The increasing prevalence of hybrid vector and relational data necessitates efficient, general support for queries that combine high-dimensional vector search with complex relational filtering. However, existing filtered search solutions are fundamentally limited by specialized indices, which restrict arbitrary filtering and hinder integration with general-purpose DBMSs. This work introduces \textsc{Compass}, a unified framework that enables general filtered search across vector and structured data without relying on new index designs. Compass leverages established index structures -- such as HNSW and IVF for vector attributes, and B+-trees for relational attributes -- implementing a principled cooperative query execution strategy that coordinates candidate generation and predicate evaluation across modalities. Uniquely, Compass maintains generality by allowing arbitrary conjunctions, disjunctions, and range predicates, while ensuring robustness even with highly-selective or multi-attribute filters. Comprehensive empirical evaluations demonstrate that Compass consistently outperforms NaviX, the only existing performant general framework, across diverse hybrid query workloads. It also matches the query throughput of specialized single-attribute indices in their favorite settings with only a single attribute involved, all while maintaining full generality and DBMS compatibility. Overall, Compass offers a practical and robust solution for achieving truly general filtered search in vector database systems.

翻译：混合向量与关系数据的日益普及，要求对结合高维向量搜索与复杂关系过滤的查询提供高效、通用的支持。然而，现有过滤搜索解决方案受限于专用索引，这些索引不仅限制了任意过滤能力，还阻碍了与通用数据库管理系统的集成。本文提出指南针框架，这是一种无需依赖新索引设计即可实现跨向量和结构化数据通用过滤搜索的统一框架。指南针利用现有索引结构（如用于向量属性的HNSW和IVF，以及用于关系属性的B+树），通过跨模态协调候选生成与谓词评估，实施一种有原则的协同查询执行策略。独特的优势在于，指南针通过允许任意合取、析取和范围谓词保持通用性，同时确保在面对高选择性或多属性过滤器时仍具稳健性。全面的实证评估表明，在多样化混合查询工作负载下，指南针始终优于现有唯一高性能通用框架NaviX。即使仅涉及单一属性，它在专用单属性索引的偏好设置中也能匹配其查询吞吐量，同时完全保持通用性和数据库管理系统兼容性。总体而言，指南针为在向量数据库系统中实现真正通用的过滤搜索提供了实用且稳健的解决方案。