.NET SDK Overview

The .NET SDKs provide comprehensive access to Azure AI services including Foundry Local, Azure Machine Learning, and Azure AI Search. This guide covers installation, authentication, and usage examples for C#.

Installation

Azure AI Foundry SDK

dotnet add package Azure.AI.Projects --prerelease
dotnet add package Azure.AI.Projects.OpenAI --prerelease
dotnet add package Azure.Identity

Foundry Local SDK

Windows (WinML)
Cross-Platform

dotnet add package Microsoft.AI.Foundry.Local.WinML

dotnet add package Microsoft.AI.Foundry.Local

Azure AI Search

dotnet add package Azure.Search.Documents
dotnet add package Azure.Identity

Azure Machine Learning

dotnet add package Azure.AI.MachineLearning
dotnet add package Azure.Identity

Authentication

All SDKs support Azure Active Directory authentication:

using Azure.Identity;

var credential = new DefaultAzureCredential();

Ensure you’re authenticated with Azure CLI: az login

Azure AI Foundry

Project Client

Connect to your Azure AI Foundry project:

using Azure.AI.Projects.OpenAI;
using Azure.Identity;

string endpoint = "https://<resource-name>.services.ai.azure.com/api/projects/<project-name>";

AIProjectClient projectClient = new(
    endpoint: new Uri(endpoint),
    tokenProvider: new DefaultAzureCredential()
);

Chat Completions

Use the OpenAI-compatible client:

using OpenAI.Responses;

#pragma warning disable OPENAI001

OpenAIResponseClient responseClient = projectClient.OpenAI.GetProjectResponsesClientForModel("gpt-5.2");

OpenAIResponse response = responseClient.CreateResponse("What is the speed of light?");

Console.WriteLine(response.GetOutputText());

#pragma warning restore OPENAI001

Streaming Responses

using OpenAI.Responses;
using System;

var responseClient = projectClient.OpenAI.GetProjectResponsesClientForModel("gpt-5.2");

var streamingResponse = responseClient.CreateResponseStreaming(
    "Explain quantum computing in simple terms"
);

await foreach (var chunk in streamingResponse)
{
    Console.Write(chunk.ContentUpdate);
}

Get Project Connections

var connections = projectClient.GetConnections();

foreach (var connection in connections)
{
    Console.WriteLine($"Connection: {connection.Name}");
    Console.WriteLine($"Type: {connection.ConnectionType}");
}

Foundry Local

Initialize Manager

using Microsoft.AI.Foundry.Local;
using Microsoft.Extensions.Logging;

var config = new Configuration
{
    AppName = "my-app",
    LogLevel = Microsoft.AI.Foundry.Local.LogLevel.Information,
    ModelCacheDir = "./models"
};

using var loggerFactory = LoggerFactory.Create(builder =>
{
    builder.SetMinimumLevel(Microsoft.Extensions.Logging.LogLevel.Information);
});

var logger = loggerFactory.CreateLogger<Program>();

await FoundryLocalManager.CreateAsync(config, logger);
var manager = FoundryLocalManager.Instance;

List and Download Models

// Get catalog
var catalog = await manager.GetCatalogAsync();

// List available models
var models = await catalog.ListModelsAsync();
Console.WriteLine($"Available models: {models.Count()}");

// Get specific model
var model = await catalog.GetModelAsync(alias: "qwen2.5-0.5b");

if (model != null)
{
    Console.WriteLine($"Model: {model.DisplayName}");
    Console.WriteLine($"Size: {model.FileSizeMb} MB");
    
    // Download model
    await model.DownloadAsync();
    
    // Load into memory
    await model.LoadAsync();
    
    Console.WriteLine($"Model loaded: {model.SelectedVariant.Name}");
}

Model Management

// List cached models
var cachedModels = await catalog.GetCachedModelsAsync();
foreach (var cachedModel in cachedModels)
{
    Console.WriteLine($"Cached: {cachedModel.Alias}");
}

// List loaded models
var loadedModels = await catalog.GetLoadedModelsAsync();
foreach (var loadedModel in loadedModels)
{
    Console.WriteLine($"Loaded: {loadedModel.DisplayName}");
}

// Unload model
if (model != null)
{
    await model.UnloadAsync();
}

Native Chat Completions

using Microsoft.AI.Foundry.Local;

// Load model
var model = await catalog.GetModelAsync(alias: "qwen2.5-0.5b");
await model.LoadAsync();

// Get model path for inference
var modelPath = await model.GetPathAsync();

// Use model with native API (implementation varies by model type)
Console.WriteLine($"Model ready at: {modelPath}");

Start Web Server (Optional)

// Start REST API server
await manager.StartWebServerAsync();

Console.WriteLine($"Server running at: {config.Web.Urls}");

// Stop server when done
await manager.StopWebServerAsync();

Azure AI Search

Search Client

using Azure.Search.Documents;
using Azure.Identity;
using System;

Uri endpoint = new Uri("https://<search-service>.search.windows.net");
string indexName = "your-index";

SearchClient searchClient = new SearchClient(
    endpoint,
    indexName,
    new DefaultAzureCredential()
);

Full-Text Search

using Azure.Search.Documents.Models;

SearchOptions options = new SearchOptions
{
    Size = 5,
    Select = { "id", "page_chunk", "page_number" }
};

SearchResults<SearchDocument> response = await searchClient.SearchAsync<SearchDocument>(
    "Phoenix urban development",
    options
);

await foreach (SearchResult<SearchDocument> result in response.GetResultsAsync())
{
    Console.WriteLine($"Score: {result.Score}");
    Console.WriteLine($"Content: {result.Document["page_chunk"]}");
    Console.WriteLine($"Page: {result.Document["page_number"]}\n");
}

Vector Search

using Azure.Search.Documents.Models;
using System.Collections.Generic;

// Generate embedding (using your embedding function)
float[] queryVector = GenerateEmbedding("Phoenix metropolitan area");

VectorizedQuery vectorQuery = new VectorizedQuery(queryVector)
{
    KNearestNeighborsCount = 5,
    Fields = { "page_embedding_text_3_large" }
};

SearchOptions options = new SearchOptions
{
    VectorSearch = new VectorSearchOptions
    {
        Queries = { vectorQuery }
    },
    Select = { "id", "page_chunk", "page_number" }
};

SearchResults<SearchDocument> response = await searchClient.SearchAsync<SearchDocument>(
    null,
    options
);

await foreach (SearchResult<SearchDocument> result in response.GetResultsAsync())
{
    Console.WriteLine($"Content: {result.Document["page_chunk"]}");
}

Upload Documents

using Azure.Search.Documents.Models;
using System.Collections.Generic;

var documents = new[]
{
    new SearchDocument
    {
        ["id"] = "doc1",
        ["page_chunk"] = "Phoenix is a major city in Arizona.",
        ["page_number"] = 104
    },
    new SearchDocument
    {
        ["id"] = "doc2",
        ["page_chunk"] = "The Phoenix metropolitan area includes Glendale.",
        ["page_number"] = 105
    }
};

IndexDocumentsResult result = await searchClient.IndexDocumentsAsync(
    IndexDocumentsBatch.Upload(documents)
);

Console.WriteLine($"Uploaded {result.Results.Count} documents");

Azure Machine Learning

Workspace Client

using Azure.AI.MachineLearning;
using Azure.Identity;
using System;

Uri endpoint = new Uri("https://<workspace>.api.azureml.ms");
string subscriptionId = "your-subscription-id";
string resourceGroup = "your-resource-group";
string workspace = "your-workspace";

var credential = new DefaultAzureCredential();

// Note: Specific client implementation varies
Console.WriteLine($"Connected to workspace: {workspace}");

Azure AI Services

Speech Recognition

using Microsoft.CognitiveServices.Speech;
using System;
using System.Threading.Tasks;

var config = SpeechConfig.FromSubscription("your-key", "your-region");
using var audioConfig = AudioConfig.FromWavFileInput("audio.wav");
using var recognizer = new SpeechRecognizer(config, audioConfig);

var result = await recognizer.RecognizeOnceAsync();

if (result.Reason == ResultReason.RecognizedSpeech)
{
    Console.WriteLine($"Recognized: {result.Text}");
}
else
{
    Console.WriteLine($"Recognition failed: {result.Reason}");
}

Speech Synthesis

var config = SpeechConfig.FromSubscription("your-key", "your-region");
config.SpeechSynthesisVoiceName = "en-US-AriaNeural";

using var synthesizer = new SpeechSynthesizer(config);
var result = await synthesizer.SpeakTextAsync("Hello, world!");

if (result.Reason == ResultReason.SynthesizingAudioCompleted)
{
    Console.WriteLine("Speech synthesized successfully");
}

Content Safety

using Azure.AI.ContentSafety;
using Azure.Identity;

Uri endpoint = new Uri("https://<resource>.cognitiveservices.azure.com");

ContentSafetyClient client = new ContentSafetyClient(
    endpoint,
    new DefaultAzureCredential()
);

var request = new AnalyzeTextOptions("Sample text to analyze");
var response = await client.AnalyzeTextAsync(request);

Console.WriteLine($"Hate: {response.Value.HateResult.Severity}");
Console.WriteLine($"Violence: {response.Value.ViolenceResult.Severity}");

Error Handling

using Azure;
using System;

try
{
    var results = await searchClient.SearchAsync<SearchDocument>("query");
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
    Console.WriteLine("Index not found");
}
catch (RequestFailedException ex)
{
    Console.WriteLine($"Error: {ex.Status} - {ex.Message}");
}
catch (Exception ex)
{
    Console.WriteLine($"Unexpected error: {ex.Message}");
}

Async Best Practices

Use ConfigureAwait(false)

In library code, use ConfigureAwait(false) to avoid deadlocks:

var result = await client.SearchAsync("query").ConfigureAwait(false);

Dispose Resources

Use using statements or IDisposable pattern:

using var client = new SearchClient(endpoint, indexName, credential);
// Client is automatically disposed

Cancellation Tokens

Support cancellation for long-running operations:

using var cts = new CancellationTokenSource(TimeSpan.FromSeconds(30));
var results = await client.SearchAsync("query", cancellationToken: cts.Token);

Reduce Application Size

For Foundry Local, exclude large execution provider libraries:

<!-- ExcludeExtraLibs.props -->
<Project>
  <!-- Remove CUDA EP on Windows x64 -->
  <Target Name="ExcludeCudaLibs" Condition="'$(RuntimeIdentifier)'=='win-x64'" AfterTargets="ResolvePackageAssets">
    <ItemGroup>
      <NativeCopyLocalItems Remove="@(NativeCopyLocalItems)"
        Condition="$([System.Text.RegularExpressions.Regex]::IsMatch('%(Filename)', '^onnxruntime.*cuda.*', RegexOptions.IgnoreCase))" />
    </ItemGroup>
  </Target>
</Project>

Import in your .csproj:

<Import Project="ExcludeExtraLibs.props" />

Package References

Package	NuGet	Documentation
Azure.AI.Projects	Link	Docs
Microsoft.AI.Foundry.Local	Link	Docs
Azure.Search.Documents	Link	Docs
Microsoft.CognitiveServices.Speech	Link	Docs

REST API

Foundry REST API reference

Python SDK

Python SDK documentation

JavaScript SDK

JavaScript and TypeScript SDK

Samples

C# code samples on GitHub

REST APIs

SDKs

​.NET SDK Overview

​Installation

​Azure AI Foundry SDK

​Foundry Local SDK

​Azure AI Search

​Azure Machine Learning

​Authentication

​Azure AI Foundry

​Project Client

​Chat Completions

​Streaming Responses

​Get Project Connections

​Foundry Local

​Initialize Manager

​List and Download Models

​Model Management

​Native Chat Completions

​Start Web Server (Optional)

​Azure AI Search

​Search Client

​Full-Text Search

​Vector Search

​Upload Documents

​Azure Machine Learning

​Workspace Client

​Azure AI Services

​Speech Recognition

​Speech Synthesis

​Content Safety

​Error Handling

​Async Best Practices

​Reduce Application Size

​Package References

​Related Resources

REST API

Python SDK

JavaScript SDK

Samples

Build docs developers (and LLMs) love

.NET SDK Overview

Installation

Azure AI Foundry SDK

Foundry Local SDK

Azure AI Search

Azure Machine Learning

Authentication

Azure AI Foundry

Project Client

Chat Completions

Streaming Responses

Get Project Connections

Foundry Local

Initialize Manager

List and Download Models

Model Management

Native Chat Completions

Start Web Server (Optional)

Azure AI Search

Search Client

Full-Text Search

Vector Search

Upload Documents

Azure Machine Learning

Workspace Client

Azure AI Services

Speech Recognition

Speech Synthesis

Content Safety

Error Handling

Async Best Practices

Reduce Application Size

Package References

Related Resources