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75 lines
3.5 KiB
C#
75 lines
3.5 KiB
C#
// Copyright (c) Microsoft. All rights reserved.
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// This sample shows how to configure ChatClientAgent 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.OpenAI;
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using Azure.Identity;
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using Microsoft.Agents.AI;
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using OpenAI.Chat;
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using SampleApp;
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var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
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var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
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// Create chat client to be used by chat client agents.
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// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
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// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
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// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
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ChatClient chatClient = new AzureOpenAIClient(
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new Uri(endpoint),
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new DefaultAzureCredential())
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.GetChatClient(deploymentName);
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// Create the ChatClientAgent with the specified name and instructions.
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ChatClientAgent agent = chatClient.AsAIAgent(name: "HelpfulAssistant", instructions: "You are a helpful assistant.");
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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($"Occupation: {response.Result.Occupation}");
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// Create the ChatClientAgent with the specified name, instructions, and expected structured output the agent should produce.
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ChatClientAgent agentWithPersonInfo = chatClient.AsAIAgent(new ChatClientAgentOptions()
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{
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Name = "HelpfulAssistant",
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ChatOptions = new() { Instructions = "You are a helpful assistant.", ResponseFormat = Microsoft.Extensions.AI.ChatResponseFormat.ForJsonSchema<PersonInfo>() }
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});
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// Invoke the agent with some unstructured input while streaming, to extract the structured information from.
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var updates = agentWithPersonInfo.RunStreamingAsync("Please provide information about John Smith, who is a 35-year-old software engineer.");
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// Assemble all the parts of the streamed output, since we can only deserialize once we have the full json,
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// then deserialize the response into the PersonInfo class.
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PersonInfo personInfo = (await updates.ToAgentResponseAsync()).Deserialize<PersonInfo>(JsonSerializerOptions.Web);
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Console.WriteLine("Assistant Output:");
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Console.WriteLine($"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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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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