Optimized Product Quantization (OPQ)

SOAR is a set of improved algorithms on top of ScaNN that accelerate vector search by introducing controlled redundancy and multi-cluster assignment, enabling faster approximate nearest neighbor retrieval with smaller indexes in large‑scale vector databases and search systems.

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.

Online Product Quantization (O-PQ) is a variant of product quantization designed to support dynamic or streaming data. It enables adaptive updating of quantization codebooks and codes in real-time, making it suitable for vector databases that handle evolving datasets.

Spectral Hashing is a method for approximate nearest neighbor search that uses spectral graph theory to generate compact binary codes, often applied in vector databases to enhance retrieval efficiency on large-scale, high-dimensional data.

Low-Rank Matrix Factorization algorithm for Approximate Nearest Neighbor Search, offering competitive performance with faster query times than leading libraries at various recall levels.

faiss-quickeradc is an extension of FAISS that implements the Quicker ADC approach to accelerate product-quantization-based approximate nearest neighbor search using SIMD, improving performance in vector database retrieval.