This blog post will integrate the Spring AI framework into a Java application. We’ll use a simple project that includes a ChatService and a ChatController to demonstrate using the Spring AI framework to generate text & image responses and horoscopes based on user input.
Update 12/28/2025 – Updated from M1 release to Spring AI 1.1.0 GA
This is part 2 of a humor-inspired take on Monoliths to microservices that I wrote a few years back: https://blogs.justenougharchitecture.com/monos-journey-from-monolith-to-microservices/. If you did not read that, please do so first.
Mikro was serving his consumers as always. He consistently met his promises (SLAs), and his life was good. Suddenly, he felt a stab and excruciating pain. “Damnit, what was that?” he said. To Mikro’s horror, he found himself being sliced and diced into smaller and smaller pieces. “But I thought I was already micro enough!” he wailed as functions were extracted from his very being.
Designing and building robust, scalable, and efficient systems is a fundamental requirement in software engineering. The AWS Well-Architected Framework is an essential resource that can significantly aid this process. Although it originates from Amazon Web Services, the principles and best practices it outlines are universally applicable. This blog aims to provide an understanding of the AWS Well-Architected Framework, its core focus areas, and its value to software engineering practices, regardless of whether you use AWS services.
As an AI and software professional, you’ve likely heard the buzz around large language models (LLMs) like GPT-3, ChatGPT, and their growing capabilities. These powerful models can handle a wide range of natural language tasks, from text generation to question answering. However, effectively leveraging LLMs in your own applications can be a complex challenge. That’s where LangChain comes in.