Retrieval Augmented Generation with Spring AI - Tomas Zezula

In our last post, we looked at enriching the OpenAI model with custom data through function calls. While this technique is useful, it has its limitations and performance trade-offs. Today, we explore a more efficient way of incorporating relevant data into prompts to receive accurate and relevant model responses. Retrieval Augmented Generation, or RAG, relies on preprocessed data that is readily available upon request. In this post, we will build an Extract, Transform, Load (ETL) pipeline that stores a large corpus of weather forecasts and learn how to efficiently retrieve relevant information from a vector store.