Local FAISS MCP Server

FAISS (Facebook AI Similarity Search) can be used as a local vector database, particularly in conjunction with an MCP (Machine Conversation Protocol) server for Retrieval-Augmented Generation (RAG) applications. FAISS as a Local Vector Database: FAISS is a library developed by Facebook AI Research for efficient similarity search and clustering of dense vectors. While not a full-fledged vector database in the sense of a client-server architecture like Pinecone or Milvus, it provides the core functionality for storing and querying vector embeddings locally. Storage: FAISS indexes can be created in memory or persisted to disk, allowing for local storage of vector embeddings. Search: It offers various indexing structures and algorithms for fast similarity search based on metrics like L2 distance, dot product, or cosine similarity. Integration: Libraries like LangChain provide convenient wrappers for using FAISS as a local vector store, simplifying its integration into RAG pipelines. MCP Server with FAISS for RAG: An MCP server can be built to expose FAISS functionality as a tool to an AI agent, enabling natural language interaction with the local vector store for RAG. Tool Definition: The MCP server defines tools (e.g., ingest_document, query_rag_store) that encapsulate FAISS operations. Document Ingestion: The ingest_document tool can handle document chunking, embedding generation, and storing these embeddings in a local FAISS index. Querying: The query_rag_store tool can perform similarity searches on the FAISS index based on a user query, retrieving relevant document chunks. Agent Interaction: An AI agent (e.g., powered by a large language model) can then use these tools to interact with the FAISS-backed vector store, enabling RAG by retrieving relevant information to augment its responses. This approach allows for building a local and self-contained RAG system where FAISS handles the vector storage and search, and an MCP server provides the interface for agent interaction.