A simple C# .NET client library for OpenAI to use though their RESTful API.
Independently developed, this is not an official library and I am not affiliated with OpenAI.
An OpenAI API account is required.
Forked from [OpenAI-API-dotnet](https://github.com/OkGoDoIt/OpenAI-API-dotnet).
More context [on Roger Pincombe's blog](https://rogerpincombe.com/openai-dotnet-api).
A simple C# .NET client library for OpenAI to use use chat-gpt, GPT-4, GPT-3.5-Turbo and Dall-E though their RESTful API (currently in beta). Independently developed, this is not an official library and I am not affiliated with OpenAI. An OpenAI API account is required.
You use the OpenAIAuthentication when you initialize the API as shown:
Pass keys directly with constructor
var api = new OpenAIClient("sk-apiKey");
Or create a OpenAIAuthentication object manually
var api = new OpenAIClient(new OpenAIAuthentication("sk-apiKey", "org-yourOrganizationId"));
Load key from configuration file
Attempts to load api keys from a configuration file, by default .openai in the current directory, optionally traversing up the directory tree or in the user's home directory.
To create a configuration file, create a new text file named .openai and containing the line:
You can also choose to use Microsoft's Azure OpenAI deployments as well.
To setup the client to use your deployment, you'll need to pass in OpenAIClientSettings into the client constructor.
var auth = new OpenAIAuthentication("sk-apiKey");
var settings = new OpenAIClientSettings(resourceName: "your-resource", deploymentId: "your-deployment-id");
var api = new OpenAIClient(auth, settings);
:new: OpenAI API Proxy
Using either the OpenAI-DotNet or com.openai.unity packages directly in your front-end app may expose your API keys and other sensitive information. To mitigate this risk, it is recommended to set up an intermediate API that makes requests to OpenAI on behalf of your front-end app. This library can be utilized for both front-end and intermediary host configurations, ensuring secure communication with the OpenAI API.
Front End Example
In the front end example, you will need to securely authenticate your users using your preferred OAuth provider. Once the user is authenticated, exchange your custom auth token with your API key on the backend.
After successful authentication, create a new OpenAIAuthentication object and pass in the custom token with the prefix sess-.
Create a new OpenAIClientSettings object and specify the domain where your intermediate API is located.
Pass your new auth and settings objects to the OpenAIClient constructor when you create the client instance.
Here's an example of how to set up the front end:
var authToken = await LoginAsync();
var auth = new OpenAIAuthentication($"sess-{authToken}");
var settings = new OpenAIClientSettings(domain: "api.your-custom-domain.com");
var api = new OpenAIClient(auth, settings);
This setup allows your front end application to securely communicate with your backend that will be using the proxy service, which then forwards requests to the OpenAI API. This ensures that your OpenAI API keys and other sensitive information remain secure throughout the process.
Back End Example
In this example, we demonstrate how to set up and use OpenAIProxyStartup in a new ASP.NET Core web app. The proxy server will handle authentication and forward requests to the OpenAI API, ensuring that your API keys and other sensitive information remain secure.
Create a new class that inherits from AbstractAuthenticationFilter and override the ValidateAuthentication method. This will implement the IAuthenticationFilter that you will use to check user session token against your internal server.
In Program.cs, create a new proxy web application by calling OpenAIProxyStartup.CreateDefaultHost method, passing your custom AuthenticationFilter as a type argument.
Create OpenAIAuthentication and OpenAIClientSettings as you would normally with your API keys, org id, or Azure settings.
public partial class Program
{
private class AuthenticationFilter : AbstractAuthenticationFilter
{
public override void ValidateAuthentication(IHeaderDictionary request)
{
// You will need to implement your own class to properly test
// custom issued tokens you've setup for your end users.
if (!request.Authorization.ToString().Contains(userToken))
{
throw new AuthenticationException("User is not authorized");
}
}
}
public static void Main(string[] args)
{
var auth = OpenAIAuthentication.LoadFromEnv();
var settings = new OpenAIClientSettings(/* your custom settings if using Azure OpenAI */);
var openAIClient = new OpenAIClient(auth, settings);
var proxy = OpenAIProxyStartup.CreateDefaultHost<AuthenticationFilter>(args, openAIClient);
proxy.Run();
}
}
Once you have set up your proxy server, your end users can now make authenticated requests to your proxy api instead of directly to the OpenAI API. The proxy server will handle authentication and forward requests to the OpenAI API, ensuring that your API keys and other sensitive information remain secure.
List and describe the various models available in the API. You can refer to the Models documentation to understand what models are available and the differences between them.
The Models API is accessed via OpenAIClient.ModelsEndpoint
Lists the currently available models, and provides basic information about each one such as the owner and availability.
var api = new OpenAIClient();
var models = await api.ModelsEndpoint.GetModelsAsync();
foreach (var model in models)
{
Console.WriteLine(model.ToString());
}
Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.
var api = new OpenAIClient();
var result = await api.CompletionsEndpoint.CreateCompletionAsync("One Two Three One Two", temperature: 0.1, model: Model.Davinci);
Console.WriteLine(result);
To get the CompletionResult (which is mostly metadata), use its implicit string operator to get the text if all you want is the completion choice.
Completion Streaming
Streaming allows you to get results are they are generated, which can help your application feel more responsive, especially on slow models like Davinci.
var api = new OpenAIClient();
await api.CompletionsEndpoint.StreamCompletionAsync(result =>
{
foreach (var choice in result.Completions)
{
Console.WriteLine(choice);
}
}, "My name is Roger and I am a principal software engineer at Salesforce. This is my resume:", maxTokens: 200, temperature: 0.5, presencePenalty: 0.1, frequencyPenalty: 0.1, model: Model.Davinci);
var api = new OpenAIClient();
await foreach (var token in api.CompletionsEndpoint.StreamCompletionEnumerableAsync("My name is Roger and I am a principal software engineer at Salesforce. This is my resume:", maxTokens: 200, temperature: 0.5, presencePenalty: 0.1, frequencyPenalty: 0.1, model: Model.Davinci))
{
Console.WriteLine(token);
}
var api = new OpenAIClient();
var chatPrompts = new List<ChatPrompt>
{
new ChatPrompt("system", "You are a helpful assistant."),
new ChatPrompt("user", "Who won the world series in 2020?"),
new ChatPrompt("assistant", "The Los Angeles Dodgers won the World Series in 2020."),
new ChatPrompt("user", "Where was it played?"),
};
var chatRequest = new ChatRequest(chatPrompts);
var result = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
Console.WriteLine(result.FirstChoice);
var api = new OpenAIClient();
var chatPrompts = new List<ChatPrompt>
{
new ChatPrompt("system", "You are a helpful assistant."),
new ChatPrompt("user", "Who won the world series in 2020?"),
new ChatPrompt("assistant", "The Los Angeles Dodgers won the World Series in 2020."),
new ChatPrompt("user", "Where was it played?"),
};
var chatRequest = new ChatRequest(chatPrompts, Model.GPT3_5_Turbo);
await api.ChatEndpoint.StreamCompletionAsync(chatRequest, result =>
{
Console.WriteLine(result.FirstChoice);
});
var api = new OpenAIClient();
var chatPrompts = new List<ChatPrompt>
{
new ChatPrompt("system", "You are a helpful assistant."),
new ChatPrompt("user", "Who won the world series in 2020?"),
new ChatPrompt("assistant", "The Los Angeles Dodgers won the World Series in 2020."),
new ChatPrompt("user", "Where was it played?"),
};
var chatRequest = new ChatRequest(chatPrompts, Model.GPT3_5_Turbo);
await foreach (var result in api.ChatEndpoint.StreamCompletionEnumerableAsync(chatRequest))
{
Console.WriteLine(result.FirstChoice);
}
Creates a new edit for the provided input, instruction, and parameters using the provided input and instruction.
var api = new OpenAIClient();
var request = new EditRequest("What day of the wek is it?", "Fix the spelling mistakes");
var result = await api.EditsEndpoint.CreateEditAsync(request);
Console.WriteLine(result);
Creates an embedding vector representing the input text.
var api = new OpenAIClient();
var model = await api.ModelsEndpoint.GetModelDetailsAsync("text-embedding-ada-002");
var result = await api.EmbeddingsEndpoint.CreateEmbeddingAsync("The food was delicious and the waiter...", model);
Console.WriteLine(result);
var api = new OpenAIClient();
var request = new AudioTranscriptionRequest(Path.GetFullPath(audioAssetPath), language: "en");
var result = await api.AudioEndpoint.CreateTranscriptionAsync(request);
Console.WriteLine(result);
var api = new OpenAIClient();
var request = new AudioTranslationRequest(Path.GetFullPath(audioAssetPath));
var result = await api.AudioEndpoint.CreateTranslationAsync(request);
Console.WriteLine(result);
var api = new OpenAIClient();
var results = await api.ImagesEndPoint.GenerateImageAsync("A house riding a velociraptor", 1, ImageSize.Small);
foreach (var result in results)
{
Console.WriteLine(result);
// result == file://path/to/image.png
}
Creates an edited or extended image given an original image and a prompt.
var api = new OpenAIClient();
var results = await api.ImagesEndPoint.CreateImageEditAsync(Path.GetFullPath(imageAssetPath), Path.GetFullPath(maskAssetPath), "A sunlit indoor lounge area with a pool containing a flamingo", 1, ImageSize.Small);
foreach (var result in results)
{
Console.WriteLine(result);
// result == file://path/to/image.png
}
var api = new OpenAIClient();
var results = await api.ImagesEndPoint.CreateImageVariationAsync(Path.GetFullPath(imageAssetPath), 1, ImageSize.Small);
foreach (var result in results)
{
Console.WriteLine(result);
// result == file://path/to/image.png
}
Returns a list of files that belong to the user's organization.
var api = new OpenAIClient();
var files = await api.FilesEndpoint.ListFilesAsync();
foreach (var file in files)
{
Console.WriteLine($"{file.Id} -> {file.Object}: {file.FileName} | {file.Size} bytes");
}
Upload a file that contains document(s) to be used across various endpoints/features. Currently, the size of all the files uploaded by one organization can be up to 1 GB. Please contact us if you need to increase the storage limit.
var api = new OpenAIClient();
var fileData = await api.FilesEndpoint.UploadFileAsync("path/to/your/file.jsonl", "fine-tune");
Console.WriteLine(fileData.Id);
var api = new OpenAIClient();
var fileData = await GetFileInfoAsync(fileId);
Console.WriteLine($"{fileData.Id} -> {fileData.Object}: {fileData.FileName} | {fileData.Size} bytes");
var api = new OpenAIClient();
var downloadedFilePath = await api.FilesEndpoint.DownloadFileAsync(fileId, "path/to/your/save/directory");
Console.WriteLine(downloadedFilePath);
Assert.IsTrue(File.Exists(downloadedFilePath));
Creates a job that fine-tunes a specified model from a given dataset.
Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.
var api = new OpenAIClient();
var request = new CreateFineTuneRequest(fileData);
var fineTuneJob = await api.FineTuningEndpoint.CreateFineTuneJobAsync(request);
Console.WriteLine(fineTuneJob.Id);
var api = new OpenAIClient();
var fineTuneJobs = await api.FineTuningEndpoint.ListFineTuneJobsAsync();
foreach (var job in fineTuneJobs)
{
Console.WriteLine($"{job.Id} -> {job.Status}");
}
var api = new OpenAIClient();
var result = await api.FineTuningEndpoint.RetrieveFineTuneJobInfoAsync(fineTuneJob);
Console.WriteLine($"{result.Id} -> {result.Status}");
Get fine-grained status updates for a fine-tune job.
var api = new OpenAIClient();
var fineTuneEvents = await api.FineTuningEndpoint.ListFineTuneEventsAsync(fineTuneJob);
Console.WriteLine($"{fineTuneJob.Id} -> status: {fineTuneJob.Status} | event count: {fineTuneEvents.Count}");
var api = new OpenAIClient();
await api.FineTuningEndpoint.StreamFineTuneEventsAsync(fineTuneJob, fineTuneEvent =>
{
Console.WriteLine($" {fineTuneEvent.CreatedAt} [{fineTuneEvent.Level}] {fineTuneEvent.Message}");
});
var api = new OpenAIClient();
await foreach (var fineTuneEvent in api.FineTuningEndpoint.StreamFineTuneEventsEnumerableAsync(fineTuneJob))
{
Console.WriteLine($" {fineTuneEvent.CreatedAt} [{fineTuneEvent.Level}] {fineTuneEvent.Message}");
}
Classifies if text violates OpenAI's Content Policy.
var api = new OpenAIClient();
var response = await api.ModerationsEndpoint.GetModerationAsync("I want to kill them.");
Assert.IsTrue(response);
License
This library is licensed CC-0, in the public domain. You can use it for whatever you want, publicly or privately, without worrying about permission or licensing or whatever. It's just a wrapper around the OpenAI API, so you still need to get access to OpenAI from them directly. I am not affiliated with OpenAI and this library is not endorsed by them, I just have beta access and wanted to make a C# library to access it more easily. Hopefully others find this useful as well. Feel free to open a PR if there's anything you want to contribute.
