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.

IVF is an indexing technique widely used in vector databases where vectors are clustered into inverted lists (partitions), enabling efficient Approximate Nearest Neighbor search by probing only a subset of relevant partitions at query time.

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.

Product Quantization (PQ) is a technique for compressing high-dimensional vectors into compact codes, enabling efficient approximate nearest neighbor (ANN) search in vector databases. PQ reduces memory footprint and search time, making it a foundational algorithm for large-scale vector search systems.

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.