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 within the constraints of a typical Function Calling approach. Let’s see how RAG can seamlessly integrate AI with domain-specific knowledge. Retrieval-Augmented Generation While listing veterinarians could have been a straightforward implementation, I chose this as an opportunity to showcase the power of Retrieval-Augmented Generation (RAG). RAG integrates large language models with real-time data retrieval to produce more accurate and contextually relevant text. Although this concept aligns with our previous work, RAG typically emphasizes data retrieval from a vector store. A vector store contains data in the form of embeddings—numerical representations that capture the meaning of the information, such as the data about our veterinarians. These embeddings are stored as high-dimensional vectors, facilitating efficient similarity searches based on semantics rather than traditional text-based…Read More