Mastering Hybrid Search: Blending Classic Ranking Functions with Vector Search for Superior Search Relevance

Building on the bedrock of classic search and ranking systems based on established algorithms like BM25, the advent of vector search has opened doors to developing hybrid search systems that deliver highly relevant results. Though ranking algorithms in Lucene-based products are powerful, they sometimes come up short, leading to low recall with zero results or poor relevancy.

In this session, we’ll explore the use of unstructured data such as images and text for fuzzy searching and enhanced search relevancy. We’ll discuss best practices of using pre/post metadata filtering as well as defining hybrid ranking that combines both BM25 scores with vector-based algorithms such as HNSW.

Additionally, we will delve into the complexity of moving from research to production, achieving real-time at scale while managing the tradeoff between performance and accuracy.

We invite participants to join us in exploring the combined might of traditional and vector search paradigms.

Ohad Levi

Hyperspace

Co-founder and CEO of Hyperspace, product leader and entrepreneur. Passionate about data, search and performance and specialized in building best-in-class products in the conjunction between data, AI and algorithm optimization. Now started Hyperspace to solve some of the most challenging search performance, scalability and cost issues, breaking the limits of real-time search.