RAG with Spring AI: VectorStore, Explained for Java Developers

What Spring AI Specifically Adds

Building RAG without any framework help means writing several distinct pieces of integration code yourself: an HTTP client for whichever vector database you picked, with that database’s own request/response shape; a separate call out to an embedding model to turn text into the numeric vectors that vector database actually stores and searches; and finally, hand-written logic that takes whatever text comes back from that search and glues it into the prompt you send to the model. None of that is really about yourapplication’s logic — it’s plumbing every RAG application needs regardless of what it’s actually for.

Spring AI’s VectorStoreinterface is exactly the same “write against one abstraction, swap the implementation underneath” idea the previous topic covered for ChatClient and chat model providers — applied here to vector databases instead. Your code calls vectorStore.add(...) to store documents and vectorStore.similaritySearch(...) to retrieve them, the same way regardless of which actual vector database sits behind that interface — a local, in-memory store while you’re learning, or a production database like PGVector once the application is real. And Spring AI ships a built-in QuestionAnswerAdvisor that wires a VectorStore’s retrieval directly into a ChatClientcall, so even the prompt-stuffing step doesn’t have to be written by hand.

The Open-Book Exam, Held by a Java Librarian

Picture a Java developer tasked with building an internal support assistant that answers questions using their company’s own product documentation. Without any help, that developer becomes, all at once, an HTTP client author for a vector database’s API, a client for an embedding-model API, and the author of whatever string-building logic combines the two results into a usable prompt — three separate, unrelated integration jobs, on top of the actual feature they set out to build.

Now picture the same developer handed a well-organized library with a librarian at the desk. The developer doesn’t need to know which shelving system the library uses, how its card catalog is physically implemented, or which specific building the archive room is in. They just ask the librarian a question, and the librarian hands back the exact right pages already pulled from the shelf. Swap that library for a different one across town — different building, different catalog system — and the developer’s request to the librarian doesn’t change at all.

VectorStoreis that librarian’s desk. The actual “library” behind it — an in-memory store today, a dedicated vector database tomorrow — is free to change, because the Java code asking “find me the documents relevant to this question” never has to know or care which one is actually doing the searching.

Fun Fact

Spring AI’s current documentation frames VectorStore around a deliberate split between reading and writing: a separate, read-only VectorStoreRetriever interface exposes only similaritySearch(...), while the full VectorStore interface (which also lets you add and delete documents) builds on top of it. A component that only ever needs to search — never to write — can depend on just the narrower interface, the same principle-of-least-privilege instinct a careful Java developer already applies to their own interfaces.

Test Yourself

Ready for the real mechanics — the current VectorStore interface, getting documents into it, retrieving them back out, and a real @RestController endpoint that answers a question grounded in your own documents? Continue to the Intermediate level →