MCP Server with FAISS for RAG
by MCP-Mirror
This project provides a proof-of-concept Machine Conversation Protocol (MCP) server that allows an AI agent to query a vector database and retrieve relevant documents for Retrieval-Augmented Generation (RAG). It integrates FastAPI, FAISS, and LLMs for a complete RAG workflow.
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What is MCP Server with FAISS for RAG?
The MCP Server is a proof-of-concept implementation designed to enable AI agents to query a vector database and retrieve relevant documents for Retrieval-Augmented Generation (RAG). It uses the Machine Conversation Protocol (MCP) to facilitate communication between the agent and the server.
How to use MCP Server with FAISS for RAG?
The server can be installed using pipx or manually. After installation, you can download Move files from GitHub, index them into the FAISS vector database, and then query the database using the provided command-line tools or the MCP API. RAG integration with LLMs is also supported for generating enhanced responses.
Key features of MCP Server with FAISS for RAG
FastAPI server with MCP endpoints
FAISS vector database integration
Document chunking and embedding
GitHub Move file extraction and processing
LLM integration for complete RAG workflow
Simple client example
Sample documents
Use cases of MCP Server with FAISS for RAG
Querying code repositories for relevant code snippets
Building AI assistants that can answer questions about code
Creating RAG pipelines for code documentation
Developing tools for analyzing and understanding code
Integrating code understanding into other AI applications
FAQ from MCP Server with FAISS for RAG
What is FAISS?
What is FAISS?
FAISS is a library for efficient similarity search and clustering of dense vectors.
What is RAG?
What is RAG?
RAG stands for Retrieval-Augmented Generation, a technique that combines information retrieval with language generation to improve the quality and relevance of generated text.
How do I configure my GitHub token?
How do I configure my GitHub token?
Set the GITHUB_TOKEN environment variable in the .env file or as a system environment variable.
How do I use RAG with an LLM?
How do I use RAG with an LLM?
Set the OPENAI_API_KEY environment variable and use the mcp-rag command or rag_integration.py script.
Where are the indexed documents stored?
Where are the indexed documents stored?
The FAISS index is stored in the data/ directory by default. The location can be customized using the --index-file option.