FAISS

FAISS (Facebook AI Similarity Search) is a library developed by Meta Research for efficient similarity search and clustering of high-dimensional dense vectors. It provides highly optimized implementations of nearest neighbor search algorithms that can handle datasets ranging from thousands to billions of vectors.

FAISS offers multiple index types for different scale and accuracy tradeoffs. IndexFlatL2 provides exact search (brute force) suitable for small datasets. IndexIVFFlat partitions the vector space into clusters for faster approximate search at moderate scale. IndexHNSW implements the Hierarchical Navigable Small World algorithm for the best speed-accuracy tradeoff at large scale. FAISS also supports product quantization to compress vectors and reduce memory usage.

In RAG systems, FAISS is commonly used for local development and self-hosted production deployments where you need fast vector search without a managed service. It runs entirely in-process (no separate server), supports both CPU and GPU execution, and can be combined with metadata filtering for more precise retrieval. For managed production deployments, teams often migrate to services like Pinecone or Weaviate while keeping FAISS for development.