Faiss
원문: faiss
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 m
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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 (invert…
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