The Excellence
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Vector compression technique representing each component as a single bit (0 or 1). Achieves 40x retrieval speedup and 28x reduced index size for embeddings centered around zero.

Vector compression technique mapping float32 dimensions to int8 representations. Achieves 4x memory compression through learned range mapping while maintaining 98-99% recall.

Vector indexing method combining inverted file index with product quantization for memory-efficient search. Reduces storage from 128x4 bytes to 32x1 bytes (1/16th) while maintaining search quality.

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.

nanopq is a lightweight product quantization library for efficient vector compression and similarity search, which is an important feature for vector databases that need to store and query large-scale vector data efficiently.

Resources and tools on quantization techniques for vectors, which are essential for optimizing storage and retrieval in vector databases.