OpenSearchCon India 2026

Enterprise search is evolving from keyword-based retrieval into intelligent, context-aware systems powered by generative AI. While many RAG examples focus on simple vector lookups, building a production-grade, multi-modal RAG platform requires more—especially at enterprise scale.

This talk presents a reference architecture for agent-assisted, multi-modal RAG systems with OpenSearch as the core retrieval and indexing layer. OpenSearch combines lexical relevance, vector similarity, and metadata filtering across text, images, audio, and video, while preserving deterministic control over retrieval in AI-driven workflows.

The session covers ingestion pipelines, chunking strategies, hybrid and multi-vector indexing, and retrieval orchestration, and explains how OpenSearch acts as the retrieval intelligence boundary between probabilistic agent reasoning and enterprise data. Key production trade-offs around relevance tuning, multi-tenant isolation, performance scaling, and cost control are also discussed, providing practical guidance for building reliable, enterprise-ready AI search platforms.