根据查询动态选择最优近似最近邻搜索方法，提升检索效率与精度平衡。
Query-aware Routing for Filtered Approximate Nearest Neighbors Search
- 基于查询特征预测各方法召回率，动态路由选择最佳方案。
- 在5个未见数据集上实现领先召回率与每秒查询数平衡。
- 轻量模型仅引入微量延迟，适合生产环境部署。
过滤型近似最近邻（Filtered ANN）搜索结合向量相似性与属性谓词，是现代向量数据库和检索增强生成的核心技术。我们在多个数据集上对主流分类过滤型ANN方法进行基准测试，发现无单一方法始终最优；甚至在同一数据集与谓词类型下，最佳方法随查询变化。为此，我们提出一种查询感知路由框架：轻量级机器学习模型预测各候选方法在当前查询上的召回率，路由器参考离线基准表中各方法及参数设置的实测召回率与每秒查询数（QPS），选取召回率- QPS权衡最优的方法。消融实验将22个候选特征压缩至3个最小集合，并采用回归而非分类作为预测目标以提高精度。模型在6个真实世界数据集上训练，应用于5个未见验证数据集。结果表明，相比现有方法，该路由器在所有5个验证数据集上均实现最优的召回率与QPS平衡，且引入可忽略的延迟开销。
原文摘要 · Abstract (English)
Filtered ANN search, which combines vector similarity with attribute predicates, is a core primitive in modern vector databases and retrieval-augmented generation. We benchmark all major categorical filtered ANN methods across multiple datasets under three predicates and find that no single method dominates. Moreover, even within a single dataset and predicate type, the best method for a query can vary. Therefore, we propose a query-aware routing framework. A lightweight ML model predicts each candidate method's recall on the query, and the router consults an offline benchmark table that maps every method and parameter setting to its measured recall and QPS, then selects the method with the best recall--QPS trade-off. Our ablation study narrows 22 candidate features to a minimal set of three and we adopt regression rather than classification as the prediction target to sharpen accuracy. Our model is trained on six real-world datasets and applied to five unseen validation datasets. The final result shows that our router achieves state-of-the-art recall and QPS balance across all five validation datasets compared to existing filtered ANN baselines, while incurring negligible latency overhead.
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