{
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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Structured Response\n",
    "\n",
    "`LLMCompletion.completion` accepts a `response_format` parameter that is a pydantic model for parsing and returning structured responses.\n"
   ]
  },
  {
   "cell_type": "code",
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    {
     "name": "stdout",
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     "text": [
      "City: Seattle\n",
      "  Temperature: 11.1 °C\n",
      "  Condition: Sunny\n",
      "City: San Francisco\n",
      "  Temperature: 23.9 °C\n",
      "  Condition: Cloudy\n"
     ]
    }
   ],
   "source": [
    "# Copyright (c) 2024 Microsoft Corporation.\n",
    "# Licensed under the MIT License\n",
    "\n",
    "import os\n",
    "\n",
    "from dotenv import load_dotenv\n",
    "from graphrag_llm.completion import LLMCompletion, create_completion\n",
    "from graphrag_llm.config import AuthMethod, ModelConfig\n",
    "from graphrag_llm.types import LLMCompletionResponse\n",
    "from pydantic import BaseModel, Field\n",
    "\n",
    "load_dotenv()\n",
    "\n",
    "\n",
    "class LocalWeather(BaseModel):\n",
    "    \"\"\"City weather information model.\"\"\"\n",
    "\n",
    "    city: str = Field(description=\"The name of the city\")\n",
    "    temperature: float = Field(description=\"The temperature in Celsius\")\n",
    "    condition: str = Field(description=\"The weather condition description\")\n",
    "\n",
    "\n",
    "class WeatherReports(BaseModel):\n",
    "    \"\"\"Weather information model.\"\"\"\n",
    "\n",
    "    reports: list[LocalWeather] = Field(\n",
    "        description=\"The weather reports for multiple cities\"\n",
    "    )\n",
    "\n",
    "\n",
    "api_key = os.getenv(\"GRAPHRAG_API_KEY\")\n",
    "model_config = ModelConfig(\n",
    "    model_provider=\"azure\",\n",
    "    model=os.getenv(\"GRAPHRAG_MODEL\", \"gpt-4o\"),\n",
    "    azure_deployment_name=os.getenv(\"GRAPHRAG_MODEL\", \"gpt-4o\"),\n",
    "    api_base=os.getenv(\"GRAPHRAG_API_BASE\"),\n",
    "    api_version=os.getenv(\"GRAPHRAG_API_VERSION\", \"2025-04-01-preview\"),\n",
    "    api_key=api_key,\n",
    "    auth_method=AuthMethod.AzureManagedIdentity if not api_key else AuthMethod.ApiKey,\n",
    ")\n",
    "llm_completion: LLMCompletion = create_completion(model_config)\n",
    "\n",
    "response: LLMCompletionResponse[WeatherReports] = llm_completion.completion(\n",
    "    messages=\"It is sunny and 52 degrees fahrenheit in Seattle. It is cloudy and 75 degrees fahrenheit in San Francisco.\",\n",
    "    response_format=WeatherReports,\n",
    ")  # type: ignore\n",
    "\n",
    "local_weather_reports: WeatherReports = response.formatted_response  # type: ignore\n",
    "for report in local_weather_reports.reports:\n",
    "    print(f\"City: {report.city}\")\n",
    "    print(f\"  Temperature: {report.temperature} °C\")\n",
    "    print(f\"  Condition: {report.condition}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6360f512",
   "metadata": {},
   "source": [
    "## Streaming\n",
    "\n",
    "Streaming is not supported when using `response_format`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e08b9ba6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error during streaming completion: response_format is not supported for streaming completions.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    response = llm_completion.completion(\n",
    "        messages=\"It is sunny and 52 degrees fahrenheit in Seattle. It is cloudy and 75 degrees fahrenheit in San Francisco.\",\n",
    "        response_format=WeatherReports,\n",
    "        stream=True,\n",
    "    )\n",
    "except Exception as e:  # noqa: BLE001\n",
    "    print(f\"Error during streaming completion: {e}\")"
   ]
  }
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