MINT: Multi-Vector Search Index Tuning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 90 references that are in the map. First posted on arXiv 28 Apr 2025.
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30 of 90 references.
References · 30
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18 more
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