The Excellence
Directory Platform Template

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.

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.

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.