Retrieving the most similar vector embeddings to a given query among a massive collection of vectors has long been a key component of countless real-world applications. The recently introduced Retrieval-Augmented Generation is one of the most prominent examples. For many of these applications, the database evolves over time by inserting new data and removing outdated data. In these cases, the retrieval problem is known as streaming similarity search. While Locally-Adaptive Vector Quantization (LVQ), a highly efficient vector compression method, yields state-of-the-art search performance for non-evolving databases, its usefulness in the streaming setting has not been yet established. In this work, we study LVQ in streaming similarity search. In support of our evaluation, we introduce two improvements of LVQ: Turbo LVQ and multi-means LVQ that boost its search performance by up to 28% and 27%, respectively. Our studies show that LVQ and its new variants enable blazing fast vector search, outperforming its closest competitor by up to 9.4x for identically distributed data and by up to 8.8x under the challenging scenario of data distribution shifts (i.e., where the statistical distribution of the data changes over time). We release our contributions as part of Scalable Vector Search, an open-source library for high-performance similarity search.

翻译：在海量向量集合中检索与给定查询最相似的向量嵌入，长期以来一直是无数实际应用的关键组成部分。近期提出的检索增强生成便是其中最显著的例子之一。在这些应用中，数据库会随时间动态变化，包括新数据的插入和旧数据的删除。在这种情况下，检索问题被称为流式相似性搜索。尽管局部自适应向量量化——一种高效的向量压缩方法——在非动态数据库中实现了最先进的搜索性能，但其在流式场景中的实用性尚未得到证实。本研究在流式相似性搜索中深入探讨了LVQ。为支撑评估，我们提出了LVQ的两种改进方法：Turbo LVQ与多均值LVQ，它们分别将搜索性能提升了高达28%和27%。研究表明，LVQ及其新变体实现了极速向量搜索，在数据同分布条件下，其性能比最接近的竞争方法快9.4倍；在更具挑战性的数据分布偏移场景（即数据的统计分布随时间变化）中，性能提升高达8.8倍。我们将研究成果作为可扩展向量搜索——一个用于高性能相似性搜索的开源库——的一部分公开发布。