Integrating ChatGPT in Spring Boot Application

ChatGPT is a powerful language model developed by OpenAI that can be used for a variety of natural language processing tasks, including language translation, text summarization, and conversation generation. In this post, we will explore how to integrate ChatGPT into a Spring Boot application, so that you can easily add natural language processing capabilities to your own projects.

1. First, you will need to create a new Spring Boot project, either through the Spring Initializer website ( https://start.spring.io/) by using the Spring Boot CLI:

Check my recent tutorial below about how to create a Spring Boot project.

( https://medium.com/@abdallah.benyouness/getting-started-with-spring-boot-a-step-by-step-guide-to-installation-and-project-creation-5680b5c010b1 )

1. Next, you will need to add the OpenAI Java SDK to your project as a dependency. You can do this by adding the following line to your pom.xml file:

<dependency>
    <groupId>ai.djl</groupId>
    <artifactId>api</artifactId>
    <version>0.9.0</version>
</dependency>

Once you have added the OpenAI Java SDK as a dependency, you can start using it in your application by creating a new instance of the Model class and loading a pre-trained ChatGPT model. You can do this with the following code:

Model model = Model.newInstance("chatbot");
model.load(Model.getModelUrl("chatbot"), "local");

Now that you have a working instance of ChatGPT, you can use it to generate text based on a given prompt. For example, the following code generates a response to the prompt “Hello, how are you?”:

String prompt = "Hello, how are you?";
String response = model.generate(prompt).getBest().getText();

You can also use the model.generate() method to specify additional parameters, such as the number of responses to generate or the maximum length of the generated text. For example, you can use the following code to generate three responses with a maximum length of 100 characters each:

List<String> responses = model.generate(prompt, 3, 100).getResponses().stream().map(t->t.getText()).collect(Collectors.toList());

To use the ChatGPT model in a web application, you can create a RESTful endpoint that takes a prompt as input and returns a response. For example, you can use the following code to create an endpoint that takes a prompt as a query parameter and returns a response in JSON format:

@RestController
public class ChatbotController {
    @GetMapping("/chat")
    public String chat(@RequestParam(value = "prompt") String prompt) {
        String response = model.generate(prompt).getBest().getText();
        return response;
    }
}

Another feature of ChatGPT is the ability to fine-tune the model to specific domains by providing a dataset. You can fine-tune the model to your specific use case by providing a dataset, the following command fine-tune the model using the dataset:

TrainingConfig config = new TrainingConfig(Optimizer.ADAM);
    config.addEvaluator(new PerplexityEvaluator());
    config.setBatchSize(32);
    model.setTrainingConfig(config);
    ...

In this post, we have discussed in detail how to integrate ChatGPT, the powerful language model developed by OpenAI, into a Spring Boot application. Spring Boot is a popular framework for building web applications and microservices, and by combining it with ChatGPT, we can easily add natural language processing capabilities to our projects.

To start, we have outlined the steps for adding the OpenAI Java SDK as a dependency in our Spring Boot project, which allows us to use the ChatGPT model in our application. We then walked through the process of loading a pre-trained ChatGPT model, and using it to generate text based on a given prompt. This can be useful for tasks such as language translation, text summarization, and conversation generation.

We also demonstrated how to use the ChatGPT model in a web application by creating a RESTful endpoint that takes a prompt as input and returns a response. This allows for easy integration with other systems and can be used to build chatbots, conversational interfaces, and other applications.

Additionally, we’ve discussed the fine-tuning feature of the model which can be used to improve the model performance on the specific use case by providing a dataset. This is a powerful feature that allows you to tailor the model to your specific needs, which can lead to improved results.

In conclusion, integrating ChatGPT into a Spring Boot application is a straightforward process that can add powerful natural language processing capabilities to your projects. With the fine-tuning feature, you can improve the performance of the model on the specific use case, resulting in even more accurate and useful results.