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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.
An open-source library for approximate nearest neighbor search in high-dimensional spaces, often used as a backend for vector databases and search engines.
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.
Locality-Sensitive Hashing (LSH) is an algorithmic technique for approximate nearest neighbor search in high-dimensional vector spaces, commonly used in vector databases to speed up similarity search while reducing memory footprint.
Vexvault is an open-source vector database designed for efficient storage, management, and similarity search of high-dimensional vector data.
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.
NMSLIB (Non-Metric Space Library) is an efficient and extensible similarity search library and toolkit for high-dimensional vector spaces, with a particular focus on generic and non-metric spaces. It is widely used for approximate nearest neighbor (ANN) search and evaluation of k-NN methods.
NMSLIB is open-source and free to use under the Apache-2.0 license.