iFVS：迈向实例优化的过滤向量搜索
iFVS: Towards Instance-Optimized Filtered Vector Search
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中文总结 AI 辅助
研究现代AI+DB系统中过滤向量搜索问题，提出iFVS技术，针对特定数据集和查询工作负载，采用特定于查询的码本生成方法，依查询向量和过滤谓词估计距离，实验证明其提升了QPS与召回率的权衡。
中文摘要 AI 辅助
过滤向量搜索（FVS）在现代人工智能+数据库系统中日益重要，它将向量相似性搜索与关系谓词相结合。量化通过对大型向量数据集进行查询处理，在这些系统中起着至关重要的作用。然而，有损方法，如乘积量化（PQ），在距离计算中会导致精度损失，从而对查询召回性能产生负面影响。在FVS中，由于相关向量空间会随关系谓词和选择性而变化，这个问题变得更具挑战性。受实例优化数据库系统组件成功的启发，我们引入了iFVS，一种实例优化的过滤向量搜索技术。给定固定的量化向量数据集和代表性的过滤向量查询工作负载，iFVS针对FVS采用特定于查询的码本生成方法，该方法针对特定数据集和查询工作负载进行实例优化。iFVS不是对所有查询使用固定码本，而是根据查询向量和过滤谓词来进行距离估计。这使得在保留紧凑的向量存储的同时，能够对压缩向量进行更准确的排序。实验表明，与固定码本量化的FVS基线相比，iFVS在多个过滤选择性区间内提高了每秒查询数（QPS）与召回率的权衡。
英文摘要
Filtered vector search (FVS) is increasingly important in modern AI + DB systems, where vector similarity search is combined with relational predicates. Quantization plays a vital role in these systems by enabling query processing over large vector datasets. However, lossy approaches, e.g., Product Quantization (PQ), incur a precision penalty during distance calculation, thereby negatively impacting the query recall performance. This problem becomes more challenging in FVS because the relevant vector space can change with the relational predicate and selectivity. Motivated by the success of instance-optimized database system components, we introduce iFVS, an Instance-Optimized Filtered Vector Search technique. Given a fixed, quantized vector dataset, and a representative workload of filtered vector queries, iFVS adopts a query-specific codebook generation approach for FVS that is instance-optimized towards a certain dataset and query workload. Instead of using a fixed codebook for all queries, iFVS conditions distance estimation on both the query vector and the filter predicate. This enables more accurate ranking over compressed vectors while preserving compact per-vector storage. Experiments show that iFVS improves the Queries Per Second (QPS)-recall tradeoff across several filter selectivity bins compared with fixed-codebook quantized FVS baselines.
