Langchain4j Rag Implementation Patterns

This implements RAG systems in Java using LangChain4j, handling the full pipeline from document ingestion through vector search to context-aware responses. You get document splitters with token counting, embedding store configuration (in-memory by default), and retrieval with configurable similarity thresholds. The skill includes validation checkpoints to verify embedding counts match segments and test retrieval before going live. It's built for Spring Boot projects and assumes you're working with OpenAI embeddings, though the patterns apply to other providers. The hierarchical RAG example is worth looking at if you're dealing with large document sets where you want to search summaries first, then drill into chunks. Code is production-ready with proper error handling and metadata support.