OpenSearch v3 major release that was introduced in the past year represents a significant leap forward in open source search technology, delivering breakthrough innovations across neural search, AI-driven search experiences and performance optimization. This talk explores the major features that define the 3.x releases and their impact on modern search applications.

We'll dive into differentiating capabilities like scalable Neural Sparse ANN Search using the SEISMIC algorithm, and the new Search Relevance Workbench for metric-driven relevance evaluation. Discover how system-generated Search Pipelines eliminate configuration overhead, automatically building Vector Search pipelines at query runtime and UI editor for AI Search workflow set up.

The release brings industry-standard search features including MMR, ColBERT’s late interaction, RRF, radial search, and one of the most popular pre-trained spare encoder model in HuggingFace positioning OpenSearch alongside leading search platforms. Performance innovations deliver dramatic improvements: Memory-Optimized and Disk-based Vector Search with efficient FAISS execution, star-tree indexes for multi-field aggregations, 2x storage savings through derived source, and reader/writer separation for independent index/search scaling and resiliency. Real-time data processing enables continuous query execution for streaming results, and ability to build vector indices remotely using GPUs, while QueryInsights helps monitor cluster’s search query performance.

Finally, we'll showcase Agentic Search capabilities—from Natural Language Agent Search to native AI agents with persistent memory, workflow UI editors for non-technical users to set up AI Search flows, and OpenSearch MCP integration with Claude code, Gemini CLI and other AI assistants to interact with OpenSearch.

This is your opportunity to hear from the OpenSearch maintainers and ambassadors about the latest and greatest in the project. Attendees will leave understanding how OpenSearch v3 addresses the full spectrum of modern search challenges: Neural and Vector Search, Search quality measurement, performance at scale, and the future of AI-powered Search experiences.