Multi-Stage Retrieval in Elasticsearch - Present and Future

<p>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.</p> <p>We’ll use 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.</p> <p>We’ll discuss where ML models and LLMs fit into the retrieval stack, from embedding generation to on-the-fly augmentation and semantic rerankers. </p> <p>Finally, we’ll look at future directions for search.</p> <p>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.</p>