{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取环境变量\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()  # 加载 .env 文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用通义千问\n",
    "from langchain_community.llms import Tongyi\n",
    "\n",
    "import os\n",
    "\n",
    "# 初始化通义模型（以qwen-max为例）\n",
    "llm_tongyi = Tongyi(\n",
    "    model_name=\"qwen-turbo\",\n",
    "    dashscope_api_key=os.getenv(\"DASHSCOPE_API_KEY\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造提示，告诉模型用 JSON 输出\n",
    "prompt = \"\"\"\n",
    "请根据以下用户请求，生成一个适合用于搜索的查询，并说明理由。\n",
    "用户请求: “{0}”\n",
    "\n",
    "请按照以下 JSON 格式返回：\n",
    "{{\n",
    "    \"search_query\": \"...\",\n",
    "    \"justification\": \"...\"\n",
    "}}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Invoke the augmented LLM\n",
    "output = llm_tongyi.invoke(prompt.format(\"How does Calcium CT score relate to high cholesterol?\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(output)"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.3"
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