Local Faiss MCP — AI skill for Claude Code

Local FAISS MCP Server

[](https://opensource.org/licenses/MIT) [](https://www.python.org/downloads/) [](https://github.com/nonatofabio/local_faiss_mcp/actions) [](https://badge.fury.io/py/local-faiss-mcp)

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-faiss command 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?"

Or use with Claude Code - configure MCP client (see [Configuration](#configuration-with-mcp-clients)) and try:

Use the ingest_document tool with: ./path/to/document.pdf
Then use query_rag_store to search for: "How does FAISS perform similarity search?"

Claude wil