rag-retriever-mcp

rag-retriever

A lightweight, local-first document retrieval engine that mounts to an agent as an MCP tool. Drop files in; the agent searches them and answers with its own LLM. There is no LLM in here — this is only the "front half" of RAG (extract → chunk → embed → store + similarity search).

your agent (owns the LLM)
   │  calls MCP tool: search("question")
   ▼
rag-retriever ──► extract ─► chunk ─► embed ─► LanceDB
   ▲                                              │
   └────────── returns relevant passages ◄────────┘
   │
   agent reads passages → answers with its own LLM

Built from the same proven pieces as Open Notebook (file extraction + bge-m3 embeddings + vector search), minus the heavyweight backend, UI, and answer/podcast generation you don't need.

Why this shape

• One LLM, not two. The retriever never answers; your agent does. You keep

full control of reasoning, prompts, and cost.

• Local-first. Default backend (fastembed) runs entirely offline, no server.
• Pluggable embeddings. Switch between fully local and a China-friendly cloud

API (SiliconFlow) with one env var — no code change.

Install

cd rag-retriever
uv sync
cp .env.example .env   # then pick your embedding backend

Configure the embedding backend (.env)

| RAG_EMBED_BACKEND | What it uses | Notes | |---|---|---| | local (default) | fastembed (ONNX, in-process) | 100% offline, no server, heavier first install | | ollama | local Ollama daemon | ollama serve + ollama pull bge-m3 | | openai | OpenAI-compatible API (e.g. SiliconFlow) | needs RAG_OPENAI_API_KEY; text leaves the machine |

⚠️ Index-time and query-time must use the same backend + model. Changing the model means re-indexing everything.

Use (CLI, for testing)

uv run rag-retriever index "C:\path\to\docs"     # a file or a whole folder
uv run rag-retriever search "什么是表见代理" -k 5
uv run rag-retriever list
uv run rag-retriever stats

Mount as an MCP server (the real entry point)

Run uv run rag-retriever-mcp (stdio). Register it with your MCP client. For Claude Code, add to your MCP config:

{
  "mcpServers": {
    "rag-retriever": {
      "command": "uv",
      "args": ["run", "--directory", "D:\\Vibe Coding Items\\rag-retriever", "rag-retriever-mcp"]
    }
  }
}

Tools exposed: index_path, search, list_sources, stats.

Supported files

pdf, docx, pptx, xlsx, html, md, txt, csv, json, epub (via markitdown). Scanned / image-only PDFs need an OCR engine (tesseract) installed separately; without it they extract empty and are reported as skipped.

Layout

rag_retriever/
  config.py     # env-driven config; picks the embedding backend
  extract.py    # file -> text (markitdown)
  chunk.py      # token-based chunking with overlap
  embed.py      # local | ollama | openai-compatible backends
  store.py      # LanceDB vector store (embedded, no server)
  pipeline.py   # ingest + search orchestration (no LLM)
  server.py     # MCP server (agent-facing)
  cli.py        # manual CLI