Observability, log analytics, GenAI systems, and RAG pipelines must query massive volumes of semantic embeddings to retrieve relevant content instantly. Today’s search systems often fall short in handling high-dimensional vector data and similarity search.

OpenSearch 3.0 brings significant architectural improvements to address these challenges. Integrating Apache Lucene 10 and JVM 21, the platform delivers 20% faster queries than its 2.x predecessor and 10× the throughput of 1.x versions. New features like GPU-accelerated vector indexing and concurrent segment search dramatically improve k-NN query performance while reducing operational costs.

The platform's expanded AI capabilities now include an advanced Vector Engine for approximate k-NN searches and neural sparse search for efficient text indexing. These innovations, combined with optimized embedding ingestion and query-time pruning, enable organizations to build performant, cost-effective search solutions that scale with their needs.

We'll explore practical applications of these features, demonstrating how OpenSearch 3.0 powers the next generation of AI-driven search experiences.

This session is sponsored by OpenSearch