langchain4j-vector-stores-configuration
LangChain4J vector store configuration for RAG applications with multiple database backends.
- 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 setups, and dimension matching for different embedding models
LangChain4J Vector Stores Configuration
Configure vector stores for Retrieval-Augmented Generation applications with LangChain4J.
Overview
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.
When to Use
- Configuring vector stores for semantic search and RAG applications
- Setting up embedding storage with metadata filtering and hybrid search
- Optimizing vector database performance for production AI workloads
Instructions
Set Up Basic Vector Store
Configure an embedding store for vector operations:
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