Rabitq Quantization With Lossless Re Ranking

via “rabitq quantization with lossless re-ranking”

A lightweight, lightning-fast, in-process vector database

Unique: Applies rotation-aware learning per segment to align high-variance dimensions before quantization, then transparently re-ranks with original vectors during query execution, achieving compression ratios comparable to product quantization while maintaining simpler parameter tuning

vs others: More memory-efficient than unquantized HNSW (8-16x compression vs 1x) while maintaining higher recall than simple scalar quantization, and requires less manual tuning than product quantization because rotation matrices are learned automatically per segment

via “vector quantization with configurable precision loss”

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Unique: Implements both product quantization and scalar quantization with quantization-aware distance metrics that account for precision loss, allowing recall to be maintained within 2-5% of full-precision search while reducing memory by 4-16x

vs others: More flexible than single-method quantization because it supports both PQ (better for high-dimensional vectors) and SQ (simpler, better for low-dimensional vectors), and quantization-aware metrics preserve recall better than naive quantization followed by standard distance computation