azure-ai-openai-dotnet — Клиент Azure OpenAI для C# приложений

--- name: azure-ai-openai-dotnet description: Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. risk: unknown source: community date_added: '2026-02-27' --- # Azure.AI.OpenAI (.NET) Client library for Azure OpenAI Service providing access to OpenAI models including GPT-4, GPT-4o, embeddings, DALL-E, and Whisper. ## Installation ```bash dotnet add package Azure.AI.OpenAI # For OpenAI (non-Azure) compatibility dotnet add package OpenAI ``` **Current Version**: 2.1.0 (stable) ## Environment Variables ```bash AZURE_OPENAI_ENDPOINT=https://<resource-name>.openai.azure.com AZURE_OPENAI_API_KEY=<api-key> # For key-based auth AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o-mini # Your deployment name ``` ## Client Hierarchy ``` AzureOpenAIClient (top-level) ├── GetChatClient(deploymentName) → ChatClient ├── GetEmbeddingClient(deploymentName) → EmbeddingClient ├── GetImageClient(deploymentName) → ImageClient ├── GetAudioClient(deploymentName) → AudioClient └── GetAssistantClient() → AssistantClient ``` ## Authentication ### API Key Authentication ```csharp 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 ID (Recommended for Production) ```csharp using Azure.Identity; using Azure.AI.OpenAI; AzureOpenAIClient client = new( new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!), new DefaultAzureCredential()); ``` ### Using OpenAI SDK Directly with Azure ```csharp 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") }); ``` ## Chat Completions ### Basic Chat ```csharp 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); ``` ### Async Chat ```csharp 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}"); ``` ### Streaming Chat ```csharp await foreach (StreamingChatCompletionUpdate update in chatClient.CompleteChatStreamingAsync(messages)) { if (update.ContentUpdate.Count > 0) { Console.Write(update.ContentUpdate[0].Text); } } ``` ### Chat with Options ```csharp ChatCompletionOptions options = new() { MaxOutputTokenCount = 1000, Temperature = 0.7f, TopP = 0.95f, FrequencyPenalty = 0, PresencePenalty = 0 }; ChatCompletion completion = await chatClient.CompleteChatAsync(messages, options); ``` ### Multi-turn Conversation ```csharp 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)); ``` ## Structured Outputs (JSON Schema) ```csharp 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($"Answer: {json.RootElement.GetProperty("final_answer")}"); ``` ## Reasoning Models (o1, o4-mini) ```csharp ChatCompletionOptions options = new() { ReasoningEffortLevel = ChatReasoningEffortLevel.Low, MaxOutputTokenCount = 100000 }; ChatCompletion completion = await chatClient.CompleteChatAsync( [ new DeveloperChatMessage("You are a helpful assistant"), new UserChatMessage("Explain the theory of relativity") ], options); ``` ## Azure AI Search Integration (RAG) ```csharp using Azure.AI.OpenAI.Chat; #pragma warning disable AOAI001 ChatCompletionOptions options = new(); options.AddDataSource(new AzureSearchChatDataSource() { Endpoint = new Uri(searchEndpoint), IndexName = searchIndex, Authentication = DataSourceAuthentication.FromApiKey(searchKey) }); ChatCompletion completion = await chatClient.CompleteChatAsync( [new UserChatMessage("What health plans are available?")], options); ChatMessageContext context = completion.GetMessageContext(); if (context?.Intent is not null) { Console.WriteLine($"Intent: {context.Intent}"); } foreach (ChatCitation citation in context?.Citations ?? []) { Console.WriteLine($"Citation: {citation.Content}"); } ``` ## Embeddings ```csharp using OpenAI.Embeddings; EmbeddingClient embeddingClient = azureClient.GetEmbeddingClient("text-embedding-ada-002"); OpenAIEmbedding embedding = await embeddingClient.GenerateEmbeddingAsync("Hello, world!"); ReadOnlyMemory<float> vector = embedding.ToFloats(); Console.WriteLine($"Embedding dimensions: {vector.Length}"); ``` ### Batch Embeddings ```csharp 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($"Index {emb.Index}: {emb.ToFloats().Length} dimensions"); } ``` ## Image Generation (DALL-E) ```csharp using OpenAI.Images; ImageClient imageClient = azureClient.GetImageClient("dall-e-3"); GeneratedImage image = await imageClient.GenerateImageAsync( "A futuristic city skyline at sunset", new ImageGenerationOptions { Size = GeneratedImageSize.W1024xH1024, Quality = GeneratedImageQuality.High, Style = GeneratedImageStyle.Vivid }); Console.WriteLine($"Image URL: {image.ImageUri}"); ``` ## Audio (Whisper) ### Transcription ```csharp 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); ``` ### Text-to-Speech ```csharp 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()); ``` ## Function Calling (Tools) ```csharp 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}"); } } ``` ## Key Types Reference | Type | Purpose | |------|---------| | `AzureOpenAIClient` | Top-level client for Azure OpenAI | | `ChatClient` | Chat completions | | `EmbeddingClient` | Text embeddings | | `ImageClient` | Image generation (DALL-E) | | `AudioClient` | Audio transcription/TTS | | `ChatCompletion` | Chat response | | `ChatCompletionOptions` | Request configuration | | `StreamingChatCompletionUpdate` | Streaming response chunk | | `ChatMessage` | Base message type | | `SystemChatMessage` | System prompt | | `UserChatMessage` | User input | | `AssistantChatMessage` | Assistant response | | `DeveloperChatMessage` | Developer message (reasoning models) | | `ChatTool` | Function/tool definition | | `ChatToolCall` | Tool invocation request | ## Best Practices 1. **Use Entra ID in production** — Avoid API keys; use `DefaultAzureCredential` 2. **Reuse client instances** — Create once, share across requests 3. **Handle rate limits** — Implement exponential backoff for 429 errors 4. **Stream for long responses** — Use `CompleteChatStreamingAsync` for better UX 5. **Set appropriate timeouts** — Long completions may need extended timeouts 6. **Use structured outputs** — JSON schema ensures consistent response format 7. **Monitor token usage** — Track `completion.Usage` for cost management 8. **Validate tool calls** — Always validate function arguments before execution ## Error Handling ```csharp 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}"); } ``` ## Related SDKs | SDK | Purpose | Install | |-----|---------|---------| | `Azure.AI.OpenAI` | Azure OpenAI client (this SDK) | `dotnet add package Azure.AI.OpenAI` | | `OpenAI` | OpenAI compatibility | `dotnet add package OpenAI` | | `Azure.Identity` | Authentication | `dotnet add package Azure.Identity` | | `Azure.Search.Documents` | AI Search for RAG | `dotnet add package Azure.Search.Documents` | ## Reference Links | Resource | URL | |----------|-----| | NuGet Package | https://www.nuget.org/packages/Azure.AI.OpenAI | | API Reference | https://learn.microsoft.com/dotnet/api/azure.ai.openai | | Migration Guide (1.0→2.0) | https://learn.microsoft.com/azure/ai-services/openai/how-to/dotnet-migration | | Quickstart | https://learn.microsoft.com/azure/ai-services/openai/quickstart | | GitHub Source | https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/openai/Azure.AI.OpenAI | ## When to Use This skill is applicable to execute the workflow or actions described in the overview.