FAISS (Facebook AI Similarity Search) is a library for efficient similarity search and clustering of dense vectors. Purpose: Find nearest neighbors in high-dimensional spaces, orders of magnitude faster than brute force. Open source from Meta. Key capabilities: GPU acceleration, billion-scale search, multiple index types, clustering, dimensionality reduction. Index types: Flat: Exact search, baseline. IVF: Inverted file, clusters for faster search. HNSW: Graph-based, best accuracy/speed tradeoff. PQ: Product quantization for compression. IVF+PQ: Combined for scale. Use pattern: Build index on embeddings, query returns k nearest vectors by ID. GPU support: Dramatic speedup for large-scale search. Index can live on GPU. Scale: Handles billion-vector datasets with appropriate indexing and sharding. Integration: Python bindings primary, C++ core. Used under the hood by many vector databases. Training: Some indexes (IVF, PQ) need to be trained on representative data before adding vectors. Comparison to vector DBs: FAISS is library/building block. Vector DBs add persistence, filtering, APIs. Use cases: Core of similarity search systems, RAG pipelines, recommendation, and more.