langchain4j-rag-implementation-patterns

This skill provides Retrieval-Augmented Generation (RAG) implementation patterns for Java developers using the LangChain4j library. It addresses the common challenge of grounding large language model responses in a curated knowledge base, so that AI assistants can answer questions over PDFs, text files, and web pages with source attribution rather than relying only on the model's training data. The skill packages ready-to-use code for document ingestion pipelines, embedding stores, and vector search wired into a Spring Boot application.

The skill walks through the full RAG workflow: initializing a project with the langchain4j Spring Boot starter and OpenAI dependencies, configuring an embedding model and embedding store, building a document ingestion service that loads files, splits them into overlapping segments (recursive, paragraph, sentence, or line splitters), and stores embeddings. It adds validation checkpoints that verify embedding counts match segment counts and that a sample query returns retrievable results. A companion API reference documents loaders (filesystem, classpath, URL), the DocumentSplitters factory, multiple embedding models (OpenAI, Vertex AI, Ollama, offline AllMiniLmL6V2), and a wide range of vector stores including in-memory, Pinecone, Weaviate, Qdrant, Chroma, Neo4j, MongoDB Atlas, pgvector, and Milvus, plus content retrievers with score thresholds and the EmbeddingStoreIngestor pipeline.

It targets Java and Spring Boot developers building chat-with-documents systems, document Q&A, knowledge-base assistants, or semantic and hybrid search over document repositories. It is most useful for teams that want production-shaped, validated RAG scaffolding rather than assembling LangChain4j primitives from scratch.