Artificial Intelligence (AI) has revolutionized software applications, making them more interactive, intelligent, and user-friendly. LangChain4j, a Java-based implementation of LangChain, allows seamless integration of AI into Spring Boot applications. In this article, we will walk you through the process of creating an AI assistant using LangChain4j in a Spring Boot application.

Introduction to LangChain4j

LangChain4j is an extension of LangChain, a powerful framework for building applications that leverage language models like OpenAI’s GPT. This Java-based library facilitates:

• Simplified AI model integration
• Context-aware chatbots
• Seamless conversation memory handling
• Connection with various data sources

Prerequisites

Before diving into coding, ensure that you have the following installed:

• Java 17 or later
• Spring Boot (3.x preferred)
• Maven or Gradle
• An OpenAI API key (or another compatible LLM API key)

Setting Up the Spring Boot Project

We start by creating a Spring Boot project using Spring Initializr.

1. Go to Spring Initializr.
2. Select Maven Project with Spring Boot 3.x.
3. Add dependencies:
   • Spring Web
   • Lombok
   • OpenAI API or any LLM API
4. Generate and extract the project.

Alternatively, you can create the project manually and add the necessary dependencies.

Adding Dependencies

Add the following dependencies to your pom.xml:

<dependencies>
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j</artifactId>
        <version>0.26.0</version>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-json</artifactId>
    </dependency>
</dependencies>

Configuring LangChain4j in Spring Boot

Setting Up API Keys

Create an application.properties file under src/main/resources/ and add:

openai.api.key=your-api-key-here

Creating the Configuration Class

We need a configuration class to set up LangChain4j with OpenAI.

package com.example.aiassistant.config;

import dev.langchain4j.service.SystemMessage;
import dev.langchain4j.service.UserMessage;
import dev.langchain4j.service.AiService;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import dev.langchain4j.OpenAiChatModel;

@Configuration
public class LangChain4jConfig {

    @Bean
    public OpenAiChatModel openAiChatModel() {
        return OpenAiChatModel.builder()
                .apiKey(System.getenv("OPENAI_API_KEY"))
                .build();
    }
}

Implementing the AI Assistant Service

Creating the AI Service Interface

package com.example.aiassistant.service;

import dev.langchain4j.service.AiService;

@AiService
public interface AiAssistantService {
    String chat(String message);
}

Implementing the AI Service

package com.example.aiassistant.service;

import org.springframework.stereotype.Service;
import dev.langchain4j.OpenAiChatModel;

@Service
public class AiAssistantServiceImpl implements AiAssistantService {

    private final OpenAiChatModel chatModel;

    public AiAssistantServiceImpl(OpenAiChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @Override
    public String chat(String message) {
        return chatModel.call(message);
    }
}

Creating the Controller

To expose the AI assistant via REST API, create a controller.

package com.example.aiassistant.controller;

import com.example.aiassistant.service.AiAssistantService;
import org.springframework.web.bind.annotation.*;

@RestController
@RequestMapping("/api/assistant")
public class AiAssistantController {

    private final AiAssistantService aiAssistantService;

    public AiAssistantController(AiAssistantService aiAssistantService) {
        this.aiAssistantService = aiAssistantService;
    }

    @PostMapping("/chat")
    public String chat(@RequestBody String message) {
        return aiAssistantService.chat(message);
    }
}

Testing the AI Assistant

Start the Spring Boot application:

mvn spring-boot:run

Then, use Postman or curl to send a request:

curl -X POST http://localhost:8080/api/assistant/chat -H "Content-Type: application/json" -d "Hello, how are you?"

You should receive an AI-generated response.

Enhancing the AI Assistant

Adding Conversation Memory

To maintain conversation context, use LangChain4j’s memory support.

@Bean
public ChatMemory chatMemory() {
    return new InMemoryChatMemory();
}

Modify the service to store past messages:

@Override
public String chat(String message) {
    chatMemory.addUserMessage(message);
    String response = chatModel.call(chatMemory.getConversation());
    chatMemory.addAiMessage(response);
    return response;
}

Connecting with External APIs

You can enhance your assistant by integrating external APIs, such as weather services or databases, to fetch real-time information.

Conclusion

Building an AI assistant using LangChain4j in a Spring Boot application offers numerous possibilities for innovation. Throughout this article, we have covered setting up the environment, implementing the AI service, creating a REST API, and enhancing the chatbot with memory capabilities.

By leveraging LangChain4j, developers can efficiently integrate AI into their applications, making interactions more dynamic and personalized. The chatbot can be further improved by implementing authentication mechanisms, expanding memory storage with databases, and connecting to external APIs for real-time responses. Moreover, fine-tuning AI models based on user behavior and feedback can lead to even better user experiences.

AI assistants can be deployed in various domains, such as customer service, healthcare, education, and enterprise solutions, making them indispensable tools for businesses and developers alike. As AI technology evolves, integrating intelligent assistants into applications will become a standard practice, opening doors to endless possibilities.

By following this guide, you have taken the first step in creating a powerful AI-powered assistant. Continue exploring and experimenting with different AI models and frameworks to further enhance your application.