OpenSearchCon India 2026

Organizations generate huge amounts of internal documentation across wikis, runbooks, architecture guides, and operational documents. Finding the right information quickly can be difficult when traditional keyword search cannot capture the intent behind a question.

In this talk, I will share how a Retrieval Augmented Generation assistant can be built using OpenSearch vector search to retrieve relevant knowledge from enterprise documentation. The system converts user queries into embeddings, retrieves the most relevant documents from OpenSearch, and provides them to a language model to generate grounded answers.

The session will walk through the architecture of the system, including document ingestion, embedding generation, vector indexing, and semantic retrieval. We will also discuss strategies for improving retrieval quality, managing latency in the query pipeline, and ensuring that enterprise data remains secure.

Attendees will learn how OpenSearch can power scalable semantic search systems and how vector search can be combined with language models to build reliable knowledge assistants for modern engineering organizations.

Modern distributed applications generate massive volumes of telemetry in the form of logs, metrics, and traces. Designing a scalable pipeline to collect, process, and analyze this data is essential for effective observability.

In this session, we explore how OpenTelemetry and OpenSearch can be combined to build a modern observability pipeline. We walk through the end-to-end telemetry flow - from instrumenting applications with OpenTelemetry SDKs to collecting and processing telemetry through the OpenTelemetry Collector and finally indexing and analyzing data in OpenSearch.

We also discuss key design considerations such as telemetry ingestion patterns, index strategies for observability workloads, and correlating logs, metrics, and traces. Attendees will see how OpenSearch can act as a scalable backend for storing, searching, and analyzing observability data.

Audience Takeaway:
Learn how to design a scalable observability pipeline using OpenTelemetry for telemetry collection and OpenSearch for storage and analysis, along with key considerations for ingesting and correlating logs, metrics, and traces.

Description
Join us to explore the latest performance improvements in OpenSearch, highlighting the integration of Lucene’s bulk collection API and enhancements to the gRPC Search API. This session will dive into how bulk collection optimizes aggregation execution, delivering measurable gains across analytical workloads by batching operations and reducing computational overhead. We will also cover the expanded features of the gRPC Search API in OpenSearch 3.4, which now supports new query types, improved bulk requests, and multiple document formats. To make these concepts clear and practical, we will showcase live demos illustrating performance boosts in aggregation workloads and expanded query handling through gRPC. Attendees will gain a clear understanding of how these advancements significantly enhance efficiency and broaden query capabilities in real‑world use cases. Key takeaways include understanding Lucene’s bulk collection benefits, learning about new gRPC query types, and discovering improvements in bulk request handling and document format support.

This session explores how to build a comprehensive code security platform using OpenSearch's powerful analytics capabilities enhanced with AI agentic workflows. I'll demonstrate how to implement a system that continuously monitors, analyzes, and remediates security vulnerabilities across your codebase. Attendees will learn how to leverage OpenSearch for storing and analyzing code security telemetry, while implementing agentic AI workflows that autonomously identify patterns, prioritize vulnerabilities, and suggest remediation strategies.

Key topics include:
• Configuring OpenSearch for efficient storage and querying of code security data
• Implementing vector search to identify similar vulnerability patterns across repositories
• Building AI agents that autonomously scan, analyze, and classify security issues
• Creating agentic workflows that coordinate specialized security analysis tasks
• Developing automated remediation suggestions using LLM-powered agents
• Visualizing security insights through OpenSearch Dashboards

This session provides a practical blueprint for security teams looking to scale their code security.

Large Language Models (LLMs) power some of today’s most advanced AI applications-from chatbots to intelligent copilots-yet monitoring their complex behavior remains a major challenge. This session demonstrates how OpenTelemetry’s auto-instrumentation capabilities, combined with OpenSearch’s powerful analytics, provide end-to-end observability for LLM-based systems.

You’ll learn how to automatically collect rich telemetry data-traces, metrics, and logs-from your LLM applications with minimal manual effort using OpenTelemetry and libraries like OpenLIT. See how this data flows into OpenSearch, enabling real-time analysis, anomaly detection, and performance monitoring tailored specifically for AI workloads.

By the end of this talk, you will get the tools and best practices to build scalable, AI-driven observability pipelines that keep your LLM applications reliable and performant.