{"slug": "rag-with-spring-boot-embeddings-and-vector-search-step-by-step-2026", "title": "RAG with Spring Boot — Embeddings and Vector Search Step by Step (2026)", "summary": "A developer published a tutorial on building a Retrieval-Augmented Generation (RAG) pipeline using Spring Boot, embeddings, and vector search. The tutorial demonstrates how to ingest documents, split them into chunks, generate embeddings, store them in a PGVector vector store, and answer user questions by retrieving similar chunks and passing them to an LLM. The code is available on GitHub and extends the AI Developer Tutorials series.", "body_md": "Canonical URL:Republished from[munonye.com]. Full code on[GitHub].\n\nLearn **how to build a RAG Spring Boot tutorial** pipeline that answers questions from your own documents. This post extends the [AI Developer Tutorials](https://www.munonye.com/ai-developer-tutorials/) series and connects to [M7-A Spring AI REST basics](https://www.munonye.com/spring-ai-tutorial-first-rest-endpoint-openai-2026/).\n\n```\nDocuments → chunk → embed → VectorStore\nUser question → embed → top-K similar chunks → prompt → LLM → answer\n<dependency>\n  <groupId>org.springframework.ai</groupId>\n  <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>\n</dependency>\n<dependency>\n  <groupId>org.springframework.ai</groupId>\n  <artifactId>spring-ai-openai-spring-boot-starter</artifactId>\n</dependency>\n@Service\npublic class DocumentIngestionService {\n  private final VectorStore vectorStore;\n  private final Resource docsFolder;\n\n  public DocumentIngestionService(VectorStore vectorStore,\n      @Value(\"classpath:docs/\") Resource docsFolder) {\n    this.vectorStore = vectorStore;\n    this.docsFolder = docsFolder;\n  }\n\n  public void ingestAll() throws IOException {\n    for (Resource file : docsFolder.getFile().listFiles()) {\n      String text = Files.readString(file.getFile().toPath());\n      List<Document> chunks = split(text, 800, 100);\n      vectorStore.add(chunks);\n    }\n  }\n\n  private List<Document> split(String text, int size, int overlap) {\n    List<Document> out = new ArrayList<>();\n    for (int i = 0; i < text.length(); i += size - overlap) {\n      out.add(new Document(text.substring(i, Math.min(i + size, text.length()))));\n    }\n    return out;\n  }\n}\n@PostMapping(\"/api/ask\")\npublic AnswerResponse ask(@RequestBody QuestionRequest req) {\n  List<Document> similar = vectorStore.similaritySearch(req.question(), 5);\n  String context = similar.stream().map(Document::getContent).collect(Collectors.joining(\"\\n---\\n\"));\n  String answer = chatClient.prompt()\n      .system(\"Answer only from the context below. Say 'I don't know' if not found.\\n\" + context)\n      .user(req.question())\n      .call()\n      .content();\n  return new AnswerResponse(answer);\n}\n```\n\n[M8-B — Structured JSON from LLMs in Angular](https://www.munonye.com/angular-function-calling-structured-json-llms/)\n\n**Full tutorial:** [RAG with Spring Boot — Embeddings and Vector Search Step by Step (2026)](https://www.munonye.com/rag-spring-boot-embeddings-vector-search-step-by-step/)", "url": "https://wpnews.pro/news/rag-with-spring-boot-embeddings-and-vector-search-step-by-step-2026", "canonical_source": "https://dev.to/alkademy/rag-with-spring-boot-embeddings-and-vector-search-step-by-step-2026-5fpk", "published_at": "2026-07-01 05:39:46+00:00", "updated_at": "2026-07-01 05:48:43.104033+00:00", "lang": "en", "topics": ["large-language-models", "developer-tools", "artificial-intelligence"], "entities": ["Spring Boot", "PGVector", "OpenAI", "GitHub", "munonye.com"], "alternates": {"html": "https://wpnews.pro/news/rag-with-spring-boot-embeddings-and-vector-search-step-by-step-2026", "markdown": "https://wpnews.pro/news/rag-with-spring-boot-embeddings-and-vector-search-step-by-step-2026.md", "text": "https://wpnews.pro/news/rag-with-spring-boot-embeddings-and-vector-search-step-by-step-2026.txt", "jsonld": "https://wpnews.pro/news/rag-with-spring-boot-embeddings-and-vector-search-step-by-step-2026.jsonld"}}