langchain4j-vector-stores-configuration

LangChain4J vector store configuration for RAG applications with multiple database backends. Configure vector stores for Retrieval-Augmented Generation applications with LangChain4J. LangChain4J provides a unified abstraction for vector stores (PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Milvus, Neo4j) with builder-based configuration, metadata filtering, and hybrid search support. Configure an embedding store for vector operations: Follow this workflow to ensure correct vector store setup: Use different stores for different use cases: Use EmbeddingStoreIngestor for automated document processing: Configure metadata-based filtering capabilities: Implement connection pooling and monitoring: Monitor vector store connectivity: For Development: For Production: Choose index types based on performance requirements: Match embedding dimensions to your model: Use batch operations for better performance: Protect sensitive configuration: For comprehensive documentation and advanced configurations, see: - Supports PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Milvus, Neo4j, and in-memory stores with unified abstraction - Includes document ingestion pipelines with configurable chunking, metadata filtering, and batch operations - Provides production patterns for connection pooling, health checks, monitoring, and index optimization - Covers semantic search implementation, multi-store...