mcp-rag-mini

mcp-rag-mini

Minimal RAG service exposing the same vector index over two interfaces:

• REST (FastAPI) — upload docs, query for top-k relevant chunks, get a suggested LLM prompt.
• MCP server (stdio) — a rag_search tool that any MCP-compatible client (Claude Desktop, custom agents) can call directly.

Both interfaces share one DocStore — ChromaDB for vectors, fastembed (ONNX) for embeddings, cosine similarity. No LLM inside; the service is a clean retrieval layer.

Why this shape

Most RAG demos mix embedding, retrieval, and generation into one script. That's fine for a notebook, but production systems separate them — the retrieval layer needs its own SLOs (recall@k, latency), its own tests, and its own scaling story. Splitting it out means:

• REST works for classic HTTP-based agents / dashboards / eval harnesses.
• MCP works for LLM tool-use (Claude, Cursor, custom loops) with no glue code.
• Same index, same guarantees — no drift between what "an LLM sees" vs "a dashboard sees".

Stack

• Python 3.12, FastAPI, Uvicorn
• ChromaDB (persistent) + fastembed (all-MiniLM-L6-v2, ONNX runtime — no torch)
• MCP Python SDK
• Docker + docker-compose

Run locally

python -m venv .venv
.venv\Scripts\activate  # Windows
# source .venv/bin/activate  # macOS/Linux

pip install -r requirements.txt
uvicorn app.api:app --reload

Or via Docker:

docker compose up --build

Try it

# Upload a document
curl -X POST http://localhost:8000/documents \
  -H "Content-Type: application/json" \
  -d '{"title":"Bitcoin whitepaper intro","text":"A purely peer-to-peer version of electronic cash..."}'

# Ask a question
curl -X POST http://localhost:8000/ask \
  -H "Content-Type: application/json" \
  -d '{"question":"What problem does Bitcoin solve?","top_k":3}'

MCP integration (Claude Desktop)

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "rag-mini": {
      "command": "python",
      "args": ["-m", "app.mcp_server"],
      "cwd": "/absolute/path/to/mcp-rag-mini"
    }
  }
}

Claude will see one tool — rag_search(query, top_k=4).

Structure

app/
├── store.py        # DocStore: chunk → embed → upsert → similarity search
├── api.py          # FastAPI: /documents, /ask, /health
├── mcp_server.py   # MCP stdio server: rag_search tool

What's intentionally NOT here

• No LLM generation — this repo is retrieval only. Bring your own model.
• No reranker — cosine top-k. Fine for demo; production needs cross-encoder rerank.
• Fixed-window chunking with overlap. Semantic chunking is a follow-up.
• No auth — mount behind a reverse proxy or add API key middleware.

Interview crib sheet

See INTERVIEW_NOTES.md — the actual reasoning behind each architectural choice, plus expected questions.