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
dotnet add package Azure.AI.OpenAI
# For OpenAI (non-Azure) compatibility
dotnet add package OpenAI
Current Version: 2.1.0 (stable)
Environment Variables
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
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)
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
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
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
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
await foreach (StreamingChatCompletionUpdate update
in chatClient.CompleteChatStreamingAsync(messages))
{
if (update.ContentUpdate.Count > 0)
{
Console.Write(update.ContentUpdate[0].Text);
}
}
Chat with Options
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
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)
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($"Answer: {json.RootElement.GetProperty("final_answer")}");
Reasoning Models (o1, o4-mini)
ChatCompletionOptions options = new()
{
ReasoningEffortLevel = ChatReasoningEffortLevel.Low,
MaxOutputTokenCount = 100000
};
ChatCompletion completion = await chatClient.CompleteChatAsync(
[
new DeveloperChatMessage("You are a helpful assistant"),
new UserChatMessage("Explain the theory of relativity")
], options);
Azure AI Search Integration (RAG)
using Azure.AI.OpenAI.Chat;
#pragma warning disable AOAI001
ChatCompletionOptions options = new();
options.AddDataSource(new AzureSearchChatDataSource()
{
Endpoint = new Uri(searchEndpoint),
IndexName = searchIndex,
Authentication = DataSourceAuthentication.FromApiKey(searchKey)
});
ChatCompletion completion = await chatClient.CompleteChatAsync(
[new UserChatMessage("What health plans are available?")],
options);
ChatMessageContext context = completion.GetMessageContext();
if (context?.Intent is not null)
{
Console.WriteLine($"Intent: {context.Intent}");
}
foreach (ChatCitation citation in context?.Citations ?? [])
{
Console.WriteLine($"Citation: {citation.Content}");
}
Embeddings
using OpenAI.Embeddings;
EmbeddingClient embeddingClient = azureClient.GetEmbeddingClient("text-embedding-ada-002");
OpenAIEmbedding embedding = await embeddingClient.GenerateEmbeddingAsync("Hello, world!");
ReadOnlyMemory<float> vector = embedding.ToFloats();
Console.WriteLine($"Embedding dimensions: {vector.Length}");
Batch Embeddings
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($"Index {emb.Index}: {emb.ToFloats().Length} dimensions");
}
Image Generation (DALL-E)
using OpenAI.Images;
ImageClient imageClient = azureClient.GetImageClient("dall-e-3");
GeneratedImage image = await imageClient.GenerateImageAsync(
"A futuristic city skyline at sunset",
new ImageGenerationOptions
{
Size = GeneratedImageSize.W1024xH1024,
Quality = GeneratedImageQuality.High,
Style = GeneratedImageStyle.Vivid
});
Console.WriteLine($"Image URL: {image.ImageUri}");
Audio (Whisper)
Transcription
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
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)
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
- Use Entra ID in production — Avoid API keys; use
DefaultAzureCredential
- Reuse client instances — Create once, share across requests
- Handle rate limits — Implement exponential backoff for 429 errors
- Stream for long responses — Use
CompleteChatStreamingAsync for better UX
- Set appropriate timeouts — Long completions may need extended timeouts
- Use structured outputs — JSON schema ensures consistent response format
- Monitor token usage — Track
completion.Usage for cost management
- Validate tool calls — Always validate function arguments before execution
Error Handling
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
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.