{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 使用 GPT-4o 模型生成结构化输出\n\n本指南演示了如何使用 GPT-4o 模型获取结构化输出。OpenAI beta 客户端 SDK 提供了一个解析助手，允许您使用自己的 Pydantic 模型，从而无需定义 JSON 模式。对于受支持的模型，推荐使用此方法。\n\n目前支持此功能的模型有：\n\n- OpenAI 上的 gpt-4o-mini\n- OpenAI 上的 gpt-4o-2024-08-06\n- Azure 上的 gpt-4o-2024-08-06\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "让我们定义一个简单的消息类型，用于携带数学问题的解释和输出\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pydantic import BaseModel\n",
        "\n",
        "\n",
        "class MathReasoning(BaseModel):\n",
        "    class Step(BaseModel):\n",
        "        explanation: str\n",
        "        output: str\n",
        "\n",
        "    steps: list[Step]\n",
        "    final_answer: str"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# 设置环境变量\n",
        "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR_ENDPOINT_DETAILS.openai.azure.com/\"\n",
        "os.environ[\"AZURE_OPENAI_API_KEY\"] = \"YOUR_API_KEY\"\n",
        "os.environ[\"AZURE_OPENAI_DEPLOYMENT_NAME\"] = \"gpt-4o-2024-08-06\"\n",
        "os.environ[\"AZURE_OPENAI_API_VERSION\"] = \"2024-08-01-preview\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import json\n",
        "import os\n",
        "from typing import Optional\n",
        "\n",
        "from autogen_core.models import UserMessage\n",
        "from autogen_ext.models.openai import AzureOpenAIChatCompletionClient\n",
        "\n",
        "\n",
        "# 获取环境变量并确保其不为 None 的函数\n",
        "def get_env_variable(name: str) -> str:\n",
        "    value = os.getenv(name)\n",
        "    if value is None:\n",
        "        raise ValueError(f\"Environment variable {name} is not set\")\n",
        "    return value\n",
        "\n",
        "\n",
        "# 使用经过类型检查的环境变量创建客户端\n",
        "client = AzureOpenAIChatCompletionClient(\n",
        "    azure_deployment=get_env_variable(\"AZURE_OPENAI_DEPLOYMENT_NAME\"),\n",
        "    model=get_env_variable(\"AZURE_OPENAI_MODEL\"),\n",
        "    api_version=get_env_variable(\"AZURE_OPENAI_API_VERSION\"),\n",
        "    azure_endpoint=get_env_variable(\"AZURE_OPENAI_ENDPOINT\"),\n",
        "    api_key=get_env_variable(\"AZURE_OPENAI_API_KEY\"),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'steps': [{'explanation': 'Start by aligning the numbers vertically.', 'output': '\\n  16\\n+ 32'}, {'explanation': 'Add the units digits: 6 + 2 = 8.', 'output': '\\n  16\\n+ 32\\n   8'}, {'explanation': 'Add the tens digits: 1 + 3 = 4.', 'output': '\\n  16\\n+ 32\\n  48'}], 'final_answer': '48'}\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "MathReasoning(steps=[Step(explanation='Start by aligning the numbers vertically.', output='\\n  16\\n+ 32'), Step(explanation='Add the units digits: 6 + 2 = 8.', output='\\n  16\\n+ 32\\n   8'), Step(explanation='Add the tens digits: 1 + 3 = 4.', output='\\n  16\\n+ 32\\n  48')], final_answer='48')"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# 定义用户消息\n",
        "messages = [\n",
        "    UserMessage(content=\"What is 16 + 32?\", source=\"user\"),\n",
        "]\n",
        "\n",
        "# 在客户端调用create方法，传入消息和额外参数。\n",
        "# extra_create_args字典包含我们上面定义的MathReasoning模型作为响应格式。\n",
        "# 提供响应格式和pydantic模型将使用beta SDK中的新parse方法\n",
        "response = await client.create(messages=messages, extra_create_args={\"response_format\": MathReasoning})\n",
        "\n",
        "# 在加载前确保响应内容是有效的JSON字符串\n",
        "response_content: Optional[str] = response.content if isinstance(response.content, str) else None\n",
        "if response_content is None:\n",
        "    raise ValueError(\"Response content is not a valid JSON string\")\n",
        "\n",
        "# 将响应内容作为JSON加载后打印出来\n",
        "print(json.loads(response_content))\n",
        "\n",
        "# 使用MathReasoning模型验证响应内容\n",
        "MathReasoning.model_validate(json.loads(response_content))"
      ]
    }
  ],
  "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": "3.11.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}