Vector databases are used as memory for AI apps, particularly those powered by large language models (LLMs). Redis has gained traction as a high-performance vector database and is now being used in the Spring community to simplify the development of AI-powered apps. A Retrieval Augmented Generation (RAG) workflow integrates data with AI models, where data is loaded into a vector database, and then used to generate responses based on user queries. The RAG workflow has been implemented using Redis as the vector database and Spring AI in this example, which uses a dataset of beers to demonstrate the concept. The implementation includes a JSON reader to parse the dataset, a RagService class that implements the RAG workflow, and a RagController class that exposes the service as a HTTP endpoint. This project showcases how combining Redis' speed and ease of use with Spring AI's abstractions makes it easier for Java developers to build responsive AI applications.