I'm Principal Software Engineer at Red Hat, where I contribute to the Apache Camel project. As we shape the future of integration within the open source community, I work with others across the developer community to drive progress, adoption, and innovation. I'm passionate about making open source integration more accessible, user-friendly, and greater for all.
When not coding, you can find me sharing knowledge and insights as a speaker at conferences, panelist or guest on tech podcasts, discussing the latest trends and innovations in technology.
In today's dynamic AI landscape, the seamless integration of Large Language Models (LLMs) into applications is a key focus for developers. While many initiatives have emerged to facilitate the integration of LLMs, the world of Java has seen limited options.
Enter Langchain4j, a powerful library designed to seamlessly integrate Java applications with LLMs. The excitement amplifies Langchain4j into Quarkus, a framework designed for building Cloud-Native applications in Java. Quarkus is tuned for Kubernetes environments boasting faster startup times and reduced memory usage compared to traditional Java applications. When Quarkus meets Langchain4j, the process of building a Java LLM-powered application becomes an enjoyable experience.
In this talk, we’ll delve into building AI applications powered by LLMs, using Quarkus and Langchain4j. We’ll leverage existing features from the ecosystem to create effective strategies for data ingestion. We’ll demonstrate how to seamlessly bring in information from a broader set of resources, with the power of Apache Camel.
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