OpenSearchCon Europe 2025

Building a communication platform that handles diverse data types—emails, chats, attachments, and files—can be complex. Storing and organizing this data in a way that makes it searchable, optimizable, and queryable is key to providing valuable insights. Using OpenSearch as the backbone for data storage and search, this presentation explores how to structure multi-modal data and integrate it with Retrieval-Augmented Generation (RAG) applications powered by Large Language Models (LLMs).

The goal is to help users query and generate contextual replies based on a wide variety of input formats. We will share a use case showcasing how OpenSearch can be leveraged to create a seamless experience for managing and retrieving text-based and multimodal data efficiently. Tips and insights from our experience building such an application will be shared, covering best practices for managing data at scale, optimizing search performance, and implementing LLM-based feedback mechanisms to assist users in formulating contextually relevant responses.