Filtered Vector Search

Advanced algorithm designed to make hybrid searches combining metadata filters and vector similarity more efficient, implemented in Apache Solr and other vector search systems.

The capability to filter vector search results based on metadata attributes before or during similarity search. Metadata filtering enables hybrid queries combining semantic search with structured constraints like dates, categories, tags, or user permissions, crucial for production RAG and search applications.

Complete guide to metadata filtering in vector search covering pre-filtering, post-filtering, and hybrid approaches. Addresses the Achilles heel of vector search with modern solutions.

NeurIPS 2023 paper presenting NHQ, an efficient and robust framework for approximate nearest neighbor search with attribute constraints, enabling hybrid queries combining vector similarity with structured filtering.

Joint Attribute Graphs for Filtered Nearest Neighbor Search, a research paper that addresses the challenge of combining vector similarity search with attribute filtering. JAG presents a novel index structure that efficiently handles filtered ANN queries common in real-world applications.

Advanced retrieval approach combining dense vectors, sparse vectors, and reranking in a multi-stage pipeline, achieving up to 48% better performance than single-method retrieval.