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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<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();
    }
}

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<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<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<TextSegment> store;
    private final EmbeddingModel embeddingModel;

    public List<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<TextSegment> vectorStore(
            @Value("${vector.store.host}") String host,
            MeterRegistry meterRegistry) {

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

        return new MonitoredEmbeddingStore<>(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<Document> documents) {
        for (List<Document> batch : Lists.partition(documents, BATCH_SIZE)) {
            List<TextSegment> segments = batch.stream()
                .map(doc -> TextSegment.from(doc.text(), doc.metadata()))
                .collect(Collectors.toList());

            List<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.

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