Search in Elasticsearch keeps evolving, from traditional BM25 keyword retrieval to multi-stage search that combine lexical, vector, and language-model-driven intelligence. In this talk, we’ll explore how Elasticsearch APIs enable developers to build hybrid search systems that mix classical scoring, dense vector search and semantic reranking in a single coherent workflow.

We’ll walk through the modern Query DSL: retrievers, hybrid search constructs, and rescorers that allow incremental refinement of results. We’ll then dive into ES|QL, Elasticsearch’s new query language, and show how constructs like FORK, FUSE, RERANK, COMPLETION, and full-text functions let you build multi-stage pipelines in a simple query.

We’ll discuss where ML models and LLMs fit into the retrieval stack, from embedding generation to on-the-fly augmentation and semantic rerankers.

Finally, we’ll look at future directions for search.

If you want a practical and forward-looking view of how search is evolving in Elasticsearch—and how to put multi-stage retrieval to work—this session is for you.