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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.

Comprehensive guide covering index optimization, quantization, caching, and parameter tuning for vector databases. Includes techniques for balancing performance, cost, and accuracy at scale.

faiss-quickeradc is an extension of FAISS that implements the Quicker ADC approach to accelerate product-quantization-based approximate nearest neighbor search using SIMD, improving performance in vector database retrieval.

Open‑source library providing fast SIMD‑accelerated implementations of similarity and distance computations (e.g., vector inner products and distances), serving as an efficient alternative to scipy.spatial.distance and numpy.inner for vector search and vector database workloads.

SOAR is a set of improved algorithms on top of ScaNN that accelerate vector search by introducing controlled redundancy and multi-cluster assignment, enabling faster approximate nearest neighbor retrieval with smaller indexes in large‑scale vector databases and search systems.

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

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.

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.

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.