OpenSearchCon Europe 2025

Search engines take in a query and return a set of results that best satisfy the need of the searcher. Words from a text box, user interface facets, and some user identity information have been the clues for good retrieval. Today, Large Language Models (LLMs) produce vectors with semantic information that augments or replaces lexical information for retrieving best results. OpenSearch's low-level APIs can connect with and use LLMs as part of an overall search solution. A full solution still requires some higher-level coding to take full advantage of these APIs. In this talk, we’ll cover some theory on similarity, and how and where vectors can help you retrieve better results. We’ll dive deep on opensearch-ml-quickstart (https://github.com/Jon-AtAWS/opensearch-ml-quickstart), an open-source example set for managing connections with OpenSearch, local models, and vector embeddings. We’ll walk through examples and code to help you get started quickly with OpenSearch’s vector capabilities: exact nearest-neighbor matching, dense vector embeddings, and sparse vectors. You’ll leave with a great tool in opensearch-ml-quickstart, along with the knowledge of how to apply it.