS,
+                confidence=0.9,
+            )
+        )
+
+        # Add an orphaned concept (no represents edges)
+        storage.add_node(ConceptNode(id="concept:orphan", name="orphan", description="No connections"))
+
+        # Cleanup should remove the orphan
+        removed = storage.cleanup_orphaned_semantic_nodes()
+        assert removed == 1
+        assert storage.get_node("concept:orphan") is None
+        assert storage.get_node("concept:revenue") is not None  # Still connected
+
+    def test_entity_with_has_concept_edges_is_orphan_without_structural_anchor(self, storage):
+        # Entity connected only to other semantic nodes (has_concept) IS orphaned
+        # — has_concept edges are semantic-layer-internal and don't count as
+        # anchors.  Only represents edges from structural nodes anchor a
+        # semantic node.
+        storage.add_node(EntityNode(id="entity:customer", name="customer", description="A person"))
+        storage.add_node(ConceptNode(id="concept:customer_id", name="customer_id", description="ID"))
+        storage.add_edge(
+            Edge(
+                id="e:hc:1",
+                source_id="entity:customer",
+                target_id="concept:customer_id",
+                type=EdgeType.HAS_CONCEPT,
+            )
+        )
+
+        removed = storage.cleanup_orphaned_semantic_nodes()
+        assert removed == 2  # Both entity and concept are orphaned — no structural anchor
+        # Their has_concept edge should also be cleaned up
+        assert storage.count_edges(EdgeType.HAS_CONCEPT) == 0
+
+    def test_entity_with_represents_from_structural_is_not_orphan(self, storage):
+        # Entity IS anchored when a structural node (Column) has a represents
+        # edge targeting its associated Concept.
+        storage.add_node(ColumnNode(id="col:1", name="customer_id", table_id="t1", dtype="int"))
+        storage.add_node(EntityNode(id="entity:customer", name="customer", description="A person"))
+        storage.add_node(ConceptNode(id="concept:customer_id", name="customer_id", description="ID"))
+        storage.add_edge(
+            Edge(
+                id="e:rep:1",
+                source_id="col:1",
+                target_id="concept:customer_id",
+                type=EdgeType.REPRESENTS,
+            )
+        )
+        storage.add_edge(
+            Edge(
+                id="e:hc:1",
+                source_id="entity:customer",
+                target_id="concept:customer_id",
+                type=EdgeType.HAS_CONCEPT,
+            )
+        )
+
+        removed = storage.cleanup_orphaned_semantic_nodes()
+        assert removed == 0  # Concept is anchored by Column → represents edge
+
+
+class TestGraphStats:
+    """Test graph statistics."""
+
+    def test_get_graph_stats(self, storage):
+        storage.add_node(TableNode(id="t1", name="t1", source="s"))
+        storage.add_node(ColumnNode(id="c1", name="c1", table_id="t1", dtype="int"))
+        storage.add_node(ConceptNode(id="concept:1", name="test_concept", description="test"))
+
+        storage.add_edge(Edge(id="e:1", source_id="t1", target_id="c1", type=EdgeType.CONTAINS))
+        storage.add_edge(
+            Edge(id="e:2", source_id="c1", target_id="concept:1", type=EdgeType.REPRESENTS, confidence=0.9)
+        )
+
+        stats = storage.get_graph_stats()
+        assert stats["total_nodes"] == 3
+        assert stats["node_type_counts"]["table"] == 1
+        assert stats["node_type_counts"]["column"] == 1
+        assert stats["node_type_counts"]["concept"] == 1
+        assert stats["total_edges"] == 2
+
+    def test_clear_all(self, storage):
+        storage.add_node(TableNode(id="t1", name="t1", source="s"))
+        storage.add_node(ColumnNode(id="c1", name="c1", table_id="t1", dtype="int"))
+        storage.clear_all()
+        assert storage.count_nodes() == 0
+
+
+class TestMetadata:
+    """Test metadata operations."""
+
+    def test_set_and_get_metadata(self, storage):
+        storage.set_metadata("build_time", "2024-01-01T00:00:00")
+        assert storage.get_metadata("build_time") == "2024-01-01T00:00:00"
+
+    def test_get_nonexistent_metadata(self, storage):
+        assert storage.get_metadata("nonexistent") is None
diff --git a/services/datalink/uv.lock b/services/datalink/uv.lock
new file mode 100644
index 00000000..a32863b8
--- /dev/null
+++ b/services/datalink/uv.lock
@@ -0,0 +1,6735 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'darwin'",
+    "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and sys_platform == 'win32'",
+    "python_full_version < '3.11' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.13.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version == '3.11.*' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "python_full_version < '3.11' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
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