ScaNN Library

Vector index type based on Google's ScaNN algorithm combining tree-like structure with Asymmetric Hashing quantization, optimized for batch queries with 10x faster index generation and smaller memory footprint.

Product Quantization is a compression and indexing technique for vector search that splits vectors into subspaces and quantizes each part separately, allowing vector databases to store large-scale embeddings compactly while supporting efficient ANN search.

Parameterless and Universal Fast Finding of Nearest Neighbors - an LSH-based library for approximate nearest neighbor search with probabilistic guarantees. Features a parameterless design requiring only memory budget and result quality specifications.

A C++ library for approximate nearest neighbor searching in arbitrarily high dimensions, developed by David Mount and Sunil Arya at the University of Maryland. Provides data structures and algorithms for both exact and approximate nearest neighbor searching.

A C++ library for performing fast approximate nearest neighbor searches in high dimensional spaces. Contains multiple ANN algorithms and automatic algorithm selection based on dataset characteristics.

Fast Library for Approximate Nearest Neighbors containing a collection of algorithms optimized for nearest neighbor search in high dimensional spaces with automatic algorithm and parameter selection.