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RAG with In-Memory Java Microservices

Abstract

Learn why Retrieval Augmented Generation (RAG) has emerged as a critical need to augment Large Language Models (LLMs) with internal, non public data. Python is usually required for GenAI programming but we will show you a native In-Memory Java approach to RAG pipelines for LLMs.

Explore GenAI in an open source, fully native Java ecosystem using the Helidon microservices framework and LangChain4j to supplement GenAI LLMs with your internal, corporate data using RAG to provide more precise and accurate responses, reduce hallucinations and increase transparency and trust. Join us as we dive into Java based RAG with LLMs and shape the future of Java and AI!

Phil Chung

Phil Chung is a Director of Product Management at Oracle, responsible for planning, strategy, and helping build an enterprise cloud native Java with GenAI, Kubernetes platform. Formerly an Oracle Cloud Infrastructure cloud architect and member of the Oracle financial services global industry solutions group, Phil has experience in software architecture and development, focused on Java-based real-time in-memory data processing, in-memory data grids, and middleware.

Phil's expertise includes a combination of deep capital markets, telco, gaming industry, and technology.