LangChain FAISS

LangChain FAISS is an adapter that connects LangChain’s VectorStore interface to Facebook AI Similarity Search (FAISS), an open-source C++/Python library optimized for in-memory, billion-scale vector searches. Using FAISS.from_documents, developers break text into chunks, generate embeddings, and index them in either a flat L2 index or an HNSW/IVF-PQ structure for sub-second querying. At runtime, LangChain embeds the user’s prompt, calls similarity_search or max_marginal_relevance_search, and returns the top-k documents in the chain or a Retrieval-Augmented Generation (RAG) agent. Because FAISS runs locally, it avoids network latency, stores data locally for GDPR compliance, and requires no overhead other than hardware. The wrapper supports persistence to disk via index.faiss and index.pkl, multi-threaded bulk updates, and cosine or dot product metrics. Replacing FAISS with Pinecone, Chroma, or Milvus — or vice versa — is a one-line code change, allowing teams to prototype on a laptop and scale to cloud vector databases as traffic grows.