MCP RAG Server

MCP RAG Server

MCP server for retrieving answers from local PDFs using RAG with FAISS and OpenAI.

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README

MCP Platform — Retrieval-Augmented Q&A

End-to-end RAG over local PDFs using FAISS, an MCP retrieval server, a FastAPI MCP client, OpenAI, and a small web UI.

Architecture

PDFs in /docs
    │
    ▼
MCP server (:8001/mcp)
  - embed + FAISS index (cached under /data/faiss_index)
  - tools: ensure_index, search_documents, list_documents, index_status
    │  Streamable HTTP (MCP)
    ▼
FastAPI backend (:8000)
  - on boot: ensure_index via MCP
  - LangGraph: retrieve → generate (OpenAI)
  - HTTP API for the UI
    │
    ▼
Frontend (/)

Requirements

  • Python 3.10+ (3.12 recommended; MCP does not support 3.9)
  • An OpenAI API key

Setup

python -m venv .venv

# Windows
.venv\Scripts\activate

# macOS / Linux
source .venv/bin/activate

pip install -r requirements.txt
copy .env.example .env   # or: cp .env.example .env

Put your OpenAI key in .env, then generate sample PDFs (optional):

python scripts/make_sample_pdfs.py

Drop any other .pdf files into docs/.

Run

Start the MCP server first:

python -m mcp_server.server

In a second terminal (venv activated):

python -m backend.main

Open http://127.0.0.1:8000.

API

Method Path Purpose
GET /api/health Backend + MCP status
GET /api/documents List PDFs
POST /api/reindex?force=true Rebuild FAISS index
POST /api/ask {"question":"..."} → answer + sources

Notes

  • Indexing runs automatically on MCP/backend startup when the PDF fingerprint changes; otherwise the on-disk FAISS index is reused.
  • Answers are grounded in retrieved chunks; the model is instructed to say it does not know when context is insufficient.

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