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.

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.

Multi-Vector Retrieval Algorithm that reduces multi-vector similarity search to single-vector similarity search via Fixed Dimensional Encodings. Achieves 10% improved recall with 90% lower latency compared to existing approaches.

A commutative vector quantization method for KV cache compression that reduces FP16 cache size by 87.5% with 2-bit quantization and enables 1-bit quantization, allowing LLaMA-3.1 8B to run with 128K context on a single RTX 4090 GPU.