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FAISS (Facebook AI Similarity Search) is a popular open-source library for efficient similarity search and clustering of dense vectors. Developed by Facebook/Meta, it supports billions of vectors and is widely used to power vector search engines and databases, especially where raw speed and scalability are needed.
NMSLIB is an efficient similarity search library and toolkit for high-dimensional vector spaces, supporting a variety of indexing algorithms for vector database use cases.
SPTAG is a distributed approximate nearest neighbor (ANN) library for building and searching large-scale vector indexes, supporting efficient and scalable vector search scenarios.
Epsilla is an open-source vector database optimized for high-performance similarity search and scalable storage of vector embeddings.
HVS is a graph-based index structure leveraging Voronoi diagrams for approximate nearest neighbor search in high-dimensional vector spaces. It is directly relevant to vector databases as it provides efficient similarity search capabilities for large-scale vector data.
KGraph is an open-source library for fast approximate nearest neighbor search in high-dimensional vector spaces, applicable to vector database solutions.
FAISS (Facebook AI Similarity Search) is an open-source library for efficient similarity search and clustering of dense vectors, developed by Facebook/Meta. It is widely used for powering vector search engines and databases, especially in applications requiring high speed and scalability.
conda install -c pytorch faiss-cpuconda install -c pytorch faiss-gpuopen-source, ann, similarity-search, scalable
SDKs & Libraries