NearestNeighbors.jl

AHPQ.jl is a Julia library providing training and inference for anisotropic hierarchical product quantization, compatible with ScaNN-style vector quantization and useful for building high-performance vector search pipelines.

A Go implementation of the HNSW approximate nearest neighbor search algorithm, enabling developers to embed efficient vector similarity search directly into Go services and custom vector database solutions.

A Rust implementation of the HNSW (Hierarchical Navigable Small World) approximate nearest neighbor search algorithm, useful for building high-performance, memory-safe vector search components in Rust-based AI and retrieval systems.

NVIDIA CAGRA is a GPU-accelerated graph-based library for approximate nearest neighbor searches, optimized for high-performance vector search leveraging modern GPU parallelism. It is suitable for scenarios requiring rapid, large-scale vector retrieval.

Amazon OpenSearch's k-NN plugin enables scalable, efficient vector search using ANN algorithms (IVF, HNSW) directly within a managed OpenSearch cluster. It is directly relevant for building, querying, and scaling vector databases on AWS.

NGT (Neighborhood Graph and Tree) is an open-source vector search engine designed for fast and scalable approximate nearest neighbor search.