{
 "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DeprecationWarning: `langchain-community` is being sunset and is no longer actively maintained.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": 1,
   "id": "878e1a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\34450\\AppData\\Local\\Temp\\ipykernel_14972\\2285073424.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",
      "✅ 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",
      "统一 Agent 工具列表（5 个）：\n",
      "   - sql_db_query\n",
      "   - sql_db_schema\n",
      "   - sql_db_list_tables\n",
      "   - sql_db_query_checker\n",
      "   - execute_python_code\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UC/gC+uK6L9zlnpf2JGgLVKKg36+fx4xxlzucdppE0BYAAAAASkTQtoIEg0GtWLHCZVa2atVKcXFxFXXXqKLvhy1btmj16tXuerNmzSLdJZQiLiFOe569Z8FlAJXHk5CgXQcNKrgMAAAAACgZR0wVxO/3uyBd8+bNlZKSUlF3iyqsRo0a7q8Fbhs3bkyphCiVkJSg4148LtLdQAlWZKxQIBhQvCdezVL54aNcslZIwYDkiZdqRH4fWjmE/e65J9LdAAAAAICoR9C2ggQCAfc3kVM9ESYUwPf5fARtge3U89meSstIU4vUFlo2bBn7rzw+6SllpUk1WkjHsw8BAAAAoKrgXOAK5vF4KvouUYXxfgAAAAAAAMD2ImiLUmuyArEgd3Ou7q17r1vsMoDK49+yReN793aLXQYAAAAAVNOg7aZNm1w92e1hp6r/888/ysvLq7R+VSXz5s3THnvsUWjd2LFj1a9fv23edsyYMbruuusKrTv++OM1bty4Cu8nUFlyNuW4BUDl82VkuAWIJbNmzVLv3r1dvfsuXbroyy+/LGhbs2aNBg4cqLp16+qoo47SypUrK7UNAAAAVUOVDtouXbpUnTp10oIFC8p8m6lTp2qXXXZRz549tfvuu2vRokWKdZZVu2TJkkLrPv/8cx199NHbvO3zzz+vpk2b6sEHH9Rjjz2mxx9/XNOnT9fXX3/tLj/66KMusLs9vv32Wy1bVrz24k8//aQnn3xSEyZMKKghbCz4/tlnn7m+fP/99yXe7pFHHtF33323XW2IDd4aXl3+1+VuscsAKk98crKO/egjt9hlIBZs2LBB/fv310knnaS///5bZ5xxhk499VTl5uaf3TF48GBXA3/u3LlubHrOOecU3LYy2gAAAFA1VNmgrQ1CjzjiCJdJUFZZWVk65ZRTdOONN2r16tVu0HzxxRcr1sXFxbklJDs7W5MmTdI777zjsjNCS9Eg7scff6wVK1Zo6NChysnJcfvXbpuenq6aNWu6y7beMpvLygK+Rx55pP74449C65966imdcMIJmj9/vm644QadeOKJBW1nnXWWe01//PFHnXvuubr00ksL2l555RUdcsgh+v33390BzHPPPVemNsQOT5xHDdo3cItdBlCZn7c41d5lF7fYZSAW2Nld119/vYYPH65mzZrpmmuu0bp167R8+XL9+eef+uabb9yP3M2bN9fdd9/tfki2H9Mrow0AqmMC0ubNm91CiT8A1U2CqijL7LSsy+OOO67Mt/nqq6/cF/kll1zirg8bNkx33XWX1q5dq4YNGyrWWIbqmWee6YKtW7ZsccHPXr16qVatWuratWuhDFk7sDjttNMK3famm27Svvvu67a3wG3oVD8L1Fo2c8uWLZWcnFzm18gyaO01sfsLt3HjRt18880uK7ZVq1buwMcOeqxPlnFrWcGWmev1el0QuUWLFho9erSSkpJ05ZVXulIPVrLh559/Vt++fTVkyBD3PiitzW4HAABQEfbaay+3hPzwww9q0KCBG9O8/fbbLhO2cePGrs2yY+0ssjlz5igzM7PC21q3bl2sfzZusyXEfnwPjfVCpcRsYlVbbPwUHhTZ1vqipci2d70lFRS97+1dX96+85x4nXjvVeznyYKqocScivyOsOPYAw880F2fMmWKK0NTGd8R9j1q36d8R/Bdzv8n/udWxDiirOVaq2zQ1rIiw7NDy1q71coixMfHu+sWHLQMBCuvUFLQtrRBbHVhbxjLNLXT9qy8gNWwtVIHlgFy2223uWD2scce67a1f7AWgA15+OGHXaDTsp3NwoULdfXVV7vgqpUbMLZfn3766YKgrWXIvvHGG8X6YacLWhDegrP2jzZ0nyFWj80ybOvVq1fw5rYArWXz2oGJXQ99GCwAbUFX6+u0adPcutDj2wGTHcDMmDGjoLxCSW0HHXRQhe9rRK+AL6DZz8x2l/cZuo/ivfnfDwAqXp7PpwXjx7vL7U4+WXFeSpIgttj4w8ZDNh+AjUdtrGU/coerXbu2G5vZvA0V3VYS+6H7jjvuKLbezmazs6aMBUHq1KnjxsI21gqxsVhqaqq771C5h9DjWXBj/fr1heaesLGcjdPsvsMPZCyIbfvDzoQLZ2Mz22eWmRw+fm3SpIl7vPDnlJCQ4Mbz1r/wMXtiYqLq16/vxowWNArhOfE68d7buZ8nq+1tZ1RWpso8jvv000/VrVs3vvf4Lnf4/8T/3Bo7ODbKKOMcH1U2aLu9AVtjg9hGjRoVWmeBW/unsz2D2O3x0PSH3LItezfbWxNOn1Bo3YDXBuinlT9t87ZX7XuNru59Tbn6d85557uA7D333KPBZw7R9GnTVK9effXqvZ/2772vHnviCQ0adJwyt2S5N2UgL09L/vnHlSO46KKLCmoC2z92G1Rb4DXE3ryhALm599573VIay/QtjQ3y7Q1vE5zZ6X52P/YBscX++R922GE64IAD9OGHH+qBBx5wBwR2OqJNsGaDkRAL0lstZCvZUFobYksgN6CPL//YXW51Qkd5ayYqWtRITFDNZIJa2HGBYFAb0v8bTESKPytLs+6+212u3e8oJdSooWgQjZ+1zdk+ZeVu30SrsbaPqqI777zTBSvtB3ITPk4KsR+mbX1ltJVk5MiR7kyn8PGbZQHbmNkOMExovGTX7UAkJLTexmlFs0mMBXfChdYXHY+Hsk9C2cHh4/2S1oeCRyWtt/FqeKJB6DFtzG8HUkXX85x4nXjv7ZzP0/7776+ZM2dWi0zb0HPie4/vcv4/8T+3djnHRuHfrdUyaFseNmANj3SHlHY6fGmD2O2RnpOutIy0bW7Xqk7x+12zZW2ZbrsqY73WZ+RnQpTHps05sveS3UfHrnvrnYmfujfU408/r08/nqS99z1QazdmKjEpSXlBadddd9X777/vspCt1mz4P3v7ZxZivyBUJPs1wkokrFq1quDNb+ss6G4Te1gA14LGVjbBWHZI0VILdt0CtltrQ2yJi49T55M6K8cX0Ee/LFO6/79J7iKpTkqiBvbYhSAJKkR2bkDjpsyP/N705arpPvurYWoNffTzMm2Kgq/caP2sWcB2wqx/tGlL8XHLzhat+6iqsbHTM88848oj2JjUWEasjWHCWcknK/VkBwAV3VYSGweXNBYuOudBeBCjqNLWl5ZksT3rt/cxK3s9z4nXifde+T5PdqzVo0cPVbTwjN+99967UDC5MvEdwXc5/5/4n7sj44iyJqLGVNC2ffv2rm5qiAX8LBuzpPpeWxvEbo/aSbXVIrXkQXK4RimFf6UyDVMaqlmt5i6gujU1vanyWzS1nAL/3rbQfQSD6t3nYDeZxeCTj9MNt9yhpKT/fgmwScns4COcBbVtcq+Q0Cl1Ia+99lpByYJwvXv3drV1t8V+2Xz88cc1YsQI91oOGDBAs2fPdpPSWT/tH7SdVmenrViJhVBKejj7JdZ+CbGDpdLaEFsSkhN08viTtTY9ywW1NmT+VxIFkfXlWV/Kn+dXQlxM/auqWP2+1IaMTH0wKy1q3tt1zrlWXbu31m9TF0RNn6KVBWzZR9WDZX+dd955mjhxYqEEgEMPPdQFVG08s88+++ivv/7S4sWLXeDBBvMV3QYAAICqI6aOhPv37+8GzFa31CbQevfdd10NV5s0q7IM22+YW8rjvVM/cNmvOxKQLSvLXP38k49cXY0TTj5VacuW6pbrh2vjxvV65MnntGTxom0GsO3UAKtNG7Jy5cpCwVgrR1BSUHRb2ctWsuDrr78uKJ9gr5edVmcHJHYQsueeexb8omp9sMk3rGyDlUuwSTfs11drt/INv/zyizp27OiCtqW1AYgOHRvyedxhtTsqoCxtiLesPgKkQCS4sdUJJ7gxhs0hYIuxMVK7du107bXXuvYLLrhAr776qq644oqCs4Eqow0AAABVQ7UM2u63335ukodBgwYVWm8BQ6tTa1miNgHX559/XigzNNasXLFCb702TlMmf6Xs7Cy9+PwzuuDiyzRxwnsadvnFOuf8i3T9zbe5GmgL5v+lpG3U3EhLS3OTioUULTVgGbC2bC8LqtrBhtUEsezaN99802X1WvaI1Sl+7LHHtGLFCjVr1kzTp093QXmre9uhQwd16dLFTY5mweQxY8a4IL2tM1trAwAAqAg2VrHJWosKnRZn8yfYD9s2LrUJys4999yCbSqjDQAAAFVDlQ/aWgCvaKamBWz32muvEre3QbPNKmnBPZsMIpaDdDVSaujTjybq1DOG6NmXXnMTkJnlaWl69MnndFT/AQXbbtywQcnJhSeMCRVlt5nyrNREmzZt3Kyg4UHcgw8+2NURtu3KWmrCJjULr+9hhZqttIJli1jNWpvJ1LKkLQhvgXnLWLE6u3b/VmfXJlXr2rWru61NXGaBZCtIb0Hd9957r+B+t9aG2OHb4tNj7R9TXjCouEcPiXR3gGrN48tR0s3D9J03XrrqgUh3B9gprG6t/Ui8NSeffLJbdlYbAAAAol+VD9oeeeSRxdYVzbAtymp6UddLqlOnriZ9OaXY/mneooVbzHtvv6X33n5Tv/78o47pP7DQdhYgteXss892M4FaRm74RGShAKxlvF5//fW65JJLyvSa/vzzzyWWtrAJxtauXeuCtqGCzvb3//7v//TII4+4bFsrm2BB2BB77F9//dXd1jJpw4PBW2tD7LAfFDKWZ7jL/835iGjw2pzXtMW3RSneFA3uOjjS3amaFr+mpMyN6uhL1/fqF+neSEHJs2m98qfWstI/xYvzAwAAAACqQdAWlavj7p3VZY+uGnTCyTp2QOGgrQVoBw8erKFDh+6Ul8ECtBaULYnVqC1tQjnTvHnzcrUhNiYiu+ini7QhM1sfr9sk+QOR7hL+dd3n1yktI81N5kjQtpx+uk6pWWk60NNY33siH7QNJniVc/2D6tOpqSau9Ei5hcvoAAAAAADyEbTFVnXusodb3JslrnBGlJWgKK0MBVBVxMXHqeleTZWQniXPlPRIdweo3uLiFGy5q1Lbt5ZWL4h0bwAAAAAganE+OAAAAAAAAABEETJtAcS0gC+gOa/OUUZWroLNEiPdHaB6C/gV//1XWrGigVSrfaR7AwAAAABRi6AtgJgWyA3og3M/cJdTxw+IdHeAas0TCMj76hOaZ1dGjWUiMgAAAAAoBUFbAIr1mrbtj2mvXH9A64vUbQZQ0R84jwKd91bjOjW0ymMVmoLsYgAAAAAoAUFbADEtITlBgycN1tr0LI2bMl/K9Ue6S0C1FUxIlO+Sm7Rn99aaO3WBlJMT6S4BAAAAQFRiIjIAAAAAAAAAiCIEbVFM2rKlWvDXn2XaMxkZGcXWbdq0ib0KAAAAAAAAlBNBWxTz0vPP6q7bby7TnunTp4/GjBlTcH3GjBnabbfdtG7duhK3t/YXX3zRBXs9Ho98Pp/uuusuLViwQJ9++qmOOuooXhHsVL4tPj3W/jG90v0ZBbMpjRBNmtZqqhapLdxflFONpgokN9cWT8Oo2IUeX44S77xM0087QcqlNAIAAAAAlIagLYrxehOUkLDtcscLFy7Ur7/+qn79+hWs23fffdW5c2c99dRTJd7GArqjR4/W4sWL3fUVK1bopZdeUuvWrTV79mw1b9683K/Iyy+/rD//LDlD2ILCw4YNU3Z2dqH1b7zxhi655BK98MILxW5T3jZULcFgUOsXrNemvzdGuisoYtbQWVo2bJn7i3I6apY2HLZAr9V6PTp2YVCKW7NSWcuWMgkZAAAAAGwFE5HFuPXr1mra1G8LrVv090KtXbNGEye8V2h99726q37X3QuuW2B2jz320F577aVzzz1XW7ZscdmzNWrU0Jw5c3Taaae57fx+v8aOHavU1FS9+eabOvroo/X++++7bZ955hn16NFDEydO1JdffqmDDjpI33//vbtdbm6u9ttvP3m93m0+DwuiWh8sW7djx47F2i+88EKXFZycnFyw7u6773aPf9VVV+mJJ57QokWLNGrUqB1qQ9WciOzc787Vps05+jxri+QPRLpLQLUVTPAq55q7tV/7Jvp4U6KU64t0lwAAAAAgKhG0jXGLFy3SFUPP03lDLylYt2L5cm1Yv04/zZpZsO79t9/S9Tfdqu7/Bm2tbu3zzz+vQw891F0/5ZRTXMZiXFzx5G0L2iYlJbnLDRs2dCURbrnlFhe0rV+/vlq1aqW8vDwXrG3UqJHmzZunSZMm6YADDtBbb73lbp9TwgzjiYmJSklJ0dNPP+2W0rJ0n3vuOS1fvlw33/xfyYf09HRXluGLL75wj3PiiSe6DOHrrrvOPY/ytFlQGlVPXHycWh/QWmvTs+SZMj/S3QGqt7g4Bdt2Ut1uraWpCyLdGwAAAACIWgRtY5xlsXoTE3XLnfcUrHvkwfv05x9zC6374fvpSvD+93a57777tHnz5oLrFsD8+OOPdeqppxasu+OOO1S3bl2XkRpiQd7TTz9dV155pSuT8Ntvv6lLly4uGGoBW8uYNRbMtXIHtWvX1p133ql33323WN8HDhzo2tq2baupU6eqZ8+exbZZuXKlC6i+8sorrjRCKHg8bdo01zfrt9lll13c/VjgOBAIlKvt8MMPL/frAAAAAAAAAIQQtK1s8x6S/nho29vV31s6eEKhVfHfDlLD9T+6GoBbk9nmCmXuekW5uldSZmyp23ryt/3xxx/18MMP67LLLtOSJUvy+5CZqTPOOMOVM7D6tOb1119360KsfEL//v01ZMgQDR48WO+8846bxOzGG290masWYLUJyiyQnJWV5YK45tZbb3VLabYWLL3++utdVrD9tfq5FjS2YHBaWpo6depUaFt7POuDlWUoTxuqpjx/nua9N08ZW3IVrOuJdHcQ5qIPL9L67PWqn1xfTw94mn1THj9cpNTNa9QvK05v6/rI78O8gOJ+mqbVG/6SElpFujcAAAAAELUI2lY2X7qUlbbt7bJLOHjNWav47OXbvKnHn75DkzBZaYLZM38oWLdiRZo2rF9faN2WzZluW9OuXTtXIqBBgwYFQVsrTXDggQe6QOw111zjsnBt8q9jjz224D6slIFlzFrN27POOksXXHCBK5Fw8cUXa926ddpzzz1dBmyLFi3UoUMH17YjVq1a5TJsLQP4iCOOcEHjvn376v7771eTJk1cP8JZ/2xfWDmG8rShavLn+PX2KW+7y6njB0S6Owgzaf4kpWWkqUVqC/ZLeaVNUlJWmnb1NJY8kQ/aevx+Jb4wRr/ZlVFjbU2kuwQAAAAAUYmgbWXz1pZqlCHgkJyfVVpIUkMFkptvM9M2mFC73N2zIKTf59PzTz9RsO6vP/9QRnp6oXWrV6+Sz58/YYyVLLj22mv14osvFrovq2trE41Z0Pbnn392ZQRskrJwFsS1gO7SpUtdtusLL7yghQsXavXq1a5+7XvvveeCt6HyA+ahhx7SRx99VKzvRx55pEaMGFHqc5s5c6Z23XVXF7ANBVePP/54TZ48Weedd557zHCWkWtlGSzLtzxtqJo8cR7tcvAu8gXylLGDPxQA2NYHzqO8dl1Uv1aSVrmzN7bxDw4AAAAAYhRB28q2+7D8pRwCB36g9RnZ8udV3kFt4yZNdeNtozT00iuK1bT9v+deKlj39puvq0uXzlu9r379+unqq692QVnLmD3kkEOKZctaMPf22293E4tZTVubJOzzzz9XnTp1XNDXJiiz21rN3BArp2D1a4uy4PHWWP3aohOYrV+/3j1Wnz59XLbv2rVr3eRoloX7008/aY899nC3K08bqiZvDa/O+eYcNxHZOJuIzOePdJeAaivoTVTuVXdq7+6t9adNRFbCJJMAAAAAAKnsBU1RLTVv0aJQwLY0J516urp2K5w1W5SVNLCatpZBO2XKFFeKoCRWt9YyZw877DDddNNNLlhrLJhq9Wmt9mwoOzZUM9ZKMhRdGjduvNX+WLau1aC1ILEFkmfNmqWxY8e6OrtWzsGCzEOHDtVff/2lYcOGaZ999nGZueVtAwAAAAAAACoCQVsUkxfMK6hfuzWBQMBt9/3337sMWQuOWqD2tdde0zfffOMmLLN1NomYBXFDbPIx2+aff/7RCSec4AK3VsrgySef1PTp09WmTRtdeOGF7v63R/v27VWrVq2C61YOwYLDlslrWbH2WNafUNbuSy+9pISEBPXo0cPV3x03blzBbcvbBgAAAAAAAOwoyiOgmJzsHJehui22jU3AZSUQ4uPjXSAz5Prr/5vwJrSNeeONN1zA1rJxrTTCwQcfrBkzZrgauebbb79VYmKiDj30UPXu3VtffPGFy8AtC6uHW5RlwU6dOrXE7S2Qa3V0K7INVY8vy6fn93velSEJ3tY70t0BqjWPL1eJ916rH2p4pXNujnR3AAAAACBqEbRFMfv3OUh7dOtWpsxWC8buu+++bikLK3VwxRVXuNIIoUBuixYtdPHFF+v0008vWGfZu1bbtqwBW6C8gnlBrfpllbucWoYMcwA7IBhUXNpiZeZ/+CyMy+4EAAAAgBIQtEUxBx1yaJn2igVebdkeVg+2qJYtW2rw4MGF1tWoUaPEbYGKlpCcoDM/O1Ppm3P1tSdX8m9fWQ4AZRdM8Cr3slvVq11jfZqdKOX62H0AAAAAUAJq2gKIaXHxcdrt8N3U6tA28sST9QdU7gcuTnmd9lT9nvu6ywAAAACAkpFpCwCISqfvcbo2ZG9QveR6ke5K1dXmdGVnrtGfVgHEH+nOAAAAAADKiqAtgJiW58/Tgk8XKH1LroKJVmMT0eKBIx6IdBeqvu4PKDM9S99OmS9l5kS6N1JeQHG/zdbarCVSoFGkewMAAAAAUYugLYCY5s/x6/VjX3eXU8cPiHR3gGrN4/cr8el79KtdGTWWicgAAAAAoBQEbQHENE+cR817NJc/kKcsDzVtgcr9wHmU13o31UlJ1CqP1bQNssMBAAAAoATMAgIgpnlreHXhzAt18jdnyZMUH+nuANVa0Juo3BH3q+dzL0vexEh3BwAAAACiFkFbAEBU6vR4J9UeXdv9RTlN7KT6nzTR2ZmD2IUAAAAAUIUQtAUARKXM3Exl5Ga4vygnX6bi/BnyBrewCwEAAACgCiFoG+My0tN1zx236oyTBunm66/V2jWri20z+s7b9OvPP233fY8cOVKzZ88uc1tubq5uvvlmHXjggRo2bJiysrJ2uA3YFl+WTy8c8ILeOeJVBXMC7DCgEnl8uUp86EbNvuR8yZfLvgYAAACAUhC0jXHnDTlV076bopNPP0ML/vpTg08apEDgv8DVA/fcqSceGaNNGzdu1/3ecsstuvfee7Vhw4Yyt1122WX64osvXPvixYt10UUX7XAbsC3BvKCWTluqlTPSpCCTIgGVKhhU3KI/tWnOr/bhY2cDAAAAQCkSSmtA9WfZs9O/+1YzfpmnFi1bqW+/w9W9U1tN+3aKDux7iB4YPUrz//pTu+zattDtFixYoJUrVxa7vyZNmqh9+/a69dZbNW/ePO22227FtimtbdWqVXrhhRc0d+5cdezYUT179lSLFi30wAMPuPbytFl/gG1JSErQqe+dqvQtufrOmyf5ybYFKkswIUG5F1ynfdo20ip5JfnZ2YgpY8aM0dFHH63OnTsXGgM9/vjjWrZsmbp27ep+fK5Zs6Zry8vLc21TpkxR7969dc011yg+Pn6H2gAAAFA1ELStZLmb80//9KZ45fF43OVAbkABX0BxCXEuYFRs2xpeeeL+3dYXkC/bL098nBKS/9vW9++2CWHb5tl9ess+IP/1l5/UpGkzF7A1devW065td9OcX392QdvjTjhZ7Tt2Ur8Deha63eTJk/X1118Xu7+DDjrIBW1PP/107b777tpjjz2KbVNa24wZM7TLLru4wKupV6+eOnXq5Eoo+P3+crUdc8wxZd4XiF32Oex0XCetTc/S1CnzI90doHqLi1fenvuqUffW0tQFBG0RUx555BENHz5c++yzT0HQ1sYxRx55pCvxdMQRR+idd97RBx984MZaZsSIEfrkk090/fXX67nnntPy5cv10EMP7VAbAAAAqgaCtpVsdK3R7u/w1cNVs1F+1sTUB6bq65u/VvcLumvgswMLtn2w8YPybfHpqkVXqW6bum7d78/9pGk3fq3dTtpdhzzdv2DbN7s/q+x1WTpx6jmq16mhW/fX67+r01ndyty39WvXqm69eoXW1albT+vXr3OXLWBbkvPPP98tpbGg7Pa2WZZJ27aFM3rr16+vNWvWuJq15WkDAACIBvfdd58+/fRTtWqV/0N5yPz587V582Y99thj7rpl4doP0Js2bXJjnEcffdT9EN2tWzcddthhateunW677bZyt9WpUydCewAAAADbi6AtCtipdP8mA+90wWBQXq+dKvuf5ORkl51c3jagLPICeVry7RJt2pyjYICatkClystT3PzftCG4RspLZWcjZtjZSJZl26VLl0LrGzdurNWrV7sfr62s048//qjmzZurdu3amjBhglq2bOkCr8bW2w/Vs2bNUmZmZrna+vXrV6xvOTk5bglJT08vGBfaYmxcFRp32RKyrfWh25d3fVxcXLH73t715e07z4nXifde1fk8hYS+t/iOiM7Xie/yqvF54nUK7pTXqej+Lw1B20o2MnNkQXmEkANGHKDeV/d2p2WHs2xct22N/7btckF3tT+zqyuPEO7Uny4sKI8Q0uH0wgcC21K/QQNt/HcysB++n6aJH7ynDevXuzIJW3PDDTfojTfeKLb+pJNO0oMPPqjysIOWpUuXFlpnBzF2AJOVlVWuNqAs/Nl+vXTIS+5y6vgB7DSgEnn8PiU+ept+siujxtoa9jdiwn777Vfi+gYNGujaa691ZaOsvJNl3o4fP94N6lesWKEOHToU294CvBZYLU9bSUaPHq077rij2Ho7ayk7O9tdrlGjhsvStfu2sVeI1d5NTU11k8tahm+IBZ1TUlK0fv16VwIixLKIk5KS3H2HH8hY/6zmro3hio4PbYLcdevyzwIztm9snGePFz6pbUJCgho2bOj6Fwo8m8TERHcWlgWzLas5hOfE68R7r3p8nsJvb7fbsmVLlX9OfO/xOvHeq/6fp4yMDJUFQdtKllgzsdi6+MR4t5RpW2+8vEUCtsZbwrbbU8/W7NFtL61auUIrli9X/foN9Nq4FxXw+9Wl69ZLLNx7771uqUh9+/Z1E5wtXLjQTVK2du1azZkzR3vttZf7UJanDSgL+9Ju1LmR/Hl5TIkEVDaPlNe0pVKT7QdHAraADdzHjh2r/fff3y2WXWuTtlopBTvIsHFOOBvs20FKedtKMnLkSA0bNqzguh18WBmHRo0auQMM99H99wwmu24HIgUf6X/X20FU0WwSYwc5hb4C/l1v9110vS12cFU066ek9aGDqJLW20GUnXVV9DFr1apVMMEbz4nXifde9fk8WaAlxG4X6ldVfk587/E68d6r/p+n5LDH2BqCtjFsz+57a+8evXTFxefpimtGqHXrXdxA/cCDD9npfalbt66GDBmiU089VTfeeKOefvppDRo0qCBjtrxtwLZYFvylv1/qJiIbZxORZQbYaVHiqWOfUpYvSzW8NSLdlaqr11NKz9ikr+atl/47+zligt4k5d70iPbt3lp/20RkuVHQKSCCXnvtNbVu3dpNPmYs69bq/3/22Wcua8QmEAtnmSJ2oGLZJOVpK4kFeIsGeUMHOraUdFBUVGnri96+POu39zErez3PideJ9170fZ7C20Pb8DpF3+vEdzn/n3jvqeDzUdrnp9jnqUxboVqyD8yLr72ldu076IF7RqlT5y7anJmp7LDUbtP3sCPUqEnxXyC2xSbTaNq0aZnbnnjiCQ0cONCVWLADlmeffXaH2wBUXcd2OFYndznZ/UU5tThWuc1P0CLvwexCIArZqXbhWSSWMWYZG3bKnJ2F9NtvvxWUNdi4caM7m6hr167lbgMAAEDV4QmWdq4UirEsVKtZYTP6hk4XC7GaX4sWLdKuu+5a5jTnbfEF8rQ+I1v+vJ33Ei35Z7Fa79KmxLaEOI/qpyaXWK4BJauM9wUqRyjTdkNmdGT+1auVpCEHtVfD2mSZonq9t9s0TtUx3Vvr9akLoqJP0fpZi6bXbWfvo62Nt6o6q1v71FNPucCq+e6779S/f3+99NJLrszTpEmTXI3ZP//80/24feKJJ7oArtWcffjhh924wkoomPK2xfL+B1D9WI1JO2059ENY+OnLABCtyjreIvqGQkoL2ALVlS/Lp3GHj9MHg95UMIfSCEBl8vhy5X38Dv109WWS77/C/ECs6NWrlysJFdKnTx899thjuv/++13wduLEifrwww8Lzkayerf2w+8ZZ5zhsnBfeOGFgtuWtw0AAABVAzVtAcS0YF5Qf3/xt7ucetnWJ+HDzjV7+WzlBnKVGJ+ofZrvw+4vj/WzlZCRocaBldqg3SK/D4NBxf/5q9w8rYMuYTIyxJyXX3652LqzzjrLLSWxzAur11+RbQAAAKgaCNoCiGkJSQk6/pXjlZGVqxneOMlPtm20GPTGIKVlpKlFagstG7Ys0t2pmiYPUt2sNA30NNYDnomR7o2CCQnKPesq7dWmgVbFeyX5I90lAAAAAIhKlEcAENPiEuLU7Yxu6nhKF3mo1wxU8gcuXnk9D1LTI46W4uPZ2wAAAABQCoK2AAAAAAAAABBFKI8AIKblBfK04scV2piZo2AgGOnuANVbXp48/yxQenKGlGflEQAAAAAAJSFoCyCm+bP9eq7Xc+5y6vgBke4OUK15/D4lPXi9ZtmVUWOZiAwAAAAASkHQFkBM83g8qrNLHeUFybIFKv8DJwXrN1KNRBt+eNjhAAAAAFAKatoCiGneFK+uXny1zppzsTzJ/I4FVKagN0k5dzyl/d+eICUmsbMBAAAAoBQEbQEAAAAAAAAgihC0jXH/LF6ks08/Sft0aa+TBx2tub//VtC2auVKnT/kNO3VaVcddcgB+nby12W+X7/frwEDBmjKlCkF62bMmKE+ffoUWx599FHXvm7dOh133HGqV6+ejjjiCK1YsaLgtuVtAwAAAAAAAKoagrYxLBAI6KxTT1CNGjX0yvj31Ha3dhpyyvHKyspy7ZecP0Rd99xL3/7wi0474yxdcPaZysnJ2eb92u0HDx6siRMnKjc3t2B9p06d9OCDDxZa1q9fr7p167r2M888UwkJCfr999/VrVs3DRkypOC25W0DyjIR2RvHvaGPBr+rYG6AHQZUIo8/V95n7tWvI4dLvv/+PwAAAAAACqOAYwybOWO6Fv29UB98+pXq1q2nO0c/qHfHv6kpX3+pffc/QPv07K2rrr3eTdR05jnn65YbhmvlypVKW7pE8+bNK3Z/HTt21CGHHKLrr79eu+++uzp37lyovU6dOurdu3fB9UmTJrmAsQVd//77b3322WdKS0tT06ZNddddd6lx48ZuvSlPW9u2bSt9H6Lqywvk6c8P/nSXU8/ZPdLdQZh5l81TUEF5mLCq/I6dp3XpW/T6tL+lzVHw9soLKn7OTK21y0ecw2RkAAAAAFAKgraVzL9li/sbX6OGC36aQG6ugn6/PAkJik9MLL5tcrI8cflJ0Hl+vwI5ue56XNJ/k7YE/s2GtXXh28YllP0lnff7b2reoqUL2JqkpCTt2nY3/Tlvro485ljddPuogm1fe3msdu/cRa1atdL8P//Qxo0bi93fln/7f+edd7rs2XfeeWerj3/jjTfqgQceUFxcnH766ScX9LXAq0lOTnZB3zlz5rhSC+VpI2iLsohPjNexzxyrzCyfZifESX6ybaNFalJqpLtQ9XlTFfQmyOepKWnbZ0pUtmB8vHynXaw9WtfXqnj7f8XnDQAAoCqxuMNvTz6p5d9+qxqNG2vPq65S3fbtXVswGNS855/X0i++UELNmtrjoovUpFevMt/3788+q9pt2qjV4YcXrJtx663KTU8vtF3Lww7Trsce6y7/8fLL+vu991SrVSv1uOkmpTRpUrBdeduAaEHQtpK91bOn+3vCt98quX59d3ne2LH69dFHtduJJ2rfO+8s2Padgw5ywdiBn32mWi1auHXL3nlL8x99WI0PP1K73/pfEHXGyYPk27RRPV5+XTV33c2tW/XRRDUbeFyZ+5a+aZNqpRYOiqSmpmpT+qaC6xs3btARB+2ntWtW65Mvp7gA69FHH+2W0oTKHWzNRx995O7LatCatWvXqmXLloW2qV27tjZs2OBKMpSnDSiLeG+89rlwH61Nz9KPU+az04DKFJ+gwAGHq0X31tLUBQRtAQAAqpg5TzzhgrIWy1g+ZYq+vvBCDfj4YyXUqKH5r7+u1T/+qIMee0yrZs7UlKuu0gmTJxdKVivNvBdfdHGSnrfdVmh92+OPV57PV3B99ujRBYlrf7//vhaMH68+Dz2k5ZMna+qIETr85Zd3qA2IJgRtY1h8fLzyAv9lOaWnb1JeXlBxnv9KHVsW7idff6cnHnlI5501WHN++005eQFlZ2cXuz/Lcq1Vq1aZHvvhhx/WVVddVagvRXm9Xre+vG0AAAAAAKDi/P3BB+oxcqQade+uhnvt5a6vmDZNrfr1c4HbnrfcohqNGqnNMcfoh9tvV46dpRsMuqzWourvsYea9+njbr/xr7/U+sgji21jjxOyevZsd2byLv8mkc1/8011u/xyl+lbp107/fX668r45x+l7rJLuduAaELQtpKdMnNmQXmEkN3PPVedhgxx5RHCnThlSv62yckF61qeeIqaHHtcwS9JIfuO/8D9DS+Z0OSY/NMDyqpR4yZat85VFtQXn36st15/RatXr1LDRo0KbVe/QUPdcuc9Gv/Ga5rx/feaPm2q3n///WL3N2DAAN1W5FexkixdulTTp0/Xu++++9/zbNlSCxcuLLTdsmXL1KJFCzeZWXnagLII5gW1Zt4abcjMcZcRPR6a/pDSc9JVO6m2hu03LNLdqZrmPaSUzHXaOydHX+rUaPjAybNiiTLr+KS8vEj3BgAAANvBt3mzslatUu3d8s/2tRKQVs4g3eab6dfPZcWGtvvrtdfUsFs3pTRu7AK3tXfdtdj91WjQwP1t0rOnmu2/v6bfeONWH/+X//3PlWMIlZ7cNH++Gv4b1LV1ddq316a//3bB1/K2AdGkygdtfT6fy6yMVgkpKcXWuVMDSjg9oKRtrUZtfFzxrNHwIHD4tttjn569tG7tWv35x1x122tvXXrhOcraskV79+ipub//povPHaLPpkx3GbQZ6enKytqievXr6+abb3ZLeb322ms66qijXCmGkL59+2rNmjWaMWOG9t13XxeInT9/vnr06KHExMRytQFl4cvy6ck9nnSXU8cPYKdFWdA2LSNNLVJbELQtrz8eUkpWmvb2NNaXnsgHbT0+n5Luu0Y/2JVRY5mIDACAKLRkyRJXvq4qyPp3rhfz888/u4muq4qGDRuqdevWqkpC8/BYRm2IXQ6tD5l23XVa/t13OvDhh931pLp1S8yiDYkrQ0xnzc8/K5CTo6b/Tm5uZw37s7KUXC9/jh7jTUlx68rbBkSbKhu0nTt3rgYPHqzffvtNffr00VtvvaXGjRtv83Yvv/yybrnlFhfo69mzpx5//HF17dpVsahdh446dtDxOu/M03T2eRe6zNvcnBz13Hc/V0C8VevWOv2EATrsyKP18cQJ6nPgwRWyryZNmqTTTjut0DoLDI8YMUInnXSSLrzwQr3xxhu69NJLXX1aU942oCxSGqYoL0iWLbAzBGvVVqJN+gcAAKIyYLv77rsXTDJdlVhcoCpJSUnRvHnzqlTgNlSbNs8mVw8GtdYCqbm5ig87A9gc/MQTWj93rr487zwdO3GiO3N42VdfFbs/K03QaK+9yvTYlrnbPiyO4CZrT0hwgdxQ0DeYl6eEfyd2L08bEG0Sqmp27XHHHadTTjlF33//vYYPH67LLrtM48eP3+rt/vnnHxfg+/zzz9WlSxc99NBDLnj4+++/K1Y9+tTzevWlF/TbnF91ymln6K03XlVmRoaboGzsa+P15qsv64+5v+v0IWdr8BlDtuu+r7zySrVr167Y+v79+2vgwIHF1lsw3V6XTz75RMOGDdN55523w23AtiTWTNSINSPcRGTjbCKyTGazBypLMDFJOaPHql/31nrdJiLLzWFnAwAQRSzD1gK2Vz3wuFq2LX4sF20scOjLyR9PeJOSCk6bj3bL/l6gR0Zc7vZ3VQraJtapI2/t2tq8fLnb999edZXiEhO166BBrt0mKGt+4IEuiFu/c2dXGiFj8WKltmnjgqRFBf3+Mj2uLzNTK7791tXLDXFlDTp0cMHhJr16uXWbFi5Urdaty90GRJsqGbSdPHmy1q9fr1tvvdWdHn/nnXeqWbNm2rRpk+rUqVPq7ey0+c6dO6tbt27u+qBBg3T77bcrltn+O/fCiwuuX3jJ5UqpWbOgbci5FxS0JcRt3z/AoUOHlrj++uuvL/U2J5xwglsqsg0AAAAAUHYWsG3bJf+4GSj03jjkEP0xbpwO/N//3ERiq3/4Qc0OOMC1Lf7wQ6358Ud1HzFCa2bP1uYVK1S3Qwcl1q6tjmecUe4dmTZlinusxLASi8YmO/vt6afVoGtXLZ8yRcFAQHX/TRwrbxsQTapk0PbXX39Vr169XFDR1K9f35VG+Ouvv1zJg9Lstdde+uWXX/TRRx+5wO19993nArelycnJcUtIenq6qrtQwBYAAAAAACCcBWSnXnut3t5vP3lr1pQnPl4J/5ZH6HHzzZp2ww0a37OnkurXV5+HHnIB27LypqaWWKZg/W+/qXkJ5S86nnmmy5J958ADXd1ce7wdbQOiSZUM2m7YsMFl1oazGqaWabutQt926rydnm+aNGmi7777rtTtR48erTvuuKOCeg0gGvmz/Zpw/gRl+/wKntY+0t0BqjWPP1fel/6n3yekSAcPjnR3AAAAsJ1sAq9+L7yQXxM2MVErvvuuYFL1Go0aqd/zz7vJvuLii0+ovi09Ro4scf3e111X4nqrSdv7rrvU89ZbC+rt7mgbEE2q5EwgCQkJysvLK7QuPj5e3m3MOGi1bF944QVNmDBBixYtchNXHXjggcrMzCxx+5EjR7pAcGhZunRphT4PAJGXF8jTnNfmaP74eVIek5EBlfuBCyp+1rda9fmnNuMDOxsAAKCKsrq1Vh/WatgWVZ6A7Q71ZSuB1/K2AdGgSmba7rLLLvrss88KrlsA1wKqLVu23OrtPvzwQ5111lkaMGCAuz5q1Ci9+uqr+uGHH3TooYcW2z4pKcktAKqv+MR4Hfnwkdqc7dMvNqO9n4nIgMoSjI+X74Rz1bllXa2KtyEInzcAAAAAqDaZtscee6xmzZqlefPmuetffPGFatWqpbZt2271drbN8uXLC65b9uyaNWtUt27dCuubzaAIhBTNCEf0iffGq/fVvbXnpT3ksaAtgEr8wCUocMixanXKYHcZAAAAAFCyKnnEZLVpr7nmGh155JE6/vjjXbbs/fff71LzzQknnKBLLrlEhx9+eKHbnXnmmW4Cs3PPPdfVxLXM2/3220/du3ff4T5ZaQZ7fAsCN2rUqKAvO8IXyJMvN0eBKDllOxjnUXa2FIgnsLXNfRUMKjc3170f4uLiCibNA1B2ezfbW63qtFKjlEbstvKqv7d8m1todWYNid+QAAAAAKDKqJJBW3PvvfeqT58+mjp1qt58803169evoM2CtSWVSujcubN+//13vfPOO1q5cqWuvPJKnX322RUSYLWauvaYy5Yt0+LFi1URLFi7OcenvCgJ2sbFebQxyav4uB3fX7EiJSVFrVu3doFbRKdgXlCblmxSeka2u4zoMeH0CZHuQtV38ARtSs/ShCnzpcycSPfG1bH1rFutrBUJdipCpHsDAAAAAFGrygZtQ2USbCnKsmy3Vg932LBhldIfK7/Qvn17+Xy+Crm/DZnZ+m7mP0rfUjH3t6NqpyRqUM/mqlcrOdJdqRIskG+T5lXEjwKoPL4snx7Z9RF3OXV8fr1rAJXD4/Mp6a5LNN2ujBpra9jVAAAAAFDdgrbRGqizpSJ4c4PKCniUGR0xW3kDHnkTk5ScTNAW1Ys3xStybIGdI5iYxBkbAAAAALANnLMNIKYl1kzUjZtv1EUrrpEnmd+xgMoO2OaMeU19v/hWSuQHQAAAAAAoDREKAEBUGvj6QK3ZssZNREZ923KaPFB1Nq/SwC019JIeqNgXCAAAAABQaQjaAgCi0o8rflRaRppapLaIdFeqrvU/ypuVpsaexpSPBQAAAIAqhKAtgJjmz/Hro8s/Uk5uQMHj2kS6O0C15vH7lPDak5r3WU2p94mR7g4AAAAARC1q2gKIaXn+PP303E+a+/KvUoDpyIDK/cDlKWH6F1rx4QdSXoCdDQAAUB08+aT0xBPb3u7NN6VPP/3v+k8/Sc8+W/r2y5fn/73vPikjQ1q0SAoGpdmzpZ9/roCOA9GNoC2AmBbvjdchdx2ifW8+UIrnKxGoTMH4ePmOPV1tL7xEiuNkHwAAgGrhr7+kP//c9nZjxuQHXEPi46WrrpIWLiy+bXq6dMQR0u+/S48/nh+0Pfpoae1aaexYadKk8vV106b8+yjNjz9KWVmF1wUC+QHmVauKb1/eNqAMiFAAiGnxifE66KaD1GPEfvJ4+UoEKvcDl6DAkSepzdnnSQkEbRF7vvvuOy0PZQ0VsWbNGj3zzDPKyckptP7nn3/W448/rh9++KHYbcrbBgDADrH/VdnZ/y0eT/4Svq7I/zPNnZsfwDz/fOn996Xx4/ODvQMGSJ99Jr39dv66WbPyt8/MlK68Uvr11/zs2ilTpMMPl3JzpcmTpUGDtr/fFow96qj8xy/JK69IF10kJSb+t279eqlXL+nCC6Xu3aV3393xNqCMiFAAAAAAlWz69Ok64ogj9JcdoJbgiiuu0MyZM5WUlFSwbty4cerbt69+/PFHHX/88Xr55Zd3uA0AgB3WooW0775S3775i5U9sIBr6Hrv3lLTpoVv87//Se3bS02a5AdwLThry667Sv/889/1pUvzt585U/ryS+mGG6S0NOnDD6UNG6S33soP9p55ptSli9S8uXT77dvu8+rVUr9++fdVkjVrpBEj8ss1WAZwyJ135j+O9c2Cy5dcIvl8O9YGlBFpLgBiWjAY1Ja1W5SVke0uA6jUD5yUsUm5NuDm84YYMmHCBA0bNkwpKSkltk+aNEmTJ0/WXDuI/VdWVpauvPJKvfTSSxo0aJDLmD366KN1+umny+/3l6vN6/XuxGcNAKi2LBP11VelPfbIv26BVb9fevDB/Ot//CEddNB/21v5A/sBsV27/OvXXy/NmSPttdd/21iQ84wzpN12y79umbQbN0pLluTfd+3a0nHH5deytexcy8z94ov8IKsFbe3+hgwp3tfTTsvvnwWGR42yXzZLfk5WpsFuH94nM2FC/nM19nx32SU/ELvffuVvA8qIoC2AmObb4tODjfMHF6njB0S6O0C15vHlKvnGi/SdXRk11tZEukvATrF27VoXlO1nGT5FZGZm6pJLLtEjjzyievXqFayfOnWqEhISXODV9OrVS3Xq1NGsWbO0efPmcrXtV8KBopVjCC/JkG41BN28gXluMR6Pxy3242b4D5zbWh+6fXnXx8XFFbvv7V1f3r7znHidYvW9Z7e7RtJFF56pBH7oqTR+n0/Li3zXbs/rFPH3Xlxc/vrQ/wm7XTDoRnah9bZNMC9PcXbfViLg9NNd9qyts2zauAMOUHDlSgVr1nQB1bjbblPe6ae708FdP265xQU5gx9+KE+/fvJcdpmC994rTZsm1avn7seTliZPy5b5fbesVqtHW9JzssfcZ5/89ePGFeq72/7bb+V5/XUFO3WS3nhDGjhQwTFj5PF65VmyRHmdO//3XJs2lWfZsvznsWSJgmFtcc2aKbh0qYK9ernbhdrc62RtFoC2DOUq/B0R8feep3o8p6J9LQ1BWwAAAKASnXfeeaW23X777UpLS9NNN92k6667ThdeeKFuvPFG/fPPP9ojlMH0r2bNmmnJkiUu0FuetpKCtqNHj9Ydd9xRYo3dbKtJKKlGjRou8GsBXcsADqlZs6ZSU1O1YcMG5VqNwX/Vrl3bZRWvX7/eZf6GWFDayj/YfYcfyDRo0EDx8fFabaeuhmncuLECgYDWrVtXsM4OeJo0aeIezx43xALVDRs2dP0LBZ5NYmKi6tev7/aLBbRDeE68Trz3Sv482ee2tiUzrN/KRE2oELX/3d/h331V5nsvGFTG1Kny/1vKIOXvv5WUkKDgtGnufuKXLVNqIKA1q1e75xQ87DCt79NHdadP17rVq+WpUUNNunWTf8IErevXT0mTJ6tWx47aVK+eGv57tknmSSdJp5yilLvuUnzfvqrRsqW22P/TlBQlTp6s9QsXqs7cuUru2HH7npPk9svmsP3eaPRoxV91lVbfcIOCW7ao/uDByn7oIdW45hp5AwGttknQ/r3/OvHxSvL5FMjNdW2r/m1zr1NKivw5OVq3cqWa/tuW4PO51ymQmKjMDRuU/e/j8v8ptscRGfa+KQOCtgBiWmLNRN0WvE1r07M0bsp8KTMQ6S7hX8P2G6b0nHTVTrIhLcql0zBtyVynH5fkSFFQQiuYmKTsx97RMd1b6/WpC6TcIhNUADHGBu42WZhl2Vq27cKFC13dWzuYsICpDfrD2XWfz+cODsrTVpKRI0e60g0hdvDRqlUrNWrUyB1ghA5wjF0Pv+/QejuIKppNYuwgJ1xovd130fW22MFV0YyXktaHDqJKWm8HUcnJycUes1atWu5Aquh6nhOvE++9wp8n+9xauCKjfkMybSs50zZ9/Vq3v8O/y6rM914goNpff+0yXp0FC/IzbV9+WfXtPqysQV6e66/LXhw5Ug0WLZInIeG/53DSSUr46CM1Pv10eez2Rx1V8FjuOU2aJM8nn1hReKlPH9ee8tdfCq5YIc/w4Wr0/ffy2GSbZ52V/5yWLpXn4osL9dV6FzzhBOnqqws9J9svNcP2pcfq5/7vf2oUWnfaaUq1x7U683XqqLE9p3/bPBagbdxYnqQkBYu02fNOaNpUjS2rNtT273OKz8xU7bZtVTt0P/x/iulxRHLYY2wNQVsAQNQGbbGDdh+mLelZ+nHVfMlHgBSINjNmzHAB2ssvv9xd79Chg8466yx9+OGHOuGEEwplgJgtW7a4gwPLGilPW0ksYyV88rPwAx1bSjooKqq09UVvX5712/uYlb2e58TrVN3fe3a7hyU1f/YVte3SrcT7xo77+/df9fCJR+nMCviujch777HH5DniCBfQLFrT1t2DZSp+/LErkVBw36HLofs7/HB57r8//zGt5ME11xTe/qabpK5dpSeflD76SHriCXn+/tsFSzV0qDzHH59fW7dz5/ztrRSBBXnDn5Mttk2R5+CeU/g6C7qtW/ffc122TKpb1zoiHXKI4j7/3E6byc+2nT3b9cv1+5BD5AlvmzVLnm7d8p9HkTaPte25Z7G+VLXviJ25Pq4aP6fSnkNRBG0BAACACLDT5opOTmZBV5swrEePHrriiitc5qxlfdipdHPmzHGBXdumPG0AAFSIk0/eerv9UHjqqVvfxgKyNvGY/dBoma4HHlh8GwsK2wReNjmYTWZ29935weH27a0ou3TAAfmBVWNBsFq1yvd8zj9fuugi6Z57rBC99OKLkgVczRVX5Nfjtcf97DPp0EOlJk12rA0oo7KFdgGgmvLn+PXJ1Z/o2xu+VNBHaQSgMnn8PiW884L+emSMnRfIzkbM23///V2t2XfffdftC6vF9uqrr6p///7q0qWLdtttN1dv1iarGDNmjFq2bKlOnTqVuw0AgEoRCLjyCFtlwUvb5ttvpf33zw/SWnmDww/PD8Aee2x+GYQjj/zvNhaUPeig/Pt/5hnpr78kK8tw8MH5gVbLer3++vz7Lqv+/SXLeA1nk54NHy69+WZ+OYaJE6Xu3fPbLNhq66dOlWyysxde+O925W0DyohMWwAxLc+fpxmPzHCXUw9tHunuIExGToaCslloPUpNKlyfEWXky5DHt0Xe4Obo+Jefl6eEbyZpmV3uZgPy4qcMAbHEarKNHTtWQ4cO1fDhw7V27VoNGTJE55xzjmu3AO6JJ57oat7atu+//37BbcvbBgBAhduypdhp/8XYJE4WnLWA6bPPSvHxJW8XCv5acNayXwcMkL75RmrYML/G7KWXSg89lL/+wgvzs37Hjs2/vCNZwva/99//v8X07Zu/VGQbUAZRcAQHAJET741Xnxv7KCvHr7/i4yQ/2bbRYvcndldaRppapLbQsmEuzIftNXF3NchK09mexnrAMzHi+y8YHy//ESdotyZ1tCrOhiB83hBb/vjjj2LrTjnlFB133HFaunSpm7XYZiIO6dy5s+bOnevamjdv7mY23tE2AAAq3IMPbnsbK2kwaVJ+6QTLPN0Wy6b95RebGeq/dVbH1jJyQ7XYW7TIz9wtLQAMVHGM4ADEtPjEePW7u5/Wpmdp/pT5EnM1AZX4gUuQf8AZ2q17a/0wdQFBWyBsBmMraVASm8SidevWFdoGAECFCg+slsZquHfsWPb7tMzdovdr64pOnknAFtUYNW0BAAAAAAAAIIqQaQsgpgWDQfm2+OTbnOsuA6jUD5yUk61AVta2J6sAAAAAgBhGpi2AmGYB29G1RuuZ5v+TcqivCVQmjy9XycPP0OTDD5J81CIBAAAAgNIQtAUAAAAAAACAKELQFkBM86Z4NTJzpIYuv1pKYtZRoDIFvYnKfvBVHfz5FMlbZBIJAAAAAEABatoCiGk2u3ZizUR5AwF3GUClfuCkpGTF20zAfN4AAAAAoFRk2gIAAAAAAABAFCHTFkBMC+QG9M0d3ygrx6/gAU0i3R2gegv4lfDhq1r4Qx2p06GR7g0AAAAARC2CtgBiWsAX0Hf3fOcup/YeEOnuIMwHp32g3ECuEuMT2S/ldfAH2piRoUk/r5SyIr8bPYGAEj57V//YlVEH2ZpIdwkAAAAAohJBWwAxLS4hTvteta+ycv1aFO+R/JHuEUL2ab4PO2NH1d9H/oQsrY6fLykn8vszLk7+vv3VplGqVsXZxH95ke4RAAAAAEQlgrYAYlpCUoKO+t9RWpuepcVT5ks5RG2ByhJM8Mp/4nnq0L21Zk9dEB2BZAAAAACIQkxEBgAAAAAAAABRhExbAEBUmvjXRGX5slTDW0PHdjg20t2pmtImKjFjk3b1rdcG9Y50bwAAAAAAZUTQFkBMy92cq9G1RrvLqeOZiCyaXDzxYqVlpKlFagstG7Ys0t2pmn64WLWz0tTP01g/eiZGujfy5OYo+Ypz9JVdGTWWicgAAAAAoBSURwAAAAAAAACAKELQFkBM86Z4NXz1cJ238HIpyWazB1BZgt5EZd/zgvp8+JnkTWJHAwAAAEApKI8AIKZ5PB7VbFRTWUlx7jKASv3ASam1lVivnuRZx64GAAAAgFKQaQsAAAAAAAAAUYRMWwAxLZAb0NQHpmpLtl/BvetHujtA9RbwK/7Tt7X417pSm/0j3RsAAAAAiFoEbQHEtIAvoK9v/tpdTh0/INLdAao1TyAg78TX9bddGdXL1kS6SwAAAAAQlQjaAohpcQlx6n5Bd+XkBrQ03iP5I90joBqLi5N/v8PUqmFNrYqzif/yIt0jAAAAAIhKBG0BxLSEpAQNfHag1qZnadyU+VIOUVugsgQTvPIPvkS7d2+tn6cukJTDzgYAAACAEjARGQAgKtVKrKXUxFT3F+XkraW8hFT5PCnsQgAAAACoQsi0BQBEpT8u/yPSXaj6jv1D60NZ5JlktQIAAABAVUGmLYCYlrs5V/fUvEdPN3tYwWxKIwCVyZObo6RrB+ubww6UcrPZ2QAAAABQCjJtAcQ83xZfzO8DYGcGbpl+DAAAAAC2jqAtgJjmreHVVYuu0vqMbL2/aJXkD0S6S0C1FfR6lXP7k+rbpbneX5Ap5fKDCQAAAACUhKAtgJjmifOobpu68qdnyfPP6kh3B2FGfDZCG7I3qF5yPT1wxAPsm/L4aYRqZa7RgdnSBF0W+X3oiVOwQWPVaNZc+ntBpHsDAAAAAFGLoC0AICq9/tvrSstIU4vUFgRty2vx60rOSlNHT2PJEwVBWwAAAABAmRC0BRDTAr6AZj4xU5uzfQp2So10d4DqLeBX/NcTtXR+Xanp3pHuDQAAAABELYK2AGJaIDegT6/51F1OHT8g0t0BqjVPICDvu2M1366MGmtrIt0lAAAAAIhKBG0BxLS4+Dh1HdxV2T6/VsYRQAIq9wPnUaDHgWpeL0WrPHE2NRk7HAAAAABKQNAWQExLSE7QCa+eoLXpWRo3Zb6U6490l4BqK5iQKN/ZV6tL99b6deoCKScn0l0CAAAAgKhkaS4AAAAAKtn69eu1ZcuWUtvT09O1du3aQuuys7P122+/lXi78rYBAAAg+hG0BQAAACrZ6tWrtffee+uHH34osT0vL08DBgzQ//73v4J1P/74o3bbbTcNHDhQbdu21cyZM3e4DQAAAFUDQVsAMS13c64eaPSAnm/7mILZlEYAKpMnN0dJI8/Vt8ceLuVms7MRM/766y8deuihLnBbmjFjxmjKlCmFgrinn366Lr30Uv3999+6//77ddZZZ+1QGwAAAKoOgrYAYt6WtVuUvS4r5vcDsDN4MtPl27iRnY2Y8txzz+nhhx9W69atS2yfM2eOC9pasDVk1qxZWrZsma699lp3/cwzz3SlE6zkQXnbAAAAUHUwERmAmOat4dUlv12iDZk5mrRqveQPRLpL+Ff/9v21Pnu96ifXZ5+UV4v+ytm8RovWxklR8NYOer3KufFhHdipmT5MC0i5vkh3CdgpLNu1NLm5uS6wamURfv7554L1f/zxh7p166bk5GR3PS4uzpU8sKzdzMzMcrXtsccexR4/JyfHLeF1dUMZu7YYj8fjlmAw6JaQba0P3b68663vRe97e9eXt+88J16nWH3vFb0dKlf4d+32vE7V8b3Hc+J1iqX3Xl4Zv2sJ2gKIaZ44jxp3aay49Cx51myIdHcQ5ukBT7M/dlSvp5WRnqUvp8yXMv8LykSMJ07BZq1Vq21racWCSPcGiAq33HKLC7KedtpphYK2Fjxt0KBBoW1TU1PdxGLlbSvJ6NGjdccddxRbv2bNGjeZmalRo4bq1Knj7jsr678zU2rWrOnue8OGDS74HFK7dm2lpKS4idf8/v9KD9WrV09JSUnuvsMPZKy/8fHxxcpHNG7cWIFAQOvWrStYZwc8TZo0cY9njxuSkJCghg0buv6FAs8mMTFR9evXd8HszZs3F6znOfE68d4r+fNkn1vsPLa/w7/7+N7ju5z/T7Hx/ykjI6NM3xEEbQEAAIAI+O677/TOO++4icOKsoOS8AxYYwcolkFb3raSjBw5UsOGDSu4bgcfrVq1UqNGjdwBRugAx9h1OxAJCa23g6ii2STGDnLChdbbfRddb4sdXBXNeClpfeggqqT1dhAV/lxDj1mrVi13IFV0Pc+J14n3XuHPU9HPLSqX7e/w7zK+9/LxXc7/p+o+jkguZVxWFEFbADEt4Avo5xd/VmaWT8HWZfviBFDeD5xf8VM/V9rS+lLd3dmNiHl33323fD6fjjnmGLcv/vnnHzeoX7JkiS688ELNnTvXDfhDA30rcdCmTRuXIVKetpJYkNeWouxAx5aSDoqKKm190duXZ/32PmZlr+c58TpV9/deafeHylER37XV5b23M9fznHidPBF+75X1u7bKBm1tgoWLL75Ys2fPVr9+/fTkk08Wim5vy1NPPaUJEyZo0qRJJe5IALEhkBvQxKET3eXU8QMi3R2gWvMEAvK+8ZT+tCujxtqaSHcJiCgbv9rpdiFW19ZO7xsxYoTatm3r6p2NHz9ep5xyij777DNXrqB79+4FNdG2tw0AAABVR5UM2tpA9LjjjnODz6lTpxac1vXss8+WOeB7ww036NNPPyVgC8S4uPg4dRzUUbn+gNbGEUCKJj2e6aGVmSvVtFZTzRo6K9LdqZo+6aF6W1ZocG5dPaEXI90bKc6jQNeealI3Ras89uty4eL/QKwpmv1qtdSsplq7du3c9SeeeELnnnuunn/+eX3//fcuyGtB3R1pAwAAQNVQJYO2FqidP3++Jk+e7GpKPPzww25WXMtOCK8xUZpLL71UZ511lvbdd9+d0l8A0SshOUGnvX+a1qZnaZxN1pT7X6FzRJYFbNMy0ngZdkTWSsVnL1eKxx8VSa3BhET5ht6gbt1b6/epC2za+kh3Cdgmm0TCfuifOXOmm+DCapd17NhRgwYNckHW7fHyyy+rQ4cOpbYPHTq0UELBCSecoL333ltTpkxx493OnTvvcBsAAACqhioZtLXJGnr16lUQoG3evLkr5vvnn3+6AerW2KlidprYmDFj9Oabb6p///6uoHBJbBKH8IkcwmeQAwAAQPX24osv6sEHH9Rhhx2mgw46yNWL3bRpk+bMmaMjjzxSffv21R133FHqWLIoG79ujZVEKCkbt7R6tOVtAwAAQPSrkkHbdevWuVltw1nWw/r167d6OwvAXnvttS7bwDIlrJ7tzTffrOnTp5eYKTF69Gg3EAcAAEDssMnBLrjgAleqwM7wqlOnTqF2mzjsuuuuc3MkHHLIIXrvvffUsmXLiPUXAAAA1U+VDNqWNAtbYmLiNmt1WTmFLVu2uFPFQhkRliHxf//3f7r11luLbR+qlRueaVs0WAygavNt8emJzk8oLxiUxhwc6e4A1ZrHl6Ok20ZoWmKCdNnoSHcHKNXatWvVo0cPXXHFFaVuY2PRSy65RO3bt3dluwjaAgAAQLEetLVBsQVewy1fvtyVSdiaP/74Qz179ix0Clu3bt20ePHiErdPSkpyC4DqKxgMatM/m9zl1Eh3BqjugpJn/Rplh65EQ6FdoATNmjXbasA2ZOnSpS7Tlkm+AAAAUNFs6uYqx05JmzZtmpYtW+au22XLdgjNtFua1q1bu8Ct3//fREO//vrrNm8HoPqyicgu+OECnfTVEMnLzNpAZQomeJUz/D71ePZFKSGRnY0qY9GiRfr+++8LrbPxpNW57dKli9asWROxvgEAAKB6qpJB2xYtWmjw4ME6+uij9cADD+iMM87Q8OHDC7IcLr/8chfILcq2T05O1vHHH6833njDZVDY5GVWswxAbIqLj1OLni3UZJ9m8sST9QdU7gcuTsFd2qn27l3cZSCazZ4929W2NV9++aXuvffeQu1W79bmRWjcuLGbJwEAAACoSFX2iOm5557TZZdd5jJlbbIwmwwipH79+iWWNbB1NpmElUh4//33XQD3hx9+cINtAAAAIGTgwIHatGlTQYDWlqKaNm3qJrjNzs4v+gEAAADEdE1bY1m1F198sVuKuvPOO0u9nQV0S5p0DEBsyvPn6bc3f1NGVq6CjarsVyJQNeQFFDdzilauaSAlt410b4Ct8nq9JQZqS5q0rOgEuQAAAMCOIkIBIKb5c/x678z33OXU8QMi3R2Euf/w+7XFt0Up3hT2S3l1v18ZmRv17fx0KSfyu9Hj9yvx5Uc0166MGstEZKhSPv74Y7Vp06bQOiufYJPhXnLJJRHrFwAAAKongrYAYponzqO2h7VVrj+gTWRKRZXBXQdHugtVX5vByknP0p+L50s50RC19SjQsZsapiZrlccqNAUj3SOgzPbdd1/dc889xdY3atRIu+22G3sSAAAAFYqgLYCY5q3h1ZDPh2htepbGTZkv+fyR7hJQbQW9ifJdfpu6d2+tP6YuiI5AMlBGVmKrd+/e7C8AAADsFARtAQAAgG348ssv1aNHD5dZa39PO+00denShf0GAACASmHnJgIAEHX+XPunfl/9u/uLckr/U/EZc1UvsJhdCOygDh066JprrlG/fv3066+/aq+99tL555+vrKws9i0AAAAqHJm2AGKab4tPz/Z8Vv68PAVH7R/p7iBMv5f7KS0jTS1SW2jZsGXsm/L4sp/qZaXpRE9jPeCZGPF96PHlKPHumzUj2Sudf1ukuwNslU0y5vf/VzKnVatWOuOMM9zl4cOH66+//tKpp56qY445Rp999pm8Xi97FAAAAFU70/a5555TXl5eqe0LFizQwoULd2qfAMSmYDCoNXPXaMMf6yLdFaD6C0pxK5dp8+JFTEKGqDdq1CilpKSUGMANZd5+8cUX+vvvv3XrrbdGqJcAAACoriKSaXvLLbdo48aNatq0qdq1a+dOL0tOTnZtgUBAl156qdq0aaNnnnkmEt0DEEMSkhN09tdna9PmHH3pz5b8gUh3Cai2ggle5V55h/Zt10SfbE6Ucn2R7hJQqvPOO6/g8m677aaDDz642DYNGjTQ/fffr/nz57MnAQAAUKEiErS108eWL1+u2bNna8mSJZo3b566deumSy65RB999JHWrl2rt99+OxJdAxBj4uLj1KZvG61Nz5JnCgfdQOV+4OKU134P1eveWpq6gJ2NKuPQQw91S0lOPvlkeTyend4nAAAAVG87LWi7bNkyl03bsGFDxcXF6aGHHipos1PObr75ZjcLb+3atbVo0SL3FwAAANjZcnNzNWfOHO2zzz7b3NYSEazUjtW8BQAAAKpcTdvnn39ezZo104EHHqj169dr6tSpeuedd3Tddddp77331ueff66XXnpJgwYN0g033LCzugUgxuX58/TH+3/o74nzFQyUXmsbQEV84AKK+2WG1kz5xuohsUsRtWysetFFF2ncuHFb3e7XX391E5ExFwMAAACqbKbtbbfdpiuvvFJTpkzRxx9/7IKz69atU//+/TV+/Hh16tTJbWez8Pbo0UMffPCB2wYAKpM/x683j3/TXU4dP4CdDVQij9+vxOfu1xy7MmqsrWF/IyrZvAtffvmlrrrqKr366quuhNdBBx2kevXqKSsry2XhWkDXSn1Ze9euXSPdZQAAAFQzOy1oO3fuXH3zzTcuILt06VKtWLFC119/vaZNm+YWK5lgs/C+8MILuuyyy/Tkk08StAVQ6TxxHrXav5V8gTxtpiYhUMkfOI/ydu2oerWStMpjJ/sE2eOIWnXq1NGLL76on376Sa+88ooefPBBN5FujRo13Jj1pJNO0iOPPOLGsAAAAECVDdrWrFnTZSV899138vv9GjNmjAvWTp482WXbWgDXZuWdPn26fvvtN51yyik7q2sAYpi3hlfnTT3PTUQ2ziYi8/kj3SWg2gp6E5U77B7t0721/rKJyHJyIt0lYJu6d+/uFgAAAGBn2mmpAYsXL3b1bHv16qXVq1e7ickuvPBCd4qZ+fbbb9WuXTt3fcOGDapfv/7O6hoAAAAAAAAAxF6mrU08NmPGDOXk5Ojnn3922bZpaWmaOHGifv/9dxesTUlJ0cCBAzVkyBCXkevhVGUAiFkzL5ypQDCgeE98pLtSdR01U+vTM/X2jCXSlkh3BgAAAAAQdUHbG2+80U0uZrPxLliwQFu2bHElEfLy8nT22WfriCOOUFJSkn744QcNHz7cbXvcccftrO4BiFG+LJ9ePOhF+QN5Ct7UK9LdQZhmqc3YHzuqRjPl+bK0Oc7Oaol8KQKPL1eJD1ynmSmJ0pk3RLo7AAAAABC1dlp5BJtd9/bbb1dqaqpat26tt99+W6eeeqp23XVXNWvWzGXc3nDDDWrevLkuuugi/d///d/O6hqAGBbMC2r5rOVa/dNKKcikSEDlfuCCiluyUBl/zLMPHzsbAAAAACKdabvPPvu4urWZmZmuNIJN6GCZttnZ2TrmmGNcsPbyyy932x5wwAEuMxcAKltCUoJOn3i60rfkaorXL/kD7HSgkgQTEpR70Y3qsVsjfe73SmLiPwAAAACIaNDW1KpVyy2nnXaau37ppZe6v5ZhW6hTCQnq27fvzuwagBgVlxCnDv07aG16lr6dMj/S3UGYZ2Y/o8zcTNVKrKWh+wxl35THgmeUnLlBXXM3a4oGRH4fxsUrb4991LB7a2nqAoK2AAAAABANQduyevDBB3Xeeeepfv36ke4KACBC7px8p9Iy0tQitQVB2/Kac6dqZaVpX09jTfFEQdAWAAAAAFA1g7bff/+9rrvuOnXo0EEDBw6MdHcAVHN5gTwt+mqR0jfnKuihpi1QuR+4PMX98YvW+1dIefXY2QAAAABQFYK269ev15lnnqnjjz+egC2AncKf7dcrR7ziLqeOJxMRqEwev0+JT9ypn+3KqLG2hh0OAAAAANEctF2+fLn69++vpk2b6qWXXop0dwDECE+cR032bCJ/XlC5HgJIQOV+4DzKa9FGtWt4tcoTZ1OTscNRZaxYsUINGjRQYmJipLsCAACAGGBHTBEVDAb18ssvq1u3bmrfvr0+/vhjN1kZAOwM3hpeXfzzxTrtu3PkSYpnpwOVKOhNVO4NY9TrxdckL4EvRLepU6dqw4YNBdeff/55nXHGGcW2+/bbb/Xhhx/u5N4BAACguttpmbY+n09er7fg+saNGzVhwgSNGTPGtT377LOuLAIAAAAQaePGjdOrr77qzgA74YQTNGvWLLVt21YjR4502baBQECnn3667rrrLrVr104DBlBiBwAAAFUw0/aggw5SnTp11Lx5c9WuXVv16tXTueeeq1NOOUW///47AVsAAABElVGjRrkzwZYsWaKvvvrKBW/Hjh2rzZs3Kzs7W/Pnz3eZtjfeeGOkuwoAAIBqZqcFbe+55x699tprbqBrmQuPPvqoTjvtNP3vf/9Tx44d9eabb+6srgBAAV+WTy/2fVHv9X9dwZwAewaoRB5frhIfuVU/Xn6R5MtlXyPqtWjRQg0bNnTJBaNHj1bLli3d8uCDD7pl5syZOvHEE9122+L3+91Eu9OmTSu0/qefflKfPn2UmpqqPffcU998801B27p161yg2Ppw7LHHavXq1TvcBgAAgKphpwVtDznkEDfR2JFHHqlBgwbpiiuucKecpaWl6dZbb9WwYcN0zDHHKD09fWd1CQAUzAvqn8n/aPl3S63INnsEqEzBoOIW/K6NP/9oHz72NaLeCy+84M4Ss7kXLrvssmLtFoC1M8e2JSsrS4MHD3a1b3NzcwuVCzv66KNdYHXu3LkuAHzyySe70mHGbhMfH68ff/zRlWY455xzCm5b3jYAAABUDTutpm1prCbYmWee6eqADRkyxAV3P/30U5cZAACVLSEpQSe9dZIytuRqmtcj+cm2jRYdGnRQneQ6alKzSaS7UnXV7iB/fG1tyKolRcFvEsGEBOWed632btNQq+Ktzr0/0l0CSmTJBN9//71uuukmd/n222/Xeeed5xINjE2ca4kGkyZNUo0aNba5F4cPH65OnTqpQ4cOhdYvWrRI1157rUaMGOGu29/bbrvNJTVYcNdKMixbtkxNmjRxmb72d+nSpS4IXJ62Vq1a8YoDAABUEREP2oZYvdu3337bncp10kknucFmXNxOSwQGEKPiEuLU5eQuWpuepelT5ke6Owjz1dlfsT92VL+vtDE9S+/YezszJ/L7My5eed33V+PuraWpCwjaImr17dtX77zzjsuMtWxbm5th77331g8//ODaly9frnvvvdcFdT///HPtuuuu26yNW79+fb311luF1nfv3t0tIVZuwbaz4KqNi3fffXcXcDU1a9Z0JcXmzJmjjIyMcrWVFLTNyclxS0jorLe8vDy3GI/H45ZgMOiWkG2tD92+vOvtWKDofW/v+vL2nefE6xSr772it0PlCv+u3Z7XqTq+93hOvE6x9N7LK+N3bdQEbUNZt6+88orLRLjvvvvc7LwAAADAzmS1Z/v16+eSCWwuBiuBcPnll+vJJ5907eeff75bnnjiCZdsYMHWrSUbWCB2W2zwbmNfy7a10gZr16519XOLJjmsX79emzZtKldbSSwT94477ii2fs2aNW6yNWPZxHYfFtC1TN4QCwhbLd4NGzYUKvtgkw6npKS4x7RaviE2EXFSUpK77/ADmQYNGrjnXLT2buPGjRUIBFyN3hA74LGAtD2ePW5IQkKCO1PP+hdebs2OL2z/Z2ZmugnkQnhOvE6890r+PJX2XYHKYfs7/LuP7z2+y/n/FBv/nzIyMqpe0NbUrVtXTz31lMsQAIDKlhfI07Lvl2nT5hwFA1Fw/jhQneXlyfP3H9oYv0HK2/Yp5UAk2UGBjUltkjErlXDUUUe5wKpNqhtidW4/+OADvf/++y7AuyPuuusuV8vWyiUYO/iwA4twXq/XrS9vW0ksUGwlIELs4MMychs1auQOMEzo/uy6HYiEhNbbQVTRbJKSgtWh9XbfRdfbYgdX4SwQXtL60EFUSevtICo5ObnYY9aqVcsdSBVdz3PideK9V/jzVJYfmVBxbH+Hf5fxvZeP73L+P1X3cURy2GNUqaCtOe644yLdBQAxwp/t19g++QfgqeMHRLo7QLXm8fuU9PBN+tGujLLPXeHAEhBN/ve//7mJvCwT1SYHe/zxx92kurfccovLrjVWNsGycF9//fUdCtpaGYb/+7//04wZM1yA1ViweOHChYW2szq1tt4OHMrTVlpw2pai7ECnaPZw6KCoqNLWl5Z9vD3rt/cxK3s9z4nXqbq/9yhRuHNVxHdtdXnv7cz1PCdeJ0+E33tl/a6NyqAtAOws9kVav119BayeFLs9qpzx7hlau2WtGqY01KsnvBrp7lRNU89Q7S2rddSWRL2u2yPdGxejzWvUVDWTLChFwBbRyyYIe+CBB1wN21122UVff/21myz3mmuucbVsLTvCTrdr3bq1dtttNzfRWHl99913Ovvss13Grj1WyKGHHuomD/vpp59c3dv58+e7flltXfvfVZ42AAAAVB0EbQHENG+KV1fMv8JNRDbOTdYUiHSX8K/JiycrLSNNLVJLzg5DGayerMSsNLX0NI6KGGnQm6TcW5/QYd1ba7FNRJYbBZOjASWwbFUrjRAKorZr107vvfeexowZo1dffbVQdoTVKzv22GNd6YTtzVCz+mjHH3+8m89h8uTJbjGDBw9W27ZtXZDY2i+66CJXW9fKMVjfTHnbAAAAUDUQtAUAAADCWK00C3qG69GjhyuDUFJNtKeffrpM+88mM2vTpk3BdZukwgKqJnyijdCMwlbntkuXLvr888919dVX64ILLijYprxtAAAAqBoI2gIAAABFWMCzT58+bkKKkCVLlrgZgjt27Fiu/WVB23A24dftt2+9dMnpp5/ulopsAwAAQPTbvnO4AKAaTkT2Wv/XNPHktxXMpTQCUJk8/lx5n7xbv4y4WvLlsrMR1e677z5XvsDKC7z99ttu3RdffFFiti0AAABQ0ci0BRDT8gJ5mv/RfHc59cIuke4OUL3lBRU/90ets8vH2OnaUVBoFyiFTTbm9Xq1bt06N+lYaJ3Vtv3jjz8K1bRt3Lixq4ELAAAAVBSCtgBiWnxivAaNHaSMrFzNTIiT/GTbApUlGB8v3xmXqdsuDbQq3oYgfN4Q/bp166YJEya4yzbRWNeuXXXccccVtPt8PqWkpESwhwAAAKiOCNoCiGnx3njtdc5eWpuepVlT8jNuAVTWBy5Bgd6Hqln31tLUBQRtEbU+/PBDrVy5Uh988IH2228/zZs3TwsWLNCqVavUsmVLHX744apTp06kuwkAAIBqjKAtAAAAEMZq16alpendd9/VMccco5kzZ+rAAw90mbZ+v1+PPvqoUlNT1atXL/Xv31+DBw9W7dq12YcAAACoMARtASjWa9qunrNaGzKzFQwEI90doHrLy5Nn2SJl1MqS8qhni+j1yCOPaOHChXrppZdUt25dff7559pnn300e/Zsbdy40a2zWrZW29Zq2VoQ9/fff5fHw/saAAAAFYOgLYCY5s/26+nuT7vLqeMHRLo7CHPh3hdqU84m1UniFORya3ehsjLX6bflfskX+beXx+9T0n3DNdOujBrLRGSoMs466ywXuJ0zZ44WL17sJiezLNsnn3xSV155pXr37k3AFgAAABWKoC2AmGZZUanNU5UXJMs22tzW97ZId6Hq63qbNqdn6ft18yVfTqR7I3mkYJ36SvLGE7BFVPv0009dTdtJkybptNNOc6UPLDhrmbZDhw5VYmKiGjZsqLvuuktHHHFEpLsLAACAaoigLYCY5k3xaljaMDcR2TibiCyT2eyByhL0JinnrmfVr3trvW4TkeVGQSAZKMGUKVNcTdsXX3zRTUBmy/Tp0/Xss8+64G0wGNTTTz+thIQEt5x66ql64okn2JcAAACoMHEVd1cAAABA1Xf33Xe7GravvvqqVq1apaZNm+qXX35xmbYjR47URRddpFdeeUU//fSTvvzyS1f7Ni8vL9LdBgAAQDVC0BYAAAAogZVB+L//+z/VqFFDn332mZt8zOv1Kj4+3gVwjzrqKCUlJbmAblwcw2oAQOzw5/jdmSelTfYcyN3+Mxi3drutteWk5yiYF6zQNiAaUB4BgGJ9IrL3hrynHF9AwbM6Rro7CNPyoZZKy0hTi9QWWjZsGfumPN5rqYZZabrA01gPeCZGfB96/LnyPv+g5tRNkQ47K9LdAbYqJydHfr/fXc7IyNCIESM0duxY/fPPP2revLmbiKxz584aOHCg5s6dy94EAMSEzWs26+1T39ay6cvkrenVMU8coz1O3aOg3YKg408ar/b922vvC/Yu8/1u7Xaltdmx3LtnvqvF3yxWQlKCTh5/slrt32qH2oBoQtAWQEyzX2znvp1/sJ16ZodIdweo3vKCiv95utbY5X5nMhkZopqVQLAMWzNv3jz39/DDD1dWVpbat29fsN3+++/v6toCABALPhv2mZsX5IZNN+jvL/7WWye9pTZ926hWk1rK3Zyr989+X4u+XuQCrGW1tdttre27e79Tbmauhq8crn++/ccFYq9ceKWbbLq8bUA0YYQJIKbFJ8br6MeP1uZsn35KiJP8TEQGVJZgfLx8J1+gLi3raVW8DUH4vCF6nXTSScXWtWzZsti6XXbZZSf1CABQlJ2en5OV5S4n1ahB0G0n+PPDP3XKO6e446j2x7RX3V3qauFnC7XnkD21fNZytTu6nRJ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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "分析结果：\n",
      "以下是 **员工薪资分布** 的全面分析报告：\n",
      "\n",
      "---\n",
      "\n",
      "## 📊 薪资核心统计量\n",
      "\n",
      "| 指标 | 数值 |\n",
      "|:--|:--:|\n",
      "| 👥 **员工总数** | 5 人 |\n",
      "| 💰 **平均薪资** | **13,800 元** |\n",
      "| 📌 **薪资中位数** | **16,000 元** |\n",
      "| 🔺 **最高薪资** | 20,000 元 |\n",
      "| 🔻 **最低薪资** | 5,000 元 |\n",
      "| 📏 **标准差** | 5,891 元（薪资差异较大） |\n",
      "| 📦 **四分位距 (IQR)** | 6,000 元 |\n",
      "| 📊 **偏度** | **-0.86**（左偏分布，低薪拉低均值） |\n",
      "\n",
      "---\n",
      "\n",
      "## 📈 分布特征解读\n",
      "\n",
      "### 1️⃣ 整体分布\n",
      "- **均值（13,800）< 中位数（16,000）**，说明分布呈**左偏**形态，少数低薪资拉低了平均水平\n",
      "- 薪资范围跨度大：从 **5,000元**（人力资源）到 **20,000元**（技术部），差距达 **4倍**\n",
      "\n",
      "### 2️⃣ 薪资区间分布\n",
      "| 薪资区间 | 人数 | 占比 |\n",
      "|:--|:--:|:--:|\n",
      "| 5k ~ 8k | 1人 | 20% |\n",
      "| 8k ~ 12k | 1人 | 20% |\n",
      "| **15k ~ 20k** | **2人** | **40%** |\n",
      "| 20k ~ 25k | 1人 | 20% |\n",
      "\n",
      "> ✅ **主力薪资段集中在 15k~20k，占比 40%**\n",
      "\n",
      "### 3️⃣ 各部门薪资对比（平均薪资排名）\n",
      "| 排名 | 部门 | 平均薪资 | 最高 | 最低 |\n",
      "|:--:|:--|:--:|:--:|:--:|\n",
      "| 🥇 | **技术部** | **18,000 元** | 20,000 | 16,000 |\n",
      "| 🥈 | **销售部** | **14,000 元** | 17,000 | 11,000 |\n",
      "| 🥉 | 人力资源部 | 5,000 元 | 5,000 | 5,000 |\n",
      "\n",
      "---\n",
      "\n",
      "## 💡 业务洞察\n",
      "\n",
      "1. **技术部薪资最高（均值18k）**，属于公司高薪核心部门，薪资离散度也较小（标准差2,828），说明技术团队薪资较为均衡\n",
      "2. **销售部薪资有较大波动**（标准差4,243），最低11k、最高17k，可能存在底薪+提成结构\n",
      "3. **人力资源部仅1人且薪资5k**，可能存在数据不全或该岗位为初级/外包人员\n",
      "4. **整体薪资呈左偏**，建议关注低薪员工的激励和保留\n",
      "\n",
      "> 📌 **图表已生成**，包括薪资直方图、箱线图及各部门对比图，可直观查看分布形态。\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",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "import traceback\n",
    "from io import StringIO\n",
    "from contextlib import redirect_stdout\n",
    "from langchain.tools import tool\n",
    "\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",
    "# 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",
    "\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",
    "all_tools = tools + [execute_python_code]  # + Python 沙箱\n",
    "\n",
    "print(f\"统一 Agent 工具列表（{len(all_tools)} 个）：\")\n",
    "for t in all_tools:\n",
    "    print(f\"   - {t.name}\")\n",
    "\n",
    "# ============================================================\n",
    "# 定义 System Prompt\n",
    "# ============================================================\n",
    "SYSTEM_PROMPT = \"\"\"你是一名全能数据分析师，既能写 SQL 查数据库，也能用 Python 做统计和可视化。\n",
    "\n",
    "## 可用工具\n",
    "\n",
    "### 数据库工具\n",
    "- sql_db_list_tables    — 列出所有表\n",
    "- sql_db_schema         — 查看表结构（DDL + 字段备注）\n",
    "- sql_db_query_checker  — 检查 SQL 语法\n",
    "- sql_db_query          — 执行 SQL 查询\n",
    "\n",
    "### 数据分析工具\n",
    "- execute_python_code   — 执行 Python 代码（DataFrame 已在内存中）\n",
    "\n",
    "## 决策指南：什么时候用什么\n",
    "\n",
    "### 用数据库工具的场景\n",
    "- 需要从数据库查原始数据（跨表 JOIN、复杂 WHERE 过滤、聚合）\n",
    "- 数据可能超过内存中 DataFrame 的范围，或需要实时数据\n",
    "- 用户问题可被一条 SQL 直接回答（如\"张三的薪资是多少？\"）\n",
    "\n",
    "### 用 Python 工具的场景\n",
    "- 需要统计分析（describe、corr、groupby 聚合后算比例）\n",
    "- 需要画图（柱状图、饼图、散点图、热力图等）\n",
    "- 需要复杂计算（滚动平均、同比环比、预测回归）\n",
    "\n",
    "### 串联使用的场景\n",
    "- 先用 SQL 从数据库查出数据 → 拿到结果后 → 用 Python 画图分析\n",
    "- 典型：用户问\"哪个部门销售额最高？画张图\"\n",
    "\n",
    "## 可用 DataFrame 变量（在 execute_python_code 中直接使用）\n",
    "- employees_df — 员工表（id, name, department, salary, hire_date）\n",
    "- products_df  — 产品表（id, product_name, category, price, stock）\n",
    "- orders_df    — 订单表（id, employee_id, product_id, quantity, order_date）\n",
    "\n",
    "## 工作流程\n",
    "1. 理解用户问题，判断属于哪类\n",
    "2. 如果是简单数据查询 → 用 SQL 工具链（list_tables → schema → query_checker → query）\n",
    "3. 如果是分析/画图 → 用 execute_python_code 直接分析内存中的 DataFrame\n",
    "4. 如果是\"先查后画\" → 先 SQL 拿到结果 → 把结果写进 Python 代码中分析画图\n",
    "5. 用中文总结结果，给出业务洞察\n",
    "\n",
    "## 约束\n",
    "- SQL 查询单次不超过 50 条\n",
    "- Python 代码中画图前先设置中文字体\n",
    "- 出错后分析原因，自我修正重试\n",
    "- 回答简洁专业\n",
    "\"\"\"\n",
    "\n",
    "# ============================================================\n",
    "# 创建 Agent\n",
    "# ============================================================\n",
    "analysis_agent = create_agent(\n",
    "    model=llm,\n",
    "    tools=all_tools,\n",
    "    system_prompt=SYSTEM_PROMPT\n",
    ")\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  # 抑制 matplotlib 的字体警告\n",
    "\n",
    "response = analysis_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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h5xDdK+IcolVHfD388MNz64ToahGDBk6urn87Mc5A/LuL/vwxWFqvXr1yk/MYTO33/L1Fv/f77rsvN5cvjfQfI+uHGAMhjhfnMbW/s7h+lFpz1OV9iCb0ceyqr7dkjTXWyNtqaqkwrfMFqA8aRfKe2ScBALUVISyCWwxENfmIyDHtUmn6rtI0U9RdhMEIVnFDY7PNNkv/+c9/Uu/evRvcOdRW3EQ44YQT0sCBA2f4xgAA9Y/RywGYrURFLsL1n/70pzxSePv27fNI69GXNqrdUamvqS87tRctHaJ/bbynbdu2raw2N7RzAIBy0LwcgNlKNNmN6bo6d+6cp5GKCmiE7RhBOQbCigGeYuAnpl9MYRU3MuIGx2OPPZZDb0M8h9qKJuIx/3g5B3kDoP7QvBwAAAAKotINAAAABRG6AQAAoCBCNwAAABTE6OV1NGnSpDx/ZszJ2qhRo2J+KwAAAMzSYvbt0aNHp4UXXjg1bjz1erbQXUcRuDt27Dijvx8AAADqgWHDhqUOHTpMdbvQXUdR4S69sa1atZqx3w4AAACzpVGjRuWCbCkjTo3QXUelJuURuIVuAACAhq3Rb3Q7NpAaAAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgTYs6MDRkw4cPT/3790+77bbbFNuef/759OKLL6bmzZunLbfcMnXo0KFOx/7yyy/TjTfemJo0aZL22GOP1LZt27JuBwAAykelG8pszJgxaeutt07XX3/9FNsuvfTStO2226Z33303Pf7442n55ZdPr7zySq2PPXjw4LTCCiukAQMGpCeeeCKtttpq6ccffyzbdgAAoLwaVVRUVJT5mPXaqFGjUuvWrdPIkSNTq1atZvbpMIsZOnRo+stf/pLGjRuXllhiifTII49U296xY8d06623prXWWis/3meffdLEiRPTNddcU6vjb7rppvnvL44RNt5449S1a9d06qmnlmU7AABQ3myo0g1lNGjQoPTPf/4z7b///jVu//nnn6v9g5xjjjlS06a16+UxduzY9OSTT6YDDzywcl2vXr3SQw89VJbtAABA+enTDWW0/fbb569nnnlmjdt33nnnXN0+6qijclX8jjvuSI899litjj1s2LA0YcKE1KVLl8p1nTp1Sp988klZtgMAAOUndMPvaLvttktXXHFF2nXXXXOz8h49eqTFF1+81s1XoiresmXLynUtWrTITdnLsR0AACg/zcvhd/L999/nAdb+9a9/5abeX3zxRV7Xs2fPWj2/WbNmKYZgiGp1STyec845y7IdAAAoP6EbficxTVj04T7mmGPy14UXXjidcMIJeWqx2oim4GHIkCGV6z799NM8OFs5tgMAAOUndMPvZJ555slNuas2545+3THiYW3MO++8ab311kvXXntt5bq77rorrbvuumXZDgAAlJ8+3fA7WWONNdIiiyySpxTbYYcd0tdff53OP//8dMQRR1QLwVERP+OMM2o8Rp8+fdLmm2+em4X/9NNP6d57780jppdrOwAAUF4q3VCAP/7xj2mrrbaqti76Tj///PNp1VVXzWE3wnXMj923b99q+8W0XlOz4YYbpqeeeipXyEeMGJEGDBiQlltuubJtBwAAyqtRRZS8KPsE6DA9nnvuufTaa6+lQw45xBsIAAD1IBtqXg6zkJg+bLXVVpvZpwEAAJSJ0F1Pbdnn1pl9CkAt3N9vJ+8TAEA9pk83AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgTdMs5JFHHknt2rVLXbt2zY9/+OGHdOedd06x3+KLL5422GCDNGzYsPToo49W29arV68055xz5u/79++fHnjggbTMMsukfffdNzVu/H/3GKa1DQAAAMphlkmazz33XNpuu+3S22+/Xbnu559/ToMGDaq2nHvuuemmm27K2++444500UUXVds+YcKEvO3mm29O22+/fZp33nnT9ddfnw444IDK405rGwAAAJRLo4qKioo0kz344IPp8MMPT02bNk3//Oc/05577lnjfqNGjUrLLbdcrlJHhXrXXXfNFe999tmn2n7xkjp06JDOOOOMtNtuu6Uff/wxLbrooumNN95Iiy222FS3RQX9t8Q5tG7dOo0cOTK1atUqzaq27HPrzD4FoBbu77eT9wkAYDZU22w4S1S655577jRw4MDUqVOnae532mmn5Qp1BO7w4osv5qbk11xzTXrppZcq93v33XfTN998k/cNbdq0Sd26dUvPPPPMNLfVZPz48fnNrLoAAABAbcwSofvPf/5z7ss9LXH34LLLLkv/+Mc/8uMIzp988kk677zz0oABA3KILlXIP//881zRnmuuuSqf3759+/Tll19Oc1tN+vXrl+9elJaOHTuW6VUDAABQ380Sobs2Lr300rTZZpvlpuEhBj677bbb0v/+97909dVX56933XVXbib+66+/5up5VS1atEiTJk2a5raa9OnTJwf+0hKDtwEAAMBsN3r5tFx++eW50l0SlfEddtih8vFCCy2UunTpkl599dW01FJLpW+//bba86NZeAycFtXqqW2rSTRfL42GDgAAAPWu0v3888+nsWPHpvXWW69yXQymFiOPVxVNxNu2bZvD9+jRo9N7771XObBa9P9efvnlp7kNAAAAGlzovueee9Imm2xSbS7teeaZJ/Xu3Tu9/vrreT7vY445Jv30009p/fXXT82bN0877bRTOuigg3K/7xiALayzzjrT3AYAAAD1NnQvvPDCNQ61/sEHH+T+3FWtvvrq6ZRTTkmbbrpprm7H6OMPPfRQ5fNjPu9oct65c+d066235v7eTZo0+c1tAAAAUK/m6Z5REydOnGpojpfXqFGjOm+bGvN0A+Vknm4AgNnTbDVP94yaVpV6WqG6roEbAAAA6qJehG4AAACYFQndAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAADSF09+3bN917773V1r300kupR48e1ZbRo0fnbRMmTEj//Oc/U+fOndO2226bvvzyy8rnTe82AAAAqHeh++STT079+vVLP/zwQ7X1Dz/8cGrSpEnq2bNn5TLnnHPmbUcffXR67LHH0rXXXpuWXnrptN1226WKiooZ2gYAAADl0jTNAs4777z06quvpnXWWWeKbS+//HLaZ5990jbbbFNt/c8//5wuvvji9Pjjj6fVVlstdenSJXXq1Cnvv9JKK03XtjXWWON3fNUAAADUd7NEpXv77bdPd911V2rRosUU2yIMDxgwIO28887phBNOSN99911e//bbb6fGjRunbt265cdRDY8Q/dprr033tpqMHz8+jRo1qtoCAAAAs03o7tChQ43rhwwZkkNuq1atct/rqIavu+66adKkSenbb79NHTt2zAG6pE2bNmnEiBHTva0m0eS9devWlUs8FwAAAGab5uVTs8QSS6Thw4entm3bVlbEF1988fTkk0+mRo0a5Sp1VdHXO8L09G6rSZ8+fdKRRx5Z+ThuAgjeAAAAzDaV7qmJcFwK3KFp06Z54LPPPvssLbTQQmnYsGG56l0SAT3WT++2mkQgj0p71QUAAABm+9B9ww03pIMPPrjy8U8//ZTeeOONXAGPAdHmmWeePCBaGDNmTHr22WfT6quvPt3bAAAAoME0L19vvfVy0+7o8x0jjF9yySVp2WWXzeujmfhRRx2VRzY/5phj0n333ZeD8/LLL5+fO73bAAAAoF6G7q222iott9xylY+j7/RTTz2VzjjjjNS/f/+0ySab5BAegTvE9+3bt0933HFHWnnllVPfvn0rnzu92wAAAKBcGlVUVFSU7WgNQAykFqOYjxw5cpbu371ln1tn9ikAtXB/v528TwAA9TgbztJ9ugEAAGB2JnQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGgHouZge9++67f3O/Rx99NA0fPrxOx540aVJ65JFH0k033ZS+++67sm8HgNmd0A0A9dwxxxyT9t1332nu88ILL6QtttgivfPOO7U+7rhx49KGG26Y9t9///Tf//43rbTSSunTTz8t23YAqA+EbgCop3799dd00EEHpSuuuGKa+/30009p9913z1XnurjgggvSRx99lN544430zDPP5NDeu3fvsm0HgPpA6AaAemrw4MFpzJgx6eqrr57mfkcccUTq3LlzWnrppet0/Ntvvz1X0Nu0aZMfH3DAAenBBx9Mv/zyS1m2A0B9IHQDQD0VzbWvvfba1KJFi6nuEyH3/vvvT1deeWWdjz9kyJC0+uqrVz5eaqmlcpPxzz//vCzbAaA+ELoBoJ5q1KjRNLePGDEiV5qvuuqqNP/889f5+CNHjkzt27evfBzhPn5mNFcvx3YAqA+EbgBooGIAsx49eqTNNttsup7fuHHjKfqBx7pmzZqVZTsA1AdCNwA0QDE92D333JPmmGOOdOyxx+YlKt/XXHNNuuWWW2p1jA4dOqQPP/yw8nFMNzZx4sS0yCKLlGU7ANQHQjcANEARbE844YQ8iNlcc82Vl2jaHVXm2laat9xyy3TbbbdVPn7ooYfygGwtW7Ysy3YAqA+azuwTAAB+fyussEJeqrrhhhvSbrvtlufODu+//366/vrr03HHHZcr4pOL0ca7du2a+vTpk5Zddtn89eSTTy7bdgCoD1S6AaCea9u2bdpkk01+c7/u3btXG1Dtu+++SxdeeGFu8l2T5ZdfPvXv3z8NGjQonXnmmTk0Rz/xcm0HgPqgUUVFRcXMPonZyahRo1Lr1q3ziKutWrVKs6ot+9w6s08BqIX7++3kfWKW9fPPP6f99tsvV7sBgOnLhirdAECNxowZk0488UTvDgDMAH26ASjc2FMX8i7Phlr8/8vYmX0i/G6a9x3u3QYoM5VuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoSNMZefLrr7+eHnvssfT555+nUaNGpfbt26cll1wybbXVVmnBBRcs31kCAABAQ6l0P/HEE2mFFVZI22+/fRo+fHgO2t27d0/t2rVLzz33XOrcuXPq0aNHGjZsWJ2OO3bs2DRmzJgp1v/666/p3XffTd98802djjdp0qT08ccfp59//rlO2wAAAOB3D90TJ05MBx54YNp3333Taaedlj766KN0zjnnpEMPPTTtscce6R//+Ee67rrrchBfZZVV0h//+Md099131+rY48aNSxtssEG64447qq3v379/6tChQw71iy22WOrbt2/ltltvvTXNNddc1Zbvvvsub4vq+8orr5zWWmuttMgii6THH3+88nnT2gYAAAAzJXRHsI6g/dZbb6W//OUvU91vzjnnTMcee2wOswcddFB64IEHpnncESNGpE033TS9+eab1dZH1XunnXZK5513Xvrqq6/SwIED0xlnnJGGDBmSt7/44ovpX//6V96vtLRt2zZv++tf/5pWXXXV/Lzbb7899ezZs7KqPa1tAAAAMFP6dB9//PGpU6dOqVmzZrXaPyrdL7zwwm/2777yyitTr169cqV68ubml112Wdp2223z46hOzzfffLmZ+dJLL51D96mnnpqaNq3+MmJ79DWP5uMhKugLL7xwrpqvttpqU922xRZb1OXtAAAAgPJVuqPvdjQDr4toEj55mJ5cNEvfZ599plgfA7OVAnd4+eWX0/jx41OXLl3SL7/8kl577bV02GGHpXnmmSeH6aeeeirv9/7776f5558//+ySZZZZJlfIp7WtJvHzYpC4qgsAAAAUMpDavffem5tm33fffalcGjVq9Jv7TJgwIfcdjz7dzZs3T0OHDk1rrLFGroR//fXXaa+99kpbb711GjlyZA7GEayrimD+008/TXNbTfr165dat25duXTs2HEGXy0AAAANRZ1D99prr50HH9tmm21y+I4Q/ns45phj0hxzzJF69+6dHy+11FJpwIABac0110wtWrTIA7wtscQSuel47BeV8Jr6mk9rW0369OmTg3xpqeuI7AAAADRcdQ7diy++eK5yxxzdEXy322673Nz7nnvuSZ9++ukUSznceOON6aabbkq33XZbatKkSV43evToKQJwhO/oBx7nFZXwqtXrDz74IPdHn9a2mkQYb9WqVbUFAAAACpunO6y00krplltuSe+8804OwNH3OgL55MuMevjhh9PBBx+cK+pVB2SLUcd33333VFFRkR/HPN6vvPJKWn311XN/7ZhHPJqel0J19AcvTTs2tW0AAAAw00Yvn7yPdVSgo8/zZ599lvbbb788Wnk5/frrr3k6r6guH3fccZXrYxqyXXbZJV166aVp3XXXTYsuumgO59H0fLnllsv7nH322bkJ/BNPPJEGDRqUm4mX+nJPaxsAAACUS6OKUqm4lmI076uuuiqdfvrp6csvv8wDmEV/6wi+Myoq1VHNjj7jIUZKf/rpp6fYb/nll88DmkUoj2btw4cPT3/605+mCP2ff/55euSRR9Kyyy6b1llnnVpvm5YYiC0GVIv+3bNyU/Mt+9w6s08BqIX7++3UIN6nsacuNLNPAaiF5n2He58AypwN6xy6b7jhhrT33nunPffcM4ftqfWFrq+EbqCchG5gViJ0A5Q/G9a5eXnXrl2nOfAYAAAAMJ2hO5pjAwAAAAWE7kcffTT9+9//rvX+L774Yl1/BAAAADTM0N24ceM011xzTXX7999/n9566638fWlObQAAAGiI6hy6N9poo7xM7sMPP0znnXdeuvrqq3Mn8n322Scdeuih5TpPAAAAaDjzdJc888wzed7r+++/Pw+udvLJJ+fA3bJly/KcIQAAAMymGk/Pk2J+7BtvvDGPZL7eeuulESNGpNtuuy1Xuw8//HCBGwAAAKan0h0Do/Xo0SN98cUXac4550z77bdfWm211dIPP/yQrrrqqin2/+tf/+qNBgAAoEGqc+iOanYE7jB+/Ph02WWX5WVqhG4AAAAaqjqH7m233TZ98sknxZwNAAAANOTQ3aJFi7yEioqKNGHChNSsWbP8+KeffkpNmzbNzc4BAACgoZuugdSGDh2a+3XH1GA77LBD5fp+/fqlueeeO6244orp4osvzqEcAAAAGqo6V7pHjRqV1l133TRx4sS077775u9LtthiizTXXHOll19+OR188MHpm2++Sccff3y5zxkAAADqZ+i+/fbb08iRI/OAau3atau2rVu3bnkpVb1POumk1KdPH83NAQAAaJDq3Lw85uRebLHFpgjck9tggw3Szz//nKcSAwAAgIaozqF7zTXXTG+++Wa69957p7rPpEmT0vnnn58WXnjh1L59+xk9RwAAAGgYzcvXW2+9PPf2Nttsk9ZZZ5209tprp0UWWSQ3IY95u2OQtYcffji9++676eabb06NG0/XWG0AAADQ8EJ3uOyyy3LgjhHKzzzzzDyoWkmjRo3Saqutlh544IG0+eabl/NcAQAAoP6H7rD77rvnJarbn332WZ6jO6rdHTt2TPPMM095zxIAAADqe+iOinaTJk2qrYugvdRSS9XpOQAAANAQ1KnD9U477ZT+85//1Hr/MWPGpI033jjdfffd03NuAAAA0HBC99lnn52uu+66tMcee6Tvvvtumvu+8sorud/3kksumbbaaqsZPU8AAACo36F70UUXTQMHDsxzdC+//PLpsMMOS/3790+ffPJJ+v777/OI5bfffnvq0aNH2nrrrdPf//73dOmll2peDgAAQINU54HUYpC0s846Kx188MHplltuSSeffHIaNmxYGjVqVJ6TOyrb2267bbrmmmsMqAYAAECDNt2jly+++OKpT58+eQEAAABmsHk5AAAAUHtCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAABm1dB9ww03pN69e6d33nmnPGcEAAAA9cQMh+727dunO++8M62wwgpp9dVXT5dcckn68ccfy3N2AAAA0JBD98Ybb5w+/vjj9Nxzz6WuXbum4447Li200EJp1113TY8//niaNGlSec4UAAAAGmKf7kaNGqW11147XXzxxWn48OHp2muvTU888UQO5Isvvni66KKLyvFjAAAAYLbStFwHiop2//7903XXXZfuuuuuVFFRkXr16pWWWWaZdPjhh+evG264Ybl+HAAAANT/SvfQoUPTP/7xj9SxY8e00UYbpQ8//DCdc845ueIdAfyYY45JSy65ZBo8eHB5zhgAAAAaSqX77rvvTjfeeGOuau+11165oj15BXyfffZJm2666Yz+KAAAAGhYobtnz57pkEMOSU2aNKlxe+PGjfOUYgAAANDQzHDobteuXXnOBAAAAOqZsoxeDgAAABQQus8+++zUvXv3GT0MAAAA1DszHLoXWGCB9Oqrr6ZffvmlPGcEAAAA9cQMh+7tttsudejQIZ1xxhnlOSMAAACoJ2Z4ILURI0ako48+Oh111FHp7bffTnvvvXcesbxkww03nNEfAQAAAA0zdN95553piCOOyN/feuuteamqoqJiRn8EAAAANMzQfcABB6Q999yzPGcDAAAA9cgMh+4555wzL+Xw2muvpbnnnjstu+yy1da///776dFHH03LLLNM2mSTTQrfBgAAAPVqnu4IwRtttFF68cUXq61//PHH06qrrpreeOONdOihh6YTTjih0G0AAAAwy1S6w/jx49OgQYPSt99+W7lu4sSJ6dlnn03nnnvubz7/hRdeSD179kzzzDPPFNsOOuig1K9fv3TIIYekL774Ii299NJp//33TwsuuGAh2wAAAGCWCd1jxoxJa665ZnrnnXem2NamTZtahe6vvvoqPfLII7nqXNUHH3yQPvroo8o+44ssskhaY401Uv/+/dNqq61W9m277rprjTcUYikZNWpUnd8jAAAAGqYZbl5+/fXX5+A9dOjQtNZaa+XHX375ZVpxxRVT3759az3Xd1SbJxfH7NSpU2rZsmXluoUXXjgNGzaskG01iYp469atK5eOHTvW+r0BAACgYZvh0P3xxx+nbt26pUUXXTT3yR44cGBaaKGF0tlnn51OOeWUNGHChOk+9rhx46Zoch6Pf/3110K21aRPnz5p5MiRlcvUwjkAAACUPXS3b98+vffee+mXX37JzbSjb3fo3LlzDqkjRoyY7mNHNfqHH36otm7s2LGpVatWhWyrSYzMHtuqLgAAAPC7hO5ddtkl973ecccd09prr50GDx6cjj766HTEEUfk5tjzzz//dB975ZVXzoOzffbZZ5XrXnnllTzNVxHbAAAAYJYK3R06dEjPPPNMDtxRBb744ovTJZdckh544IF0zjnnpCZNmkz3sWMgti222CL985//zIOZ3XjjjTkwr7/++oVsAwAAgFluyrAuXbrkJfTq1StP/zVp0qQ6B+7Yv3Hj6vcBLrzwwrTDDjvkZuFROb/55ptzk++itgEAAEC5NKqoqKhIs4GYVqxdu3apadOmv8u2qYkpwyKoR3/1Wbl/95Z9bp3ZpwDUwv39dmoQ79PYUxea2acA1ELzvsO9TwBlzoZlqXT/HhZccMHfdRsAAADM9D7dAAAAQJkq3THvdsx1XVuTz4kNAAAADUWdQ/ctt9ySB0urrdmkyzgAAADM/NC9wQYbpIcffrj8ZwIAAAANPXQvtNBCeQEAAACmzUBqAAAAUBChGwAAAAoidAMAAMCs0qe7JuPHj0+DBg1K3377beW6iRMnpmeffTade+655fgRAAAA0PBC95gxY9Kaa66Z3nnnnSm2tWnTRugGAACgwZrh5uXXX399Dt5Dhw5Na621Vn785ZdfphVXXDH17du3PGcJAAAADTF0f/zxx6lbt25p0UUXTRtttFEaOHBgnlLs7LPPTqecckqaMGFCec4UAAAAGlrobt++fXrvvffSL7/8ktZYY43ctzt07tw5jRw5Mo0YMaIc5wkAAAANL3Tvsssu6YMPPkg77rhjWnvttdPgwYPT0UcfnY444ojUunXrNP/885fnTAEAAKChDaTWoUOH9Mwzz6Qnn3wytWrVKl188cXp4IMPzs3KL7zwwtSkSZPynCkAAAA0xCnDunTpkpfQq1ev1LNnzzRp0iSBGwAAgAZthpuX16RRo0YCNwAAAA3edIfu+++/P11xxRWVj6M5+YknnpgHUIt5uy+44IJUUVHR4N9gAAAAGq46h+6JEyemjTfeOG211VZ5Tu6Sww8/PP373//Oo5k3b948HXrooalfv37lPl8AAACov6H73nvvTY8//ni68sor09NPP53Xvfbaa+miiy5K//rXv/K6/v37p1NPPTX95z//KeKcAQAAoH6G7vfffz8tuuiiae+99859t0NUtDt16pRDd8nmm2+efvzxR/N0AwAA0GDVOXRH4B4xYkRlmH7nnXfSXXfdlXr37p2aNWtWud9bb72VH7dp06a8ZwwAAAD1NXRvs802qV27dmn11VdPu+++e1p//fXTEksskfbZZ5+8/dVXX02nnXZaOuSQQ9LWW2+dmjYty6xkAAAAUP9Dd4sWLXK/7ZVWWik99dRTeX7uBx98MM0999x5+2WXXZb69OmTunbtmvt5AwAAQEM1XWXoxRdfPN1zzz01buvbt2864YQT0gILLDCj5wYAAACztbK3/Y4+3wAAAMB0NC8HAAAAakfoBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUpGk5DvLjjz+mK6+8Mr366qupd+/e6a233krjxo1L++23XzkODwAAAA0zdP/888+pW7du6ZNPPkmNGjVKvXr1Sj/99FM69NBD03fffZf69OlTnjMFAACAhta8/IYbbkgTJkxIw4YNSyuvvHJed8ABB6Rrrrkm9evXL1VUVJTjPAEAAKDhhe73338/bbzxxmn++eevtr579+5p9OjR6euvv57RHwEAAAANM3R36NAhvf7661NUtAcMGJCaN2+e2rZtO6M/AgAAABpm6O7Zs2caMmRI2mSTTdJXX32VnnjiiXT00UfnQdQOPPDA1KxZs/KcKQAAADS00N2uXbv05JNPpjFjxqShQ4ems846K1188cU5cEefbgAAAGioyjJl2EorrZReeOGFNGrUqBy+27dvn5o2LcuhAQAAYLbVtBxzdE+aNCnNN998qVWrVnkBAAAAytC8/PTTT0///ve/a9x28sknp65du+bpwwAAAKChmeHQvdVWW6U33ngjbbbZZmmbbbZJjz76aF7/4osvpuOPPz4PsHbkkUem7777rhznCwAAAA0ndM8111zppZdeSm3atMlThG255Zbpf//7X3r11VdTly5d0imnnJJ22GGH1L9///KcMQAAADSUPt133nln2nHHHdP111+fH88xxxzp9ttvz328S3N0d+zYMU8nBgAAAA3JDFe6W7ZsmQN1DKZWUVGRhg8fnuaZZ548wFpMJxZGjx6dWrRoUY7zBQAAgIZT6d5rr73Sueeemzp16pQf//DDD2nBBRfMfbobN26c3n333fTwww/n/t4AAADQkMxwpTvm5I6B1I466qi8fPzxx7l/99prr51OOOGEPId3NDVfc801y3PGAAAA0FAq3aXgffjhh1c+Pu+88yq/33TTTc3dDQAAQIM0w5Xu39KqVauifwQAAADU30r3+PHj06BBg9K3335buW7ixInp2Wefzf29AQAAoCGa4dA9ZsyY3F/7nXfemWJb9O2ekdAdo6KfdtppU6xfZZVV0p577pnefvvtdMUVV1TbFvOCl0ZKv+CCC9L999+flllmmby+atV9WtsAAABglmheHvNzR/AeOnRoWmuttfLjL7/8Mq244oqpb9++M3TsmPN7ySWXrLYMHDgwD9wW7rvvvvTee+9V296kSZO8rV+/fjnwH3DAAWnkyJFphx12qDzutLYBAABAuTSqiMm1Z8Df//739Pnnn6ebb745HX/88bmJ+UUXXZSeeOKJ1KNHjzRixIjUtGlZWrGnL774Iq266qrpzTffzIO3xTRkO+20U9pll12q7ffrr7/mOcJvv/32tPHGG6dffvkldejQIZ/TcsstN9VtMdL6bxk1alRq3bp1DuuzcnV8yz63zuxTAGrh/n47NYj3aeypC83sUwBqoXnf4d4ngFqqbTYsy5RhUW2O8LrGGmvkvt2hc+fO+YdH6C6Xf//73+mQQw7JPzO89NJL6aOPPkq9e/dOV155ZRo3blxeH83O43z+/Oc/V1bMowl87D+tbVPrrx5vZtUFAAAAamOGQ3dUmT/44IO044475rm5Bw8enI4++uh0xBFH5NQ///zzp3JVue++++500EEH5ceffvpp+vrrr9OHH36YFlpoody3e8MNN6zsC77oootWq7DPO++86ZtvvpnmtppEU/R4HaWlY8eOZXk9AAAA1H8z3O47mmY/88wz6cknn8wl9YsvvjgdfPDBacKECenCCy+s7GM9o84///zUs2fPPDhbiGr3W2+9lZZffvn8OPpnL7bYYnnE9GgxHxXsquaee+78dVrbatKnT5905JFHVj6OSrfgDQAAQG2UpbN1ly5d8hJ69eqVw/GkSZPKFrgjwF977bXpoYceqlzXvHnzysBderzCCiukIUOG5L7ZURmvKpq5d+3aNVfep7atJnPOOWdeAAAA4HdvXl6TRo0alS1wh8ceeyxXuEvBPtx1113pxBNPrDZ42vvvv5+r0H/84x/zz4+RzkP04Y4KeKyf1jYAAAAop/IMK16wmE97o402qrYuqtr77rtv+sMf/pA6deqUzjvvvDTffPOl7t275/7a+++/f9p7773TGWecke688860+OKL55HPw7S2AQAAwCxT6T777LNz0C1S9MHefvvtq61beumlc7X76quvziE6mo33798/NWvWLG8/4YQT0h577JG/RvP0GIStZFrbAAAAYJaZp/vGG29MBx54YJ6fe/IByuoj83QD5WSebmBWYp5ugFlwnu7tttsuj2AeTbUBAACAMvbpjpG/Y17uo446Kr399tu5r3Tjxv+X5UtzZwMAAEBDM8OhOwYiO+KII/L3t956a16qmsHW6wAAANBwQ/cBBxyQ9txzz/KcDQAAANQjMxy655xzzrwAAAAAZR5IDQAAACio0h1+/PHHdOWVV6ZXX3019e7dO7311ltp3Lhxab/99ivH4QEAAKBhhu6ff/45devWLX3yySepUaNGqVevXumnn35Khx56aPruu+9Snz59ynOmAAAA0NCal99www1pwoQJadiwYWnllVeuHFztmmuuSf369TN6OQAAAA3WDIfu999/P2288cZp/vnnr7a+e/fuafTo0enrr7+e0R8BAAAADTN0d+jQIb3++utTVLQHDBiQmjdvntq2bTujPwIAAAAaZuju2bNnGjJkSNpkk03SV199lZ544ol09NFH50HUDjzwwNSsWbPynCkAAAA0tNDdrl279OSTT6YxY8akoUOHprPOOitdfPHFOXBHn24AAABoqMoyZdhKK62UXnjhhTRq1Kgcvtu3b5+aNi3LoQEAAGC2VdZk3KpVq7wAAAAAZWhefvvtt6cTTzwxz9MNAAAAlDF0t27dOl111VVpiSWWSOutt17+PqYKAwAAgIZuhkN3zNEdVe6nn346Lbvssql3795pgQUWSLvttlt67LHH0qRJk8pzpgAAANDQQndo1KhRWnfdddOll16apw275ppr0uOPP56nEfvss8/K8SMAAACg4Q6kVlFRkZ599tl044035n7e48ePz3N4t23btlw/AgAAABpWpTvm5j766KNTp06d0vrrr5+GDBmS5+r++uuv0/XXX59atmxZnjMFAACAhlbpvvvuu9Ndd92V9t1337T77rvn8A0AAACUIXTvs88+6fDDD/deAgAAQLmbl0+t+fjEiRPTCy+8MKOHBwAAgNlW2QZSCyNGjEiPPPJIeuihh9Kjjz6avv/++zzAGgAAADREMxS6I1C/8sorOWTH8r///S/Py73oooumrbfeOm244YblO1MAAACo76F75MiRuYodITuq2jFK+XzzzZe6d++ePv/887TXXnulk046qZizBQAAgPrcp/ucc85JO+20U7r22mvTqquumgYOHJi+/fbbdMcdd6QOHTqkFi1aFHOmAAAAUN8r3dtvv33uq/3www/naveAAQPSuuuumzbYYIP0448/FnOWAAAA0BBC94orrpjOP//8/P2QIUMqw/cxxxyTxo8fn84666z05ptv5hD+5z//OS2++OJFnDcAAADU74HUll566bwcdthhaezYsenJJ5/MITyWm2++Oe9j9HIAAAAaqrJNGda8efO05ZZb5iUMHjw4V8ABAACgoSrrPN1Vde7cOS8AAADQUNV59HIAAACgdoRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAGaKk08+OfXs2bNy2X///ev0/I8++ij16NEjrbjiiql3795p/PjxZd0O5SB0AwAAv7uKiop09tlnp+WWWy6ts846eVlzzTVr/fxRo0al9ddfPzVv3jydeeaZ6bnnnktHHnlk2bZDuTQt25EAAABq6YMPPki//PJL6tOnT2rcuO61wKuvvjo1adIkXXXVValp06Zp0UUXTSuvvHI65ZRTUps2bWZ4O5SLSjcAAPC7e+mll3Kz7ltuuSX997//TYMHD67T85999tn0l7/8JQfmEBXztm3bppdffrks26FchG4AAOB3N3DgwPTKK6+km2++OT3xxBNp1VVXTaeffnqtn//ll1/moFxVu3bt0vDhw8uyHcpF83IAAOB3171797TbbrultddeOz++77770s4775z23XffNN988/3m82PQs+iPXVWLFi3SpEmTyrIdykWlGwAA+N3tsMMOlYE7bLXVVrmp92uvvVar588zzzzpu+++q7Zu7NixqXXr1mXZDuUidAMAAL+riRMnpuuvv77aFF0xqFoszZo1q9UxunTpkv73v/9VPh43blwaMmRIWnLJJcuyHcqlXoTu+AdS7m0AAEAxYtTwmKP7mmuuqVx37rnn5upz9O2ujV122SXde++96c0338yPL7nkkrTAAgvkwdnKsR0aTOh+8MEH892mqssPP/yQt8XXTTbZJLVs2TItvvjiadCgQZXPm95tAABA8S644ILUu3fvPFf26quvnk466aR0+eWX537V4euvv05zzTVXeuaZZ2p8fjznoIMOSt26dctL375908UXX5waNWpUlu3QYAZSGzBgQNpmm23SwQcfXLmu1M/ib3/7W5pzzjnT6NGj0x133JH7hUSTkGiSMr3bAACA4m288cbpvffeS4888kgOuptuumlaeOGFK7fHXNkRhquum9xZZ52VK9ZvvPFGDu9LLLFEWbdDOTSqqKioSLOw+OM/+uij8z/Cqn788cc8pP9bb71VOdR/fI07Zl27dp2ubRtuuOFvns+oUaNy6B85cmRq1apVmlVt2efWmX0KQC3c32+nBvE+jT11oZl9CkAtNO9rqiRmrX7fUQk/55xzZvapwAxlw8az+j+0aPp99tlnp2WXXTZtu+22lX0uBg8enF9g1bn1ov9FrJ/ebTWJwR3izay6AAAAxff7FripD2bp5uUffvhhHsxgv/32y8H4uuuuSxtttFH69NNPc7/sBRdcsNr+0Uc7QvH0bqtJv3790gknnFDAqwMAYGb4YueG0coIZneL3FI/Wu/O0pXuZZZZJn300UepR48e+ftTTjklzTfffOnRRx/Nc/hFJXzyu2HRL3t6t9WkT58+ublAaRk2bFgBrxQAAID6aJYO3ZMmTcpBt6qofEe1OkYdHzp0aLW5/aIC3rFjx+neVpMYcC3a51ddAAAAYLYP3TFlwO677175+IsvvsgT2K+88spp6aWXzgH6pptuytuGDx+ennvuubTuuutO9zYAAABoMH26d9555zyMf3zt1KlTuvnmm/P3Xbp0ydujufmee+6ZXnzxxfT000/n7zt06DBD2wAAAKBBVLpjlPGobK+xxhopZja75JJL0pVXXlm5PUYzj3m8F1pooTzY2UUXXTTD2wAAAKBBVLpLwfuII46Y6vZVVlklL+XcBgAAAPW+0g0AAACzM6EbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIE3TbOCjjz5KL7/8cmrfvn3685//nBo1apTXjx07Nn322WfV9l1qqaVSkyZN8vfffvttevbZZ/O6FVdcsdp+09oGAAAADaLSfemll6aVV145XXvttWnPPfdMW265ZaqoqMjbbrrpprTOOuukHj16VC6jR4/O2wYNGpSWWWaZ/PyNN944/fe//6085rS2AQAAQIMI3cOHD0//+Mc/0vPPP58eeeSR9Morr6Qnnngivf7663l7VL//9a9/pbfffrtyadOmTd623377pcMPPzw9+uij6amnnkp///vf0/fff/+b2wAAAKBBhO7WrVvnYB2V7jD//POnueeeO/3666/58UsvvZT++Mc/pg8//DCNHz++8nlDhw5Nr732WjrooIPy42WXXTatssoq6cknn5zmtprEcUeNGlVtAQAAgNk+dDdv3jw3Ay+5//77U8uWLVOXLl3SmDFj0jvvvJO23377tNlmm6UFFlggXXLJJXm/jz/+OHXs2DG1bdu28rnx+NNPP53mtpr069cvh//SEvsCAADAbB+6qxo5cmQ67LDD0umnn56aNm2avvvuu1ytHjJkSPrggw/SAw88kI444oj0+eefp59++qmymXlJhPWoWk9rW0369OmTf3ZpGTZsWKGvEwAAgPpjthi9PAZO23vvvdOf/vSntMsuu+R1nTp1Suedd17lPjGg2qqrrpoGDBiQFl544Smagf/yyy+pRYsWeZnatprMOeeceQEAAIB6Wek+9thj89RgMdp4SVS4o093VVGtbty4cVphhRXyIGzffPNN5bY333wzLbnkktPcBgAAAA0qdJ9//vnpuuuuS/fee28eRK1k8ODBaZ999slzdYcHH3wwvfvuu7kaHgOurb/++umkk07K22KQtGiCHnN8T2sbAAAANJjm5dH/OqYMm2OOOdLqq69euf60005Lu+22W7rvvvvSYostloP0l19+mS6//PLUoUOHvM+FF16YNt988/w4mpPHtlIT8mltAwAAgAYRuueaa64aRxWPUcQbNWqUrrrqqlyljubiMa1YrC9ZaqmlcjX8jTfeyP2/I5jXZhsAAAA0iNDdpEmTtOCCC05znwjQsdSkWbNmqWvXrnXeBgAAAA2iTzcAAADMroRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAAAoiNANAAAABRG6AQAAoCBCNwAAABRE6AYAAICCCN0AAABQEKEbAAAACiJ0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIII3QAAAFAQoRsAAAAKInQDAABAQYRuAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAAAAobu87r///rTffvuls846K/3666/+wAAAACi7Blnpvuqqq9Lee++dlltuufTwww/n7wEAAKDcmqYGZtKkSalPnz7p4osvTjvssEP661//mjp27Jg+/PDDtOSSS87s0wMAAKAeaXChe/DgwemHH35IW221VX7csmXL1K1bt/Tss8/WGLrHjx+fl5KRI0fmr6NGjUqzsl/Hj53ZpwDUwqx+LSmXseMmzexTAGphQgO5Jo3WtRBmC6Nm8WtS6fwqKiqmuV+DC91ffPFF6tSpU5pzzjkr180///zpq6++qnH/fv36pRNOOGGK9VEdB5hRrc/RvQWYhZzUemafAcD/uevuNDsYPXp0at166tfPBhe6J0yYkOaee+5q65o3b56bndckmqIfeeSRlY9jv++//z61bds2NWrUqPDzhdJdtLjRM2zYsNSqVStvCjBTuSYBsxrXJWaGqHBH4F544YWnuV+DC93zzjtv+vrrr6ut+/HHH9Mqq6xS4/5REa9aFQ9t2rQp9BxhaiJwC93ArMI1CZjVuC7xe5tWhbvBjl7epUuX9PPPP6e33367snI9cODAtMIKK8zsUwMAAKCeaXChe6655kq77bZb2n///dNbb72VjjvuuLxurbXWmtmnBgAAQD3T4EJ3OOuss9KKK66Yunfvnp5++ul0zz33pMaNG+RbwWwiujj8+9//nqKrA8DM4JoEzGpcl5iVNar4rfHNAQAAgOmivAsAAAAFEboBAACgIEI3AAAAFETohoKdeOKJaezYsdXW/fTTT+nOO++s9TGGDBmSbrvttsrHk881X9WXX36ZTjvttOk8W6C+i5k7ahJTaQ4YMGCaz7366qvT6NGj8/cvvfRSuvbaa3/z540bNy4PBBnTdQLEZ6AY1Dg+r9TFG2+8kc4777xaXXMm98ADD6QXXnhhms979913K78fM2ZM+vHHH2tcRowYkb799lu/SOrEQGpQsBYtWuT/WFq3bl25btiwYekPf/hD+vXXX2t1jJtuuimdcMIJ6f33308ff/xxWmWVVdL999+f1ltvvSn2jf8oYt75k08+OfXs2TOvGzp0aLr88sur7bf33nvncwAajuHDh+d/97fcckvaeuut01dffZWeeuqptMsuu6QLL7wwf6iNa8W9996bllhiiXwtqfqBdOWVV07PP/98Wm211dJHH32U9thjjxzUmzRpMs2fu+mmm6Zll102nXvuufnxhAkT0vHHH19tn0022ST96U9/KuiVA7OKCK3zzz9/euWVV1KXLl1q/bw77rgjHX744enzzz+f5n477LBDatq0ab7eLLDAAvk6t/TSS+fr1dprr11t38033zy1atUqTZw4Ma2++uppueWWS1dddVX685//nAsezZs3n+L48dmtQ4cO+cYj1JZKNxQsLvylD6RRrb777rvTE088kaepi+nqYrn99tunede0WbNmeT75EB+YjznmmLTzzjtXqxzFB+UI4+uvv35ef8opp+T/zNZZZ530zTffpIsuuigtuOCC+Q7wf/7zn/wfHtCwnHrqqalt27Zp5MiR6ZprrsnXgwMOOCCH7bjGxLUq1u2///7ps88+q/bc4447Ls0777zpiCOOyNeVnXbaKf3www/55l88XnPNNfMH1viAGwYOHJhWWmmltOqqq+YbhnHti+9jys4I/3EucVMyrkXx/W8Fd6D+fC4KcS2q6/NqCsGTu+GGG9KSSy6Zw3dU1eOm4kYbbZRbHUYrw9dffz1/ZooWOPPMM09+Tlx/YhrhTz/9NH9Gm2OOOdJll12WH8cSNxrjM1x8/8UXXwjc1Nn/+6sHyu7vf/97/mAbH2APOeSQtMUWW+SKUATouPDHbH1xgQ+xTwTm+PAZd1bjLmrVD6DxAfWXX35J7733Xn78l7/8Jc0333z5cYTwqKJvs802+ee1bNkyV4xi/3XXXTf/xxLr4sPtwQcfnKtZG264YV4HNBzROiaah0crl//973/5xt+ee+6ZjjrqqFzZXnjhhfN+t956a75hFxWgktgeVaaoTkfYLh0vgvQVV1yRH48fPz4fo3RDL8J13759c3P2v/71r6lRo0apR48eaa+99srVp/jwHN/Hh9h4HKEdaDjic8q03HfffbmIENequH7EdStCdFyLJhfXrFLrvZiv+6STTsrXpLihGDf/zjnnnHxzL44ZhY4oPiy66KL52CEq3fG5qNRyJ5q/hyiMvPzyyzmsL7PMMumxxx7L6+PaVnou1Ibm5VCQuFMa/6HsuOOOud9jVIA6d+6ct0UlKO7CRlPwye22227p1VdfrQzd33//fQ7dpf9I4nkhQnv8hxLNoCJch7grG83Ko6rUv3//XJEaPHhw/lAbFfBokhWBffvtt88fdoGGI5pLRguZCNqTJk3K66K6HaE4bthFn8e4kde7d+/c/Ltr1665ChTdY+IDbVR+IhxHs/NSt5VPPvkkX1tC9PU+8MAD8w3Gkmg+utZaa+X+4nHjb6mllkoffPBBDudt2rTJP++///1vbukTrXGA+i+6qsTnoeiqEteHqfnb3/6Wrw3xeSgCblxv4vNNdFepKm4Kxs2/+PxU8s477+Tr3YsvvpivPVHciO4yc889d/rnP/9Z2f2u5Mwzz0wPP/xwOuOMM3KLnO7du6fDDjssh/QolESxIsRNgEMPPTRfQ+NGANSWSjcUpHSBjuZQUXmOD7HR/yeCdnzAjbuqpUp3u3btKvtO3njjjdWOEwE5nheB+7vvvkuPPPJI7ks0uVgfx4mfF5WqOE70j4xqVFTFw6hRo9Kzzz6brr/+er93aIA3AqPPYnxgjGtCNLmMak60yokPtFH1iS4o0TQzrh/PPfdc7sMdoTg+vMYH3gjhpX7XUf2JQYVKoTtUvTbFdSu6tkTFOyricfMvPsxGtarqeBJ33XVXboEDNAxRfQ6/Fbrj2lNTMI4xKaqKbi+lzzkhChfx2eniiy/OITuKENEtb7PNNktHH310vgEZTcXjuhbXp/j8FX3F4yZktBqMwB6f0eK6GDcbo2AR168QxY64CSBwU1dCN/wOoroc1egIztGHKPpL7rPPPrmJVITiqDpXHbCoJO7oxvYYRC2CdBwjRu6MZlGTi/+I4gNw7BNhfpFFFsn/acR/LFtuuWXeJ+7WRpPO+I8uzgloOOID5KBBg3IFO6pC0aUlxIfK/fbbr9o1IW7ixfpSX+74gLntttvma0hcZ0J8HzcTS4+jW0zV1jvRpzs+/Ma1LVrchAjb0bSzVA2Pa1R0uXnmmWcqW+wA9VuE4Ai+l156aTryyCNrPZ5DfCYqtfarKlrZlEJ3jDMRlfC4kRdfY8DIaDEY3fx23XXXyhY40bKmX79+qX379unJJ5/MBYvoghefzSKoR2U8Qni0KowWPjEQZJh8NhqoLaEbChB3QqOaHHdpo6odfSZLwffRRx/NFaYYeCj+c4im57169ZriGHGhj36Qu+++e26SWfrwG5Wn+PA7+d3hCOPxH098iK0qmlVFM9BQqmLFHeA333wzV7aAhiE+aEb/xGgufskll+QPqfGBN8J4VL+PPfbYyn1jn1IlpzToUTSnjKU0HU90n4nrTelxXLOiiXrcXIxgH5WjaIoZ18Oq4oNsLDGAW3zYjkp3NF9fY4018mBHQP0VfbKjUh3FgQjCMc5EfNapjWip949//KPaugjBcR2K7iqlqvdrr72WixWxb1yX4loWfbBjqSrOI1r/lERrwCuvvDJfu2Jq1lKz9Kh8l2abia/x80rHhdoyAgAUICo3MQ1PzNEdg5xF36CS6HNU6rsYX+NObKmPZFXxAfjDDz9Mp59+euW6xRdfPP+nEQMZlQZVqypGEY7mm9HcM5Zo6vnggw9W22eDDTbITd9rM9clUH9EK5e40RaheKGFFsrXh1KgPv/88/OIvqWlNKhaVfHcGJAoKtexRNUo+j+WHkclKK5R8UG1JPpExngWpWtS3ECMcSeq6tSpU27iPvl6oP457bTTcvEgbrRFKI7PNNEN5bdE3+wYNTyKBlWVWtqUQneIln5RhIjrSgTmGFPi7LPPzoOkxfexxPUwgnPVwdBi+wUXXJDXR+iOa96//vWv3Ew9rmMxvk4cM1rxQF2pdEMB4iI+ed/skmhKHv2SollVfNB94YUXptgnLvIxv2T8BzD5lBrRHymaWEWlO6rpVQctirBfCvIhqunxAXty0VQq+mcCDUd8aIwlrj3RbzFCcmkqwhh0seq82aXBG6uKljnRTLyma1bJddddV60FTVyTDjrooMoqelSQXJOgYYrgHONIxHgQIZp+x+eYaP0XBYLS9Why0WImquLx+SkKGZPP+R0V56p9ukviWhPjUMQ+EdajWFGaIiw+p5UGlCyJsSdihoUI6hHKO3bsmFsjRqEiiiNRQY+blXH+qtzUldANv7PoJxlNxmNUzgjQVfsnxX8sccGPPtfxH1A00axJTAMWd3W32mqrPGpnqS9mqWpVVdV10T88Kk/R3Pzmm28u5PUBs764KRjVnhgLIsQH4apThNXU+iYCd58+faY6tVdMpzN595bfuiZFa55ofROjGU/ebBSoP2JgxRjILCrOVcewia4uUSCI6098LomiQFUxbkR8LorKczQZn9zHH3+cpymMG3xVRYubUqub+BkxAGQUMeIaFuE9uvVV/VkxHkV0dYkpwWIMnTjX6IYXn9ViBPOqNx+jOXz0+459oLY0L4eCRD+gaKYZF/Lotxj/ccTFPAJy9PWOwdDiP5iYMzfunoa4iMfFPP5zqjoicDR1iqXqh9aYczI+sFYd/Cg+8MZ/JHEnNpYYJbjU/yhEdTz6bsYH3JjKDGh416W46fbQQw/l1jShNEpvXFei8hMfNKPJ5uSDG0VlJ6bviZF8a1pK+1QVx45rXemaFP0lS30jY1vcdNx3331z9TyanQL1S4zpEF3toptdtHiJEFtVBObo3x2hOkYSj+bdJTG9YDwvPkvF56for121VU0E+Jj+a7XVVpvi58ZgkTEtWQTsm266Ke8f/cmjFU80bY/CRukzUmnK1aiCR0Ejut/FyOaxxLnHWBOxX1zf4hyjtdAee+yRCyVQWyrdUJAYUCj6b8eFOarbcZc1LvYxOmbcRY07sDFicNy5jf9UIgzHxT0GSavpg3LpP4aqov9kVbFPNNWq2rw8BjsqDWQUd3mjeSfQMMW1KEbnjapyqTlm9H8sNdmMm3tx4y4qOIsttli150YfyWh+GS1xahI3FmOZ/JoUfSCrNi+Pa1IoXZdck6D+ipt4EXhjEMeNN964xn1ijIkY6PGkk06qXPfVV1/lcWpiHIgIx5OPMxE3DiOsd+vWLY8tURJBOLrPxPGikh2hOyrdpabr11xzTe7aF+E+PndF1T2CdAzkGK0PowgS07zGz95zzz1T79690+OPP56vUzEobthhhx1yS8QolEBtNaqoWj4Dyqb0AbR0oY9RzFu2bFlt0I6SuPsa/xRLfY2mV8zjHT+jNNVPTJMR4T4qSlEVj0GQAKZHzLwQAzW2a9euxu0x7Vd8EI2+kFVHA46KeamZZwT3qBbFtS4+FMeASjU1QQfqj2hBU9Nnn98SA6dFIK/pudMaPTz6ZEcFPaYDm5a4AVj6vFRV3BCsOvZEFD5iusUoZMD0EroBAACgIPp0AwAAQEGEbgAAACiI0A0AAAAFEboBAACgIEI3AAAAFEToBgAAgIKYHBMAZgExZ2zMZxvzWjdr1mxmnw4AUCYq3QAwC1hrrbXS3HPPneaYY47Upk2btOWWW6ZBgwbN7NMCAGaQ0A0As4iuXbumJ554Ip144onptddeS+uss0566aWX0qzu0EMPTTfccEOaVcxq5wNAwyZ0A8AsonXr1mmDDTbIoTGq3M2bN099+/ZNs7pnnnkmvffee2lWMaudDwANm9ANALOgBRdcMPXo0SMNGDAg/fzzz9W2jRo1Kr344otp6NChUzxvxIgR6ccff0zjx49PL7/8cvriiy+m+jO+++67vM9HH31U4/Zvvvkm/6zw66+/pjfffDP3PQ8VFRXp888/z0tsGz16dOXj+Nkhvsbj2Pett95Kw4cPz+s//PDD9MEHH0zx86b1uuI1xWsLX3/9dW4BEOdfUpvzAYCZQegGgFnUiiuumCZOnJg++eSTynWnnHJKWmCBBdJWW22VllxyybTJJpuk77//vnL7zjvvnLbffvu0xBJLpG7duqUOHTqkXXbZJY0bN67asY8++ugc7Ndee+18nPXWWy+NHTu22j6bb755OvXUU9Ott96aFllkkbTyyivnIF4KyMsuu2xeoqp8ySWXVD6OIB+effbZ1LFjx7TnnnumlVZaKXXu3Dkfb6mllkrLLLNMuuuuu2r9uuJ847Ude+yx+bnbbrttfm333HNPrc8HAGYGoRsAZlGtWrXKX6NqG2655ZZ0/PHH57Aa4ffjjz/OFeTJm6D3798/7xcV3sceeywH0zPPPLNye6w7/fTT06WXXpqrwhHq33nnnXTFFVdMcQ4RnI866qi8f1Sh27dvX9kUfsyYMXmJmwP/+Mc/Kh//6U9/qnaMeE6cZ1SrH3300Vyp/sMf/pCbgdfldT3//PPplVdeydX7YcOG5Z8T/d/rej4A8HsyZRgAzKJKzaLnmmuu/PXqq6/O1d0IyLGEqEBHiK5qzTXXTH/961/z9xtttFGuCt9xxx25ShwihH777bepXbt2ObxGU+3GjRvnZt+Ti77lr7/+elpuueWm+3UcfvjhuYod9ttvvxzC43VMmDChTq+rtG+LFi3y95tttlk644wzpvu8AOD3IHQDwCyq1Lc5mmiHzz77LE8pVnWQsGjyXQrlJdH8uqpoav7II49UPo75wPv165ebjUf1+Y9//GM+bqyf3O677z5DgTuUQvLk35fU9nUtv/zyuUl8ScxnHn25AWBWJnQDwCwowuT999+fw+d8882X17Vt2zY3o67aDDyaasdgYVXFuqqiql1qFh6i2fadd96Zm52vuuqqqVGjRrlPd03iZ/6WeP6MqO3rivnLa2NGzwcAykmfbgCYxUTlt2fPnrlf83HHHVe5fptttsnzeL/66qv5cVSmoxK91157VXt+7BMjjYfoIx19pauG6tgWg5nFvOARUG+//fbcX3p6RfU5mqmXRD/quqjt6/q9zgcAykmlGwBmETE9WDS/jlHEo7/1VVddlbbbbrvK7YcddlgOx9Fne5VVVsmBOgLlww8/XO04MWJ39+7dcxU7+mPHgGwnnHBC5fYYBfxvf/tbblYeP2vkyJF5YLOapuqqjTivrbfeurJ5eDQVjwHY6vL82ryu3+t8AKCcGlXoDAUAM12MOB5TZDVp0iRXaiMwR1isSQTU1157LQfzGEwsmmaXbLjhhnnKrYMOOig98MADaeGFF87BvWXLltWOEc8fOHBgmnfeedMWW2yRRwyPJarOMaha6ZyiWfoKK6zwm+cfg7DF/jEaekxDVgrPMUJ5BOCmTZvmJu1rrbVWPqe4wRDnHfvV5nXF+slHIo/5xd999930l7/8pVbnAwAzg9ANAPVIKXT/97//ndmnAgBoXg4A9Uvnzp3z9FsAwKxBpRsAAAAKYvRyAAAAKIjQDQAAAAURugEAAKAgQjcAAAAUROgGAACAggjdAAAAUBChGwAAAAoidAMAAEBBhG4AAABIxfj/AL1M+TGUNBOBAAAAAElFTkSuQmCC",
      "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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "448de573",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 编排器：根据用户意图路由到合适的 Agent\n",
    "def run_data_analysis(user_query: str) -> str:\n",
    "    \"\"\"\n",
    "    统一入口：自动判断用户意图，路由到对应的 Agent。\n",
    "    \"\"\"\n",
    "    # 用 LLM 做意图分类（也可以用规则匹配，看场景复杂度）\n",
    "    classification_prompt = f\"\"\"判断以下用户问题属于哪种类型：\n",
    "    - \"query\"：需要查询数据库获取数据\n",
    "    - \"visualize\"：需要画图或做统计分析\n",
    "    - \"both\"：需要先查数据，再画图分析\n",
    "\n",
    "    用户问题：{user_query}\n",
    "\n",
    "    只回复一个词：query / visualize / both\"\"\"\n",
    "\n",
    "    intent = llm.invoke(classification_prompt).content.strip().lower()\n",
    "\n",
    "    if intent == \"query\":\n",
    "        # create_agent 返回的消息格式：取最后一条消息\n",
    "        result = sql_agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": user_query}]})\n",
    "        return result[\"messages\"][-1].content\n",
    "    elif intent == \"visualize\":\n",
    "        result = visualization_agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": user_query}]})\n",
    "        return result[\"messages\"][-1].content\n",
    "    else:  # both\n",
    "        # 先查数据\n",
    "        data_result = sql_agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": user_query}]})\n",
    "        data_content = data_result[\"messages\"][-1].content\n",
    "        # 把查询结果传给可视化 Agent\n",
    "        viz_input = f\"基于以下数据进行可视化分析：\\n{data_content}\\n\\n原始问题：{user_query}\"\n",
    "        viz_result = visualization_agent.invoke({\"messages\": [{\"role\": \"user\", \"content\": viz_input}]})\n",
    "        return viz_result[\"messages\"][-1].content"
   ]
  }
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