Langchain4j is my favorite framework for working with large language models and Java. In the last couple of weeks, both Bazlur and I have presented to multiple user groups and conferences. This week, we had the privilege of presenting at the Jakarta Tech Talk, which both of us were looking very much forward to.
We now have so much demo code on the topic that we cannot present all the variations in one hour. We are still building along as we learn here.
Konveyor AI is a tool used to migrate Java applications to different Java frameworks, such as from JavaEE to Quarkus or Spring or from Spring 5 to 6, using Generative AI and static code analysis. I wrote a detailed post about this last year for the Java Advent Calendar.
Most recently, we have all been hard at work, bringing a preview for our community of users. In this post, I will outline how you can install and configure Konveyor AI using OpenAI and make meaningful generations. However, I have chosen OpenAI for the sake of simplicity in this post. Users can choose many other models, which are documented here.
Java is an amazing language to work with. Millions of developers use it for daily work routines, and many mission-critical applications run on Java today. Whether we talk about banks, stock exchanges, or space, Java is prevalent and a language of choice.
With the advent of Large Language Models(LLM), new opportunities are at play. While Python has been the dominating language runtime for apparent reasons, there is a misconception that creating applications, agents, or other components for LLMs should also be done in Python. Most of the integration in LLMs is achieved by using REST API. Java is not short on anything in that space. System integration has been pretty awesome in Java, with many tools in that space. So why not Java?
Konveyor’s main strength lies in its comprehensive approach to migration and modernization. At the core of Konveyor’s functionality is its powerful analysis engine. This engine performs static source code analysis, identifying anti-patterns and issues that might hinder the application’s operation on a target platform. Utilizing community standards like the Language Server Protocol, Konveyor’s analysis engine uses rules designed to aid in various migration scenarios. Users can also create custom rules to address specific migration needs, enhancing Konveyor’s flexibility and adaptability.
