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arXiv · 2504.20018

MINT: Multi-Vector Search Index Tuning

Jiongli Zhu·Yue Wang·Bailu Ding·Philip A. Bernstein·Vivek Narasayya·Surajit Chaudhuri
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Abstract

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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Jiongli Zhu, Yue Wang, Bailu Ding, Philip A. Bernstein, Vivek Narasayya, Surajit Chaudhuri. 2025-04-28. MINT: Multi-Vector Search Index Tuning. https://arxiv.org/abs/2504.20018

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