Provides document query capabilities using a hybrid retrieval RAG pipeline with Google's Gemini for generation.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RAG Query MCP Server What is the company's policy on remote work?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Hybrid-retrieval RAG (dense + BM25 sparse fused in Pinecone, Jina reranking, Gemini generation) over a single document.
pip install -r requirements.txtCreate 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 endpointRelated MCP server: PDF MCP Server
Chunks and embeds data/*.pdf into Pinecone, and fits the BM25 index.
python ingest.pyAll three answer questions through the same pipeline.
ask.py)python ask.py "What is the standard meal expense cap during business travel at Texazdi X?" # one-shot
python ask.py # interactive promptapp.py, FastAPI)python -m uvicorn app:app --host 127.0.0.1 --port 8000Then 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
mcp_server.py)Exposes a query_documents tool over MCP (stdio):
python mcp_server.pypython -m evals.evaluateRecent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
No tool schema history has been recorded yet.
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Ingest, manage, and retrieve documents for RAG-powered AI applications
Search your knowledge bases from any AI assistant using hybrid RAG.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
Medical RAG: semantic search for clinical guidelines, drug interactions, diagnoses & EHR data.
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