graphrag-ts

TypeScript GraphRAG for Markdown corpora. Build a knowledge graph, detect communities, and answer with evidence-backed retrieval.

Benchmarks / 基准报告 English report · 中文报告

Overview

graphrag-ts is a TypeScript GraphRAG reference implementation for Markdown corpora. It builds a knowledge graph in PostgreSQL and answers questions with hybrid retrieval and source evidence, without requiring a Python runtime.

• Markdown ingestion, entity/relationship extraction, and community detection

• Full and incremental builds, with unchanged community summaries reused

• Hybrid vector, keyword, and graph retrieval, plus corpus-wide Map-Reduce queries

• MCP server with isolated workspaces and a local browser showcase

See the architecture guide for the pipeline and module details.

Related MCP server: nvim-markdown-notes-memgraph

Quick start

MCP with Docker Compose

The Compose stack starts PostgreSQL/pgvector and the MCP service. You must provide reachable chat and embedding endpoints; see the MCP guide for configuration, client setup, and workspace behavior.

cp .env.example .env
# Configure the database password and model endpoints in .env.
docker compose up -d --build
curl http://127.0.0.1:3000/healthz

Connect an MCP client to http://127.0.0.1:3000/mcp. For the optional local browser showcase, configure the models and run docker compose --profile showcase up -d --build --wait, then open http://127.0.0.1:8080. The showcase is a local demo, not an authentication layer; do not expose it to untrusted users.

Use the library

pnpm install
pnpm run db:generate
cp .env.example .env
# Configure DATABASE_URL and RAG_* model settings; see the guides below.
pnpm run db:push
bun run examples/demo.ts

The library requires Bun, PostgreSQL with pgvector, and compatible chat and embedding endpoints. See the English guide or 中文指南 for environment setup, API examples, adapters, and tuning.

Benchmark

The reported in-corpus run reached 10/12 strict hits and 95.8% combined lexical recall on 12 questions from five indexed chapters. This is a small project benchmark, not human semantic grading or a cross-project comparison. The reports include methodology, per-query results, and limitations.

The English and Chinese reports are linked at the top of this page. To run the three-question Docker smoke benchmark, use pnpm benchmark:docker:quick with the model endpoints configured and loaded. See the MCP guide for prerequisites and the report for benchmark scope.

Documentation

• docs/architecture.md: architecture and data flow

• docs/en-US.md: English user guide

• docs/zh-CN.md: Chinese user guide

• docs/comparison.md: comparison notes

• CONTRIBUTING.md: contribution guide

Contributing

Contributions are welcome through issues and pull requests. The project is organized around small, testable modules, so the most useful changes usually fit into one of these areas:

• graph construction quality

• retrieval quality and ranking

• namespace isolation

• model-loading robustness

• documentation and examples

License

MIT