Langchain4j Vector Stores Configuration

@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();
}

@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();
    }
}

@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();
}

// 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();

@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();
}

@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();
        }
    }
}

@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"));
    }
}

@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();
    }
}

@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);
    }
}