This session explores how Gaia’s decentralized AI platform integrates with OpenSearch to create scalable, privacy-preserving solutions for semantic search, generative AI applications, and real-time analytics.

In this session, I intend to demonstrate a use case where Gaia’s federated learning models enhance OpenSearch’s vector database capabilities, enabling dynamic personalization and efficient retrieval-augmented generation (RAG) workflows.

Gaia’s AI Framework: How Gaia’s decentralized architecture complements OpenSearch’s ecosystem, particularly in scenarios requiring data privacy and distributed model training (e.g., healthcare or financial analytics).

Demo a RAG Pipeline: Build a real-world demo/example using Gaia’s language models to generate embeddings stored in OpenSearch’s binary vector indexes (new in v2.19), coupled with OpenSearch Flow for automated pipeline configuration.

Community Impact: A look at how this integration supports OpenSearch’s goal of becoming the preferred backend for generative AI while adhering to open governance principles.