qwenpaw-data-context

The context management + graph memory foundation of QwenPaw Data. It organizes data metadata, topology, and business knowledge into a connected, retrievable, and evolvable graph memory, exposing only semantic information needs — never internal graph traversal or vector retrieval topology. Think Mem0 / Zep / MemOS for LLM applications, with one key difference: this package manages both fact / episode records and structured nodes such as tables, columns, metrics, and formulas.

Core architecture: three graphs (Metadata Graph / Topology Graph / Knowledge Graph) + five stages (Build → Store → Retrieve → Learn → Govern).

This package was merged from the formerly standalone context-management project and serves as the host-agnostic memory foundation shared by the CLI host, plugins, and skills.

Directory layout

packages/qwenpaw-data-context/
├── src/
│   ├── qwenpaw_data/context/          ← QwenPaw Data namespace placeholder (public API reserved)
│   ├── context_manager/          ← CM core: graph build / retrieval / pipelines / FastAPI (api/server.py)
│   └── semantic_config/          ← semantic-config editing layer (SQLite CRUD + Excel import + weave)
├── frontend/                     ← DataBridge frontend (Vite, fixed port 3000)
├── scripts/
│   ├── serve.py                  ← Web / API service entry point
│   └── setup/                    ← graph building, dataset download, vector indexing
├── config/
│   ├── agent_explorer.json       ← Explorer / Agent hyperparameters
│   └── datasources.json          ← datasource registry
├── Makefile                      ← shortcuts (serve / setup)
├── pyproject.toml                ← package definition + dependencies (hatchling)
├── requirements.txt              ← annotated dependency notes
├── requirements.lock.txt         ← verified exact-version snapshot (reproducible installs)
├── semantic_config.db            ← editing-layer SQLite (local, holds connection info, not committed)
└── .venv/                        ← package-scoped uv virtual environment (isolated, not committed)

frontend/ and the API belong to the same DataBridge local runtime and are initialized and started together by the repository scripts.

Prerequisites

• Python 3.12 (see .python-version)
• uv (virtual environments and dependency management)
• Node.js + npm (DataBridge frontend)
• Neo4j 5.20+ Community (graph store; optional, only needed for graph capabilities)

The service starts even when the graph store / PG is down: GET /api/health returns a minimal liveness status, and the SQLite-based semantic-config editing layer (/api/semantic-config/*) supports CRUD without the graph store.

Start the database (optional, Docker)

# Neo4j
export NEO4J_PASSWORD="$(openssl rand -hex 32)"
docker run -d --name neo4j \
  -p 127.0.0.1:7474:7474 -p 127.0.0.1:7687:7687 \
  -e NEO4J_AUTH="neo4j/${NEO4J_PASSWORD}" \
  -e NEO4J_PLUGINS='["apoc"]' \
  -e NEO4J_dbms_security_procedures_unrestricted='apoc.*' \
  neo4j:5.20-community

Installation

The recommended way is to initialize DataBridge Python and frontend dependencies from the repository root:

scripts/init_databridge.sh

To install the package-scoped uv virtual environment manually, run the following inside packages/qwenpaw-data-context/:

# 1) Create an isolated virtual environment (Python 3.12)
uv venv --python 3.12 .venv

# 2) Install dependencies
VIRTUAL_ENV="$(pwd)/.venv" uv pip install -r requirements.lock.txt

# 3) Register this package in editable mode (context_manager / semantic_config / qwenpaw_data.context)
VIRTUAL_ENV="$(pwd)/.venv" uv pip install -e . --no-deps

To re-resolve dependencies from declared constraints (instead of the locked snapshot), use uv pip install -e ., which follows dependencies in pyproject.toml.

Configure environment variables

cd ../..
cp .env.example .env

Edit .env at the repository root and fill in the model configuration:

OPENAI_API_KEY=sk-xxx
OPENAI_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
LLM_MODEL=qwen-max
EMBED_MODEL=text-embedding-v3
EMBED_DIM=1024

Replace NEO4J_PASSWORD=YOUR_PASSWORD in .env with your local Neo4j password. The local Neo4j URI and username have working defaults; set NEO4J_URI and NEO4J_USER only when connecting to an external instance.

Datasource connection details (PostgreSQL / MySQL / ODPS / Hologres, etc.) are managed in the semantic-config layer (/api/semantic-config/datasource) and do not need to be set in .env.

Start the service

# Recommended: start the DataBridge frontend and API together
scripts/start_databridge.sh

# Start only the databases and the API
scripts/start_databridge.sh --skip-frontend

On a successful start you will see:

INFO api.server: Neo4j driver opened: bolt://localhost:7687
INFO api.server: semantic-config SQLite initialized
INFO:     Uvicorn running on http://127.0.0.1:8765

Default service addresses:

DataBridge UI:      http://localhost:3000
DataBridge API:     http://localhost:8765
OpenAPI:            http://localhost:8765/docs

The frontend is served by Vite with hot reload on the fixed port 3000; if the port is occupied, startup fails explicitly instead of silently switching to another port.

Common flags: --reload (backend source hot reload), --host, --log-level, --skip-frontend. Frontend source updates are always handled by Vite HMR.

API overview

The service hosts three route families in a single process on one port (default 8765).

CM semantic capabilities (REST prefix /api/v1/cm, MCP prefix /mcp/v1/cm)

L1 — intent understanding:

L2 — context operations: /explore_entity, /compare_entities, /search_event, /execute_sql (all POST).

L3 — entity queries (GET): /domains, /domain-overview, /metrics, /search-metrics, /north-star-metrics, /dimensions, /dimension-hierarchy, /dimension-values, /datasets, /dataset-relations.

Semantic-config editing layer (prefix /api/semantic-config)

Manages datasources, business domains, datasets, dimensions, metrics, and related entities in local SQLite (CRUD + Excel import), then pushes the configuration into the CM graph through "weave". Runs in the same process and port as CM.

• Main routes: /datasource, /biz-domain, /dataset-meta, /dataset-column-meta, /dataset-dimension, /dimension, /metric-lib, /metric-formula-lib, /import/excel, /weave-task/*.
• Weave: POST /weave-task/submit assembles the semantic payload for a whole datasource and calls CM's semantic import logic in-process (context_manager.graph.semantic_import_service.run_semantic_import_async) to write into the graph store; the callback address is configured via WEAVE_CALLBACK_URL in .env.
• Storage does not depend on Neo4j/PG: CRUD and Excel import work even without the graph store online (only "weave" requires it).
• Error protocol: /api/semantic-config/* returns {timestamp,status,error,message}; CM /api/v1/* keeps its own protocol.
• Positioning: designed for local-first, single-user deployments. All API routes require Bearer token authentication (QWENPAW_DATA_API_TOKEN or scoped QWENPAW_DATA_API_KEYS); only liveness endpoints are exempt. Review the security model in the repository root README before exposing anything beyond 127.0.0.1.

Graph browsing / operations / exploration (prefix /api)

/api/health, /api/agent_query, /api/chat_stream, /api/execute_sql, /api/global_graph, /api/domain_graph, /api/search_nodes, and more (used by the frontend pages and scripts). See /docs for the full list.

Paths and configuration notes (post-merge)

• Importable backend packages live in src/ (context_manager, semantic_config); operational directories and assets (scripts/, config/, semantic_config.db) live at the package root; environment variables are read from the repository root .env.
• Path derivation in context_manager and semantic_config is anchored to the package root (absolute paths based on __file__), independent of the startup directory (CWD), so the service can be started from anywhere.
• .venv/, the repository root .env, and *.db files are ignored via .gitignore (semantic_config.db holds datasource connection info — do not commit it).

Bundled demo assets

qwenpaw-data-context ships a self-contained GAAP demo for the docker-compose one-shot setup in the main QwenPaw repository:

• context_manager/demo/assets/demo_semantic_config.xlsx — semantic-layer workbook
• context_manager/demo/assets/seed-postgres.sql — idempotent PostgreSQL seed script

These assets are accessible from a PyPI install via importlib.resources:

from context_manager.demo.loader import seed_postgres_sql, semantic_workbook_bytes

sql = seed_postgres_sql()
workbook_bytes = semantic_workbook_bytes()

See docs/demo.md for the asset lifecycle and regeneration instructions.