Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for hi

FAISS Efficient Similarity Search Facebook AI's library for billion scale vector similarity search. When to use FAISS Use FAISS when: Need fast similarity search on large vector datasets (millions/billions) GPU acceleration required Pure vector similarity (no metadata filtering needed) High throughput, low latency critical Offline/batch processing of embeddings Metrics : 31,700+ GitHub stars Meta/Facebook AI Research Handles billions of vectors C++ with Python bindings Use alternatives instead : Chroma/Pinecone : Need metadata filtering Weaviate : Need full database features Annoy : Simpler, fewer features Quick start Installation Basic usage Index types 1. Flat (exact search) 2. IVF (inverted file) Fast approximate 3. HNSW (Hierarchical NSW) Best quality/speed 4. Product Quantization Memory efficient Save and load GPU acceleration LangChain integration LlamaIndex integration Best practices 1. Choose right index type Flat for <10K, IVF for 10K 1M, HNSW for quality 2. Normalize for cosine Use IndexFlatIP with normalized vectors 3. Use GPU for large datasets 10 100× faster 4. Save trained indices Training is expensive 5. Tune nprobe/ef search Balance speed/accuracy 6. Monitor memory PQ for large datasets 7. Batch queries Better GPU utilization Performance Index Type Build Time Search Time Memory Accuracy Flat Fast Slow High 100% IVF Medium Fast Medium 95 99% HNSW Slow Fastest High 99% PQ Medium Fast Low 90 95% Resources GitHub : https://github.com/facebookresearch/faiss ⭐ 31,700+ Wiki : https://github.com/facebookresearch/faiss/wiki License : MIT