{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2671054f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\34450\\AppData\\Local\\Temp\\ipykernel_16268\\3464868009.py:2: DeprecationWarning: `langchain-community` is being sunset and is no longer actively maintained. See https://github.com/langchain-ai/langchain-community/issues/674 for details and migration guidance toward standalone integration packages.\n",
      "  from langchain_community.utilities import SQLDatabase\n"
     ]
    },
    {
     "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": [
    "import os\n",
    "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  # langchain 1.3.1 的新 API\n",
    "from dotenv import load_dotenv \n",
    "\n",
    "load_dotenv()  # 默认加载当前目录或父目录中的 .env 文件\n",
    "# ============================================================\n",
    "# 第一步：连接数据库\n",
    "# ============================================================\n",
    "# SQLDatabase.from_uri 接受标准的数据库连接 URI\n",
    "# LangChain 内部会用 SQLAlchemy 管理连接池\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",
    "# 验证连接：打印可用的表名\n",
    "print(f\"数据库连接成功\")\n",
    "print(f\"   可用表：{db.get_usable_table_names()}\")\n",
    "\n",
    "# ============================================================\n",
    "# 第二步：初始化大模型\n",
    "# ============================================================\n",
    "llm = ChatOpenAI(\n",
    "    model=os.getenv(\"DEEPSEEK_MODEL\"),\n",
    "    api_key=os.getenv(\"DEEPSEEK_API_KEY\"),\n",
    "    base_url=os.getenv(\"DEEPSEEK_BASE_URL\"),\n",
    "    temperature=0,  # SQL 生成需要确定性输出\n",
    ")\n",
    "\n",
    "# ============================================================\n",
    "# 第三步：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",
    "\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",
    "# ============================================================\n",
    "# 第四步：定义 System Prompt（Agent 的\"岗位说明书\"）\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",
    "# ============================================================\n",
    "# 第五步：组装 Agent（langchain 1.3.1 一行搞定）\n",
    "# ============================================================\n",
    "# create_agent 返回一个可直接调用的 Runnable，无需再套 AgentExecutor\n",
    "# 它内部自动处理工具调用、循环迭代、错误重试等逻辑\n",
    "sql_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=tools,\n",
    "    system_prompt=SQL_AGENT_PROMPT,\n",
    ")\n",
    "\n",
    "print(\"\\nNL2SQL Agent 创建完成，可以开始提问了！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd1fef44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：公司共有 **5 名员工**。\n"
     ]
    }
   ],
   "source": [
    "# 简单问题：Agent 只需一条 COUNT SQL 就能搞定\n",
    "# create_agent 的 invoke 接口使用 messages 格式\n",
    "response = sql_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": 6,
   "id": "a7025030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：## 查询结果\n",
      "\n",
      "技术部共有 **2 名员工**，按薪资从高到低排序如下：\n",
      "\n",
      "| 姓名 | 薪资 |\n",
      "|------|------|\n",
      "| 张三 | 20,000.00 |\n",
      "| 王五 | 16,000.00 |\n",
      "\n",
      "**业务洞察：**\n",
      "- 技术部员工平均薪资为 **18,000 元**。\n",
      "- 张三薪资最高（20,000元），比第二名王五高出 **25%**。\n"
     ]
    }
   ],
   "source": [
    "# 中等难度：需要 JOIN + WHERE + GROUP BY\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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cb41a941",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回答：查询结果如下：\n",
      "\n",
      "---\n",
      "\n",
      "### 📊 销售部订单统计\n",
      "\n",
      "| 指标 | 数据 |\n",
      "|------|------|\n",
      "| **订单总数** | **3 笔** |\n",
      "| **订单总金额** | **¥14,979.00** |\n",
      "\n",
      "### 👥 员工明细\n",
      "\n",
      "| 员工 | 订单数 | 金额 |\n",
      "|-----|:-----:|:----:|\n",
      "| **李四** | 2 笔 | ¥11,985.00 |\n",
      "| **钱七** | 1 笔 | ¥2,994.00 |\n",
      "\n",
      "**业务洞察：**\n",
      "- 销售部共 **2 名员工**，累计下单 **3 笔**，总金额 **14,979 元**。\n",
      "- 李四贡献了总金额的 **80%**（11,985 元），是销售部的核心业务人员。\n"
     ]
    }
   ],
   "source": [
    "# 高难度：需要 JOIN 三张表 + 聚合计算\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": 4,
   "id": "5ce44c51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "最终回答：每个部门各有多少人？\n",
      "\n",
      "步骤 2 [AIMessage]：\n",
      "  调用工具：sql_db_list_tables\n",
      "  参数：{}\n",
      "  工具返回：employees, orders, products\n",
      "\n",
      "步骤 4 [AIMessage]：\n",
      "  调用工具：sql_db_schema\n",
      "  参数：{'table_names': 'employees'}\n",
      "  工具返回：\n",
      "CREATE TABLE employees (\n",
      "\tid INTEGER NOT NULL AUTO_INCREMENT, \n",
      "\tname VARCHAR(50) NOT NULL, \n",
      "\tdepartment VARCHAR(50) NOT NULL, \n",
      "\tsalary DECIMAL(10, 2) NOT NULL, \n",
      "\thire_date DATE, \n",
      "\tPRIMARY KEY (id)\n",
      ")E\n",
      "\n",
      "步骤 6 [AIMessage]：\n",
      "  调用工具：sql_db_query_checker\n",
      "  参数：{'query': 'SELECT department, COUNT(*) AS 人数 FROM employees GROUP BY department ORDER BY 人数 DESC'}\n",
      "  工具返回：SELECT department, COUNT(*) AS 人数 FROM employees GROUP BY department ORDER BY 人数 DESC\n",
      "\n",
      "步骤 8 [AIMessage]：\n",
      "  调用工具：sql_db_query\n",
      "  参数：{'query': 'SELECT department, COUNT(*) AS 人数 FROM employees GROUP BY department ORDER BY 人数 DESC'}\n",
      "  工具返回：[('技术部', 2), ('销售部', 2), ('人力资源', 1)]\n",
      "\n",
      "最终回答：查询完成，以下是 **各部门人数统计**：\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",
      "**业务洞察：** 目前公司共有 5 名员工，技术部和销售部各占 2 人，人力资源部 1 人。技术部和销售部人员配置相对均衡。\n"
     ]
    }
   ],
   "source": [
    "# 遍历所有消息，还原 Agent 的完整推理链\n",
    "response = sql_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"每个部门各有多少人？\"}]\n",
    "})\n",
    "\n",
    "for i, msg in enumerate(response[\"messages\"]):\n",
    "    msg_type = msg.__class__.__name__\n",
    "\n",
    "    # AIMessage 中如果有 tool_calls，说明 Agent 调用了工具\n",
    "    if hasattr(msg, \"tool_calls\") and msg.tool_calls:\n",
    "        print(f\"\\n步骤 {i+1} [{msg_type}]：\")\n",
    "        for tc in msg.tool_calls:\n",
    "            print(f\"  调用工具：{tc['name']}\")\n",
    "            print(f\"  参数：{tc.get('args', {})}\")\n",
    "\n",
    "    # ToolMessage 是工具返回的结果\n",
    "    elif msg_type == \"ToolMessage\":\n",
    "        print(f\"  工具返回：{msg.content[:200]}\")\n",
    "\n",
    "    # AIMessage 的最终文本回答\n",
    "    elif msg.content:\n",
    "        print(f\"\\n最终回答：{msg.content}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "019a2781",
   "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": [
    "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",
    "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": 8,
   "id": "4c043990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Python 代码执行沙箱创建成功\n"
     ]
    }
   ],
   "source": [
    "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}\"\n",
    "\n",
    "\n",
    "print(\"✅ Python 代码执行沙箱创建成功\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dbeb1792",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据可视化 Agent 创建完成\n"
     ]
    }
   ],
   "source": [
    "from langchain.agents import create_agent\n",
    "\n",
    "# ============================================================\n",
    "# 定义可视化 Agent 的 System Prompt\n",
    "# ============================================================\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",
    "# ============================================================\n",
    "# 组装可视化 Agent（langchain 1.3.1 写法）\n",
    "# ============================================================\n",
    "viz_tools = [execute_python_code]\n",
    "\n",
    "# 同样用 create_agent 一行搞定，和 SQL Agent 的创建方式完全一致\n",
    "visualization_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=viz_tools,\n",
    "    system_prompt=VISUALIZATION_PROMPT,\n",
    ")\n",
    "\n",
    "print(\"数据可视化 Agent 创建完成\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "423c5583",
   "metadata": {},
   "outputs": [
    {
     "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 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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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、核心数据一览\n",
      "\n",
      "| 指标 | 数值 |\n",
      "|------|------|\n",
      "| 员工总数 | **5 人**（3 个部门） |\n",
      "| **平均薪资** | **13,800** |\n",
      "| **中位薪资** | **16,000** |\n",
      "| **最高薪资** | **20,000**（技术部·张三） |\n",
      "| **最低薪资** | **5,000**（人力资源·赵六） |\n",
      "| 薪资极差 | **15,000** |\n",
      "| 最高/最低倍数 | **4.0 倍** |\n",
      "\n",
      "---\n",
      "\n",
      "### 二、分布形态分析\n",
      "\n",
      "> **均值（13,800）< 中位数（16,000）**，呈现 **左偏（负偏）分布**。\n",
      "\n",
      "这意味着大部分员工的薪资集中在 16,000~20,000 之间，但有一位低薪员工（赵六，5,000 元）显著拉低了整体平均水平。如果剔除人力资源部，其余员工薪资区间为 11,000~20,000，相对集中。\n",
      "\n",
      "---\n",
      "\n",
      "### 三、薪资差异分析\n",
      "\n",
      "- **变异系数（CV）高达 42.7%** —— 远高于 30% 的警戒线，说明公司内部 **薪资差异较大，存在公平性隐患**。\n",
      "- 最高薪资（20,000）是最低薪资（5,000）的 **4 倍**，这在不同部门间尤为明显。\n",
      "\n",
      "---\n",
      "\n",
      "### 四、部门对比洞察\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",
      "- **技术部**薪资最高（均值 18,000），且内部差距较小（16,000 ~ 20,000）\n",
      "- **人力资源部**仅 1 人，薪资 5,000，与公司整体水平差距悬殊\n",
      "- **部门间最高/最低均值差距达 13,000**，建议关注人力资源岗位的薪酬合理性\n",
      "\n",
      "---\n",
      "\n",
      "### 五、💡 业务建议\n",
      "\n",
      "1. **关注人力资源岗位薪酬**：人力资源部薪资（5,000）远低于公司平均水平，可能存在招聘留任风险，建议对标行业水平进行调整。\n",
      "2. **建立薪资宽带机制**：当前薪资极差达 4 倍，建议设计更透明的薪资等级制度，控制内部公平性。\n",
      "3. **扩充数据样本**：当前仅 5 条记录，分析结论代表性有限，建议补充更多员工数据后做更深入的回归分析。\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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c3fb906",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 薪资统计概览 ===\n",
      "count        5.00000\n",
      "mean     13800.00000\n",
      "std       5890.67059\n",
      "min       5000.00000\n",
      "25%      11000.00000\n",
      "50%      16000.00000\n",
      "75%      17000.00000\n",
      "max      20000.00000\n",
      "Name: salary, dtype: float64\n",
      "\n",
      "中位数薪资：16,000 元\n",
      "薪资范围：5,000 ~ 20,000 元\n",
      "薪资标准差：5,891 元\n"
     ]
    }
   ],
   "source": [
    "# 数据探索\n",
    "print(\"=== 薪资统计概览 ===\")\n",
    "print(employees_df['salary'].describe())\n",
    "\n",
    "# 计算额外统计量\n",
    "median_salary = employees_df['salary'].median()\n",
    "print(f\"\\n中位数薪资：{median_salary:,.0f} 元\")\n",
    "print(f\"薪资范围：{employees_df['salary'].min():,.0f} ~ {employees_df['salary'].max():,.0f} 元\")\n",
    "print(f\"薪资标准差：{employees_df['salary'].std():,.0f} 元\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d5978d54",
   "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 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "---\n",
      "\n",
      "## 📊 各部门平均薪资对比分析\n",
      "\n",
      "### 核心数据\n",
      "\n",
      "| 部门 | 平均薪资 |\n",
      "|------|--------:|\n",
      "| 🥇 **技术部** | **18,000 元** |\n",
      "| 🥈 **销售部** | **14,000 元** |\n",
      "| 🥉 **人力资源部** | **5,000 元** |\n",
      "| **全公司平均** | **13,800 元** |\n",
      "\n",
      "---\n",
      "\n",
      "### 💡 业务洞察\n",
      "\n",
      "1. **技术部薪资最高（18,000 元）**：比公司平均值高出约 **30%**，符合技术人才在市场上的高溢价特征。\n",
      "\n",
      "2. **人力资源部薪资最低（5,000 元）**：仅为技术部的 **28%**，低于公司平均水平约 **64%**。该部门仅有 1 人（赵六），薪资水平可能存在**岗位职级偏低**或**数据样本不足**的情况，建议核实是否该员工为初级岗位。\n",
      "\n",
      "3. **部门间薪资差距显著**：最高（技术部）与最低（人力资源部）相差 **13,000 元**，倍数达 **3.6 倍**。这种差距在科技公司中常见，但可以关注人力资源团队的薪酬竞争力，避免人才流失。\n",
      "\n",
      "4. **销售部表现居中（14,000 元）**：略高于公司平均，业绩导向岗位通常还有提成/奖金，实际总收入可能更高。\n",
      "\n",
      "> ⚠️ **注意**：当前数据仅 5 条记录，样本量较小，以上结论为初步分析，建议结合更多员工数据进一步验证。\n"
     ]
    }
   ],
   "source": [
    "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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10ed556c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 按部门计算平均薪资\n",
    "dept_salary = employees_df.groupby('department')['salary'].mean().sort_values(ascending=False)\n",
    "\n",
    "# 画柱状图\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(dept_salary.index, dept_salary.values, color=['#4C78A8', '#F58518', '#E45756'])\n",
    "plt.title('Average Salary by Department', fontsize=16)\n",
    "plt.xlabel('Department', fontsize=12)\n",
    "plt.ylabel('Average Salary (CNY)', fontsize=12)\n",
    "\n",
    "# 在柱子上方标注具体数值\n",
    "for bar in bars:\n",
    "    height = bar.get_height()\n",
    "    plt.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{height:,.0f}', ha='center', va='bottom', fontsize=11)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4cf2a29a",
   "metadata": {},
   "outputs": [
    {
     "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",
      "产品表中共有 **4 个产品**，涵盖 **2 个类别**：\n",
      "| 类别 | 产品数量 | 总库存 |\n",
      "|------|---------|--------|\n",
      "| **电子产品** | 3 个（笔记本电脑、机械键盘、显示器） | **1,900 件** |\n",
      "| **办公用品** | 1 个（办公椅） | **300 件** |\n",
      "\n",
      "### 业务洞察\n",
      "\n",
      "1. **电子产品是库存主力** 🏆\n",
      "   - 电子产品总库存 **1,900 件**，占总库存的 **86.4%**\n",
      "   - 其中机械键盘库存最高（1,000 件），适合作为引流爆品\n",
      "\n",
      "2. **办公用品库存较少**\n",
      "   - 办公用品仅 300 件（办公椅），占比 **13.6%**\n",
      "   - 考虑到办公椅体积大、单价适中，建议保持合理库存水位，避免资金占用\n",
      "\n",
      "3. **库存结构建议**\n",
      "   - 电子产品类别覆盖了高、中、低三个价位段（¥399 ~ ¥6,999），品类搭配合理\n",
      "   - 办公用品仅有一种产品，可考虑拓展品类（如打印机、文件柜等），平衡整体库存结构\n",
      "\n",
      "> 💡 上图已在水平条形图上**标注了具体数值**，方便快速查看每个类别的库存总量。\n"
     ]
    }
   ],
   "source": [
    "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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60a16762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "Agent 生成的第 2 段代码：\n",
      "============================================================\n",
      "# 先探索数据\n",
      "print(\"产品表基本信息：\")\n",
      "products_df.info()\n",
      "print(\"\\n前5行数据：\")\n",
      "print(products_df.head())\n",
      "print(\"\\n类别分布：\")\n",
      "print(products_df['category'].value_counts())\n",
      "print(\"\\n价格统计描述：\")\n",
      "print(products_df['price'].describe())\n",
      "\n",
      "\n",
      "--- 执行结果 ---\n",
      "执行成功：\n",
      "产品表基本信息：\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   id            4 non-null      int64  \n",
      " 1   product_name  4 non-null      str    \n",
      " 2   category      4 non-null      str    \n",
      " 3   price         4 non-null      float64\n",
      " 4   stock         4 non-null      int64  \n",
      "dtypes: float64(1), int64(2), str(2)\n",
      "memory usage: 292.0 bytes\n",
      "\n",
      "前5行数据：\n",
      "   id product_name categ\n",
      "\n",
      "============================================================\n",
      "Agent 生成的第 4 段代码：\n",
      "============================================================\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "# 设置中文字体\n",
      "plt.rcParams['font.sans-serif'] = ['SimHei', 'PingFang SC', 'DejaVu Sans']\n",
      "plt.rcParams['axes.unicode_minus'] = False\n",
      "\n",
      "# 计算各产品类别的平均价格\n",
      "category_avg_price = products_df.groupby('category')['price'].mean().round(2)\n",
      "print(\"各产品类别的平均价格：\")\n",
      "print(category_avg_price)\n",
      "print(f\"\\n{'='*40}\")\n",
      "print(f\"最高平均价格类别：{category_avg_price.idxmax()}（{category_avg_price.max():.2f}元）\")\n",
      "print(f\"最低平均价格类别：{category_avg_price.idxmin()}（{category_avg_price.min():.2f}元）\")\n",
      "print(f\"两者差距：{category_avg_price.max() - category_avg_price.min():.2f}元\")\n",
      "\n",
      "# 绘制饼图\n",
      "plt.figure(figsize=(8, 8))\n",
      "colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']\n",
      "explode = [0.03] * len(category_avg_price)  # 轻微分离\n",
      "\n",
      "wedges, texts, autotexts = plt.pie(\n",
      "    category_avg_price.values,\n",
      "    labels=category_avg_price.index,\n",
      "    autopct='%1.1f%%',\n",
      "    startangle=90,\n",
      "    colors=colors[:len(category_avg_price)],\n",
      "    explode=explode,\n",
      "    shadow=True,\n",
      "    textprops={'fontsize': 14}\n",
      ")\n",
      "\n",
      "# 突出显示最大值\n",
      "max_idx = np.argmax(category_avg_price.values)\n",
      "autotexts[max_idx].set_fontweight('bold')\n",
      "autotexts[max_idx].set_fontsize(16)\n",
      "\n",
      "plt.title('Average Price by Product Category', fontsize=16, fontweight='bold', pad=20)\n",
      "\n",
      "# 添加图例，显示具体数值\n",
      "legend_labels = [f'{cat}: {val:.2f}元' for cat, val in zip(category_avg_price.index, category_avg_price.values)]\n",
      "plt.legend(legend_labels, loc='upper right', bbox_to_anchor=(1.3, 1), fontsize=12)\n",
      "\n",
      "plt.axis('equal')\n",
      "plt.tight_layout()\n",
      "plt.show()\n",
      "\n",
      "\n",
      "--- 执行结果 ---\n",
      "执行成功：\n",
      "各产品类别的平均价格：\n",
      "category\n",
      "办公用品     499.0\n",
      "电子产品    2866.0\n",
      "Name: price, dtype: float64\n",
      "\n",
      "========================================\n",
      "最高平均价格类别：电子产品（2866.00元）\n",
      "最低平均价格类别：办公用品（499.00元）\n",
      "两者差距：2367.00元\n",
      "\n",
      "\n",
      "============================================================\n",
      "最终回答：\n",
      "## 📊 分析结果与业务洞察\n",
      "\n",
      "### 各产品类别平均价格\n",
      "\n",
      "| 产品类别 | 平均价格（元） | 占比 |\n",
      "|---------|:-----------:|:---:|\n",
      "| 🖥️ **电子产品** | **2,866.00** | **85.2%** |\n",
      "| 🪑 **办公用品** | **499.00** | **14.8%** |\n",
      "\n",
      "### 🎯 核心洞察\n",
      "\n",
      "1. **电子产品价格远高于办公用品**：电子产品的平均价格（2,866 元）是办公用品（499 元）的 **5.7 倍**，差距达 2,367 元。\n",
      "\n",
      "2. **电子产品是营收主力**：从饼图可见，电子产品占平均价格总额的 **85.2%**，是绝对的价格高地。\n",
      "\n",
      "3. **⚠️ 数据局限性说明**：当前产品表仅有 **4 条记录**（电子产品 3 个，办公用品 1 个），样本量较小，分析结论可能存在偏差。建议后续补充更多产品数据后重新验证。\n",
      "\n",
      "> 💡 **业务建议**：电子产品单价高、利润空间大，建议作为重点品类进行精细化运营（如组合营销、会员折扣）；办公用品可考虑通过捆绑销售或套餐形式提升客单价。\n"
     ]
    }
   ],
   "source": [
    "# 查看 Agent 的完整推理过程，重点是它生成了什么代码\n",
    "response = visualization_agent.invoke({\n",
    "    \"messages\": [{\"role\": \"user\", \"content\": \"分析各产品类别的平均价格，画一个饼图\"}]\n",
    "})\n",
    "\n",
    "for i, msg in enumerate(response[\"messages\"]):\n",
    "    msg_type = msg.__class__.__name__\n",
    "\n",
    "    # AIMessage 中的 tool_calls 包含 Agent 生成的代码\n",
    "    if msg_type == \"AIMessage\" and hasattr(msg, \"tool_calls\") and msg.tool_calls:\n",
    "        print(f\"\\n{'='*60}\")\n",
    "        for tc in msg.tool_calls:\n",
    "            if tc[\"name\"] == \"execute_python_code\":\n",
    "                print(f\"Agent 生成的第 {i+1} 段代码：\")\n",
    "                print(f\"{'='*60}\")\n",
    "                print(tc[\"args\"].get(\"code\", \"\"))\n",
    "\n",
    "    # ToolMessage 是代码执行的结果\n",
    "    elif msg_type == \"ToolMessage\":\n",
    "        print(f\"\\n--- 执行结果 ---\")\n",
    "        print(msg.content[:500])\n",
    "\n",
    "# 最终回答\n",
    "final_msg = response[\"messages\"][-1]\n",
    "print(f\"\\n{'='*60}\")\n",
    "print(f\"最终回答：\\n{final_msg.content}\")"
   ]
  }
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