Build RAG in Spring Boot with Spring AI

Quick Start

Retrieval-augmented generation, or RAG, lets an AI answer using approved documents—similar to an open-book exam. This Byte explains the essential flow before implementing it with Spring AI, so prior RAG or LangChain knowledge is not required.

Optional foundation: If you want a slower conceptual introduction first, read What Is RAG?.

Meet the Scenario

Rahul asks Divya: "A model can write a fluent answer, but how does it know PaisaWise's actual refund policy?" Divya says: "We retrieve evidence from the approved documents before generating the answer. Spring AI supports this RAG flow through VectorStore and the Advisors API."

Core Concept

RAG has four stages: split documents into useful chunks, convert them into "meaning fingerprints" called embeddings, store them in a searchable index, and retrieve relevant evidence before answering. Spring AI provides a VectorStore abstraction for the searchable index and a QuestionAnswerAdvisor for the "search, then answer" flow.

Think of the VectorStore as a plug socket — PostgreSQL/pgvector, MongoDB Atlas, Pinecone, Qdrant, and others all fit the same socket. You write your RAG logic once, and swap the underlying storage the same way you'd swap a database driver.

How It Works Under the Hood

Step 1: Load and store documents (usually done once, as a setup/ingestion task):

01@Service02public class PolicyIngestionService {0304    private final VectorStore vectorStore;0506    public PolicyIngestionService(VectorStore vectorStore) {07        this.vectorStore = vectorStore;08    }0910    public void ingest(List<Document> policyChunks) {11        vectorStore.add(policyChunks);12    }13}

Document is Spring AI's representation of one chunk. Think of each chunk as a labelled index card that can be retrieved when it matches a question.

Step 2: Answer questions using retrieved context, via the Advisors API:

01@Service02public class PolicyQAService {0304    private final ChatClient chatClient;0506    public PolicyQAService(ChatClient.Builder builder, VectorStore vectorStore) {07        this.chatClient = builder08                .defaultAdvisors(new QuestionAnswerAdvisor(vectorStore))09                .build();10    }1112    public String answer(String question) {13        return chatClient.prompt()14                .user(question)15                .call()16                .content();17    }18}

Attaching QuestionAnswerAdvisor means every call searches vectorStore for relevant chunks and places that evidence into the model context before it answers. The retrieval step happens behind this configuration while the service method stays compact.

Try It Yourself (Small Snippet)

Configuring pgvector as the storage backend in application.properties:

01spring.ai.vectorstore.pgvector.index-type=HNSW02spring.ai.vectorstore.pgvector.dimensions=1536

HNSW is an approximate nearest-neighbour index that helps pgvector find similar embeddings efficiently. Spring AI connects the Java application to this underlying pgvector capability.

Real Company Angle

Spring AI's Advisors API and auto-configuration for vector stores like PostgreSQL/pgvector, MongoDB Atlas, Pinecone, and Qdrant reflect a broader industry pattern: Java teams don't need to build RAG pipelines from scratch. With Document ETL support that handles ingesting PDFs, S3 files, and other formats with proper chunking, enterprises can go from "raw policy PDFs" to a working, queryable knowledge base largely through configuration and a handful of service classes — rather than the substantial custom pipeline work RAG required just a couple of years ago.

Common Mistakes

1. Re-ingesting the same documents on every application restart — ingestion should typically be a separate, controlled step, not something that runs automatically every time the app boots.
2. Ignoring chunking quality — Spring AI handles the plumbing, but incomplete or oversized chunks still produce weak retrieval and weak answers.
3. Assuming QuestionAnswerAdvisor guarantees correct answers — retrieval can miss the right chunk and the model can add unsupported detail, so evaluation is still necessary.
4. Hardcoding vector dimensions incorrectly — the dimensions property must match your embedding model's actual output size, or storage/retrieval will silently misbehave.

Persona Wrap-Up

Rahul summarizes the flow: "Chunk, embed, store, retrieve, then generate—and Spring AI maps those steps onto VectorStore and QuestionAnswerAdvisor." Meena tests it: "Now it answers using our approved refund policy instead of a generic guess." Divya adds: "We still need to inspect the evidence and evaluate answer quality before trusting it."

Compare & Contrast

Mini Practice Task

Sketch (on paper) how you'd extend PolicyQAService to only search a specific category of documents (say, only "refund policy" documents, not all company documents). What would you need to add when calling vectorStore.add(...) during ingestion to make that possible later at search time?

Key Takeaways

• Spring AI's VectorStore is an interchangeable storage abstraction for embedded document chunks.
• QuestionAnswerAdvisor automates the "retrieve relevant chunks, then answer" flow, attached to ChatClient with one line of configuration.
• Chunking, embeddings, retrieval quality and evaluation remain essential regardless of implementation language.
• Spring AI supports production vector stores including pgvector, MongoDB Atlas, Pinecone and Qdrant.
• RAG's known failure modes (stale documents, bad chunking, poor retrieval) apply just as much in Java — the language changes, the underlying discipline doesn't.

FAQ / Knowledge Check

Q1: What Spring AI class automates the "search documents, then answer" RAG flow? QuestionAnswerAdvisor, attached to a ChatClient via .defaultAdvisors(...).

Q2: Does moving RAG from Python to Java change the underlying vector database options? No — Spring AI supports the same production vector databases (pgvector, MongoDB Atlas, Pinecone, Qdrant, and others).

Q3: Does using QuestionAnswerAdvisor eliminate the need for RAG evaluation? No. Retrieval can still miss the right chunk and the model can still add unsupported detail; test whether the answer is supported by the retrieved evidence.

Knowledge Check:

1. What Spring AI interface plays the role of a vector database abstraction?
2. True/False: Chunking quality no longer matters once you're using Spring AI's QuestionAnswerAdvisor.
3. Name one vector database supported by Spring AI.

(Answers: 1. VectorStore; 2. False — chunking quality still directly affects answer quality, regardless of language; 3. pgvector (PostgreSQL))

Next byte: AI Agents and Tool Calling in Spring Boot — giving your Java-based AI assistant the ability to take actions, not just answer questions.

Interactive Knowledge Check

References and Further Reading

• Spring AI Reference
• Spring AI ChatClient
• Spring Boot Reference