RAG Config Generator MCP
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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: RAG Factory
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
Build a RAG pipeline station by station and search your own documents with cited answers.
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.
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