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

arXiv: https://arxiv.org/abs/2501.13442 · HTML: https://mlsanity.com/paper/2501.13442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 2 of 2)

- [LANNS: A Web-Scale Approximate Nearest Neighbor Lookup System](https://mlsanity.com/paper/2010.09426/) (2020)
- [Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval](https://mlsanity.com/paper/2210.05521/) (2022)

## Abstract (arXiv)

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.