langchain4j-rag-implementation-patterns

# LangChain4j RAG Implementation Patterns ## Overview Implements RAG systems with LangChain4j: document ingestion pipelines, embedding stores, and vector search for chat-with-documents and knowledge-enhanced AI applications. ## When to Use This Skill - Building chat-with-documents systems or document Q&A over PDFs, text files, or web pages - Creating AI assistants with access to company knowledge bases or external sources - Implementing semantic search or hybrid search over document repositories - Building domain-specific AI with curated knowledge and source attribution ## Instructions ### Initialize RAG Project Create a new Spring Boot project with required dependencies: **pom.xml**: ```xml <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-spring-boot-starter</artifactId> <version>1.8.0</version> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> <version>1.8.0</version> </dependency> ``` ### Setup Document Ingestion Configure document loading and processing with validation: **Validation Checkpoint**: After ingestion, verify embedding count matches segment count and test retrieval with a sample query. ```java @Configuration public class RAGConfiguration { @Bean public EmbeddingModel embeddingModel() { return OpenAiEmbeddingModel.builder() .apiKey(System.getenv("OPENAI_API_KEY")) .modelName("text-embedding-3-small") ...