Spectral Hashing

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.

Advanced quantization technique that applies per-vector normalization and scalar quantization, adapting the quantization bounds individually for each vector. Achieves four-fold reduction in vector size while maintaining search accuracy with 26-37% overall memory footprint reduction.

A RAG optimization technique that compresses retrieved documents by extracting only the most relevant portions relative to the query. Reduces token usage and improves LLM response quality by removing irrelevant context.

Extreme vector compression technique converting each dimension to a single bit (0 or 1), achieving 32x memory reduction and enabling ultra-fast Hamming distance calculations with acceptable accuracy trade-offs.

Vector compression technique that splits high-dimensional vectors into subvectors and quantizes each independently, achieving significant memory reduction while enabling approximate similarity search.

Vector compression technique reducing precision of each vector component from 32-bit floats to 8-bit integers, achieving 4x memory reduction with minimal accuracy loss for vector search.