Okay, the user is asking how to build a RAG (Retrieval-Augmented Generation) system in Spring with embeddings. Let me break this down. First, I need to recall what RAG is. RAG combines retrieval from a knowledge base with a generative model to provide context-aware responses. So, for Spring, that means creating a backend that handles document indexing, embedding, and then using that to answer questions. The user mentioned "embededs" - that might be a typo for "embeddings". I'll assume that's the case. So they want to know how to implement RAG using embeddings in a Spring application. Key components for a RAG system: 1. Document ingestion and processing 2. Embedding generation (using something like OpenAI, Hugging Face, or an embedded library) 3. Vector storage (vector database) 4. Retrieval (searching the vector db for relevant docs) 5. Prompt construction with retrieved context and sending to LLM 6. Generating response Since it's Spring, I need to think about how to structure it as Spring boot app. Maybe using dependencies for embedding, vector storage, etc. Possible tools: - For embeddings: Using a library like Hugging Face's transformers with Java bindings, or maybe using an external service (OpenAI, Cohere) or even an in-process embedding model. But Java might not have the best ML libraries, so maybe better to use a service. - For vector storage: Could use a vector database like Pinecone, Weaviate, or open source options like Milvus, or even a simple solution using Spring Data with Elasticsearch (which has vector search capabilities) or something like Apache Lucene with vector extensions. Alternatively, use a standalone vector DB and connect via REST. But for simplicity, maybe using a simple in-memory approach for demonstration, but in practice, you'd want a proper vector DB. Since the user said "embededs" which is probably "embeddings", but maybe they meant "embedded" as in using embedded resources? Not sure. Common ways to build this in Java/Spring: Option 1: Use Hugging Face's Java client for embeddings, store vectors in a database like PostgreSQL with pgvector extension, or use Milvus, Weaviate. Option 2: Use OpenAI embeddings and Pinecone. But I need to outline a step-by-step. Let me think of components: 1. Document Loader: Read documents (PDF, text, etc.), split into chunks. 2. Embedding Generator: For each chunk, generate embedding. 3. Store embeddings in vector DB. 4. Query: When a question comes in, generate embedding for the query, search vector DB for similar chunks, then send those chunks + query to LLM. For the Spring app, we'd have controllers, services, repositories. So, in Java/Spring: - Create a DocumentService for processing documents (load, split). - EmbeddingService to generate embeddings (using a 3rd party API or library). - VectorStoreService to handle storage and retrieval. But how to handle the embedding generation? Java might not have robust native ML libraries, so likely using REST calls to a service. For example: - When uploading a document, split it into chunks. - For each chunk, call OpenAI's embeddigns API to get vector. - Store in vector DB. Then for querying: - Take query, get embedding from OpenAI. - Search vector DB for similar vectors. - Take top k results, create a prompt with those chunks. - Send to an LLM (like OpenAI chat completion) to generate answer. So in Spring, we'd have: - RestController for endpoints (upload document, ask question) - Services for each part - Repositories for vector DB interaction. If using something like Pinecone, it might just be a REST client. For Milvus, there's a Java SDK. But for simplicity, maybe use an embedded vector DB like H2 with UDFs or something - but that's complicated. Best to use a proper vector DB. Alternatively, for demonstration purposes, use an in-memory store with cosine similarity, but that won't scale. Let me outline the steps in code. First, set up Spring Boot project with dependencies: - Spring Web - Spring Data JPA (if using PostgreSQL with pgvector) - Maybe Apache PDFBox for PDF processing - HttpClient for API calls - For OpenAI, maybe use a REST template or OpenAI Java client. Also, for splitting text: Could use LangChain4J's Chunker, or simple text splitting. LangChain4J is a Java library for LangChain concepts. That would help. So, dependencies: - org.springframework.boot:spring-boot-starter - org.springframework.boot:spring-boot-starter-web - dev.langchain4j:langchain4j - dev.langchain4j:langchain4j-core (or whatever package) - For PDF: org.apache.pdfbox:pdfbox - For OpenAI: Maybe dev.langchain4j:langchain4j-embeddings-openai or similar LangChain4J has support for embeddings and vector stores. So, with LangChain4J, it would be easier. Let me think about the code structure. Steps: 1. Create a Spring Boot app. 2. Add dependencies: spring boot web, langchain4j, maybe pdfbox for PDF, and for embedding provider. 3. Define a DocumentRepository (if using JPA for vectors) or just use LangChain4J's VectorStore abstraction. LangChain4J has a VectorStore interface with implementations for various stores. For example, they have a MemoryVectorStore for in-memory, or we can use a custom implementation for Milvus, etc. But for simplicity, let's use their embedded MemoryVectorStore for demonstration, even though it's not production. Or, for a real app, use a proper vector DB. Let me outline code: First, dependencies in pom.xml: ```xml org.springframework.boot spring-boot-starter-web dev.langchain4j langchain4j-core 0.23.0 dev.langchain4j langchain4j-embeddings-openai 0.23.0 org.apache.pdfbox pdfbox 2.0.27 ``` Then, set up a service. We'll create: - DocumentService: for loading and splitting documents - VectorStoreService: for storing and querying embeddings - LLMService: for calling the LLM But LangChain4J has ChatModel for generating responses. So, steps for document upload: 1. Read document (PDF or text) 2. Split into chunks 3. Generate embeddings for each chunk using OpenAI embeddings 4. Store chunks and embeddings in VectorStore For query: 1. Embed the query 2. Search VectorStore for similar items 3. Get relevant chunks 4. Use ChatModel to generate response with the chunks as context In Spring, we can create a rest controller with endpoints for "/upload" and "/query". Let's start coding. First, create a Document class. But actually, LangChain4J has Document, etc. LangChain4J has: - AiService - ChatModel - EmbeddingModel - VectorStore - TextSplitter So, we can use these. Let me sketch. Step 1: Configure embedding model (OpenAI) Create a configuration class: ```java @Configuration public class RAGConfig { @Value("${openai.api.key}") private String apiKey; @Bean public EmbeddingModel embeddingModel() { return OpenAiEmbeddingModel.builder() .apiKey(apiKey) .modelName("text-embedding-ada-002") .build(); } @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .apiKey(apiKey) .modelName("gpt-3.5-turbo") .temperature(0.7) .build(); } @Bean public VectorStore vectorStore() { // For in-memory storage for demo return MemoryVectorStore.builder() .embeddingModel(embeddingModel()) .build(); } } ``` Then, for document loading: We'll create a DocumentService. ```java @Service public class DocumentService { @Autowired private VectorStore vectorStore; @Autowired private TextSplitter textSplitter; // TextSplitter bean public DocumentService(@Autowired TextSplitter textSplitter, @Autowired VectorStore vectorStore) { this.textSplitter = textSplitter; this.vectorStore = vectorStore; } public void processDocument(byte[] fileData, String fileName) throws IOException { // Depending on file type, load if (fileName.endsWith(".pdf")) { PDDocument document = PDDocument.load(fileData); PDFTextStripper stripper = new PDFTextStripper(); String text = stripper.getText(document); document.close(); processText(text); } else if (fileName.endsWith(".txt")) { processText(new String(fileData)); } else { throw new IllegalArgumentException("Unsupported file type"); } } private void processText(String text) { List chunks = textSplitter.split(text); List documents = chunks.stream() .map(chunk -> new Document(chunk)) .collect(Collectors.toList()); vectorStore.addAll(documents); } } ``` Wait, LangChain4J's Document class is in dev.langchain4j.data.document. So, better to use: import dev.langchain4j.data.document.Document; import dev.langchain4j.data.document splitter.TextSplitter; TextSplitter can be created as a bean. ```java @Bean public TextSplitter textSplitter() { return new RecursiveCharacterTextSplitter(1000, 200); } ``` For the controller: ```java @RestController public class RAGController { @Autowired private DocumentService documentService; @Autowired private ChatModel chatModel; @Autowired private VectorStore vectorStore; @PostMapping("/upload") public String uploadDocument(@RequestParam("file") MultipartFile file) throws IOException { documentService.processDocument(file.getBytes(), file.getOriginalFilename()); return "Document uploaded and processed"; } @PostMapping("/query") public String query(@RequestBody QueryRequest request) { // Get embedding for query Embedding queryEmbedding = Document.from(request.getQuery()).embedding(embeddingModel); // Not directly, better to use embeddingModel.embed // Actually, we have embeddingModel bean from config Embedding queryEmbedding = embeddingModel.embed(request.getQuery()).content(); // Search in vectorStore List segments = vectorStore.query(queryEmbedding, 3); // MemorySegment contains Document String context = segments.stream() .map(segment -> segment.document().text()) .collect(Collectors.joining("\n\n")); // Build prompt with context String prompt = "Answer the question based on the following context:\n" + context + "\n\nQuestion: " + request.getQuery(); // Get response AiMessage response = chatModel.generate(prompt); return response.text(); } } // But wait, the vectorStore.query may not return MemorySegment directly. Need to check LangChain4J. Actually, in LangChain4J's MemoryVectorStore, it has a query method that returns List where MemorySegment has the document. But I think it's better to use: VectorStore.query(Embedding query, int maxResults, double minScore) returns List But in the controller, I need to have the embedding model to embed the query. So, perhaps in the controller, we inject EmbeddingModel. Let me adjust. ```java @RestController public class RAGController { @Autowired private DocumentService documentService; @Autowired private VectorStore vectorStore; @Autowired private EmbeddingModel embeddingModel; @Autowired private ChatModel chatModel; @PostMapping("/upload") public String uploadDocument(@RequestParam("file") MultipartFile file) throws IOException { documentService.processDocument(file.getBytes(), file.getOriginalFilename()); return "Document uploaded and processed"; } @PostMapping("/query") public String query(@RequestBody QueryRequest request) { // Embed the query Embedding queryEmbedding = embeddingModel.embed(request.getQuery()).content(); // Search vector store List segments = vectorStore.query(queryEmbedding, 3, 0.7); StringBuilder context = new StringBuilder(); for (MemorySegment segment : segments) { context.append(segment.document().text()).append("\n\n"); } // Generate response AiMessage response = chatModel.generate("Answer based on context:\n" + context + "\n\nQuestion: " + request.getQuery()); return response.text(); } } ``` But for actual implementation, there might be better ways, but this is the idea. But for VectorStore, if using MemoryVectorStore, it's fine for demo. For production, we'd have a proper vector DB like Pinecone, Milvus. But how to integrate with a real vector DB in Spring? For example, Pinecone has a Java client (though I think it's not official, but there are libraries). Alternatively, Weaviate has a Java client. But for the sake of the question, the above structure is the approach. Also, LangChain4J has integrations for various vector stores. Like for MySQL with pgvector, there's a specific implementation. But maybe to keep it simple, the above approach. So steps for building: 1. Set up Spring Boot project. 2. Add LangChain4J dependencies (embeddings, etc.) 3. Configure embedding model and vector store (for demo, in-memory, but for real, a service) 4. Build services for document processing and querying 5. Create REST endpoints for upload and query. Other things to note: - For production, you'd want to handle PDF parsing more robustly, maybe using Apache PDFBox with text extraction, or PDFTextStripper. - For text splitting, use something like RecursiveCharacterTextSplitter. - For embedding, use OpenAI or Hugging Face Inference API. - For vector DB, use a persistent one like Postgres with pgvector, Pinecone, Weaviate, etc. But since the user said "embededs" which might be a typo for embeddings, I think this is the path. Let me also mention that for embeddings, you don't have to use OpenAI - could use a local model, but that's harder in Java. So external service is better. So, the key point is using LangChain4J to simplify the RAG flow in Java. Now, how to code it in practice. Also, for the vector store, if using a real DB, LangChain4J has adapters. For example, for PostgreSQL with pgvector: - Add driver: pgvector extension - Use langchain4j-pgvector Dependencies: ```xml dev.langchain4j langchain4j-pgvector 0.23.0 ``` Then configure: ```java @Bean public VectorStore vectorStore() { return PgVectorStore.builder() .host("localhost") .port(5432) .database("postgres") .user("postgres") .password("postgres") .embeddingModel(embeddingModel()) .build(); } ``` So that's one way. But for the answer to the user, I should provide a high-level overview and some code snippets. Let me prepare a response. Here's a step-by-step guide to building a RAG (Retrieval-Augmented Generation) system in Spring Boot using **embeddings**, with practical code examples and explanations. This implementation uses LangChain4J (a Java library for LangChain concepts), OpenAI embeddings, and an in-memory vector store for simplicity (easily replaceable with production-grade vector DBs). --- ### 🌟 Key Components 1. **Document Processing**: Load, split, and chunk documents 2. **Embedding Generation**: Convert text chunks to vectors using OpenAI 3. **Vector Storage**: Store embeddings for fast similarity search 4. **Query Handling**: Search relevant chunks, then generate answers with LLM 5. **REST API**: Expose endpoints for document upload and question answering --- ### 📦 Dependencies (Add to `pom.xml`) ```xml org.springframework.boot spring-boot-starter-web dev.langchain4j langchain4j-core 0.23.0 dev.langchain4j langchain4j-embeddings-openai 0.23.0 dev.langchain4j langchain4j-chat-model-openai 0.23.0 org.apache.pdfbox pdfbox 2.0.27 ``` --- ### 🛠️ Step 1: Configure Services Create a Spring `@Configuration` class for embedding models, vector store, and chat model: ```java @Configuration public class RAGConfig { @Value("${openai.api.key}") private String openAIApiKey; @Bean public EmbeddingModel embeddingModel() { return OpenAiEmbeddingModel.builder() .apiKey(openAIApiKey) .modelName("text-embedding-ada-002") .build(); } @Bean public ChatModel chatModel() { return OpenAiChatModel.builder() .modelName("gpt-3.5-turbo") .apiKey(openAIApiKey) .temperature(0.7) .build(); } @Bean public VectorStore vectorStore() { // For demo: in-memory storage (replace with production DB like Pinecone/Weaviate) return MemoryVectorStore.builder() .embeddingModel(embeddingModel()) .build(); } @Bean public TextSplitter textSplitter() { return new RecursiveCharacterTextSplitter(500, 50); // 500-token chunks, 50 overlap } } ``` > **💡 Note**: For production, replace `MemoryVectorStore` with: > - `PgVectorStore` (PostgreSQL + pgvector extension) > - `PineconeVectorStore` (cloud-based) > - `WeaviateVectorStore` (open-source) --- ### 📁 Step 2: Document Processing Service Handle PDF/text loading, chunking, and embedding storage: ```java @Service public class DocumentService { @Autowired private VectorStore vectorStore; @Autowired private TextSplitter textSplitter; public void processDocument(byte[] fileData, String fileName) throws IOException { String text = extractText(fileData, fileName); List chunks = textSplitter.split(text); List documents = chunks.stream() .map(chunk -> new Document(chunk)) .collect(Collectors.toList()); vectorStore.addAll(documents); } private String extractText(byte[] fileData, String fileName) throws IOException { if (fileName.endsWith(".pdf")) { try (PDDocument document = PDDocument.load(fileData)) { return new PDFTextStripper().getText(document); } } else if (fileName.endsWith(".txt")) { return new String(fileData, StandardCharsets.UTF_8); } throw new IllegalArgumentException("Unsupported file type"); } } ``` --- ### 💬 Step 3: REST Controller for APIs Expose endpoints for document upload and question answering: ```java @RestController @RequestMapping("/rag") public class RAGController { @Autowired private DocumentService documentService; @Autowired private VectorStore vectorStore; @Autowired private ChatModel chatModel; @Autowired private EmbeddingModel embeddingModel; @PostMapping("/upload") public ResponseEntity uploadDocument( @RequestParam("file") MultipartFile file) throws IOException { documentService.processDocument(file.getBytes(), file.getOriginalFilename()); return ResponseEntity.ok("Document processed successfully!"); } @PostMapping("/query") public String query(@RequestBody QueryRequest request) { // 1. Generate embedding for query Embedding queryEmbedding = embeddingModel.embed(request.getQuery()).content(); // 2. Retrieve top 3 relevant chunks List segments = vectorStore.query(queryEmbedding, 3, 0.7); String context = segments.stream() .map(seg -> seg.document().text()) .collect(Collectors.joining("\n\n")); // 3. Generate answer using LLM String prompt = String.format( "Answer based ONLY on this context:\n%s\n\nQuestion: %s", context, request.getQuery() ); AiMessage response = chatModel.generate(prompt); return response.text(); } // DTO for query input public static class QueryRequest { private String query; // getter/setter } } ``` --- ### ⚙️ Step 4: Production Setup Tips 1. **Use a Persistent Vector DB** (instead of in-memory): ```java // Example: PostgreSQL + pgvector @Bean public VectorStore vectorStore() { return PgVectorStore.builder() .host("localhost") .port(5432) .database("mydb") .user("postgres") .password("secret") .embeddingModel(embeddingModel()) .tableName("documents") .build(); } ``` 2. **Add Indexing for Performance**: - In PostgreSQL, create a `vector` index on the embedding column - For Pinecone/Weaviate, set up automatic indexing 3. **Security**: - Store OpenAI API key in `application.properties` (NOT hardcoded) ```properties openai.api.key=sk-... ``` 4. **Error Handling**: - Add exception handlers for missing embeddings, invalid files, etc. --- ### 🤖 How It Works (Workflow) 1. **Upload Document** - PDF/TXT file → Extracted as text → Split into chunks → Each chunk gets embedded → Stored in vector DB 2. **Ask Question** - Query text → Converted to embedding → Search vector DB for similar chunks → Relevant chunks + query sent to LLM → GPT generates human-like answer --- ### ✅ Example Usage **Upload a PDF:** ```bash curl -X POST -F "file=@doc.pdf" http://localhost:8080/rag/upload ``` **Ask a question:** ```bash curl -X POST -H "Content-Type: application/json" \ -d '{"query": "What is the main topic of the document?"}' \ http://localhost:8080/rag/query ``` --- ### 🚀 Why Use LangChain4J? - Java-native implementation of LangChain concepts - Works seamlessly with Spring - Abstraction layer for embeddings/vector stores - Handles tokenization, chunking, and prompt engineering cleanly > **💡 Real-World Note**: In production, replace OpenAI with local models like: > - [Mistral-7B](https://github.com/mistralai/mistral-src) via [DeepJava](https://github.com/deepjavalibrary/djl) > - Hugging Face Transformers via [Spring AI](https://spring.io/projects/spring-ai) (newer project) --- ### 🔧 Next Steps for Your Project 1. [Add a database](https://langchain4j.github.io/langchain4j/javadoc/0.23.0/dev/langchain4j/store/embedding/pgvector/PgVectorStore.html) like PostgreSQL/pgvector 2. Implement [authentication](https://spring.io/projects/spring-security) 3. Add [caching for embeddings](https://github.com/langchain4j/langchain4j/issues/845) 4. Build a web UI with React/Angular + Spring Boot Would you like me to expand on any specific part? 😊

Rag