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 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.
You use the OpenAIAuthentication when you initialize the API as shown:
Pass keys directly with constructor
:warning: We recommended using the environment variables to load the API key instead of having it hard coded in your source. It is not recommended use this method in production, but only for accepting user credentials, local testing and quick start scenarios.
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:
your-resource-name The name of your Azure OpenAI Resource.
deployment-id The deployment name you chose when you deployed the model.
api-version The API version to use for this operation. This follows the YYYY-MM-DD format.
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-name", deploymentId: "deployment-id", apiVersion: "api-version");
var api = new OpenAIClient(auth, settings);
Authenticate with MSAL as usual and get access token, then use the access token when creating your OpenAIAuthentication. Then be sure to set useAzureActiveDirectory to true when creating your OpenAIClientSettings.
// get your access token using any of the MSAL methods
var accessToken = result.AccessToken;
var auth = new OpenAIAuthentication(accessToken);
var settings = new OpenAIClientSettings(resourceName: "your-resource", deploymentId: "deployment-id", apiVersion: "api-version", useActiveDirectoryAuthentication: true);
var api = new OpenAIClient(auth, settings);
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 OpenAI-DotNet-Proxy, 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.
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());
}
var api = new OpenAIClient();
var assistantsList = await OpenAIClient.AssistantsEndpoint.ListAssistantsAsync();
foreach (var assistant in assistantsList.Items)
{
Console.WriteLine($"{assistant} -> {assistant.CreatedAt}");
}
Create an assistant with a model and instructions.
var api = new OpenAIClient();
var request = new CreateAssistantRequest("gpt-3.5-turbo-1106");
var assistant = await OpenAIClient.AssistantsEndpoint.CreateAssistantAsync(request);
var api = new OpenAIClient();
var assistant = await OpenAIClient.AssistantsEndpoint.RetrieveAssistantAsync("assistant-id");
Console.WriteLine($"{assistant} -> {assistant.CreatedAt}");
var api = new OpenAIClient();
var createRequest = new CreateAssistantRequest("gpt-3.5-turbo-1106");
var assistant = await api.AssistantsEndpoint.CreateAssistantAsync(createRequest);
var modifyRequest = new CreateAssistantRequest("gpt-4-1106-preview");
var modifiedAssistant = await api.AssistantsEndpoint.ModifyAsync(assistant.Id, modifyRequest);
// OR AssistantExtension for easier use!
var modifiedAssistantEx = await assistant.ModifyAsync(modifyRequest);
var api = new OpenAIClient();
var isDeleted = await api.AssistantsEndpoint.DeleteAssistantAsync("assistant-id");
// OR AssistantExtension for easier use!
var isDeleted = await assistant.DeleteAsync();
Assert.IsTrue(isDeleted);
var api = new OpenAIClient();
var filesList = await api.AssistantsEndpoint.ListFilesAsync("assistant-id");
// OR AssistantExtension for easier use!
var filesList = await assistant.ListFilesAsync();
foreach (var file in filesList.Items)
{
Console.WriteLine($"{file.AssistantId}'s file -> {file.Id}");
}
Create an assistant file by attaching a File to an assistant.
var api = new OpenAIClient();
var filePath = "assistant_test_2.txt";
await File.WriteAllTextAsync(filePath, "Knowledge is power!");
var fileUploadRequest = new FileUploadRequest(filePath, "assistant");
var file = await api.FilesEndpoint.UploadFileAsync(fileUploadRequest);
var assistantFile = await api.AssistantsEndpoint.AttachFileAsync("assistant-id", file.Id);
// OR use extension method for convenience!
var assistantFIle = await assistant.AttachFileAsync(file);
Assistant extension method, for extra convenience!
var api = new OpenAIClient();
var filePath = "assistant_test_2.txt";
await File.WriteAllTextAsync(filePath, "Knowledge is power!");
var assistantFile = await assistant.UploadFileAsync(filePath);
var api = new OpenAIClient();
var assistantFile = await api.AssistantsEndpoint.RetrieveFileAsync("assistant-id", "file-id");
// OR AssistantExtension for easier use!
var assistantFile = await assistant.RetrieveFileAsync(fileId);
Console.WriteLine($"{assistantFile.AssistantId}'s file -> {assistantFile.Id}");
var api = new OpenAIClient();
var isRemoved = await api.AssistantsEndpoint.RemoveFileAsync("assistant-id", "file-id");
// OR use extension method for convenience!
var isRemoved = await assistant.RemoveFileAsync("file-id");
Assert.IsTrue(isRemoved);
var api = new OpenAIClient();
var thread = await api.ThreadsEndpoint.CreateThreadAsync();
Console.WriteLine($"Create thread {thread.Id} -> {thread.CreatedAt}");
var api = new OpenAIClient();
var assistant = await api.AssistantsEndpoint.CreateAssistantAsync(
new CreateAssistantRequest(
name: "Math Tutor",
instructions: "You are a personal math tutor. Answer questions briefly, in a sentence or less.",
model: "gpt-4-1106-preview"));
var messages = new List<Message> { "I need to solve the equation `3x + 11 = 14`. Can you help me?" };
var threadRequest = new CreateThreadRequest(messages);
var run = await assistant.CreateThreadAndRunAsync(threadRequest);
Console.WriteLine($"Created thread and run: {run.ThreadId} -> {run.Id} -> {run.CreatedAt}");
var api = new OpenAIClient();
var thread = await api.ThreadsEndpoint.RetrieveThreadAsync("thread-id");
// OR if you simply wish to get the latest state of a thread
thread = await thread.UpdateAsync();
Console.WriteLine($"Retrieve thread {thread.Id} -> {thread.CreatedAt}");
var api = new OpenAIClient();
var thread = await api.ThreadsEndpoint.CreateThreadAsync();
var metadata = new Dictionary<string, string>
{
{ "key", "custom thread metadata" }
}
thread = await api.ThreadsEndpoint.ModifyThreadAsync(thread.Id, metadata);
// OR use extension method for convenience!
thread = await thread.ModifyAsync(metadata);
Console.WriteLine($"Modify thread {thread.Id} -> {thread.Metadata["key"]}");
var api = new OpenAIClient();
var isDeleted = await api.ThreadsEndpoint.DeleteThreadAsync("thread-id");
// OR use extension method for convenience!
var isDeleted = await thread.DeleteAsync();
Assert.IsTrue(isDeleted);
var api = new OpenAIClient();
var messageList = await api.ThreadsEndpoint.ListMessagesAsync("thread-id");
// OR use extension method for convenience!
var messageList = await thread.ListMessagesAsync();
foreach (var message in messageList.Items)
{
Console.WriteLine($"{message.Id}: {message.Role}: {message.PrintContent()}");
}
var api = new OpenAIClient();
var thread = await api.ThreadsEndpoint.CreateThreadAsync();
var request = new CreateMessageRequest("Hello world!");
var message = await api.ThreadsEndpoint.CreateMessageAsync(thread.Id, request);
// OR use extension method for convenience!
var message = await thread.CreateMessageAsync("Hello World!");
Console.WriteLine($"{message.Id}: {message.Role}: {message.PrintContent()}");
var api = new OpenAIClient();
var message = await api.ThreadsEndpoint.RetrieveMessageAsync("thread-id", "message-id");
// OR use extension methods for convenience!
var message = await thread.RetrieveMessageAsync("message-id");
var message = await message.UpdateAsync();
Console.WriteLine($"{message.Id}: {message.Role}: {message.PrintContent()}");
var api = new OpenAIClient();
var metadata = new Dictionary<string, string>
{
{ "key", "custom message metadata" }
};
var message = await api.ThreadsEndpoint.ModifyMessageAsync("thread-id", "message-id", metadata);
// OR use extension method for convenience!
var message = await message.ModifyAsync(metadata);
Console.WriteLine($"Modify message metadata: {message.Id} -> {message.Metadata["key"]}");
var api = new OpenAIClient();
var fileList = await api.ThreadsEndpoint.ListFilesAsync("thread-id", "message-Id");
// OR use extension method for convenience!
var fileList = await thread.ListFilesAsync("message-id");
var fileList = await message.ListFilesAsync();
foreach (var file in fileList.Items)
{
Console.WriteLine(file.Id);
}
var api = new OpenAIClient();
var file = await api.ThreadsEndpoint.RetrieveFileAsync("thread-id", "message-id", "file-id");
// OR use extension method for convenience!
var file = await message.RetrieveFileAsync();
Console.WriteLine(file.Id);
var api = new OpenAIClient();
var runList = await api.ThreadsEndpoint.ListRunsAsync("thread-id");
// OR use extension method for convenience!
var runList = await thread.ListRunsAsync();
foreach (var run in runList.Items)
{
Console.WriteLine($"[{run.Id}] {run.Status} | {run.CreatedAt}");
}
var api = new OpenAIClient();
var assistant = await api.AssistantsEndpoint.CreateAssistantAsync(
new CreateAssistantRequest(
name: "Math Tutor",
instructions: "You are a personal math tutor. Answer questions briefly, in a sentence or less.",
model: "gpt-4-1106-preview"));
var thread = await OpenAIClient.ThreadsEndpoint.CreateThreadAsync();
var message = await thread.CreateMessageAsync("I need to solve the equation `3x + 11 = 14`. Can you help me?");
var run = await thread.CreateRunAsync(assistant);
Console.WriteLine($"[{run.Id}] {run.Status} | {run.CreatedAt}");
var api = new OpenAIClient();
var run = await api.ThreadsEndpoint.RetrieveRunAsync("thread-id", "run-id");
// OR use extension method for convenience!
var run = await thread.RetrieveRunAsync("run-id");
var run = await run.UpdateAsync();
Console.WriteLine($"[{run.Id}] {run.Status} | {run.CreatedAt}");
var api = new OpenAIClient();
var metadata = new Dictionary<string, string>
{
{ "key", "custom run metadata" }
};
var run = await api.ThreadsEndpoint.ModifyRunAsync("thread-id", "run-id", metadata);
// OR use extension method for convenience!
var run = await run.ModifyAsync(metadata);
Console.WriteLine($"Modify run {run.Id} -> {run.Metadata["key"]}");
When a run has the status: requires_action and required_action.type is submit_tool_outputs, this endpoint can be used to submit the outputs from the tool calls once they're all completed. All outputs must be submitted in a single request.
var api = new OpenAIClient();
var function = new Function(
nameof(WeatherService.GetCurrentWeather),
"Get the current weather in a given location",
new JsonObject
{
["type"] = "object",
["properties"] = new JsonObject
{
["location"] = new JsonObject
{
["type"] = "string",
["description"] = "The city and state, e.g. San Francisco, CA"
},
["unit"] = new JsonObject
{
["type"] = "string",
["enum"] = new JsonArray { "celsius", "fahrenheit" }
}
},
["required"] = new JsonArray { "location", "unit" }
});
testAssistant = await api.AssistantsEndpoint.CreateAssistantAsync(new CreateAssistantRequest(tools: new Tool[] { function }));
var run = await testAssistant.CreateThreadAndRunAsync("I'm in Kuala-Lumpur, please tell me what's the temperature in celsius now?");
// waiting while run is Queued and InProgress
run = await run.WaitForStatusChangeAsync();
var toolCall = run.RequiredAction.SubmitToolOutputs.ToolCalls[0];
Console.WriteLine($"tool call arguments: {toolCall.FunctionCall.Arguments}");
var functionArgs = JsonSerializer.Deserialize<WeatherArgs>(toolCall.FunctionCall.Arguments);
var functionResult = WeatherService.GetCurrentWeather(functionArgs);
var toolOutput = new ToolOutput(toolCall.Id, functionResult);
run = await run.SubmitToolOutputsAsync(toolOutput);
// waiting while run in Queued and InProgress
run = await run.WaitForStatusChangeAsync();
var messages = await run.ListMessagesAsync();
foreach (var message in messages.Items.OrderBy(response => response.CreatedAt))
{
Console.WriteLine($"{message.Role}: {message.PrintContent()}");
}
var api = new OpenAIClient();
var runStepList = await api.ThreadsEndpoint.ListRunStepsAsync("thread-id", "run-id");
// OR use extension method for convenience!
var runStepList = await run.ListRunStepsAsync();
foreach (var runStep in runStepList.Items)
{
Console.WriteLine($"[{runStep.Id}] {runStep.Status} {runStep.CreatedAt} -> {runStep.ExpiresAt}");
}
var api = new OpenAIClient();
var runStep = await api.ThreadsEndpoint.RetrieveRunStepAsync("thread-id", "run-id", "step-id");
// OR use extension method for convenience!
var runStep = await run.RetrieveRunStepAsync("step-id");
var runStep = await runStep.UpdateAsync();
Console.WriteLine($"[{runStep.Id}] {runStep.Status} {runStep.CreatedAt} -> {runStep.ExpiresAt}");
var api = new OpenAIClient();
var isCancelled = await api.ThreadsEndpoint.CancelRunAsync("thread-id", "run-id");
// OR use extension method for convenience!
var isCancelled = await run.CancelAsync();
Assert.IsTrue(isCancelled);
var api = new OpenAIClient();
var messages = new List<Message>
{
new Message(Role.System, "You are a helpful assistant."),
new Message(Role.User, "Who won the world series in 2020?"),
new Message(Role.Assistant, "The Los Angeles Dodgers won the World Series in 2020."),
new Message(Role.User, "Where was it played?"),
};
var chatRequest = new ChatRequest(messages, Model.GPT4);
var response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
var choice = response.FirstChoice;
Console.WriteLine($"[{choice.Index}] {choice.Message.Role}: {choice.Message} | Finish Reason: {choice.FinishReason}");
var api = new OpenAIClient();
var messages = new List<Message>
{
new Message(Role.System, "You are a helpful assistant."),
new Message(Role.User, "Who won the world series in 2020?"),
new Message(Role.Assistant, "The Los Angeles Dodgers won the World Series in 2020."),
new Message(Role.User, "Where was it played?"),
};
var chatRequest = new ChatRequest(messages);
var response = await api.ChatEndpoint.StreamCompletionAsync(chatRequest, partialResponse =>
{
Console.Write(choice.Delta.ToString());
});
var choice = response.FirstChoice;
Console.WriteLine($"[{choice.Index}] {choice.Message.Role}: {choice.Message} | Finish Reason: {choice.FinishReason}");
var api = new OpenAIClient();
var messages = new List<Message>
{
new Message(Role.System, "You are a helpful assistant."),
new Message(Role.User, "Who won the world series in 2020?"),
new Message(Role.Assistant, "The Los Angeles Dodgers won the World Series in 2020."),
new Message(Role.User, "Where was it played?"),
};
var cumulativeDelta = string.Empty;
var chatRequest = new ChatRequest(messages);
await foreach (var partialResponse in OpenAIClient.ChatEndpoint.StreamCompletionEnumerableAsync(chatRequest))
{
foreach (var choice in partialResponse.Choices.Where(choice => choice.Delta?.Content != null))
{
cumulativeDelta += choice.Delta.Content;
}
}
Console.WriteLine(cumulativeDelta);
var api = new OpenAIClient();
var messages = new List<Message>
{
new Message(Role.System, "You are a helpful weather assistant."),
new Message(Role.User, "What's the weather like today?"),
};
foreach (var message in messages)
{
Console.WriteLine($"{message.Role}: {message}");
}
// Define the tools that the assistant is able to use:
var tools = new List<Tool>
{
new Function(
nameof(WeatherService.GetCurrentWeather),
"Get the current weather in a given location",
new JsonObject
{
["type"] = "object",
["properties"] = new JsonObject
{
["location"] = new JsonObject
{
["type"] = "string",
["description"] = "The city and state, e.g. San Francisco, CA"
},
["unit"] = new JsonObject
{
["type"] = "string",
["enum"] = new JsonArray {"celsius", "fahrenheit"}
}
},
["required"] = new JsonArray { "location", "unit" }
})
};
var chatRequest = new ChatRequest(messages, tools: tools, toolChoice: "auto");
var response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
messages.Add(response.FirstChoice.Message);
Console.WriteLine($"{response.FirstChoice.Message.Role}: {response.FirstChoice} | Finish Reason: {response.FirstChoice.FinishReason}");
var locationMessage = new Message(Role.User, "I'm in Glasgow, Scotland");
messages.Add(locationMessage);
Console.WriteLine($"{locationMessage.Role}: {locationMessage.Content}");
chatRequest = new ChatRequest(messages, tools: tools, toolChoice: "auto");
response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
messages.Add(response.FirstChoice.Message);
if (!string.IsNullOrEmpty(response.ToString()))
{
Console.WriteLine($"{response.FirstChoice.Message.Role}: {response.FirstChoice} | Finish Reason: {response.FirstChoice.FinishReason}");
var unitMessage = new Message(Role.User, "celsius");
messages.Add(unitMessage);
Console.WriteLine($"{unitMessage.Role}: {unitMessage.Content}");
chatRequest = new ChatRequest(messages, tools: tools, toolChoice: "auto");
response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
}
var usedTool = response.FirstChoice.Message.ToolCalls[0];
Console.WriteLine($"{response.FirstChoice.Message.Role}: {usedTool.Function.Name} | Finish Reason: {response.FirstChoice.FinishReason}");
Console.WriteLine($"{usedTool.Function.Arguments}");
var functionArgs = JsonSerializer.Deserialize<WeatherArgs>(usedTool.Function.Arguments.ToString());
var functionResult = WeatherService.GetCurrentWeather(functionArgs);
messages.Add(new Message(usedTool, functionResult));
Console.WriteLine($"{Role.Tool}: {functionResult}");
// System: You are a helpful weather assistant.
// User: What's the weather like today?
// Assistant: Sure, may I know your current location? | Finish Reason: stop
// User: I'm in Glasgow, Scotland
// Assistant: GetCurrentWeather | Finish Reason: tool_calls
// {
// "location": "Glasgow, Scotland",
// "unit": "celsius"
// }
// Tool: The current weather in Glasgow, Scotland is 20 celsius
:warning: Beta Feature
Currently, GPT-4 with vision does not support the message.name parameter, functions/tools, nor the response_format parameter.
var api = new OpenAIClient();
var messages = new List<Message>
{
new Message(Role.System, "You are a helpful assistant."),
new Message(Role.User, new List<Content>
{
"What's in this image?",
new ImageUrl("https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg", ImageDetail.Low)
})
};
var chatRequest = new ChatRequest(messages, model: "gpt-4-vision-preview");
var response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
Console.WriteLine($"{response.FirstChoice.Message.Role}: {response.FirstChoice.Message.Content} | Finish Reason: {response.FirstChoice.FinishDetails}");
When using JSON mode, always instruct the model to produce JSON via some message in the conversation, for example via your system message. If you don't include an explicit instruction to generate JSON, the model may generate an unending stream of whitespace and the request may run continually until it reaches the token limit. To help ensure you don't forget, the API will throw an error if the string "JSON" does not appear somewhere in the context.
The JSON in the message the model returns may be partial (i.e. cut off) if finish_reason is length, which indicates the generation exceeded max_tokens or the conversation exceeded the token limit. To guard against this, check finish_reason before parsing the response.
JSON mode will not guarantee the output matches any specific schema, only that it is valid and parses without errors.
var messages = new List<Message>
{
new Message(Role.System, "You are a helpful assistant designed to output JSON."),
new Message(Role.User, "Who won the world series in 2020?"),
};
var chatRequest = new ChatRequest(messages, "gpt-4-1106-preview", responseFormat: ChatResponseFormat.Json);
var response = await OpenAIClient.ChatEndpoint.GetCompletionAsync(chatRequest);
foreach (var choice in response.Choices)
{
Console.WriteLine($"[{choice.Index}] {choice.Message.Role}: {choice} | Finish Reason: {choice.FinishReason}");
}
response.GetUsage();
var api = new OpenAIClient();
var request = new SpeechRequest("Hello World!");
async Task ChunkCallback(ReadOnlyMemory<byte> chunkCallback)
{
// TODO Implement audio playback as chunks arrive
await Task.CompletedTask;
}
var response = await api.AudioEndpoint.CreateSpeechAsync(request, ChunkCallback);
await File.WriteAllBytesAsync("../../../Assets/HelloWorld.mp3", response.ToArray());
var api = new OpenAIClient();
var request = new AudioTranscriptionRequest(Path.GetFullPath(audioAssetPath), language: "en");
var response = await api.AudioEndpoint.CreateTranscriptionAsync(request);
Console.WriteLine(response);
var api = new OpenAIClient();
var request = new AudioTranslationRequest(Path.GetFullPath(audioAssetPath));
var response = await api.AudioEndpoint.CreateTranslationAsync(request);
Console.WriteLine(response);
var api = new OpenAIClient();
var request = new ImageGenerationRequest("A house riding a velociraptor", Models.Model.DallE_3);
var imageResults = await api.ImagesEndPoint.GenerateImageAsync(request);
foreach (var image in imageResults)
{
Console.WriteLine(image);
// image == url or b64_string
}
Creates an edited or extended image given an original image and a prompt.
var api = new OpenAIClient();
var request = new ImageEditRequest(imageAssetPath, maskAssetPath, "A sunlit indoor lounge area with a pool containing a flamingo", size: ImageSize.Small);
var imageResults = await api.ImagesEndPoint.CreateImageEditAsync(request);
foreach (var image in imageResults)
{
Console.WriteLine(image);
// image == url or b64_string
}
var api = new OpenAIClient();
var request = new ImageVariationRequest(imageAssetPath, size: ImageSize.Small);
var imageResults = await api.ImagesEndPoint.CreateImageVariationAsync(request);
foreach (var image in imageResults)
{
Console.WriteLine(image);
// image == url or b64_string
}
Returns a list of files that belong to the user's organization.
var api = new OpenAIClient();
var fileList = await api.FilesEndpoint.ListFilesAsync();
foreach (var file in fileList)
{
Console.WriteLine($"{file.Id} -> {file.Object}: {file.FileName} | {file.Size} bytes");
}
Upload a file that can be used across various endpoints. The size of all the files uploaded by one organization can be up to 100 GB.
The size of individual files can be a maximum of 512 MB. See the Assistants Tools guide to learn more about the types of files supported. The Fine-tuning API only supports .jsonl files.
var api = new OpenAIClient();
var file = await api.FilesEndpoint.UploadFileAsync("path/to/your/file.jsonl", "fine-tune");
Console.WriteLine(file.Id);
var api = new OpenAIClient();
var file = await GetFileInfoAsync(fileId);
Console.WriteLine($"{file.Id} -> {file.Object}: {file.FileName} | {file.Size} bytes");
Downloads the file content to the specified directory.
var api = new OpenAIClient();
var downloadedFilePath = await api.FilesEndpoint.DownloadFileAsync(fileId, "path/to/your/save/directory");
Console.WriteLine(downloadedFilePath);
Assert.IsTrue(File.Exists(downloadedFilePath));
Creates a job that fine-tunes a specified model from a given dataset.
Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete.
var api = new OpenAIClient();
var fileId = "file-abc123";
var request = new CreateFineTuneRequest(fileId);
var job = await api.FineTuningEndpoint.CreateJobAsync(Model.GPT3_5_Turbo, request);
Console.WriteLine($"Started {job.Id} | Status: {job.Status}");
var api = new OpenAIClient();
var jobList = await api.FineTuningEndpoint.ListJobsAsync();
foreach (var job in jobList.Items.OrderByDescending(job => job.CreatedAt)))
{
Console.WriteLine($"{job.Id} -> {job.CreatedAt} | {job.Status}");
}
var api = new OpenAIClient();
var job = await api.FineTuningEndpoint.GetJobInfoAsync(fineTuneJob);
Console.WriteLine($"{job.Id} -> {job.CreatedAt} | {job.Status}");
Creates an embedding vector representing the input text.
var api = new OpenAIClient();
var response = await api.EmbeddingsEndpoint.CreateEmbeddingAsync("The food was delicious and the waiter...", Models.Embedding_Ada_002);
Console.WriteLine(response);
Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.
The Completions API is accessed via OpenAIClient.CompletionsEndpoint
var api = new OpenAIClient();
var response = await api.CompletionsEndpoint.CreateCompletionAsync("One Two Three One Two", temperature: 0.1, model: Model.Davinci);
Console.WriteLine(response);
To get the CompletionResponse (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(response =>
{
foreach (var choice in response.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 partialResponse 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(partialResponse);
}
Classifies if text violates OpenAI's Content Policy.
var api = new OpenAIClient();
var isViolation = await api.ModerationsEndpoint.GetModerationAsync("I want to kill them.");
Assert.IsTrue(isViolation);
Additionally you can also get the scores of a given input.
var response = await OpenAIClient.ModerationsEndpoint.CreateModerationAsync(new ModerationsRequest("I love you"));
Assert.IsNotNull(response);
Console.WriteLine(response.Results?[0]?.Scores?.ToString());
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 response = await api.EditsEndpoint.CreateEditAsync(request);
Console.WriteLine(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.
