MINT: Revolutionizing Multi-Vector Search with AI-Powered Index Tuning

Vector search has become a cornerstone of modern AI applications, from recommendation systems to retrieval-augmented generation (RAG). But as businesses increasingly rely on multi-modal data—combining text, images, and more—traditional single-vector search methods are hitting their limits. Enter MINT, a groundbreaking framework from Microsoft Research that’s redefining how we approach multi-vector search.

The Multi-Vector Challenge

In today's data landscape, items are rarely represented by a single vector. Consider an e-commerce product: it might have text descriptions, images, video demos, and technical specifications—each modality encoded as a separate vector. Current approaches either:

1. Create one index per query combination (one-index-per-query): This quickly becomes unsustainable as storage costs explode with each new query type.
2. Index each column independently (one-index-per-column): While storage-efficient, this forces expensive re-ranking operations that slow down queries.

MINT introduces a third way: intelligently tuned multi-column indexes that balance speed, accuracy, and storage efficiency.

How MINT Works

At its core, MINT solves two NP-hard problems:

1. Query Planning: Given a set of indexes, determine the most efficient way to execute a query while meeting recall targets.
2. Configuration Search: Select the optimal set of indexes to create for an entire workload within storage constraints.

The system uses:

• Dynamic programming algorithms to navigate the exponential search space efficiently
• Sampling-based estimators to predict index performance without full construction
• Beam search to explore promising index combinations

Real-World Performance

MINT delivers staggering improvements:

• 2.1× to 8.3× faster queries compared to traditional per-column indexing
• Near-optimal performance while using 33-50% less storage than per-query approaches
• Minutes to tune million-vector databases

A case study on an e-commerce-style workload showed MINT reducing items scanned by 87% for complex multi-column queries through smart use of combined indexes.

Why This Matters for Business

For enterprises leveraging AI, MINT offers:

1. Cost Savings: Reduced infrastructure needs from smarter storage utilization
2. Better UX: Faster response times for multi-modal searches
3. Future-Proofing: A flexible framework that adapts as new vector types emerge

As AI systems increasingly rely on rich, multi-modal data, tools like MINT will be crucial for maintaining performance at scale. This isn't just an academic advance—it's a practical solution to a growing business challenge in the age of AI.

The research team has made MINT compatible with popular vector indexes like HNSW and DiskANN, with plans to expand support further. As vector databases become standard enterprise infrastructure, expect to see MINT's influence grow across industries.