Use OpenSearch as Your Vector Search Database To Optimize GenAI Accuracy!

Use OpenSearch as Your Vector Search Database To Optimize GenAI Accuracy! - Anil Inamdar, NetApp Instaclustr Vector search capabilities are critical for reducing hallucinations and improving contextual understanding in generative AI systems. The good news for organizations already using OpenSearch? There’s no need to invest in costly proprietary vector databases. The vector search functionality within OpenSearch provides enterprise-grade performance for RAG implementations while avoiding vendor lock-in…if used strategically. This OpenSearchCon session digs in to specific techniques for transforming your existing OpenSearch deployment into a high-performance vector search engine for AI applications, including: -Configuring and optimizing OpenSearch for vector search workloads -Implementing efficient embedding storage and retrieval strategies -Designing a scalable RAG architecture using OpenSearch -Performance tuning for high-throughput AI applications -Real-world examples of OpenSearch vector search reducing hallucinations by 40%+ Attendees will leave with actionable methods to leverage OpenSearch’s vector capabilities, concrete implementation patterns for RAG workflows, and benchmarks to evaluate success.