How to Use ChatGPT in a .NET Application?
As the tech landscape rapidly evolves, integrating artificial intelligence (AI) into your applications is not just a fancy upgrade; it has become a necessity. One of the most exciting prospects in this realm is leveraging the power of OpenAI’s ChatGPT in a .NET application. The benefits are vast, ranging from streamlining content generation to offering engaging conversational interfaces. Let’s dive into the details of how to seamlessly integrate ChatGPT into your .NET application.
Getting Started with ChatGPT and .NET
Before we jump into code, let’s ensure you have all the prerequisites covered. First and foremost, you need an account with OpenAI. This will give you access to the API key necessary for your application to communicate with ChatGPT. If you’ve got that handled, let’s move on to the exciting part—building your application!
Step 1: Create a New Project
Launch your preferred Integrated Development Environment (IDE), such as Visual Studio, and create a new project. Select “ASP.NET Core Web API” from the template options. This bit is crucial as we are building a web API to serve our chatbot functionalities.
- Configure your new project as desired.
- In the Solution Explorer, right-click on the solution project.
- Select “Add” > “New Project.”
In your new solution, add a class library and name it [SolutionName].ApplicationService for your business logic layer. This layer will handle requests and responses. Then, create another class library, naming it [SolutionName].Infrastructure, which will manage external API calls, including accessing OpenAI’s services. You should now have the following project structure:
– [SolutionName] – ApplicationService – Infrastructure – ADGenerateBot.API
Step 2: Install OpenAI NuGet Client
To get started with OpenAI in your .NET application, you need to install the OpenAI NuGet package. Here’s how you can do that:
- Go to Tools > NuGet Package Manager.
- Select Manage NuGet Packages for Solution.
- Search for « OpenAI » and install the latest version.
If you prefer using the command line, simply run:
Install-Package OpenAI
Step 3: Configure OpenAI in AppSettings
After installing the NuGet package, you must configure your application to use the OpenAI API. Open the appsettings.json file in your project and add your API key and the model you intend to use. Here’s what your settings might look like:
« Appsettings »: { « GChatAPIKEY »: « sk-################# », « Model »: « text-davinci-003 » }
You can find your API key from your OpenAI account. Ensure you reference the OpenAI documentation to confirm which model suits your needs. For this tutorial, we’ll be using “text-davinci-003” model for generating completions.
Step 4: Create a Data Model
In the Solution Explorer, add a new folder within the [SolutionName].ApplicationService project and name it ADProduct. Next, add a new class to this folder called ADGenerateRequestModelDTO.cs.
This class will contain a prompt property, which will hold the user’s query to be sent to ChatGPT. Here’s how the class should look:
public class ADGenerateRequestModelDTO { public string prompt { get; set; } }
Step 5: Add an Interface Class
Think of interfaces as contracts that define what methods a class must implement. So, let’s create one. Right-click on the [SolutionName].ApplicationService project, create a new folder named Interfaces, then add an interface named IBotAPIService.cs.
csharp public interface IBotAPIService { Task<List<string>> GenerateContent(ADGenerateRequestModelDTO generateRequestModel); }
This interface method returns a list of strings based on the request parameter.
Step 6: Implement the Service Class
Now we need to implement the interface we created. Right-click on the [SolutionName].Infrastructure project, create a folder called Network, and add a class called BotAPIService.cs.
Here’s the code to implement:
csharp public class BotAPIService : IBotAPIService { private readonly IConfiguration _configuration;
public BotAPIService(IConfiguration configuration) { _configuration = configuration; }
public async Task
- > GenerateContent(ADGenerateRequestModel generateRequestModel) { var apiKey = _configuration.GetSection(« Appsettings:GChatAPIKEY »).Value; var apiModel = _configuration.GetSection(« Appsettings:Model »).Value;
List<string> rq = new List<string>(); string rs = « »; OpenAIAPI api = new OpenAIAPI(new APIAuthentication(apiKey)); var completionRequest = new OpenAI_API.Completions.CompletionRequest() { Prompt = generateRequestModel.prompt, Model = apiModel, Temperature = 0.5, MaxTokens = 100, TopP = 1.0, FrequencyPenalty = 0.0, PresencePenalty = 0.0, }; var result = await api.Completions.CreateCompletionsAsync(completionRequest); foreach (var choice in result.Completions) { rs = choice.Text; rq.Add(choice.Text); } return rq;
} }
This code creates a connection to OpenAI and handles the response from the API.
Step 7: Create More Model Classes
Continuing with our project, we need additional model classes for handling customer requests and processing responses. Right-click on the [SolutionName].ApplicationService.ADProduct folder, and add the following classes:
- CustomerRequestModel.cs: csharp public class CustomerRequestModel { public string Message { get; set; } }
- ADProductResponseModel.cs: csharp public class ADProductResponseModel { public List<string> ADContent { get; set; } public bool Success { get; set; } }
Step 8: Add Interface Class for the AD Product
Let’s create another interface to handle ad generation requests. Right-click on the [SolutionName].ApplicationService.Interfaces folder. Create a class named IADProductService.cs with this code:
csharp public interface IADProductService { Task<ADProductResponseModel> GenerateAdContent(CustomerRequestModel aDGenerateRequestModel); }
Step 9: Implement AD Product Service Class
Now, let’s implement our ad-generation logic. Right-click on the [SolutionName].ApplicationService.ADProduct folder, create a class named ADProductService.cs, and incorporate the following:
csharp public class ADProductService : IADProductService { private readonly IBotAPIService _botAPIService;
public ADProductService(IBotAPIService botAPIService) { _botAPIService = botAPIService; }
public async Task GenerateAdContent(CustomerRequestModel aDGenerateRequestModel) { if (string.IsNullOrEmpty(aDGenerateRequestModel.Message)) { return new ADProductResponseModel { Success = false, ADContent = null }; }
var userMessage = new ADGenerateRequestModelDTO { prompt = aDGenerateRequestModel.Message }; var generateAD = await _botAPIService.GenerateContent(userMessage); return new ADProductResponseModel { Success = generateAD.Count > 0, ADContent = generateAD };
} }
This implementation orchestrates calls to the bot API service and handles responses, demonstrating the complete workflow of generating ad content based on user input.
Step 10: Register the Services
Next, you need to tell the .NET dependency injection system about your services. In program.cs, add these lines:
csharp builder.Services.AddScoped<IADProductService, ADProductService>(); builder.Services.AddScoped<IBotAPIService, BotAPIService>();
Step 11: Create the Controller
To expose our service to the outside world, we need to create a controller. Right-click on the [SolutionName].ADGenerateBot.API.Controllers folder and add a new controller named ADGeneratorController.cs. Use the following code:
csharp [Route(« api/adgenerator/[action] »)] [ApiController] public class ADGeneratorController : ControllerBase { private readonly IADProductService _adProduct;
public ADGeneratorController(IADProductService adProduct) { _adProduct = adProduct; }
[HttpPost] public async Task> GenerateAD(CustomerRequestModel aDGenerateRequestModel) { try { var response = await _adProduct.GenerateAdContent(aDGenerateRequestModel); return response; } catch (System.Exception ex) { return null; // Handle error better in a real application } } }
Step 12: Run the Application
Congratulations! You have successfully built a .NET application integrated with ChatGPT. Now, it’s time to run your application. Click on the IIS Express and then call the endpoint /api/adgenerator/GenerateAd with your question in the body as a POST request.
If all is configured correctly, you should receive a thoughtful response generated by ChatGPT. You’ve just made an API call to OpenAI, received the AI’s response, and processed it within your application.
Conclusion
In this article, you learned the step-by-step process to integrate ChatGPT into your .NET application. From setting up your project to crafting the api layer, every step was crucial to creating a seamless experience where AI can enhance your application’s functionality. As technology continues to evolve, integrating conversational AI can deliver real value to users and could potentially transform the way they interact with your services.
So, what’s next? In the upcoming articles, we will explore further integrations, like utilizing Twilio for programmable messaging services, which will be a game-changer for businesses looking to enhance their customer interaction strategy. Stay tuned!