Georgios works for Red Hat as a Principal Software Engineer and is currently the most active contributor for Quarkus, where he works in all sorts of areas, including but not limited to LangChain4j, RESTEasy Reactive, Spring compatibility, Kubernetes support, testing, Kotlin and more.
He is also an enthusiastic promoter of Quarkus that never misses a chance to spread the Quarkus love!
Generative AI has taken the world by storm, and it seems like every executive leader out there is telling us “regular” Java devs to “add AI” to our apps. Does that mean we need to drop everything we’ve built and become data scientists instead now?
Fortunately, we can infuse AI models built by actual AI experts into our applications in a fairly straightforward way. We promise it’s not as complicated as you might think! Thanks to the ease of use and superb developer experience of Quarkus and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy.
In this session, you’ll explore a variety of AI capabilities. We’ll start from the Quarkus DevUI where you can try out AI models before writing any code. Then we’ll get get into the code and explore LangChain4j features such as prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); and discovering ways to run (and train) models locally using tools like Ollama and/or Podman AI Lab. In addition, we’ll take a look at observability and fault tolerance of the AI integration. We might even try some new features, such as MCP.
Come to this session to learn how to build AI-infused applications in Java. This is also an opportunity to provide feedback to the maintainers of these projects and contribute back to the community.
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