Faiss

Faiss is a library developed by Meta's FAIR team for efficient similarity search and clustering of dense vectors. It enables fast nearest neighbor search in large-scale vector sets, supporting both exact and approximate search. Faiss is written in C++ with Python wrappers and includes GPU-accelerated indices. It provides a variety of indexing methods including inverted files, product quantization, and HNSW. Key features include non-exhaustive search, batch processing, range search, and support for maximum inner product search. Faiss is open-source and used in machine learning for retrieval, recommendation, and clustering tasks.

Key Benefits

• Efficient large-scale similarity search
• GPU acceleration for key algorithms
• Open-source with active development
• Supports multiple indexing methods