RAG Config Generator MCP
Generates RAG pipeline configurations from natural language descriptions, providing recommendations for chunking, embeddings, and vector stores.
README
RAG Config Generator MCP
An MCP (Model Context Protocol) server that generates RAG (Retrieval-Augmented Generation) pipeline configurations based on natural language descriptions.
Live Demo
- UI: Hugging Face Spaces
- API: Vercel Deployment
Features
- Generate Full Config: Describe your RAG use case in natural language
- Chunking Strategy: Get chunking recommendations based on document type
- Embeddings: Get embedding model recommendations based on use case and budget
- Vector Store: Get vector store recommendations based on data size and latency
API Endpoints
| Endpoint | Description |
|---|---|
GET / |
Server info |
GET /generate-rag-config?description=... |
Generate full RAG config |
GET /suggest-chunking?doc_type=...&doc_size=... |
Chunking recommendations |
GET /suggest-embeddings?use_case=...&budget=... |
Embedding recommendations |
GET /suggest-vector-store?data_size=...&latency=... |
Vector store recommendations |
Installation
pip install -r requirements.txt
Usage
Run Gradio UI
python app.py
Opens at http://localhost:7860
Run MCP Server
python server.py
The MCP server will be available at http://localhost:8000/mcp
Project Structure
mcp/
├── server.py # FastAPI + MCP server
├── rag_configs.py # RAG config templates and logic
├── app.py # Gradio UI
├── api/
│ └── index.py # Vercel serverless function
├── requirements.txt # Python dependencies
├── vercel.json # Vercel configuration
└── README.md # Documentation
Tech Stack
- Backend: Python, FastAPI, FastAPI-MCP
- Frontend: Gradio, HTML/CSS/JS
- Deployment: Vercel (API), Hugging Face Spaces (UI)
License
MIT
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