根据查询特征动态选择最优检索策略，提升向量搜索效率。
Efficient Filtered-ANN via Learning-based Query Planning
- 基于数据与查询统计信息，智能判断先过滤还是先近似搜索
- 实验显示最高提速4倍，召回率保持在90%以上
- 适用于各类过滤条件和后端索引，适合大规模向量检索系统
过滤型近似最近邻（Filtered ANN）搜索在向量检索中日益重要，但系统面临执行顺序的困境：预过滤（先过滤再检索）需昂贵的谓词索引构建；后过滤（先检索再过滤）在低选择性场景下可能浪费计算并降低召回率。本文提出一种基于学习的查询规划框架，通过轻量级预测（如维度、语料库大小、分布特征和谓词统计）动态选择最优执行路径。该框架支持类别/关键词及范围等多种过滤类型，兼容任意后端ANN索引。实验表明，相比强基线方法，本方法实现最高4倍加速，且召回率不低于90%。
原文摘要 · Abstract (English)
Filtered ANN search is an increasingly important problem in vector retrieval, yet systems face a difficult trade-off due to the execution order: Pre-filtering (filtering first, then ANN over the passing subset) requires expensive per-predicate index construction, while post-filtering (ANN first, then filtering candidates) may waste computation and lose recall under low selectivity due to insufficient candidates after filtering. We introduce a learning-based query planning framework that dynamically selects the most effective execution plan for each query, using lightweight predictions derived from dataset and query statistics (e.g., dimensionality, corpus size, distribution features, and predicate statistics). The framework supports diverse filter types, including categorical/keyword and range predicates, and is generic to use any backend ANN index. Experiments show that our method achieves up to 4x acceleration with >= 90% recall comparing to the strong baselines.
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