technology

Building AI Assistant Application in Java

dmitrykornilov , , , , , ,

In my previous article, I discussed how Helidon integrates with LangChain4J. While the article provided a solid foundation, some readers pointed out the lack of a complete, hands-on example. This time, we’ll fix that by building a fully functional, practical AI-powered Java application.

We’ll create the Helidon Assistant — a chatbot with a web UI trained to answer questions about the Helidon framework. By “trained,” I mean it will be capable of answering questions based on the full Helidon documentation.

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Converting Spring Boot Projects to Helidon with AI

dmitrykornilov , , , , , , ,

My previous article on migrating the Spring Petclinic Rest project to Helidon (see here) received a lot of positive feedback, which encouraged me to explore this area further.

The manual conversion process, while feasible, is time-consuming and requires careful attention to detail. However, it’s often more technical than creative—tedious, in other words. Automating this process would save time and reduce errors. While simple rule-based approaches (e.g., replacing <some Spring annotation> with <Helidon annotation>) can handle basic cases, they fall short for more complex conversions, such as transforming Spring REST Controllers to JAX-RS. This is where AI can help.

In this article, I’ll explain the approaches I took, the challenges I faced, and the results (spoiler alert: the results are very positive). I’ll start with a test project I created for the conversion.

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