🤖 AI Summary
Existing vector similarity search systems face a fundamental efficiency–accuracy trade-off when supporting joint queries with attribute filtering. Method: This paper proposes a filtering-condition-centric vector indexing framework that directly embeds attribute logic into the vector space. Its core innovation is a geometric transformation ψ(v, f, α), provably guaranteeing retrieval accuracy and enabling plug-and-play filtering enhancement for mainstream ANN indexes—including HNSW, FAISS, and ANNOY—without modifying their internal structures. The transformation exhibits theoretical robustness against data distribution shifts. A meta-index architecture and theory-driven filtering encoding further ensure scalability and stability. Results: The method matches state-of-the-art recall while achieving 2.6–3.0× higher throughput; it maintains consistent performance across multi-filter scenarios and distributional variations, significantly outperforming both decoupled and jointly optimized alternatives.
📝 Abstract
The explosive growth of vector search applications demands efficient handling of combined vector similarity and attribute filtering; a challenge where current approaches force an unsatisfying choice between performance and accuracy. We introduce Filter-Centric Vector Indexing (FCVI), a novel framework that transforms this fundamental trade-off by directly encoding filter conditions into the vector space through a mathematically principled transformation $psi(v, f, alpha)$. Unlike specialized solutions, FCVI works with any existing vector index (HNSW, FAISS, ANNOY) while providing theoretical guarantees on accuracy. Our comprehensive evaluation demonstrates that FCVI achieves 2.6-3.0 times higher throughput than state-of-the-art methods while maintaining comparable recall. More remarkably, FCVI exhibits exceptional stability under distribution shifts; maintaining consistent performance when filter patterns or vector distributions change, unlike traditional approaches that degrade significantly. This combination of performance, compatibility, and resilience positions FCVI as an immediately applicable solution for production vector search systems requiring flexible filtering capabilities.