{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5478a48b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yaffa\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv(dotenv_path=\"./../.env\")\n",
    "\n",
    "print(os.getenv(\"MY_NAME\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31ed8fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据库连接成功：mysql+pymysql://root:admin@127.0.0.1:3306/analytics_demo\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.utilities import SQLDatabase\n",
    "# 连接数据库\n",
    "db_uri = (\n",
    "    f\"mysql+pymysql://{os.getenv('DB_USER')}:{os.getenv('DB_PASSWORD')}\"\n",
    "    f\"@{os.getenv('DB_HOST')}:{os.getenv('DB_PORT')}/{os.getenv('DB_NAME')}\"\n",
    ")\n",
    "db = SQLDatabase.from_uri(db_uri)\n",
    "\n",
    "print(f\"数据库连接成功：{db_uri}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b91b6741",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "# 初始化大模型\n",
    "llm = ChatOpenAI(\n",
    "    model=\"deepseek-v4-flash\",\n",
    "    api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n",
    "    base_url=\"https://api.deepseek.com\",\n",
    "    temperature=0,  # SQL 生成需要确定性输出\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97272c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
    "# ============================================================\n",
    "# 创建 SQL 工具包\n",
    "# ============================================================\n",
    "# SQLDatabaseToolkit 会自动注册 4 个工具：\n",
    "#   1. sql_db_list_tables    — 列出数据库中所有表\n",
    "#   2. sql_db_schema         — 获取指定表的 DDL 结构\n",
    "#   3. sql_db_query_checker  — 检查 SQL 语法是否正确\n",
    "#   4. sql_db_query          — 执行 SQL 并返回结果\n",
    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
    "tools = toolkit.get_tools()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c581dfca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# ============================================================\n",
    "# 配置 matplotlib 中文显示\n",
    "# ============================================================\n",
    "# Windows 用 SimHei（黑体），macOS 用 PingFang SC\n",
    "# 如果还是乱码，试试安装 fonts-noto-cjk 并清除缓存\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\", \"PingFang SC\", \"DejaVu Sans\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False  # 解决负号显示为方块的问题\n",
    "\n",
    "# ============================================================\n",
    "# 从数据库加载数据到 Pandas DataFrame\n",
    "# ============================================================\n",
    "# 用 SQLAlchemy engine 复用连接，避免重复创建连接池\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "engine = create_engine(db_uri)\n",
    "\n",
    "employees_df = pd.read_sql(\"SELECT * FROM employees\", engine)\n",
    "products_df  = pd.read_sql(\"SELECT * FROM products\", engine)\n",
    "orders_df    = pd.read_sql(\"SELECT * FROM orders\", engine)\n",
    "\n",
    "import traceback\n",
    "from io import StringIO\n",
    "from contextlib import redirect_stdout\n",
    "from langchain.tools import tool\n",
    "\n",
    "# ============================================================\n",
    "# 定义沙箱的\"白名单\"——只有这些库和数据可以被代码访问\n",
    "# ============================================================\n",
    "# 这是一种简单的安全策略：不在白名单里的东西，代码碰不到\n",
    "SANDBOX_GLOBALS = {\n",
    "    # 数据：Agent 可以分析这三张表\n",
    "    \"employees_df\": employees_df,\n",
    "    \"products_df\":  products_df,\n",
    "    \"orders_df\":    orders_df,\n",
    "    # 工具库：Agent 可以用这些库做分析和画图\n",
    "    \"pd\":  pd,       # pandas — 数据处理\n",
    "    \"plt\": plt,      # matplotlib — 基础绑图\n",
    "    \"sns\": sns,      # seaborn — 统计可视化\n",
    "    \"np\":  np,       # numpy — 数值计算\n",
    "}\n",
    "\n",
    "\n",
    "@tool\n",
    "def execute_python_code(code: str) -> str:\n",
    "    \"\"\"\n",
    "    执行 Python 代码进行数据分析和可视化。\n",
    "\n",
    "    可用变量：\n",
    "      - employees_df: 员工表 DataFrame（字段：id, name, department, salary, hire_date）\n",
    "      - products_df:  产品表 DataFrame（字段：id, product_name, category, price, stock）\n",
    "      - orders_df:    订单表 DataFrame（字段：id, employee_id, product_id, quantity, order_date）\n",
    "      - pd:   pandas 库\n",
    "      - plt:  matplotlib.pyplot\n",
    "      - sns:  seaborn\n",
    "      - np:   numpy\n",
    "\n",
    "    使用示例：\n",
    "      # 统计各部门平均薪资\n",
    "      result = employees_df.groupby('department')['salary'].mean()\n",
    "      print(result)\n",
    "\n",
    "      # 画柱状图\n",
    "      plt.figure(figsize=(10, 6))\n",
    "      employees_df.groupby('department')['salary'].mean().plot(kind='bar')\n",
    "      plt.title('各部门平均薪资')\n",
    "      plt.show()\n",
    "    \"\"\"\n",
    "    # 准备隔离的执行环境\n",
    "    # globals_dict 提供白名单变量，locals_dict 收集执行过程中产生的新变量\n",
    "    exec_globals = dict(SANDBOX_GLOBALS)\n",
    "    exec_locals = {}\n",
    "\n",
    "    # 用 StringIO 捕获 print() 的输出\n",
    "    # 这样 Agent 生成的代码里所有的 print 语句都会被收集\n",
    "    output_buffer = StringIO()\n",
    "\n",
    "    try:\n",
    "        # redirect_stdout 会把标准输出重定向到我们的 buffer\n",
    "        with redirect_stdout(output_buffer):\n",
    "            exec(code, exec_globals, exec_locals)\n",
    "\n",
    "        result = output_buffer.getvalue()\n",
    "\n",
    "        # 如果代码没有 print 任何东西，给个默认提示\n",
    "        if not result.strip():\n",
    "            result = \"✅ 代码执行成功（无文本输出，可能已生成图表）\"\n",
    "\n",
    "        return f\"执行成功：\\n{result}\"\n",
    "\n",
    "    except Exception as e:\n",
    "        # 出错时返回完整的错误堆栈，方便 Agent 自我修正\n",
    "        error_detail = traceback.format_exc()\n",
    "        return f\"❌ 执行出错：{e}\\n\\n{error_detail}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ccb237a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MY Agent 创建完成，可以开始提问了！\n"
     ]
    }
   ],
   "source": [
    "from langchain.agents import create_agent\n",
    "ONE_AGENT_PROMPT = \"\"\"你是一名资深数据分析师，精通 Python、Pandas 和 Matplotlib 数据可视化。\n",
    "## 可用数据\n",
    "数据表：\n",
    "- employees\n",
    "- products\n",
    "- orders\n",
    "\n",
    "## 工具\n",
    "- sql_db_list_tables: 列出数据库中所有表\n",
    "- sql_db_schema: 获取指定表的 DDL 结构\n",
    "- sql_db_query_checker: 检查 SQL 语法是否正确\n",
    "- sql_db_query: 执行 SQL 并返回结果\n",
    "- pd：Pandas 数据库\n",
    "- plt：Matplotlib 图表\n",
    "- sns：Seaborn 图表\n",
    "- np：NumPy 数组\n",
    "- execute_python_code：执行 Python 代码的工具\n",
    "\n",
    "## 工作流程\n",
    "1. 理解用户的分析需求\n",
    "2. 自行决定使用哪些工具\n",
    "3. 先做数据探索（head、describe、info），再做深入分析\n",
    "4. 用中文解释分析结果，给出业务洞察\n",
    "\n",
    "## 代码规范\n",
    "- 绑图前设置中文字体：plt.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'DejaVu Sans']\n",
    "- 设置 plt.rcParams['axes.unicode_minus'] = False\n",
    "- 图表尺寸统一用 plt.figure(figsize=(10, 6))\n",
    "- 必须添加标题、坐标轴标签，让图表自解释\n",
    "- 用 print() 输出关键统计量，不要只画图不说话\n",
    "- 图表标题用英文（避免渲染问题），但用中文向用户解释结果\n",
    "\n",
    "## 注意事项\n",
    "- 每次只执行一段完整的代码，不要拆成多段\n",
    "- 先探索数据结构，再做分析——不要上来就画图\n",
    "- 结果要有业务洞察，不只是\"最大值是 XXX\"\n",
    "\"\"\"\n",
    "one_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=[*tools, execute_python_code],\n",
    "    system_prompt=ONE_AGENT_PROMPT,\n",
    ")\n",
    "\n",
    "print(\"\\nMY Agent 创建完成，可以开始提问了！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9f9a687d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：公司一共有 **5 名员工**。\n",
      "\n",
      "从数据来看，员工分布在不同的部门，后续如果需要了解部门分布、薪资情况等，我也可以继续帮您分析！\n"
     ]
    }
   ],
   "source": [
    "response = one_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"公司一共有多少名员工？\"}]\n",
    "})\n",
    "\n",
    "# 最终回答在最后一条消息里\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"回答：{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a4c081c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：以下是查询结果：\n",
      "\n",
      "---\n",
      "\n",
      "### 📋 技术部员工薪资排名（从高到低）\n",
      "\n",
      "| 排名 | 姓名 | 薪资（元） |\n",
      "|:----:|:----:|:----------:|\n",
      "| 🥇 | **张三** | **20,000.00** |\n",
      "| 🥈 | **王五** | **16,000.00** |\n",
      "\n",
      "---\n",
      "\n",
      "### ✅ 查询结果说明\n",
      "\n",
      "技术部共有 **2 名员工**，按薪资从高到低排序如下：\n",
      "\n",
      "1. **张三** — 薪资 **20,000 元**\n",
      "2. **王五** — 薪资 **16,000 元**\n",
      "\n",
      "目前技术部的人员规模较小，但平均薪资达到了 **18,000 元**，其中张三的薪资最高，达到 **2 万元**。如果需要进一步分析技术部与其他部门的薪资对比或人员结构，请随时告诉我！\n"
     ]
    }
   ],
   "source": [
    "response = one_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"列出技术部所有员工的姓名和薪资，按薪资从高到低排序\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"回答：{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a716b1be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<string>:104: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 26415 (\\N{CJK UNIFIED IDEOGRAPH-672F}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 37096 (\\N{CJK UNIFIED IDEOGRAPH-90E8}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 38144 (\\N{CJK UNIFIED IDEOGRAPH-9500}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 21806 (\\N{CJK UNIFIED IDEOGRAPH-552E}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 20154 (\\N{CJK UNIFIED IDEOGRAPH-4EBA}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 21147 (\\N{CJK UNIFIED IDEOGRAPH-529B}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from font(s) DejaVu Sans.\n",
      "<string>:104: UserWarning: Glyph 28304 (\\N{CJK UNIFIED IDEOGRAPH-6E90}) missing from font(s) DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20803 (\\N{CJK UNIFIED IDEOGRAPH-5143}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 25216 (\\N{CJK UNIFIED IDEOGRAPH-6280}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 26415 (\\N{CJK UNIFIED IDEOGRAPH-672F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 37096 (\\N{CJK UNIFIED IDEOGRAPH-90E8}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 38144 (\\N{CJK UNIFIED IDEOGRAPH-9500}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21806 (\\N{CJK UNIFIED IDEOGRAPH-552E}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 20154 (\\N{CJK UNIFIED IDEOGRAPH-4EBA}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 21147 (\\N{CJK UNIFIED IDEOGRAPH-529B}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 36164 (\\N{CJK UNIFIED IDEOGRAPH-8D44}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/widya/Documents/IT/projects-python/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 28304 (\\N{CJK UNIFIED IDEOGRAPH-6E90}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "---\n",
      "\n",
      "## 📊 员工薪资分布分析报告\n",
      "\n",
      "### 一、全公司薪资概况\n",
      "\n",
      "| 统计指标 | 数值 | 说明 |\n",
      "|---------|:----:|:----|\n",
      "| **员工总数** | **5 人** | 当前全公司在编人数 |\n",
      "| **均值** | **13,800 元** | 整体薪资平均水平 |\n",
      "| **中位数** | **16,000 元** | 中间位置员工的薪资水平 |\n",
      "| **最高薪资** | **20,000 元** | 张三（技术部） |\n",
      "| **最低薪资** | **5,000 元** | 赵六（人力资源） |\n",
      "| **标准差** | **5,890.67 元** | 薪资差异较大 |\n",
      "| **极差** | **15,000 元** | 最高与最低相差 3 倍 |\n",
      "\n",
      "### 二、关键洞察\n",
      "\n",
      "#### 🔍 1. 薪资分布呈**左偏**形态（偏度 = -0.86）\n",
      "- 均值（13,800） **低于** 中位数（16,000），说明大部分员工薪资集中在较高区间，但被**低薪极端值（赵六，5,000元）**拉低了整体平均水平。\n",
      "- 这意味着：**超过半数的员工薪资 ≥ 16,000元**，整体薪资水平其实不错。\n",
      "\n",
      "#### 🔍 2. 薪资两极分化明显（极差 15,000 元）\n",
      "- 最高薪资（技术部 20,000 元）是**最低薪资（人力资源 5,000 元）的 4 倍**。\n",
      "- 四分位距（IQR）为 6,000 元，中间 50% 的员工薪资集中在 **11,000 ~ 17,000 元**之间。\n",
      "\n",
      "#### 🔍 3. 部门间薪资差异显著\n",
      "\n",
      "| 部门 | 平均薪资 | 员工数 | 特点 |\n",
      "|:---:|:-------:|:-----:|:----|\n",
      "| 🥇 **技术部** | **18,000 元** | 2人 | 薪资最高，两位员工薪资均较高（16k ~ 20k） |\n",
      "| 🥈 **销售部** | **14,000 元** | 2人 | 薪资中等，内部差异也不小（11k ~ 17k） |\n",
      "| 🥉 **人力资源** | **5,000 元** | 1人 | 薪资最低，明显低于其他部门 |\n",
      "\n",
      "### 三、业务建议\n",
      "\n",
      "1. **关注人力资源岗位的薪资合理性**：该岗位薪资（5,000元）远低于公司平均水平，可能存在**人才流失风险**，建议对标市场行情适当调整。\n",
      "2. **技术部薪资具有竞争力**：技术部平均薪资 18,000 元，有利于吸引和留住技术人才，但也要注意与销售部的薪资差距是否合理。\n",
      "3. **考虑建立薪资带宽和晋升机制**：目前薪资离散度较大，建议建立统一的职级薪资体系，让员工有明确的成长路径。\n",
      "\n",
      "---\n",
      "\n",
      "> 📎 以上分析已通过直方图、箱线图和部门对比图进行了可视化展示，图表标题使用英文展示，关键统计量已在图中标注。\n"
     ]
    }
   ],
   "source": [
    "response = one_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"分析一下员工薪资的分布情况，包括均值、中位数、最大最小值等统计量\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n分析结果：\\n{final_msg.content}\")"
   ]
  }
 ],
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