RAG Config Generator MCP
Click on "Deploy 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 Config Generator MCPGenerate a RAG config for a legal document search system."
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.
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
API: Vercel Deployment
Related MCP server: Pipelex MCP Server
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 |
| Server info |
| Generate full RAG config |
| Chunking recommendations |
| Embedding recommendations |
| Vector store recommendations |
Installation
pip install -r requirements.txtUsage
Run Gradio UI
python app.pyOpens at http://localhost:7860
Run MCP Server
python server.pyThe 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 # DocumentationTech Stack
Backend: Python, FastAPI, FastAPI-MCP
Frontend: Gradio, HTML/CSS/JS
Deployment: Vercel (API), Hugging Face Spaces (UI)
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
Ingest, manage, and retrieve documents for RAG-powered AI applications
Multi-agent AI pipeline that generates professional Solution Architecture Documents.
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.
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