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arXiv · 2501.13442

Billion-scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering

Simeon Emanuilov·Aleksandar Dimov
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Abstract

This paper presents a novel approach for similarity search with complex filtering capabilities on billion-scale datasets, optimized for CPU inference. Our method extends the classical IVF-Flat index structure to integrate multi-dimensional filters. The proposed algorithm combines dense embeddings with discrete filtering attributes, enabling fast retrieval in high-dimensional spaces. Designed specifically for CPU-based systems, our disk-based approach offers a cost-effective solution for large-scale similarity search. We demonstrate the effectiveness of our method through a case study, showcasing its potential for various practical uses.

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BibTeXRIS

Simeon Emanuilov, Aleksandar Dimov. 2025-01-23. Billion-scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering. https://doi.org/10.2478/cait-2024-0035

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