Canonical URL: Republished from munonye.com. Full code on GitHub.
Learn how to build a RAG Spring Boot tutorial pipeline that answers questions from your own documents. This post extends the AI Developer Tutorials series and connects to M7-A Spring AI REST basics.
RAG architecture
Documents → chunk → embed → VectorStore
User question → embed → top-K similar chunks → prompt → LLM → answer
Dependencies
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-openai-spring-boot-starter</artifactId>
</dependency>
Ingestion service
@Service
public class DocumentIngestionService {
private final VectorStore vectorStore;
private final Resource docsFolder;
public DocumentIngestionService(VectorStore vectorStore,
@Value("classpath:docs/") Resource docsFolder) {
this.vectorStore = vectorStore;
this.docsFolder = docsFolder;
}
public void ingestAll() throws IOException {
for (Resource file : docsFolder.getFile().listFiles()) {
String text = Files.readString(file.getFile().toPath());
List<Document> chunks = split(text, 800, 100);
vectorStore.add(chunks);
}
}
private List<Document> split(String text, int size, int overlap) {
List<Document> out = new ArrayList<>();
for (int i = 0; i < text.length(); i += size - overlap) {
out.add(new Document(text.substring(i, Math.min(i + size, text.length()))));
}
return out;
}
}
Question endpoint
@PostMapping("/api/ask")
public AnswerResponse ask(@RequestBody QuestionRequest req) {
List<Document> similar = vectorStore.similaritySearch(req.question(), 5);
String context = similar.stream().map(Document::getContent).collect(Collectors.joining("\n---\n"));
String answer = chatClient.prompt()
.system("Answer only from the context below. Say 'I don't know' if not found.\n" + context)
.user(req.question())
.call()
.content();
return new AnswerResponse(answer);
}
Next: function calling from Angular
M8-B — Structured JSON from LLMs in Angular
Full tutorial: RAG with Spring Boot — Embeddings and Vector Search Step by Step (2026)