azure-ai-openai-dotnet

by sickn33Skill

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants.

Source on GitHub

Install this skill

Install with the open skills CLI (global, non-interactive — available in every Claude Code session):

npx skills add sickn33/agentic-awesome-skills --skill "azure-ai-openai-dotnet" -g -a claude-code -y

Or manually — copy the SKILL.md below into:

~/.claude/skills/azure-ai-openai-dotnet-sickn33-2/SKILL.md

SKILL.md

---
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.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Details

Repository: sickn33/agentic-awesome-skills
License: MIT
Last push: Jul 14, 2026

Categories & sources

AI & LLM Integration Languages & Frameworks

Listed in: seed:sickn33/agentic-awesome-skills, aggregator

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