Billion-scale Similarity Search Using A Hybrid Indexing Approach With Advanced Filtering
Simeon Emanuilov, Aleksandar Dimov . Cybernetics and Information Technologies 2025 – 2 citations
[Paper]
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.