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.

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.

Billion-scale graph-based ANNS index with direct insertion capabilities. Achieves <1ms search latency with >10x less memory than in-memory indexes through GC-free design and update combining.

Disk-based approximate nearest neighbor search framework with page-aligned graph structure. Achieves 1.85x-10.83x higher throughput than state-of-the-art methods through optimized SSD utilization.

Low-latency, billion-scale updatable graph-based vector store on SSD. Achieves <1ms search latency with 10x less memory than in-memory indexes through alignment of best-first search with SSD characteristics.

Pure-Rust implementation of HNSW algorithm for approximate nearest neighbor search. Decouples graph from vector storage for flexible deployment. Supports dense floating point and quantized int8 vectors. This is an OSS library.