{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36449e38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据库 analytics_demo 已创建\n"
     ]
    }
   ],
   "source": [
    "# 老师，作业在最后一个cell\n",
    "\n",
    "\n",
    "# NL2SQL 查询 Agent\n",
    "\n",
    "import mysql.connector\n",
    "\n",
    "conn = mysql.connector.connect(\n",
    "    host=\"127.0.0.1\",\n",
    "    port=3306,\n",
    "    user=\"root\",\n",
    "    password=\"123456\",\n",
    ")\n",
    "cursor = conn.cursor()\n",
    "cursor.execute(\"CREATE DATABASE IF NOT EXISTS analytics_demo\")\n",
    "cursor.close()\n",
    "conn.close()\n",
    "print(\"✅ 数据库 analytics_demo 已创建\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e9d981f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据库初始化完成\n",
      "   - employees 表：5 条员工记录\n",
      "   - products  表：4 条产品记录\n",
      "   - orders    表：5 条订单记录\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import mysql.connector\n",
    "\n",
    "conn = mysql.connector.connect(\n",
    "    host=\"127.0.0.1\",\n",
    "    port=3306,\n",
    "    user=\"root\",\n",
    "    password=\"123456\",\n",
    "    database=\"analytics_demo\",\n",
    ")\n",
    "\n",
    "cursor = conn.cursor()\n",
    "\n",
    "cursor.execute(\"\"\"\n",
    "CREATE TABLE IF NOT EXISTS employees (\n",
    "    id          INT PRIMARY KEY AUTO_INCREMENT,  -- 自增主键\n",
    "    name        VARCHAR(50)  NOT NULL,           -- 姓名\n",
    "    department  VARCHAR(50)  NOT NULL,           -- 所属部门\n",
    "    salary      DECIMAL(10,2) NOT NULL,          -- 月薪（精确到分）\n",
    "    hire_date   DATE                             -- 入职日期\n",
    ") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;\n",
    "\"\"\")\n",
    "\n",
    "cursor.execute(\"\"\"\n",
    "CREATE TABLE IF NOT EXISTS products (\n",
    "    id           INT PRIMARY KEY AUTO_INCREMENT,  -- 自增主键\n",
    "    product_name VARCHAR(100) NOT NULL,           -- 商品名称\n",
    "    category     VARCHAR(50)  NOT NULL,           -- 商品分类\n",
    "    price        DECIMAL(10,2) NOT NULL,          -- 单价\n",
    "    stock        INT DEFAULT 0                    -- 当前库存量\n",
    ") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;\n",
    "\"\"\")\n",
    "\n",
    "cursor.execute(\"\"\"\n",
    "CREATE TABLE IF NOT EXISTS orders (\n",
    "    id           INT PRIMARY KEY AUTO_INCREMENT,  -- 自增主键\n",
    "    employee_id  INT NOT NULL,                    -- 下单员工（外键）\n",
    "    product_id   INT NOT NULL,                    -- 购买商品（外键）\n",
    "    quantity     INT NOT NULL,                    -- 购买数量\n",
    "    order_date   DATE NOT NULL,                   -- 下单日期\n",
    "    FOREIGN KEY (employee_id) REFERENCES employees(id),   -- 关联员工表\n",
    "    FOREIGN KEY (product_id)  REFERENCES products(id)     -- 关联产品表\n",
    ") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;\n",
    "\"\"\")\n",
    "\n",
    "employees_data = [\n",
    "    (1, \"张三\", \"技术部\",   20000.00, \"2023-01-15\"),\n",
    "    (2, \"李四\", \"销售部\",   11000.00, \"2023-02-20\"),\n",
    "    (3, \"王五\", \"技术部\",   16000.00, \"2022-11-10\"),\n",
    "    (4, \"赵六\", \"人力资源\", 5000.00, \"2023-03-01\"),\n",
    "    (5, \"钱七\", \"销售部\",   17000.00, \"2022-12-05\"),\n",
    "]\n",
    "\n",
    "products_data = [\n",
    "    (1, \"笔记本电脑\", \"电子产品\", 6999.00, 500),\n",
    "    (2, \"机械键盘\",   \"电子产品\", 399.00,  1000),\n",
    "    (3, \"办公椅\",     \"办公用品\", 499.00,  300),\n",
    "    (4, \"显示器\",     \"电子产品\", 1200.00, 400),\n",
    "]\n",
    "\n",
    "orders_data = [\n",
    "    (1, 1, 1, 2, \"2024-01-15\"),  # 张三买了 2 台笔记本\n",
    "    (2, 2, 2, 15, \"2024-01-16\"),  # 李四买了 15 个键盘\n",
    "    (3, 3, 1, 10, \"2024-01-17\"),  # 王五买了 10 台笔记本\n",
    "    (4, 5, 3, 6, \"2024-01-18\"),  # 钱七买了 6 把办公椅\n",
    "    (5, 2, 4, 5, \"2024-01-19\"),  # 李四买了 5 台显示器\n",
    "]\n",
    "\n",
    "cursor.executemany(\"INSERT IGNORE INTO employees VALUES (%s,%s,%s,%s,%s)\", employees_data)\n",
    "cursor.executemany(\"INSERT IGNORE INTO products  VALUES (%s,%s,%s,%s,%s)\", products_data)\n",
    "cursor.executemany(\"INSERT IGNORE INTO orders     VALUES (%s,%s,%s,%s,%s)\", orders_data)\n",
    "\n",
    "conn.commit()\n",
    "conn.close()\n",
    "\n",
    "print(\"✅ 数据库初始化完成\")\n",
    "print(\"   - employees 表：5 条员工记录\")\n",
    "print(\"   - products  表：4 条产品记录\")\n",
    "print(\"   - orders    表：5 条订单记录\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0094720d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据库连接成功\n",
      "   可用表：['employees', 'orders', 'products']\n",
      "\n",
      "SQL 工具包已加载（4 个工具）：\n",
      "   - sql_db_query\n",
      "   - sql_db_schema\n",
      "   - sql_db_list_tables\n",
      "   - sql_db_query_checker\n",
      "\n",
      "NL2SQL Agent 创建完成，可以开始提问了！\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.utilities import SQLDatabase\n",
    "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain.agents import create_agent\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()\n",
    "api_key= os.getenv('DS_API_KEY')\n",
    "base_url= os.getenv('DS_BASE_URL')\n",
    "\n",
    "mysql_url = \"mysql+pymysql://root:123456@localhost:3306/analytics_demo\"\n",
    "db = SQLDatabase.from_uri(mysql_url) #连接数据库\n",
    "print(f\"数据库连接成功\")\n",
    "print(f\"   可用表：{db.get_usable_table_names()}\")\n",
    "\n",
    "# 初始化大模型\n",
    "llm = ChatOpenAI(\n",
    "    model_name=\"deepseek-v4-pro\",              # DeepSeek 的对话模型\n",
    "    api_key=api_key,                    # 在 platform.deepseek.com 获取\n",
    "    base_url=base_url,                  # DeepSeek API 地址\n",
    "    temperature=0\n",
    ")\n",
    "\n",
    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
    "tools = toolkit.get_tools()\n",
    "print(f\"\\nSQL 工具包已加载（{len(tools)} 个工具）：\")\n",
    "for t in tools:\n",
    "    print(f\"   - {t.name}\")\n",
    "\n",
    "SQL_AGENT_PROMPT = \"\"\"你是一名专业的 SQL 数据分析师。\n",
    "\n",
    "## 工作流程\n",
    "1. 先用 sql_db_list_tables 查看数据库中有哪些表\n",
    "2. 用 sql_db_schema 获取相关表的字段结构和类型\n",
    "3. 生成 SQL 之前，用 sql_db_query_checker 检查语法\n",
    "4. 确认无误后，用 sql_db_query 执行查询\n",
    "5. 用中文总结查询结果，给出简洁的业务洞察\n",
    "\n",
    "## 约束\n",
    "- 只使用数据库中实际存在的表和字段，不要凭空编造\n",
    "- 单次查询结果限制在 50 条以内\n",
    "- 如果查询出错，分析错误原因后重新生成 SQL\n",
    "- 回答要简洁专业，不要啰嗦\n",
    "\"\"\"\n",
    "\n",
    "sql_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=tools,\n",
    "    system_prompt=SQL_AGENT_PROMPT\n",
    ")\n",
    "print(\"\\nNL2SQL Agent 创建完成，可以开始提问了！\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "32a3e658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：查询结果如下：\n",
      "\n",
      "---\n",
      "\n",
      "## 📊 公司员工统计\n",
      "\n",
      "| 部门 | 人数 |\n",
      "|------|------|\n",
      "| 技术部 | 2 人 |\n",
      "| 销售部 | 2 人 |\n",
      "| 人力资源 | 1 人 |\n",
      "\n",
      "> **公司共计 5 名员工**，分布在 3 个部门。其中技术部和销售部各占 2 人，人力资源部有 1 人。整体来看团队规模较小，结构相对精简。\n",
      "回答：## 查询结果\n",
      "\n",
      "技术部共有 **2 名员工**，按薪资从高到低排列：\n",
      "\n",
      "| 排名 | 姓名 | 月薪（元） |\n",
      "|------|------|------------|\n",
      "| 1    | 张三 | 20,000.00  |\n",
      "| 2    | 王五 | 16,000.00  |\n",
      "\n",
      "**业务洞察**：技术部目前仅两人，张三薪资领先王五约 25%（高出 4,000 元），整体薪资水平较高，符合技术岗位的市场定位。\n",
      "回答：## 查询结果\n",
      "\n",
      "| 指标 | 数值 |\n",
      "|------|------|\n",
      "| **订单数量** | 3 单 |\n",
      "| **订单总金额** | ¥14,979.00 |\n",
      "\n",
      "---\n",
      "\n",
      "### 📊 业务洞察\n",
      "\n",
      "销售部员工共产生 **3 笔订单**，累计金额 **¥14,979.00**。目前数据库中仅有一位销售部员工（李四），平均每单约 ¥4,993，客单价处于中等水平。如果销售团队规模扩大，建议关注人均订单量和客单价的增长趋势。\n"
     ]
    }
   ],
   "source": [
    "# 执行\n",
    "response = sql_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"公司一共有多少名员工？分别在哪个部门？\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"回答：{final_msg.content}\")\n",
    "\n",
    "response = sql_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}\")\n",
    "\n",
    "response = sql_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"销售部的员工总共下了多少订单？订单总金额是多少？\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"回答：{final_msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c7370f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据可视化 Agent\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "264b4f91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据加载完成\n",
      "   员工表：5 行 × 5 列\n",
      "   产品表：4 行 × 5 列\n",
      "   订单表：5 行 × 5 列\n",
      "\n",
      "📋 员工表示例：\n",
      " id name department  salary  hire_date\n",
      "  1   张三        技术部 20000.0 2023-01-15\n",
      "  2   李四        销售部 11000.0 2023-02-20\n",
      "  3   王五        技术部 16000.0 2022-11-10\n"
     ]
    }
   ],
   "source": [
    "from sqlalchemy import create_engine\n",
    "\n",
    "engine = create_engine(mysql_url)\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",
    "print(\"✅ 数据加载完成\")\n",
    "print(f\"   员工表：{len(employees_df)} 行 × {len(employees_df.columns)} 列\")\n",
    "print(f\"   产品表：{len(products_df)} 行 × {len(products_df.columns)} 列\")\n",
    "print(f\"   订单表：{len(orders_df)} 行 × {len(orders_df.columns)} 列\")\n",
    "print(f\"\\n📋 员工表示例：\")\n",
    "print(employees_df.head(3).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "db0930e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Python 代码执行沙箱创建成功\n"
     ]
    }
   ],
   "source": [
    "# 构建代码执行沙箱\n",
    "\n",
    "import traceback\n",
    "from io import StringIO\n",
    "from contextlib import redirect_stdout\n",
    "from langchain.tools import tool\n",
    "\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",
    "@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",
    "    exec_globals = dict(SANDBOX_GLOBALS)\n",
    "    exec_locals = {}\n",
    "    output_buffer = StringIO()\n",
    "\n",
    "    try:\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",
    "        error_detail = traceback.format_exc()\n",
    "        return f\"❌ 执行出错：{e}\\n\\n{error_detail}\"\n",
    "    \n",
    "print(\"✅ Python 代码执行沙箱创建成功\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "82985918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据可视化 Agent 创建完成\n"
     ]
    }
   ],
   "source": [
    "# 创建可视化agent\n",
    "\n",
    "from langchain.agents import create_agent\n",
    "VISUALIZATION_PROMPT = \"\"\"你是一名资深数据分析师，精通 Python、Pandas 和 Matplotlib 数据可视化。\n",
    "\n",
    "## 可用数据\n",
    "1. employees_df — 员工表（字段：id, name, department, salary, hire_date）\n",
    "2. products_df  — 产品表（字段：id, product_name, category, price, stock）\n",
    "3. orders_df    — 订单表（字段：id, employee_id, product_id, quantity, order_date）\n",
    "\n",
    "## 工作流程\n",
    "1. 理解用户的分析需求\n",
    "2. 用 execute_python_code 工具编写并执行 Python 代码\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",
    "\n",
    "viz_tools = [execute_python_code]\n",
    "visualization_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=viz_tools,\n",
    "    system_prompt=VISUALIZATION_PROMPT,\n",
    ")\n",
    "print(\"数据可视化 Agent 创建完成\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "70299507",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n",
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "---\n",
      "\n",
      "## 📊 员工薪资分析报告\n",
      "\n",
      "### 一、数据概览\n",
      "\n",
      "当前数据集共 **5 名员工**，分布在 3 个部门（技术部、销售部、人力资源）。\n",
      "\n",
      "---\n",
      "\n",
      "### 二、核心统计量\n",
      "\n",
      "| 指标 | 数值 |\n",
      "|------|------|\n",
      "| **均值** | ¥13,800 |\n",
      "| **中位数** | ¥16,000 |\n",
      "| **最小值** | ¥5,000（赵六，人力资源） |\n",
      "| **最大值** | ¥20,000（张三，技术部） |\n",
      "| **标准差** | ¥5,891 |\n",
      "| **Q1 / Q3** | ¥11,000 / ¥17,000 |\n",
      "| **偏度** | -0.86 |\n",
      "\n",
      "---\n",
      "\n",
      "### 三、关键洞察\n",
      "\n",
      "#### 1️⃣ 薪资分布呈「左偏」形态\n",
      "\n",
      "中位数（¥16,000）**高于**均值（¥13,800），偏度为 **-0.86**。这说明少数低薪员工（赵六 ¥5,000）大幅拉低了整体均值，而大多数员工集中在较高薪资区间。从业务角度看，**人力资源部门的赵六薪资明显偏低**，是拉低整体均值的关键因素。\n",
      "\n",
      "#### 2️⃣ 部门间薪资差距悬殊\n",
      "\n",
      "| 部门 | 人数 | 均值 | 中位数 |\n",
      "|------|------|------|--------|\n",
      "| 技术部 | 2 | ¥18,000 | ¥18,000 |\n",
      "| 销售部 | 2 | ¥14,000 | ¥14,000 |\n",
      "| 人力资源 | 1 | ¥5,000 | ¥5,000 |\n",
      "\n",
      "- **技术部**薪资最高，平均 ¥18,000，且两人薪资接近（¥16K / ¥20K），内部公平性较好。\n",
      "- **销售部**居中，平均 ¥14,000，两人差距 ¥6,000，可能存在绩效差异。\n",
      "- **人力资源部**仅 1 人，薪资 ¥5,000，与技术部最高差 **3 倍**，部门间差距较大。\n",
      "\n",
      "#### 3️⃣ 离散程度分析\n",
      "\n",
      "标准差 ¥5,891，变异系数（CV）= 42.7%。这个值偏高，说明员工之间薪资**分化较明显**——并非均匀分布，而是存在明显的层级差距。\n",
      "\n",
      "#### 4️⃣ 没有极端异常值\n",
      "\n",
      "IQR = ¥6,000，箱线图显示无离群点，所有数据都在合理范围内。最高 ¥20,000 和最低 ¥5,000 虽然差距大，但在统计上不算异常。\n",
      "\n",
      "---\n",
      "\n",
      "### 四、建议\n",
      "\n",
      "> - **关注人力资源岗位**：¥5,000 的薪资是否与市场水平匹配？如果该岗位职责不小，可能需要调薪以提升留存率。\n",
      "> - **建立薪资带宽**：当前 5 人薪资离散度较大（CV 42.7%），建议按岗位级别建立薪资带宽，避免新老员工倒挂。\n",
      "> - **样本量有限**：目前仅 5 条数据，统计结论的参考意义有限，建议后续扩充数据后再做更稳健的分析。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n",
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "---\n",
      "\n",
      "## 📊 分析结果总结\n",
      "\n",
      "### 数据概览\n",
      "\n",
      "公司共有 **5 名员工**，分布在 **3 个部门**。数据量虽小，但部门间薪资差异已经非常明显。\n",
      "\n",
      "### 各部门平均薪资（降序）\n",
      "\n",
      "| 部门 | 平均薪资 | 人数 |\n",
      "|------|---------|------|\n",
      "| 🔵 技术部 | ¥18,000 | 2人 |\n",
      "| 🟢 销售部 | ¥14,000 | 2人 |\n",
      "| 🔴 人力资源 | ¥5,000 | 1人 |\n",
      "\n",
      "- **公司整体均值**：¥13,800（图中红色虚线）\n",
      "- **极差**：¥13,000（最高与最低相差近 3.6 倍）\n",
      "\n",
      "### 💡 业务洞察\n",
      "\n",
      "1. **技术部薪资领跑**：以 ¥18,000 位居榜首，高出公司均值 **30%**，符合技术岗位市场溢价规律。\n",
      "2. **人力资源部明显偏低**：仅 ¥5,000，不足公司均值的一半，且仅 1 人，可能存在岗位层级偏低或数据样本不足的问题，建议进一步核实该岗位的职级定位。\n",
      "3. **销售部处于中等水平**：¥14,000 与公司均值基本持平，薪酬结构合理。\n",
      "4. **样本量提醒**：当前仅 5 条数据，统计结论仅供参考。随着团队扩张，部门间的对比会更具有参考价值。\n",
      "\n",
      "> 柱状图中红色虚线代表公司整体平均薪资线，高于该线的部门属于「高薪部门」，低于该线的则有薪酬优化空间。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "## 📊 分析结果\n",
      "\n",
      "### 数据概览\n",
      "\n",
      "产品表共 **4 条记录**，涵盖 **2 个产品类别**：\n",
      "\n",
      "| 类别 | 产品数 | 库存总量 |\n",
      "|------|--------|----------|\n",
      "| 电子产品 | 3（笔记本电脑、机械键盘、显示器） | **1,900** 件 |\n",
      "| 办公用品 | 1（办公椅） | **300** 件 |\n",
      "\n",
      "---\n",
      "\n",
      "### 🔍 业务洞察\n",
      "\n",
      "1. **库存严重向电子产品倾斜**：电子产品库存总量（1,900 件）是办公用品（300 件）的 **6.3 倍**。这一方面是因为电子产品有 3 款 SKU，办公用品仅有 1 款，但也反映出公司在品类布局上高度聚焦 3C 数码。\n",
      "\n",
      "2. **办公用品种类过于单薄**：仅有 1 款产品（办公椅），且库存只有 300 件。如果这是主营品类之一，建议扩充 SKU 丰富度，否则该品类对整体业绩贡献将非常有限。\n",
      "\n",
      "3. **单品库存健康度需关注**：机械键盘库存 1,000 件，远超其他单品。需结合订单流转速度判断是否存在滞销风险，避免资金被库存占用。\n",
      "\n",
      "4. **数据量偏小**：当前产品表仅有 4 条记录，以上分析仅供参考。若这是真实业务场景的完整数据，建议尽快扩充产品线；若只是示例数据，分析框架已就绪，接入完整数据后可直接复用。\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # 抑制 matplotlib 的字体警告\n",
    "\n",
    "response = visualization_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}\")\n",
    "\n",
    "response = visualization_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"画一个柱状图，展示各部门的平均薪资对比\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n分析结果：\\n{final_msg.content}\")\n",
    "\n",
    "response = visualization_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"计算每个产品类别的库存总量，用水平条形图展示，并标注具体数值\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n分析结果：\\n{final_msg.content}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "94d45a94",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Failed to find font weight bold, now using 400.\n",
      "findfont: Failed to find font weight bold, now using 400.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "从分析结果来看，公司各部门薪资情况如下：\n",
      "\n",
      "---\n",
      "\n",
      "### 📊 各部门平均薪资\n",
      "\n",
      "| 排名 | 部门 | 平均薪资 |\n",
      "|:---:|------|--------:|\n",
      "| 🥇 | **技术部** | ¥18,000 |\n",
      "| 🥈 | **销售部** | ¥14,000 |\n",
      "| 🥉 | **人力资源** | ¥5,000 |\n",
      "\n",
      "- 公司整体平均薪资为 **¥13,800**\n",
      "\n",
      "---\n",
      "\n",
      "### 💡 业务洞察\n",
      "\n",
      "1. **技术部遥遥领先**：平均薪资 ¥18,000，是人力资源部的 **3.6 倍**，比公司整体水平高出 **30%+**，反映了公司对技术人才的重视和市场竞争程度。\n",
      "\n",
      "2. **人力资源部薪资偏低**：仅 ¥5,000，远低于整体平均线（差距 ¥8,800），可能需要评估是否存在人才流失风险。\n",
      "\n",
      "3. **销售部居中**：¥14,000 接近整体均值，属于合理区间。如果销售团队业绩贡献大，可考虑适当提高激励。\n",
      "\n",
      "柱状图中红色虚线为整体平均薪资 ¥13,800，可以直观看到仅技术部高于均值，销售部和人力资源部均低于均值，薪资结构存在一定两极分化。\n"
     ]
    }
   ],
   "source": [
    "# 把 SQL 工具和 Python 代码执行工具都挂到同一个 Agent 上，让 LLM 自己决定调用哪些工具\n",
    "\n",
    "all_tools = tools + [execute_python_code]\n",
    "\n",
    "UNIFIED_PROMPT = \"\"\"你是一名全能数据分析师，既精通 SQL 查询，也精通 Python 数据分析与可视化。\n",
    "\n",
    "  ## 你的工具\n",
    "  - sql_db_list_tables / sql_db_schema：侦察数据库结构\n",
    "  - sql_db_query_checker：检查 SQL 语法\n",
    "  - sql_db_query：执行 SQL 查询，返回结构化数据\n",
    "  - execute_python_code：执行 Python 代码，可访问 employees_df、products_df、orders_df 三张表，以及 pd、plt、sns、np\n",
    "\n",
    "  ## 决策规则\n",
    "  1. 需要从数据库查原始数据 → 用 SQL 工具\n",
    "  2. 需要统计分析、画图表 → 用 execute_python_code\n",
    "  3. 需要先查数据再画图 → 先用 SQL 工具，再用 execute_python_code\n",
    "  4. 数据已经在 DataFrame 里（如统计各部门人数）→ 直接用 execute_python_code，不必重复查 SQL\n",
    "\n",
    "  ## 注意事项\n",
    "  - 查询结果限制在 50 条以内\n",
    "  - 画图前设置中文字体\n",
    "  - 用中文给出业务洞察\n",
    "  \"\"\"\n",
    "\n",
    "unified_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=all_tools,\n",
    "    system_prompt=UNIFIED_PROMPT\n",
    ")\n",
    "\n",
    "response = unified_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"查询每个部门的平均薪资，然后画一个柱状图展示\"}]\n",
    "})\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n分析结果：\\n{final_msg.content}\")"
   ]
  }
 ],
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