langchain4j-vector-stores-configuration

LangChain4J Vector Stores Configuration

Configure vector stores for Retrieval-Augmented Generation applications with LangChain4J.

Overview

Vector stores are essential components for RAG (Retrieval-Augmented Generation) systems, enabling efficient storage and retrieval of document embeddings for semantic search. LangChain4J provides a unified abstraction over multiple vector database backends including PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Milvus, Neo4j, and in-memory stores for development.

When to Use

To configure vector stores when:

Building RAG applications requiring embedding storage and retrieval
Implementing semantic search in Java applications
Integrating LLMs with vector databases for context-aware responses
Configuring multi-modal embedding storage for text, images, or other data
Setting up hybrid search combining vector similarity and full-text search
Migrating between different vector store providers
Optimizing vector database performance for production workloads
Building AI-powered applications with memory and persistence
Implementing document chunking and embedding pipelines
Creating recommendation systems based on vector similarity

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(); }

Configure Multiple Vector Stores

Use different stores for different use cases:

@Configuration public class MultiVectorStoreConfiguration {

@Bean
@Qualifier("documentsStore")
public EmbeddingStore&#x3C;TextSegment> documentsEmbeddingStore() {
    return PgVectorEmbeddingStore.builder()
        .table("document_embeddings")
        .dimension(1536)
        .build();
}

@Bean
@Qualifier("chatHistoryStore")
public EmbeddingStore&#x3C;TextSegment> chatHistoryEmbeddingStore() {
    return MongoDbEmbeddingStore.builder()
        .collectionName("chat_embeddings")
        .build();
}

}

Implement Document Ingestion

Use EmbeddingStoreIngestor for automated document processing:

@Bean public EmbeddingStoreIngestor embeddingStoreIngestor( EmbeddingStore<TextSegment> embeddingStore, EmbeddingModel embeddingModel) {

return EmbeddingStoreIngestor.builder()
    .documentSplitter(DocumentSplitters.recursive(
        300,  // maxSegmentSizeInTokens
        20,   // maxOverlapSizeInTokens
        new OpenAiTokenizer(GPT_3_5_TURBO)
    ))
    .embeddingModel(embeddingModel)
    .embeddingStore(embeddingStore)
    .build();

}

Set Up Metadata Filtering

Configure metadata-based filtering capabilities:

// MongoDB with metadata field mapping IndexMapping indexMapping = IndexMapping.builder() .dimension(1536) .metadataFieldNames(Set.of("category", "source", "created_date", "author")) .build();

// Search with metadata filters EmbeddingSearchRequest request = EmbeddingSearchRequest.builder() .queryEmbedding(queryEmbedding) .maxResults(10) .filter(and( metadataKey("category").isEqualTo("technical_docs"), metadataKey("created_date").isGreaterThan(LocalDate.now().minusMonths(6)) )) .build();

Configure Production Settings

Implement connection pooling and monitoring:

@Bean public EmbeddingStore<TextSegment> optimizedPgVectorStore() { HikariConfig hikariConfig = new HikariConfig(); hikariConfig.setJdbcUrl("jdbc:postgresql://localhost:5432/vectordb"); hikariConfig.setUsername("username"); hikariConfig.setPassword("password"); hikariConfig.setMaximumPoolSize(20); hikariConfig.setMinimumIdle(5); hikariConfig.setConnectionTimeout(30000);

DataSource dataSource = new HikariDataSource(hikariConfig);

return PgVectorEmbeddingStore.builder()
    .dataSource(dataSource)
    .table("embeddings")
    .dimension(1536)
    .useIndex(true)
    .build();

}

Implement Health Checks

Monitor vector store connectivity:

@Component public class VectorStoreHealthIndicator implements HealthIndicator {

private final EmbeddingStore&#x3C;TextSegment> embeddingStore;

@Override
public Health health() {
    try {
        embeddingStore.search(EmbeddingSearchRequest.builder()
            .queryEmbedding(new Embedding(Collections.nCopies(1536, 0.0f)))
            .maxResults(1)
            .build());

        return Health.up()
            .withDetail("store", embeddingStore.getClass().getSimpleName())
            .build();
    } catch (Exception e) {
        return Health.down()
            .withDetail("error", e.getMessage())
            .build();
    }
}

}

Examples

Basic RAG Application Setup

@Configuration public class SimpleRagConfig {

@Bean
public EmbeddingStore&#x3C;TextSegment> embeddingStore() {
    return PgVectorEmbeddingStore.builder()
        .host("localhost")
        .database("rag_db")
        .table("documents")
        .dimension(1536)
        .build();
}

@Bean
public ChatLanguageModel chatModel() {
    return OpenAiChatModel.withApiKey(System.getenv("OPENAI_API_KEY"));
}

}

Semantic Search Service

@Service public class SemanticSearchService {

private final EmbeddingStore&#x3C;TextSegment> store;
private final EmbeddingModel embeddingModel;

public List&#x3C;String> search(String query, int maxResults) {
    Embedding queryEmbedding = embeddingModel.embed(query).content();

    EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
        .queryEmbedding(queryEmbedding)
        .maxResults(maxResults)
        .minScore(0.75)
        .build();

    return store.search(request).matches().stream()
        .map(match -> match.embedded().text())
        .toList();
}

}

Production Setup with Monitoring

@Configuration public class ProductionVectorStoreConfig {

@Bean
public EmbeddingStore&#x3C;TextSegment> vectorStore(
        @Value("${vector.store.host}") String host,
        MeterRegistry meterRegistry) {

    EmbeddingStore&#x3C;TextSegment> store = PgVectorEmbeddingStore.builder()
        .host(host)
        .database("production_vectors")
        .useIndex(true)
        .indexListSize(200)
        .build();

    return new MonitoredEmbeddingStore&#x3C;>(store, meterRegistry);
}

}

Best Practices

Choose the Right Vector Store

For Development:

Use InMemoryEmbeddingStore for local development and testing
Fast setup, no external dependencies
Data lost on application restart

For Production:

PostgreSQL + pgvector: Excellent for existing PostgreSQL environments
Pinecone: Managed service, good for rapid prototyping
MongoDB Atlas: Good integration with existing MongoDB applications
Milvus/Zilliz: High performance for large-scale deployments

Configure Appropriate Index Types

Choose index types based on performance requirements:

// For high recall requirements .indexType(IndexType.FLAT) // Exact search, slower but accurate

// For balanced performance .indexType(IndexType.IVF_FLAT) // Good balance of speed and accuracy

// For high-speed approximate search .indexType(IndexType.HNSW) // Fastest, slightly less accurate

Optimize Vector Dimensions

Match embedding dimensions to your model:

// OpenAI text-embedding-3-small .dimension(1536)

// OpenAI text-embedding-3-large .dimension(3072)

// Sentence Transformers .dimension(384) // all-MiniLM-L6-v2 .dimension(768) // all-mpnet-base-v2

Implement Batch Operations

Use batch operations for better performance:

@Service public class BatchEmbeddingService {

private static final int BATCH_SIZE = 100;

public void addDocumentsBatch(List&#x3C;Document> documents) {
    for (List&#x3C;Document> batch : Lists.partition(documents, BATCH_SIZE)) {
        List&#x3C;TextSegment> segments = batch.stream()
            .map(doc -> TextSegment.from(doc.text(), doc.metadata()))
            .collect(Collectors.toList());

        List&#x3C;Embedding> embeddings = embeddingModel.embedAll(segments)
            .content();

        embeddingStore.addAll(embeddings, segments);
    }
}

}

Secure Configuration

Protect sensitive configuration:

// Use environment variables @Value("${vector.store.api.key:#{null}}") private String apiKey;

// Validate configuration @PostConstruct public void validateConfiguration() { if (StringUtils.isBlank(apiKey)) { throw new IllegalStateException("Vector store API key must be configured"); } }

References

For comprehensive documentation and advanced configurations, see:

API Reference - Complete API documentation
Examples - Production-ready examples

Constraints and Warnings

Vector dimensions must match the embedding model; mismatched dimensions will cause errors.
Large vector collections require proper indexing configuration for acceptable search performance.
Embedding generation can be expensive; implement batching and caching strategies.
Different vector stores have different distance metric support; verify compatibility.
Connection pooling is critical for production deployments to prevent connection exhaustion.
Metadata filtering capabilities vary between vector store implementations.
Vector stores consume significant memory; monitor resource usage in production.
Migration between vector store providers may require re-embedding all documents.
Batch operations are more efficient than single-document operations.
Always validate configuration during application startup to fail fast.

langchain4j-vector-stores-configuration

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