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OpenSearch's ML Commons plugin enables deploying embedding models directly on your cluster—yet many developers still rely on external services. This session demonstrates building a complete RAG system using OpenSearch as both vector store and ML inference engine.
We'll cover deploying sentence-transformer models via ML Commons, processing PDFs, generating embeddings within OpenSearch, configuring knn_vector indices, and implementing semantic search. The highlight: voice search using local Whisper (STT) model creating a fully self-contained system.
What I hope to achieve: Share practical patterns for leveraging OpenSearch's ML capabilities and gather community feedback.
What attendees gain: Step-by-step knowledge to deploy ML models on OpenSearch, build RAG pipelines, and integrate local voice processing—reproducible techniques they can apply immediately.
How this helps the ecosystem: Showcases OpenSearch's native ML features, reducing dependency on external services. Demonstrates OpenSearch as a complete AI platform, not just a search engine.
Target audience: Developers building RAG applications, OpenSearch operators exploring ML capabilities.
I'm a Staff/Lead Software Engineer at Genesys, working on the Conversational AI team. I build intelligent search and knowledge systems using OpenSearch, focusing on vector search and RAG pipelines. I'm passionate about exploring OpenSearch's AI/ML capabilities—particularly ML Commons... Read More →
