All-in-one Graph-based Indexing for Hybrid Search on GPUs
Zhonggen Li, Yougen Li, Yifan Zhu, Congcong Ge, Zhaoqiang Chen, Yunjun Gao

TL;DR
Allan-Poe is a GPU-accelerated, unified graph index that efficiently supports hybrid lexical and semantic search, improving accuracy, flexibility, and storage efficiency for complex retrieval tasks.
Contribution
The paper presents Allan-Poe, a novel all-in-one graph index that unifies multiple retrieval paths and enables flexible, efficient hybrid search on GPUs without index reconstruction.
Findings
Achieves 1.5x-186.4x higher throughput than state-of-the-art methods.
Significantly reduces storage overhead for hybrid search indexes.
Demonstrates superior accuracy on 6 real-world datasets.
Abstract
Hybrid search has emerged as a promising paradigm that combines lexical and semantic retrieval, enhancing accuracy for applications such as recommendations, information retrieval, and Retrieval-Augmented Generation. However, existing methods are constrained by a trilemma: they sacrifice flexibility for efficiency, suffer from accuracy degradation, or incur prohibitive storage overhead for flexible combinations of retrieval paths. This paper introduces Allan-Poe, a novel all-in-one graph index accelerated by GPUs for efficient hybrid search. We first analyze the limitations of existing retrieval paradigms and extract key design principles for an effective hybrid index. Guided by the principles, we architect a unified graph-based index that flexibly integrates three retrieval paths (dense vector, sparse vector, and full-text) within a single, cohesive structure. To enable efficient…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGraph Theory and Algorithms · Information Retrieval and Search Behavior · Advanced Image and Video Retrieval Techniques
