A practical guide to implementing RAG (Retrieval-Augmented Generation) with Spring AI. Covers embedding models (EmbeddingModel interface with OpenAI, Ollama, Azure, Vertex implementations), storing embeddings in vector databases (SimpleVectorStore, PgVectorStore, ChromaVectorStore, etc.), reading documents via DocumentReaders (JSON, Text, PDF), and wiring it all together in a REST controller that retrieves semantically relevant documents and passes them as context to an LLM to answer domain-specific questions. Includes full Java code examples for each step.
Table of contents
IntroductionUnderstand Retrieval-Augmented Generation (RAG)Embedding APIsVector DatabaseDocumentReader and DocumentWriterImplementing RAG (Retrieval-Augmented Generation)ConclusionSort: