Langchain4j Vector Stores Configuration

This handles LangChain4J vector store setup for RAG applications across PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Milvus, and Neo4j. You get builder-based configuration patterns, connection pooling with HikariCP, metadata filtering with multiple field types, and health check implementations. The validation workflow is smart: it walks you through connection testing, dimension validation, and test queries before you ingest production data. Most useful when you're wiring up semantic search or need to switch between vector databases without rewriting your embedding logic. The examples cover everything from basic in-memory stores for development to production setups with monitoring and connection pools. Good reference if you're tired of reading vendor-specific documentation for each database.