Building Enterprise Conversational AI with LangChain4j and Spring Boot

Introduction

In the era of AI-powered customer experiences, building conversational assistants that truly understand context, maintain state, and integrate with business systems has become a critical enterprise capability. Traditional chatbots with rigid decision trees fail to deliver the natural, helpful interactions that modern customers expect.

This article explores ConversationalAIAssistant, a production-ready implementation of an AI-powered customer service agent for a car rental company. Built with LangChain4j and Spring Boot, this architecture demonstrates how to create intelligent assistants that can lookup reservations, handle cancellations, and answer policy questions - all through natural conversation.

Key Insight: LangChain4j brings the power of LangChain to the Java ecosystem, enabling enterprise developers to build sophisticated AI agents with familiar tools and patterns.

Why LangChain4j for Enterprise Conversational AI?

LangChain4j is the Java port of LangChain - a framework for rapidly assembling conversational AI applications. It addresses the fundamental challenges of building production AI systems:

The framework allows Java developers to leverage their existing Spring Boot expertise while building AI capabilities that would otherwise require significant machine learning expertise.

Architecture Overview

The ConversationalAIAssistant follows a modular architecture that separates concerns while enabling seamless coordination between AI components:

Core Components

The architecture consists of five key layers:

1. User Interface Layer: Handles incoming chat requests via REST or UI
2. AI Agent Layer: Orchestrates conversation flow, memory, and retrieval
3. Business Logic Layer: Implements domain operations as callable tools
4. AI Services: Provides language understanding, generation, and embeddings
5. Data Layer: Stores reservations and policy documents

The LangChain4j Agent Model

At the heart of the system is the LangChain4j agent model, which coordinates all AI capabilities through a unified interface:

• Agent Interface: Defines conversation capabilities like chat()
• LangChain Agent: Production wrapper integrating all components
• Language Model: Provides natural language understanding and generation (OpenAI GPT)
• Tools: Business logic APIs exposed for workflow automation
• Memory: Maintains user context across conversation turns
• Retrievers: Matches questions to indexed documents for accurate answers

Defining the Agent Interface

The ReservationSupportAgent interface defines the contract for the conversational assistant:

public interface ReservationSupportAgent {

    @SystemMessage({
        "You are a customer support agent of a car rental company named 'Miles of Gonnect Ltd'.",
        "Before providing information about reservation or cancelling booking, you MUST always check:",
        "reservation number, member name and surname.",
        "Today is {{current_date}}."
    })
    String chat(String userMessage);
}

Key design decisions:

• System Message: Provides the AI with its role, constraints, and context
• Security Requirements: Mandates identity verification before sensitive operations
• Dynamic Context: Injects current date for time-sensitive policy decisions
• Simple Interface: Single chat() method hides complexity from consumers

Implementing Business Logic as Tools

The ReservationToolService exposes business operations that the AI agent can invoke:

@Component
public class ReservationToolService {

    @Autowired
    private ReservationRepository reservationRepository;

    @Tool
    public Reservation getReservationDetails(
            String reservationNumber,
            String memberName,
            String memberSurname) {

        log.info("Getting details for reservation {} for member {} {}",
            reservationNumber, memberName, memberSurname);

        return reservationRepository.getReservationDetails(
            reservationNumber, memberName, memberSurname);
    }

    @Tool
    public void cancelReservation(
            String reservationNumber,
            String memberName,
            String memberSurname) {

        log.info("Cancelling reservation {} for member {} {}",
            reservationNumber, memberName, memberSurname);

        reservationRepository.cancelReservation(
            reservationNumber, memberName, memberSurname);
    }
}

The @Tool annotation is the key to LangChain4j's power:

• Automatic Discovery: The agent automatically discovers available tools
• Parameter Extraction: LangChain4j extracts parameters from natural language
• Error Handling: Exceptions are gracefully communicated back to users
• Logging Integration: Standard logging provides observability

Semantic Embeddings for Knowledge Retrieval

One of the most powerful features is the ability to answer questions about company policies using semantic search. This is achieved through the embedding store:

Document Ingestion Pipeline

The ReservationHelpMeApplicationConfigurer sets up the embedding pipeline:

@Bean
EmbeddingStore<TextSegment> embeddingStore(
        EmbeddingModel embeddingModel,
        ResourceLoader resourceLoader) throws IOException {

    // Initialize in-memory vector store
    EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();

    // Load company terms and conditions
    Resource resource = resourceLoader.getResource(
        "classpath:gonnect-miles-terms-and-condition.txt");
    Document document = loadDocument(
        resource.getFile().toPath(),
        new TextDocumentParser());

    // Configure document splitting (100 tokens per segment)
    DocumentSplitter documentSplitter = DocumentSplitters.recursive(
        100, 0,
        new OpenAiTokenizer(GPT_3_5_TURBO));

    // Build and execute ingestion pipeline
    EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
        .documentSplitter(documentSplitter)
        .embeddingModel(embeddingModel)
        .embeddingStore(embeddingStore)
        .build();

    ingestor.ingest(document);

    return embeddingStore;
}

The AllMiniLmL6V2 Embedding Model

The application uses the AllMiniLmL6V2EmbeddingModel for generating vector representations:

@Bean
EmbeddingModel embeddingModel() {
    return new AllMiniLmL6V2EmbeddingModel();
}

This model enables:

• Embedding document segments during indexing
• Generating question embeddings at query time
• Measuring cosine similarity for relevance matching

Configuring the Retriever

The retriever connects the embedding store to the agent:

@Bean
Retriever<TextSegment> fetch(
        EmbeddingStore<TextSegment> embeddingStore,
        EmbeddingModel embeddingModel) {

    int maxResultsRetrieved = 1;
    double minScore = 0.6;

    return EmbeddingStoreRetriever.from(
        embeddingStore,
        embeddingModel,
        maxResultsRetrieved,
        minScore);
}

Key configuration parameters:

• maxResultsRetrieved: Number of matching segments to return (tuned for precision)
• minScore: Minimum cosine similarity threshold (0.6 = 60% match required)

These values should be tuned based on your specific data and use case.

Wiring It All Together

The final configuration assembles all components into a working agent:

@Bean
ReservationSupportAgent reservationSupportAgent(
        ChatLanguageModel chatLanguageModel,
        ReservationToolService reservationToolService,
        Retriever<TextSegment> retriever) {

    return AiServices.builder(ReservationSupportAgent.class)
        .chatLanguageModel(chatLanguageModel)
        .chatMemory(MessageWindowChatMemory.withMaxMessages(20))
        .tools(reservationToolService)
        .retriever(retriever)
        .build();
}

The AiServices.builder() pattern:

• Type-safe: Generates implementation from interface
• Composable: Combines memory, tools, and retrieval
• Configurable: Memory window size, tool selection, etc.

Complete Conversation Flow

Here is the complete sequence of a typical customer interaction:

Class Architecture

The following diagram shows the relationships between key classes:

Project Dependencies

The application requires the following LangChain4j dependencies:

<dependencies>
    <!-- Spring Boot Web -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>

    <!-- LangChain4j OpenAI Integration -->
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
        <version>0.25.0</version>
    </dependency>

    <!-- LangChain4j Core -->
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j</artifactId>
        <version>0.25.0</version>
    </dependency>

    <!-- Embedding Model -->
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>
        <version>0.25.0</version>
    </dependency>
</dependencies>

Running the Application

Prerequisites

1. Java 17 or higher
2. Maven 3.6+
3. OpenAI API key

Configuration

Set up your OpenAI API key as an environment variable:

export OPENAI_API_KEY=<your_api_key>

Build and Test

# Build the application
mvn clean install -DskipTests

# Run tests (requires OpenAI API key)
mvn test

The test suite demonstrates complete conversation flows including:

• Reservation lookup with identity verification
• Cancellation attempts (success and failure cases)
• Policy questions using semantic retrieval

Enterprise Enhancement Opportunities

The base implementation can be extended for production environments:

Personalization

@Tool
public List<Recommendation> getPersonalizedRecommendations(
        String memberId,
        String travelDates) {
    // Use customer history and preferences
    return recommendationService.generate(memberId, travelDates);
}

Real-time Integration

@Tool
public VehicleAvailability checkAvailability(
        String vehicleClass,
        String location,
        String dates) {
    // Connect to live inventory system
    return inventoryService.checkAvailability(vehicleClass, location, dates);
}

Payment Processing

@Tool
public PaymentResult processPayment(
        String reservationId,
        PaymentDetails payment) {
    // Integrate with payment gateway
    return paymentService.process(reservationId, payment);
}

Best Practices for Production

Security Considerations

Performance Optimization

• Embedding Cache: Cache frequently accessed document embeddings
• Connection Pooling: Use connection pools for database and API calls
• Async Processing: Consider async for non-blocking operations
• Memory Management: Tune conversation window size based on use case

Monitoring and Observability

@Tool
public Reservation getReservationDetails(...) {
    Timer.Sample sample = Timer.start(meterRegistry);
    try {
        Reservation result = repository.getReservationDetails(...);
        sample.stop(meterRegistry.timer("tool.reservation.lookup", "status", "success"));
        return result;
    } catch (Exception e) {
        sample.stop(meterRegistry.timer("tool.reservation.lookup", "status", "error"));
        throw e;
    }
}

Conclusion

The ConversationalAIAssistant project demonstrates how enterprise Java developers can build sophisticated AI-powered customer service applications using familiar tools and patterns. By combining LangChain4j with Spring Boot, the architecture provides:

• Natural Language Understanding: Customers interact in plain English
• Semantic Knowledge Retrieval: Accurate answers from company documents
• Business Process Automation: Reservations, cancellations, and more
• Conversation Memory: Context maintained across interactions
• Production-Ready Patterns: Security, logging, and error handling

The modular design allows teams to start simple and progressively enhance capabilities - from basic Q&A to fully integrated customer service automation.

For the complete implementation with working examples, visit the ConversationalAIAssistant repository.

Further Reading

• LangChain4j Documentation
• Spring Boot Reference
• OpenAI API Documentation
• Vector Embeddings Explained
• Building AI Agents