What is FAISS?
FAISS (Facebook AI Similarity Search) is a highly optimized open-source library developed by Meta AI for efficient similarity search and clustering of dense vectors. It provides algorithms and data structures specifically designed to handle billion-scale vector collections with minimal latency, supporting both exact and approximate nearest neighbor search. FAISS is widely used as the vector search engine underlying many production AI systems.
The library offers multiple indexing strategies optimized for different scenarios: flat indexes for exact search on smaller datasets, IVF (Inverted File) indexes that partition the vector space for faster approximate search, HNSW (Hierarchical Navigable Small World) graphs for excellent recall-speed tradeoffs, and product quantization techniques that compress vectors to reduce memory requirements. FAISS also supports GPU acceleration for dramatically faster search on large datasets.
FAISS has become a foundational component in the vector search ecosystem. While it operates at a lower level than full-featured vector databases (requiring more manual management of indexing and persistence), its performance and flexibility make it a popular choice for building custom retrieval systems. Many higher-level tools and vector databases use FAISS internally, and it integrates well with frameworks like LangChain for building RAG applications and agent memory systems.