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处理工作流中的请求和响应

本教程演示如何使用 Agent Framework 工作流处理工作流中的请求和响应。 你将了解如何创建交互式工作流，以便暂停执行以从外部源（如人类或其他系统）请求输入，然后在提供响应后恢复。

涵盖的概念

在 .NET 中，人机交互工作流使用 RequestPort 和外部请求处理来暂停执行并收集用户输入。 此模式支持交互式工作流，其中系统可以在执行过程中从外部源请求信息。

先决条件

安装 NuGet 包

首先，安装 .NET 项目所需的包：

dotnet add package Microsoft.Agents.AI.Workflows --prerelease

关键组件

RequestPort 和外部请求

充当 RequestPort 工作流和外部输入源之间的桥梁。 当工作流需要输入时，它会生成 RequestInfoEvent 由您的应用程序处理。

// Create a RequestPort for handling human input requests
RequestPort numberRequestPort = RequestPort.Create<NumberSignal, int>("GuessNumber");

信号类型

定义信号类型以传达不同的请求类型：

/// <summary>
/// Signals used for communication between guesses and the JudgeExecutor.
/// </summary>
internal enum NumberSignal
{
    Init,     // Initial guess request
    Above,    // Previous guess was too high
    Below,    // Previous guess was too low
}

工作流执行程序

创建处理用户输入并提供反馈的执行程序：

/// <summary>
/// Executor that judges the guess and provides feedback.
/// </summary>
internal sealed class JudgeExecutor : Executor<int>("Judge")
{
    private readonly int _targetNumber;
    private int _tries;

    public JudgeExecutor(int targetNumber) : this()
    {
        _targetNumber = targetNumber;
    }

    public override async ValueTask HandleAsync(int message, IWorkflowContext context, CancellationToken cancellationToken)
    {
        _tries++;
        if (message == _targetNumber)
        {
            await context.YieldOutputAsync($"{_targetNumber} found in {_tries} tries!", cancellationToken)
                         .ConfigureAwait(false);
        }
        else if (message < _targetNumber)
        {
            await context.SendMessageAsync(NumberSignal.Below, cancellationToken).ConfigureAwait(false);
        }
        else
        {
            await context.SendMessageAsync(NumberSignal.Above, cancellationToken).ConfigureAwait(false);
        }
    }
}

生成工作流

在反馈循环中连接 RequestPort 和执行程序：

internal static class WorkflowHelper
{
    internal static ValueTask<Workflow<NumberSignal>> GetWorkflowAsync()
    {
        // Create the executors
        RequestPort numberRequestPort = RequestPort.Create<NumberSignal, int>("GuessNumber");
        JudgeExecutor judgeExecutor = new(42);

        // Build the workflow by connecting executors in a loop
        return new WorkflowBuilder(numberRequestPort)
            .AddEdge(numberRequestPort, judgeExecutor)
            .AddEdge(judgeExecutor, numberRequestPort)
            .WithOutputFrom(judgeExecutor)
            .BuildAsync<NumberSignal>();
    }
}

执行交互式工作流

在工作流执行期间处理外部请求：

private static async Task Main()
{
    // Create the workflow
    var workflow = await WorkflowHelper.GetWorkflowAsync().ConfigureAwait(false);

    // Execute the workflow
    await using StreamingRun handle = await InProcessExecution.StreamAsync(workflow, NumberSignal.Init).ConfigureAwait(false);
    await foreach (WorkflowEvent evt in handle.WatchStreamAsync().ConfigureAwait(false))
    {
        switch (evt)
        {
            case RequestInfoEvent requestInputEvt:
                // Handle human input request from the workflow
                ExternalResponse response = HandleExternalRequest(requestInputEvt.Request);
                await handle.SendResponseAsync(response).ConfigureAwait(false);
                break;

            case WorkflowOutputEvent outputEvt:
                // The workflow has yielded output
                Console.WriteLine($"Workflow completed with result: {outputEvt.Data}");
                return;
        }
    }
}

请求处理

处理不同类型的输入请求：

private static ExternalResponse HandleExternalRequest(ExternalRequest request)
{
    switch (request.DataAs<NumberSignal?>())
    {
        case NumberSignal.Init:
            int initialGuess = ReadIntegerFromConsole("Please provide your initial guess: ");
            return request.CreateResponse(initialGuess);
        case NumberSignal.Above:
            int lowerGuess = ReadIntegerFromConsole("You previously guessed too large. Please provide a new guess: ");
            return request.CreateResponse(lowerGuess);
        case NumberSignal.Below:
            int higherGuess = ReadIntegerFromConsole("You previously guessed too small. Please provide a new guess: ");
            return request.CreateResponse(higherGuess);
        default:
            throw new ArgumentException("Unexpected request type.");
    }
}

private static int ReadIntegerFromConsole(string prompt)
{
    while (true)
    {
        Console.Write(prompt);
        string? input = Console.ReadLine();
        if (int.TryParse(input, out int value))
        {
            return value;
        }
        Console.WriteLine("Invalid input. Please enter a valid integer.");
    }
}

实现概念

RequestInfoEvent 流程

  1. 工作流执行：工作流进程，直到需要外部输入
  2. 请求生成：RequestPort 生成包含请求详细信息的 RequestInfoEvent。
  3. 外部处理：应用程序捕获事件并收集用户输入
  4. 响应提交：发送 ExternalResponse 返回以继续工作流
  5. 工作流恢复：工作流继续使用提供的输入进行处理

工作流生命周期

  • 流式执行：利用 StreamAsync 实时监视事件
  • 事件处理：RequestInfoEvent用于输入请求和WorkflowOutputEvent用于完成的过程
  • 响应协调：使用工作流的响应处理机制匹配对请求的响应

实施流程

  1. 工作流初始化：工作流首先向 RequestPort 发送一个 NumberSignal.Init 。

  2. 请求生成：RequestPort 生成一个请求以获取用户的初始猜测。

  3. 工作流暂停：当应用程序处理请求时，工作流将暂停并等待外部输入。

  4. 人工响应：外部应用程序收集用户输入并发送 ExternalResponse 回工作流。

  5. 处理和反馈：处理 JudgeExecutor 猜测并完成工作流或发送新信号（上/下方）以请求另一个猜测。

  6. 循环延续：该过程将重复，直到猜出正确的数字。

框架优势

  • 类型安全性：强键入可确保维护请求响应协定
  • 事件驱动：丰富的事件系统提供工作流执行的可见性
  • 暂停执行：工作流在等待外部输入时可以无限期暂停
  • 状态管理：工作流状态在暂停恢复周期之间保留
  • 灵活集成：RequestPorts 可与任何外部输入源（UI、API、控制台等）集成。

完整示例

有关完整实现，请参阅 Human-in-the-Loop Basic 示例。

此模式可生成复杂的交互式应用程序，用户可在自动化工作流中的关键决策点提供输入。

你将构建的内容

你将创建一个交互式数字猜测游戏工作流，用于演示请求-响应模式：

  • 发出智能猜测的 AI 代理
  • 能够直接使用request_info API发送请求的执行程序
  • 一个协调代理与人工交互的轮次管理器，使用 @response_handler
  • 用于实时反馈的交互式控制台输入/输出

先决条件

  • Python 3.10 或更高版本
  • 已配置 Azure OpenAI 部署
  • 已配置 Azure CLI 身份验证（az login）
  • 基本了解 Python 异步编程

关键概念

请求和响应功能

执行程序具有内置的请求和响应功能，可实现人工循环交互：

  • 调用 ctx.request_info(request_data=request_data, response_type=response_type) 以发送请求
  • 使用@response_handler修饰器处理响应
  • 定义没有继承要求的自定义请求/响应类型

请求-响应流程

执行器可以使用ctx.request_info()直接发送请求，并使用@response_handler修饰器处理响应：

  1. 执行器调用 ctx.request_info(request_data=request_data, response_type=response_type)
  2. 工作流发出包含请求数据的RequestInfoEvent
  3. 外部系统（人工、API 等）处理请求
  4. 通过 send_responses_streaming() 发送响应
  5. 工作流将恢复并传送响应给执行程序的@response_handler方法。

设置环境

首先，安装所需的包：

pip install agent-framework-core --pre
pip install azure-identity

定义请求和响应模型

首先定义请求-响应通信的数据结构：

import asyncio
from dataclasses import dataclass
from pydantic import BaseModel

from agent_framework import (
    AgentExecutor,
    AgentExecutorRequest,
    AgentExecutorResponse,
    ChatMessage,
    Executor,
    RequestInfoEvent,
    Role,
    WorkflowBuilder,
    WorkflowContext,
    WorkflowOutputEvent,
    WorkflowRunState,
    WorkflowStatusEvent,
    handler,
    response_handler,
)
from agent_framework.azure import AzureOpenAIChatClient
from azure.identity import AzureCliCredential

@dataclass
class HumanFeedbackRequest:
    """Request message for human feedback in the guessing game."""
    prompt: str = ""
    guess: int | None = None

class GuessOutput(BaseModel):
    """Structured output from the AI agent with response_format enforcement."""
    guess: int

这个 HumanFeedbackRequest 是用于结构化请求负载的简单数据类。

  • 请求有效负载的强类型
  • 向前兼容的验证
  • 明确响应与语义相关的关联
  • 丰富的 UI 提示的背景字段（如先前猜测的字段）

创建轮次管理器

轮次管理器协调 AI 代理和人工之间的流：

class TurnManager(Executor):
    """Coordinates turns between the AI agent and human player.

    Responsibilities:
    - Start the game by requesting the agent's first guess
    - Process agent responses and request human feedback
    - Handle human feedback and continue the game or finish
    """

    def __init__(self, id: str | None = None):
        super().__init__(id=id or "turn_manager")

    @handler
    async def start(self, _: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
        """Start the game by asking the agent for an initial guess."""
        user = ChatMessage(Role.USER, text="Start by making your first guess.")
        await ctx.send_message(AgentExecutorRequest(messages=[user], should_respond=True))

    @handler
    async def on_agent_response(
        self,
        result: AgentExecutorResponse,
        ctx: WorkflowContext,
    ) -> None:
        """Handle the agent's guess and request human guidance."""
        # Parse structured model output (defensive default if agent didn't reply)
        text = result.agent_run_response.text or ""
        last_guess = GuessOutput.model_validate_json(text).guess if text else None

        # Craft a clear human prompt that defines higher/lower relative to agent's guess
        prompt = (
            f"The agent guessed: {last_guess if last_guess is not None else text}. "
            "Type one of: higher (your number is higher than this guess), "
            "lower (your number is lower than this guess), correct, or exit."
        )
        # Send a request using the request_info API
        await ctx.request_info(
            request_data=HumanFeedbackRequest(prompt=prompt, guess=last_guess),
            response_type=str
        )

    @response_handler
    async def on_human_feedback(
        self,
        original_request: HumanFeedbackRequest,
        feedback: str,
        ctx: WorkflowContext[AgentExecutorRequest, str],
    ) -> None:
        """Continue the game or finish based on human feedback."""
        reply = feedback.strip().lower()
        # Use the correlated request's guess to avoid extra state reads
        last_guess = original_request.guess

        if reply == "correct":
            await ctx.yield_output(f"Guessed correctly: {last_guess}")
            return

        # Provide feedback to the agent for the next guess
        user_msg = ChatMessage(
            Role.USER,
            text=f'Feedback: {reply}. Return ONLY a JSON object matching the schema {{"guess": <int 1..10>}}.',
        )
        await ctx.send_message(AgentExecutorRequest(messages=[user_msg], should_respond=True))

生成工作流

创建连接所有组件的主工作流：

async def main() -> None:
    # Create the chat agent with structured output enforcement
    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    agent = chat_client.create_agent(
        instructions=(
            "You guess a number between 1 and 10. "
            "If the user says 'higher' or 'lower', adjust your next guess. "
            'You MUST return ONLY a JSON object exactly matching this schema: {"guess": <integer 1..10>}. '
            "No explanations or additional text."
        ),
        response_format=GuessOutput,
    )

    # Create workflow components
    turn_manager = TurnManager(id="turn_manager")
    agent_exec = AgentExecutor(agent=agent, id="agent")

    # Build the workflow graph
    workflow = (
        WorkflowBuilder()
        .set_start_executor(turn_manager)
        .add_edge(turn_manager, agent_exec)  # Ask agent to make/adjust a guess
        .add_edge(agent_exec, turn_manager)  # Agent's response goes back to coordinator
        .build()
    )

    # Execute the interactive workflow
    await run_interactive_workflow(workflow)

async def run_interactive_workflow(workflow):
    """Run the workflow with human-in-the-loop interaction."""
    pending_responses: dict[str, str] | None = None
    completed = False
    workflow_output: str | None = None

    print("🎯 Number Guessing Game")
    print("Think of a number between 1 and 10, and I'll try to guess it!")
    print("-" * 50)

    while not completed:
        # First iteration uses run_stream("start")
        # Subsequent iterations use send_responses_streaming with pending responses
        stream = (
            workflow.send_responses_streaming(pending_responses)
            if pending_responses
            else workflow.run_stream("start")
        )

        # Collect events for this turn
        events = [event async for event in stream]
        pending_responses = None

        # Process events to collect requests and detect completion
        requests: list[tuple[str, str]] = []  # (request_id, prompt)
        for event in events:
            if isinstance(event, RequestInfoEvent) and isinstance(event.data, HumanFeedbackRequest):
                # RequestInfoEvent for our HumanFeedbackRequest
                requests.append((event.request_id, event.data.prompt))
            elif isinstance(event, WorkflowOutputEvent):
                # Capture workflow output when yielded
                workflow_output = str(event.data)
                completed = True

        # Check workflow status
        pending_status = any(
            isinstance(e, WorkflowStatusEvent) and e.state == WorkflowRunState.IN_PROGRESS_PENDING_REQUESTS
            for e in events
        )
        idle_with_requests = any(
            isinstance(e, WorkflowStatusEvent) and e.state == WorkflowRunState.IDLE_WITH_PENDING_REQUESTS
            for e in events
        )

        if pending_status:
            print("🔄 State: IN_PROGRESS_PENDING_REQUESTS (requests outstanding)")
        if idle_with_requests:
            print("⏸️  State: IDLE_WITH_PENDING_REQUESTS (awaiting human input)")

        # Handle human requests if any
        if requests and not completed:
            responses: dict[str, str] = {}
            for req_id, prompt in requests:
                print(f"\n🤖 {prompt}")
                answer = input("👤 Enter higher/lower/correct/exit: ").lower()

                if answer == "exit":
                    print("👋 Exiting...")
                    return
                responses[req_id] = answer
            pending_responses = responses

    # Show final result
    print(f"\n🎉 {workflow_output}")

运行示例

有关完整的人机交互猜谜游戏示例的实现，请参阅人机交互猜谜游戏示例。

工作原理

  1. 工作流初始化：工作流从请求 AI 代理提供一个初步猜测开始TurnManager。

  2. 代理响应：AI 代理进行猜测并返回结构化的 JSON，JSON 数据将流回TurnManager。

  3. 人工请求：处理TurnManager代理的猜测，并调用ctx.request_info()带有HumanFeedbackRequest。

  4. 工作流暂停：工作流发出一个 RequestInfoEvent 并继续进行，直到无法采取进一步操作，然后等待人工输入。

  5. 人类响应：外部应用程序收集人类输入，并使用send_responses_streaming()发送响应。

  6. 恢复并继续：工作流恢复，TurnManager 的 @response_handler 方法处理人工反馈，并结束游戏或向代理发送另一个请求。

主要优势

  • 结构化通信：类型安全请求和响应模型可防止运行时错误
  • 相关性：请求 ID 确保响应与正确的请求匹配
  • 暂停执行：工作流在等待外部输入时可以无限期暂停
  • 状态保留：工作流状态在暂停恢复周期内保持
  • 事件驱动：丰富的事件系统提供工作流状态和转换的可见性

此模式支持构建复杂的交互式应用程序，其中 AI 代理和人类可在结构化工作流中无缝协作。

后续步骤

了解检查点和恢复工作流

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