Azure.AI.OpenAI (.NET)
用于 Azure OpenAI 服务的客户端库，提供对 OpenAI 模型（包括 GPT-4、GPT-4o、嵌入、DALL-E 和 Whisper）的访问。
安装
dotnet add package Azure.AI.OpenAI
# 对于 OpenAI（非 Azure）兼容性
dotnet add package OpenAI
当前版本：2.1.0（稳定版）
环境变量
AZURE_OPENAI_ENDPOINT=https://<资源名称>.openai.azure.com # 必需：Azure OpenAI 端点
AZURE_OPENAI_API_KEY=<api-key> # 仅当使用 AzureKeyCredential 认证时需要
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini # 必需：模型部署名称
AZURE_TOKEN_CREDENTIALS=prod # 仅当在生产环境使用 DefaultAzureCredential 时需要
客户端层次结构
AzureOpenAIClient（顶层）
├── GetChatClient(deploymentName) → ChatClient
├── GetEmbeddingClient(deploymentName) → EmbeddingClient
├── GetImageClient(deploymentName) → ImageClient
├── GetAudioClient(deploymentName) → AudioClient
└── GetAssistantClient() → AssistantClient
认证
API 密钥认证
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 令牌凭据
using Azure.Identity;
using Azure.AI.OpenAI;
// 本地开发：DefaultAzureCredential。生产环境：设置 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=<特定凭据>
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// 或者在生产环境中直接使用特定凭据：
// 请参阅 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);
使用 OpenAI SDK 直接与 Azure 配合
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")
});
聊天补全
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);
异步聊天
ChatCompletion completion = await chatClient.CompleteChatAsync(
[
new SystemChatMessage("You are a helpful assistant."),
new UserChatMessage("Explain cloud computing in simple terms.")
]);
Console.WriteLine($"响应: {completion.Content[0].Text}");
Console.WriteLine($"使用的令牌数: {completion.Usage.TotalTokenCount}");
流式聊天
await foreach (StreamingChatCompletionUpdate update
in chatClient.CompleteChatStreamingAsync(messages))
{
if (update.ContentUpdate.Count > 0)
{
Console.Write(update.ContentUpdate[0].Text);
}
}
带选项的聊天
ChatCompletionOptions options = new()
{
MaxOutputTokenCount = 1000,
Temperature = 0.7f,
TopP = 0.95f,
FrequencyPenalty = 0,
PresencePenalty = 0
};
ChatCompletion completion = await chatClient.CompleteChatAsync(messages, options);
多轮对话
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));
结构化输出（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($"答案: {json.RootElement.GetProperty("final_answer")}");
推理模型（o1，o4-mini）
ChatCompletionOptions options = new()
{
ReasoningEffortLevel = ChatReasoningEffortLevel.Low,
MaxOutputTokenCount = 100000
};
ChatCompletion completion = await chatClient.CompleteChatAsync(
[
new DeveloperChatMessage("You are a helpful assistant"),
new UserChatMessage("Explain the theory of relativity")
], options);
Azure AI Search 集成（RAG）
using Azure.AI.OpenAI.Chat;
#pragma warning disable AOAI001
ChatCompletionOptions options = new();
options.AddDataSource(new AzureSearchChatDataSource()
{
Endpoint = new Uri(searchEndpoint),
IndexName = searchIndex,
Authentication = DataSourceAuthentication.FromApiKey(searchKey)
});
ChatCompletion completion = await chatClient.CompleteChatAsync(
[new UserChatMessage("What health plans are available?")],
options);
ChatMessageContext context = completion.GetMessageContext();
if (context?.Intent is not null)
{
Console.WriteLine($"意图: {context.Intent}");
}
foreach (ChatCitation citation in context?.Citations ?? [])
{
Console.WriteLine($"引用: {citation.Content}");
}
嵌入
using OpenAI.Embeddings;
EmbeddingClient embeddingClient = azureClient.GetEmbeddingClient("text-embedding-ada-002");
OpenAIEmbedding embedding = await embeddingClient.GenerateEmbeddingAsync("Hello, world!");
ReadOnlyMemory<float> vector = embedding.ToFloats();
Console.WriteLine($"嵌入维度: {vector.Length}");
批量嵌入
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($"索引 {emb.Index}: {emb.ToFloats().Length} 维度");
}
图像生成（DALL-E）
using OpenAI.Images;
ImageClient imageClient = azureClient.GetImageClient("dall-e-3");
GeneratedImage image = await imageClient.GenerateImageAsync(
"A futuristic city skyline at sunset",
new ImageGenerationOptions
{
Size = GeneratedImageSize.W1024xH1024,
Quality = GeneratedImageQuality.High,
Style = GeneratedImageStyle.Vivid
});
Console.WriteLine($"图像 URL: {image.ImageUri}");
音频（Whisper）
转录
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);
文本转语音
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());
函数调用（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($"函数: {toolCall.FunctionName}");
Console.WriteLine($"参数: {toolCall.FunctionArguments}");
}
}
关键类型参考
| 类型 | 用途 |
|---|---|
AzureOpenAIClient | Azure OpenAI 的顶层客户端 |
ChatClient | 聊天补全 |
EmbeddingClient | 文本嵌入 |
ImageClient | 图像生成（DALL-E） |
AudioClient | 音频转录/TTS |
ChatCompletion | 聊天响应 |
ChatCompletionOptions | 请求配置 |
StreamingChatCompletionUpdate | 流式响应分块 |
ChatMessage | 基本消息类型 |
SystemChatMessage | 系统提示 |
UserChatMessage | 用户输入 |
AssistantChatMessage | 助手响应 |
DeveloperChatMessage | 开发者消息（推理模型） |
ChatTool | 函数/工具定义 |
ChatToolCall | 工具调用请求 |
最佳实践
- 在生产环境使用 Entra ID — 避免使用 API 密钥；使用
DefaultAzureCredential - 复用客户端实例 — 创建一次，跨请求共享
- 处理速率限制 — 对 429 错误实施指数退避策略
- 对长响应使用流式处理 — 使用
CompleteChatStreamingAsync以获得更好的用户体验 - 设置适当的超时 — 长补全可能需要延长超时时间
- 使用结构化输出 — JSON schema 确保一致的响应格式
- 监控令牌使用 — 跟踪
completion.Usage以管理成本 - 验证工具调用 — 在执行前始终验证函数参数
错误处理
using Azure;
try
{
ChatCompletion completion = await chatClient.CompleteChatAsync(messages);
}
catch (RequestFailedException ex) when (ex.Status == 429)
{
Console.WriteLine("达到速率限制。延迟后重试。");
await Task.Delay(TimeSpan.FromSeconds(10));
}
catch (RequestFailedException ex) when (ex.Status == 400)
{
Console.WriteLine($"错误请求: {ex.Message}");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Azure OpenAI 错误: {ex.Status} - {ex.Message}");
}
相关 SDK
| SDK | 用途 | 安装 |
|---|---|---|
Azure.AI.OpenAI | Azure OpenAI 客户端（本 SDK） | dotnet add package Azure.AI.OpenAI |
OpenAI | OpenAI 兼容性 | dotnet add package OpenAI |
Azure.Identity | 认证 | dotnet add package Azure.Identity |
Azure.Search.Documents | 用于 RAG 的 AI Search | dotnet add package Azure.Search.Documents |