Name: OpenAI
Author: OpenAI

OpenAI v2.6.0 — nugetz

dotnet add package OpenAI

using OpenAI.Chat;

ChatClient client = new(model: "gpt-4o", apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

ChatCompletion completion = client.CompleteChat("Say 'this is a test.'");

Console.WriteLine($"[ASSISTANT]: {completion.Content[0].Text}");

using OpenAI;
using OpenAI.Chat;

ChatClient client = new(
    model: "MODEL_NAME",
    credential: new ApiKeyCredential(Environment.GetEnvironmentVariable("OPENAI_API_KEY")),
    options: new OpenAIClientOptions() 
    { 
        Endpoint = new Uri("BASE_URL")
    }
);

Namespace	Client class
`OpenAI.Assistants`	`AssistantClient`
`OpenAI.Audio`	`AudioClient`
`OpenAI.Batch`	`BatchClient`
`OpenAI.Chat`	`ChatClient`
`OpenAI.Embeddings`	`EmbeddingClient`
`OpenAI.Evals`	`EvaluationClient`
`OpenAI.FineTuning`	`FineTuningClient`
`OpenAI.Files`	`OpenAIFileClient`
`OpenAI.Images`	`ImageClient`
`OpenAI.Models`	`OpenAIModelClient`
`OpenAI.Moderations`	`ModerationClient`
`OpenAI.Realtime`	`RealtimeClient`
`OpenAI.Responses`	`OpenAIResponseClient`
`OpenAI.VectorStores`	`VectorStoreClient`

ChatCompletion completion = await client.CompleteChatAsync("Say 'this is a test.'");

using OpenAI;

OpenAIClient client = new(Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

AudioClient ttsClient = client.GetAudioClient("tts-1");
AudioClient whisperClient = client.GetAudioClient("whisper-1");

builder.Services.AddSingleton<ChatClient>(serviceProvider =>
{
    var apiKey = Environment.GetEnvironmentVariable("OPENAI_API_KEY");
    var model = "gpt-4o";

    return new ChatClient(model, apiKey);
});

[ApiController]
[Route("api/[controller]")]
public class ChatController : ControllerBase
{
    private readonly ChatClient _chatClient;

    public ChatController(ChatClient chatClient)
    {
        _chatClient = chatClient;
    }

    [HttpPost("complete")]
    public async Task<IActionResult> CompleteChat([FromBody] string message)
    {
        ChatCompletion completion = await _chatClient.CompleteChatAsync(message);
        
        return Ok(new { response = completion.Content[0].Text });
    }
}

CollectionResult<StreamingChatCompletionUpdate> completionUpdates = client.CompleteChatStreaming("Say 'this is a test.'");

Console.Write($"[ASSISTANT]: ");
foreach (StreamingChatCompletionUpdate completionUpdate in completionUpdates)
{
    if (completionUpdate.ContentUpdate.Count > 0)
    {
        Console.Write(completionUpdate.ContentUpdate[0].Text);
    }
}

AsyncCollectionResult<StreamingChatCompletionUpdate> completionUpdates = client.CompleteChatStreamingAsync("Say 'this is a test.'");

Console.Write($"[ASSISTANT]: ");
await foreach (StreamingChatCompletionUpdate completionUpdate in completionUpdates)
{
    if (completionUpdate.ContentUpdate.Count > 0)
    {
        Console.Write(completionUpdate.ContentUpdate[0].Text);
    }
}

private static string GetCurrentLocation()
{
    // Call the location API here.
    return "San Francisco";
}

private static string GetCurrentWeather(string location, string unit = "celsius")
{
    // Call the weather API here.
    return $"31 {unit}";
}

private static readonly ChatTool getCurrentLocationTool = ChatTool.CreateFunctionTool(
    functionName: nameof(GetCurrentLocation),
    functionDescription: "Get the user's current location"
);

private static readonly ChatTool getCurrentWeatherTool = ChatTool.CreateFunctionTool(
    functionName: nameof(GetCurrentWeather),
    functionDescription: "Get the current weather in a given location",
    functionParameters: BinaryData.FromBytes("""
        {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. Boston, MA"
                },
                "unit": {
                    "type": "string",
                    "enum": [ "celsius", "fahrenheit" ],
                    "description": "The temperature unit to use. Infer this from the specified location."
                }
            },
            "required": [ "location" ]
        }
        """u8.ToArray())
);

List<ChatMessage> messages = 
[
    new UserChatMessage("What's the weather like today?"),
];

ChatCompletionOptions options = new()
{
    Tools = { getCurrentLocationTool, getCurrentWeatherTool },
};

bool requiresAction;

do
{
    requiresAction = false;
    ChatCompletion completion = client.CompleteChat(messages, options);

    switch (completion.FinishReason)
    {
        case ChatFinishReason.Stop:
            {
                // Add the assistant message to the conversation history.
                messages.Add(new AssistantChatMessage(completion));
                break;
            }

        case ChatFinishReason.ToolCalls:
            {
                // First, add the assistant message with tool calls to the conversation history.
                messages.Add(new AssistantChatMessage(completion));

                // Then, add a new tool message for each tool call that is resolved.
                foreach (ChatToolCall toolCall in completion.ToolCalls)
                {
                    switch (toolCall.FunctionName)
                    {
                        case nameof(GetCurrentLocation):
                            {
                                string toolResult = GetCurrentLocation();
                                messages.Add(new ToolChatMessage(toolCall.Id, toolResult));
                                break;
                            }

                        case nameof(GetCurrentWeather):
                            {
                                // The arguments that the model wants to use to call the function are specified as a
                                // stringified JSON object based on the schema defined in the tool definition. Note that
                                // the model may hallucinate arguments too. Consequently, it is important to do the
                                // appropriate parsing and validation before calling the function.
                                using JsonDocument argumentsJson = JsonDocument.Parse(toolCall.FunctionArguments);
                                bool hasLocation = argumentsJson.RootElement.TryGetProperty("location", out JsonElement location);
                                bool hasUnit = argumentsJson.RootElement.TryGetProperty("unit", out JsonElement unit);

                                if (!hasLocation)
                                {
                                    throw new ArgumentNullException(nameof(location), "The location argument is required.");
                                }

                                string toolResult = hasUnit
                                    ? GetCurrentWeather(location.GetString(), unit.GetString())
                                    : GetCurrentWeather(location.GetString());
                                messages.Add(new ToolChatMessage(toolCall.Id, toolResult));
                                break;
                            }

                        default:
                            {
                                // Handle other unexpected calls.
                                throw new NotImplementedException();
                            }
                    }
                }

                requiresAction = true;
                break;
            }

        case ChatFinishReason.Length:
            throw new NotImplementedException("Incomplete model output due to MaxTokens parameter or token limit exceeded.");

        case ChatFinishReason.ContentFilter:
            throw new NotImplementedException("Omitted content due to a content filter flag.");

        case ChatFinishReason.FunctionCall:
            throw new NotImplementedException("Deprecated in favor of tool calls.");

        default:
            throw new NotImplementedException(completion.FinishReason.ToString());
    }
} while (requiresAction);

List<ChatMessage> messages =
[
    new UserChatMessage("How can I solve 8x + 7 = -23?"),
];

ChatCompletionOptions options = new()
{
    ResponseFormat = ChatResponseFormat.CreateJsonSchemaFormat(
        jsonSchemaFormatName: "math_reasoning",
        jsonSchema: BinaryData.FromBytes("""
            {
                "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
            }
            """u8.ToArray()),
        jsonSchemaIsStrict: true)
};

ChatCompletion completion = client.CompleteChat(messages, options);

using JsonDocument structuredJson = JsonDocument.Parse(completion.Content[0].Text);

Console.WriteLine($"Final answer: {structuredJson.RootElement.GetProperty("final_answer")}");
Console.WriteLine("Reasoning steps:");

foreach (JsonElement stepElement in structuredJson.RootElement.GetProperty("steps").EnumerateArray())
{
    Console.WriteLine($"  - Explanation: {stepElement.GetProperty("explanation")}");
    Console.WriteLine($"    Output: {stepElement.GetProperty("output")}");
}

// Chat audio input and output is only supported on specific models, beginning with gpt-4o-audio-preview
ChatClient client = new("gpt-4o-audio-preview", Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

// Input audio is provided to a request by adding an audio content part to a user message
string audioFilePath = Path.Combine("Assets", "realtime_whats_the_weather_pcm16_24khz_mono.wav");
byte[] audioFileRawBytes = File.ReadAllBytes(audioFilePath);
BinaryData audioData = BinaryData.FromBytes(audioFileRawBytes);
List<ChatMessage> messages =
    [
        new UserChatMessage(ChatMessageContentPart.CreateInputAudioPart(audioData, ChatInputAudioFormat.Wav)),
    ];

// Output audio is requested by configuring ChatCompletionOptions to include the appropriate
// ResponseModalities values and corresponding AudioOptions.
ChatCompletionOptions options = new()
{
    ResponseModalities = ChatResponseModalities.Text | ChatResponseModalities.Audio,
    AudioOptions = new(ChatOutputAudioVoice.Alloy, ChatOutputAudioFormat.Mp3),
};

ChatCompletion completion = client.CompleteChat(messages, options);

void PrintAudioContent()
{
    if (completion.OutputAudio is ChatOutputAudio outputAudio)
    {
        Console.WriteLine($"Response audio transcript: {outputAudio.Transcript}");
        string outputFilePath = $"{outputAudio.Id}.mp3";
        using (FileStream outputFileStream = File.OpenWrite(outputFilePath))
        {
            outputFileStream.Write(outputAudio.AudioBytes);
        }
        Console.WriteLine($"Response audio written to file: {outputFilePath}");
        Console.WriteLine($"Valid on followup requests until: {outputAudio.ExpiresAt}");
    }
}

PrintAudioContent();

// To refer to past audio output, create an assistant message from the earlier ChatCompletion, use the earlier
// response content part, or use ChatMessageContentPart.CreateAudioPart(string) to manually instantiate a part.

messages.Add(new AssistantChatMessage(completion));
messages.Add("Can you say that like a pirate?");

completion = client.CompleteChat(messages, options);

PrintAudioContent();

OpenAIResponseClient client = new(
    model: "o3-mini",
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

OpenAIResponse response = await client.CreateResponseAsync(
    userInputText: "What's the optimal strategy to win at poker?",
    new ResponseCreationOptions()
    {
        ReasoningOptions = new ResponseReasoningOptions()
        {
            ReasoningEffortLevel = ResponseReasoningEffortLevel.High,
        },
    });

await foreach (StreamingResponseUpdate update
    in client.CreateResponseStreamingAsync(
        userInputText: "What's the optimal strategy to win at poker?",
        new ResponseCreationOptions()
        {
            ReasoningOptions = new ResponseReasoningOptions()
            {
                ReasoningEffortLevel = ResponseReasoningEffortLevel.High,
            },
        }))
{
    if (update is StreamingResponseOutputItemAddedUpdate itemUpdate
        && itemUpdate.Item is ReasoningResponseItem reasoningItem)
    {
        Console.WriteLine($"[Reasoning] ({reasoningItem.Status})");
    }
    else if (update is StreamingResponseOutputItemAddedUpdate itemDone
        && itemDone.Item is ReasoningResponseItem reasoningDone)
    {
        Console.WriteLine($"[Reasoning DONE] ({reasoningDone.Status})");
    }
    else if (update is StreamingResponseOutputTextDeltaUpdate delta)
    {
        Console.Write(delta.Delta);
    }
}

OpenAIResponseClient client = new(
    model: "gpt-4o-mini",
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

ResponseTool fileSearchTool
    = ResponseTool.CreateFileSearchTool(
        vectorStoreIds: [ExistingVectorStoreForTest.Id]);
OpenAIResponse response = await client.CreateResponseAsync(
    userInputText: "According to available files, what's the secret number?",
    new ResponseCreationOptions()
    {
        Tools = { fileSearchTool }
    });

foreach (ResponseItem outputItem in response.OutputItems)
{
    if (outputItem is FileSearchCallResponseItem fileSearchCall)
    {
        Console.WriteLine($"[file_search] ({fileSearchCall.Status}): {fileSearchCall.Id}");
        foreach (string query in fileSearchCall.Queries)
        {
            Console.WriteLine($"  - {query}");
        }
    }
    else if (outputItem is MessageResponseItem message)
    {
        Console.WriteLine($"[{message.Role}] {message.Content.FirstOrDefault()?.Text}");
    }
}

OpenAIResponseClient client = new(
    model: "gpt-4o-mini",
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

OpenAIResponse response = await client.CreateResponseAsync(
    userInputText: "What's a happy news headline from today?",
    new ResponseCreationOptions()
    {
        Tools = { ResponseTool.CreateWebSearchTool() },
    });

foreach (ResponseItem item in response.OutputItems)
{
    if (item is WebSearchCallResponseItem webSearchCall)
    {
        Console.WriteLine($"[Web search invoked]({webSearchCall.Status}) {webSearchCall.Id}");
    }
    else if (item is MessageResponseItem message)
    {
        Console.WriteLine($"[{message.Role}] {message.Content?.FirstOrDefault()?.Text}");
    }
}

using OpenAI.Embeddings;

EmbeddingClient client = new("text-embedding-3-small", Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

string description = "Best hotel in town if you like luxury hotels. They have an amazing infinity pool, a spa,"
    + " and a really helpful concierge. The location is perfect -- right downtown, close to all the tourist"
    + " attractions. We highly recommend this hotel.";

OpenAIEmbedding embedding = client.GenerateEmbedding(description);
ReadOnlyMemory<float> vector = embedding.ToFloats();

EmbeddingGenerationOptions options = new() { Dimensions = 512 };

OpenAIEmbedding embedding = client.GenerateEmbedding(description, options);

using OpenAI.Images;

ImageClient client = new("dall-e-3", Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

string prompt = "The concept for a living room that blends Scandinavian simplicity with Japanese minimalism for"
    + " a serene and cozy atmosphere. It's a space that invites relaxation and mindfulness, with natural light"
    + " and fresh air. Using neutral tones, including colors like white, beige, gray, and black, that create a"
    + " sense of harmony. Featuring sleek wood furniture with clean lines and subtle curves to add warmth and"
    + " elegance. Plants and flowers in ceramic pots adding color and life to a space. They can serve as focal"
    + " points, creating a connection with nature. Soft textiles and cushions in organic fabrics adding comfort"
    + " and softness to a space. They can serve as accents, adding contrast and texture.";

ImageGenerationOptions options = new()
{
    Quality = GeneratedImageQuality.High,
    Size = GeneratedImageSize.W1792xH1024,
    Style = GeneratedImageStyle.Vivid,
    ResponseFormat = GeneratedImageFormat.Bytes
};

GeneratedImage image = client.GenerateImage(prompt, options);
BinaryData bytes = image.ImageBytes;

using FileStream stream = File.OpenWrite($"{Guid.NewGuid()}.png");
bytes.ToStream().CopyTo(stream);

using OpenAI.Audio;

AudioClient client = new("whisper-1", Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

string audioFilePath = Path.Combine("Assets", "audio_houseplant_care.mp3");

AudioTranscriptionOptions options = new()
{
    ResponseFormat = AudioTranscriptionFormat.Verbose,
    TimestampGranularities = AudioTimestampGranularities.Word | AudioTimestampGranularities.Segment,
};

AudioTranscription transcription = client.TranscribeAudio(audioFilePath, options);

Console.WriteLine("Transcription:");
Console.WriteLine($"{transcription.Text}");

Console.WriteLine();
Console.WriteLine($"Words:");
foreach (TranscribedWord word in transcription.Words)
{
    Console.WriteLine($"  {word.Word,15} : {word.StartTime.TotalMilliseconds,5:0} - {word.EndTime.TotalMilliseconds,5:0}");
}

Console.WriteLine();
Console.WriteLine($"Segments:");
foreach (TranscribedSegment segment in transcription.Segments)
{
    Console.WriteLine($"  {segment.Text,90} : {segment.StartTime.TotalMilliseconds,5:0} - {segment.EndTime.TotalMilliseconds,5:0}");
}

using OpenAI.Assistants;
using OpenAI.Files;

OpenAIClient openAIClient = new(Environment.GetEnvironmentVariable("OPENAI_API_KEY"));
OpenAIFileClient fileClient = openAIClient.GetOpenAIFileClient();
AssistantClient assistantClient = openAIClient.GetAssistantClient();

using Stream document = BinaryData.FromBytes("""
    {
        "description": "This document contains the sale history data for Contoso products.",
        "sales": [
            {
                "month": "January",
                "by_product": {
                    "113043": 15,
                    "113045": 12,
                    "113049": 2
                }
            },
            {
                "month": "February",
                "by_product": {
                    "113045": 22
                }
            },
            {
                "month": "March",
                "by_product": {
                    "113045": 16,
                    "113055": 5
                }
            }
        ]
    }
    """u8.ToArray()).ToStream();

OpenAIFile salesFile = fileClient.UploadFile(
    document,
    "monthly_sales.json",
    FileUploadPurpose.Assistants);

AssistantCreationOptions assistantOptions = new()
{
    Name = "Example: Contoso sales RAG",
    Instructions =
        "You are an assistant that looks up sales data and helps visualize the information based"
        + " on user queries. When asked to generate a graph, chart, or other visualization, use"
        + " the code interpreter tool to do so.",
    Tools =
    {
        new FileSearchToolDefinition(),
        new CodeInterpreterToolDefinition(),
    },
    ToolResources = new()
    {
        FileSearch = new()
        {
            NewVectorStores =
            {
                new VectorStoreCreationHelper([salesFile.Id]),
            }
        }
    },
};

Assistant assistant = assistantClient.CreateAssistant("gpt-4o", assistantOptions);

ThreadCreationOptions threadOptions = new()
{
    InitialMessages = { "How well did product 113045 sell in February? Graph its trend over time." }
};

ThreadRun threadRun = assistantClient.CreateThreadAndRun(assistant.Id, threadOptions);

do
{
    Thread.Sleep(TimeSpan.FromSeconds(1));
    threadRun = assistantClient.GetRun(threadRun.ThreadId, threadRun.Id);
} while (!threadRun.Status.IsTerminal);

CollectionResult<ThreadMessage> messages
    = assistantClient.GetMessages(threadRun.ThreadId, new MessageCollectionOptions() { Order = MessageCollectionOrder.Ascending });

foreach (ThreadMessage message in messages)
{
    Console.Write($"[{message.Role.ToString().ToUpper()}]: ");
    foreach (MessageContent contentItem in message.Content)
    {
        if (!string.IsNullOrEmpty(contentItem.Text))
        {
            Console.WriteLine($"{contentItem.Text}");

            if (contentItem.TextAnnotations.Count > 0)
            {
                Console.WriteLine();
            }

            // Include annotations, if any.
            foreach (TextAnnotation annotation in contentItem.TextAnnotations)
            {
                if (!string.IsNullOrEmpty(annotation.InputFileId))
                {
                    Console.WriteLine($"* File citation, file ID: {annotation.InputFileId}");
                }
                if (!string.IsNullOrEmpty(annotation.OutputFileId))
                {
                    Console.WriteLine($"* File output, new file ID: {annotation.OutputFileId}");
                }
            }
        }
        if (!string.IsNullOrEmpty(contentItem.ImageFileId))
        {
            OpenAIFile imageInfo = fileClient.GetFile(contentItem.ImageFileId);
            BinaryData imageBytes = fileClient.DownloadFile(contentItem.ImageFileId);
            using FileStream stream = File.OpenWrite($"{imageInfo.Filename}.png");
            imageBytes.ToStream().CopyTo(stream);

            Console.WriteLine($"<image: {imageInfo.Filename}.png>");
        }
    }
    Console.WriteLine();
}

[USER]: How well did product 113045 sell in February? Graph its trend over time.

[ASSISTANT]: Product 113045 sold 22 units in February【4:0†monthly_sales.json】.

Now, I will generate a graph to show its sales trend over time.

* File citation, file ID: file-hGOiwGNftMgOsjbynBpMCPFn

[ASSISTANT]: <image: 015d8e43-17fe-47de-af40-280f25452280.png>
The sales trend for Product 113045 over the past three months shows that:

- In January, 12 units were sold.
- In February, 22 units were sold, indicating significant growth.
- In March, sales dropped slightly to 16 units.

The graph above visualizes this trend, showing a peak in sales during February.

OpenAIClient openAIClient = new(Environment.GetEnvironmentVariable("OPENAI_API_KEY"));
OpenAIFileClient fileClient = openAIClient.GetOpenAIFileClient();
AssistantClient assistantClient = openAIClient.GetAssistantClient();

OpenAIFile pictureOfAppleFile = fileClient.UploadFile(
    Path.Combine("Assets", "images_apple.png"),
    FileUploadPurpose.Vision);

Uri linkToPictureOfOrange = new("https://raw.githubusercontent.com/openai/openai-dotnet/refs/heads/main/examples/Assets/images_orange.png");

Assistant assistant = assistantClient.CreateAssistant(
    "gpt-4o",
    new AssistantCreationOptions()
    {
        Instructions = "When asked a question, attempt to answer very concisely. "
            + "Prefer one-sentence answers whenever feasible."
    });

AssistantThread thread = assistantClient.CreateThread(new ThreadCreationOptions()
{
    InitialMessages =
        {
            new ThreadInitializationMessage(
                MessageRole.User,
                [
                    "Hello, assistant! Please compare these two images for me:",
                    MessageContent.FromImageFileId(pictureOfAppleFile.Id),
                    MessageContent.FromImageUri(linkToPictureOfOrange),
                ]),
        }
});

CollectionResult<StreamingUpdate> streamingUpdates = assistantClient.CreateRunStreaming(
    thread.Id,
    assistant.Id,
    new RunCreationOptions()
    {
        AdditionalInstructions = "When possible, try to sneak in puns if you're asked to compare things.",
    });

foreach (StreamingUpdate streamingUpdate in streamingUpdates)
{
    if (streamingUpdate.UpdateKind == StreamingUpdateReason.RunCreated)
    {
        Console.WriteLine($"--- Run started! ---");
    }
    if (streamingUpdate is MessageContentUpdate contentUpdate)
    {
        Console.Write(contentUpdate.Text);
    }
}

--- Run started! ---
The first image depicts a multicolored apple with a blend of red and green hues, while the second image shows an orange with a bright, textured orange peel; one might say it’s comparing apples to oranges!

AzureOpenAIClient azureClient = new(
    new Uri("https://your-azure-openai-resource.com"),
    new DefaultAzureCredential());
ChatClient chatClient = azureClient.GetChatClient("my-gpt-35-turbo-deployment");

ChatCompletion completion = chatClient.CompleteChat(
    [
        // System messages represent instructions or other guidance about how the assistant should behave
        new SystemChatMessage("You are a helpful assistant that talks like a pirate."),
        // User messages represent user input, whether historical or the most recen tinput
        new UserChatMessage("Hi, can you help me?"),
        // Assistant messages in a request represent conversation history for responses
        new AssistantChatMessage("Arrr! Of course, me hearty! What can I do for ye?"),
        new UserChatMessage("What's the best way to train a parrot?"),
    ]);

Console.WriteLine($"{completion.Role}: {completion.Content[0].Text}");

ChatClient client = new("gpt-4o", Environment.GetEnvironmentVariable("OPENAI_API_KEY"));

BinaryData input = BinaryData.FromBytes("""
    {
       "model": "gpt-4o",
       "messages": [
           {
               "role": "user",
               "content": "Say 'this is a test.'"
           }
       ]
    }
    """u8.ToArray());

using BinaryContent content = BinaryContent.Create(input);
ClientResult result = client.CompleteChat(content);
BinaryData output = result.GetRawResponse().Content;

using JsonDocument outputAsJson = JsonDocument.Parse(output.ToString());
string message = outputAsJson.RootElement
    .GetProperty("choices"u8)[0]
    .GetProperty("message"u8)
    .GetProperty("content"u8)
    .GetString();

Console.WriteLine($"[ASSISTANT]: {message}");

public bool ContainsSecretWord(AudioClient client, string audioFilePath, string secretWord)
{
    AudioTranscription transcription = client.TranscribeAudio(audioFilePath);
    return transcription.Text.Contains(secretWord);
}

// Instantiate mocks and the AudioTranscription object.

Mock<AudioClient> mockClient = new();
Mock<ClientResult<AudioTranscription>> mockResult = new(null, Mock.Of<PipelineResponse>());
AudioTranscription transcription = OpenAIAudioModelFactory.AudioTranscription(text: "I swear I saw an apple flying yesterday!");

// Set up mocks' properties and methods.

mockResult
    .SetupGet(result => result.Value)
    .Returns(transcription);

mockClient.Setup(client => client.TranscribeAudio(
        It.IsAny<string>(),
        It.IsAny<AudioTranscriptionOptions>()))
    .Returns(mockResult.Object);

// Perform validation.

AudioClient client = mockClient.Object;
bool containsSecretWord = ContainsSecretWord(client, "<audioFilePath>", "apple");

Assert.That(containsSecretWord, Is.True);

OpenAIOfficial/OpenAIv2.6.0

Get Started

Readme

OpenAI .NET API library

Table of Contents

Getting started

Prerequisites

Install the NuGet package

Using the client library

Using a custom base URL and API key

Namespace organization

Using the async API

Using the `OpenAIClient` class

How to use dependency injection

How to use chat completions with streaming

How to use chat completions with tools and function calling

How to use chat completions with structured outputs

How to use chat completions with audio

How to use responses with streaming and reasoning

How to use responses with file search

How to use responses with web search

How to generate text embeddings

How to generate images

How to transcribe audio

How to use assistants with retrieval augmented generation (RAG)

How to use assistants with streaming and vision

How to work with Azure OpenAI

Advanced scenarios

Using protocol methods

Mock a client for testing

Automatically retrying errors

Observability