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This commit is contained in:
wehub-resource-sync
2026-07-13 13:39:25 +08:00
commit db620d33df
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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFrameworks>net10.0</TargetFrameworks>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<ProjectReference Include="..\..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
</ItemGroup>
</Project>
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// Copyright (c) Microsoft. All rights reserved.
// This sample shows how to configure an agent to produce structured output.
using System.ComponentModel;
using System.Text.Json;
using System.Text.Json.Serialization;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
using SampleApp;
#pragma warning disable CA5399
string endpoint = Environment.GetEnvironmentVariable("FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("FOUNDRY_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("FOUNDRY_MODEL") ?? "gpt-5.4-mini";
// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new DefaultAzureCredential());
AIAgent agent = aiProjectClient.AsAIAgent(new ChatClientAgentOptions
{
Name = "StructuredOutputAssistant",
ChatOptions = new()
{
ModelId = deploymentName,
Instructions = "You are a helpful assistant that extracts structured information about people.",
ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
}
});
// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output.
AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
// Access the structured output via the Result property of the agent response.
Console.WriteLine("Assistant Output:");
Console.WriteLine($"Name: {response.Result.Name}");
Console.WriteLine($"Age: {response.Result.Age}");
Console.WriteLine($"Occupation: {response.Result.Occupation}");
// Invoke the agent with streaming support, then deserialize the assembled response.
IAsyncEnumerable<AgentResponseUpdate> updates = agent.RunStreamingAsync("Please provide information about Jane Doe, who is a 28-year-old data scientist.");
PersonInfo personInfo = JsonSerializer.Deserialize<PersonInfo>((await updates.ToAgentResponseAsync()).Text, JsonSerializerOptions.Web)
?? throw new InvalidOperationException("Failed to deserialize the streamed response into PersonInfo.");
Console.WriteLine("\nStreaming Assistant Output:");
Console.WriteLine($"Name: {personInfo.Name}");
Console.WriteLine($"Age: {personInfo.Age}");
Console.WriteLine($"Occupation: {personInfo.Occupation}");
namespace SampleApp
{
/// <summary>
/// Represents information about a person.
/// </summary>
[Description("Information about a person including their name, age, and occupation")]
public class PersonInfo
{
[JsonPropertyName("name")]
public string? Name { get; set; }
[JsonPropertyName("age")]
public int? Age { get; set; }
[JsonPropertyName("occupation")]
public string? Occupation { get; set; }
}
}
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# Structured Output with the Responses API
This sample demonstrates how to configure an agent to produce structured output using JSON schema.
## What this sample demonstrates
- Using `RunAsync<T>()` to get typed structured output from the agent
- Deserializing streamed responses into structured types
- No server-side agent creation or cleanup required
## Prerequisites
- .NET 10 SDK or later
- Microsoft Foundry service endpoint and deployment configured
- An authenticated Azure identity (for example, sign in with `az login`)
Set the following environment variables:
```powershell
$env:FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project"
$env:FOUNDRY_MODEL="gpt-5.4-mini"
```
## Run the sample
```powershell
cd dotnet/samples/02-agents/AgentProviders/foundry
dotnet run --project .\Agent_Step05_StructuredOutput
```