Local FAISS MCP Server
A Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for Retrieval-Augmented Generation (RAG) applications.
Features
Core Capabilities
- Local Vector Storage: Uses FAISS for efficient similarity search without external dependencies
- Document Ingestion: Automatically chunks and embeds documents for storage
- Semantic Search: Query documents using natural language with sentence embeddings
- Persistent Storage: Indexes and metadata are saved to disk
- MCP Compatible: Works with any MCP-compatible AI agent or client
v0.2.0 Highlights
- CLI Tool:
local-faisscommand for standalone indexing and search - Document Formats: Native PDF/TXT/MD support, DOCX/HTML/EPUB with pandoc
- Re-ranking: Two-stage retrieve and rerank for better results
- Custom Embeddings: Choose any Hugging Face embedding model
- MCP Prompts: Built-in prompts for answer extraction and summarization
Quickstart
# Install
pip install local-faiss-mcp
# Index documents
local-faiss index document.pdf
# Search
local-faiss search "What is this document about?"
