Local FAISS
Provides local vector database functionality using FAISS for document ingestion, semantic search, and Retrieval-Augmented Generation (RAG) applications with persistent storage and customizable embedding models.
What is Local FAISS?
Local FAISS is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to provides local vector database functionality using faiss for document ingestion, semantic search, and retrieval-augmented generation (rag) applications with persistent storage and customizable embeddi...
Provides local vector database functionality using FAISS for document ingestion, semantic search, and Retrieval-Augmented Generation (RAG) applications with persistent storage and customizable embedding models.
This server falls under the Knowledge & Memory category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Provides local vector database functionality using FAISS for
Use Cases
Maintainer
Works with
Installation
PIP
pip install local-faiss-mcpManual Installation
pip install local-faiss-mcpConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Local FAISS
Local FAISS MCP Server provides a fully local vector database for AI assistants using Facebook's FAISS library, enabling document ingestion, semantic search, and Retrieval-Augmented Generation (RAG) without sending data to external APIs. It supports ingesting PDFs, text files, Markdown, and more, and persists the vector index to disk so your knowledge base survives between sessions. Developers use it to build private RAG applications where documents contain sensitive information that must not leave the local machine.
Prerequisites
- Python 3.9+ installed
- pip for installing the local-faiss-mcp package
- Optional: pandoc installed for DOCX, HTML, and EPUB file support
- An MCP-compatible client such as Claude Desktop or Claude Code
Install from PyPI
Install the local-faiss-mcp package with pip. This installs FAISS, sentence-transformers, and all required dependencies.
pip install local-faiss-mcpChoose an index storage directory
Decide where you want the FAISS index files to be stored on disk. This directory will persist your vector database between sessions. Create it if it does not exist.
mkdir -p ~/faiss-indexRun the server to verify it starts
Test the server with your chosen index directory and the default embedding model (all-MiniLM-L6-v2) to confirm everything is installed correctly.
python -m local_faiss_mcp --index-dir ~/faiss-indexAdd the server to your MCP client config
Register local-faiss-mcp in your Claude Desktop or Claude Code configuration, specifying the index directory and optionally a custom embedding model.
{
"mcpServers": {
"local-faiss": {
"command": "python",
"args": [
"-m", "local_faiss_mcp",
"--index-dir", "/Users/you/faiss-index",
"--embed", "all-MiniLM-L6-v2"
]
}
}
}Ingest your first document
Use the ingest_document tool to add a document to the vector store. You can provide a file path or raw text content. The document is chunked, embedded, and stored in FAISS.
Query the vector store
Use the query_rag_store tool to perform semantic search over your ingested documents. Results are returned as the most relevant chunks with optional re-ranking.
Local FAISS Examples
Client configuration
Claude Desktop configuration for Local FAISS MCP server with a custom index directory and re-ranking model.
{
"mcpServers": {
"local-faiss": {
"command": "python",
"args": [
"-m", "local_faiss_mcp",
"--index-dir", "/Users/you/faiss-index",
"--embed", "all-MiniLM-L6-v2",
"--rerank", "cross-encoder/ms-marco-MiniLM-L-6-v2"
]
}
}
}Prompts to try
Example prompts for ingesting documents and querying the local vector store.
- "Ingest the file /Users/me/docs/architecture.pdf into the vector store"
- "Search the vector store for information about authentication flows"
- "What does my documentation say about error handling?"
- "Summarize the key topics covered across all ingested documents"
- "Find the most relevant chunks about database migrations in my docs"Troubleshooting Local FAISS
ImportError for faiss or sentence_transformers on install
On Apple Silicon Macs, FAISS may need to be installed separately: 'pip install faiss-cpu'. On Linux, use 'pip install faiss-cpu' for CPU-only or 'faiss-gpu' for GPU support. Then retry 'pip install local-faiss-mcp'.
DOCX or HTML files fail to ingest
These formats require pandoc as an external dependency. Install pandoc from https://pandoc.org/installing.html and ensure the 'pandoc' command is available in your PATH. PDF, TXT, and MD files work without pandoc.
Semantic search returns irrelevant results
The default embedding model (all-MiniLM-L6-v2) is fast but general-purpose. For domain-specific corpora, try a more specialized embedding model via the --embed argument. Adding the --rerank flag with a cross-encoder model significantly improves result relevance.
Frequently Asked Questions about Local FAISS
What is Local FAISS?
Local FAISS is a Model Context Protocol (MCP) server that provides local vector database functionality using faiss for document ingestion, semantic search, and retrieval-augmented generation (rag) applications with persistent storage and customizable embedding models. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Local FAISS?
Install via pip with: pip install local-faiss-mcp. Then configure your AI client to connect to this MCP server.
Which AI clients work with Local FAISS?
Local FAISS works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Local FAISS free to use?
Yes, Local FAISS is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
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