Build Intelligent Spring Boot Apps with ChatGPT

Table of Contents:

1. Introduction
2. Step 1: Generate Project
3. Step 2: Set up ChatGPT API secret key
4. Step 3: Create a controller with endpoint
5. Step 4: Endpoint checkup
6. Step 5: ChatGPT integration
7. Step 6: Create a ChatGptRequest class
8. Step 7: Extend request entities
9. Step 8: Create GptRequestBuilder service
10. Step 9: Authorize server in ChatGPT API
11. Step 10: Execute requests and get ChatGPT responses
12. Conclusion

How to Incorporate OpenAI ChatGPT APIs into Your Spring Boot Projects

In this tutorial, I will guide You through the process of incorporating OpenAI ChatGPT APIs into your Spring Boot projects. OpenAI's ChatGPT API allows you to integrate AI-powered chatbots into your applications, enabling you to have interactive conversations with users.

Step 1: Generate Project

The first step is to generate a Spring Boot project using the Spring initializer Website. You need to provide the Group ID, Artifact ID, and name for your project. We will use Maven as the build tool. For this tutorial, you only need to add two dependencies: Spring Web and Lombok. Once the project is generated, open it in your IDE.

Step 2: Set up ChatGPT API secret key

To work with the ChatGPT API, you need to generate a secret key from the OpenAI platform website. If you don't have an account, please create one first. Once you have the account, go to the profile icon and select "View API keys". From there, you can create a new secret key. Copy the key and paste it into the application.yml or any other appropriate configuration file in your project. Create a property called ai and set the secret key as the value for ai.secret.

Step 3: Create a controller with endpoint

Now, let's create the structure of your project. Create a folder called "controller" and inside it, create a class called ApiController. Annotate the class with @RequestMapping and @RestController annotations. Add an endpoint in the controller that will accept requests from the user, process them, and return a response. In the same folder, create a folder called "entities" and add a class called ApiRequest. This class will be a simple POJO that will hold the text sent by the user. Annotate it with @RequestBody to accept the JSON payload.

Step 4: Endpoint checkup

After setting up the endpoint, it's time to check if it works properly. Launch the project, open up Postman, and execute a POST request to the endpoint's URL. If everything is working fine, you should see a message from your logger in the console of the server. This indicates that the endpoint is functioning correctly.

Step 5: ChatGPT integration

Now, let's start integrating with the ChatGPT API. Create a POJO class called ChatGptRequest that will hold all the data required for the request to the ChatGPT API. This class should have two properties: the model (a STRING) and the messages (a List of messages). The messages property should be another POJO with role and content properties. Create a custom constructor to build the ChatGptRequest object Based on the ApiRequest received from the controller.

Step 6: Extend request entities

Add additional properties to the request entities. Include a String id property to represent the ID of the request/response. Create another custom POJO class called Choice that will hold data for the so-called Choice data Type. The Choice class should consist of an int index and a Message class. The Message class should consist of properties like role and content.

Step 7: Create GptRequestBuilder service

Create a service called GptRequestBuilder that will convert the ApiRequest received from the controller into a format that the ChatGPT API can understand. This service will also handle the execution of requests to the ChatGPT API and receive the Relevant response. Inject the ai.secret and ai.url values from the application.yml file. Use the RestTemplate class to make the HTTP requests.

Step 8: Authorize server in ChatGPT API

Authorization is required to make requests to the ChatGPT API. Include the secret key obtained earlier in the request as an Authorization header. Use the HttpHeaders and HttpEntity classes to add the header to the POST request. The response from the ChatGPT API will be stored in a ResponseEntity.

Step 9: Execute requests and get ChatGPT responses

Everything is ready to test your flow. Launch the project and use Postman to make the first request to the ChatGPT API. Start by asking a simple question like "Who am I talking to right now?". The AI model built by OpenAI will respond with an identifier. The response object may be nested, so you can adjust it by creating an ApiResponse class that extracts the relevant response message from the ChatGPT response. Update the controller and services to return the ApiResponse class.

Conclusion

Congratulations! You have successfully incorporated OpenAI ChatGPT APIs into your Spring Boot projects. You can now use this integration to build intelligent chatbots and engage users in interactive conversations. Stay tuned for more educational videos coming soon.

Highlights:

• Learn how to incorporate OpenAI ChatGPT APIs into Spring Boot projects
• Generate a Spring Boot project using the Spring initializer website
• Set up the ChatGPT API secret key for authentication
• Create a controller with an endpoint to accept user requests
• Check the endpoint functionality using Postman
• Integrate with the ChatGPT API and handle the request/response flow
• Extend the request entities and create the necessary POJO classes
• Create services to handle the request flow and API integration
• Authorize the server to make requests to the ChatGPT API
• Test the flow by executing requests and getting ChatGPT responses

FAQ: Q: Can I use a different build tool instead of Maven? A: Yes, you can use other build tools like Gradle if you prefer.

Q: Can I add more fields to the ChatGptRequest class? A: Yes, you can modify the ChatGptRequest class to include additional properties as per your requirements.

Q: How can I validate the response from the ChatGPT API? A: You can add validation logic in the services to ensure the response from the ChatGPT API meets your expectations.

Q: Can I use this integration in a production environment? A: Yes, after thoroughly testing and securing your application, you can use this integration in a production environment.

Q: Are there any limitations or restrictions when using the ChatGPT API? A: Please refer to the documentation provided by OpenAI for any limitations or restrictions applicable to the ChatGPT API.