Holy Cummins

Senior Principal Software Engineer, Red Hat

Speaker's Bio

In this session, we’ll explore how to infuse AI capabilites into Java applications, using LangChain4j and its Quarkus integration. We’ll start from the Quarkus DevUI where you can try out AI models even before writing any code. Then we’ll 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 and compile the app to a native binary.

In this session, we’ll explore how to infuse AI capabilites into Java applications, using LangChain4j and its Quarkus integration. We’ll start from the Quarkus DevUI where you can try out AI models even before writing any code. Then we’ll 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 and compile the app to a native binary.

Final Lottery – Closing

Panel Discussion: Debate on Software Craftsmanship in the AI Era: The Balance Between Low-Code/AI Solutions and Deep Craftsmanship

Hackathon final pitching + Award

NetCompany – Gold Sponsor

Lottery

Coffee – Networking