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95 lines
2.7 KiB
Python
95 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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"""
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.. _handoffs:
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Handoffs
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========================================
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Handoffs 是由 OpenAI 提出的工作流模式，通过调用子智能体的方式来完成目标任务。
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在 AgentScope 中通过工具调用的方式实现 handoffs 非常简单。首先，我们创建一个函数来允许协调者动态创建子智能体。
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.. figure:: ../../_static/images/handoffs.png
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:width: 80%
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:align: center
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:alt: 协调者-子智能体工作流
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*Handoffs 示例*
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"""
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import asyncio
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import os
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from agentscope.agent import ReActAgent
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from agentscope.formatter import DashScopeChatFormatter
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from agentscope.memory import InMemoryMemory
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from agentscope.message import Msg
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from agentscope.model import DashScopeChatModel
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from agentscope.tool import (
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ToolResponse,
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Toolkit,
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execute_python_code,
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)
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# 创建子智能体的工具函数
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async def create_worker(
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task_description: str,
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) -> ToolResponse:
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"""创建一个子智能体来完成给定的任务。子智能体配备了 Python 执行工具。
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Args:
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task_description (``str``):
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子智能体要完成的任务描述。
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"""
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# 为子智能体智能体配备一些工具
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toolkit = Toolkit()
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toolkit.register_tool_function(execute_python_code)
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# 创建子智能体智能体
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worker = ReActAgent(
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name="Worker",
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sys_prompt="你是一个智能体。你的目标是完成给定的任务。",
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model=DashScopeChatModel(
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model_name="qwen-max",
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api_key=os.environ["DASHSCOPE_API_KEY"],
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stream=False,
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),
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formatter=DashScopeChatFormatter(),
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toolkit=toolkit,
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)
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# 让子智能体完成任务
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res = await worker(Msg("user", task_description, "user"))
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return ToolResponse(
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content=res.get_content_blocks("text"),
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)
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async def run_handoffs() -> None:
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"""交接工作流示例。"""
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# 初始化协调者智能体
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toolkit = Toolkit()
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toolkit.register_tool_function(create_worker)
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orchestrator = ReActAgent(
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name="Orchestrator",
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sys_prompt="你是一个协调者智能体。你的目标是通过将任务分解为更小的任务并创建子智能体来完成它们，从而完成给定的任务。",
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model=DashScopeChatModel(
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model_name="qwen-max",
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api_key=os.environ["DASHSCOPE_API_KEY"],
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stream=False,
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),
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memory=InMemoryMemory(),
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formatter=DashScopeChatFormatter(),
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toolkit=toolkit,
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)
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# 任务描述
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task_description = "在 Python 中执行 hello world"
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# 创建子智能体来完成任务
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await orchestrator(Msg("user", task_description, "user"))
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asyncio.run(run_handoffs())
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