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.
There are 3 ways to provide your API keys, in order of precedence:
[!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.
[!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.
using var api = new OpenAIClient("sk-apiKey");
Or create a OpenAIAuthentication object manually
using var api = new OpenAIClient(new OpenAIAuthentication("sk-apiKey", "org-yourOrganizationId", "proj_yourProjectId"));
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:
[!NOTE]
Organization and project id entries are optional.
You can also load the configuration file directly with known path by calling static methods in OpenAIAuthentication:
Loads the default .openai config in the specified directory:
using var api = new OpenAIClient(OpenAIAuthentication.LoadFromDirectory("path/to/your/directory"));
Loads the configuration file from a specific path. File does not need to be named .openai as long as it conforms to the json format:
using var api = new OpenAIClient(OpenAIAuthentication.LoadFromPath("path/to/your/file.json"));
Use System Environment Variables
Use your system's environment variables specify an api key and organization to use.
Use OPENAI_API_KEY for your api key.
Use OPENAI_ORGANIZATION_ID to specify an organization.
Use OPENAI_PROJECT_ID to specify a project.
using var api = new OpenAIClient(OpenAIAuthentication.LoadFromEnv());
Handling OpenAIClient and HttpClient Lifecycle
OpenAIClient implements IDisposable to manage the lifecycle of the resources it uses, including HttpClient. When you initialize OpenAIClient, it will create an internal HttpClient instance if one is not provided. This internal HttpClient is disposed of when OpenAIClient is disposed of. If you provide an external HttpClient instance to OpenAIClient, you are responsible for managing its disposal.
If OpenAIClient creates its own HttpClient, it will also take care of disposing it when you dispose OpenAIClient.
If an external HttpClient is passed to OpenAIClient, it will not be disposed of by OpenAIClient. You must manage the disposal of the HttpClient yourself.
Please ensure to appropriately dispose of OpenAIClient to release resources timely and to prevent any potential memory or resource leaks in your application.
Typical usage with an internal HttpClient:
using var api = new OpenAIClient();
Custom HttpClient (which you must dispose of yourself):
using var customHttpClient = new HttpClient();
// set custom http client properties here
var api = new OpenAIClient(client: customHttpClient);
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");
using 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);
using 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");
using 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 OpenAIProxy 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 OpenAIProxy.CreateWebApplication 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 async Task ValidateAuthenticationAsync(IHeaderDictionary request)
{
await Task.CompletedTask; // remote resource call to verify token
// 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(TestUserToken))
{
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 */);
using var openAIClient = new OpenAIClient(auth, settings);
OpenAIProxy.CreateWebApplication<AuthenticationFilter>(args, openAIClient).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.
using var api = new OpenAIClient();
var models = await api.ModelsEndpoint.GetModelsAsync();
foreach (var model in models)
{
Console.WriteLine(model.ToString());
}
Delete a fine-tuned model. You must have the Owner role in your organization.
using var api = new OpenAIClient();
var isDeleted = await api.ModelsEndpoint.DeleteFineTuneModelAsync("your-fine-tuned-model");
Assert.IsTrue(isDeleted);
using var api = new OpenAIClient();
var assistantsList = await api.AssistantsEndpoint.ListAssistantsAsync();
foreach (var assistant in assistantsList.Items)
{
Console.WriteLine($"{assistant} -> {assistant.CreatedAt}");
}
Create an assistant with a model and instructions.
using var api = new OpenAIClient();
var request = new CreateAssistantRequest(Model.GPT4o);
var assistant = await api.AssistantsEndpoint.CreateAssistantAsync(request);
using var api = new OpenAIClient();
var assistant = await api.AssistantsEndpoint.RetrieveAssistantAsync("assistant-id");
Console.WriteLine($"{assistant} -> {assistant.CreatedAt}");
using var api = new OpenAIClient();
var createRequest = new CreateAssistantRequest(Model.GPT4_Turbo);
var assistant = await api.AssistantsEndpoint.CreateAssistantAsync(createRequest);
var modifyRequest = new CreateAssistantRequest(Model.GPT4o);
var modifiedAssistant = await api.AssistantsEndpoint.ModifyAssistantAsync(assistant.Id, modifyRequest);
// OR AssistantExtension for easier use!
var modifiedAssistantEx = await assistant.ModifyAsync(modifyRequest);
using var api = new OpenAIClient();
var isDeleted = await api.AssistantsEndpoint.DeleteAssistantAsync("assistant-id");
// OR AssistantExtension for easier use!
var isDeleted = await assistant.DeleteAsync();
Assert.IsTrue(isDeleted);
[!NOTE]
Assistant stream events can be easily added to existing thread calls by passing Func<IServerSentEvent, Task> streamEventHandler callback to any existing method that supports streaming.
using var api = new OpenAIClient();
var thread = await api.ThreadsEndpoint.CreateThreadAsync();
Console.WriteLine($"Create thread {thread.Id} -> {thread.CreatedAt}");
using 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: Model.GPT4o));
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($"Created thread and run: {run.ThreadId} -> {run.Id} -> {run.CreatedAt}");
Create Thread and Run Streaming
Create a thread and run it in one request while streaming events.
using var api = new OpenAIClient();
var tools = new List<Tool>
{
Tool.GetOrCreateTool(typeof(WeatherService), nameof(WeatherService.GetCurrentWeatherAsync))
};
var assistantRequest = new CreateAssistantRequest(tools: tools, instructions: "You are a helpful weather assistant. Use the appropriate unit based on geographical location.");
var assistant = await api.AssistantsEndpoint.CreateAssistantAsync(assistantRequest);
ThreadResponse thread = null;
async Task StreamEventHandler(IServerSentEvent streamEvent)
{
switch (streamEvent)
{
case ThreadResponse threadResponse:
thread = threadResponse;
break;
case RunResponse runResponse:
if (runResponse.Status == RunStatus.RequiresAction)
{
var toolOutputs = await assistant.GetToolOutputsAsync(runResponse);
foreach (var toolOutput in toolOutputs)
{
Console.WriteLine($"Tool Output: {toolOutput}");
}
await runResponse.SubmitToolOutputsAsync(toolOutputs, StreamEventHandler);
}
break;
default:
Console.WriteLine(streamEvent.ToJsonString());
break;
}
}
var run = await assistant.CreateThreadAndRunAsync("I'm in Kuala-Lumpur, please tell me what's the temperature now?", StreamEventHandler);
run = await run.WaitForStatusChangeAsync();
var messages = await thread.ListMessagesAsync();
foreach (var response in messages.Items.Reverse())
{
Console.WriteLine($"{response.Role}: {response.PrintContent()}");
}
using var api = new OpenAIClient();
var thread = await api.ThreadsEndpoint.RetrieveThreadAsync("thread-id");
// OR if you simply wish to get the latest state of a thread
thread = await thread.UpdateAsync();
Console.WriteLine($"Retrieve thread {thread.Id} -> {thread.CreatedAt}");
using 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($"Modify thread {thread.Id} -> {thread.Metadata["key"]}");
using var api = new OpenAIClient();
var isDeleted = await api.ThreadsEndpoint.DeleteThreadAsync("thread-id");
// OR use extension method for convenience!
var isDeleted = await thread.DeleteAsync();
Assert.IsTrue(isDeleted);
using var api = new OpenAIClient();
var messageList = await api.ThreadsEndpoint.ListMessagesAsync("thread-id");
// OR use extension method for convenience!
var messageList = await thread.ListMessagesAsync();
foreach (var message in messageList.Items)
{
Console.WriteLine($"{message.Id}: {message.Role}: {message.PrintContent()}");
}
using 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($"{message.Id}: {message.Role}: {message.PrintContent()}");
using var api = new OpenAIClient();
var message = await api.ThreadsEndpoint.RetrieveMessageAsync("thread-id", "message-id");
// OR use extension methods for convenience!
var message = await thread.RetrieveMessageAsync("message-id");
var message = await message.UpdateAsync();
Console.WriteLine($"{message.Id}: {message.Role}: {message.PrintContent()}");
using 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($"Modify message metadata: {message.Id} -> {message.Metadata["key"]}");
using var api = new OpenAIClient();
var runList = await api.ThreadsEndpoint.ListRunsAsync("thread-id");
// OR use extension method for convenience!
var runList = await thread.ListRunsAsync();
foreach (var run in runList.Items)
{
Console.WriteLine($"[{run.Id}] {run.Status} | {run.CreatedAt}");
}
using 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: Model.GPT4o));
var thread = await api.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($"[{run.Id}] {run.Status} | {run.CreatedAt}");
Create Thread Run Streaming
Create a run and stream the events.
using var api = new OpenAIClient();
var assistant = await api.AssistantsEndpoint.CreateAssistantAsync(
new CreateAssistantRequest(
name: "Math Tutor",
instructions: "You are a personal math tutor. Answer questions briefly, in a sentence or less. Your responses should be formatted in JSON.",
model: Model.GPT4o,
responseFormat: ChatResponseFormat.Json));
var thread = await api.ThreadsEndpoint.CreateThreadAsync();
var message = await thread.CreateMessageAsync("I need to solve the equation `3x + 11 = 14`. Can you help me?");
var run = await thread.CreateRunAsync(assistant, async streamEvent =>
{
Console.WriteLine(streamEvent.ToJsonString());
await Task.CompletedTask;
});
var messages = await thread.ListMessagesAsync();
foreach (var response in messages.Items.Reverse())
{
Console.WriteLine($"{response.Role}: {response.PrintContent()}");
}
using 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($"[{run.Id}] {run.Status} | {run.CreatedAt}");
using var api = new OpenAIClient();
var metadata = new Dictionary<string, string>
{
{ "key", "custom run metadata" }
};
var run = await api.ThreadsEndpoint.ModifyRunAsync("thread-id", "run-id", metadata);
// OR use extension method for convenience!
var run = await run.ModifyAsync(metadata);
Console.WriteLine($"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.
using var api = new OpenAIClient();
var tools = new List<Tool>
{
// Use a predefined tool
Tool.Retrieval, Tool.CodeInterpreter,
// Or create a tool from a type and the name of the method you want to use for function calling
Tool.GetOrCreateTool(typeof(WeatherService), nameof(WeatherService.GetCurrentWeatherAsync)),
// Pass in an instance of an object to call a method on it
Tool.GetOrCreateTool(api.ImagesEndPoint, nameof(ImagesEndpoint.GenerateImageAsync)),
// Define func<,> callbacks
Tool.FromFunc("name_of_func", () => { /* callback function */ }),
Tool.FromFunc<T1,T2,TResult>("func_with_multiple_params", (t1, t2) => { /* logic that calculates return value */ return tResult; })
};
var assistantRequest = new CreateAssistantRequest(tools: tools, instructions: "You are a helpful weather assistant. Use the appropriate unit based on geographical location.");
var testAssistant = await api.AssistantsEndpoint.CreateAssistantAsync(assistantRequest);
var run = await testAssistant.CreateThreadAndRunAsync("I'm in Kuala-Lumpur, please tell me what's the temperature now?");
// waiting while run is Queued and InProgress
run = await run.WaitForStatusChangeAsync();
// Invoke all of the tool call functions and return the tool outputs.
var toolOutputs = await testAssistant.GetToolOutputsAsync(run.RequiredAction.SubmitToolOutputs.ToolCalls);
foreach (var toolOutput in toolOutputs)
{
Console.WriteLine($"tool call output: {toolOutput.Output}");
}
// submit the tool outputs
run = await run.SubmitToolOutputsAsync(toolOutputs);
// waiting while run in Queued and InProgress
run = await run.WaitForStatusChangeAsync();
var messages = await run.ListMessagesAsync();
foreach (var message in messages.Items.OrderBy(response => response.CreatedAt))
{
Console.WriteLine($"{message.Role}: {message.PrintContent()}");
}
Structured Outputs is the evolution of JSON mode. While both ensure valid JSON is produced, only Structured Outputs ensure schema adherence.
[!IMPORTANT]
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.
var mathSchema = new JsonSchema("math_response", @"
{
""type"": ""object"",
""properties"": {
""steps"": {
""type"": ""array"",
""items"": {
""type"": ""object"",
""properties"": {
""explanation"": {
""type"": ""string""
},
""output"": {
""type"": ""string""
}
},
""required"": [
""explanation"",
""output""
],
""additionalProperties"": false
}
},
""final_answer"": {
""type"": ""string""
}
},
""required"": [
""steps"",
""final_answer""
],
""additionalProperties"": false
}");
var assistant = await OpenAIClient.AssistantsEndpoint.CreateAssistantAsync(
new CreateAssistantRequest(
name: "Math Tutor",
instructions: "You are a helpful math tutor. Guide the user through the solution step by step.",
model: "gpt-4o-2024-08-06",
jsonSchema: mathSchema));
ThreadResponse thread = null;
try
{
var run = await assistant.CreateThreadAndRunAsync("how can I solve 8x + 7 = -23",
async @event =>
{
Console.WriteLine(@event.ToJsonString());
await Task.CompletedTask;
});
thread = await run.GetThreadAsync();
run = await run.WaitForStatusChangeAsync();
Console.WriteLine($"Created thread and run: {run.ThreadId} -> {run.Id} -> {run.CreatedAt}");
var messages = await thread.ListMessagesAsync();
foreach (var response in messages.Items)
{
Console.WriteLine($"{response.Role}: {response.PrintContent()}");
}
}
finally
{
await assistant.DeleteAsync(deleteToolResources: thread == null);
if (thread != null)
{
var isDeleted = await thread.DeleteAsync(deleteToolResources: true);
Assert.IsTrue(isDeleted);
}
}
using 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($"[{runStep.Id}] {runStep.Status} {runStep.CreatedAt} -> {runStep.ExpiresAt}");
}
using var api = new OpenAIClient();
var runStep = await api.ThreadsEndpoint.RetrieveRunStepAsync("thread-id", "run-id", "step-id");
// OR use extension method for convenience!
var runStep = await run.RetrieveRunStepAsync("step-id");
var runStep = await runStep.UpdateAsync();
Console.WriteLine($"[{runStep.Id}] {runStep.Status} {runStep.CreatedAt} -> {runStep.ExpiresAt}");
using 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);
using var api = new OpenAIClient();
var vectorStores = await OpenAIClient.VectorStoresEndpoint.ListVectorStoresAsync();
foreach (var vectorStore in vectorStores.Items)
{
Console.WriteLine(vectorStore);
}
using var api = new OpenAIClient();
var createVectorStoreRequest = new CreateVectorStoreRequest("test-vector-store");
var vectorStore = await api.VectorStoresEndpoint.CreateVectorStoreAsync(createVectorStoreRequest);
Console.WriteLine(vectorStore);
using var api = new OpenAIClient();
var vectorStore = await api.VectorStoresEndpoint.GetVectorStoreAsync("vector-store-id");
Console.WriteLine(vectorStore);
using var api = new OpenAIClient();
var metadata = new Dictionary<string, object> { { "Test", DateTime.UtcNow } };
var vectorStore = await api.VectorStoresEndpoint.ModifyVectorStoreAsync("vector-store-id", metadata: metadata);
Console.WriteLine(vectorStore);
using var api = new OpenAIClient();
var isDeleted = await api.VectorStoresEndpoint.DeleteVectorStoreAsync("vector-store-id");
Assert.IsTrue(isDeleted);
using var api = new OpenAIClient();
var files = await api.VectorStoresEndpoint.ListVectorStoreFilesAsync("vector-store-id");
foreach (var file in vectorStoreFiles.Items)
{
Console.WriteLine(file);
}
Create a vector store file by attaching a file to a vector store.
using var api = new OpenAIClient();
var file = await api.VectorStoresEndpoint.CreateVectorStoreFileAsync("vector-store-id", "file-id", new ChunkingStrategy(ChunkingStrategyType.Static));
Console.WriteLine(file);
using var api = new OpenAIClient();
var file = await api.VectorStoresEndpoint.GetVectorStoreFileAsync("vector-store-id", "vector-store-file-id");
Console.WriteLine(file);
Delete a vector store file. This will remove the file from the vector store but the file itself will not be deleted. To delete the file, use the delete file endpoint.
using var api = new OpenAIClient();
var isDeleted = await api.VectorStoresEndpoint.DeleteVectorStoreFileAsync("vector-store-id", vectorStoreFile);
Assert.IsTrue(isDeleted);
using var api = new OpenAIClient();
var files = new List<string> { "file_id_1","file_id_2" };
var vectorStoreFileBatch = await api.VectorStoresEndpoint.CreateVectorStoreFileBatchAsync("vector-store-id", files);
Console.WriteLine(vectorStoreFileBatch);
using var api = new OpenAIClient();
var vectorStoreFileBatch = await api.VectorStoresEndpoint.GetVectorStoreFileBatchAsync("vector-store-id", "vector-store-file-batch-id");
// you can also use convenience methods!
vectorStoreFileBatch = await vectorStoreFileBatch.UpdateAsync();
vectorStoreFileBatch = await vectorStoreFileBatch.WaitForStatusChangeAsync();
using var api = new OpenAIClient();
var files = await api.VectorStoresEndpoint.ListVectorStoreBatchFilesAsync("vector-store-id", "vector-store-file-batch-id");
foreach (var file in files.Items)
{
Console.WriteLine(file);
}
Cancel a vector store file batch. This attempts to cancel the processing of files in this batch as soon as possible.
using var api = new OpenAIClient();
var isCancelled = await api.VectorStoresEndpoint.CancelVectorStoreFileBatchAsync("vector-store-id", "vector-store-file-batch-id");
using 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.GPT4o);
var response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
var choice = response.FirstChoice;
Console.WriteLine($"[{choice.Index}] {choice.Message.Role}: {choice.Message} | Finish Reason: {choice.FinishReason}");
using var api = new OpenAIClient();
var messages = new List<Message>
{
new Message(Role.System, "You are a helpful assistant."),
new Message(Role.User, "Who won the world series in 2020?"),
new Message(Role.Assistant, "The Los Angeles Dodgers won the World Series in 2020."),
new Message(Role.User, "Where was it played?"),
};
var chatRequest = new ChatRequest(messages);
var response = await api.ChatEndpoint.StreamCompletionAsync(chatRequest, async partialResponse =>
{
Console.Write(partialResponse.FirstChoice.Delta.ToString());
await Task.CompletedTask;
});
var choice = response.FirstChoice;
Console.WriteLine($"[{choice.Index}] {choice.Message.Role}: {choice.Message} | Finish Reason: {choice.FinishReason}");
using 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);
using var api = new OpenAIClient();
var messages = new List<Message>
{
new(Role.System, "You are a helpful weather assistant. Always prompt the user for their location."),
new Message(Role.User, "What's the weather like today?"),
};
foreach (var message in messages)
{
Console.WriteLine($"{message.Role}: {message}");
}
// Define the tools that the assistant is able to use:
// 1. Get a list of all the static methods decorated with FunctionAttribute
var tools = Tool.GetAllAvailableTools(includeDefaults: false, forceUpdate: true, clearCache: true);
// 2. Define a custom list of tools:
var tools = new List<Tool>
{
Tool.GetOrCreateTool(objectInstance, "TheNameOfTheMethodToCall"),
Tool.FromFunc("a_custom_name_for_your_function", ()=> { /* Some logic to run */ })
};
var chatRequest = new ChatRequest(messages, tools: tools, toolChoice: "auto");
var response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
messages.Add(response.FirstChoice.Message);
Console.WriteLine($"{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($"{locationMessage.Role}: {locationMessage.Content}");
chatRequest = new ChatRequest(messages, tools: tools, toolChoice: "auto");
response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
messages.Add(response.FirstChoice.Message);
if (response.FirstChoice.FinishReason == "stop")
{
Console.WriteLine($"{response.FirstChoice.Message.Role}: {response.FirstChoice} | Finish Reason: {response.FirstChoice.FinishReason}");
var unitMessage = new Message(Role.User, "Fahrenheit");
messages.Add(unitMessage);
Console.WriteLine($"{unitMessage.Role}: {unitMessage.Content}");
chatRequest = new ChatRequest(messages, tools: tools, toolChoice: "auto");
response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
}
// iterate over all tool calls and invoke them
foreach (var toolCall in response.FirstChoice.Message.ToolCalls)
{
Console.WriteLine($"{response.FirstChoice.Message.Role}: {toolCall.Function.Name} | Finish Reason: {response.FirstChoice.FinishReason}");
Console.WriteLine($"{toolCall.Function.Arguments}");
// Invokes function to get a generic json result to return for tool call.
var functionResult = await toolCall.InvokeFunctionAsync();
// If you know the return type and do additional processing you can use generic overload
var functionResult = await toolCall.InvokeFunctionAsync<string>();
messages.Add(new Message(toolCall, functionResult));
Console.WriteLine($"{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 39°C.
[!WARNING]
Beta Feature. API subject to breaking changes.
using 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: Model.GPT4o);
var response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
Console.WriteLine($"{response.FirstChoice.Message.Role}: {response.FirstChoice.Message.Content} | Finish Reason: {response.FirstChoice.FinishDetails}");
The evolution of Json Mode. While both ensure valid JSON is produced, only Structured Outputs ensure schema adherence.
[!IMPORTANT]
When using JSON mode, always instruct the model to produce JSON via some message in the conversation, for example via your system message. If you don't include an explicit instruction to generate JSON, the model may generate an unending stream of whitespace and the request may run continually until it reaches the token limit. To help ensure you don't forget, the API will throw an error if the string "JSON" does not appear somewhere in the context.
The JSON in the message the model returns may be partial (i.e. cut off) if finish_reason is length, which indicates the generation exceeded max_tokens or the conversation exceeded the token limit. To guard against this, check finish_reason before parsing the response.
var messages = new List<Message>
{
new(Role.System, "You are a helpful math tutor. Guide the user through the solution step by step."),
new(Role.User, "how can I solve 8x + 7 = -23")
};
var mathSchema = new JsonSchema("math_response", @"
{
""type"": ""object"",
""properties"": {
""steps"": {
""type"": ""array"",
""items"": {
""type"": ""object"",
""properties"": {
""explanation"": {
""type"": ""string""
},
""output"": {
""type"": ""string""
}
},
""required"": [
""explanation"",
""output""
],
""additionalProperties"": false
}
},
""final_answer"": {
""type"": ""string""
}
},
""required"": [
""steps"",
""final_answer""
],
""additionalProperties"": false
}");
var chatRequest = new ChatRequest(messages, model: new("gpt-4o-2024-08-06"), jsonSchema: mathSchema);
var response = await OpenAIClient.ChatEndpoint.GetCompletionAsync(chatRequest);
foreach (var choice in response.Choices)
{
Console.WriteLine($"[{choice.Index}] {choice.Message.Role}: {choice} | Finish Reason: {choice.FinishReason}");
}
response.GetUsage();
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, Model.GPT4o, responseFormat: ChatResponseFormat.Json);
var response = await api.ChatEndpoint.GetCompletionAsync(chatRequest);
foreach (var choice in response.Choices)
{
Console.WriteLine($"[{choice.Index}] {choice.Message.Role}: {choice} | Finish Reason: {choice.FinishReason}");
}
response.GetUsage();
using var api = new OpenAIClient();
var request = new SpeechRequest("Hello World!");
async Task ChunkCallback(ReadOnlyMemory<byte> chunkCallback)
{
// TODO Implement audio playback as chunks arrive
await Task.CompletedTask;
}
var response = await api.AudioEndpoint.CreateSpeechAsync(request, ChunkCallback);
await File.WriteAllBytesAsync("../../../Assets/HelloWorld.mp3", response.ToArray());
using var api = new OpenAIClient();
using var request = new AudioTranscriptionRequest(Path.GetFullPath(audioAssetPath), language: "en");
var response = await api.AudioEndpoint.CreateTranscriptionTextAsync(request);
Console.WriteLine(response);
You can also get detailed information using verbose_json to get timestamp granularities:
using var api = new OpenAIClient();
using var request = new AudioTranscriptionRequest(transcriptionAudio, responseFormat: AudioResponseFormat.Verbose_Json, timestampGranularity: TimestampGranularity.Word, temperature: 0.1f, language: "en");
var response = await api.AudioEndpoint.CreateTranscriptionTextAsync(request);
foreach (var word in response.Words)
{
Console.WriteLine($"[{word.Start}-{word.End}] \"{word.Word}\"");
}
using var api = new OpenAIClient();
using var request = new AudioTranslationRequest(Path.GetFullPath(audioAssetPath));
var response = await api.AudioEndpoint.CreateTranslationTextAsync(request);
Console.WriteLine(response);
using 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.
using 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
}
using 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.
using var api = new OpenAIClient();
var fileList = await api.FilesEndpoint.ListFilesAsync();
foreach (var file in fileList)
{
Console.WriteLine($"{file.Id} -> {file.Object}: {file.FileName} | {file.Size} bytes");
}
Upload a file that can be used across various endpoints. The size of all the files uploaded by one organization can be up to 100 GB.
The size of individual files can be a maximum of 512 MB. See the Assistants Tools guide to learn more about the types of files supported. The Fine-tuning API only supports .jsonl files.
using var api = new OpenAIClient();
var file = await api.FilesEndpoint.UploadFileAsync("path/to/your/file.jsonl", FilePurpose.FineTune);
Console.WriteLine(file.Id);
using var api = new OpenAIClient();
var file = await api.FilesEndpoint.GetFileInfoAsync(fileId);
Console.WriteLine($"{file.Id} -> {file.Object}: {file.FileName} | {file.Size} bytes");
Downloads the file content to the specified directory.
using 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.
using 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($"Started {job.Id} | Status: {job.Status}");
using var api = new OpenAIClient();
var jobList = await api.FineTuningEndpoint.ListJobsAsync();
foreach (var job in jobList.Items.OrderByDescending(job => job.CreatedAt))
{
Console.WriteLine($"{job.Id} -> {job.CreatedAt} | {job.Status}");
}
using var api = new OpenAIClient();
var job = await api.FineTuningEndpoint.GetJobInfoAsync(fineTuneJob);
Console.WriteLine($"{job.Id} -> {job.CreatedAt} | {job.Status}");
using var api = new OpenAIClient();
var batches = await api.await OpenAIClient.BatchEndpoint.ListBatchesAsync();
foreach (var batch in listResponse.Items)
{
Console.WriteLine(batch);
}
Creates and executes a batch from an uploaded file of requests
using var api = new OpenAIClient();
var batchRequest = new CreateBatchRequest("file-id", Endpoint.ChatCompletions);
var batch = await api.BatchEndpoint.CreateBatchAsync(batchRequest);
using var api = new OpenAIClient();
var batch = await api.BatchEndpoint.RetrieveBatchAsync("batch-id");
// you can also use convenience methods!
batch = await batch.UpdateAsync();
batch = await batch.WaitForStatusChangeAsync();
Cancels an in-progress batch. The batch will be in status cancelling for up to 10 minutes, before changing to cancelled, where it will have partial results (if any) available in the output file.
using var api = new OpenAIClient();
var isCancelled = await api.BatchEndpoint.CancelBatchAsync(batch);
Assert.IsTrue(isCancelled);
Creates an embedding vector representing the input text.
using var api = new OpenAIClient();
var response = await api.EmbeddingsEndpoint.CreateEmbeddingAsync("The food was delicious and the waiter...", Models.Embedding_Ada_002);
Console.WriteLine(response);
Classifies if text violates OpenAI's Content Policy.
using 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.
using var api = new OpenAIClient();
var response = await api.ModerationsEndpoint.CreateModerationAsync(new ModerationsRequest("I love you"));
Assert.IsNotNull(response);
Console.WriteLine(response.Results?[0]?.Scores?.ToString());
