Embeddings

A Spring AI RAG pipeline lets an LLM answer using your documents instead of just its training data. RAG — retrieval-augmented generation — is three moves: take the user’s question, find the most relevant chunks from your data (using embeddings and a vector store), and send those chunks plus the question to the model. The model answers grounded in what you gave it. Spring AI’s ChatClient and document/vector abstractions make this surprisingly little code. This is Part 4 of the Spring AI series and it ties the previous parts together. ...

RAG rests on two primitives: embeddings (turning text into vectors) and a vector store (saving those vectors and finding the nearest ones to a query). Spring AI gives you one interface for each — EmbeddingModel and VectorStore — and you choose the implementation with a dependency and config, exactly like the chat client in Part 2. This is Part 3 of the Spring AI series, and it’s the groundwork for the RAG pipeline in Part 4. ...