{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 快速入门\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "通过AgentChat，您可以使用预设代理快速构建应用程序。\n为了说明这一点，我们将从创建一个能够使用工具的单代理开始。\n\n首先，我们需要安装AgentChat和扩展包。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "vscode": {
          "languageId": "shellscript"
        }
      },
      "outputs": [],
      "source": [
        "pip install -U \"autogen-agentchat\" \"autogen-ext[openai,azure]\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "本示例使用OpenAI模型，但您也可以使用其他模型。\n只需将`model_client`更新为所需的模型或模型客户端类即可。\n\n要使用Azure OpenAI模型和AAD身份验证，\n您可以按照[此处](./tutorial/models.ipynb#azure-openai)的说明操作。\n要使用其他模型，请参阅[模型](./tutorial/models.ipynb)。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "---------- user ----------\n",
            "What is the weather in New York?\n",
            "---------- weather_agent ----------\n",
            "[FunctionCall(id='call_bE5CYAwB7OlOdNAyPjwOkej1', arguments='{\"city\":\"New York\"}', name='get_weather')]\n",
            "---------- weather_agent ----------\n",
            "[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_bE5CYAwB7OlOdNAyPjwOkej1', is_error=False)]\n",
            "---------- weather_agent ----------\n",
            "The current weather in New York is 73 degrees and sunny.\n"
          ]
        }
      ],
      "source": [
        "from autogen_agentchat.agents import AssistantAgent\n",
        "from autogen_agentchat.ui import Console\n",
        "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
        "\n",
        "# 定义一个模型客户端。\n",
        "# 您可以使用其他实现了`ChatCompletionClient`接口的模型客户端。\n",
        "model_client = OpenAIChatCompletionClient(\n",
        "    model=\"gpt-4o\",\n",
        "    # api_key=\"YOUR_API_KEY\",\n",
        ")\n",
        "\n",
        "\n",
        "# 定义一个简单的函数工具供代理使用。本例中，\n",
        "# 我们使用一个模拟天气工具进行演示。\n",
        "async def get_weather(city: str) -> str:\n",
        "    \"\"\"Get the weather for a given city.\"\"\"\n",
        "    return f\"The weather in {city} is 73 degrees and Sunny.\"\n",
        "\n",
        "\n",
        "# 定义一个AssistantAgent，配置模型、工具、\n",
        "# 系统消息并启用反思功能。系统消息通过自然语言指导代理行为。\n",
        "agent = AssistantAgent(\n",
        "    name=\"weather_agent\",\n",
        "    model_client=model_client,\n",
        "    tools=[get_weather],\n",
        "    system_message=\"You are a helpful assistant.\",\n",
        "    reflect_on_tool_use=True,\n",
        "    model_client_stream=True,  # 启用从模型客户端流式传输令牌的功能。\n",
        ")\n",
        "\n",
        "\n",
        "# 运行代理并将消息流式传输到控制台。\n",
        "async def main() -> None:\n",
        "    await Console(agent.run_stream(task=\"What is the weather in New York?\"))\n",
        "    # 关闭与模型客户端的连接。\n",
        "    await model_client.close()\n",
        "\n",
        "\n",
        "# 注意：如果在 Python 脚本中运行此代码，需要使用 asyncio.run(main())。\n",
        "await main()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 后续步骤\n\n现在您已经掌握了单个代理的基本使用方法，建议继续学习[教程](./tutorial/index.md)来了解 AgentChat 的其他功能特性。\n"
      ]
    }
  ],
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      "display_name": ".venv",
      "language": "python",
      "name": "python3"
    },
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.7"
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