Azure.AI.OpenAI (.NET)
提供對 OpenAI 模型（包括 GPT-4、GPT-4o、嵌入、DALL-E 和 Whisper）存取能力的 Azure OpenAI 服務用戶端程式庫。
安裝
dotnet add package Azure.AI.OpenAI
# 若需相容 OpenAI（非 Azure）
dotnet add package OpenAI
目前版本：2.1.0（穩定版）
環境變數
AZURE_OPENAI_ENDPOINT=https://<resource-name>.openai.azure.com # 必要：Azure OpenAI 端點
AZURE_OPENAI_API_KEY=<api-key> # 僅在使用 AzureKeyCredential 驗證時需要
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini # 必要：模型部署名稱
AZURE_TOKEN_CREDENTIALS=prod # 僅在生產環境中使用 DefaultAzureCredential 時需要
用戶端階層
AzureOpenAIClient（頂層）
├── GetChatClient(deploymentName) → ChatClient
├── GetEmbeddingClient(deploymentName) → EmbeddingClient
├── GetImageClient(deploymentName) → ImageClient
├── GetAudioClient(deploymentName) → AudioClient
└── GetAssistantClient() → AssistantClient
驗證
API 金鑰驗證
using Azure;
using Azure.AI.OpenAI;
AzureOpenAIClient client = new(
new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),
new AzureKeyCredential(Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY")!));
Microsoft Entra 權杖憑證
using Azure.Identity;
using Azure.AI.OpenAI;
// 本機開發：使用 DefaultAzureCredential。生產環境：設定 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=<特定憑證>
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// 或者直接在生產環境中使用特定憑證：
// 請參閱 https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
AzureOpenAIClient client = new(
new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),
credential);
直接使用 OpenAI SDK 搭配 Azure
using Azure.Identity;
using OpenAI;
using OpenAI.Chat;
using System.ClientModel.Primitives;
#pragma warning disable OPENAI001
BearerTokenPolicy tokenPolicy = new(
new DefaultAzureCredential(),
"https://cognitiveservices.azure.com/.default");
ChatClient client = new(
model: "gpt-4o-mini",
authenticationPolicy: tokenPolicy,
options: new OpenAIClientOptions()
{
Endpoint = new Uri("https://YOUR-RESOURCE.openai.azure.com/openai/v1")
});
聊天補全
基本聊天
using Azure.AI.OpenAI;
using OpenAI.Chat;
AzureOpenAIClient azureClient = new(
new Uri(endpoint),
new DefaultAzureCredential());
ChatClient chatClient = azureClient.GetChatClient("gpt-4o-mini");
ChatCompletion completion = chatClient.CompleteChat(
[
new SystemChatMessage("You are a helpful assistant."),
new UserChatMessage("What is Azure OpenAI?")
]);
Console.WriteLine(completion.Content[0].Text);
非同步聊天
ChatCompletion completion = await chatClient.CompleteChatAsync(
[
new SystemChatMessage("You are a helpful assistant."),
new UserChatMessage("Explain cloud computing in simple terms.")
]);
Console.WriteLine($"Response: {completion.Content[0].Text}");
Console.WriteLine($"Tokens used: {completion.Usage.TotalTokenCount}");
串流聊天
await foreach (StreamingChatCompletionUpdate update
in chatClient.CompleteChatStreamingAsync(messages))
{
if (update.ContentUpdate.Count > 0)
{
Console.Write(update.ContentUpdate[0].Text);
}
}
帶選項的聊天
ChatCompletionOptions options = new()
{
MaxOutputTokenCount = 1000,
Temperature = 0.7f,
TopP = 0.95f,
FrequencyPenalty = 0,
PresencePenalty = 0
};
ChatCompletion completion = await chatClient.CompleteChatAsync(messages, options);
多輪對話
List<ChatMessage> messages = new()
{
new SystemChatMessage("You are a helpful assistant."),
new UserChatMessage("Hi, can you help me?"),
new AssistantChatMessage("Of course! What do you need help with?"),
new UserChatMessage("What's the capital of France?")
};
ChatCompletion completion = await chatClient.CompleteChatAsync(messages);
messages.Add(new AssistantChatMessage(completion.Content[0].Text));
結構化輸出（JSON Schema）
using System.Text.Json;
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 = await chatClient.CompleteChatAsync(
[new UserChatMessage("How can I solve 8x + 7 = -23?")],
options);
using JsonDocument json = JsonDocument.Parse(completion.Content[0].Text);
Console.WriteLine($"Answer: {json.RootElement.GetProperty("final_answer")}");
推理模型（o1, o4-mini）
ChatCompletionOptions options = new()
{
ReasoningEffortLevel = ChatReasoningEffortLevel.Low,
MaxOutputTokenCount = 100000
};
ChatCompletion completion = await chatClient.CompleteChatAsync(
[
new DeveloperChatMessage("You are a helpful assistant"),
new UserChatMessage("Explain the theory of relativity")
], options);
Azure AI Search 整合（RAG）
using Azure.AI.OpenAI.Chat;
#pragma warning disable AOAI001
ChatCompletionOptions options = new();
options.AddDataSource(new AzureSearchChatDataSource()
{
Endpoint = new Uri(searchEndpoint),
IndexName = searchIndex,
Authentication = DataSourceAuthentication.FromApiKey(searchKey)
});
ChatCompletion completion = await chatClient.CompleteChatAsync(
[new UserChatMessage("What health plans are available?")],
options);
ChatMessageContext context = completion.GetMessageContext();
if (context?.Intent is not null)
{
Console.WriteLine($"Intent: {context.Intent}");
}
foreach (ChatCitation citation in context?.Citations ?? [])
{
Console.WriteLine($"Citation: {citation.Content}");
}
嵌入
using OpenAI.Embeddings;
EmbeddingClient embeddingClient = azureClient.GetEmbeddingClient("text-embedding-ada-002");
OpenAIEmbedding embedding = await embeddingClient.GenerateEmbeddingAsync("Hello, world!");
ReadOnlyMemory<float> vector = embedding.ToFloats();
Console.WriteLine($"Embedding dimensions: {vector.Length}");
批次嵌入
List<string> inputs = new()
{
"First document text",
"Second document text",
"Third document text"
};
OpenAIEmbeddingCollection embeddings = await embeddingClient.GenerateEmbeddingsAsync(inputs);
foreach (OpenAIEmbedding emb in embeddings)
{
Console.WriteLine($"Index {emb.Index}: {emb.ToFloats().Length} dimensions");
}
圖片生成（DALL-E）
using OpenAI.Images;
ImageClient imageClient = azureClient.GetImageClient("dall-e-3");
GeneratedImage image = await imageClient.GenerateImageAsync(
"A futuristic city skyline at sunset",
new ImageGenerationOptions
{
Size = GeneratedImageSize.W1024xH1024,
Quality = GeneratedImageQuality.High,
Style = GeneratedImageStyle.Vivid
});
Console.WriteLine($"Image URL: {image.ImageUri}");
音訊（Whisper）
轉錄
using OpenAI.Audio;
AudioClient audioClient = azureClient.GetAudioClient("whisper");
AudioTranscription transcription = await audioClient.TranscribeAudioAsync(
"audio.mp3",
new AudioTranscriptionOptions
{
ResponseFormat = AudioTranscriptionFormat.Verbose,
Language = "en"
});
Console.WriteLine(transcription.Text);
文字轉語音
BinaryData speech = await audioClient.GenerateSpeechAsync(
"Hello, welcome to Azure OpenAI!",
GeneratedSpeechVoice.Alloy,
new SpeechGenerationOptions
{
SpeedRatio = 1.0f,
ResponseFormat = GeneratedSpeechFormat.Mp3
});
await File.WriteAllBytesAsync("output.mp3", speech.ToArray());
函式呼叫（工具）
ChatTool getCurrentWeatherTool = ChatTool.CreateFunctionTool(
functionName: "get_current_weather",
functionDescription: "Get the current weather in a given location",
functionParameters: BinaryData.FromString("""
{
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"]
}
},
"required": ["location"]
}
"""));
ChatCompletionOptions options = new()
{
Tools = { getCurrentWeatherTool }
};
ChatCompletion completion = await chatClient.CompleteChatAsync(
[new UserChatMessage("What's the weather in Seattle?")],
options);
if (completion.FinishReason == ChatFinishReason.ToolCalls)
{
foreach (ChatToolCall toolCall in completion.ToolCalls)
{
Console.WriteLine($"Function: {toolCall.FunctionName}");
Console.WriteLine($"Arguments: {toolCall.FunctionArguments}");
}
}
關鍵型別參考
| 型別 | 用途 |
|---|---|
AzureOpenAIClient | Azure OpenAI 的頂層用戶端 |
ChatClient | 聊天補全 |
EmbeddingClient | 文字嵌入 |
ImageClient | 圖片生成（DALL-E） |
AudioClient | 音訊轉錄/TTS |
ChatCompletion | 聊天回應 |
ChatCompletionOptions | 請求設定 |
StreamingChatCompletionUpdate | 串流回應片段 |
ChatMessage | 基本訊息型別 |
SystemChatMessage | 系統提示 |
UserChatMessage | 使用者輸入 |
AssistantChatMessage | 助手回應 |
DeveloperChatMessage | 開發者訊息（推理模型） |
ChatTool | 函式/工具定義 |
ChatToolCall | 工具呼叫請求 |
最佳實務
- 在生產環境中使用 Entra ID — 避免 API 金鑰；改用
DefaultAzureCredential - 重複使用用戶端執行個體 — 建立一次，並在各請求間共用
- 處理速率限制 — 對 429 錯誤實作指數退避
- 針對長回應使用串流 — 使用
CompleteChatStreamingAsync以獲得更好的使用者體驗 - 設定適當的逾時時間 — 較長的補全可能需要延長逾時時間
- 使用結構化輸出 — JSON Schema 可確保一致的回應格式
- 監控權杖使用量 — 追蹤
completion.Usage以管理成本 - 驗證工具呼叫 — 執行前務必驗證函式引數
錯誤處理
using Azure;
try
{
ChatCompletion completion = await chatClient.CompleteChatAsync(messages);
}
catch (RequestFailedException ex) when (ex.Status == 429)
{
Console.WriteLine("Rate limited. Retry after delay.");
await Task.Delay(TimeSpan.FromSeconds(10));
}
catch (RequestFailedException ex) when (ex.Status == 400)
{
Console.WriteLine($"Bad request: {ex.Message}");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Azure OpenAI error: {ex.Status} - {ex.Message}");
}
相關 SDK
| SDK | 用途 | 安裝 |
|---|---|---|
Azure.AI.OpenAI | Azure OpenAI 用戶端（此 SDK） | dotnet add package Azure.AI.OpenAI |
OpenAI | OpenAI 相容性 | dotnet add package OpenAI |
Azure.Identity | 驗證 | dotnet add package Azure.Identity |
Azure.Search.Documents | 用於 RAG 的 AI Search | dotnet add package Azure.Search.Documents |