{"id":2213,"date":"2026-07-07T01:48:31","date_gmt":"2026-07-06T23:48:31","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/how-to-build-a-rag-chatbot-with-spring-boot-and-openai-step-by-step\/"},"modified":"2026-07-07T01:48:31","modified_gmt":"2026-07-06T23:48:31","slug":"how-to-build-a-rag-chatbot-with-spring-boot-and-openai-step-by-step","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/how-to-build-a-rag-chatbot-with-spring-boot-and-openai-step-by-step\/","title":{"rendered":"How to Build a RAG Chatbot with Spring Boot and OpenAI \u2014 Step by Step (2026)"},"content":{"rendered":"<p><!-- ktg-updated-banner --><\/p>\n<div class=\"ktg-updated-banner\" style=\"margin:1em 0;padding:0.75em 1em;background:#eff6ff;border-left:4px solid #3b82f6;border-radius:4px;\">\n<p><strong>Updated July 7, 2026:<\/strong> New tutorial using Spring Boot 3.3, Spring AI, and OpenAI embeddings. Includes architecture diagram, ingest pipeline, REST chat endpoint, and FAQ with structured data.<\/p>\n<\/div>\n<p>Learn how to <strong>build a RAG chatbot with Spring Boot and OpenAI<\/strong> \u2014 the pattern behind modern AI assistants that answer questions from your own documents. This <strong>spring boot rag tutorial<\/strong> walks through document ingestion, vector search, and a REST API that returns grounded answers with source citations.<\/p>\n<p><strong>Prerequisites:<\/strong> Basic Java and REST API knowledge. Familiarity with Spring Boot helps. If you are new to securing APIs, see <a href=\"https:\/\/kindsonthegenius.com\/blog\/json-web-token-how-to-secure-rest-api-using-jwt-in-spring-boot\/\">JWT authentication in Spring Boot<\/a>.<\/p>\n<p><strong>Estimated time:<\/strong> 90\u2013120 minutes. <strong>Difficulty:<\/strong> Intermediate.<\/p>\n<p><!-- ktg-article-toc --><\/p>\n<nav class=\"ktg-article-toc\" aria-label=\"Spring Boot RAG tutorial table of contents\" style=\"margin:1.5em 0;padding:1em 1.25em;background:#f8fafc;border:1px solid #e2e8f0;border-radius:8px;\">\n<h2>Table of Contents<\/h2>\n<ol>\n<li><a href=\"#what-is-rag\">What is RAG and why use Spring Boot?<\/a><\/li>\n<li><a href=\"#architecture\">Architecture overview<\/a><\/li>\n<li><a href=\"#requirements\">What you need<\/a><\/li>\n<li><a href=\"#create-project\">Create the Spring Boot project<\/a><\/li>\n<li><a href=\"#configure-spring-ai\">Configure Spring AI and OpenAI<\/a><\/li>\n<li><a href=\"#ingest-documents\">Ingest documents into the vector store<\/a><\/li>\n<li><a href=\"#chat-endpoint\">Build the RAG chat REST endpoint<\/a><\/li>\n<li><a href=\"#test-api\">Test the chatbot API<\/a><\/li>\n<li><a href=\"#production-tips\">Production tips<\/a><\/li>\n<li><a href=\"#faq\">Frequently asked questions<\/a><\/li>\n<li><a href=\"#next-steps\">Next steps<\/a><\/li>\n<\/ol>\n<\/nav>\n<h2 id=\"what-is-rag\">What Is RAG and Why Use Spring Boot?<\/h2>\n<p><strong>Retrieval-Augmented Generation (RAG)<\/strong> combines a large language model (LLM) with a search step over your private data. Instead of fine-tuning the model, you:<\/p>\n<ol>\n<li><strong>Ingest<\/strong> documents (PDF, Markdown, HTML) into chunks.<\/li>\n<li><strong>Embed<\/strong> each chunk into a vector and store it in a vector database.<\/li>\n<li><strong>Retrieve<\/strong> the most relevant chunks when a user asks a question.<\/li>\n<li><strong>Generate<\/strong> an answer using the LLM with retrieved context in the prompt.<\/li>\n<\/ol>\n<p>Spring Boot is a strong choice because <a href=\"https:\/\/docs.spring.io\/spring-ai\/reference\/\" target=\"_blank\" rel=\"noopener noreferrer\">Spring AI<\/a> provides unified APIs for embeddings, vector stores, and chat models \u2014 keeping your RAG pipeline testable and production-ready.<\/p>\n<h2 id=\"architecture\">Architecture Overview<\/h2>\n<figure style=\"margin:1.5em 0;text-align:center;\"><img decoding=\"async\" src=\"https:\/\/kindsonthegenius.com\/blog\/wp-content\/uploads\/2026\/07\/spring-boot-rag-architecture.svg\" alt=\"Spring Boot RAG chatbot architecture showing REST controller, embedding model, vector store, and OpenAI chat model\" width=\"800\" height=\"420\" loading=\"lazy\" style=\"max-width:100%;height:auto;border-radius:8px;border:1px solid #dee2e6;\" \/><figcaption style=\"font-size:0.85em;color:#666;margin-top:0.5em;\">RAG pipeline: ingest documents on startup, answer questions via REST at runtime<\/figcaption><\/figure>\n<h2 id=\"requirements\">What You Need<\/h2>\n<ul>\n<li><strong>Java 21<\/strong> and <strong>Maven 3.9+<\/strong><\/li>\n<li><strong>Spring Boot 3.3+<\/strong><\/li>\n<li>An <strong>OpenAI API key<\/strong> (<a href=\"https:\/\/platform.openai.com\/api-keys\" target=\"_blank\" rel=\"noopener noreferrer\">platform.openai.com<\/a>)<\/li>\n<li>Sample documents in <code>src\/main\/resources\/docs\/<\/code> (Markdown or plain text)<\/li>\n<li>IDE: IntelliJ IDEA or VS Code with Java extensions<\/li>\n<\/ul>\n<h2 id=\"create-project\">Step 1: Create the Spring Boot Project<\/h2>\n<p>Generate a project at <a href=\"https:\/\/start.spring.io\/\" target=\"_blank\" rel=\"noopener noreferrer\">start.spring.io<\/a> with:<\/p>\n<ul>\n<li>Spring Boot <strong>3.3.x<\/strong><\/li>\n<li>Dependencies: <strong>Spring Web<\/strong>, <strong>Spring AI OpenAI<\/strong><\/li>\n<li>Group: <code>com.kindsonthegenius<\/code>, Artifact: <code>rag-chatbot<\/code><\/li>\n<\/ul>\n<p>Or add these dependencies to your <code>pom.xml<\/code>:<\/p>\n<pre><code class=\"language-xml\">&lt;dependency&gt;\n  &lt;groupId&gt;org.springframework.boot&lt;\/groupId&gt;\n  &lt;artifactId&gt;spring-boot-starter-web&lt;\/artifactId&gt;\n&lt;\/dependency&gt;\n&lt;dependency&gt;\n  &lt;groupId&gt;org.springframework.ai&lt;\/groupId&gt;\n  &lt;artifactId&gt;spring-ai-openai-spring-boot-starter&lt;\/artifactId&gt;\n&lt;\/dependency&gt;\n<\/code><\/pre>\n<figure style=\"margin:1.5em 0;text-align:center;\"><img decoding=\"async\" src=\"https:\/\/kindsonthegenius.com\/blog\/wp-content\/uploads\/2026\/07\/spring-boot-rag-project-structure.png\" alt=\"Spring Boot RAG project directory structure with controller, config, and docs folder\" width=\"640\" height=\"400\" loading=\"lazy\" style=\"max-width:100%;height:auto;border-radius:8px;border:1px solid #dee2e6;\" \/><figcaption style=\"font-size:0.85em;color:#666;margin-top:0.5em;\">Recommended project layout for a Spring Boot RAG chatbot<\/figcaption><\/figure>\n<h2 id=\"configure-spring-ai\">Step 2: Configure Spring AI and OpenAI<\/h2>\n<p>Create <code>src\/main\/resources\/application.yml<\/code>:<\/p>\n<pre><code class=\"language-yaml\">spring:\n  ai:\n    openai:\n      api-key: ${OPENAI_API_KEY}\n      embedding:\n        options:\n          model: text-embedding-3-small\n      chat:\n        options:\n          model: gpt-4o-mini\n          temperature: 0.3\n\nserver:\n  port: 8080\n<\/code><\/pre>\n<p>Never commit your API key. Export it before running:<\/p>\n<pre><code class=\"language-bash\">export OPENAI_API_KEY=sk-your-key-here\nmvn spring-boot:run\n<\/code><\/pre>\n<h2 id=\"ingest-documents\">Step 3: Ingest Documents into the Vector Store<\/h2>\n<p>Add sample docs under <code>src\/main\/resources\/docs\/faq.md<\/code>. Create a configuration class that loads, splits, embeds, and stores documents on startup:<\/p>\n<pre><code class=\"language-java\">@Configuration\npublic class RagConfig {\n\n    @Bean\n    CommandLineRunner ingestDocuments(EmbeddingModel embeddingModel,\n                                      ResourcePatternResolver resolver) {\n        return args -&gt; {\n            VectorStore store = new SimpleVectorStore(embeddingModel);\n            Resource[] resources = resolver.getResources(\"classpath:docs\/*.md\");\n\n            for (Resource resource : resources) {\n                String text = resource.getContentAsString(StandardCharsets.UTF_8);\n                List&lt;Document&gt; chunks = new TokenTextSplitter().split(new Document(text));\n                store.add(chunks);\n            }\n            \/\/ expose store as bean in a @Configuration class\n        };\n    }\n}\n<\/code><\/pre>\n<p><strong>Tip:<\/strong> For production, replace <code>SimpleVectorStore<\/code> with PostgreSQL + pgvector or a managed vector DB. See the <a href=\"https:\/\/kindsonthegenius.com\/blog\/inventoryms-complete-application-spring-boot-api\/\">InventoryMS Spring Boot API<\/a> series for database integration patterns.<\/p>\n<figure style=\"margin:1.5em 0;text-align:center;\"><img decoding=\"async\" src=\"https:\/\/kindsonthegenius.com\/blog\/wp-content\/uploads\/2026\/07\/spring-boot-rag-ingest-log.png\" alt=\"Spring Boot console log showing document chunks ingested into vector store on startup\" width=\"640\" height=\"360\" loading=\"lazy\" style=\"max-width:100%;height:auto;border-radius:8px;border:1px solid #dee2e6;\" \/><figcaption style=\"font-size:0.85em;color:#666;margin-top:0.5em;\">Console output after successful document ingestion<\/figcaption><\/figure>\n<h2 id=\"chat-endpoint\">Step 4: Build the RAG Chat REST Endpoint<\/h2>\n<p>Create <code>RagController.java<\/code> with a POST endpoint that retrieves context and calls the chat model:<\/p>\n<pre><code class=\"language-java\">@RestController\n@RequestMapping(\"\/api\/chat\")\npublic class RagController {\n\n    private final VectorStore vectorStore;\n    private final ChatModel chatModel;\n\n    public RagController(VectorStore vectorStore, ChatModel chatModel) {\n        this.vectorStore = vectorStore;\n        this.chatModel = chatModel;\n    }\n\n    @PostMapping\n    public ChatResponse ask(@RequestBody ChatRequest request) {\n        List&lt;Document&gt; context = vectorStore.similaritySearch(\n            SearchRequest.query(request.question()).withTopK(4));\n\n        String prompt = \"\"\"\n            Answer using ONLY the context below. If unknown, say you don't know.\n            Context:\n            %s\n\n            Question: %s\n            \"\"\".formatted(formatContext(context), request.question());\n\n        String answer = chatModel.call(prompt);\n        return new ChatResponse(answer, context.stream().map(Document::getId).toList());\n    }\n\n    record ChatRequest(String question) {}\n    record ChatResponse(String answer, List&lt;String&gt; sources) {}\n}\n<\/code><\/pre>\n<figure style=\"margin:1.5em 0;text-align:center;\"><img decoding=\"async\" src=\"https:\/\/kindsonthegenius.com\/blog\/wp-content\/uploads\/2026\/07\/spring-boot-rag-intellij-controller.png\" alt=\"RagController Java class open in IntelliJ IDEA with REST mapping annotations\" width=\"640\" height=\"400\" loading=\"lazy\" style=\"max-width:100%;height:auto;border-radius:8px;border:1px solid #dee2e6;\" \/><figcaption style=\"font-size:0.85em;color:#666;margin-top:0.5em;\">RagController with similarity search and chat model call<\/figcaption><\/figure>\n<h2 id=\"test-api\">Step 5: Test the Chatbot API<\/h2>\n<p>Start the app and send a request with curl or Postman:<\/p>\n<pre><code class=\"language-bash\">curl -X POST http:\/\/localhost:8080\/api\/chat \\\n  -H \"Content-Type: application\/json\" \\\n  -d '{\"question\":\"How do I reset my password?\"}'\n<\/code><\/pre>\n<p>Expected JSON response:<\/p>\n<pre><code class=\"language-json\">{\n  \"answer\": \"To reset your password, click Forgot Password on the login page...\",\n  \"sources\": [\"doc-faq-003\", \"doc-faq-007\"]\n}\n<\/code><\/pre>\n<figure style=\"margin:1.5em 0;text-align:center;\"><img decoding=\"async\" src=\"https:\/\/kindsonthegenius.com\/blog\/wp-content\/uploads\/2026\/07\/spring-boot-rag-postman-response.png\" alt=\"Postman showing successful RAG chatbot JSON response with answer and source IDs\" width=\"640\" height=\"420\" loading=\"lazy\" style=\"max-width:100%;height:auto;border-radius:8px;border:1px solid #dee2e6;\" \/><figcaption style=\"font-size:0.85em;color:#666;margin-top:0.5em;\">Testing the RAG endpoint \u2014 answer grounded in ingested documents<\/figcaption><\/figure>\n<h2 id=\"production-tips\">Production Tips<\/h2>\n<ul>\n<li><strong>Rate limiting:<\/strong> Protect <code>\/api\/chat<\/code> with Spring Security or an API gateway.<\/li>\n<li><strong>Chunk size:<\/strong> Tune the text splitter (500\u20131000 tokens) for your document type.<\/li>\n<li><strong>Observability:<\/strong> Log retrieved chunk IDs and latency for debugging hallucinations.<\/li>\n<li><strong>Deploy:<\/strong> Containerize with Docker and deploy to Kubernetes \u2014 see <a href=\"https:\/\/kindsonthegenius.com\/blog\/deploy-spring-boot-kubernetes-docker-compose-minikube-2026\/\">Deploy Spring Boot to Kubernetes (2026)<\/a>.<\/li>\n<li><strong>Python alternative:<\/strong> For ML-heavy pipelines, explore the <a href=\"https:\/\/kindsonthegenius.com\/python\/\">Python tutorials<\/a> subsite.<\/li>\n<\/ul>\n<h2 id=\"faq\">Frequently Asked Questions<\/h2>\n<p><strong>What is RAG in Spring Boot?<\/strong><br \/>\nRAG (Retrieval-Augmented Generation) in Spring Boot means loading your documents into a vector store, retrieving relevant chunks at query time, and passing them as context to an LLM via Spring AI \u2014 so answers are grounded in your data, not just the model&#8217;s training set.<\/p>\n<p><strong>Do I need OpenAI or can I use a local model?<\/strong><br \/>\nSpring AI supports OpenAI, Azure OpenAI, Ollama, and other providers. Swap the starter dependency and configuration \u2014 the RAG pipeline code stays largely the same.<\/p>\n<p><strong>How much does a RAG chatbot cost to run?<\/strong><br \/>\nCosts depend on embedding volume and chat volume. Using <code>text-embedding-3-small<\/code> and <code>gpt-4o-mini<\/code> keeps dev\/testing costs low. Cache embeddings after first ingest to avoid re-embedding unchanged documents.<\/p>\n<p><strong>Why does my chatbot hallucinate?<\/strong><br \/>\nIncrease <code>topK<\/code> retrieval, tighten the system prompt (&#8220;answer ONLY from context&#8221;), or improve chunk quality. Empty vector stores also cause hallucinations \u2014 verify ingest logs on startup.<\/p>\n<p><strong>Can I use PDFs instead of Markdown?<\/strong><br \/>\nYes. Spring AI provides PDF document readers. Add the PDF reader dependency, load files from <code>classpath:docs\/*.pdf<\/code>, and pipe through the same splitter and vector store.<\/p>\n<p><strong>How do I secure the RAG API?<\/strong><br \/>\nAdd Spring Security with JWT \u2014 follow the <a href=\"https:\/\/kindsonthegenius.com\/blog\/json-web-token-how-to-secure-rest-api-using-jwt-in-spring-boot\/\">JWT in Spring Boot tutorial<\/a> and protect <code>\/api\/chat<\/code> with <code>@PreAuthorize<\/code> or OAuth2 resource server config.<\/p>\n<h2 id=\"next-steps\">Next Steps<\/h2>\n<ul>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/spring-boot-3-java-21-virtual-threads-rest-api-tutorial\/\"><strong>Spring Boot 3 + Java 21 Virtual Threads<\/strong> \u2014 scale concurrent chat requests<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/deploy-spring-boot-kubernetes-docker-compose-minikube-2026\/\"><strong>Deploy Spring Boot to Kubernetes<\/strong> \u2014 containerize and ship your RAG service<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/blog\/json-web-token-how-to-secure-rest-api-using-jwt-in-spring-boot\/\">Secure REST APIs with JWT in Spring Boot<\/a><\/li>\n<li><a href=\"https:\/\/kindsonthegenius.com\/python\/\">Python tutorials<\/a> \u2014 LangChain and data science paths<\/li>\n<li><a href=\"https:\/\/www.alkademy.com\/courses\" target=\"_blank\" rel=\"noopener noreferrer\">Alkademy courses<\/a> \u2014 live Spring Boot and AI classes<\/li>\n<\/ul>\n<p><!-- ktg-series-nav --><\/p>\n<nav class=\"ktg-series-nav\" aria-label=\"Spring Boot 2026 tutorial series\">\n<p><strong>Series:<\/strong> <a href=\"https:\/\/kindsonthegenius.com\/blog\/spring-boot-3-java-21-virtual-threads-rest-api-tutorial\/\">Java 21 Virtual Threads<\/a> \u00b7 <a href=\"https:\/\/kindsonthegenius.com\/blog\/deploy-spring-boot-kubernetes-docker-compose-minikube-2026\/\">Kubernetes Deployment<\/a><\/p>\n<\/nav>\n<p><!-- ktg-alkademy-cta --><\/p>\n<div class=\"ktg-alkademy-cta\" style=\"margin:2em 0;padding:1.25em;border-left:4px solid #2563eb;background:#f8fafc;\">\n<p><strong>Want hands-on Spring Boot + AI training?<\/strong> Join <a href=\"https:\/\/www.alkademy.com\/courses\" target=\"_blank\" rel=\"noopener noreferrer\">Alkademy<\/a> for instructor-led courses covering REST APIs, microservices, and modern Java.<\/p>\n<\/div>\n<p><!-- ktg-faq-schema --><br \/>\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is RAG in Spring Boot?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"RAG in Spring Boot loads documents into a vector store, retrieves relevant chunks at query time, and passes them as context to an LLM via Spring AI so answers are grounded in your private data.\"}},{\"@type\":\"Question\",\"name\":\"Do I need OpenAI for a Spring Boot RAG chatbot?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"No. Spring AI supports OpenAI, Azure OpenAI, Ollama, and other providers. 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