Philipp Krenn

Vector search is only a feature. Search engines and information retrieval have retaken their position as the foundation of RAG. This workshop takes you through decades of research, what has been working for a long time, and how it got better with Machine Learning.

Every vector database out there is both faster and slower than any other competitor — if you believe all the benchmarketing out there. Let's turn the marketing into useful benchmarks that actually help you: 1. How not to benchmark (spoiler: don’t trust the glossy charts). 2. What’s uniquely tricky about benchmarking vector search. 3. How to build meaningful benchmarks tailored to your use case.

Reciprocal rank fusion (RRF) combines multiple result sets with different relevance indicators — like lexical and dense or sparse vector search — into a single result set. One of the great attributes of RRF is that it requires no tuning, and the different relevance indicators do not have to be related to each other to achieve high-quality results.