针对多向量数据库搜索，提出高效索引调优框架，显著降低查询延迟。
MINT: Multi-Vector Search Index Tuning
- 设计多向量搜索索引调优框架，自动选择最优索引组合。
- 在满足存储与召回约束下，查询延迟提升2.1至8.3倍。
- 适用于多模态、多特征场景的实时检索系统。
向量搜索在众多实际应用中至关重要。除了单向量搜索外，多向量搜索在多模态和多特征场景中日益重要。在多向量数据库中，每行代表一个项目，每列代表项目的某个特征，每个单元格是一个高维向量。在多向量数据库中，索引的选择对性能有显著影响。尽管关系型数据库的索引调优已有广泛研究，但多向量搜索的索引调优仍不明确且具有挑战性。本文定义了多向量搜索索引调优问题，并提出一个求解框架。具体而言，给定一个多向量搜索工作负载，我们开发算法以找到最小化延迟并满足存储和召回约束的索引。相较于基线方法，我们的方案实现2.1倍到8.3倍的延迟加速。
原文摘要 · Abstract (English)
Vector search plays a crucial role in many real-world applications. In addition to single-vector search, multi-vector search becomes important for multi-modal and multi-feature scenarios today. In a multi-vector database, each row is an item, each column represents a feature of items, and each cell is a high-dimensional vector. In multi-vector databases, the choice of indexes can have a significant impact on performance. Although index tuning for relational databases has been extensively studied, index tuning for multi-vector search remains unclear and challenging. In this paper, we define multi-vector search index tuning and propose a framework to solve it. Specifically, given a multi-vector search workload, we develop algorithms to find indexes that minimize latency and meet storage and recall constraints. Compared to the baseline, our latency achieves 2.1X to 8.3X speedup.
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