🤖 AI Summary
To address the challenge of efficient similarity search and multi-dimensional attribute filtering over billion-scale datasets on CPU-only systems, this paper proposes a disk-resident hybrid indexing paradigm. Our method deeply integrates multi-dimensional discrete filtering into the IVF-Flat architecture for the first time, jointly encoding dense embeddings and discrete features. We design a disk-aware hierarchical filtering mechanism and a CPU-native vectorized computation pipeline. The approach supports complex queries with millisecond-level latency while significantly improving filtering accuracy by 32% and reducing memory footprint by 58% compared to state-of-the-art CPU-based methods such as FAISS and Annoy. By unifying dense and discrete representations within a disk-efficient framework, our solution achieves a superior balance among retrieval efficiency, accuracy, and resource utilization—particularly critical for large-scale, memory-constrained CPU deployments.
📝 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.