MCP Servers

RAG Query MCP Server

Enables querying a hybrid-retrieval RAG pipeline (dense + BM25) over ingested PDF documents, returning answers generated by Gemini.

README

RAG Pipeline

Hybrid-retrieval RAG (dense + BM25 sparse fused in Pinecone, Jina reranking, Gemini generation) over a single document.

Setup

pip install -r requirements.txt

Create a .env in the project root:

GOOGLE_API_KEY=your_key
PINECONE_API_KEY=your_key
PINECONE_INDEX_NAME=rag-hybrid
JINA_API_KEY=your_key
API_KEY=your_choice          # protects the FastAPI endpoint

Ingest (run once before querying)

Chunks and embeds data/*.pdf into Pinecone, and fits the BM25 index.

python ingest.py

Run the endpoints

All three answer questions through the same pipeline.

1. CLI (`ask.py`)

python ask.py "What is the standard meal expense cap during business travel at Texazdi X?"   # one-shot
python ask.py                                             # interactive prompt

2. HTTP API (`app.py`, FastAPI)

python -m uvicorn app:app --host 127.0.0.1 --port 8000

Then query it (send the API_KEY from your .env as the x-api-key header):

curl -X POST http://127.0.0.1:8000/query \
  -H "Content-Type: application/json" \
  -H "x-api-key: your_choice" \
  -d '{"question": " How many days of paid annual leave can be carried over to the next year, and what is the total annual leave allotment?"}'

Health check: GET http://127.0.0.1:8000/health

3. MCP server (`mcp_server.py`)

Exposes a query_documents tool over MCP (stdio):

python mcp_server.py

Evaluation (optional)

python -m evals.evaluate

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