OpenSearchCon Europe 2025

In today's rapidly evolving AI landscape, Agentic Retrieval-Augmented Generation (RAG) systems represent a significant leap forward in intelligent search capabilities. This presentation introduces how Agentic RAG enhances traditional RAG by incorporating autonomous agents that can both retrieve and act on information. We'll demonstrate a practical implementation using OpenSearch's vector database, showcasing how this combination enables agents to understand user queries, retrieve relevant information, and execute meaningful actions based on the context.

The presentation will focus on three key areas: the fundamental architecture of Agentic RAG, its integration with OpenSearch vector search capabilities, and practical scaling considerations for enterprise applications. Through demonstration steps, we'll show how this system handles real-world scenarios, highlighting the potential impact on business operations. We'll conclude with insights into deployment strategies and best practices for maintaining system performance at scale, providing attendees with actionable knowledge they can apply in their own organizations.

OpenSearch provides several tools for search and information retrieval, important in many stages of prompt processing. However, to be used in a varied range of industries and regulations such as healthcare, telco or finance, the solution must be proven to be secure, robust and highly available.

In this talk we will discuss how to build a secure conversational chat infra from the ground up to answer your prompts, using OpenSearch. We will explore how we can use existing features of OpenSearch to build the basis of this infra, e.g. vector search and RAG. We will discuss the latest technologies such as confidential computing and hardware acceleration to get the most out of the environment. We will also explore an OpenSearch VM operator that helps with the lifecycle of the cluster. It provides the features needed to scale, upgrade, specialize nodes in different roles according to the needs and manage security such as certificate rotation and user management.

Versioning OpenSearch indexes is critical for maintaining data integrity, ensuring seamless updates, and enabling rollback capabilities in modern CICD pipelines. This presentation explores how Terraform and Python can be combined to manage the versioning of OpenSearch indexes effectively. By leveraging Terraform’s infrastructure-as-code capabilities and Python’s scripting flexibility, teams can automate the lifecycle management of indexes, ensuring consistent reintegration and streamlined operations. Key benefits include enhanced automation, improved traceability, and reduced manual overhead.

Join us for an in-depth exploration of tiered caching in OpenSearch, a powerful search and analytics engine. Caching plays a crucial role in optimizing performance for performance-intensive applications like OpenSearch. This session delves into the limitations of on-heap caching and introduces tiered caching, a multi-layered caching approach that balances performance and capacity. Learn how tiered caching leverages both on-heap and disk-based caches to store larger datasets efficiently without consuming excessive heap memory. Discover the workings of tiered caching, its benefits, and when to implement it. Uncover performance test results comparing tiered caching with the default on-heap cache, and get insights into future enhancements in tiered caching technology. Don't miss this opportunity to enhance your understanding of caching strategies in OpenSearch for improved query response times and application performance.

Performance testing OpenSearch clusters requires careful consideration of numerous variables and methodologies. While OpenSearch Benchmark (OSB) and its associated workloads provide powerful tools for cluster evaluation, many users struggle to implement effective testing strategies that yield meaningful, reproducible results.

In this comprehensive session, we'll dive more deeply into OSB best practices, moving beyond basic benchmarking to explore topics such as how to select and size appropriate workloads, handle common pitfalls and leverage more advanced features like custom workloads and gradual ramp-up to simulate realistic production scenarios. An understanding of critical aspects that are often overlooked such as warm-up periods, proper load generator sizing and techniques to analyze statistical significance will help you obtain actionable insights into how your OpenSearch cluster scales and performs under various operational conditions.

Whether you're a DevOps engineer, database administrator, or performance analyst, this talk will equip you with the tools and methodologies needed to unlock the full potential of your OpenSearch deployment.