Billion-scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915117784891392 |
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| author | Emanuilov, Simeon Dimov, Aleksandar |
| author_facet | Emanuilov, Simeon Dimov, Aleksandar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13442 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Billion-scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering Emanuilov, Simeon Dimov, Aleksandar Information Retrieval Databases Distributed, Parallel, and Cluster Computing Machine Learning 68P20, 62H30, 68T07 H.3.3; I.2.6; G.1.0 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. |
| title | Billion-scale Similarity Search Using a Hybrid Indexing Approach with Advanced Filtering |
| topic | Information Retrieval Databases Distributed, Parallel, and Cluster Computing Machine Learning 68P20, 62H30, 68T07 H.3.3; I.2.6; G.1.0 |
| url | https://arxiv.org/abs/2501.13442 |