标题： 基于图的统一索引用于GPU上的混合搜索 显示英文标题

标题： All-in-one Graph-based Indexing for Hybrid Search on GPUs

摘要： 混合搜索作为一种有前景的范式，已出现以克服单一路径检索的局限性，提高推荐、信息检索和检索增强生成等应用的准确性。 然而，现有方法受到三重困境的限制：它们为了效率而牺牲灵活性，由于独立检索导致准确性下降，或在灵活组合检索路径时产生高昂的存储开销。 本文介绍了Allan-Poe，一种由GPU加速的新型一体化图索引，用于高效的混合搜索。 我们首先分析了现有检索范式的局限性，并提炼出有效混合搜索索引的关键设计原则。 在这些原则的指导下，我们构建了一个统一的基于图的索引，能够在单一、连贯的结构中灵活集成四种检索路径——密集向量、稀疏向量、全文和知识图谱。 为了实现高效的构建，我们设计了一个GPU加速的流水线，包括线程束级别的混合距离内核、RNG-IP联合剪枝和关键词感知的邻居回收。 对于查询处理，我们引入了一个动态融合框架，无需索引重建即可支持任何检索路径和权重的组合，利用知识图谱中的逻辑边来解决复杂的多跳查询。 在6个真实数据集上的大量实验表明，Allan-Poe实现了优越的端到端查询准确性，并在吞吐量上比最先进的方法高出1.5-186.4倍，同时显著降低了存储开销。 显示英文摘要

摘要： Hybrid search has emerged as a promising paradigm to overcome the limitations of single-path retrieval, enhancing accuracy for applications like recommendations, information retrieval, and Retrieval-Augmented Generation. However, existing methods are constrained by a trilemma: they sacrifice flexibility for efficiency, suffer from accuracy degradation due to separate retrievals, 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 distill key design principles for an effective hybrid search index. Guided by these principles, we architect a unified graph-based index that flexibly integrates four retrieval paths-dense vector, sparse vector, full-text, and knowledge graph-within a single, cohesive structure. To enable efficient construction, we design a GPU-accelerated pipeline featuring a warp-level hybrid distance kernel, RNG-IP joint pruning, and keyword-aware neighbor recycling. For query processing, we introduce a dynamic fusion framework that supports any combination of retrieval paths and weights without index reconstruction, leveraging logical edges from the knowledge graph to resolve complex multi-hop queries. Extensive experiments on 6 real-world datasets demonstrate that Allan-Poe achieves superior end-to-end query accuracy and outperforms state-of-the-art methods by 1.5-186.4x in throughput, while significantly reducing storage overhead.