artificial-intelligence

Building AI Assistant Application in Java

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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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Developing AI-Powered Applications with Helidon and LangChain4J

dmitrykornilov , , ,

Introduction

The rise of Large Language Models (LLMs) has opened new doors for AI-powered applications, enabling dynamic interactions, natural language processing, and retrieval-augmented generation (RAG). However, integrating these powerful models into Java applications can be challenging. This is where LangChain4J comes in – a framework designed to simplify AI development in Java.

To take things a step further, in version 4.2, Helidon introduced seamless LangChain4J integration, making it easier to build AI-driven applications while leveraging Helidon’s programming model and style. In this blog post, we’ll explore how this integration simplifies AI application development and how you can use it in your projects.

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Converting Spring Boot Projects to Helidon with AI

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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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