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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFrameworks>net10.0</TargetFrameworks>
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<Nullable>enable</Nullable>
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<ImplicitUsings>enable</ImplicitUsings>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Azure.AI.Projects" />
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<PackageReference Include="Azure.Identity" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.AzureAI\Microsoft.Agents.AI.AzureAI.csproj" />
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</ItemGroup>
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</Project>
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// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to configure an agent to produce structured output.
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using System.ComponentModel;
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using System.Text.Json;
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using System.Text.Json.Serialization;
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using Azure.AI.Projects;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using SampleApp;
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#pragma warning disable CA5399
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string endpoint = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_FOUNDRY_PROJECT_ENDPOINT is not set.");
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string deploymentName = Environment.GetEnvironmentVariable("AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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const string AssistantInstructions = "You are a helpful assistant that extracts structured information about people.";
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const string AssistantName = "StructuredOutputAssistant";
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// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
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AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
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// Create ChatClientAgent directly
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ChatClientAgent agent = await aiProjectClient.CreateAIAgentAsync(
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model: deploymentName,
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new ChatClientAgentOptions()
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{
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Name = AssistantName,
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ChatOptions = new()
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{
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Instructions = AssistantInstructions,
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ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
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}
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});
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// Set PersonInfo as the type parameter of RunAsync method to specify the expected structured output from the agent and invoke the agent with some unstructured input.
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AgentResponse<PersonInfo> response = await agent.RunAsync<PersonInfo>("Please provide information about John Smith, who is a 35-year-old software engineer.");
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// Access the structured output via the Result property of the agent response.
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Console.WriteLine("Assistant Output:");
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Console.WriteLine($"Name: {response.Result.Name}");
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Console.WriteLine($"Age: {response.Result.Age}");
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Console.WriteLine($"Occupation: {response.Result.Occupation}");
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// Create the ChatClientAgent with the specified name, instructions, and expected structured output the agent should produce.
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ChatClientAgent agentWithPersonInfo = await aiProjectClient.CreateAIAgentAsync(
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model: deploymentName,
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new ChatClientAgentOptions()
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{
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Name = AssistantName,
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ChatOptions = new()
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{
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Instructions = AssistantInstructions,
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ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>()
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}
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});
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// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
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IAsyncEnumerable<AgentResponseUpdate> updates = agentWithPersonInfo.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
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// Assemble all the parts of the streamed output, since we can only deserialize once we have the full json,
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// then deserialize the response into the PersonInfo class.
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PersonInfo personInfo = (await updates.ToAgentResponseAsync()).Deserialize<PersonInfo>(JsonSerializerOptions.Web);
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Console.WriteLine("Assistant Output:");
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Console.WriteLine($"Name: {personInfo.Name}");
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Console.WriteLine($"Age: {personInfo.Age}");
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Console.WriteLine($"Occupation: {personInfo.Occupation}");
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// Cleanup by agent name removes the agent version created.
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await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
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namespace SampleApp
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{
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/// <summary>
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/// Represents information about a person, including their name, age, and occupation, matched to the JSON schema used in the agent.
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/// </summary>
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[Description("Information about a person including their name, age, and occupation")]
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public class PersonInfo
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{
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[JsonPropertyName("name")]
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public string? Name { get; set; }
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[JsonPropertyName("age")]
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public int? Age { get; set; }
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[JsonPropertyName("occupation")]
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public string? Occupation { get; set; }
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}
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}
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# Structured Output with AI Agents
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This sample demonstrates how to configure AI agents to produce structured output in JSON format using JSON schemas.
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## What this sample demonstrates
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- Configuring agents with JSON schema response formats
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- Using generic RunAsync<T> method for structured output
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- Deserializing structured responses into typed objects
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- Running agents with streaming and structured output
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- Managing agent lifecycle (creation and deletion)
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## Prerequisites
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Before you begin, ensure you have the following prerequisites:
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- .NET 10 SDK or later
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- Azure Foundry service endpoint and deployment configured
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- Azure CLI installed and authenticated (for Azure credential authentication)
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**Note**: This demo uses Azure CLI credentials for authentication. Make sure you're logged in with `az login` and have access to the Azure Foundry resource. For more information, see the [Azure CLI documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
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Set the following environment variables:
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```powershell
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$env:AZURE_FOUNDRY_PROJECT_ENDPOINT="https://your-foundry-service.services.ai.azure.com/api/projects/your-foundry-project" # Replace with your Azure Foundry resource endpoint
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$env:AZURE_FOUNDRY_PROJECT_DEPLOYMENT_NAME="gpt-4o-mini" # Optional, defaults to gpt-4o-mini
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```
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## Run the sample
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Navigate to the FoundryAgents sample directory and run:
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```powershell
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cd dotnet/samples/GettingStarted/FoundryAgents
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dotnet run --project .\FoundryAgents_Step05_StructuredOutput
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```
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## Expected behavior
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The sample will:
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1. Create an agent named "StructuredOutputAssistant" configured to produce JSON output
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2. Run the agent with a prompt to extract person information
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3. Deserialize the JSON response into a PersonInfo object
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4. Display the structured data (Name, Age, Occupation)
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5. Run the agent again with streaming and deserialize the streamed JSON response
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6. Clean up resources by deleting the agent
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