Billion-scale hybrid retrieval in a single query

“Vector databases became the de facto solution for embedding-based retrieval, which reveals its limits as users realize that similarity is not relevance. As a workaround, current solutions offer “hybrid retrieval” implemented as separate queries on disjoint indexes with late fusion of partial results based on rank or scores. In this talk, we present a fundamentally different model for billion‑scale hybrid retrieval, built from the ground up to address these challenges. Our storage format and query engine were designed to unify dense & sparse vectors, keywords, filters, and user‑defined scoring functions into a single distributed query, without relying on separate indexes and late fusion. This approach gives us a flexible query language that enables search practitioners to optimize relevance in their respective domains without having to manage and sync multiple data stores.”

Marek Galovic

TopK, Inc.