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:

@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 documents to verify ingestion works
5. Run test query: Execute a sample semantic search to confirm retrieval accuracy
6. Proceed to production: Only after all steps pass, proceed with full data ingestion

Configure Multiple Vector Stores

Use different stores for different use cases:

@Configuration
public class MultiVectorStoreConfiguration {

    @Bean
    @Qualifier("documentsStore")
    public EmbeddingStore<TextSegment> documentsEmbeddingStore() {
        return PgVectorEmbeddingStore.builder()
            .table("document_embeddings")
            .dimension(1536)
            .build();
    }

    @Bean
    @Qualifier("chatHistoryStore")
    public EmbeddingStore<TextSegment> chatHistoryEmbeddingStore() {
        return MongoDbEmbeddingStore.builder()
            .collectionName("chat_embeddings")
            .build();
    }
}