{"slug": "building-rag-applications-with-spring-ai-connect-llms-to-your-own-data", "title": "Building RAG Applications with Spring AI: Connect LLMs to Your Own Data", "summary": "A developer demonstrates building a Retrieval-Augmented Generation (RAG) application using Spring AI, connecting large language models to private data sources. The post outlines the RAG pipeline, including document loading, chunking, embeddings, vector databases, and similarity search, with code examples in Java.", "body_md": "Large Language Models are powerful.\n\nBut there is one fundamental limitation:\n\nAn LLM doesn't automatically know your application's private data.\n\nYour company policies, product documentation, internal knowledge base, customer records, PDFs, technical documentation, or database content are not necessarily part of the model's training data.\n\nThis is where **RAG — Retrieval-Augmented Generation** comes in.\n\nInstead of asking an LLM to answer directly, we first retrieve relevant information from our own data and provide that information as context to the model.\n\nIn this article, we'll build the foundation of a **RAG application using Spring AI**.\n\nRAG stands for:\n\n**Retrieval-Augmented Generation**\n\nThe idea is simple:\n\n```\nUser Question\n      ↓\nRetrieve Relevant Information\n      ↓\nAdd Retrieved Context to Prompt\n      ↓\nLLM\n      ↓\nGenerated Answer\n```\n\nFor example, imagine we have a company's internal documentation.\n\nA user asks:\n\n```\nWhat is our refund policy for annual subscriptions?\n```\n\nInstead of expecting the LLM to magically know the answer, our application:\n\nThis is the core idea behind RAG.\n\nA normal LLM application looks like this:\n\n```\nUser\n  ↓\nApplication\n  ↓\nLLM\n  ↓\nAnswer\n```\n\nThe problem?\n\nThe LLM only has access to the information available to it.\n\nWith RAG, the architecture becomes:\n\n```\n                  ┌──────────────┐\n                  │  Documents   │\n                  └──────┬───────┘\n                         ↓\n                    Chunking\n                         ↓\n                    Embeddings\n                         ↓\n                  Vector Database\n                         ↑\n                         │\nUser → Query → Similarity Search\n                         ↓\n                  Relevant Context\n                         ↓\n                        LLM\n                         ↓\n                      Answer\n```\n\nNow the model can work with information from our own knowledge base.\n\nA production RAG pipeline typically contains these stages:\n\n```\nDocuments\n    ↓\nDocument Loading\n    ↓\nChunking\n    ↓\nEmbeddings\n    ↓\nVector Database\n    ↓\nSimilarity Search\n    ↓\nRelevant Context\n    ↓\nPrompt\n    ↓\nLLM\n    ↓\nAnswer\n```\n\nLet's understand each step.\n\nFirst, we need to get our data into the application.\n\nThe source could be:\n\nSpring AI provides abstractions for working with documents.\n\nA document can be represented using Spring AI's `Document`\n\nabstraction.\n\nConceptually:\n\n```\nDocument document = new Document(\n    \"Spring Boot is a framework for building Java applications...\"\n);\n```\n\nWe now have content that can be processed by our RAG pipeline.\n\nWe shouldn't usually store an entire document as a single vector.\n\nImagine a 100-page PDF.\n\nA user asks:\n\n```\nHow do I configure authentication?\n```\n\nWe don't want to retrieve the entire PDF.\n\nInstead, we split the document into smaller pieces called **chunks**.\n\nFor example:\n\n```\nDocument\n   ↓\nChunk 1\nChunk 2\nChunk 3\nChunk 4\n...\nChunk 100\n```\n\nEach chunk represents a smaller piece of knowledge.\n\nA simple example:\n\n```\nChunk 1:\nIntroduction to Spring Security\n\nChunk 2:\nConfiguring authentication\n\nChunk 3:\nCreating users\n\nChunk 4:\nJWT authentication\n\nChunk 5:\nRole-based authorization\n```\n\nNow when the user asks about JWT authentication, we can retrieve the relevant chunk instead of the entire document.\n\nThis is where things get interesting.\n\nA computer doesn't understand the semantic meaning of text in the same way humans do.\n\nWe need a way to represent text numerically.\n\nThat's where **embeddings** come in.\n\nAn embedding model converts text into a vector.\n\nFor example:\n\n```\n\"How do I configure JWT authentication?\"\n```\n\nmight become something conceptually like:\n\n```\n[0.12, -0.42, 0.87, 0.31, ...]\n```\n\nThe actual vector contains many dimensions.\n\nThe important part is:\n\nSemantically similar text produces vectors that are relatively close together in vector space.\n\nFor example:\n\n```\n\"How can I configure JWT?\"\n        ↓\n[0.12, 0.81, 0.42, ...]\n\n\"JWT authentication configuration\"\n        ↓\n[0.15, 0.78, 0.45, ...]\n```\n\nThese vectors should have high similarity.\n\nNow we need somewhere to store these embeddings.\n\nThat's where a **vector database** comes in.\n\nPopular choices include:\n\nFor a Spring Boot application, PostgreSQL with `pgvector`\n\nis an especially interesting option because you can keep your relational data and vector data within the same ecosystem.\n\nConceptually:\n\n```\nDocument Chunk\n      ↓\nEmbedding Model\n      ↓\nVector\n      ↓\nVector Database\n```\n\nOur database might contain something conceptually like:\n\n```\nID | Content                    | Embedding\n---|----------------------------|----------------\n1  | JWT configuration...       | [0.12,...]\n2  | OAuth2 configuration...    | [0.42,...]\n3  | Database configuration...  | [0.71,...]\n```\n\nNow suppose the user asks:\n\n```\nHow do I configure JWT authentication in Spring Boot?\n```\n\nWe first generate an embedding for the question.\n\n```\nUser Query\n    ↓\nEmbedding Model\n    ↓\nQuery Vector\n```\n\nThen we search the vector database for similar vectors.\n\n```\nQuery Vector\n     ↓\nVector Database\n     ↓\nSimilarity Search\n     ↓\nTop K Relevant Chunks\n```\n\nFor example:\n\n```\nResult 1 → JWT configuration\nResult 2 → Spring Security authentication\nResult 3 → SecurityFilterChain configuration\n```\n\nThese results become our **context**.\n\nNow we combine:\n\nFor example:\n\n```\nContext:\n\nSpring Security can be configured using SecurityFilterChain.\nJWT authentication can be implemented using a custom\nauthentication filter...\n\nQuestion:\n\nHow do I configure JWT authentication in Spring Boot?\n```\n\nThe LLM receives this information and generates the answer.\n\nThis is the **Augmented** part of Retrieval-Augmented Generation.\n\nThe final flow looks like:\n\n```\nUser Question\n      ↓\nEmbedding\n      ↓\nVector Search\n      ↓\nRelevant Documents\n      ↓\nPrompt + Context\n      ↓\nLLM\n      ↓\nAnswer\n```\n\nThe LLM isn't searching the database itself.\n\nOur application retrieves the information first and provides it to the model.\n\nNow let's look at how Spring AI simplifies this architecture.\n\nA typical Spring AI RAG application contains:\n\n```\nSpring Boot\n     │\n     ├── Document Reader\n     │\n     ├── Text Splitter\n     │\n     ├── Embedding Model\n     │\n     ├── Vector Store\n     │\n     └── Chat Model\n```\n\nThe exact model and vector store can be swapped without rewriting the entire application.\n\nThat's one of the strengths of Spring AI's abstraction-based approach.\n\nLet's create a Spring Boot project.\n\nWe'll need Spring AI dependencies for:\n\nFor example, with Maven:\n\n```\n<dependency>\n    <groupId>org.springframework.ai</groupId>\n    <artifactId>spring-ai-starter-model-bedrock-converse</artifactId>\n</dependency>\n\n<dependency>\n    <groupId>org.springframework.ai</groupId>\n    <artifactId>spring-ai-starter-vector-store-pgvector</artifactId>\n</dependency>\n```\n\nYou should use versions compatible with the Spring AI version used by your project.\n\nIf we're using AWS Bedrock, our application needs AWS credentials and model configuration.\n\nConceptually:\n\n```\nspring:\n  ai:\n    bedrock:\n      aws:\n        region: us-east-1\n```\n\nThe exact configuration depends on the Spring AI version and Bedrock model you're using.\n\nFor production environments, don't hard-code AWS credentials inside `application.yml`\n\n.\n\nUse:\n\n```\nEnvironment Variables\n        ↓\nIAM Roles\n        ↓\nAWS Credentials Provider Chain\n```\n\nWhenever possible, prefer IAM roles over static credentials.\n\nFor PostgreSQL + pgvector, the architecture looks like:\n\n```\nSpring Boot\n     ↓\nSpring AI VectorStore\n     ↓\nPostgreSQL\n     ↓\npgvector\n```\n\nThe vector store becomes the bridge between our application and the vector database.\n\nConceptually:\n\n```\nVectorStore vectorStore;\n```\n\nWe can then add documents:\n\n```\nvectorStore.add(documents);\n```\n\nAnd later search:\n\n```\nList<Document> results =\n        vectorStore.similaritySearch(\n                SearchRequest.builder()\n                        .query(question)\n                        .topK(5)\n                        .build()\n        );\n```\n\nThe important abstraction here is:\n\n```\nVectorStore\n```\n\nOur application doesn't need to manually implement vector similarity calculations.\n\nLet's say we have a PDF containing product documentation.\n\nSpring AI provides document readers that can load different document formats.\n\nConceptually:\n\n``` js\nvar documents = reader.get();\n```\n\nWe then split the documents into chunks.\n\nFor example:\n\n```\nTokenTextSplitter splitter = new TokenTextSplitter();\n\nList<Document> chunks =\n        splitter.apply(documents);\n```\n\nThe exact splitter and configuration should be chosen based on your document structure and model context window.\n\nThe embedding model converts every chunk into a vector.\n\nConceptually:\n\n```\nDocument Chunk\n      ↓\nEmbedding Model\n      ↓\nVector\n```\n\nSpring AI handles this through the embedding/vector-store integration.\n\nThen we can store the documents:\n\n```\nvectorStore.add(chunks);\n```\n\nOur ingestion pipeline is now:\n\n```\nPDF\n ↓\nDocuments\n ↓\nChunks\n ↓\nEmbeddings\n ↓\nVector Store\n```\n\nNow let's handle the user's question.\n\nSuppose the user asks:\n\n```\nHow does authentication work in our application?\n```\n\nWe search the vector store:\n\n```\nList<Document> results =\n        vectorStore.similaritySearch(\n                SearchRequest.builder()\n                        .query(question)\n                        .topK(5)\n                        .build()\n        );\n```\n\nWe now have the most relevant pieces of information.\n\nFor example:\n\n```\nResult 1\nJWT authentication uses...\n\nResult 2\nThe authentication filter...\n\nResult 3\nSecurity configuration...\n```\n\nWe can now construct a prompt using the retrieved documents.\n\nFor example:\n\n```\nString context = results.stream()\n        .map(Document::getText)\n        .collect(Collectors.joining(\"\\n\\n\"));\n\nString prompt = \"\"\"\n        Answer the question using only the provided context.\n\n        Context:\n        %s\n\n        Question:\n        %s\n        \"\"\".formatted(context, question);\n```\n\nThen send it to the chat model.\n\nConceptually:\n\n```\nChatResponse response =\n        chatModel.call(\n                new Prompt(prompt)\n        );\n```\n\nAnd we return:\n\n```\nresponse.getResult()\n        .getOutput()\n        .getText();\n```\n\nThat's a basic RAG implementation.\n\nPutting everything together:\n\n```\n                 INGESTION PIPELINE\n\n              ┌───────────────┐\n              │   Documents   │\n              └───────┬───────┘\n                      ↓\n                Text Splitting\n                      ↓\n                 Embeddings\n                      ↓\n               Vector Store\n                      │\n                      │\n                      │\n                      ▼\n                 RETRIEVAL\n                      ▲\n                      │\n               User Question\n                      ↓\n                  Embedding\n                      ↓\n               Similarity Search\n                      ↓\n                Top K Chunks\n                      ↓\n                   Context\n                      ↓\n                    Prompt\n                      ↓\n                     LLM\n                      ↓\n                   Answer\n```\n\nThis is the basic architecture behind many modern knowledge-based AI applications.\n\nOne of the most underestimated parts of RAG is chunking.\n\nBad chunking can produce bad retrieval.\n\nImagine this document:\n\n```\nSpring Security\n\nAuthentication allows the application to verify\nthe identity of a user.\n\nAuthorization determines whether the authenticated\nuser has permission to access a resource.\n```\n\nIf we split this badly:\n\n```\nChunk 1:\nAuthentication allows...\n\nChunk 2:\nthe identity of a user. Authorization determines...\n\nChunk 3:\nwhether the authenticated user...\n```\n\nwe may destroy important semantic relationships.\n\nA better strategy is to preserve meaningful boundaries where possible.\n\nDepending on the data, you may experiment with:\n\n```\nChunk Size\nOverlap\nSentence boundaries\nParagraph boundaries\nMarkdown headings\nSemantic sections\n```\n\nThere is no universal chunk size that works for every RAG application.\n\nWhen performing similarity search, we usually retrieve the top K results.\n\nFor example:\n\n```\n.topK(5)\n```\n\nmeans:\n\n```\nReturn the 5 most relevant chunks.\n```\n\nBut bigger isn't always better.\n\nIf we retrieve too little:\n\n```\nK = 1\n```\n\nwe may miss important context.\n\nIf we retrieve too much:\n\n```\nK = 50\n```\n\nwe may introduce irrelevant information and increase token usage.\n\nA common approach is to start with a small value and evaluate retrieval quality.\n\nFor example:\n\n```\nK = 3\nK = 5\nK = 10\n```\n\nThen measure which configuration works best for your dataset.\n\nA common question is:\n\nWhy not fine-tune the model instead?\n\nRAG and fine-tuning solve different problems.\n\nBest when:\n\nUseful when you want to change:\n\nA useful mental model is:\n\n```\nRAG\n→ Give the model the right information.\n\nFine-tuning\n→ Change how the model behaves.\n```\n\nIn many production systems, they can also be used together.\n\nA basic RAG pipeline is only the beginning.\n\nProduction RAG systems often introduce additional stages:\n\n```\nQuery\n ↓\nQuery Transformation\n ↓\nHybrid Retrieval\n ↓\nMetadata Filtering\n ↓\nVector Search\n ↓\nReranking\n ↓\nContext Compression\n ↓\nPrompt Construction\n ↓\nLLM\n```\n\nYou might eventually introduce techniques such as:\n\nThis is where RAG becomes an engineering discipline rather than simply \"put documents into a vector database.\"\n\nIf you're building RAG for production, don't stop at:\n\n```\nPDF → Vector DB → LLM\n```\n\nYou also need to think about:\n\nHow frequently are documents updated?\n\n```\nNew document\n     ↓\nProcess\n     ↓\nChunk\n     ↓\nEmbed\n     ↓\nUpdate Vector Store\n```\n\nStore useful metadata alongside chunks:\n\n```\ndocument_id\nsource\npage_number\ntenant_id\ncreated_at\nupdated_at\ndocument_type\n```\n\nThis becomes extremely useful for filtering.\n\nFor example:\n\n```\ntenant_id = \"company-123\"\n```\n\ncan ensure that users only retrieve documents belonging to their tenant.\n\nThis is critical.\n\nA RAG system must not retrieve documents that the current user isn't authorized to access.\n\nYour retrieval layer should respect application permissions.\n\n```\nUser\n ↓\nAuthentication\n ↓\nAuthorization\n ↓\nMetadata Filters\n ↓\nRetrieval\n ↓\nLLM\n```\n\nNever assume that because the LLM can't \"see\" a document directly, the document is secure.\n\nTrack things such as:\n\n```\nRetrieval latency\nEmbedding latency\nLLM latency\nToken usage\nRetrieved chunks\nSimilarity scores\nFailure rate\nAnswer quality\n```\n\nWithout observability, debugging RAG becomes extremely difficult.\n\nIf you're new to RAG, remember this:\n\n```\nEmbedding Model\n    =\n    \"Convert meaning into numbers\"\n\nVector Database\n    =\n    \"Store and search those meanings\"\n\nRetriever\n    =\n    \"Find relevant information\"\n\nLLM\n    =\n    \"Use that information to generate an answer\"\n```\n\nTogether:\n\n```\nRetrieve → Augment → Generate\n```\n\nThat's RAG.\n\nSpring AI gives Java developers abstractions around many of these building blocks.\n\nInstead of manually wiring every AI provider and vector database integration, we can work with abstractions such as:\n\n```\nChatModel\nEmbeddingModel\nVectorStore\nDocument\nDocumentReader\n```\n\nThis allows us to focus more on the application architecture rather than provider-specific implementation details.\n\nAnd that's particularly useful when building enterprise Java applications where we may want to change:\n\n```\nAWS Bedrock\n     ↓\nAnother Model Provider\n```\n\nwithout completely rewriting our application.\n\nA production-oriented Spring AI RAG application can eventually look like this:\n\n```\n                    ┌───────────────────┐\n                    │    User / App     │\n                    └─────────┬─────────┘\n                              ↓\n                       Spring Boot API\n                              ↓\n                       Query Processing\n                              ↓\n                       Retrieval Layer\n                              ↓\n                    ┌───────────────────┐\n                    │   Vector Store    │\n                    │   PostgreSQL      │\n                    │    + pgvector     │\n                    └─────────┬─────────┘\n                              ↓\n                     Relevant Context\n                              ↓\n                      Prompt Assembly\n                              ↓\n                    ┌───────────────────┐\n                    │    Spring AI     │\n                    └─────────┬─────────┘\n                              ↓\n                    ┌───────────────────┐\n                    │   AWS Bedrock    │\n                    └─────────┬─────────┘\n                              ↓\n                           Answer\n```\n\nAnd separately:\n\n```\nDocuments\n    ↓\nDocument Reader\n    ↓\nChunking\n    ↓\nEmbedding Model\n    ↓\nPostgreSQL + pgvector\n```\n\nWe started this series by exploring how Spring AI can connect Java applications with modern AI models.\n\nWith AWS Bedrock, we can access powerful foundation models without managing the underlying model infrastructure.\n\nNow, with RAG, we can take another major step:\n\n**We can connect those models to our own data.**\n\nThe journey now looks like:\n\n```\nLLM\n ↓\nSpring AI\n ↓\nAWS Bedrock\n ↓\nRAG\n ↓\nVector Database\n ↓\nOur Own Data\n```\n\nBut there is another important capability missing.\n\nWhat if we don't just want the model to **answer questions**?\n\nWhat if we want the model to **take actions**?\n\nFor example:\n\n```\nUser\n ↓\nAI Agent\n ↓\nDecide what to do\n ↓\nCall a Tool\n ↓\nExecute Action\n ↓\nReturn Result\n```\n\nThat's where **tool calling and AI agents** come in.\n\n**Next up: Building AI Agents with Spring AI — Tool Calling, Memory, and Autonomous Workflows.**\n\nIf you're building AI applications with Java and Spring Boot, **RAG is one of the most important patterns to understand.**", "url": "https://wpnews.pro/news/building-rag-applications-with-spring-ai-connect-llms-to-your-own-data", "canonical_source": "https://dev.to/ayshriv/building-rag-applications-with-spring-ai-connect-llms-to-your-own-data-4bkc", "published_at": "2026-08-31 13:07:35+00:00", "updated_at": "2026-08-31 13:22:46.707529+00:00", "lang": "en", "topics": ["large-language-models", "generative-ai", "developer-tools"], "entities": ["Spring AI", "LLM"], "alternates": {"html": 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