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.
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A 2024 paper introducing CANDY, a benchmark for continuous ANN search with a focus on dynamic data ingestion, crucial for next-generation vector databases.
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.
Optimized Product Quantization (OPQ) enhances Product Quantization by optimizing space decomposition and codebooks, leading to lower quantization distortion and higher accuracy in vector search. OPQ is widely used in advanced vector databases for improving recall and search quality.
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.
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.