VADER: Filtered Vector Search with Declarative Recall

2026-09-29

AI Summary

Approximate filtered vector search suffers from execution complexity due to the coupling between predicate selectivity and relevance, rendering it highly dependent on manual parameter tuning. This work proposes the first hyperparameter-free, declarative recall approach: users need only specify a target recall rate, and the system automatically optimizes the query. The core innovations lie in the design of a filter-aware recall predictor and a dynamic early-stopping strategy, which together enable cross-scenario generalization and near-optimal adaptive execution. Experimental results demonstrate that the proposed method achieves up to 53% speedup over the best baseline while improving result quality by 28%.

Abstract

Approximate filtered vector search (FVS), a core operation in many data management tasks that combine structured data with vector embeddings, exhibits increased complexity due to the characteristics of filtering predicates. Each predicate is defined by selectivity (i.e., the fraction of vectors that satisfy the predicate) and correlation (i.e., the relationship between the filter and the vector space), which can significantly affect search difficulty even for the same query vector. This poses a key challenge for users aiming to integrate vector search with structured data, as efficient execution often requires extensive manual tuning of algorithm parameters. In this paper, we present VADER, the first approach that eliminates hyperparameter tuning by introducing declarative recall for approximate filtered vector search. With declarative recall, users specify a desired recall target, and VADER executes FVS queries to meet this target without requiring manual configuration. VADER achieves this by employing a filter-aware recall predictor that generalizes across varying selectivities and correlations without explicit tuning, and by performing early termination once the predicted recall reaches the user-defined target. Through extensive experimental evaluation, we show that VADER achieves near-optimal early termination, while providing significant speedups of up to 53% faster and improved result quality of 28% compared to the best-performing baseline.

Problem

• filtered vector search
• declarative recall
• hyperparameter tuning
• selectivity
• correlation

Innovation

• Approximate Filtered Vector Search
• Declarative Recall
• Filter-aware Recall Predictor
• Early Termination
• Hyperparameter-free