langchain4j-rag-implementation-patterns

Complete Retrieval-Augmented Generation systems with LangChain4j for knowledge-enhanced AI applications. Implements RAG systems with LangChain4j: document ingestion pipelines, embedding stores, and vector search for chat-with-documents and knowledge-enhanced AI applications. Create a new Spring Boot project with required dependencies: pom.xml : Configure document loading and processing with validation: Validation Checkpoint : After ingestion, verify embedding count matches segment count and test retrieval with a sample query. Create document ingestion service: Setup content retrieval with filtering: Validation Checkpoint : After configuration, test retrieval with a known query to verify embeddings are searchable. Define AI service with context retrieval: Embedding Count Mismatch : Thrown when segments != embeddings. Check splitter configuration and model availability. Empty Retrieval Results : Call validateIngestion(testQuery) to verify embeddings are searchable. Check if document was ingested successfully.