Recap of Part I

In the first part of this blog series, we explored the basics of integrating Spring AI with large language models. We walked through building a custom ChatClient, leveraging Function Calling for dynamic interactions, and refining our prompts to suit the Spring Petclinic use case. By the end, we had a functional AI assistant capable of understanding and processing requests related to our veterinary clinic domain.

Now, in Part II, we’ll go a step further by exploring Retrieval-Augmented Generation (RAG), a technique that enables us to handle large datasets that wouldn’t fit…

Introduction

In this two-parts blog post, I will discuss the modifications I made to Spring Petclinic to incorporate an AI assistant that allows users to interact with the application using natural language.

Introduction to Spring Petclinic

Spring Petclinic serves as the primary reference application within the Spring ecosystem. According to GitHub, the repository was created on January 9, 2013. Since then, it has become the model application for writing simple, developer-friendly code using Spring Boot. As of this writing, it has garnered over 7,600 stars and 23,000 forks.

The application…