azure-ai-projects-dotnet

โดย microsoft

SDK ระดับสูงสำหรับการดำเนินงานโปรเจกต์ Azure AI Foundry ซึ่งรวมถึงเอเจนต์ การเชื่อมต่อ ชุดข้อมูล การปรับใช้ การประเมินผล และดัชนี

npx skills add https://github.com/microsoft/skills --skill azure-ai-projects-dotnet

Azure.AI.Projects (.NET)

High-level SDK for Azure AI Foundry project operations including agents, connections, datasets, deployments, evaluations, and indexes.

Installation

dotnet add package Azure.AI.Projects
dotnet add package Azure.Identity

# Optional: For versioned agents with OpenAI extensions
dotnet add package Azure.AI.Projects.OpenAI --prerelease

# Optional: For low-level agent operations
dotnet add package Azure.AI.Agents.Persistent --prerelease

Current Versions: GA v1.1.0, Preview v1.2.0-beta.5

Environment Variables

PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>  # Required: Azure AI project endpoint
MODEL_DEPLOYMENT_NAME=gpt-4o-mini  # Required: model deployment name
CONNECTION_NAME=<your-connection-name>  # Optional: project connection name
AI_SEARCH_CONNECTION_NAME=<ai-search-connection>  # Optional: Azure AI Search connection name
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

Authentication

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

var endpoint = Environment.GetEnvironmentVariable("PROJECT_ENDPOINT");
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
var credential = new DefaultAzureCredential(
    DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
AIProjectClient projectClient = new AIProjectClient(
    new Uri(endpoint), 
    credential);

Client Hierarchy

AIProjectClient
├── Agents          → AIProjectAgentsOperations (versioned agents)
├── Connections     → ConnectionsClient
├── Datasets        → DatasetsClient
├── Deployments     → DeploymentsClient
├── Evaluations     → EvaluationsClient
├── Evaluators      → EvaluatorsClient
├── Indexes         → IndexesClient
├── Telemetry       → AIProjectTelemetry
├── OpenAI          → ProjectOpenAIClient (preview)
└── GetPersistentAgentsClient() → PersistentAgentsClient

Core Workflows

1. Get Persistent Agents Client

// Get low-level agents client from project client
PersistentAgentsClient agentsClient = projectClient.GetPersistentAgentsClient();

// Create agent
PersistentAgent agent = await agentsClient.Administration.CreateAgentAsync(
    model: "gpt-4o-mini",
    name: "Math Tutor",
    instructions: "You are a personal math tutor.");

// Create thread and run
PersistentAgentThread thread = await agentsClient.Threads.CreateThreadAsync();
await agentsClient.Messages.CreateMessageAsync(thread.Id, MessageRole.User, "Solve 3x + 11 = 14");
ThreadRun run = await agentsClient.Runs.CreateRunAsync(thread.Id, agent.Id);

// Poll for completion
do
{
    await Task.Delay(500);
    run = await agentsClient.Runs.GetRunAsync(thread.Id, run.Id);
}
while (run.Status == RunStatus.Queued || run.Status == RunStatus.InProgress);

// Get messages
await foreach (var msg in agentsClient.Messages.GetMessagesAsync(thread.Id))
{
    foreach (var content in msg.ContentItems)
    {
        if (content is MessageTextContent textContent)
            Console.WriteLine(textContent.Text);
    }
}

// Cleanup
await agentsClient.Threads.DeleteThreadAsync(thread.Id);
await agentsClient.Administration.DeleteAgentAsync(agent.Id);

2. Versioned Agents with Tools (Preview)

using Azure.AI.Projects.OpenAI;

// Create agent with web search tool
PromptAgentDefinition agentDefinition = new(model: "gpt-4o-mini")
{
    Instructions = "You are a helpful assistant that can search the web",
    Tools = {
        ResponseTool.CreateWebSearchTool(
            userLocation: WebSearchToolLocation.CreateApproximateLocation(
                country: "US",
                city: "Seattle",
                region: "Washington"
            )
        ),
    }
};

AgentVersion agentVersion = await projectClient.Agents.CreateAgentVersionAsync(
    agentName: "myAgent",
    options: new(agentDefinition));

// Get response client
ProjectResponsesClient responseClient = projectClient.OpenAI.GetProjectResponsesClientForAgent(agentVersion.Name);

// Create response
ResponseResult response = responseClient.CreateResponse("What's the weather in Seattle?");
Console.WriteLine(response.GetOutputText());

// Cleanup
projectClient.Agents.DeleteAgentVersion(agentName: agentVersion.Name, agentVersion: agentVersion.Version);

3. Connections

// List all connections
foreach (AIProjectConnection connection in projectClient.Connections.GetConnections())
{
    Console.WriteLine($"{connection.Name}: {connection.ConnectionType}");
}

// Get specific connection
AIProjectConnection conn = projectClient.Connections.GetConnection(
    connectionName, 
    includeCredentials: true);

// Get default connection
AIProjectConnection defaultConn = projectClient.Connections.GetDefaultConnection(
    includeCredentials: false);

4. Deployments

// List all deployments
foreach (AIProjectDeployment deployment in projectClient.Deployments.GetDeployments())
{
    Console.WriteLine($"{deployment.Name}: {deployment.ModelName}");
}

// Filter by publisher
foreach (var deployment in projectClient.Deployments.GetDeployments(modelPublisher: "Microsoft"))
{
    Console.WriteLine(deployment.Name);
}

// Get specific deployment
ModelDeployment details = (ModelDeployment)projectClient.Deployments.GetDeployment("gpt-4o-mini");

5. Datasets

// Upload single file
FileDataset fileDataset = projectClient.Datasets.UploadFile(
    name: "my-dataset",
    version: "1.0",
    filePath: "data/training.txt",
    connectionName: connectionName);

// Upload folder
FolderDataset folderDataset = projectClient.Datasets.UploadFolder(
    name: "my-dataset",
    version: "2.0",
    folderPath: "data/training",
    connectionName: connectionName,
    filePattern: new Regex(".*\\.txt"));

// Get dataset
AIProjectDataset dataset = projectClient.Datasets.GetDataset("my-dataset", "1.0");

// Delete dataset
projectClient.Datasets.Delete("my-dataset", "1.0");

6. Indexes

// Create Azure AI Search index
AzureAISearchIndex searchIndex = new(aiSearchConnectionName, aiSearchIndexName)
{
    Description = "Sample Index"
};

searchIndex = (AzureAISearchIndex)projectClient.Indexes.CreateOrUpdate(
    name: "my-index",
    version: "1.0",
    index: searchIndex);

// List indexes
foreach (AIProjectIndex index in projectClient.Indexes.GetIndexes())
{
    Console.WriteLine(index.Name);
}

// Delete index
projectClient.Indexes.Delete(name: "my-index", version: "1.0");

7. Evaluations

// Create evaluation configuration
var evaluatorConfig = new EvaluatorConfiguration(id: EvaluatorIDs.Relevance);
evaluatorConfig.InitParams.Add("deployment_name", BinaryData.FromObjectAsJson("gpt-4o"));

// Create evaluation
Evaluation evaluation = new Evaluation(
    data: new InputDataset("<dataset_id>"),
    evaluators: new Dictionary<string, EvaluatorConfiguration> 
    { 
        { "relevance", evaluatorConfig } 
    }
)
{
    DisplayName = "Sample Evaluation"
};

// Run evaluation
Evaluation result = projectClient.Evaluations.Create(evaluation: evaluation);

// Get evaluation
Evaluation getResult = projectClient.Evaluations.Get(result.Name);

// List evaluations
foreach (var eval in projectClient.Evaluations.GetAll())
{
    Console.WriteLine($"{eval.DisplayName}: {eval.Status}");
}

8. Get Azure OpenAI Chat Client

using Azure.AI.OpenAI;
using OpenAI.Chat;

ClientConnection connection = projectClient.GetConnection(typeof(AzureOpenAIClient).FullName!);

if (!connection.TryGetLocatorAsUri(out Uri uri) || uri is null)
    throw new InvalidOperationException("Invalid URI.");

uri = new Uri($"https://{uri.Host}");

AzureOpenAIClient azureOpenAIClient = new AzureOpenAIClient(uri, new DefaultAzureCredential());
ChatClient chatClient = azureOpenAIClient.GetChatClient("gpt-4o-mini");

ChatCompletion result = chatClient.CompleteChat("List all rainbow colors");
Console.WriteLine(result.Content[0].Text);

Available Agent Tools

ToolClassPurpose
Code InterpreterCodeInterpreterToolDefinitionExecute Python code
File SearchFileSearchToolDefinitionSearch uploaded files
Function CallingFunctionToolDefinitionCall custom functions
Bing GroundingBingGroundingToolDefinitionWeb search via Bing
Azure AI SearchAzureAISearchToolDefinitionSearch Azure AI indexes
OpenAPIOpenApiToolDefinitionCall external APIs
Azure FunctionsAzureFunctionToolDefinitionInvoke Azure Functions
MCPMCPToolDefinitionModel Context Protocol tools

Key Types Reference

TypePurpose
AIProjectClientMain entry point
PersistentAgentsClientLow-level agent operations
PromptAgentDefinitionVersioned agent definition
AgentVersionVersioned agent instance
AIProjectConnectionConnection to Azure resource
AIProjectDeploymentModel deployment info
AIProjectDatasetDataset metadata
AIProjectIndexSearch index metadata
EvaluationEvaluation configuration and results

Best Practices

  1. Use DefaultAzureCredential for production authentication
  2. Use async methods (*Async) for all I/O operations
  3. Poll with appropriate delays (500ms recommended) when waiting for runs
  4. Clean up resources — delete threads, agents, and files when done
  5. Use versioned agents (via Azure.AI.Projects.OpenAI) for production scenarios
  6. Store connection IDs rather than names for tool configurations
  7. Use includeCredentials: true only when credentials are needed
  8. Handle pagination — use AsyncPageable<T> for listing operations

Error Handling

using Azure;

try
{
    var result = await projectClient.Evaluations.CreateAsync(evaluation);
}
catch (RequestFailedException ex)
{
    Console.WriteLine($"Error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}

Related SDKs

SDKPurposeInstall
Azure.AI.ProjectsHigh-level project client (this SDK)dotnet add package Azure.AI.Projects
Azure.AI.Agents.PersistentLow-level agent operationsdotnet add package Azure.AI.Agents.Persistent
Azure.AI.Projects.OpenAIVersioned agents with OpenAIdotnet add package Azure.AI.Projects.OpenAI

Reference Links

ResourceURL
NuGet Packagehttps://www.nuget.org/packages/Azure.AI.Projects
API Referencehttps://learn.microsoft.com/dotnet/api/azure.ai.projects
GitHub Sourcehttps://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Projects
Sampleshttps://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Projects/samples

Skills เพิ่มเติมจาก microsoft

oss-growth
microsoft
บุคลิกภาพนักเติบโตโอเอสเอส
official
winapp-ui-automation
microsoft
ตรวจสอบและโต้ตอบกับ UI ของแอปพลิเคชัน Windows ที่กำลังทำงานจากบรรทัดคำสั่งโดยใช้ UI Automation (UIA) ใช้เมื่อเอเจนต์ AI หรือนักพัฒนาจำเป็นต้องตรวจสอบ UI...
official
accessibility-aria-expert
microsoft
ตรวจจับและแก้ไขปัญหาการเข้าถึงใน React/Fluent UI webviews ใช้เมื่อตรวจสอบโค้ดเพื่อความเข้ากันได้กับโปรแกรมอ่านหน้าจอ แก้ไขป้ายกำกับ ARIA ทำให้มั่นใจว่า…
official
generate-canvas-app
microsoft
[เลิกใช้งานแล้ว — ใช้ canvas-app แทน] สร้างแอป Canvas ของ Power Apps ที่สมบูรณ์
official
django
microsoft
แนวทางปฏิบัติที่ดีที่สุดสำหรับการพัฒนาเว็บ Django รวมถึงโมเดล วิว เทมเพลต และการทดสอบ
official
github-issue-creator
microsoft
แปลงบันทึกดิบ บันทึกข้อผิดพลาด การเขียนตามคำบอก หรือภาพหน้าจอ ให้เป็นรายงานปัญหาที่ชัดเจนในรูปแบบ GitHub-flavored markdown ใช้เมื่อผู้ใช้วางข้อมูลบั๊ก ข้อผิดพลาด...
official
python-package-management
microsoft
ใช้ uv สำหรับการจัดการ dependencies และ poethepoet สำหรับการทำงานอัตโนมัติ
official
runtime-validation
microsoft
การตรวจสอบความถูกต้องขณะรันไทม์สำหรับแอปพลิเคชันที่ถูกย้าย — ครอบคลุมกลยุทธ์การทดสอบ (ระยะการวางแผน) และการดำเนินการทดสอบ (ระยะการตรวจสอบความถูกต้อง): การตรวจสอบการเริ่มต้นระบบ,…
official