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Similarity search in the blink of an eye with compressed indices
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Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity search, of relevance for a wide range of applications. Graph-based indices are currently the best performing techniques for billion-scale similarity search. However, their random-access memory pattern presents challenges to realize their full potential. In this work, we present new techniques and systems for creating faster and smaller graph-based indices. To this end, we introduce a novel vector compression method, Locally-adaptive Vector Quantization (LVQ), that uses per-vector scaling and scalar quantization to improve search performance with fast similarity computations and a reduced effective bandwidth, while decreasing memory footprint and barely impacting accuracy. LVQ, when combined with a new high-performance computing system for graph-based similarity search, establishes the new state of the art in terms of performance and memory footprint. For billions of vectors, LVQ outcompetes the second-best alternatives: (1) in the low-memory regime, by up to 20.7x in throughput with up to a 3x memory footprint reduction, and (2) in the high-throughput regime by 5.8x with 1.4x less memory.
Forward citations
Cited by 6 Pith papers
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MERIT: Efficient In-Place Deletion for Dynamic Graph-Based Approximate Nearest Neighbor Indexes
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A benchmark of six cloud CPUs shows that the best choice for vector search depends on the index type and quantization, with Graviton3 winning on queries per dollar and Zen4 on raw IVF throughput.
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A graph-based ANN search method that integrates RaBitQ quantization and SIMD batching, with implicit re-ranking and batch-aligned graph refinement, sets a new time-accuracy state of the art.
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OneDB: A Distributed Multi-Metric Data Similarity Search System
A Spark-based system that indexes each data modality separately and combines them with learned weights to support exact distributed similarity search across multi-modal data.
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CHASE: A Native Relational Database for Hybrid Queries on Structured and Unstructured Data
CHASE natively integrates ANN vector search into a compiled relational engine, cuts redundant similarity computation in hybrid queries, and reports up to 7,500x speedups.
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Arctic-Embed 2.0: Multilingual Retrieval Without Compromise
Arctic-Embed 2.0 delivers open multilingual embedding models with competitive MTEB-R and CLEF retrieval scores and strong 256-dimension MRL compression, plus new evidence on cross-lingual transfer.
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