langchain4j-vector-stores-configuration

Configure LangChain4j vector stores by selecting a provider—PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, or Neo4j—then initialize the EmbeddingStore with connection credentials, embedding dimensions, and indexing settings. This enables storing and retrieving document embeddings for semantic search and RAG workflows.

LangChain4j supports multiple production vector stores: PostgreSQL with pgvector extension, Pinecone, MongoDB, Milvus, and Neo4j. Each provider offers different trade-offs in scalability, query performance, and operational overhead.

Build a RAG pipeline by ingesting documents, embedding them via LangChain4j, storing embeddings in your configured vector store, then retrieving semantically similar documents at query time to augment LLM context. This requires document ingestion, embedding, retrieval, and orchestration steps.

Yes. LangChain4j vector stores support metadata filtering to narrow retrieval results and hybrid search combining semantic similarity with keyword matching. Configure filtering rules during store setup and apply them in retrieval queries.

Deploy with connection pooling, health checks, monitoring patterns, and cross-store orchestration. These configurations ensure reliability, detect failures early, track performance metrics, and enable failover across multiple vector store providers.

Yes. Optimize by configuring appropriate batch sizes for document ingestion, tuning embedding dimensions, indexing strategies per provider, connection pooling, and query timeouts. Production performance depends on these settings and your document volume.