{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 序列化组件\n\nAutoGen 提供了一个 {py:class}`~autogen_core.Component` 配置类，用于定义将组件序列化/反序列化为声明式规范的行为。我们可以分别通过调用 `.dump_component()` 和 `.load_component()` 来实现这一点。这对于调试、可视化甚至与他人分享您的工作非常有用。在本笔记本中，我们将演示如何将多个组件序列化为声明式规范（如 JSON 文件）。\n\n\n```{warning}\n\n仅从可信来源加载组件。\n\n对于序列化的组件，每个组件都实现了其序列化和反序列化的逻辑——即如何生成声明式规范以及如何将其转换回对象。\n\n在某些情况下，创建对象可能包括执行代码（例如序列化的函数）。仅从可信来源加载组件。\n\n```\n\n\n### 终止条件示例\n\n在下面的示例中，我们将在 Python 中定义终止条件（代理团队的一部分），将其导出为字典/JSON，并演示如何从字典/JSON 加载终止条件对象。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Config:  {\"provider\":\"autogen_agentchat.base.OrTerminationCondition\",\"component_type\":\"termination\",\"version\":1,\"component_version\":1,\"description\":null,\"config\":{\"conditions\":[{\"provider\":\"autogen_agentchat.conditions.MaxMessageTermination\",\"component_type\":\"termination\",\"version\":1,\"component_version\":1,\"config\":{\"max_messages\":5}},{\"provider\":\"autogen_agentchat.conditions.StopMessageTermination\",\"component_type\":\"termination\",\"version\":1,\"component_version\":1,\"config\":{}}]}}\n"
          ]
        }
      ],
      "source": [
        "from autogen_agentchat.conditions import MaxMessageTermination, StopMessageTermination\n",
        "\n",
        "max_termination = MaxMessageTermination(5)\n",
        "stop_termination = StopMessageTermination()\n",
        "\n",
        "or_termination = max_termination | stop_termination\n",
        "\n",
        "or_term_config = or_termination.dump_component()\n",
        "print(\"Config: \", or_term_config.model_dump_json())\n",
        "\n",
        "new_or_termination = or_termination.load_component(or_term_config)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 代理示例\n\n在下面的示例中，我们将在 Python 中定义一个代理，将其导出为字典/JSON，并演示如何从字典/JSON 加载代理对象。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "from autogen_agentchat.agents import AssistantAgent, UserProxyAgent\n",
        "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
        "\n",
        "# 创建一个使用 OpenAI GPT-4o 模型的代理。\n",
        "model_client = OpenAIChatCompletionClient(\n",
        "    model=\"gpt-4o\",\n",
        "    # api_key=\"YOUR_API_KEY\",\n",
        ")\n",
        "agent = AssistantAgent(\n",
        "    name=\"assistant\",\n",
        "    model_client=model_client,\n",
        "    handoffs=[\"flights_refunder\", \"user\"],\n",
        "    # tools=[], # 目前不支持序列化工具\n",
        "    system_message=\"Use tools to solve tasks.\",\n",
        ")\n",
        "user_proxy = UserProxyAgent(name=\"user\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\"provider\":\"autogen_agentchat.agents.UserProxyAgent\",\"component_type\":\"agent\",\"version\":1,\"component_version\":1,\"description\":null,\"config\":{\"name\":\"user\",\"description\":\"A human user\"}}\n"
          ]
        }
      ],
      "source": [
        "user_proxy_config = user_proxy.dump_component()  # 转储组件\n",
        "print(user_proxy_config.model_dump_json())\n",
        "up_new = user_proxy.load_component(user_proxy_config)  # 加载组件"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\"provider\":\"autogen_agentchat.agents.AssistantAgent\",\"component_type\":\"agent\",\"version\":1,\"component_version\":1,\"description\":null,\"config\":{\"name\":\"assistant\",\"model_client\":{\"provider\":\"autogen_ext.models.openai.OpenAIChatCompletionClient\",\"component_type\":\"model\",\"version\":1,\"component_version\":1,\"config\":{\"model\":\"gpt-4o\"}},\"handoffs\":[{\"target\":\"flights_refunder\",\"description\":\"Handoff to flights_refunder.\",\"name\":\"transfer_to_flights_refunder\",\"message\":\"Transferred to flights_refunder, adopting the role of flights_refunder immediately.\"},{\"target\":\"user\",\"description\":\"Handoff to user.\",\"name\":\"transfer_to_user\",\"message\":\"Transferred to user, adopting the role of user immediately.\"}],\"model_context\":{\"provider\":\"autogen_core.model_context.UnboundedChatCompletionContext\",\"component_type\":\"chat_completion_context\",\"version\":1,\"component_version\":1,\"config\":{}},\"description\":\"An agent that provides assistance with ability to use tools.\",\"system_message\":\"Use tools to solve tasks.\",\"reflect_on_tool_use\":false,\"tool_call_summary_format\":\"{result}\"}}\n"
          ]
        }
      ],
      "source": [
        "agent_config = agent.dump_component()  # 转储组件\n",
        "print(agent_config.model_dump_json())\n",
        "agent_new = agent.load_component(agent_config)  # 加载组件"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "类似的方法可用于序列化 `MultiModalWebSurfer` 智能体。\n\n```python\nfrom autogen_ext.agents.web_surfer import MultimodalWebSurfer\n\nagent = MultimodalWebSurfer(\n    name=\"web_surfer\",\n    model_client=model_client,\n    headless=False,\n)\n\nweb_surfer_config = agent.dump_component()  # 转储组件\nprint(web_surfer_config.model_dump_json())\n\n```\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 团队示例\n\n在下面的示例中，我们将用Python定义一个团队，将其导出为字典/JSON格式，并演示如何从字典/JSON加载团队对象。\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{\"provider\":\"autogen_agentchat.teams.RoundRobinGroupChat\",\"component_type\":\"team\",\"version\":1,\"component_version\":1,\"description\":null,\"config\":{\"participants\":[{\"provider\":\"autogen_agentchat.agents.AssistantAgent\",\"component_type\":\"agent\",\"version\":1,\"component_version\":1,\"config\":{\"name\":\"assistant\",\"model_client\":{\"provider\":\"autogen_ext.models.openai.OpenAIChatCompletionClient\",\"component_type\":\"model\",\"version\":1,\"component_version\":1,\"config\":{\"model\":\"gpt-4o\"}},\"handoffs\":[{\"target\":\"flights_refunder\",\"description\":\"Handoff to flights_refunder.\",\"name\":\"transfer_to_flights_refunder\",\"message\":\"Transferred to flights_refunder, adopting the role of flights_refunder immediately.\"},{\"target\":\"user\",\"description\":\"Handoff to user.\",\"name\":\"transfer_to_user\",\"message\":\"Transferred to user, adopting the role of user immediately.\"}],\"model_context\":{\"provider\":\"autogen_core.model_context.UnboundedChatCompletionContext\",\"component_type\":\"chat_completion_context\",\"version\":1,\"component_version\":1,\"config\":{}},\"description\":\"An agent that provides assistance with ability to use tools.\",\"system_message\":\"Use tools to solve tasks.\",\"reflect_on_tool_use\":false,\"tool_call_summary_format\":\"{result}\"}}],\"termination_condition\":{\"provider\":\"autogen_agentchat.conditions.MaxMessageTermination\",\"component_type\":\"termination\",\"version\":1,\"component_version\":1,\"config\":{\"max_messages\":2}}}}\n"
          ]
        }
      ],
      "source": [
        "from autogen_agentchat.agents import AssistantAgent, UserProxyAgent\n",
        "from autogen_agentchat.conditions import MaxMessageTermination\n",
        "from autogen_agentchat.teams import RoundRobinGroupChat\n",
        "from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
        "\n",
        "# 创建一个使用OpenAI GPT-4o模型的智能体。\n",
        "model_client = OpenAIChatCompletionClient(\n",
        "    model=\"gpt-4o\",\n",
        "    # api_key=\"YOUR_API_KEY\",\n",
        ")\n",
        "agent = AssistantAgent(\n",
        "    name=\"assistant\",\n",
        "    model_client=model_client,\n",
        "    handoffs=[\"flights_refunder\", \"user\"],\n",
        "    # tools=[], # 序列化工具暂不支持\n",
        "    system_message=\"Use tools to solve tasks.\",\n",
        ")\n",
        "\n",
        "team = RoundRobinGroupChat(participants=[agent], termination_condition=MaxMessageTermination(2))\n",
        "\n",
        "team_config = team.dump_component()  # 转储组件\n",
        "print(team_config.model_dump_json())\n",
        "\n",
        "await model_client.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": ".venv",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}