{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6945138b",
   "metadata": {},
   "source": [
    "# Templating\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3322e6a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rendered message to parse: \n",
      "It is sunny and 52 degrees fahrenheit in Seattle.\n",
      "\n",
      "It is cloudy and 75 degrees fahrenheit in San Francisco.\n",
      "\n",
      "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 (\n",
    "    AuthMethod,\n",
    "    ModelConfig,\n",
    "    TemplateEngineConfig,\n",
    "    TemplateEngineType,\n",
    "    TemplateManagerType,\n",
    ")\n",
    "from graphrag_llm.templating import create_template_engine\n",
    "from graphrag_llm.types import LLMCompletionResponse\n",
    "from pydantic import BaseModel, Field\n",
    "\n",
    "load_dotenv()\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",
    "\n",
    "template_engine = create_template_engine()\n",
    "\n",
    "# The above default is the same as the following configuration:\n",
    "template_engine = create_template_engine(\n",
    "    TemplateEngineConfig(\n",
    "        type=TemplateEngineType.Jinja,\n",
    "        template_manager=TemplateManagerType.File,\n",
    "        base_dir=\"templates\",\n",
    "        template_extension=\".jinja\",\n",
    "        encoding=\"utf-8\",\n",
    "    )\n",
    ")\n",
    "\n",
    "msg = template_engine.render(\n",
    "    # Name of the template file without extension\n",
    "    template_name=\"weather_listings\",\n",
    "    # Values to fill in the template\n",
    "    context={\n",
    "        \"weather_reports\": [\n",
    "            {\"city\": \"Seattle\", \"temperature_f\": 52, \"condition\": \"sunny\"},\n",
    "            {\"city\": \"San Francisco\", \"temperature_f\": 75, \"condition\": \"cloudy\"},\n",
    "        ]\n",
    "    },\n",
    ")\n",
    "\n",
    "\n",
    "print(f\"The rendered message to parse: {msg}\")\n",
    "\n",
    "\n",
    "# Structured response parsing using pydantic\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",
    "response: LLMCompletionResponse[WeatherReports] = llm_completion.completion(\n",
    "    messages=msg,\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}\")"
   ]
  }
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