langchain4j-vector-stores-configuration

# 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: ```java @Bean public EmbeddingStore<TextSegment> embeddingStore() { return PgVectorEmbeddingStore.builder() .host("localhost") .port(5432) .database("vectordb") .user("username") .password("password") .table("embeddings") .dimension(1536) // OpenAI embedding dimension .createTable(true) .useIndex(true) .build(); } ``` ### Validation Workflow Follow this workflow to ensure correct vector store setup: 1. **Configure**: Build the embedding store with required dimensions and connection parameters 2. **Test connection**: Verify store connectivity with a health check before ingesting data 3. **Validate dimensions**: Confirm embedding model dimensions match store configuration 4. **Ingest test data**: Add a small batch of test do...