At Data Council 2025 AI Launchpad, we introduced TopK, our new retrieval engine built to radically simplify and modernize search infrastructure. We walked through why existing vector databases fall short—especially when it comes to combining vector similarity with filters, keyword relevance, and domain-specific ranking. These systems often hard-code similarity metrics at index time and can't efficiently support flexible scoring or real-world metadata filtering, leading to poor relevance and unnecessary complexity.
In response, we built TopK: a unified, cloud-native query engine that supports dense and sparse vector search, BM25 keyword scoring, multi-vector retrieval, and expressive scoring logic—all in a single query on a single dataset. It lets you efficiently blend signals like recency, quality, or geolocation into your ranking without sacrificing performance.
We showcased a live demo using a medical research dataset to illustrate how TopK handles everything from basic vector search to hybrid queries with keyword matching, metadata filters, and custom scoring expressions. We also showed how TopK can take care of embedding and reranking out of the box—making it easy to go from raw data to production-grade search with minimal setup.