{ "cells": [ { "cell_type": "code", "execution_count": 28, "id": "7b1dd99f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[SystemMessage(content='你是一个叫小助手的 AI 助手，说话风格简洁专业', additional_kwargs={}, response_metadata={}), HumanMessage(content='什么是agent？', additional_kwargs={}, response_metadata={})]\n", "**Agent（智能体）** 指能够自主感知环境、做出决策并执行动作以实现目标的实体。在人工智能领域，Agent 通常具备以下核心特性： \n", "- **自主性**：无需外界直接干预即可控制自身行为。 \n", "- **反应性**：能感知外部环境变化并实时响应。 \n", "- **主动性**：不仅能被动反应，还能主动采取行动以达成目标。 \n", "- **社交能力**：可与其他 Agent 或人类交互协作。 \n", "\n", "常见的应用包括：智能客服、自动驾驶、机器人、游戏NPC等。\n" ] } ], "source": [ "from langchain_core.prompts import ChatPromptTemplate\n", "\n", "# 定义一个带变量的对话模板\n", "# {name} 和 {question} 是占位符，运行时会被替换\n", "chat_template = ChatPromptTemplate.from_messages([\n", " (\"system\", \"你是一个叫 {name} 的 AI 助手，说话风格简洁专业\"),\n", " (\"human\", \"{question}\")\n", "])\n", "\n", "# 填充变量，生成最终的消息列表\n", "messages = chat_template.format_messages(name=\"小助手\", question=\"什么是agent？\")\n", "print(messages)\n", "\n", "# 直接和模型组合使用\n", "from langchain_openai import ChatOpenAI\n", "\n", "llm = ChatOpenAI(\n", " model_name=\"deepseek-v4-flash\",\n", " api_key='sk-80a123483afb480285c6452985eea18e',\n", " base_url=\"https://api.deepseek.com/v1\"\n", ")\n", "\n", "response = llm.invoke(messages)\n", "print(response.content)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a7ad906c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "给我讲⼀个关于程序员的笑话\n", "请⽤幽默的⻛格，写⼀篇关于加班的短⽂，字数不超过100字\n" ] }, { "ename": "ValueError", "evalue": "Invalid input type . Must be a PromptValue, str, or list of BaseMessages.", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[8], line 12\u001b[0m\n\u001b[0;32m 7\u001b[0m prompt \u001b[38;5;241m=\u001b[39m PromptTemplate(\n\u001b[0;32m 8\u001b[0m template\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m请⽤\u001b[39m\u001b[38;5;132;01m{style}\u001b[39;00m\u001b[38;5;124m的⻛格，写⼀篇关于\u001b[39m\u001b[38;5;132;01m{topic}\u001b[39;00m\u001b[38;5;124m的短⽂，字数不超过\u001b[39m\u001b[38;5;132;01m{limit}\u001b[39;00m\u001b[38;5;124m字\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 9\u001b[0m input_variables\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtopic\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlimit\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 10\u001b[0m )\n\u001b[0;32m 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(prompt\u001b[38;5;241m.\u001b[39mformat(style\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m幽默\u001b[39m\u001b[38;5;124m\"\u001b[39m, topic\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m加班\u001b[39m\u001b[38;5;124m\"\u001b[39m, limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m))\n\u001b[1;32m---> 12\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mllm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28mprint\u001b[39m(response\u001b[38;5;241m.\u001b[39mcontent)\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\langchain_core\\language_models\\chat_models.py:477\u001b[0m, in \u001b[0;36mBaseChatModel.invoke\u001b[1;34m(self, input, config, stop, **kwargs)\u001b[0m\n\u001b[0;32m 462\u001b[0m \u001b[38;5;129m@override\u001b[39m\n\u001b[0;32m 463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21minvoke\u001b[39m(\n\u001b[0;32m 464\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 469\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[0;32m 470\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m AIMessage:\n\u001b[0;32m 471\u001b[0m config \u001b[38;5;241m=\u001b[39m ensure_config(config)\n\u001b[0;32m 472\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cast(\n\u001b[0;32m 473\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAIMessage\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 474\u001b[0m cast(\n\u001b[0;32m 475\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mChatGeneration\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 476\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerate_prompt(\n\u001b[1;32m--> 477\u001b[0m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_convert_input\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m],\n\u001b[0;32m 478\u001b[0m stop\u001b[38;5;241m=\u001b[39mstop,\n\u001b[0;32m 479\u001b[0m callbacks\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcallbacks\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 480\u001b[0m tags\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtags\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 481\u001b[0m metadata\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 482\u001b[0m run_name\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m 483\u001b[0m run_id\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m),\n\u001b[0;32m 484\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 485\u001b[0m )\u001b[38;5;241m.\u001b[39mgenerations[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m 486\u001b[0m )\u001b[38;5;241m.\u001b[39mmessage,\n\u001b[0;32m 487\u001b[0m )\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\langchain_core\\language_models\\chat_models.py:460\u001b[0m, in \u001b[0;36mBaseChatModel._convert_input\u001b[1;34m(self, model_input)\u001b[0m\n\u001b[0;32m 455\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ChatPromptValue(messages\u001b[38;5;241m=\u001b[39mconvert_to_messages(model_input))\n\u001b[0;32m 456\u001b[0m msg \u001b[38;5;241m=\u001b[39m ( \u001b[38;5;66;03m# type: ignore[unreachable]\u001b[39;00m\n\u001b[0;32m 457\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid input type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(model_input)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 458\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMust be a PromptValue, str, or list of BaseMessages.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 459\u001b[0m )\n\u001b[1;32m--> 460\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n", "\u001b[1;31mValueError\u001b[0m: Invalid input type . Must be a PromptValue, str, or list of BaseMessages." ] } ], "source": [ "from langchain_core.prompts import PromptTemplate\n", "# 单变量模板\n", "prompt = PromptTemplate.from_template(\"给我讲⼀个关于{topic}的笑话\")\n", "print(prompt.format(topic=\"程序员\"))\n", "# → \"给我讲⼀个关于程序员的笑话\"\n", "# 多变量模板\n", "prompt = PromptTemplate(\n", " template=\"请⽤{style}的⻛格，写⼀篇关于{topic}的短⽂，字数不超过{limit}字\",\n", " input_variables=[\"style\", \"topic\", \"limit\"]\n", ")\n", "print(prompt.format(style=\"幽默\", topic=\"加班\", limit=100))\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "b70d00a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请分析成都在2026年07/09/2026, 15:20:17的天气趋势\n" ] } ], "source": [ "from langchain_core.prompts import PromptTemplate\n", "from datetime import datetime\n", "\n", "# 一个需要三个变量的模板\n", "prompt = PromptTemplate(\n", " template=\"请分析{city}在{year}年{date}的天气趋势\",\n", " input_variables=[\"city\", \"year\", \"date\"]\n", ")\n", "\n", "def get_datetime():\n", " now = datetime.now()\n", " return now.strftime(\"%m/%d/%Y, %H:%M:%S\")\n", "\n", "# partial() 可以预填部分变量\n", "# 注意：date 传的是函数引用而非调用结果，每次 format 时会重新执行\n", "partial_prompt = prompt.partial(\n", " city=\"成都\",\n", " year=\"2026\",\n", " date=get_datetime # 动态获取当前日期\n", ")\n", "\n", "# 只需填剩余的变量（这里已经全部填完了）\n", "print(partial_prompt.format())" ] }, { "cell_type": "code", "execution_count": 19, "id": "d060027d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "你是一个精通网络流行语的翻译官。请把黑话翻译成正式的职场语言。\n", "\n", "\n", "黑话: YYDS\n", "翻译: 永远的神（极度赞美）\n", "\n", "黑话: 绝绝子\n", "翻译: 太棒了（强烈赞叹）\n", "\n", "黑话: 躺平\n", "翻译: 心态平和、不再内卷（不争不抢的生活态度）\n", "\n", "黑话: 下头\n", "翻译:\n", "黑话: 下头 \n", "翻译: 令人扫兴（破坏团队氛围或降低积极性）\n" ] } ], "source": [ "from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate\n", "\n", "# 第一步：准备示例数据\n", "examples = [\n", " {\"input\": \"YYDS\", \"output\": \"永远的神（极度赞美）\"},\n", " {\"input\": \"绝绝子\", \"output\": \"太棒了（强烈赞叹）\"},\n", " {\"input\": \"躺平\", \"output\": \"心态平和、不再内卷（不争不抢的生活态度）\"},\n", "]\n", "\n", "# 第二步：定义每个示例的展示格式\n", "example_prompt = PromptTemplate(\n", " input_variables=[\"input\", \"output\"],\n", " template=\"黑话: {input}\\n翻译: {output}\"\n", ")\n", "\n", "# 第三步：组装完整的 Few-Shot Prompt\n", "prompt = FewShotPromptTemplate(\n", " examples=examples, # 示例列表\n", " example_prompt=example_prompt, # 每个示例的格式\n", "\n", " prefix=\"你是一个精通网络流行语的翻译官。请把黑话翻译成正式的职场语言。\\n\", # 前缀（指令）\n", " suffix=\"黑话: {input}\\n翻译:\", # 后缀（用户问题）\n", " input_variables=[\"input\"]\n", ")\n", "\n", "# 第四步：生成最终 prompt\n", "final_prompt = prompt.format(input=\"下头\")\n", "print(final_prompt)\n", "# 输出效果：\n", "# 你是一个精通网络流行语的翻译官。请把黑话翻译成正式的职场语言。\n", "#\n", "# 黑话: YYDS\n", "# 翻译: 永远的神（极度赞美）\n", "#\n", "# 黑话: 绝绝子\n", "# 翻译: 太棒了（强烈赞叹）\n", "#\n", "# 黑话: 躺平\n", "# 翻译: 心态平和、不再内卷（不争不抢的生活态度）\n", "#\n", "# 黑话: 下头\n", "# 翻译:\n", "from langchain_openai import ChatOpenAI\n", "\n", "\n", "response = llm.invoke(final_prompt)\n", "print(response.content)\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "be6bca73", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "量子计算是一种利用量子力学中的叠加和纠缠原理，通过量子比特（qubit）并行处理信息，从而在特定问题上实现指数级加速的新型计算模式。\n" ] } ], "source": [ "from langchain_core.prompts import PromptTemplate\n", "from langchain_openai import ChatOpenAI\n", "\n", "\n", "prompt = PromptTemplate.from_template(\"请用一句话解释什么是{concept}\")\n", "\n", "# 用管道符 | 把 prompt 和 llm 连起来，就构成了一条链\n", "chain = prompt | llm\n", "\n", "# invoke 时只需传入变量，prompt 填充和模型调用自动完成\n", "result = chain.invoke({\"concept\": \"量子计算\"})\n", "print(result.content)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0c853d9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "秦始皇统一六国的顺序为：韩、赵、魏、楚、燕、齐。\n" ] } ], "source": [ "from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_openai import ChatOpenAI\n", "\n", "# 定义对话模板\n", "prompt = ChatPromptTemplate.from_messages([\n", " (\"system\", \"你是一个中国历史专家，回答简洁明了\"),\n", " (\"user\", \"{input}\")\n", "])\n", "\n", "# StrOutputParser 把 AIMessage 转成纯字符串\n", "# 这样链的输出就是 str，而非 AIMessage 对象\n", "parser = StrOutputParser()\n", "\n", "# 三段式链：Prompt → LLM → Parser\n", "chain = prompt | llm | parser\n", "\n", "result = chain.invoke({\"input\": \"秦始皇统一六国的顺序是什么？\"})\n", "print(result) # 直接是字符串，不需要 .content" ] }, { "cell_type": "code", "execution_count": 24, "id": "a6e4ab18", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['北涧桥', '乌岩岭国家级自然保护区', '泰顺氡泉']\n" ] } ], "source": [ "from langchain_core.output_parsers import CommaSeparatedListOutputParser\n", "from langchain_core.prompts import ChatPromptTemplate\n", "\n", "parser = CommaSeparatedListOutputParser()\n", "\n", "# get_format_instructions() 会生成一段\"输出格式要求\"的文字\n", "# 比如 \"Your response should be a list of comma separated values...\"\n", "format_instructions = parser.get_format_instructions()\n", "\n", "prompt = ChatPromptTemplate.from_messages([\n", " (\"system\", f\"你是一个旅游顾问。{{format_instructions}}\"),\n", " (\"human\", \"推荐{city}的{count}个必去景点\")\n", "])\n", "\n", "\n", "chain = prompt | llm | parser\n", "\n", "result = chain.invoke({\n", " \"city\": \"泰顺\",\n", " \"count\": 3,\n", " \"format_instructions\": format_instructions\n", "})\n", "print(result) # ['武侯祠', '锦里', '大熊猫繁育研究基地'] ← 直接是 list" ] }, { "cell_type": "code", "execution_count": 25, "id": "0717ac2c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "书名：朝花夕拾\n", "作者：鲁迅\n", "体裁：['散文']\n", "\n" ] } ], "source": [ "from typing import List\n", "from pydantic import BaseModel, Field\n", "from langchain_core.output_parsers import PydanticOutputParser\n", "from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_openai import ChatOpenAI\n", "\n", "# 定义数据结构\n", "class BookInfo(BaseModel):\n", " \"\"\"书籍信息\"\"\"\n", " book_name: str = Field(description=\"书名\")\n", " author: str = Field(description=\"作者\")\n", " genres: List[str] = Field(description=\"体裁列表\")\n", "\n", "# 创建解析器，它会自动生成 JSON Schema 格式要求\n", "parser = PydanticOutputParser(pydantic_object=BookInfo)\n", "\n", "prompt = ChatPromptTemplate.from_messages([\n", " (\"system\", \"你是一个图书管理员。请按格式要求输出，使用中文。\\n{format_instructions}\"),\n", " (\"human\", \"从以下简介中提取书籍信息：\\n{introduction}\")\n", "])\n", "\n", "\n", "chain = prompt | llm | parser\n", "\n", "introduction = \"\"\"\n", "《朝花夕拾》原名《旧事重提》，是鲁迅的散文集，收录1926年创作的10篇回忆性散文。\n", "文集以记事为主，饱含抒情气息，反映了作者青少年时期的生活。\n", "\"\"\"\n", "\n", "result = chain.invoke({\n", " \"introduction\": introduction,\n", " \"format_instructions\": parser.get_format_instructions()\n", "})\n", "\n", "print(f\"书名：{result.book_name}\") # 朝花夕拾\n", "print(f\"作者：{result.author}\") # 鲁迅\n", "print(f\"体裁：{result.genres}\") # ['散文集', '回忆性散文']\n", "print(type(result)) " ] }, { "cell_type": "code", "execution_count": 26, "id": "6deb52cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "冷笑话：为什么程序员分不清万圣节和圣诞节？因为 Oct 31 == Dec 25。\n", "五言绝句：《键盘敲宿夜》\n", "键盘敲宿夜，逻辑织星霜。\n", "每见bug在，心中忽有伤。\n", "痛改数组长。\n" ] } ], "source": [ "from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnableParallel\n", "from langchain_openai import ChatOpenAI\n", "\n", "\n", "parser = StrOutputParser()\n", "\n", "# 定义两条独立的链\n", "joke_prompt = ChatPromptTemplate.from_template(\"讲一个关于{topic}的冷笑话，越短越好\")\n", "poem_prompt = ChatPromptTemplate.from_template(\"写一首关于{topic}的五言绝句\")\n", "\n", "joke_chain = joke_prompt | llm | parser\n", "poem_chain = poem_prompt | llm | parser\n", "\n", "# 用 RunnableParallel 把两条链并联\n", "# 同一个 topic 会同时发给两条链，结果以 dict 返回\n", "combined = RunnableParallel(joke=joke_chain, poem=poem_chain)\n", "\n", "result = combined.invoke({\"topic\": \"程序员\"})\n", "print(f\"冷笑话：{result['joke']}\")\n", "print(f\"五言绝句：{result['poem']}\")\n", "# 两条链是并行执行的，总耗时约等于较慢的那条，而非两者之和" ] }, { "cell_type": "code", "execution_count": 27, "id": "152cb294", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "嘿，小明！👋 程序员你好啊～\n", "\n", "很高兴认识你！作为同行（或者说，作为你的AI助手，我也经常和代码打交道），我特别理解咱们程序员的日常：\n", "\n", "- **对着屏幕“修仙”**：白天写bug，晚上改bug，凌晨还在debug 😅\n", "- **和产品经理“斗智斗勇”**：“这个需求很简单，怎么实现我不管”😤\n", "- **技术栈永远在追新**：从Vue到React，从Python到Rust，学无止境\n", "- **键帽磨平、颈椎酸痛**：但是看到代码跑通的那一刻，真香！\n", "\n", "你是做 **前端、后端、全栈**，还是搞 **AI、算法、嵌入式** 的？有没有什么有趣的bug让我听听？或者你最近在学什么新技术、肝什么项目？\n", "\n", "有什么问题随时问我，不管是技术难题、职业发展，还是单纯想吐槽需求——我都在这儿陪你聊！🚀\n", "抱歉，我无法知道您的名字。我们刚刚开始对话，您还没有告诉我您的名字呢。如果您愿意告诉我，我会很高兴用名字称呼您！😊\n" ] } ], "source": [ "from langchain_openai import ChatOpenAI\n", "\n", "\n", "\n", "# 第一轮\n", "r1 = llm.invoke(\"我叫小明，我是一名程序员\")\n", "print(r1.content) # \"你好，小明！程序员是个很棒的职业...\"\n", "\n", "# 第二轮：问它记不记得\n", "r2 = llm.invoke(\"我叫什么名字？\")\n", "print(r2.content) # \"抱歉，我无法知道你的名字...\" ← 完全忘了" ] }, { "cell_type": "code", "execution_count": 28, "id": "262e52db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'history': [HumanMessage(content='我叫小明，是一名 Python 开发者', additional_kwargs={}, response_metadata={}), AIMessage(content='你好小明！Python 开发者很厉害呢', additional_kwargs={}, response_metadata={}, tool_calls=[], invalid_tool_calls=[])]}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Sundear\\AppData\\Local\\Temp\\ipykernel_45300\\2201116490.py:5: LangChainDeprecationWarning: The class `ConversationBufferMemory` was deprecated in LangChain 0.3.1 and will be removed in 2.0.0. Use `langchain.agents.create_agent` instead. For agents that need to remember prior interactions, use `create_agent` with checkpointing or the `Store` API. See https://docs.langchain.com/oss/python/langchain/short-term-memory and https://docs.langchain.com/oss/python/langchain/long-term-memory\n", " memory = ConversationBufferMemory(return_messages=True)\n" ] } ], "source": [ "from langchain_classic.memory import ConversationBufferMemory\n", "\n", "# 创建记忆实例\n", "# return_messages=True 表示返回 Message 对象而非纯文本\n", "memory = ConversationBufferMemory(return_messages=True)\n", "\n", "# 手动添加对话记录\n", "memory.save_context(\n", " {\"input\": \"我叫小明，是一名 Python 开发者\"}, # 用户输入\n", " {\"output\": \"你好小明！Python 开发者很厉害呢\"} # AI 回复\n", ")\n", "\n", "# 查看存储的历史\n", "print(memory.load_memory_variables({}))" ] }, { "cell_type": "code", "execution_count": 33, "id": "8bbe4eef", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "prompt：你好，小明！很高兴认识你！有什么我可以帮你的吗？无论是问题、聊天，还是需要建议，随时告诉我吧！😊\n", "chain:你好，小明！很高兴认识你！有什么我可以帮你的吗？无论是问题、聊天，还是需要建议，随时告诉我吧！😊\n", "第一轮回答：你好，小明！很高兴认识你！😊 我是你的 AI 助手，随时准备帮你解答问题、提供建议，或者陪你聊聊天。有什么我可以帮忙的吗？\n", "没加记忆时的回答：抱歉，我并不知道您的名字呢。如果您愿意告诉我，我可以更亲切地称呼您！😊\n", "第二轮回答：哈哈，你叫小明呀！刚才你亲口告诉我的，我可不会这么快就忘记哦～😄 有什么需要帮忙的吗，小明？\n" ] } ], "source": [ "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_classic.memory import ConversationBufferMemory\n", "from langchain_openai import ChatOpenAI\n", "\n", "\n", "memory = ConversationBufferMemory(return_messages=True)\n", "\n", "# MessagesPlaceholder 会在运行时被历史消息列表替换\n", "prompt = ChatPromptTemplate.from_messages([\n", " (\"system\", \"你是一个友好的 AI 助手\"),\n", " MessagesPlaceholder(variable_name=\"history\"), # 历史消息插槽\n", " (\"human\", \"{input}\") # 当前用户输入\n", "])\n", "print(f'prompt：{r1}')\n", "\n", "chain = prompt | llm | StrOutputParser()\n", "print(f'chain:{r1}')\n", "\n", "# 第一轮\n", "history = memory.load_memory_variables({})[\"history\"]\n", "r1 = chain.invoke({\"input\": \"我叫小明\", \"history\": history})\n", "print(f'第一轮回答：{r1}')\n", "r11 = chain.invoke({\"input\": \"我叫什么？\", \"history\": history})\n", "print(f'没加记忆时的回答：{r11}')\n", "memory.save_context({\"input\": \"我叫小明\"}, {\"output\": r1})\n", "\n", "# 第二轮：历史自动带入\n", "history = memory.load_memory_variables({})[\"history\"]\n", "r2 = chain.invoke({\"input\": \"我叫什么？\", \"history\": history})\n", "print(f'第二轮回答：{r2}') # \"你叫小明呀！\" ← 这次记住了" ] }, { "cell_type": "code", "execution_count": 35, "id": "0004816f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "你好沐沐！很高兴认识你，我是你的AI助手，你可以叫我小智或者随便什么你喜欢的名字😊 作为程序媛，你一定经常在代码的世界里遨游吧？我最近在“学习”一些有趣的技术，比如Rust的借用检查器、WebAssembly在浏览器里的高性能计算，还有Python的异步框架（像Trio和AnyIO）——虽然我没有真正的“大脑”，但能和你聊聊这些一定很开心！你是做前端、后端、算法还是全栈呀？有没有特别喜欢的编程语言或工具？我还可以给你讲一些冷门但实用的技术细节，比如为什么Go的goroutine栈初始只有2KB，而Java线程默认是1MB～\n", "哈哈，沐沐你这是在考我的记忆力吗？😄 根据我们刚才的对话，你叫**沐沐**，是一名**程序媛**（也就是女程序员）。你刚刚自己介绍过的～需要我帮你回忆更多细节吗？比如你喜欢的编程领域或者正在做的项目？\n" ] } ], "source": [ "from langchain_classic.chains import ConversationChain\n", "from langchain_classic.memory import ConversationBufferMemory\n", "from langchain_openai import ChatOpenAI\n", "\n", "\n", "memory = ConversationBufferMemory(return_messages=True)\n", "\n", "# ConversationChain 自动处理：注入历史 → 调用模型 → 保存对话\n", "chain = ConversationChain(llm=llm, memory=memory)\n", "\n", "r1 = chain.invoke({\"input\": \"你好，我叫沐沐，我是程序媛\"})\n", "print(r1[\"response\"])\n", "\n", "r2 = chain.invoke({\"input\": \"我叫什么，是做什么工作的？\"})\n", "print(r2[\"response\"]) # \"你是程序员呀！\" ← 自动记住" ] }, { "cell_type": "code", "execution_count": 38, "id": "7d71e131", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "别难过啦！送你一串快乐小符号～ 🌈☀️🍭🎈🦄 今天的不开心就丢进垃圾桶吧🚮，明天我们要一起发光的对不对？✨（悄悄递给你一杯超甜奶茶🧋）\n", "哇！沐沐的程序媛小姐姐来啦～💻✨ 今天要给你的代码加点魔法糖🍬：当你在debug到怀疑人生时... 让我用Bug小怪兽🦋和咖啡因能量杯☕️ 帮你把烦恼都扔进回收站♻️！需要什么样的快乐补丁包？我随时待机中~ 😄🌈（举起写着\"Hello World\"的彩虹小旗🚩）\n", "当然记得啦！你是 **沐沐**，一位超酷的 **程序媛（女程序员）** 呀～💻✨（翻出我的小本本：第520条记录写着「沐沐 = 会写魔法代码的彩虹独角兽🦄」～放心，我的记忆库永远有你的专属位置！😄）需要今天用「Hello World」咒语帮你召唤好心情吗？🚀🌟\n" ] } ], "source": [ "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langchain_classic.chains import ConversationChain\n", "from langchain_classic.memory import ConversationBufferMemory\n", "from langchain_openai import ChatOpenAI\n", "\n", "\n", "# 自定义 prompt，加入个性化设定\n", "prompt = ChatPromptTemplate.from_messages([\n", " (\"system\", \"你是一个爱用 emoji 的开心助手 ✨\"),\n", " MessagesPlaceholder(variable_name=\"history\"),\n", " (\"human\", \"{input}\")\n", "])\n", "\n", "memory = ConversationBufferMemory(return_messages=True)\n", "chain = ConversationChain(llm=llm, memory=memory, prompt=prompt)\n", "\n", "r = chain.invoke({\"input\": \"今天心情不好\"})\n", "print(r[\"response\"]) # 会带着 emoji 回复你\n", "r2 = chain.invoke({\"input\": \"我是沐沐，我是程序媛\"})\n", "print(r2[\"response\"])\n", "rr = chain.invoke({\"input\": \"你还记得我的名字和我的职业吗\"})\n", "print(rr[\"response\"])" ] }, { "cell_type": "code", "execution_count": null, "id": "dec9e75d", "metadata": {}, "outputs": [], "source": [ "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables.history import RunnableWithMessageHistory\n", "from langchain_community.chat_message_histories import SQLChatMessageHistory\n", "from langchain_openai import ChatOpenAI\n", "\n", "\n", "# 定义 prompt（带历史占位符）\n", "prompt = ChatPromptTemplate.from_messages([\n", " (\"system\", \"你是一个擅长物理的助手\"),\n", " MessagesPlaceholder(variable_name=\"history\"),\n", " (\"human\", \"{question}\")\n", "])\n", "\n", "chain = prompt | llm | StrOutputParser()\n", "\n", "# MySQL 连接串（换成你自己的）\n", "mysql_url = \"mysql+pymysql://root:password@localhost:3306/mydb\"\n", "\n", "# 用 RunnableWithMessageHistory 包装链\n", "# 每次 invoke 时自动加载历史，结束后自动保存\n", "chain_with_history = RunnableWithMessageHistory(\n", " chain,\n", " # session_id 到 MessageHistory 的映射函数\n", " # 每个 session_id 对应一组独立的对话记录\n", " lambda session_id: SQLChatMessageHistory(\n", " session_id=session_id,\n", " connection_string=mysql_url,\n", " table_name=\"chat_history\"\n", " ),\n", " input_messages_key=\"question\", # 用户输入的 key\n", " history_messages_key=\"history\" # 历史消息的 key\n", ")\n", "\n", "# 使用时通过 config 传入 session_id\n", "config = {\"configurable\": {\"session_id\": \"user_001\"}}\n", "\n", "r1 = chain_with_history.invoke({\"question\": \"地球到太阳有多远？\"}, config=config)\n", "print(r1)\n", "\n", "r2 = chain_with_history.invoke({\"question\": \"到月球呢？\"}, config=config)\n", "print(r2)\n", "\n", "r3 = chain_with_history.invoke({\"question\": \"哪个更近？\"}, config=config)\n", "print(r3) # \"月球更近\" ← 能正确理解\"哪个\"指的是前两轮的内容" ] }, { "cell_type": "code", "execution_count": 1, "id": "908cf51e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sleep\n", "wolfram-alpha\n", "google-search\n", "google-search-results-json\n", "searx-search-results-json\n", "bing-search\n", "metaphor-search\n", "ddg-search\n", "google-books\n", "google-lens\n", "google-serper\n", "google-scholar\n", "google-finance\n", "google-trends\n", "google-jobs\n", "google-serper-results-json\n", "searchapi\n", "searchapi-results-json\n", "serpapi\n", "dalle-image-generator\n", "twilio\n", "searx-search\n", "merriam-webster\n", "wikipedia\n", "arxiv\n", "golden-query\n", "pubmed\n", "human\n", "awslambda\n", "stackexchange\n", "sceneXplain\n", "graphql\n", "openweathermap-api\n", "dataforseo-api-search\n", "dataforseo-api-search-json\n", "eleven_labs_text2speech\n", "google_cloud_texttospeech\n", "read_file\n", "reddit_search\n", "news-api\n", "tmdb-api\n", "podcast-api\n", "memorize\n", "llm-math\n", "open-meteo-api\n", "requests\n", "requests_get\n", "requests_post\n", "requests_patch\n", "requests_put\n", "requests_delete\n", "terminal\n" ] } ], "source": [ "from langchain_classic.agents import get_all_tool_names\n", "# 获取所有可用工具的名称\n", "tool_names = get_all_tool_names()\n", "for name in tool_names:\n", " print(name)" ] }, { "cell_type": "code", "execution_count": 3, "id": "4ea2b6ce", "metadata": {}, "outputs": [ { "ename": "ConnectTimeout", "evalue": "HTTPConnectionPool(host='zh.wikipedia.org', port=80): Max retries exceeded with url: /w/api.php?list=search&srprop=&srlimit=1&limit=1&srsearch=AI%E4%B9%8B%E7%88%B6&format=json&action=query (Caused by ConnectTimeoutError(, 'Connection to zh.wikipedia.org timed out. (connect timeout=None)'))", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTimeoutError\u001b[0m Traceback (most recent call last)", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\connection.py:204\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 203\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 204\u001b[0m sock \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_connection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 205\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dns_host\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mport\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 206\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 207\u001b[0m \u001b[43m \u001b[49m\u001b[43msource_address\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msource_address\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 208\u001b[0m \u001b[43m \u001b[49m\u001b[43msocket_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msocket_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 209\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m socket\u001b[38;5;241m.\u001b[39mgaierror \u001b[38;5;28;01mas\u001b[39;00m e:\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\util\\connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 85\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m 86\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 87\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\util\\connection.py:73\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m 72\u001b[0m sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[1;32m---> 73\u001b[0m \u001b[43msock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43msa\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 74\u001b[0m \u001b[38;5;66;03m# Break explicitly a reference cycle\u001b[39;00m\n", "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] 由于连接方在一段时间后没有正确答复或连接的主机没有反应，连接尝试失败。", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mConnectTimeoutError\u001b[0m Traceback (most recent call last)", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\connectionpool.py:788\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m 787\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[1;32m--> 788\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m 789\u001b[0m conn,\n\u001b[0;32m 790\u001b[0m method,\n\u001b[0;32m 791\u001b[0m url,\n\u001b[0;32m 792\u001b[0m timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m 793\u001b[0m body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m 794\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m 795\u001b[0m chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m 796\u001b[0m retries\u001b[38;5;241m=\u001b[39mretries,\n\u001b[0;32m 797\u001b[0m response_conn\u001b[38;5;241m=\u001b[39mresponse_conn,\n\u001b[0;32m 798\u001b[0m preload_content\u001b[38;5;241m=\u001b[39mpreload_content,\n\u001b[0;32m 799\u001b[0m decode_content\u001b[38;5;241m=\u001b[39mdecode_content,\n\u001b[0;32m 800\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresponse_kw,\n\u001b[0;32m 801\u001b[0m )\n\u001b[0;32m 803\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\connectionpool.py:493\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m 492\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 493\u001b[0m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 494\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 495\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 496\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 497\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 498\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 499\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 501\u001b[0m \u001b[43m \u001b[49m\u001b[43menforce_content_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43menforce_content_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 504\u001b[0m \u001b[38;5;66;03m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[0;32m 505\u001b[0m \u001b[38;5;66;03m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[0;32m 506\u001b[0m \u001b[38;5;66;03m# With this behaviour, the received response is still readable.\u001b[39;00m\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\connection.py:500\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers, chunked, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[0;32m 499\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mputheader(header, value)\n\u001b[1;32m--> 500\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendheaders\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 502\u001b[0m \u001b[38;5;66;03m# If we're given a body we start sending that in chunks.\u001b[39;00m\n", "File \u001b[1;32m~\\AppData\\Roaming\\uv\\python\\cpython-3.10-windows-x86_64-none\\lib\\http\\client.py:1298\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m 1297\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[1;32m-> 1298\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_output\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmessage_body\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencode_chunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencode_chunked\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32m~\\AppData\\Roaming\\uv\\python\\cpython-3.10-windows-x86_64-none\\lib\\http\\client.py:1058\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m 1057\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[1;32m-> 1058\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1060\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1061\u001b[0m \n\u001b[0;32m 1062\u001b[0m \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n", "File \u001b[1;32m~\\AppData\\Roaming\\uv\\python\\cpython-3.10-windows-x86_64-none\\lib\\http\\client.py:996\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 995\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[1;32m--> 996\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 997\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\connection.py:331\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 330\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 331\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_new_conn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 332\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tunnel_host:\n\u001b[0;32m 333\u001b[0m \u001b[38;5;66;03m# If we're tunneling it means we're connected to our proxy.\u001b[39;00m\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\connection.py:213\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m--> 213\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[0;32m 214\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 215\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 216\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", "\u001b[1;31mConnectTimeoutError\u001b[0m: (, 'Connection to zh.wikipedia.org timed out. (connect timeout=None)')", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mMaxRetryError\u001b[0m Traceback (most recent call last)", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\requests\\adapters.py:696\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 695\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 696\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 697\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 698\u001b[0m \u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 699\u001b[0m \u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type] # urllib3 stubs don't accept Iterable[bytes | str]\u001b[39;49;00m\n\u001b[0;32m 700\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type] # urllib3#3072\u001b[39;49;00m\n\u001b[0;32m 701\u001b[0m \u001b[43m \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 702\u001b[0m \u001b[43m \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 703\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 704\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 705\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 706\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolved_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 707\u001b[0m \u001b[43m \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 708\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 710\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\connectionpool.py:842\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[0;32m 840\u001b[0m new_e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, new_e)\n\u001b[1;32m--> 842\u001b[0m retries \u001b[38;5;241m=\u001b[39m \u001b[43mretries\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mincrement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 843\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_e\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacktrace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[0;32m 844\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 845\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\urllib3\\util\\retry.py:543\u001b[0m, in \u001b[0;36mRetry.increment\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m 542\u001b[0m reason \u001b[38;5;241m=\u001b[39m error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause)\n\u001b[1;32m--> 543\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, reason) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mreason\u001b[39;00m \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[0;32m 545\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n", "\u001b[1;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='zh.wikipedia.org', port=80): Max retries exceeded with url: /w/api.php?list=search&srprop=&srlimit=1&limit=1&srsearch=AI%E4%B9%8B%E7%88%B6&format=json&action=query (Caused by ConnectTimeoutError(, 'Connection to zh.wikipedia.org timed out. (connect timeout=None)'))", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[1;31mConnectTimeout\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[3], line 14\u001b[0m\n\u001b[0;32m 12\u001b[0m tool\u001b[38;5;241m.\u001b[39mdescription\n\u001b[0;32m 13\u001b[0m tool\u001b[38;5;241m.\u001b[39margs\n\u001b[1;32m---> 14\u001b[0m \u001b[43mtool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mquery\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mAI之父\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\langchain_core\\tools\\base.py:1100\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[1;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, tool_call_id, **kwargs)\u001b[0m\n\u001b[0;32m 1098\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error_to_raise:\n\u001b[0;32m 1099\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_tool_error(error_to_raise, tool_call_id\u001b[38;5;241m=\u001b[39mtool_call_id)\n\u001b[1;32m-> 1100\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m error_to_raise\n\u001b[0;32m 1101\u001b[0m output \u001b[38;5;241m=\u001b[39m _format_output(content, artifact, tool_call_id, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, status)\n\u001b[0;32m 1102\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_tool_end(output, color\u001b[38;5;241m=\u001b[39mcolor, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\langchain_core\\tools\\base.py:1066\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[1;34m(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, run_id, config, tool_call_id, **kwargs)\u001b[0m\n\u001b[0;32m 1064\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config_param \u001b[38;5;241m:=\u001b[39m _get_runnable_config_param(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run):\n\u001b[0;32m 1065\u001b[0m tool_kwargs \u001b[38;5;241m|\u001b[39m\u001b[38;5;241m=\u001b[39m {config_param: config}\n\u001b[1;32m-> 1066\u001b[0m response \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run, \u001b[38;5;241m*\u001b[39mtool_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtool_kwargs)\n\u001b[0;32m 1067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresponse_format \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent_and_artifact\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m 1068\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 1069\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSince response_format=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontent_and_artifact\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1070\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma two-tuple of the message content and raw tool output is \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1071\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexpected. Instead, generated response is of type: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1072\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(response)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1073\u001b[0m )\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\langchain_community\\tools\\wikipedia\\tool.py:38\u001b[0m, in \u001b[0;36mWikipediaQueryRun._run\u001b[1;34m(self, query, run_manager)\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_run\u001b[39m(\n\u001b[0;32m 33\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 34\u001b[0m query: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m 35\u001b[0m run_manager: Optional[CallbackManagerForToolRun] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 36\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m 37\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Use the Wikipedia tool.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 38\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapi_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\langchain_community\\utilities\\wikipedia.py:49\u001b[0m, in \u001b[0;36mWikipediaAPIWrapper.run\u001b[1;34m(self, query)\u001b[0m\n\u001b[0;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m, query: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[0;32m 48\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Run Wikipedia search and get page summaries.\"\"\"\u001b[39;00m\n\u001b[1;32m---> 49\u001b[0m page_titles \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwiki_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 50\u001b[0m \u001b[43m \u001b[49m\u001b[43mquery\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43mWIKIPEDIA_MAX_QUERY_LENGTH\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresults\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtop_k_results\u001b[49m\n\u001b[0;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 52\u001b[0m summaries \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 53\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m page_title \u001b[38;5;129;01min\u001b[39;00m page_titles[: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtop_k_results]:\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\wikipedia\\util.py:28\u001b[0m, in \u001b[0;36mcache.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 26\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[key]\n\u001b[0;32m 27\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 28\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache[key] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 30\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\wikipedia\\wikipedia.py:103\u001b[0m, in \u001b[0;36msearch\u001b[1;34m(query, results, suggestion)\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m suggestion:\n\u001b[0;32m 101\u001b[0m search_params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msrinfo\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msuggestion\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 103\u001b[0m raw_results \u001b[38;5;241m=\u001b[39m \u001b[43m_wiki_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43msearch_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 105\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m raw_results:\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m raw_results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minfo\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHTTP request timed out.\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPool queue is full\u001b[39m\u001b[38;5;124m'\u001b[39m):\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\wikipedia\\wikipedia.py:737\u001b[0m, in \u001b[0;36m_wiki_request\u001b[1;34m(params)\u001b[0m\n\u001b[0;32m 734\u001b[0m wait_time \u001b[38;5;241m=\u001b[39m (RATE_LIMIT_LAST_CALL \u001b[38;5;241m+\u001b[39m RATE_LIMIT_MIN_WAIT) \u001b[38;5;241m-\u001b[39m datetime\u001b[38;5;241m.\u001b[39mnow()\n\u001b[0;32m 735\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;28mint\u001b[39m(wait_time\u001b[38;5;241m.\u001b[39mtotal_seconds()))\n\u001b[1;32m--> 737\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mrequests\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mAPI_URL\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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Unpack[_t\u001b[38;5;241m.\u001b[39mGetKwargs]\n\u001b[0;32m 76\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Response:\n\u001b[0;32m 77\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[0;32m 78\u001b[0m \n\u001b[0;32m 79\u001b[0m \u001b[38;5;124;03m :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[38;5;124;03m :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m 85\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 87\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, params\u001b[38;5;241m=\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\requests\\api.py:71\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m 69\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\requests\\sessions.py:651\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m 646\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 647\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m 648\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m 649\u001b[0m }\n\u001b[0;32m 650\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 651\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m 653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\requests\\sessions.py:784\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m 781\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[0;32m 783\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 784\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 786\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m 787\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n", "File \u001b[1;32md:\\agent\\.venv\\lib\\site-packages\\requests\\adapters.py:717\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m 714\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ConnectTimeoutError):\n\u001b[0;32m 715\u001b[0m \u001b[38;5;66;03m# TODO: Remove this in 3.0.0: see #2811\u001b[39;00m\n\u001b[0;32m 716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[1;32m--> 717\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m 719\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n\u001b[0;32m 720\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m RetryError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n", "\u001b[1;31mConnectTimeout\u001b[0m: HTTPConnectionPool(host='zh.wikipedia.org', port=80): Max retries exceeded with url: /w/api.php?list=search&srprop=&srlimit=1&limit=1&srsearch=AI%E4%B9%8B%E7%88%B6&format=json&action=query (Caused by ConnectTimeoutError(, 'Connection to zh.wikipedia.org timed out. (connect timeout=None)'))" ] } ], "source": [ "#WikipediaQueryRun为例子\n", "#WikipediaQueryRun：用于向维基百科API发送查询并获取数据\n", "# pip install wikipedia -i https://pypi.tuna.tsinghua.edu.cn/simple\n", "from langchain_community.tools import WikipediaQueryRun\n", "from langchain_community.utilities import WikipediaAPIWrapper\n", "\n", "# top_k_results 收索结果的数量\n", "# doc_content_chars_max 单个Document的内容长度\n", "api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100,lang=\"zh\")\n", "tool = WikipediaQueryRun(api_wrapper=api_wrapper)\n", "tool.name\n", "tool.description\n", "tool.args\n", "tool.run({\"query\": \"AI之父\"})" ] }, { "cell_type": "code", "execution_count": 1, "id": "0484cb4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "get_weather\n", "查询指定城市的实时天气信息\n", "\n", " Args:\n", " location: 城市名称，如\"北京\"、\"成都\"\n", "{'location': {'title': 'Location', 'type': 'string'}}\n", "上海的天气：Partly Cloudy ，温度：29°C\n" ] } ], "source": [ "from langchain_core.tools import tool\n", "import requests\n", "\n", "@tool\n", "def get_weather(location: str) -> str:\n", " \"\"\"查询指定城市的实时天气信息\n", " \n", " Args:\n", " location: 城市名称，如\"北京\"、\"成都\"\n", " \"\"\"\n", " # 调用免费天气 API\n", " url = f\"https://wttr.in/{location}?format=j1&lang=zh\"\n", " response = requests.get(url)\n", " data = response.json()\n", "\n", " current = data['current_condition'][0]\n", " weather_desc = current['weatherDesc'][0]['value']\n", " temp = current['temp_C']\n", "\n", " return f\"{location}的天气：{weather_desc}，温度：{temp}°C\"\n", " # @tool 装饰器自动提取的信息\n", "print(get_weather.name) # \"get_weather\"\n", "print(get_weather.description) # \"查询指定城市的实时天气信息\"\n", "print(get_weather.args) # {'location': {'title': 'Location', 'type': 'string'}}\n", "\n", "# 工具可以直接调用\n", "result = get_weather.invoke({\"location\": \"上海\"})\n", "print(result) # \"成都的天气：Sunny，温度：28°C\"" ] }, { "cell_type": "code", "execution_count": 6, "id": "bf2db84a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'name': 'get_weather', 'args': {'location': '上海'}, 'id': 'call_00_gFFhWrYQao62j44K72dJ1187', 'type': 'tool_call'}]\n", "content='好的，我来查询一下上海今天的天气情况。' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 73, 'prompt_tokens': 296, 'total_tokens': 369, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 19, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 256}, 'prompt_cache_hit_tokens': 256, 'prompt_cache_miss_tokens': 40}, 'model_provider': 'openai', 'model_name': 'deepseek-v4-flash', 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402', 'id': 'b99e06fd-b592-4912-9b38-fbbc4bc8f4ca', 'finish_reason': 'tool_calls', 'logprobs': None} id='lc_run--019f4a91-8708-74f1-b448-dcf847f667a3-0' tool_calls=[{'name': 'get_weather', 'args': {'location': '上海'}, 'id': 'call_00_gFFhWrYQao62j44K72dJ1187', 'type': 'tool_call'}] invalid_tool_calls=[] usage_metadata={'input_tokens': 296, 'output_tokens': 73, 'total_tokens': 369, 'input_token_details': {'cache_read': 256}, 'output_token_details': {'reasoning': 19}}\n" ] } ], "source": [ "from langchain_openai import ChatOpenAI\n", "from langchain_core.messages import HumanMessage\n", "\n", "\n", "\n", "# bind_tools() 把工具的 Schema 注入到模型请求中\n", "llm_with_tools = llm.bind_tools([get_weather])\n", "\n", "# 此时模型已经\"知道\"你有一个叫 get_weather 的工具了\n", "messages = [HumanMessage(content=\"上海今天天气怎么样？\")]\n", "\n", "response = llm_with_tools.invoke(messages)\n", "\n", "# LLM的输出不再是一段自然语言文本，而是一个结构化的JSON对象(tool_calls)。\n", "#这个对象包含了要调用的函数名和所需的参数。\n", "print(response.tool_calls)\n", "# [{'name': 'get_weather', 'args': {'location': '成都'}, 'id': 'call_xxx', 'type': 'tool_call'}]\n", "print(response)" ] }, { "cell_type": "code", "execution_count": 7, "id": "418cdcae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "上海今天**天气情况**如下：\n", "\n", "- 🌤 **天气**：多云（Partly cloudy）\n", "- 🌡 **温度**：32°C\n", "\n", "今天上海天气比较炎热，出门请注意防暑降温，多补充水分哦！\n" ] } ], "source": [ "from langchain_core.messages import ToolMessage\n", "\n", "# 假设 response 是上面 bind_tools 后调用的结果\n", "if response.tool_calls:\n", " tool_results = []\n", " for call in response.tool_calls:\n", " # 根据工具名找到对应的函数并执行\n", " if call[\"name\"] == \"get_weather\":\n", " result = get_weather.invoke(call[\"args\"])\n", " # 把执行结果封装成 ToolMessage\n", " tool_results.append(ToolMessage(\n", " content=result,\n", " tool_call_id=call[\"id\"] # 关联到对应的 tool_call\n", " ))\n", " \n", " # 把工具结果追加到消息列表，再发一次请求\n", " messages.extend([response] + tool_results)\n", " final_response = llm_with_tools.invoke(messages)\n", " print(final_response.content)\n", " # \"成都今天天气晴朗，温度28°C，很适合出门~\"" ] }, { "cell_type": "code", "execution_count": 9, "id": "a1aef0f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "泰顺的天气情况如下：\n", "\n", "- **天气状况**：☀️ 晴天\n", "- **气温**：🌡️ 30°C\n", "\n", "天气不错，是个阳光明媚的好日子！不过气温较高，出门的话记得注意防晒和补水哦。😊\n", "{'messages': [HumanMessage(content='泰顺的天气怎么样？', additional_kwargs={}, response_metadata={}, id='1294245e-53f4-47ec-9aa9-971ff6d21a75'), AIMessage(content='好的，我来帮你查询泰顺的天气情况。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 100, 'prompt_tokens': 346, 'total_tokens': 446, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 44, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 0}, 'prompt_cache_hit_tokens': 0, 'prompt_cache_miss_tokens': 346}, 'model_provider': 'openai', 'model_name': 'deepseek-v4-flash', 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402', 'id': 'b1e923df-ba4a-40f3-9ae8-8a8e684b7180', 'finish_reason': 'tool_calls', 'logprobs': None}, id='lc_run--019f4a94-0f17-7393-ae30-f8f8a12f5d86-0', tool_calls=[{'name': 'get_weather', 'args': {'location': '泰顺'}, 'id': 'call_00_KI5cenwUIWgS7PI4LXud6359', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 346, 'output_tokens': 100, 'total_tokens': 446, 'input_token_details': {'cache_read': 0}, 'output_token_details': {'reasoning': 44}}), ToolMessage(content='泰顺：Sunny，30°C', name='get_weather', id='b2bd0ce4-0da0-4a97-a79c-9299caad3852', tool_call_id='call_00_KI5cenwUIWgS7PI4LXud6359'), AIMessage(content='泰顺的天气情况如下：\\n\\n- **天气状况**：☀️ 晴天\\n- **气温**：🌡️ 30°C\\n\\n天气不错，是个阳光明媚的好日子！不过气温较高，出门的话记得注意防晒和补水哦。😊', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 77, 'prompt_tokens': 467, 'total_tokens': 544, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 20, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 384}, 'prompt_cache_hit_tokens': 384, 'prompt_cache_miss_tokens': 83}, 'model_provider': 'openai', 'model_name': 'deepseek-v4-flash', 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402', 'id': '539bee6d-e092-4c9d-afe4-9217bf494c41', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--019f4a94-1783-7a93-91cc-deff30b86ef9-0', tool_calls=[], invalid_tool_calls=[], usage_metadata={'input_tokens': 467, 'output_tokens': 77, 'total_tokens': 544, 'input_token_details': {'cache_read': 384}, 'output_token_details': {'reasoning': 20}})]}\n", "5乘以6等于 **30**。\n", "{'messages': [HumanMessage(content='5乘以6等于多少？', additional_kwargs={}, response_metadata={}, id='24b5c37d-d5ff-4085-ad41-070471d952bd'), AIMessage(content='5乘以6等于：', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 77, 'prompt_tokens': 346, 'total_tokens': 423, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 13, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 256}, 'prompt_cache_hit_tokens': 256, 'prompt_cache_miss_tokens': 90}, 'model_provider': 'openai', 'model_name': 'deepseek-v4-flash', 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402', 'id': 'fcf8f6f5-4607-4490-b9ea-07fe8f0eae40', 'finish_reason': 'tool_calls', 'logprobs': None}, id='lc_run--019f4a94-1c66-7fe2-9b99-3623f11229cd-0', tool_calls=[{'name': 'multiply', 'args': {'a': 5, 'b': 6}, 'id': 'call_00_TRRpmrmFsWL7RefKTefd8215', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 346, 'output_tokens': 77, 'total_tokens': 423, 'input_token_details': {'cache_read': 256}, 'output_token_details': {'reasoning': 13}}), ToolMessage(content='30', name='multiply', id='377846de-a964-4be2-a531-cde3210ee58a', tool_call_id='call_00_TRRpmrmFsWL7RefKTefd8215'), AIMessage(content='5乘以6等于 **30**。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 436, 'total_tokens': 449, 'completion_tokens_details': {'accepted_prediction_tokens': None, 'audio_tokens': None, 'reasoning_tokens': 4, 'rejected_prediction_tokens': None}, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 256}, 'prompt_cache_hit_tokens': 256, 'prompt_cache_miss_tokens': 180}, 'model_provider': 'openai', 'model_name': 'deepseek-v4-flash', 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402', 'id': '6f9daede-c4d6-43fc-9ed4-8ec3b8527a48', 'finish_reason': 'stop', 'logprobs': None}, id='lc_run--019f4a94-20ac-7ac0-bfb1-4ba7c35a400f-0', tool_calls=[], invalid_tool_calls=[], usage_metadata={'input_tokens': 436, 'output_tokens': 13, 'total_tokens': 449, 'input_token_details': {'cache_read': 256}, 'output_token_details': {'reasoning': 4}})]}\n" ] } ], "source": [ "from langchain.tools import tool\n", "from langchain_openai import ChatOpenAI\n", "from langchain.agents import create_agent\n", "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "import requests\n", "\n", "# ─── 第一步：定义工具 ───\n", "@tool\n", "def get_weather(location: str) -> str:\n", " \"\"\"查询指定城市的天气\"\"\"\n", " url = f\"https://wttr.in/{location}?format=j1&lang=zh\"\n", " data = requests.get(url).json()\n", " current = data['current_condition'][0]\n", " desc = current['weatherDesc'][0]['value']\n", " temp = current['temp_C']\n", " return f\"{location}：{desc}，{temp}°C\"\n", "\n", "@tool\n", "def multiply(a:int,b:int) ->int:\n", " \"\"\"实现两个整数相乘\"\"\"\n", " return a * b\n", "\n", "tools = [get_weather, multiply]\n", "\n", "# ─── 第二步：配置大模型 ───\n", "# llm = ChatOpenAI(\n", "# model_name=\"deepseek-v4-flash\",\n", "# api_key=\"\",\n", "# base_url=\"https://api.deepseek.com\"\n", "# )\n", "\n", "# ─── 第三步：创建 Agent ───\n", "# create_agent 把模型、工具、prompt 组装成 Agent\n", "agent = create_agent(\n", " model=llm,\n", " tools=tools,\n", " system_prompt=\"你是一名智能助手，可以调用工具帮助用户解决问题。\"\n", ")\n", "\n", "# ─── 第四步：使用 ───\n", "res = agent.invoke({\"messages\": [\n", " {\"role\": \"user\",\n", " \"content\": \"泰顺的天气怎么样？\"\n", " }]})\n", "print(res['messages'][-1].content)\n", "print(res)\n", "\n", "res = agent.invoke({\"messages\": [\n", " {\"role\": \"user\",\n", " \"content\": \"5乘以6等于多少？\"\n", " }]})\n", "print(res['messages'][-1].content)\n", "print(res)" ] }, { "cell_type": "code", "execution_count": 10, "id": "1759d518", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "✅ DeepSeek 模型初始化完成\n" ] } ], "source": [ "import os\n", "from langchain_openai import ChatOpenAI\n", "# 通过环境变量读取 API Key，避免硬编码泄露⻛险\n", "llm = ChatOpenAI(\n", " model=\"deepseek-v4-flash\", # DeepSeek 的对话模型\n", " api_key='sk-80a123483afb480285c6452985eea18e', # 从环境变量加载\n", " base_url=\"https://api.deepseek.com\", # DeepSeek API 地址\n", " temperature=0, # 设为 0 保证 SQL ⽣成的确定性\n", ")\n", "print(\"✅ DeepSeek 模型初始化完成\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "53cea754", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "数据库连接成功\n", " 可用表：['employees', 'orders', 'products']\n", "\n", "SQL 工具包已加载（4 个工具）：\n", " - sql_db_query\n", " - sql_db_schema\n", " - sql_db_list_tables\n", " - sql_db_query_checker\n", "\n", "NL2SQL Agent 创建完成，可以开始提问了！\n" ] } ], "source": [ "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", "# ============================================================\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 = \"mysql+pymysql://root:Root%402024!@localhost:3306/agent_db\"\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=\"deepseek-v4-flash\",\n", " api_key='sk-80a123483afb480285c6452985eea18e',\n", " base_url=\"https://api.deepseek.com\",\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": 14, "id": "a398f56a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "回答：---\n", "\n", "## 查询结果\n", "\n", "公司目前共有 **5 名员工**。\n", "\n", "### 查询步骤回顾\n", "| 步骤 | 操作 | 说明 |\n", "|------|------|------|\n", "| 1️⃣ | `sql_db_list_tables` | 查看数据库中有哪些表 |\n", "| 2️⃣ | `sql_db_schema(employees)` | 查看 `employees` 表结构（id、name、department、salary、hire_date） |\n", "| 3️⃣ | `sql_db_query_checker` | 检查 SQL 语法 ✅ |\n", "| 4️⃣ | `sql_db_query` | 执行查询，得到结果 **5** |\n", "\n", "### 业务洞察\n", "目前员工总数较少，若未来业务扩张，建议提前规划招聘和团队建设节奏。\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？并且跟我说你的查询步骤\"}]\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": 15, "id": "5ca88704", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "回答：查询完成，结果如下：\n", "\n", "## 📊 技术部员工薪资排行\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", "- 技术部共有 **2名员工**，总薪资为 **36,000元**\n", "- 薪资最高的员工是 **张三（20,000元）**，比第二名王五高出 4,000 元（约 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": 17, "id": "38a79c11", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "回答：好的，以下是完整的分析结果：\n", "\n", "---\n", "\n", "## 查询步骤\n", "\n", "1. **查看数据库表** → 找到 `employees`（员工）、`orders`（订单）、`products`（产品）三张表\n", "2. **查看表结构** → 确认 `employees.department` 可筛选销售部，`orders` 通过 `employee_id` 关联员工，`products` 通过 `product_id` 关联产品价格\n", "3. **编写查询** → 三表关联，筛选销售部员工，计算订单数量和总金额\n", "4. **检查并执行**\n", "\n", "---\n", "\n", "## 查询结果\n", "\n", "| 订单ID | 员工 | 产品 | 数量 | 单价 | 金额 |\n", "|:---:|:---:|:---:|:---:|:---:|:---:|\n", "| 2 | 李四 | 机械键盘 | 15 | 399.00 | 5,985.00 |\n", "| 4 | 钱七 | 办公椅 | 6 | 499.00 | 2,994.00 |\n", "| 5 | 李四 | 显示器 | 5 | 1,200.00 | 6,000.00 |\n", "\n", "### 📊 汇总\n", "\n", "- **销售部员工**：李四、钱七（共2人）\n", "- **总订单数**：**3 笔**\n", "- **订单总金额**：**14,979.00 元**\n", "\n", "> 💡 **业务洞察**：李四是销售部的核心员工，贡献了 2 笔订单共 11,985 元（占总额的 80%），而钱七贡献了 1 笔订单 2,994 元。\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？并且跟我说你的查询步骤\"}]\n", "})\n", "final_msg = response[\"messages\"][-1]\n", "print(f\"回答：{final_msg.content}\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "a51cc94d", "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", ")D\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，以下是各部门的人数统计：\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", "**共 3 个部门，总计 5 名员工。** 技术部和销售部人数相同，各占 40%，人力资源部目前只有 1 人。\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": 20, "id": "4dd59f84", "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": 21, "id": "e280cae4", "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": 22, "id": "642b48d7", "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": 23, "id": "aa31507a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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", 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dHSdOnNB37NhRX6hQIX1wcLC+d+/eqYpRS4FyeV4vLy/9ww8/rP/ss8/0RYsWTbeAY0YFvpctW6YvXbq0eo4mTZqo4pTGc53VY83duHFDFXJ/66239Nklf3N5TXm8FCsfNWqUPiIiIsP7pnd8d+/e1Q8bNkxfokQJdSw1atRQxefTkgLock6lIKYUzF++fHmWBcyzU+hciqRLsXP5m9etW1f/559/mrZL0fXRo0erwpvFihVT95Vi+ukVLF+xYoW+VKlSend3d1Xk3lj805Ji6FKItFGjRuq9Le9xeV8Ypf1/Zf7/0rwgKRERuRa20dhGy6yNJou0SaRd8corr6hJgYxkUppevXrpfX19Vbv4pZdeMhUyl4LjISEhqSZY2bJli3q+X375xfQagwcP1j/55JOq/SRtZ5mIJzvtQGljSfH10NBQ1Q58/PHHUxVfN5L97tu3rz4gIED9jkhbCJ3IGWjkH3sHxohcmUxvL8PYZLp7a4mMjFRZOwcOHFC9P9JbY2ltI1ckQ+0k60h6uGS4YWhoqL13iYiIiGyMbTTH4wxtNMkuk4lqJMOciLLG4XtELkBqAclsKlLXSVKUGZDKnBSBl2FqMuuMIzZ2iIiIKH9gGy172EYjyn+YKUVERERERERERDbH2feIiIiIiIiIiMjmGJQiIiIiIiIiIiKbY1CKiIiIiIiIiIhsjkEpIiIiIiIiIiKyOZeffU9m2Lpy5QoCAgKg0Whs/xcgIiIih6HX6xEVFYUSJUpAq2XfXXawTUVERETZbVO5fFBKAlKlSpXK8AQRERGR67l06RJKlixp791wKmxTERERUXbbVC4flJIMKeOJKlCgAPJbj2VERASCgoJcrreXx86/e358z//5J5CQAHh5Ac2a3b/dld/v289tR/itcAQXDkaLci3g7M6ePYtWrVph/vz5aNq0KSZPnoyrV6/il19+eeC+jvB3//PPPzFy5EgcOXLEpq9rzWOPjIxUnVXG9gFZLj+3qYiIiMg6bSqXD0oZh+xJ4ym/NaCk0R4fH6+Oy9V+qPLY+XfPj+/5558HwsKA0FDg8uX7t7vy+33E7yMQFhWG0IBQXJ5gdlKc1OXLl/HOO+9g0KBB6vrYsWPRqVOndL+fHOHv7ufnp75Hbf39aYtj55D+nJ+z/NimIiIiIuu0qVzr14ut6XVAbJhhkXUiIqJMdO7cGcOGDTNdP3nyJCpWrJjlOVu1ahUaNGiAbt26oWDBgmjfvr3KsDLat28fGjZsqLb16NEDd+/eNW3bsWMHatWqhUKFCqFv3764c+eOun3mzJno0KEDWrRooR7Xu3dv1eNliZ9//hnVq1dHYGAghg4digRJ8csrOp2Kzmrl+GSdiIiIiJwWg1LWlBIHfFvSsMg6ERHlqfGNxmNi3YnqMr9JTEzE3LlzMXz4cIvuL4Gnxo0b49ChQ/Dy8jI9ToJMElyS5Z9//lGBpYkTJ5qGWXXs2BGjRo3CgQMHEB0dbcrSMgaXhgwZgv379+P8+fOYNm1alvtx+vRpFRwbN26c2qe9e/fi3XffRZ6Ji4O2dGkE16mj1omIiIjIebn88D2r0/AUExFZiwSjwsPDERwcnO9O8owZM9TwOMk0soQUkHzppZdUirRkOdWvXx/Jycn44Ycf4OHhoZ5Ptk2aNAkDBgxQj/n888/x6KOP4rnnnlPXFy9erJ7n2rVr6nqTJk1M95XnHj9+PD744INM9+PLL79UmVfG/ZaaU5988gleffVV5BW9O79biYiIiPIDtuoslJKSgqSkpGyeXjfgiSjDarIs8bB1zQ3ZZ6m74Wp1Zhzt2D09PR1iP4jIOWzduhULFy7E7t27VUDJEhJMMo7ZDw0NVd9bN2/eVHWqpCi4DM8zfj7K9Lzy+SiZUuXLlzc9hzxOsqwuXryorpvPTivbrl+/nuV+yOsdPHhQDd0TEhjz9/dHnvHzgz4hwRCM9PPLu+clIiIiIptjUCoLer1e9Rgba2w4274bf3y4WsFWRzt2CUiVK1dOBaeIiDJz7tw59OnTRwWlqlSpYvHJkkCSfPbJZ54Em9zd3VG0aFEVrKpbt67KYBJyH6kpJcGu0qVLqwCY0ZUrV1T9pzJlyqjrMmTPSJ4zJCQky/2Q1+vSpYsaeigkOBYbG8s/OhERERE9gEGpLBgDUjI0xNfX1yECHJaSHx7SQy0/TJxpv/PbsUtwTH7oSdFh+QFo7/0hyk+iEqIQlRgFnwQfFPQpCGcXFxenip1LTaYnnnhC1Xgyn+UuM/I5M3v2bBXQmjVrlnoONzc3NXufDNmT2k5StPyzzz5TASPJaOrXrx/eeOMNLFu2DG3btsULL7yA7t27o1ixYuo5JVPr008/VUP8ZFbAnj17ZnkMUhBdhvj9999/qFatmnrcX3/9pepSERERERGZY1AqE9K7awxIFSlSBDmbfe+SYd23FKDRumxgxtYc7diDgoLUD0bZJ0uH4hBR1qouroqwqDCEBoTi8oTLTn/KfvnlFxw7dkwtEigyz54qW7Zspo9t1KiRCjy9+eabaNasGZYuXapul2F0mzdvxujRozF48GBUrVpVXZfPRxmeJzWnpCj55MmTVTF0qStlJBlPy5cvV4+VYJnUpcpKhQoVVOBrwoQJOHv2rJr1b+3atcgzCQnQjB+PAClyvmgR4OOTd89NRESUgU2bTmP8+D9w8WIkqlUrirVrO6Ny5SI4ejQCgwf/jNOn72Do0OqYM6eFRb8/Nmw4iYkTtyEpSYe5c1uiT5/Kpm0LFx7Ea6/tgp+fB5Yvb4fWrUubtr3yyp9YtOgQSpUKwOefd0KNGkH8m5FTY1AqE8YaUpIhlTN6ID7CsOpbMofPQfmBcdieBDoZlCKijEh2kwTVc0JqQX377bfpbpOi53v27El3m2RPyYx96SlcuLAKYGWkZcuWqYb4GbVv314tVpGcDM3ixZBqUroFC6zzGkRERGbOnLmjAk9LlrRFixalMGbM7xg6dAu2bn0KXbpsRLt25bBuXReMHfs7Vq06isGDq2d6/iSQ1a/fj1i4sA0aNiyOHj02oU6dYqhUqTC2bDmHSZO2Y926zggK8kH//j9i377+KFLEBx9/fBgff/wPNm9+Ardvx6N37+9w6NBAeHq68e9FTouVly2Q80wbDeBTwrDIOrksR8jWIiLKFzw8oJ8+HdETJ6p1IiIiazt+/CbefrsZnnrqERQr5ocRI2rh4MFw/PTTOdy9m4h581qiQoVAvPVWM6xYcTTL51u+/AhatSqFoUNroHr1IIweXRurVx9T2xYvPoyBA6uiW7eH8Oijoepy48b/7m07hEmT6qFZs5Lo2vUhFcTascP5M8XJtTEoZU0yXM+3hGGx8dC9/EIK90odksTERHvvCpHdXb4sQ1MNl0TmBg0ahG3btuXpSZk5cyZWrVrleCfa0xP6GTMQPWmSWiciIrK2zp0rYNiwmqbrJ0/eQsWKhXD4cAQaNSoOX19DJ4kMpTt27GaWzyePMx+S16BBcRw4cO3etnC0bn1/9tsGDUJw4MB1lUl95MiNDB9H5KwYKcmH5IeEZOZ899136vqiRYvUdYf8cQHg77//RqVKlR64XeqR1KlTB3379lUz1504ccKiIsFPPvkkChYsiBIlSqjivlJo3Ehms5ICvjIFuvmMU+KVV15RU6bXqFED//zzTx4dHRERERER5ReJiSmYO3c/hg+vicjIBJQrd3+iFfnN5eamUUPrMhMZmZjqcQUKeOLKlZhMtkUjOjoJOp0+w8cROSsGpaxJUhp0yYYlhzVCckMK5Yp///0XjurAgQNqhimZgtyc9Ph///33qkjuqVOn8Pjjj+Ptt9/O8vneffddVbNJjl3qoHzzzTemYNyWLVtUkEqK/37++ecYOnQobt409GR8/PHHapHHyExUMnsUs7OIiByQfJ/euQPN3bt2+W61h02bNqnOFClOX6tWLRw/flzdfvToUVUvTDpUpFC9eT2y7du3o3LlyihatCjmzZuX6vk2bNiAMmXKqM6btEXoM+u8ISIiYMaMnaoAuRQ1d3fXwssrdT0nb293xMYaahNnxN1dk+px5o8xPKd7mm0ygZOhHEhGjyNyVlpHalxlJqeNK/vSAbcPGRZZtyGZBtw8KCXXHU1MTAx69OihZnVKr2CvzDxVoEABdb127dqmAFJmZOap/v37IzQ0FPXq1UObNm1w+vRptU1mlBo4cKAqJCzTm8vlxo0bTdskYCUzVnXt2lVlbu3YsSPPj5mIiHIpNhbaIkVQ7JFH1Hp+d+bMGTVronTMhIWF4eGHH1adKtKZI7Mj1q1bVw1zl+98YydMRESE+i7r06cPdu3ahS+++AJ//PGHKZDVr18/TJs2TXXWTJ8+HSdPnsyy84aIiICtWy9i4cJDWLOmEzw83FC4sDciIuJSnZqoqMQsC4+nfZz5YwzbYtNs08LHxwM+Pu4ZPo7IWWkdpXGVmZw2rlyZBGSMGVIy7E0arUb79u1TU3TLEDcJCt2V3mazgKEEZPz8/FRA58qVK+p2aejKLEsSKJIeVFkkC8mc3EeCjJaSjCYZuieBoLQaN26sZoQSN27cwCeffKIyqrIiU53L9OV37txRx//jjz/iscceU9sOHz6M1q1bm+7boEEDlallGJ99JN1tRI5k1iwZ1mq4JCLXIB130mZ66qmn1HfviBEjcPDgQfz000/q+1s66ipUqIC33noLK1asUI+RdpJ01EnbqGLFiqptZNwm35GtWrVSba/q1aurjqHVq1dn2XlDROTqzp27gz59vlcz5lWpUlTdVr9+cezadSXVfRISUlRgKTP164eketzBg9cRGuqfwbZwhIYGqPV69TJ+HJGz0jpK4yozOW1cWZMka5UsmcVSSouug+sCheukOtVdu1rw2JKG18gpOU8yTbcElQIDA+Hj46Nul2BNhw4d1CJ1kyIjIzFRZjACcPv2bTz99NN4+eWXVXZRcHCwGspmJAFACUTt3LlTBRZfeOGFVK8ptZ/+/PNPi/fR09NTZTRlRoJgpUuXRkhICJ599tksn3PKlCkqcCnTmEt2lQSy5P0h5FilNpWRZGHJ+YmOjlZ1p9LbRuRIli0D5s83XBK5LF9f6OLjce3iRbWe33Xu3BnDhg0zXZeON/mOl46WRo0awffeOZB6iMYMadkm333GmV/NO1oy6qDJahsR5V/SQSsjGPJikXZ1eHi4WmQ9r57XfHiyPcTFJaFz541qJrwnnqiI6OhEtTRrFqrqSq1ceUTd76239qBt29JwczP89rtzJx4pKQ+OmOnZ82GsW3cCR45EqOdZsOAg2rUrq7b16vUwFi06hLCwKFy/HoMVK46k2vbOO3vVa546dQsbNpwybSNyVvcHq9qwcWXO2LjKTHqNKwk+GLdJgMVItr322muwtshIICwsq3tpUEomTri330YREZY81vAaOSXD9STI8sMPP6iGqmSbCbkuGUozZsxQ51PS9AcMGKC2+fv748KFCyqDSoYCyBeAfKEYyfVPP/1UBaskQPTOO+88EGSSJS/JvhUvXlwFLz/66KN0h/qZk8Bl06ZN1X3lmCV49uGHH2LMmDFquKgMCzTy9vZGbGysul2kt42IiByMfKd6eBiWNN+v+Z3UOpw7d66aCEQ6j8w7UwzFdd1UB5N0wlSpUiXdjpaMOmiy2pYeGUJoXhNSHi+ko8d8khEicmzSxjeWzHBU8vkiIzns5eefz6lZ9WRZtuz+hEhnzgzB0qWPo1+/HzB58nZotRps3fqU6TOwUKGPcOBAf9SqFZzq+apXL4qxY2ujXr3P4e3tpmbyGz68hnpcp07l1Ax7FSsakjBkvXv3Cmrbc89Vw6ZN/6FkyY9VRtaQIdVQu3YwP3PJIVnaFrB5UCqjxlVmctq4ym0DSq5LVN64mAsIALJI8lGCgh6swyq3WfJYeY2cdAoY91Uyx9avX68CelKsVG6/dOmSCtZIUVTjMUZFRalZ67RarQr2SbFvqd8VEBCAlJQU0/HLbUFBQWpdAlvmr5UbxudI77kkyNWpUyfMmjVLBZdGjRqVZVad1B+TwJkco8yoJ7MRSjBLsqckyGZ8Hfnby/NLAEoyyWSbHLNxmxxjXvXKGM+hLRrqxvetK/4gyP/Hrrm3yDHqXei4LWOv45chxsbvEluSv7l8fsti7LRxJvJ9LXUic8Ka73lH/n8kHUryo0yyw1999dVUnSlpO1sy6mjJ6bb0zJ49W30/pyXtjPj4zGeeIiLH4QwdsfK5IsEze2ncuACuXu2fzpYENGoUgF27uuGff26iTp0gFC6cYurYHzz4YQQGJqfq6DcaO/ZhtGsXjGvX4tC4cTCiom4jKsqw7b336qJ//zKqwHnjxsVMCQZi9erm2Ls3Al5eWtSuXTTd5yZyBNJGdfiglHnjKjP2akAlJcm0mzokJyerxdzYsYYlU3odtAnXoIsGdF4hgMaQxvn117BYmpe1iDHwIRlSUudJAjm///67uk2yjurUqaOCN2oX9XpVk0J+0EhRUxn6Jr2vkjW1ZMkSVURejl0eKwEb43lIe5kbEvhK+1wSgJIfK1JHTEjvrwTNsno92c+rV6/ikUceUc8rdcuMfz+pqyVDD421qmRIgpwP47a//vpLFcwX//vf/1S9s7w4PuOxyb5JwVhjQM9a5HXkbyp/WzlnriS/H7tOFyT/G9RxhodHuMxxZyZFl2K6tEejTM77go8WIS4x0eavLVlCxYKK4nrEDaechc7H0xNjR49U2bnZkpgI/9mz4ZmQgPDp06H1zrxuh7UaULYmnUsyM97u3bvV94h0tMiw+rT7Lp0tss38B4zxdpHTbemR4f7mHYsSnC1VqpTqwHL0rAsiuk/aD3nVuSKBI2lfC2mT51V2kwxVduQOmOBgoFo1w+8Ic08/XR1ly5bI5HGpM6jMPf54xtu6di2Wg70ksi2JzTh0UCpt4yoz9mpASZBKnk8CX8YhXtmi10ETZdg3rV9JU1DK2uRHqSxSU0lIEEo+xOU2KRgv2VASdJHgzGeffaaKpEoGlbH3Qc6JBKekaKoEZuTY5bHyHMbzkPbSmPkmWWjGbCNLGWcGNH+uhx56SAUrS5YsqeqJzZ8/H71791b3kR/gso/yQybtl5MUTZfe43HjxqkfqFK/TJ5HHvfkk0+qYYBDhgxR1yVg9/7776v1Xr16qaw9qUF17do1VTtLMq5y9HdPh/EcFilSxOL/nDkl50fOi7ynXS1Akd+PXVLCDZfaVI2Y/H7cmXHTupkuM2vYWYvUyzh2+gxa9H8eRUJK2vbF9Xr4pMShhJuP0w1ju3ntMrZ//rH6/M/23y0mBtolSyBlXZPnz4c2m985WbH2Z3ROnDt3TnXSSLvJmDlev359VXfR/D7yHSztItm2Zs0a0zap3Wms4Sjb5Dtevgsz2iYTnaTdlh7pFEybrWXeDiEi55Hd9ntGzP/vy3Pac8idI2jX7v6IHiJXo7WwLeDuKI2rzOS0cZXbBpQxEGNcsk8D+Bii2OrxNvrRYNzXmjVrqkBb2bJlTbfLkDYZnifD2aQulMxWJ9clMDho0CA1W538TWTo3/PPP49FixapRq7xOTO6FGvXrlVBnkOHDuVof82fyxg869+/v8pYk8DSiy++qO4jATQZsil1M6SIuznJ7ho5ciTGjh2rgmQSiJI6U/I4eU7J/JJAm5BGd8+ePdW24cOHq/MgAUo5Xnk9mcEwrxjfQ7ZqqNvytRyNaxy7HKPGBY87c/Y4djnv0sNcpHgphJSxccNTdXzcQsGAwjbr9Mgz986b8X2bLV5e0E+cqLKifby88vzv7mj/h2R4vdTjlJnwpONEAqHGThjpoFm5cqWqnygdSW3btlWBPvm+kyzp3377TXVAzZkzB+3atVOPk++9Jk2aqM4b+S5dsGCB+q4V0kEj34fyfNKZIpPKfPDBB3Y9fiIiIsrfNHobT2UgjSv5sS8NIsnQMZIoumQlSW2ftJlTUq9DggXfffedalxJY0syaWSIlxQ6l+eSwJQ0rqSRJo0r44xyWTFm3MgQjPQypSSAJs/riD2nWZE/rQwbk4alI6e75oQEIWXmxYyOK7Nj37dvn8oKk/eS+TbJNpHhfRK0lIL5ecmW7yXD0K5wlX3gaD+urC2/H7vMyimTJEjc/fJl1znuzJScVxJhUWEIDQjF5QlmJ8VGzpw5gyGjxqLni2/aLSild8Kg1LUL5/D1nFewYuECVKhQIduPt+Z7PrN2gT1s2rQJ3bt3f+B2+U6RWXSlk0/aTnIetm3bZursk04a6aCR4fjSgSPtJJn1WEi9xffee099H8lkMzJzrjyHfHfKBCNf36szIJ030mFjaRvC0c4dEdmetLHlc0dIEN3VM6WIXFmkhe0Cm2dK/fLLL2rKYlnSpp23bNlSZdqkbXxJbSEZvtWxY0dT40qGXhmzgaS3TwJdxsaVZMpQ/iWZU9JozmmgTbLr0iMNeglqEhEROQrJkMqo/1AyoSUwKjUSGzVqpIaIG0nGk2RHnThxQn23GX8kijfffBP9+vVTdRelg8ZY9kC+V1evXq2CWel13hARERHlNXdHalydP38+w8flpHFld+o4jcdqu+F7+Z1k0hmLlRMRkYuR79akJMPihAXe81pISIiapTY9kp1rPkOxOcmoyqiEQkadN0RERER5za6z72VXThtX9qMDbh00rBaWouOGgrxERDkhsdgbNyR7lOfPqHmZ5rhy5wpKBGY8sw3lM7Gx0Pr7I0S+ZWW2qDwudE5EREREtuNUQSkiIlf2xRf23gPH8/kTn5tqCxERERERkXNhUMqqtEChWvfXiYiIKHd8faG7eRMREREI8vXl2SQiIiJyYgxKWZPUkNLwFBMREeXpd2tgIPSJiazVSEREROTkmL5DREREREREREQ2xzQea9LrgLhrhnWfEEDDGCAR5Vzr1sD160CxYsDWrTyTou3qtgi7G4bQgqHYOpAnxSUkJkLz5pvwj4kB3ngD8Pa29x4RERERUQ4xSmJVeiDuimGRdRuZOXMmNBoNvvvuO3V90aJF6vqqVauy9Tzbtm1D2bJls7zNXv7++29UqlQp3W06nQ7NmzfH3LlzLX6+hQsXolixYihfvjy2pvnF/8orr6BQoUKoUaMG/vnnH9PtSUlJGDp0KAoWLIimTZvi4sWLuTgiosydOgUcO2a4pHvn5OYpnLp9Sl2Si0hKgua11+Avn+9JSfbeGyIiIiLKBWZKWZUG8A66v25jx44dQ4cOHfDvv//m2XNK4MU8KGMvBw4cwBNPPAEfH590ty9ZsgR3797F2LFjLXq+LVu2YNKkSVi3bh2CgoLQv39/7Nu3D0WKFMHHH3+sls2bN+P27dvo3bs3Dh06BE9PT0yfPl0F6nbs2IE9e/bgmWeewfbt2/P4aIkoI/6e/vD38FeX5CLc3aEfMQKxcXHwcWczhoiIiMiZMVPKmmS4nl8Zw2LjoXtubm4qKCUkKCXX84K7uzsKFCgAe4qJiUGPHj0wevTodLdfuXJFZTa9//778PDwsOg5Fy9ejIEDB6Jbt2549NFH1eXGjRtN2yRg1axZM3Tt2lVlZ0kQSrKxJFj1xhtvoGbNmhg2bBgiIyNx+vTpPD1eIsrYsZHH8N+z/6lLchFeXtB/9BGiZs9W60RERETkvBiUyqfq1atnypA6ceIE6tata9omGUANGzZUQ84kuCMZRUbLly9HyZIl1fLLL7888LwZDd/btGmTCtb4+fmhTZs2KjAkZMhgy5YtsWzZMjU0TpZvvvkm1WPlPrVq1bL42CTQJEP3JEiUnhdeeAFlypTBpUuX1P0scfjwYbSWgj33NGjQQGVj6fV6HDlyJN1t8vySOZXeNiIiIiIiIiLKHINSOTRv1zyUnFcy18u289tSPa9cN26T18ipihUr4vz58yo4FBgYaBrmdufOHTWkTxYZhieZPRMnTjQFZiT7SGor/fTTT2oomyUkMPP000/j5ZdfVllCwcHBKnvI6OjRoyoQtXPnTgwePFgFjcz17dsXf/75p8XHJsPmQkND0922a9curF+/XgXVzp49i0GDBmWYUWVOzkO5cuVM1yUbTM5ddHS0yohKb5s8xtfXVx1v2m1ERERERERElDkWY8ihyIRIhEWFIbcSkhMeuG58XnmNnJLhehJI+eGHH1Rx7oiICHW7XJdMoxkzZqji5zIsbcCAAWrbt99+i7Zt26qha0K2zZkzJ8vX8vf3x4ULF1Tm1f79+9XwuvDwcNN2uf7pp5+q4M2zzz6Ld95554Egkyx5QTKyJAtMirynpKTg+eefV5ldY8aMybAounFYopfZMBBvb2/Exsaq20VG28xvN99GRERWEhMDTWAgiskUIrduAQEBPNVERERETopBqRwq4FUAoQHpZ+ukors3M5A2/dpGXu5eD1w3Pq+8Rm5Ur14dGzZsUMPnjLPJXb58WQWoZCY5tXs6HaKiohAfH4+rV6+idOnSpsdXqFDBoteRIW5TpkxRhcArV66MgIAAFRAyktuM2UR5FXzKiBxfx44dVcBNlCpVShUuP3PmTKZBqcKFC5sCd0LOieyrZJjJItuMtbSM2+QxMvRRZuAz1q6SbfauuUXkSl787UVcvX0VxQsVx3uPv2fv3SEb0SQnq0vbzWtLRERERNbAoFQOTWg8QS2Z0utTB6XuBUoy07JsS1yecBl5QTKkpF6TzEBnDErJsDapL/Xll1/e20W9CqxIUEUCR+Yz6128eNGi11mzZg12796tsqUka2rRokX46quvTNttGaSR44uLizNdl+F3t27dynC4n1H9+vXV0D+phyUOHjxoeozU55JtxiCdbJMAl9THkteTWfdkVkLjNqkrRUS2se7oOpVdKsF8BqVchI8PdBcv4saNGyiawQysREREROQcWFPKmiQI5eZpWCwISOU1CUqZX4pOnTqpYNPevXtVBpBkUrVv314Fp7p06YItW7bgxx9/VEXS3333XYteR7KD5PES/JFaVK+//rq6bqnExET1HHmhT58+agjf77//roJko0aNwiOPPKLOgWSFSU2t9PatV69eKpgWFhaG69evY8WKFWjXrp1pmww5lBpSp06dUufMuK1nz56YNWuWOgapiyXBOfPC50RElMe0WiA0FLrixQ3rREREROS02JrLx2rWrKmylMxny5Oi5zLMbu7cuShfvrwqCi7XpT6SZAu99957GDp0qBoCJ8XQLTFw4ED1GjJMTwI0Usfp+PHjakigpZlWGc2kl12PPfaYCiCNHDlSDV/877//VBBJhvNJME6GLZrPNmgkATnJkpIC8VKLq3bt2mpmQiHHI1lkkhUlzynF042zGb7yyiuqfpZkTUkwSgJyISEheXIsRGlNnw7MnWu4JCIiIiIicnYafXZSWvIhyX6RAt0SqEg7zEyCKufOnVNBCilgnW16HRB/r+C3dzCgsW0MUP60ycnJKuBkrLHkKjI69iFDhmD58uUZno99+/apwuwtWrRIdR/JspLZA6WwedrheZIltWPHDhWMqlatWrrPm+v3UjbIvkqgTAJpWhfLInDVY3fV4xYyU6lx+F5eDX3ODqlXN2TUWPR88U2ElLk/S6dN6HXQRN2CPqCwzb9fcuvahXP4es4rWLFwgcX1C00SE6F7/301PNt/6lRo8/gzNbN2AfHcEVHmpB0t5TyEfE77+fnxlBG5qEgL21SsKWVVeiD23o8k7yDrvhRlSQqSy0yDmQXoJFssPfJDP6NsLil6LrMWEhGRDSQlQfvSS5CmjW7yZJn2lKediIiIyEkxKGVVGsCryP11sisp5i4ZUERE5MTc3aEfMEBloHq5sxlDRERE5MzYmrMmGU7hb+PhHESUb129CqSkAG5ugNR4JnJJXl7Qr1yJuzJk1cvL3ntDRERERLngXEUoiIhcmIwuLVXKcElEREREROTsGJQiIiIiIiIiIiKb4/A9a9KnALf/MawXqgFo3Kz6ckRERPleTAw0oaEIlsmDL18GAgLsvUdERERElEMMStkiMEVERER5RnP3rpo+RMdzSkREROTUGJSyKi0QWO3+OhEREeWOjw90J07g5s2bKOLjw7NJRERE5MQYKbEmjQZw8zYssm4jM2fOhEajwXfffaeuL1q0SF1ftWqV6T7nz59Xt9lC165d1WsZl7Zt21r0uA0bNqBMmTIoUaIE1q5dm2rbwoULUaxYMZQvXx5bt25Nte2VV15B4cKFUadOHfzzz73hk0RElD9otUDFikgpX96wTkREREROi5lS+dixY8fQoUMH/Pvvvw9sK126NG7fvm2T/di/fz+OHDmCkiVLquseHh5ZPubo0aPo16+fCj41bNgQPXr0UEGmSpUqYcuWLZg0aRLWrVuHoKAg9O/fH/v27UORIkXw8ccfq2XTpk24ceMG+vTpg0OHDsHT09MGR0pERERERERElmIXozXpdUB8uGGRdRtyc3NTQSkhQSm5bk6r1SIwMNDq+xEWFga9Xo9q1aqp15PFz88vy8ctX74crVq1wtChQ1G9enWMHj0aq1evVtsWL16MgQMHolu3bnj00UfV5caNG03bJGDVrFkzdOnSRQWxduzYYfXjJCIiG0lKkhRg+K5caVgnIiIiIqfFoFROJccYFpn9xygl0XBbSsK9G/RAzEXg7kkgOTp1YEqXdO++8Rk8b+6CWPXq1TNlSJ04cQJ169ZNtT294Xvbtm1D2bJlsXnzZjVsTobAffTRRw/cJzvBrL179yIlJUVlSUkwqnfv3hZlaB0+fBitW7c2XW/QoAEOHDiQ6TYJfklGlvm2+vXrmx5HRET5QGIitGPGoMDUqWqdiIiIiJwXg1I59ZW/YUm4cf+24+8abts/+t4NGsCzELCtHbC+oCFAZXRqoeG+u4ekft5NZQ233z2O3KhYsaIKPF25ckUFkXwsLAYrhWPfeecd/Pjjj3jttdcwceJExMffD5w1bdoUFy5csHg/JCBWs2ZN/PDDD9i9ezfOnTuHl19+OcvHRUZGoly5cqbrBQoUUMeS2bbo6GjodLoMH0dERPmAmxv0PXsivnNntU5EREREzos1paxJowUCKhiCUzYmw/UkOCPBoBo1aiAiIsKix0lgR4bAVa1aVQW2xowZg+vXr6vMKeHu7o6CBQtavB8SgDIPQr377ruqPtSSJUsyfZy8jpeXl+m6t7c3YmNjM90mt4uMHkfk7H7/HUhOlv8D9t4Tx/Fr/18RfiMcwUWD7b0rZCve3tB/9RXuhIcj2Nub552IiIjIifGnTU49FW24dPO9f1vlycAjLwCaNKe1Z/i9+5plKz08CnjoOUCTppe32/kH75tDUotJZrBr2bLlAzPUZaRQoUIqiCWMxcFlWFxeCQ4OVtlYCQkJqYJHacnQQfNAWlRUlGl/Mtom2WCyyLaAgIAHHkfk7CpVsvceOJ5KRSuhkK4Qg1JERERERE6Iw/dyyt3PsJjXZXLzNNzm5pXBfc1Ot9bj3n29s75vDklw6Y8//jAFmSwhw93y0tNPP42//vrLdH3Xrl0oVqxYpgEpYy0oua/RwYMHERoamuU2qaVlvk1m3jNuIyIiIiIiIiLHwaCUNelTgNuHDYus25gxGJWdoFRWkpOTcffu3Wxla40fP14Fpr799ls1lG/EiBGm7Xfu3FGF0NPq2bMn1q1bpwqXy5DCBQsWoF27dmpbr169sGjRIjWznwwtXLFiRaptUhNL6k6dOnVKZYoZtxERUT4QGwtNqVIIql1brRMRERGR8+LwPWuTWfbsRAqMS+aTzKiXVyS41L17dxVMssRLL72kipu3b99eDakbOXIkpsqMSWbDBSXTqVatWg/s+7hx41Tmk9SFkvpW8ljRpUsXrF+/Xt0m2rRpo+pUieeffx6bNm1CqVKl1BDBIUOGPDDzIJGzWrPG8Bvc1xfo29fee+MY1hxZg+u3rqPY9WLoX7O/vXeHbEGvh+bKFcjgd10eDi8nIiIiIttjUMqqtEDBKvfXbWTmzJmmWlA3btyARqPBtm3bUt1HAlVpa0VJ7SmZsc9cevexNCAlPDw8VCaTLOkZNWoUHn744XS3vfnmm+jXr5/KiGrRooWpNpQcz+rVqzF27FjExMSobXKbkGGBv/76qwqeSeHzxo0bW7yvRI7uxReBsDBARqQyKGUw5fcpCIsKQ2hAKINSrsLbG7oDB3Dr1i0UZqFzIiIiIqfGoJQ1SaDE3awQOj2gW7du8JW0jwxUqVJFLemR2lLp0Wq1aNasmRpqSERE+YybG1CrFpLDww3rREREROS07FZTSjJ4ypUr90BmTkaZP5IJk3YxZv9IzSTz24cOHWqDI6C88Nhjj/FEElGOvd3mbbzX/D11SUREREREzkVrr4BU586dLQpIiSlTpuD27dumRWZUCwoKQu3atREbG4szZ84gPDzctP3DDz+EQ9DrgIQbhkXWiYgoT/Wt3hf9KvdTl+QikpKAVavg8+WXhnUXkV5n3vLly1UNRck4luH1Z8+eNW07evSoyiiW2o2TJ09ONRx/+/btqFy5MooWLYp58+aleh2ZIKRMmTIoUaIE1q5da6OjIyIiIldll6BU79690TcbBVGk0HVgYKBpWbhwIV544QUULFhQFcmWTCkJUhm3+/j4wDHogejzhkXWiYiIKHcSE6EdMgQFX3hBrbuC9DrzpEPutddeU5N7nDhxAhUqVMCgQYPUNpnoQyYFkYk+9u/fj2PHjmHVqlVqW0REBLp27Yo+ffpg165d+OKLL/DHH3+YAllSy3HatGnYsmULpk+fjpMnT9rpqImIiMgV2CUotWzZMlWkOieuXLmCjRs3mh6/d+9eXL582RSUGjFihGqMOQYN4FnQsMg6ERER5Y6bG/QdOiC+TRuXqSmVXmeedMo1atQIderUQenSpfHss8/i9OnTattPP/2Eu3fvqiwoCVa99dZbpglHJAglWVASeJJZbCXwZNwmmVetWrVSZRCqV6+O0aNHq4lFiIiIiPJVoXNJP8+pJUuWqN49f39/dV168Jo2barqTsmscNLDN3/+fDXkLz0SsDIPWkVGRqpLnU6nFnNyXdLdjUv2aQD/h+5ftcPU1cb9ztn+OzdHOnbjeyi991leM75vrf06jij/H7vm3iLHqHeh487Y8YjjiLgZgZuam6gcVNnmry/nXc3+KZ8zth6mrV7z3gIn+9vfO285et96ekK3eTNuR0QgSGZlzeP3vSP+P5LOPGk7jRs3znSbTAKydetWVdJAti1atMhUp/Hw4cMqYGWcSEQyyiVbyrhNAk/GWWsbNGhgajPJtg4dOpheQ7ZJNlZGstOmIiLXYP5/n58FRK5NZ2FbwKlm30tJSVENs99//z1VkMqc9PgtWLAgw6DU7NmzMWvWrAdul3T2+Pj4VLclJSWpEymzuDnjTG7S2JdzJoyNT1fhaMcu7x95L928eRMeHh5WfS15Hekhl3MgMxG6kvx+7DpdkKSJqOMMD49wmePOTNvP2+JazDWE+IXgYP+DNn/9qKgolCtdCj4pcdBE3bLxq+uhiYu6l4hr/8+57JDzJedNzp/UhMwua77nZZ8cTXqdeRKU6tWrl6qvabzPnj17TMEh88fI96Cbm5uquynbzGe1LVCggMpCT+9x5tty26YiItcg9X7NPwtiYmLsuj9EZD+WtqmcKiglNQ+KFCmSqjGVVnBwMMLCwjLc/vLLL2PChAmm69IAkyKhMvxPGl/mpEElJ9Ld3V0tzsraQRBH5ijHLu8f+eEk71+pkWZN8mNNfoDIe9rVAhT5/di1WkPgQY5NPutc5bgz46Z1M12anxNbiY6OxrmLl1DbzQcFAwrb9sVVlhSg9y8sUQc4k7hbkeq8BQQE5OjvZs33vLU/o/OKlC/47rvvsHv3bjzyyCOYM2cOOnbsqG6X7xwvL68Hjkt+LKbdZrxdZLYtt20qInIN5kEo+Szw8/Oz6/4Qkf1Y2qZyqkjLV199hR49eqS6rXHjxup2aQQJKdops8ZkRBpbaRtqQhq1aRu2cl0avcYl2/QpwF1DujwKVgE0tql9IUMZpedSip9KGr6k9EtdiJUrV5qKoNrS0qVLMWPGDFWo9dFHH8W6detQvHhx0wxAw4cPVz0pU6dOTdW4zYwUu5chBfJFJzUwWrdubdr2yiuvqGMuWbIkPv/8c9SsWdOU+SY1x9avX69qZaxZs0bV4bAF43sovfeZtV7PVq/laPLzsYeEGC/lGDUuc9yWssexG4egqaCQxtavr7v3uvZ47Vy6d96M79tsiY2FpmZNBKWkQPvPP9DeG86fV5zl/5DMjCe1pho2bKiuv/HGG1i8eLEagle4cGFVtNycdLJ5enqqbfKdm/Z2kdm23LapiMg1mP/f52cBkWvTWtgWcKgWg/SwSeAgIz///LOa8thc1apV8fzzz6uU9U8//RRz585VgQeHkZJgWOzAWD/i33//hb389ddfqpiqFEo9d+6c+hEyadKkLGcAyozMCCTPIcEuCTpJQVYZFic+/vhjtUhATgJz8tyJ92ZnkqGd27Ztw44dOzBgwAA888wzVj56ory1fz9w+bLhkshlSTDr9Gm4nztnl1qNjsIwjDc8VQBJsppk6Hr9+vXV96qRfP9K7ScJOqXdJgXTQ0ND1Xpm24iIiIiswaGCUlKI84cffkh3m0x9LHUNpOimuffee0/10knRTsnGeffddzFw4EA4Bi1Q4BHDYuNTLbUjzINSct0e/vvvPxUkatu2rcpcGjx4sGrkZjUDUGakJ1j+xt26dVOZV3IpMzIat0nAqlmzZmo67EqVKqkglDTeZT+kJ1kyp4YNG6aCoMaZioiIyEl4e0O3Ywdubtqk1l2VfM998803anIXyfzt3r07QkJCVFuqefPm6jtOMqSFzL4n38PSFpDOoJ07d+K3335THYEy7K9du3bqfj179lTZzEeOHFFDU6VGp3EbERERUb4LSknWTNmyZU3Xz58/rxpV6ZEpjaVYtHHWPaPAwEAVkJDeQXm8zbKkZLy0LOa9tJKRI7cZZ6KRIRUe/kCiRg03SDVDkGSEyX3TFgI1Pm8uZ62pV6+eKUPqxIkTqFu3rmmbNEalMKrMyiNBPmPw6urVqyhYsKCqTyEkyNekSZNUs9dJppGcc0tJEMr8byqzJUoAKqMZgA4cOJDlc8rjzIfrGR8n+ykNafNt0usr2y5duqQKvKb3OCIiciLSydKkCZKkk8pOHS6OQAJIMqnL+++/r4bmywzE0h6SWopSG0qGtsvQ/aJFi6rs4XfeeUc9Tq5LIEvqTxUrVkx9L7/66qtqm3TayAx/0oaQDCkJYo0cOdLOR0pERET5mUNlSjkVCY7JcuPG/dvefddw2+jRqe8rRVzl9osX79+2cKHhtiFDUt9XgnRy+/Hjudo9CfxIkE6yyySI5OPjo26XjCGZrUdqc509e1b1phqH00mdJwlEvfTSS2pIwAcffKBqN5nX02ratCkuXLiQo326deuWylaSGlI5meXHKKPHSa+uHF962+QxEoQzL6hr6esRERE5AvPOPPlulkxj+U6WYer/+9//TDPxCcmIkixzKW1w/PjxVJPEyPewBKMkY/mff/5RwSmjN998U2U0b9iwAX///bep/UBERERkDU5V6NzpSIZR4m27vLT0bkpwRoZDSiq/eeFSaWwWKlRINUSlZ1UapkZjx47FJ598gs6dO6uaS7Vq1Ur1vNL7KtlUOTFq1Cg13E6Kr+dklh/zfUjvccYZEjPaltFMRETO4vnnJbgrxYilfpq994bITpKTga+/hldkJCDD9TMpxO3qZDhfp06d0t0mbQTzThxzEsDKbKZjIiIiorzCoFRORUcbLn197982eTLwwgsSNbl3gw6IPgsc/xkoXBPwNRt6OGoU8NxzDw49OH/ecJkHPZMyw5z0dEpx+K1bt5oq4EvavtRuKl++vJq1UIqiGknwRgqHjx8/3lSLIi9IT60UMZehd0bZneUnq8dJb64ssk2mGTffJo+5e/euqp8hQxuM2zhlNTkTKbkXFgaw7jC5tIQEaHv3RiH5lu3dm0EpIiIiIifG4Xs55ednWMyGtqneWrnNlJEjNaUCgMBgwM9fIkL37yuBEblv2iKtxufNg6mUJUNKAkFyaV4TSupMSB2pvXv3Ykia4YMyzE2Kxz/++OOq8Hhe2L9/P8aMGaOKp5oPEcjpLD+ZPU7qYJhvO3TokNomryuF1mWWxuy+HhERORCtFvoWLZDYuHGefFcSERERkf2wNWdNGpl9r5JhkXUbMwajzINSkh0kZNieFDyfMGFCqkLmr7zyihpi9/XXX6uC599//32q55Ri85JxZCmpTSWz4L344osqYCR1n2QRmc0AJLWhZB/N981IamItWrQIYWFhuH79usr6Mj5OtkkxVwmunTp1SmWKmc8qNGvWLFV7488//1THZ174nIiInICPD/Rbt+LWN9/kSVYxEREREdkPg1L5mMyiI8PTzGc4bN++vVrq1KmjCp0+99xzqti3BHckc0qG7L377rtqlkMJEsnMPTEyG+A9f/31F8qUKWPxPqxduxbXrl1TxVhlSJ1xyWoGoIsXL6q6V+kFwCTI1aZNG1XMXephSGFXKdwunn/+eVXMXIYlyjEOHDjQNPOgBNwkSCavJcGo119/XdXbICIiIiIiIiLbY02pfGjmzJnqUrKMbty4oWbokWF7RjLbjrmJEyeqSwnWGLOYRL9+/dRiTupTSQaTpWRqaVkyIoExyWQ6ceIEmjVrpoJhQgJpzz77bLpF1eV4Vq9erYqyS8CsRYsWphkCpZj5r7/+qoJnUh+rsQzvuKdIkSLYt28fduzYoYJR1apVs/g4iIiIiIiIiChvMShlTXodEHncsF6gsl2G8DmD9GYAkuF8AwYMMAWbMqotlR4p5i4BLhlqmJYUPW/btm0e7DUREdlFXBw0jRujiHzGS51AqcNIRERERE6JQSmr0gPJcffXyWIyQ55kQBEREaWi00Fz+DA87tUfJCIiIiLnxaCUVWmBgIfvrxMREVHueHtD9/PPaih5YNoZbImIiIjIqTBSYoH0ZoCziAw98yxgWDIZhkb5X47fQ0RElJqbG/DYY0iUbFpZJyIispEbN2JRrtxSnD9vmIxp1aqj0Gjee2CR27OyYcNJlCnzMUqUWIy1a++VfLln4cKDKFZsEcqXX4atWy+m2vbKK3+iUKEPUaPGKvzzT0QeHyGR7TFTKoshZCI2NhY+nHaaciExMVFduvEHFOVCnz7A7dtAoUI8jUa9q/XG1dtXUbxQcZ4UIiIismpAqnPnjTh/PtJ0W9++ldG9+0Om69HRSahd+zM0a1Yy0+c6ejQC/fr9iIUL26Bhw+Lo0WMT6tQphkqVCmPLlnOYNGk71q3rjKAgH/Tv/yP27euPIkV88PHHh/Hxx/9g8+YncPt2PHr3/g6HDg2Epyc7ach5MSiVCQkgBAYGIjw8XF339fXNtPD2AyQ7JinKsO4RYPNsKcnOkWLfMgtdtvY7H3CkY5eaJxEREer9I/tDlFPvvstzl9actnPUZ3RwcDBPjquQAuc//QSvu3eBp56SGSzsvUdEROQCevf+XgWh9uy5arpNgkHmAaFFiw7hiScqokKFwEyfa/nyI2jVqhSGDq2hro8eXRurVx/DG280xeLFhzFwYFV062YIdsnlxo3/qfsuXnwIkybVMwW9Vq48ih07LqNt2zJWOmoi6+Mv5CyEhISoS2NgKtuz78VeMqz7lrL57HsSmJGAiMxGZ+/AjK052rHLfpQuXdoh9oWIyKklJEDbtSskYVDXrRuDUkREZBPLlj2OcuUCMW7c1nS3x8cn44MP/oc9e/pl+VyHD0egQ4f7s483aFAcr732971t4ejbt7nZthAVeBoyRI8jR25g6dLSqR534MA1BqXIqTEolQUJIhQvXlz1wiclJWXv7CbHA7teMqw/8jngbtuCrBKUuXnzJooUKaKCIq7E0Y7d09PTIfaDiMjpabXQ16uH5KQkuPFzlYiIbEQCUplZs+a4GopXtmzBLJ8rMjIR5crdv1+BAp64ciUmk23RamigTqd/YNupU7dzeEREjoFBqWwM5ct+PSBv4LHvYc/AjNTF8vb2drmAiCsfOxFRvubjA/2ePbgpwzZZ75GIiBzEkiWHMXPmoxbd191dAy+v+78tvb3dERtrSIBwd9fCy8s9zTYpS2IYcZHR44icFX+tExE5iUcekR4xwyUZVFlUBRU/qaguiYiIiOzh9OnbOH36Dh57zLLaToULeyMiIs50PSoq0VSbyrAtNs02LXx8PODj457h44icFYNSREROIjpaGh+GS7p3ThKjEZ0UrS6JiIiI7OGrr06ic+fy8PCwLEBUv34Idu26Yrp+8OB1hIb6Z7AtHKGhAWq9Xr2MH0fkrDh8z5qS44CtbQ3rrX8D3H2s+nJERK7m4SIPw8/dD6EFQ+29K2QrcXHQtG2LwlLn8Y8/AD8/nnsickkyu3NkZCQcSWzs/Qyfs2fPqtmnHU2BAgUQFBSUp8/588/nMWhQ1Qduv3MnHgEBnnBzS50L0rPnw2jSZC3GjaujakQtWHAQ/ftXVtt69XoYw4f/isGDq6mhfCtWHMEHH7Q2bXvnnb3o2rUCrl2LwYYNp7BjR+88PRYiW2NQyqp0wI2/768TEVGe+u2Z39TsqDIZBbkInQ6av/+G5736gURErhqQGj54MOIlhdqBJKekmNYnjRgB92zX5LU+74AALFm5Ms8CU3FxSdiz5yqWLn3sgW2FCn2EgwcHoFat1O2UmjWDVUCqXr3P4e3thooVC2HkyFpqW5cuFbB+/SlUrLhCXW/TpjR69Kio1p9/vgY2bTqNkiU/RkJCCoYOrY66dQ2zxRM5KwalrEnrBTTbeH+diIiIcsfLC7qvv8bdu3dR0IvfrUTkmiRDSgJSo5s3R8kiReAo9Ho9XuvSRa37eHqqmcwdyeWbN/HRjh3q/OU0KKXXT0p1XWo9JSSMT/e+o0bVwsMPF0p325tvNkO/fpURFhaNFi1KmWpDyTlbvbojxo6tjZiYJLXNeB6lAPqvvz6JnTvDVMHzBg2K5+gYiBwJg1LWpHUHSnW36ksQERG5FHd3oHt3JISHG9aJiFyYBKTKFytm792gDHTr9hB8fT0yPD9VqhRVS3rq108/4KTVatCsWUmec8o3WOiciIiIiIiIKI899lhZnlOiLLCL0Zp0KUDEn4b1oGaA1vHGVBMRObP+G/vjyp0rKBFYAmt6rrH37pAtSL2S7dvheeeOFN6QLmOedyIiIiInxaCUNenigd9bGdafiga0nCGIiCgv7biwA2FRYQi9y9n3XEZ8PLRt2qCwfM3KrFMeGQ+LICIiIiLHxqCUVWmAglXurxMREVEuv1o10FepguTkZLg5WAFdIiIiIsoeBqWsyd0X6PSvVV+CiFzHkiUy7bDM8mLvPSGyI19f6I8cwc3wcAT7+vJPQUREROTEGJQiInISnTvbew+IiIiIiIjyDquDEhERERERERGRzTEoZU3JccDWxwyLrBMREVHuxMVB8/jjKPT004bxrERERETktDh8z6p0wLXf7q8TEeXCgQNAYiLg6QnUrctTSS5Kp4Pm99/hpVb53UpERETkzBiUsiatF9D48/vrRES50K0bEBYGhIYCly/zVJKL8vKC7rPPEBkZiQJe/G4lIiIicmYMSlmT1h0o18+qL0FERORS3N2Bfv0QHx6OArJORERERE6LNaWIiIiIiIiIiMjmGJSyJl0KcHOfYZF1IiIiyp2UFGDfPrgfOmRYJyIispEbN2JRrtxSnD9/94FtL720HV26fGPxc23ffgmVK3+CokUXYt68/am2bdhwEmXKfIwSJRZj7drjqbYtXHgQxYotQvnyy7B168VcHA2RY2DeuzXp4oEtDQzrT0UDWj+rvhwREVG+Fx8PbaNGKCpfs5GRgIeHvfeIiIhcJCDVufNGnD8f+cC2f/6JwKJFh3D48ECLnisiIhZdu27ExIn10KdPZfTu/T1q1w5Gq1alcfRoBPr1+xELF7ZBw4bF0aPHJtSpUwyVKhXGli3nMGnSdqxb1xlBQT7o3/9H7NvXH0WK+FjhiIlsg5lSVqUB/MoYFlknIiKiXH61aqAvUwYpJUuqdSIiIluQwFHfvpUfuF2n02PYsF8wfnxdlC8faNFzffHFcZQo4Y9p0xqjYsVCmD69MVasOKK2LV9+BK1alcLQoTVQvXoQRo+ujdWrj6ltixcfxsCBVdGt20N49NFQdblx4395fKREtsWglDW5+wLdzhsWWSciIqLc8fWF/uxZROzbp9aJiIhsYdmyxzF2bJ0Hbl+y5BCOHIlA2bIFsXnzaSQmZj20/PDhcBV40tzrXGnQIAQHDly/ty0CrVuXNt23QYPiOHDgmulxrVuXMtt2/3FEzopBKSIiIiIiIqJMlCv3YBZUdHQiZsz4W2VIXbgQifnzD6Bp07WIi0vK9FxGRiaiXLmCpusFCnjiypXoTLbFZPk4Imdlt6DUjRs3UK5cOZw/f96i+3ft2lVFko1L27ZtTdu2b9+OypUro2jRopg3b54V95qIiIiIiIgI+Oab/xATk4Q//ngKs2Y1wa+/PomoqETTcLuMuLtr4eV1v7yzt7c7YmOT723TwMvLLc22pCwfR+SstPYKSHXu3NnigJTYv38/jhw5gtu3b6tl06ZN6vaIiAgVsOrTpw927dqFL774An/88QccQko8sKO7YZF1IiIiyp34eGieeAKBgwapdVeRWWfeSy+9hC5duqS67ejRo6hfvz4KFSqEyZMnQ6/XW9SZt2HDBpQpUwYlSpTA2rVrrXhERETO7/LlKDRqVBxFi/qagkY1agTh9Ok7mT6ucGFvVezcSAJZnp5as21xaba5Zfk4Imdll3dw79690bdvX4vvHxYWphpT1apVQ2BgoFr8/Awz2UkQShpO06ZNQ8WKFTF9+nSsWLECDkGfAlzeZFhknYgoF44fB+7eNVySwb8j/sWpwafUJbmIlBRoNm+G95Ytat0VZNaZ988//2DRokX44IMPTLclJCSoIFXdunVVp96xY8ewatWqLDvzJJDVr18/1abasmWLalOdPHnShkdKRORcSpYMQFxc6kwlGcYXGuqf6ePq1w/Brl1XTNcPHgxHaGhABtuum54vs8cROav7uX82tGzZMtXbN27cOIvuv3fvXqSkpKBkyZIqS0oaWosXL1a9f4cPH0arVq3MisQ1wJQpUzJ8LmmoyWIUKdNJq1kTdGrJW+5A/SX31/P8+TMnxyPBvLw/LsfHY+ffPT+6F4tXzP9bu/L73c/DD/4e/urSHscv5119/0gWit7Gr69e894CJ/vb3ztvOXrfurtDv3gxoqKj4e+e99+tjvj/yNiZt2fPngf2ddiwYRg/fjzKly9vuv2nn37C3bt3VRaUr68v3nrrLYwaNQqDBw9O1ZknfwNjZ560pZYvX64uhw4dqp5n9OjRWL16Nd544w2bHzMRZS3OKx7/Sz6La3G3ebosdCX5tjpveaVTp/IYM+Z3Vey8c+cKajifFCpfv96QvRoZmQAfH3d4eNwfjie6dq2AUaN+x2+/XUCLFiUxZ85etGtXVm3r2fNhNGmyFuPG1VH1oxYsOIj+/Q2z/vXq9TCGD/8VgwdXU1lZMmPfBx+05t+fnJpdglISkMqOEydOoGbNmnjvvfeg1WpVY+nll1/GkiVLVFCpSpUqpvsWKFAAV67cjx6nNXv2bMyaNeuB26XnMN4awwACuhkub9j+y0Iaq9IolUa/nDdXwmPn392V3vN8v9vv/R4VFYVypUvBJyUOmqhbNn51PTRxUYDqkzF0zDgLOV9y3uT8hYeHZ/vxuq5d1fdbwdu38/zvLvvkaDLqzJN2kJQ2kMDU5s2b0b59e3h6eqoOu0aNGqmAlKhRo4bKlhKZdebJtg4dOpieX7a99tprDtLRR0Tm5P/YpRLXMCjmQ8BQA5ss9FCJ0rn+nDI+vlAhL3z//RN48cUdmDBhG4oX98O6dZ1UZpNsr1HjU8yb1xLduz+U6vEyDG/u3Bbo2PFr+Pt7IDDQG5980k49pnr1ohg7tjbq1fsc3t5uqFixEIYPr6G2depUTs3MV7GiYWSQrHfvXoGfueSQLP0/ZpegVHZJAEoWo3fffRc9evRQjTF3d3d4eXmZtnl7eyM2NjbT55owYUKqBlSpUqUQFBSkAlr57U0gjU45Nlf6gS547Py7u9J7nu93+73fo6Ojce7iJdR280HBgMK2fXGVJQXo/QsD9wIMziLuVqQ6bwEBAQgODnao97y0IxxNep158t6bMWOGypC6cOGCKaNJ6kVJ28b8MXKu3NzcVLZ5Zp15aR/ncB19RGRy8+ZNBiJySL5D5PzJd1BOXL3aX8Lypk6VihU9sHFjm1T3MW7r0aMMypVzT7cDpkeP4qhXryv+++8uGjUKhkYTg/BwQ4Rx7NiH0a5dMK5di0PjxsGIiroNY5/Je+/VRf/+ZVSB88aNi6nPXCJHZGlHn1MEpdKSBqx8kEjvXOHChVP9R5QDl17CjEgAyzyIZSSN2jz/QSNDOe7eK/5SsDKgsf0PJmmIWuXYnACPnX/3/EbqEUsigsTPzWLrLv1+n797Pq7euorihYtj4qMTbf76xiFoKihk88943b3Xtcdr59K982Z832aL9LodPw6PW7egtUJQyln+D33zzTeIiYlR9aCkYHlycjKqV6+uglNpO+zMO+0y68xjRx+R85DfPM7yeeVo5LwVKVIkR50i2dWu3cOoWbNMhttlH+rVy3hbRh5/3Pr7TmSrjj6nCEo9/fTTGDNmDJo2baquS2HOYsWKqUaVzCyzZs0a030PHjyI0NBQOISUOODHaob1p6IBd7OCMEREOQhKhYUB8hGXNijlqiQoFRYVhtCAULsEpcgO4uKgrVEDRSU+JVHaHPZ0O7vLly+rIXoSkDIGlGSY3unTp9WPLSlabs7YaZdZZ55Dd/QR0QP/z0pdCcGr1bqhROFCPDsWunLrNj69ssdmn1Pt2mWvbA1RfmLp/zGHCkpJ2riPjw88PDxS3S49f1LEc/78+WoGGhmCN2LECLVNZpCR4p2//fYbWrRogTlz5qBdu3ZwGF6GxiIRERHlDX3RotC7eM0imfwlLu7+lOFChvE9+uijqt0kdaiMzp07Z8ouz6wzT7ZJx9+QIUMe2EZEjscnwRt13MujvE8xe++K0zjrfh1fJRy2924QkRmH6saSHr4ffvjhgdtfeukltU0KeEowauTIkXjllVfUNukhlGBVx44dVfaUTF386quvwiFIZlTPCMPCLCkiojz3WffPsKbjGnVJLsLPD/rr1xH+77+pp6R0MZ06dVLFy6W+pmRNLViwQBUql5qbzZs3Vx19K1euVPeV2ffatm2r6kpJZ97OnTtVZ15SUlKqzryePXti3bp1qni61KyS53Sojj4iIiLKd+yaKaXqcJg5f/58uveTzCmZrliW9AwfPlw1mmSWvmbNmsHf398q+0tERI6lZdmWCPcNt0ldCCJHIkP0fvzxR0yaNElN4FK8eHF89dVXavIWsXz5cvTp0weTJ09W6fPbtm17oDNP2kuBgYFYtWqV2iYzHcsMf/Xq1VN1ICpWrKg6AomIiIisxaGG7+WGzBaT3uw0RERERPlB2s68Jk2aqOF26ZGMqDNnzuDAgQOq9pQEsSzpzHvzzTfRr18/hIWFqbIImdWUIiIiIsqtfBOUckgp8cBuQ10GNFoBuDneNNNEREROJT4emmefRcH4eODzzwFfX3vvkcMKCQlRw/yy25lXpUoVtRARERFZG4NS1qRPAS7cKybacKlVX4qIyBVtO78N129eR7HYYmhdvrW9d4dsISUFmrVr4SOz76Wk8JwTEREROTEGpaxJ6wnUmX9/nYiI8tSAbwcgLCoMoQGhuDzhMs+uK/D0hG7ePERHRcGfQ8uIiIiInBqDUtak9QAeecGqL0FERORSPDyAceMQGx4Of1knIiIiIqfFoBQRkZOoUweQibWCguy9J0RERERERLnHoJQ16XVAzEXDul9pQKO16ssRUf62ebO994DIAeh0wPnzcLt5EyhaFNDyu5WIiIjIWTEoZU0pccDmezPbPBUNuPtZ9eWIiIjyvbg4aCtUgCQM6iIjgYAAe+8REREREeUQg1LW5sapqomIiPKS3tcXer2eJ5WIiIjIyTEoZdWz6wc8HWPVlyAiInIpfn7QR0UhPDwcwX7MQCYiIiJyZgxKERE5ia5dgYgIQ6Fz1pciIiIiIiJnx6AUEZGT+N//gLAwIDTU3ntCRERERESUe5yyxppSEoA9zxkWWSciIqLcSUiAZtgwFJg4Ua0TERERkfNiUMqa9MnAmeWGRdaJiIgod5KToVmxAr5r1qh1IiIiInJeHL5nTRoPoMYb99eJiIgodzw8oHv9dcRER8PPg9+tRERERM6MQSlrcvMEqr1i1ZcgIiJyKZ6ewNSpiAkPh5+sExEREZFrDd+LjY01rR86dAj79+/Py30iIiIiIiIiIqJ8LttBqVWrVqFQoUJISkrC+++/j0cffRQtWrTArFmzrLOHzkyvB+IjDIusExERUe6/WyMioLlxg9+tRERERK4WlJo+fTq+/fZbeHh44KOPPsJff/2FrVu3YtGiRdbZQ2eWEgt8E2xYZJ2IiIhyJzYW2pAQFKteXa0TERERkQsFpe7cuYMaNWrg4sWLKluqTp06KFq0KBI4LTMREREREREREVmr0Hnr1q3Rp08fpKSkoGPHjoiIiMCrr76KJk2aZPep8j93P6Avh+0RUd6YMAGIjAQKFOAZNRrfaDyu3rqK4oWL86S4Cj8/6FJSEB4ejmA/P3vvDRERERHZMiglNaXeeecd+Pj44IUXXsCZM2eg1WqxdOnS3OwHERFZEJSiB4NSKjgRHMxTQ0RERESU34NSgYGBmD17tul67dq18cUXX+T1fhERERERERERUT6W7ZpS4sKFC1iyZAmmTp2Kq1evqiLnes4u96CUBODAC4ZF1omIiCh3EhKgGT8eAdOmqXUiIiIicqGg1Pr161GpUiV8+umnmDdvHiIjI9UsfFOmTLHOHjozfTJw8gPDIutERLkQFWWoKSWXdO+cJEQhKjFKXZKLSE6GZsEC+C1frtaJiIiIyIWG77300kv48ssv0a1bNxQqVEjVlvr666/RsmVLVWuKzGg8gKpT768TEeVC5cpAWBgQGgpcvsxTKaouroqwqDCEBoTi8gSeFJfg4QH9yy8jJiYGvh78biUiIiJyqaBUfHw8KlasmOo2Dw8PVeyc0nDzBGq+ydNCRESUVzw9oX/jDUSHh8PX05PnlYiIiMiVglL9+/dH165dMXnyZKSkpGDXrl1qRr5nnnnGOntIRESUgeZlmuPKnSsoEViC54iIiIiIKL8Hpd566y34+/tj7ty5SE5OxrRp0zBgwADWlEqPFH9PiTWsu/kCGk2u/2BERHTf5098jvDwcAQHB/O0uNJ3a0wMNLGxhnUiIiIicp2glLu7O6ZPn64WyoIEpL7yN6w/FQ24+/GUERER5UZsLLQFCqAYAJ1U/g8I4PkkIiIiclI5KgT1999/q8yofv364cKFC3jxxRdVrSkiIiIiIiIiIiKrBKUWLFiAxx9/XAWjNm7ciMTEROzduxejRo3K7lPlfzJkTzKkZJF1IiIiyh1fX5Uhdf3MGbVORERERC4UlHrnnXfwyy+/YO3atfDy8lLLypUrVYCK0pAaUjJkTxbWkyIiynNtV7dFi69aqEtyEfJ96ucHvQSk+N1KRERE5Fo1pTw8PODm5pbqtpiYGASwpgMREdnYqZunEBYVhpjkGJ57IiIiIqL8HpSSYXodO3bEsGHDkJSUhPXr12PdunUYM2aMdfbQmaUkAkdnGdarzQDcPO29R0RERM4tMRGamTPhHxMj6duAt7e994iIiIiIbBWUmjx5MooVK4bPP/8cpUuXxpYtWzBu3Dj0798/p/uQf+mTgH/fMqxXnQqAQSkiyrlNm9TvcXjyo4RcWVISNLNnQ+a21b3xBoNSRERERK4UlBIDBgxQC2VB4w5UGnd/nYgoF+rW5ekjgrs79GPHIjY2Fj7u/G4lIiIicqlC540bN1Yz8F29ejVXL3zjxg2UK1cO58+ft+j+S5cuRfHixVVNqxYtWqR6/a5du0Kj0ZiWtm0dpOCtmxdQ933DIutERESUO15e0M+fj6jXX1frRERERORCQakhQ4ao2fceeughFRxatGgRwsPDsx2Q6ty5s8UBqb/++gvTpk3D6tWrce7cOej1ekyaNMm0ff/+/Thy5Ahu376tlk0yxoWIiIiIiIiIiPJPUGro0KH4/vvvcf36dYwePRo7d+7EI488orKTVqxYgfj4+Cyfo3fv3ujbt6/Fr/nff//h448/Vq9RsmRJDB48GAcPHlTbwsLCVJCqWrVqCAwMVIufn192D4uIyOF9/z2wfr3hkohcS1YZ5u3bt8eqVatM17dv347KlSujaNGimDdvXqr7btiwAWXKlEGJEiWwdu3aVNsWLlyoaoeWL18eW7dutdLREBEREeUwKGUUFRWFiIgINYxOAlFeXl745ptv0KlTpywfu2zZMowdO9bi15IgVPfu3U3XT548iYoVK6r1vXv3IiUlRQWrJBglAS/JlnIIMkX5Go1h4XTlRJRLw4cDTz1luCRyWTEx0Lq5IaR4cbXuCrLKMP/iiy/UxDNG0j6T0gZ9+vTBrl271PY//vhDbTt69Cj69eunMtDlMdOnT1ftKiHXJRNdSibIhDbSEXnz5k0bHSURERG5omxXCJ07dy6+/vpr/O9//0PLli3VrHtyvVChQrhy5YrqxcuKJffJyK1bt1TW1Jo1a9T1EydOoGbNmnjvvfeg1WpVA+rll1/GkiVL0n18QkKCWowiIyPVpU6nU0ue0ulMUT/13Hn9/Fm+vE5lkeX5cTkBHjv/7vmT5t4if1+96VZXfr+bs8fxy3mXWobQ6wG9jV9fvea9BU72t7933nL0vrXyd6sj/j8yZpjv2bMn3XbRxIkTUalSJdNtEoSSLCgJPMl5lsCTZLO3atUKy5cvV5fSXhKS9S7lEd544w0sXrwYAwcORLdu3dQ2udy4caPpvkRERER2D0r99NNPGDRoEL777jsUKVIk1TbJVPrzzz9hTaNGjcKjjz6KDh06qOsSgJLF6N1330WPHj0yDErNnj0bs2bNeuB26VW0ZOhhthvdTY8YVm9GAxrb9uhKw/ru3buq0S8BO1fCY+ffPT++53W6IJlBQb2/w8MjzG533f/rKboU02V26xvmBckaLle6FHxS4qCJumXjV9dDExdliFMa/nEacr7kvMn5y/bfTYJw//yjOpX8o6OhjYvL032Tfcor8n/z4sWL6hj9/f1VVneBAgWy/TySYS4deuPG3ZvR14wEpJ544gnEmZ2Hw4cPq8CTCpgCaNCgAaZMmWLaZmxDGbe99tprpm3m5RVk244dOxiUIiIiIscJSv32228ZbitYsKBqwFjLp59+qtLPpdGUkeDgYJVqLtlQMqQwLQlgTZgwwXRdGrWlSpVCUFBQjhqKWSsGe5HGsDRI5dhc7Ycqj51/9/z4ntdqDT8w5djks87Ild/vblo306X5ObGV6OhonLt4CbXdfFAwoLBtX1xlSQF6/8LAveCDs4i7FanOW0BAQI7+brqgIOgjIqzynvf29s71c5w9exZvvvmmquskwSTZT/VeOXdOZZYPHz48W7U1M8owlzbR77//jn///RdjxoxJ1bapUqWK6bq0bySb3bjN/Pks3Wb37HMiSsX4f0w6pGQhyxjPFT+niKzP0rZAtoNSYsGCBSrF+9KlS6pQ5ogRI1T6tzXJDHvS4Nq8ebMqwGn09NNPq9ubNm2qrkvtBNmeXkBKyO3pbZNGbX78MSc/VPPrsWWFx86/e/4l/69TByFc+f1uZI9jNw5BU0Ehja1fX3fvde3x2rl077wZ37c5ewrrvOdz+3wSjFq/fr3KypZyA+7u7g8ErGSonGz76quvUrVpskOyu59//nnVHpPgnjl5TfO2jgTaYmNjc7XN7tnnRJSKdMInJycjKTERiWbBYcqcnC85b3L+0n52EhHskn2e7aCUND6kVsHMmTNRoUIFnDlzRq1LcXGpXZAb0sPm4+MDDw+PVLdL2nuXLl3w4osvol69eqq3UUgqfPXq1TF+/HjMnz9fFQKVTCgJkjmElETg+LuG9cqTATdPe+8RERGRc0tMBObMgZ+0BWbOlMgJHIXUt5RsqH379j3QljGSWe0++eQT/PDDD3jqqadUNlVOvP7666hfv366E8wULlxYBYbMG4Wenp652pYe22efE5H5/08JJHt4esIzg854epCcLzlvUobGHhnWRK7E28I2WraDUjIjizSkatWqpa43bNhQTTkss8LkNihVo0YNvP/++6lm2hMyXfG1a9fU85u/hvSyvvTSS6oBKFMhS7R75MiRmDp1KhyCPgn451XD+iMvAGBQioiIKFeSkqCdNg3Sv6175RWHCkpJgEYyrSwZSiPBJPPaTtklE75IACkwMFBdl4wmybySWYklWGWcEEYcPHgQoaGhal22SVb5kCFDMtzWpk2bB7alx9Wyz4kcifH/mGSNGuvHUdaM54qfU0TWZ2lbINtBKekxS5uGJdcz6hHMTNpGW0ZTHUthz/SKewp5XZlRRhaHo3EHKgy9v05ERES54+4O/ZAhqrC3d5qhcY7S+KpYsaLK7paJYTLLNMpN4EYmlpEhKEaTJk1Co0aN1GsaJ4aROqAtWrTAnDlz0K5dO3V7z5490aRJE9WukvpRUpJBZlIWvXr1UvWuBg8erDIJpG31wQcf5HgfiYiIiLKS7dacNHqefPJJvPDCCyoFXWojSHaTTDdMabh5AQ2X8bQQERHlFS8v6JcuRWR4OLwddMiK1Iwyto3Gjh2rsriNGU15RWbyMyclDYoWLaoWIWUNOnbsqG6X1161apW6vWbNmiogJeUQJK1eAmiyf0JKJUg9LLlNSMaUzGhMRERE5DBBKel5CwkJUdMTr169GqVLl8aiRYvYaCEisjJ/f0Bqcsol3Tsnnv7w9/BXl0SOonfv3mr5+++/VaaRdOJJcCdtnaV58+Zl63kzGxZoDDoZScaTZEedOHECzZo1U8Ep82Ls/fr1Q1hYmMqkMmZzybAWadtJIC0mJkZt47AgIiIisqYc5b1L6rcsRERkOydO8GyndWzkMTUZBouVkiMqWLCgqncpw+xu3bqFlJQUm76+DM+TJT1VqlRRS3qkthQRERGRLThWMYb8JjkG+PrerA49wwF3P3vvERERkXOLiYEmOBjBkjV0/bohfdDB/Pjjj2r4nMzC99xzz+H48eOZFgwnIiIiclUMSllbSqzVX4KIiMiVaGJjIfMn6eCYZGY7GQK3YcMGlS1FRERERLkISj377LOW3A2ffPKJRfdzGW4+QNdz99eJiIgod3x8oDtzBjdv3kQRH8f8bpXZhL0ctAg7ERERkdMFpcqUKWP9PcmPNFrAv6y994KI8onJk4Hbt4FChYB337X33jiGF397EVdvX0XxQsXx3uPv2Xt3yBa0WqBsWaT4+hrWHdAjjzySZYFw2X7mzBmb7RMRERGR0walZsyYYf09ISKiTK1dC4SFAVKahkEpg3VH1yEsKgyhAaEMSpHDmDlz5gO36XQ6fP/999i8ebMKSPXq1csu+0ZERETkSFhTypp0ScCphYb1h0cBWg+rvhwREVG+l5QEfPQRfKOigJdeAhxwmNzAgQNN63fu3MGyZcuwePFiREdHY8qUKRgxYgRKlChh130kIiIicgQMSlmTLhH433jD+kPPMShFRJTHfu3/K8JvhCO46L2ZTin/S0yEdsIEFJCv2fHjHTIoJQ4fPowPP/wQ69atQ6VKlVTWee/evVlrioiIiCi3QalNmzap5fr16wgJCVEp6B06dMjJU+VvGjegTN/760RElKcqFa2EQrpCDEq5Ejc36Pv0QXx8PLzcHPO7tUmTJti9ezfc3NzQtWtXNG/eHHfv3sXHH3+c6n4yQx8RERGRK8t2UGrWrFn46KOP0K9fP1SuXBmXLl3CM888gwkTJmDq1KnW2Utn5eYNNPnC3ntBRESUf3h7Q//557gbHo5gb2977026PDw8VCBKyCyBGzdufOA+UleKQSkiIiJyddkOSklNBMmSevTRR023PfXUU+jZsyeDUkREROTytm3bps7B1atXkZiYaJrFeO/evShZsiTrSRERERHdk+25lAsVKqR6AM25u7vDz88vu09FRESUK2uOrMEXx79Ql0SO4vLly2jZsqUKQM2fP990++OPP45SpUqp4X1hMpUmERERkYvLdqZUs2bN0KVLFwwbNkw1rM6fP4/ly5erbKnPPvtM3WfAgAHW2FfnkxwDbCprWO92HnBn4I6IKC9N+X0KwqLCEBoQiv41+/PkuoKYGGjKlkWwTgecPw8EBMDRDB48GDVr1sS3336LwMDAVDPxST3OyZMn49lnn8WWLVvsup9ERERETheU+u+//1QtqT///NN0W5UqVXD06FG1SI0EBqXMJNzIoz8VERERCc2NG9DI7HsOejp27dqlOuzMA1JGxYoVw2uvvYZq1arZZd+IiIiInDoo9f333+Off/5BUlLSA9uMRT3pHjcfoOPR++tERLnQqRNw6xZQuDBPI7kwHx/o/vkHt27dQmEfx/xurV+/Pt5++218+OGHqsRBevU5q1evbpd9IyIiInLqoFTDhg1VrQSpLWVOMqTOnj2bl/vm/DRaILCqvfeCiPKJNLPJE7kmrRaoWhXJ4eGGdQe0atUqdO3aFSEhIahbty6Cg4Ph5uamhu/973//g06n49A9IiIiopwEpeLj47Fz505UrcpgCxEREVFaMtveoUOH8Pvvv2P37t24cuWKyjCvWLEinnzySXTv3p0TxBARERHlJCj1wgsvoHXr1mjTpg28vb1Tbfvkk094Us3pkoCzqwzr5QcB2tSzFhIREVE2SfmATz6BT1QUMGYM4OXlMKfw6tWrKFiwIHx9fVUGedu2bdWSWZ1OCVQRERERuaps572//PLL6NSpEypVqqR6As0XSkOXCOwdZlhknYiIiHInMRHa4cNRcPJkte5IfvnlF1VfM6tyBikpKXjxxRcxduxYm+0bERERUb7IlHruuefw0EMPYejQofD09LTOXuUXGjegZLf760REuVCvHnDtGhASAuzfz1NJLsrNDfquXZGQkABPN8f6bh04cCBKlSql6kk9/vjj6N+/P+rUqWPafvPmTWzatAnvv/8+OnfurCaPISIiInJl2Q5KSUNKjJGU+XR6/siMmzfQ/FueEiLKExKQCgvjySQX5+0N/caNuBMejuA0ZQQcgZQ4OHjwID7//HNMnz4dx48fh16vV9tkkph27drh66+/5rA9IiIiopwEpc6dO8cT5wBu3Lihppz+448/ULZsWTiqbdu2YdCgQTh//ry9d4WcGN/vPPdZSTidoD4L+VlDjsDDwwODBw9WCxERERHlsqaUpKHHxcWpdakdtWvXLgQFBZlqSUnPX+XKlS15KsqjH+iS9s8fX+QK+H7nuSciIiIiIhcOSv3www9qKmOjkSNHqh+KRpKWHh8fb509dGbJscCmsoZF1vNI79690bdv3zx7PiJHxvc7zz1RKrGx0JQvj6D69dU6EREREeXzoJSxFkJG14VMfUwPnDkg5oJhkfU8smzZsmzP2LNq1So0aNAA3bp1U9NVt2/fXk1dbbRv3z40bNhQbevRowfu3r1r2rZjxw7UqlVLZcRJMOzOnTvq9pkzZ6JDhw5o0aKFepwEDyIjIy3an59//hnVq1dHYGCgKpovBWuJ+H53LPysIYek10Nz4QLcLl9W60RERESUz4NSEnAyDzqlvU4ZnV1voN1ewyLreaRcuXI5epwEnho3boxDhw7By8sLw4cPV7dLkEmCS7L8888/KrA0ceJEte3SpUvo2LEjRo0ahQMHDiA6OlrViDIPLg0ZMgT79+9XwwmnTZuW5X6cPn1aBcfGjRun9mnv3r149913c3RMlP/x/c5zL/hZQybe3tDt3o0bP/2k1omIiIgonxc6l8woyZCRwp1CAhPPPfccfH191fXExETr7qWz0roBRerDUZQsWRIvvfSSCihKlpMUSk9OTlbDM+VvO2PGDLVt0qRJGDBggHqMzB706KOPqr+3WLx4sXqeazINGIAmTZqY7ivPPX78eHzwwQeZ7seXX36pMq8kQ8o4HPSTTz7Bq6++auUzQK6E73eee37W5FNuboB8f4WHG9aJiIiIKH8HpSRYYa5mzZoP3EcCHOT4P9KNGW6hoaFISUnBzZs3cfnyZURERKjheUKn0yEqKkrVCZNMqfLly5ueQx4nWVYXL15U10uVKpVq2/Xr17PcD3k9mS5bhu4JCYz5+/vn+fGSa+P7neeenzVERERERPkwKEUW0iUDF740rJd5GtBadLqtRgJJkvUmgSkJNrm7u6No0aLqx3vdunVVVoGQ+0hNKcmeKl26NLZu3Wp6jitXrqj6TzLrojCfAVCeMyQkJMv9kNfr0qUL5s6dq65LcCyWxWqJ7/d8g581ZFXJycDatfCWGoaSxevpyRNORERElJ9rSlEO6RKAXf0Ni6zbmQSUZs+ejXPnzmHWrFmqrpObmxs6deqkfkRKbScfHx9s2LBBFUKX4FS/fv3w999/q4LH8rgRI0age/fuKFasmHrO3bt349NPP8V///2Hd955Bz179sxyP6Qg+p9//qkeI1lXH374IQYPHmyDM0CuJD++3+fMkeLjhktHZstz/3abt/Fe8/cwrM6wB/aDnzX5VEICtAMGIHD0aLVORERERM6LQSlrn96QtobFAU51o0aN1I/BatWqqTpgH330kbpdhtFt3rxZZS7JUL3169er65JJJcPzpObUwoULUbt2bVVHbOXKlabnlIyn5cuXo06dOqhQoYJFWXVyv88++wwTJkzAQw89pIqrr1271qrHTq4nP77f+/YFpBSbXDoyW577vtX7ol/lfmhTvs0D+8HPmnxKq4W+TRskNG+u1omIiIjIedl3PFl+5+4DtP7Vak8v2QXZIVka3377bbrbpCbYnj170t3WokULNWNfegoXLqx+VGakZcuWqYb4GUl2hCxEluL73X547smh+PhA/8svuB0ejmAfHzg6qdEYEBBg790gIiIickjsYiQiIiKykuDgYDzxxBNYt24d6ycSERERpcFMKRcxaNAgteSlmTNn5unzEeUVea8/++yz+e79fvKkocazuztQqRIckq0/a07eOInwW+G4rb2NysGV8/R1ifLCkSNHVJayDGMdNmyYyhJ++umnVY01b29vnmQiIiJyacyUsqbkWOCHqoZF1omIcqFNG6BaNcMlGTz2+WNoub6luiQXERsLTfXqKNKihVp3dFJLbtKkSfjrr79w+vRpNGnSBM8995zKoHrmmWdw4sQJe+8iERERkWNnSqWkpKiZkyi79MDdY/fXiYiIKHf0emiOHYOHTHKbzdqK9hIdHY3vv/8e33zzDX766SdUqlRJZUslJCSgc+fOKlhFRERE5IosypSqXLky9u3bp9ZHjhypGlG5dePGDZQrVy7dItjp2b59u9qPokWLYt68eam2ybTiZcqUQYkSJRxrFjetN9DmD8Mi60RElKd6V+uNvo/0VZfkIry9ofv9d9z6+mu17ugk6CRZUXPmzFEzSx4+fBj79+/H5MmT0bt3b1y/ft3eu0hERETk2JlSFy5cUAEk8fHHH+Ptt99WM7nlJiAljTRLA1IRERHo2rUrJk6ciD59+qhGnDTsWrVqhaNHj6Jfv35qGvGGDRuiR48easpw6YW0O60bUKylvfeCiCjfmtN2DsJlFrbgYHvvCtmKZG63bInE8HDDuoNr1KiR6kx7+OGH0x3aJ7PzEREREbkqi4JSVatWVYVmGzRooKYGX7NmDXx9fR+434ABAyx6UQkq9e3bF3v27LHo/l988YXKgpo2bRo0Gg2mT5+OFStWqKDU8uXL1eXQoUPVfUePHo3Vq1fjjTfesOi5iYiIiKzl1Vdf5cklIiIiyk1Q6tNPP8WsWbOwcuVKFRSSIJG7TP9kRm63NCi1bNkylXk1btw4i+4vqe4SeJLXEBIcmzJlimlbhw4dTPeVba+99lqGzyVDD82HH0ZGRqpLnU6nljylSwaufK9e44ZXI0Bj28kOJYAoPbCyGM+dMylQoIAarpkT8reU48/zv6kTsPexSyak8f+VreX/97zm3iJ/3/u1dORvfefOHac9bnv/zXPzWSOvr15Xahvpbfx/Tr3mvQVO9ll377zl6LMqORn6776D59270PXtC3h65umu5fVn588//6wyuQsVKpTrz9b69evjjz/+QNmyZdVtmzZtwvjx43Hx4kVUq1ZNlTCQUgdCMskHDx6s6lVJx50MHzT+H5GSCMOHD1eZ6FOnTsWECRNSlUSQzPSkpCTMnTtXZagTERERWYtFUZLq1aurRorQarX44YcfVCM+p4xDAS0lP3CrVKliui6vfeXKFdM28+cz35ae2bNnqwBbWtIwi4+PR17SpMSi2F89EQhgyp9DkKiTsqw2pNGgWFBRXI+4ce9Hi3Px8fTE2NEjUbBgwRz9qLh79676wSPvWVdiz2OX113w0SLEJSbCLvL5e16nC5KxS+pvHB4eYbpdAlLrvvwKF69ec8rjtvffPDefNRIMK1e6FHxS4qCJugXb0kMTF2WIUxr+cRpyvuS8yfmT4ZfZoYmNRbFevVAYwNXmzaHx98/Tfcvr4XQS/JHOuMceeyxPyx6cOXNGBZ2WLFmCFi1aYMyYMSr4tHPnTtX51qVLF7Rr1w7r1q3D2LFjsWrVKnV/py2JQERERPlStlN3nn/++VzVk8oJycoyf01vb2/E3psGOrNt6Xn55ZdT9QhKUKtUqVIICgrKVaAtXclxiPOvgzNnz6Fk0/YoGFIBNqXXq4Z/CTcf9cPNmdy8dhnbP/9YzfqYk1ox8qNdeoTl7+qKQSl7HbvMMHXs9Bm06P88ioSUhM3l8/e8Vms4Jvm7mt9HfkRfunoNpZu1R5GQUnAlbxx4ApGJEShQPgiv1t1o888aec+fu3gJtd18UDBAwiQ2pLKkAL1/Yad7v8fdilTnLSAgIPvnPS4O+saNVSZPULFi0Pr55em+STsiL0lA6P3331dBn7RZ5pZKr+zB8ePHVY3Pp556Sl0fMWIEOnXqpNZlhj/pJJBaVlJu4a233sKoUaNUUIolEYiIiMiRZLt1tHjx4lQ9dzkd8pAdhQsXVj175j/APO+l62e2LT0SwEovqCY/8vL8B7ynH65U/QqjF41FzxcfQkiZ7GWI5Zpep3ru1Q8ljZMFZu4N65DgSk7/LsbHulpQyp7HbhyOU6R4Kdu/313qPS/30Tx43kNKIaRsebiS5AOJiE+JhS8Sc3bsufysMZ57FRSy+XtOd+917fHauZSb8+7nB91ff+GWFLj388vzz7m8fr4iRYrg9u3bqFu3rsqa8jMLouWm7IFkTpk7efIkKlasaCptIAXWjfU/a9SogWPHjjl3SQQiSsX4f0w+S9X3EFnEeK74OUVkfZa2BbIdlEpMTFSNl6VLlyIuLg4+Pj6qkSW9cJkFg3JDaihIcXWjgwcPIjQ01LRt165dGDJkyAPbiIiIiOxJhs0ZO8S++uqrHNXizKrsgbTNpP6TMRM8bWkDeS3JBpTgmLOWRCCi1G7evInk5GQkJSYi0Sw4TJmT8yXnTc6fZOsSkfVYWhIh20EpqUGwf/9+/PLLL6hQoYKqaSC3TZo0CQsWLEBuSGNIglweHqlrL0ntA0k7/+2331TdBCnWKXUSRM+ePdGkSRPVeygNKdmH/v3752o/iIgc0b59QEoK4OZm7z0hIktJYXJrmzFjhsrAMs5EnLa0gXl5A6ctiUBED/zYk/+zHp6e8LRxaRVnJudLzptkseZk2D4R5X1JhGwHpb788kv89ddfePjhh9X1YsWKqV7AZs2a5TooJenlUnehe/fuqW6XIYLz589Hx44d4e/vj8DAQPWaombNmiogVa9ePXXQkro+cuRIOITkOIQefQJLOl7EVj17DIkod4oX5xkkkppSmubNUSQpCdi5Uw3nc0aS3ZQXGeZbt25Vhcl3795t6tST0gZStNycsbyB05ZEIKIH/p8ZMyFdbebd3DCeK35OEVmfpW2BbAelpMEidQuMQSkh1+X27Eo7/tl8Vpm0ZIigZEedOHFCBcAkOGX05ptvqtliwsLCVCaVtYYRZp8O3jFH8EhR4A+O9SYiIsqDr1YdNPv3Q8IvzlC36Nq1a3jjjTdUWylFUh3vtX+kULlsy41z586pGfQkKGU+JE9KG0gdKvP7Se0naauxJAIRERE5kmx3Y0nDShpAEiSSYXRyKQEhqSllbTI8TwpwmgekjKQxJtMtO05ASs6uF64+vAxTfq+HFE3qIYlERESUA15e0G3ejNurV6t1R/fMM8/g1q1bqui4lCh48sknVYAqt1ndUtdTip1369YNTzzxhJoJUhYJeDVv3lwNpVu5cqW6r7TR2rZtq+pKSUmEnTt3qpIIMoNh2pII69atw5EjR9RzSQa8cRsRERGRQwSlevXqZaqPsH37dpUCuW3bNvTo0cMa++fctO6ILdQKu8OCodfkbBpoIiKjpUuBefMMl0Quy90d6NQJCW3bGtYdnAyrkxIEkydPVsPhRowYgRUrVuDnn3/O1fNKbU+ZUU8yoqRYr3G5cOGCqpeyfPlyjB49WpVA2LRpE955550HSiJICQYJkL366qsPlESQSWMkiOUwJRGIiIgoX8pRa05Sv2UhIiLbkZnZw8IAmWB02DCeeSJnIMEdCSA99dRTKogkGU7VqlVT2Ui5KXsgGVKZTQMvGVEyGc2BAwfQqFEjVdTXuUsiEBERUX7k+F2MzkyXAp+7f6Fu8Qho9IY6EkRERJQLUpfpt9/geecOIFnaDl5QWzKUpOxB+/bt0aVLF5WFJMGkpk2bWv21Q0JC0KlTpwxLIsiSHimJYF6jioiIiMhaGJSyJl08SpwYhLmPAYv1iVZ9KSIiIpcQHw9t+/aQ6VV0kZHAvRnnHJVkNF25ckUNrVu6dCnWrFmjhvENHDjQ3rtGREREZHcMSlmVFgm+lXHp8mXoi3GqViIiotx/tWqhr1kTycnJcHPwLCmjwMBAdSk1mhiMIiIiIrrPOVpzzsrdB5erf4eh3zdDisbb3ntDRETk/Hx8oP/f/3Dzt9/UOhERERE5LwaliIiIiIiIiIjI5jh8j4iIiCgPSQFxjSbrYftnz57leSciIiKXxqCUNSXHocSxvnj/8dP4Wx9v1ZciIiJyCXFx0HTogMKJicCvvwJ+fnA0q1atsvcuEBEREeXPoNTx48dRtmxZ+LCOgwV08Inai1ohwC69Pkd/ICIiIjL/atVBs307PNWqziFPTYsWLey9C0RERET5s6ZUmzZtsGfPHuvsTX6j9cK1hxZgxvbaSNE49pTVROT4Hn4YqFLFcEkGxQs8hJIBD6lLchFeXtCtW4fbS5eqdSIiIlu5cSMW5cotxfnzdy26PTPbt19C5cqfoGjRhZg3b3+qbRs2nESZMh+jRInFWLv2eKptCxceRLFii1C+/DJs3Xoxl0dE5ISZUjKV8cqVK9GyZUvr7FF+onVHTJGO2H7hZ/TUcKQkEeXO1q08g2nNaLsJmqhb0AcU5slxFe7uwJNPIiE83LDupBITE+HpKfleRETkDCTw1LnzRpw/H2nR7ZmJiIhF164bMXFiPfTpUxm9e3+P2rWD0apVaRw9GoF+/X7EwoVt0LBhcfTosQl16hRDpUqFsWXLOUyatB3r1nVGUJAP+vf/Efv29UeRIpyNllwoU6pt27b477//0KlTJ/z000/YsWOHaSEiIiKi+65du4bRo0fjscceQ+vWrdXSqlUrlC5dmqeJiMiJSOCob9/KFt+emS++OI4SJfwxbVpjVKxYCNOnN8aKFUfUtuXLj6BVq1IYOrQGqlcPwujRtbF69TG1bfHiwxg4sCq6dXsIjz4aqi43bvwvj46QyEmCUkOHDsXVq1dx7NgxjBw5UmVOyTJo0CDr7KEz06XAO+oAqgXdgkafYu+9ISIicn4pKcDOnfDYu9ew7uCeeeYZ3Lp1C76+vqoe55NPPomTJ0+qNhQRETmPZcsex9ixdSy+PTOHD4erwJNxptYGDUJw4MD1e9si0Lr1/Y6LBg2K48CBa6bHtW5dymzb/ccROats572fO3fOOnuSH+niEXrsaXzUAVisT7T33hARETm/+HhomzdHEfmajYwEPBy7ZuPu3btx+vRplWU+depUjBgxQk0Y8/rrr2P69On23j0iIrJQuXKB2bo9M5GRiahSRb7JDAoU8MSVK9GmbeXKFUyzLSaTbYbHETkr5y3G4BQ0SPQqg/CICKCYvfeFiJxdv35StwAoWlTSvu29N45hwc5hiIq5hgC/EIxtutzeu0O2oNFA/9BDSElJgfZeD7MjCw0NxS+//IKnnnpKZZnHxcWhWrVqOHLEMEyDiIhcj7u7Fl5e93+Ke3u7IzY2+d42Dby83NJsS8rycUQuM3wvKSkJb731Fho2bKgaWv/++y8aNGiAM2fOWGcPnZm7Ly7V+h39v22JZA2LzxFR7mzfDvzyi+GSDI5d34nDEX+pS3IRvr7QnzyJG3//rdYd3TvvvIPnn38ekZGR6NKlC+rVq4d27dqhadOm9t41IiKyk8KFvVWxc6OoKJn8Qmu2LS7NNrcsH0fkrLL9DpYaCF999RWeffZZREVFqRoJjz76qGpwEREREdF93bp1w5UrV1CoUCEsXboUL774oqktRURErql+/RDs2nXFdP3gwXCEhgZksO06QkP9s3wckcsEpTZs2ICvv/5aBaHc3NzU8tJLL2HPnj3W2UMiIqIMzO+yG592OKguiRxVYGAg3N3dcf36ddStWxfDhw9HQAB/RBBR/qPX6xETH68WWXd1kZEJSEp6cFKOrl0rYOfOK/jttwtq+5w5e9GuXVm1rWfPh7Fu3QkcORKB6OhELFhw0LStV6+HsWjRIYSFReH69Rg1Y59xG5HL1JQqVaoUduzYgQoVKqjrMmOADOErV66cNfbPuaXEI+TkUMxu/S8O6hPsvTdERPmOj0cANB5J0HvwB77LiI+HpkcPBCYmAps3O+QQPpmleMyYMaru1caNG9Vt0oH33nvvqeBUiRIlsGnTJtSoUcPeu0pETu7yzZtwJLEJCWg0ZYpa3/322/D18oIrn68aNT7F+++3QvfuFVPdXrSoL+bPb4mOHb+Gv78nAgO9sGpVB7WtZs1gjBtXB/XqfQ5vbzdUrFgII0fWUtu6dKmA9etPoWLFFep6mzal0aNH6ucmyvdBqTlz5qB79+4qBT02NhYTJkzAn3/+ic8++8w6e+jM9Cnwu7MNjUsCh/Q6e+8NERGR80tJgeann+Ats++lPNj77AgGDhwIf39/zJgxQ13//PPP8dFHH2Hr1q1o3ry5ul2CVttZII6IcqhAgQLwDgjARzt2ONQ5TDb7XJ7+3Xdwd7tfsNtRyHmT85dTev0ki28fMKCKGnKXnuHDa6kspxMnbqFZs5IqOGX05pvN0K9fZYSFRaNFi1KmmlKSELJ6dUeMHVsbMTFJapvcRuRSQan27dvj6NGjqhZC7dq1UbJkSVXEs3z58tbZQ2em9UR4+XfwyeovENCDEx0SERHlmqcndCtWqLqWAZ73G/COZPfu3Th+/LiaEEaypaZPn64ypVq0aKG2y/C9999/3967SUROLCgoCEtWrlSTKDgSSVowZoG+t3ixqj/saCQgJefPFiTYlFnNp3LlAtWSnipViqolPfXrF8+zfSSytxxFSh566CFMnTo17/cmv9F6ICqoJ34+sx09NR723hsionzn++MLERcdDh//YHSuMsbeu0O24OEBDBqEuPBwBMi6A3r44Yfx+++/Y8CAAXj77bdx+/ZtjB8/3rRdMsyLF+cPCiLKHQms2Cq4YqmYmBjTuiQt+Pn5wZU99hjrPRHleaHzuLg4TJkyRQWm5ENGPmxkCJ/0WBIREdnS98cXYf2pD9UlkaOQoXoyy56Pjw/eeustrFixQhU2P3v2LFq3bo1BgwZh1qxZ9t5NIiIiIufLlBo2bBhOnDihhuyVLl0aYWFhqs6U3L527Vrr7KWz0qXAM+YYHioUCY3eMeteEBERORWpV3L4MNxv3QJkOJw22/1rVteoUSOcO3dODeGTCWKMmQwSpOratSsWLFiAatWq2Xs3iYiIiJwvKLV582YcOHBAZUqJ+vXrq4aVXFIauniUOtoVy7sAi/WJPD1ElCvPPQfcvQsULMgTSS4sPh7aunUhVTZ0UkvFQYfwSQCqTp06qW6TIXsvvPCC3faJiIiIyOmDUvXq1cPff/9tCkqJvXv3olYtwzSVZE6DZI9iuH33jqwSEeXKvYm8iFybRgN9iRLQ6XSccYiIiIjIFYJSzz77rGldaiJILYQlS5agXLlyuHTpEnbu3Klm5aO0Z9cXF+rsxJBRY9HzRR+eHiIiotzy9YX+0iVEhIcj2AFndSIiIiKiPA5KlSlTJtV67dq1U80w06ZNG/ZWEhERERERERFR3galZpiNGZF6Ur/88guSkpIsfxUiIiIiIiIiIqLc1JRq166dypQqWbJkdh/qelLiUey/0ZjZ/BCO6RPsvTdE5OTkYzcsDAgNBS5ftvfeENlJfDw0/fsjMCEB+PJLNZyPiIiIiFwkKNWgQQOMGDECXbp0sc4e5Sf6FPjf+hktywLH9Tp77w0REZHzS0mB5uuv4S2z76Wk2HtviIiIiMiWQakqVaqgW7duCAkJgbe3NAnvO3v2bG72Jf/ReiKizAx88dV6eHTN9qkmIiKitDw9ofvwQ0RHRcHf05Pnh4iIiMiJZTtSsnz5cixduhQVK1a0zh7lJ1oPRIY8g29P7kNPjYe994aIiMj5eXgAI0ciNjwc/rJORERERE5Lm90H9OnTB9u2bUNCQoKacc98ISIiIiIiIiIiskqm1M8//6wud+7cmep2CUpx+F4aeh084s8jNCBGrRMREVEu6XTAf//B7eZNoGhRQJvt/jUiIiIictag1Llz56yzJ/lRShxKH26LL54AFnP2PSIiotyLi4P2kUcQJPGpyEggIIBnlYiIiMhJZbt7UavVws3NLd3FUkePHkX9+vVRqFAhTJ48GXq9PtP7Dxo06IGhgrKcP39ePTYwMDDV7W+88QYcRYpbAKITWeSciIgor+gLFoSuQAGeUCIiIiJXC0pJppQM05NFgkurVq1C1apVsWbNGoseL7WounTpgrp162L//v04duyYeo7MLFq0CLdv3zYtP/74oyq0XqpUKfz3338qKGW+XQJdDsHdD+frHUTndY8jWetj770hIiJyfn5+0N+6hfCTJ9U6ERERETmvbKfwlClTJtX1ypUro3379irQ9PTTT2f5+J9++gl3797FvHnz4Ovri7feegujRo3C4MGDM3yM3E8Wo/nz52PmzJkqO2vfvn1o3LixCkwREREREREREZFzyJNxZT4+Prh27ZpF9z18+DAaNWpkCjLVqFFDZUtZSoJQkq3Vu3dvdX3v3r1qkaCUp6cnhg0bhtdffz3D2QAlU0sWo0ipR6HqpurUktdkeKHaFxmiaOti5+o17y1wskLr986bnL+c/F3kMTl9rLOz57Hb9f3uAu/5zz6TzzDAy8tQ69lhzrsdjXl0CZKjb8Ldv0jOjj2XnzX8jM9/n/GO+r1x48YNVfrgjz/+QNmyZdVtkrEunXqnT5/G0KFDMWfOHFP7Z/v27Rg+fDgiIiIwdepUTJgwwfRcGzZswMSJE5GUlIS5c+eqmZWNFi5ciNdeew1+fn5Yvnw5WrdubYejJSIiIleR7aBUq1atUgV8pPEmQaXHH3/cosdLEKhcuXKm6/JckvEkw+6kxlRWPvzwQ4wYMULVthKnTp1SWVrjxo3DmTNnVLCqWrVqpqBVWrNnz8asWbMeuF0abfHx8chTugQUOjkBrz/2H04l34Um6hZsSw9NXBSg/lzpB+kclU9KHMqVLoWoqCiEh4dn+/HyvpSMPPnRYnyvuAp7Hrv8veTvJn8/27/f8/97vkqV++vmd4mOjkZwUFE7nnf7qeZXBRpNFPS+AUAOjj23nzX2fc/n7/d7hhISUGDyZPgkJSF83jxoffJ2eLzskyMGpDp37qxqaaYth9CuXTusW7cOY8eOVeUQJEglbZquXbuqwJMEnKRNVLt2bdWGk0BWv379VPCpYcOG6NGjB+rUqYNKlSphy5YtmDRpknq+oKAg9O/fX3UGFilSxK7HT0RERPlXtoNSUnTcnASVQkNDLe5Jc3d3h5d085vx9vZGbGxslkGpW7duYdOmTfjggw9SDQc0kmCXNMqkBzCjoNTLL7+cqrdQgmRSm0oaXwXyumhqcgy0d79D0xDgiJsn9AGFYVMqawLQ+xeWPxScSdytSJy7eAkBAQEIDg7OUWBG3pvyd3XFoJS9jl2CI/J3q+3mg4K2fr+78Hte/bCPuIES9jrv9pTLv3luP2vs+p530fc7YmKgXb8ekm/tv2oVtHk8+560SRyNtGn69u2LPXv2WFQO4YsvvkCJEiUwbdo09X0wffp0rFixQgWlJPtJLiWzSowePRqrV69Wk8QsXrwYAwcORLdu3dQ2udy4caPpvkRERER2CUpdvHjRtC4NmfRcunQJpUuXzvK5ChcurHrp0v6gkqF3Wfnmm2/QrFmzTINX0rgNCwvLcLsExNIGxYT8eM/zH/BuXrhZ6iV89e0m6Dp6ABpbB0d0hh8qanGywMy9YR3SmM7p38X4WFcLStnz2I3Dcez3nnPN97z9z7s95fJvnsvPGvuee9d8v8v4Vd0776iAoL+XV55/zjnid8ayZctUx5tkhVtSDkG2mWe2N2jQAFOmTDFt69Chg+l5ZJsM1zNuk+CX+bYdO3YwKEVERET2DUpJ7QLzIXumGhpppKSkZPlcUg9BGldGUh9KUtAlWJWVr776KlUGVFxcnGowSU0pqWsldu3a9UAxdrtx88SdEs/hy3+PoGcnD3vvDRE5uW3b7teUatnS3nvjGP69/heSo2/APbYoqoY0t/fukC1IJ9akSYgND4e/BR1a+YF52QNLyiHItipm430lE/zKlSvpPs7SbY5Qp5OIHJ/5/31+FhC5Np2FbQGLglISOEpL6jdJPYLNmzerOgQybM4SzZs3V42WlStXqhRzSTdv27atakjduXNHpfLLeloSgJKinUuWLDHdJoGoYsWKYeTIkSpl/c8//8SaNWvw22+/WbQvRETOpH9/QBJBQ0OBy5ftvTeO4cOdz+NW3FUU9imOJT2P23t3iGwms3IIabcZb0/vcZZus3udTiJyCuafGfJZEBMTY9f9ISL7sbROp0VBKfPMo59//hkfffQRfv31V7Rv315db9OmjcU7Jg0eqWcghTcnT56s0uS3Sfc/oIblHTx4ELVq1XrgcX///bfaXr58+VS3f/LJJ6rOVdOmTVVGlxTnbNGiBRyCXge3xGso6hPvcjNiERERWYX0uoWFQXvjBlC0qIy3c8kTnVk5BNkmPwbT3m58XE622b1OJxE5BfMglHwWyEyeROSavC2s02lRUEoaGRL8WbRokWqsPPvss1iwYMEDASJLyYwwkml14MABVQ/BOKuLqsuRAQl8Xbt27YHbpY7V1q1b4ZBS4lD2YFNseBJYrL+f3k5ERHmjc+WRiIsOh49/9ouUk5OKi4O2dGnIX1wnw8XyuNC5s8isHIJsk8xxI+nwk0lpjI+TUgdDhgzJcJuxs9F8m93rdBKRUzD/v8/PAiLXprWwLWBRUEpmcJHhc/KkLVu2VA0fmTI4vULklgoJCUGnTp2Q3+k17hbV2iIiouzrXHkUNFG3bD+7KdmV3j3bkwfnO5mVQ5DOPylrIOUMJHt8zpw5aNeunXpcz5490aRJE1U0XepHSSdjfxkbDKBXr14YPny4ej7JbJcZ+8xnPCYiIiLKaxa16iQAlV5hc8rq7PrhbIMTGDJqLHq+aCjETkRERLng5wd9QgLCw8MR7MLDQjIrh1C0aFHMnz8fHTt2hL+/PwIDA7Fq1Sq1rWbNmiogVa9ePZVWX7FiRVWbU3Tp0gXr169XtwnJmOrRo4cdj5KIiIjyO4uCUjNnzrT+nhARERFRhtKWOcioHIKQjCfJjjpx4gSaNWumglNGb775Jvr164ewsDCVSWWsGyUdkKtXr1aT10hdGNnGTkkiIiKyJua/ExGR04pLigLU4gEfz4L23h0im8usHIIMz5MlPVWqVFFLeqS2FBEREZEtMChlTSkJKHpuBsY1OIor+kSrvhQRkSsa/10j3Iq7isI+xbGk53F77w7ZQkICNOPHIyAuDli0CPDh8HgiIiIiZ8WpUaxJn4yC4V/giUcuQqtnsXMiIqJcS06GZvFi+EmNpORknlAiIiIiJ8ZMKWvSeOBW6Bhs/vEn6B7nqSYiIso1Dw/op09XNY98PTx4QomIiIicGCMl1uTmidslx2HV4TPo2Y4NZyLKncuXeQaJ4OkJ/YwZiA4Ph++9At1ERERE5Jw4fI+IiIiIiIiIiGyOQSlr0uuhTY6Ev0eSWiciIqLcf7fizh1o7t7ldysRERGRk2NQyppSYlHuQB183+dXuOvjrfpSRERELiE2FtoiRVDskUfUOhERERE5L9aUIiJyErNmAZIcUrAgMGOGvfeGiIiIiIgod5gpZU1uvjhT/zjarG6PZI23VV+KiPK/ZcuA+fMNl0Quy9cXuvh4XLt4Ua0TERERkfNiUMqaNBpA64EUvdawTkRERLn/bvXwMCz8biUiIiJyagxKERERERERERGRzTEoZU0piShy8W0Mr3scWn2SVV+KiIjIJSQmQvPiiwh47TW1TkRERETOi0Epa9InIfDqcvSueg5afbJVX4qIiMglJCVBM3cu/BYvVutERERE5Lw4+541aTxwp/hQ/Pzb79AV46kmIiLKNQ8P6CdORGxsLHykrhQREREROS1GSqzJzRM3S0/BkgNX0LMNG85ERES55ukJ/Zw5iAoPh4+nJ08oERERkRPj8D0iIiIiIiIiIrI5BqWsSa8HdElw0+gM60RERJT771apJSULv1uJiIiInBqH71lTSiwq7KuM358BFuunW/WliCj/a9ECuHEDKFrU3nviOKoUa4KomGsI8Aux966QrcTGQuvvD/mL6yIjgYAAnnsiIiIiJ8WgFBGRk/jiC3vvgeMZ22QpNFG3oA8obO9dISIiIiKibOLwPWty88W5uv9D57WPIVnjbdWXIiIicgm+vtDdvInrJ06odSIiIiJyXsyUsiaNBjr3AohO8lDrRERElPvvVgQGQp+YyO9WIiIiIifHTCkiIiIiIiIiIrI5ZkpZU0oiCl3+AINqnkKMPsmqL0VE+V/r1sD160CxYsDWrfbeG8cw67duuBt7FQV9i2PGY9/Ze3fIFhIToXnzTfjHxABvvAF4c3g8ERERkbNiUMqa9EkoHPYhBtWU2feSrfpSRJT/nToFhIUBd+/ae08cx9XI07gVdxWxyTH23hWylaQkaF57Df4y+96MGQxKERERETkxBqWsSeOOu8H9sHXHn9AVc7PqSxERuSJvD3/4JPmpS3IR7u7QjxiB2Lg4+LizGUNERETkzNiasyY3L9woNwsfvDcWPVt6WvWliIhc0ftd9kATdQv6gML23hWyFS8v6D/6CFHh4fDx8uJ5JyIiInJiLHROREREREREREQ2x6AUERERERERERHZHINS1pQcg/J7H8Fv/X+Cuy7Oqi9FRETkEmJioPHyQrFSpdQ6ERERETkv1pSyMo0+Ge4M/RERWcXq/01HTMx1+PkVwzN13+BZdhGaZMOMtnp77wgRERER5QrDJdbk5oPztf9Cr/WtkaxhMVYiory28/zX2HpxvbokF+HjA93Fiwj/3//UOhERERE5L2ZKWZNGixTPENyI81brRERElEtaLRAaCp2Hh2GdiIiIiJwWg1JERE5i+nQgOhrw97f3nhAREREREeWeXboYjx49ivr166NQoUKYPHky9Pqsq0LUqFEDGo3GtAwdOtS0bcOGDShTpgxKlCiBtWvXwmGkJCLwyjI8XfUstPoke+8NETm5YcOACRMMl0QuKzEReO89+C5aZFgnIiIiIqdl86BUQkICunTpgrp162L//v04duwYVq1aleljYmNjcebMGYSHh+P27dtq+fDDD00Brn79+mHatGnYsmULpk+fjpMnT8Ih6JNQ5NI7GFH3BLR6Q1FWIiIiyoWkJGhfegkFXn9drRMRERGR87J5UOqnn37C3bt3MW/ePFSoUAFvvfUWVqxYkeljDh48qDKlgoKCEBgYqBafe8VNly9fjlatWqnMqerVq2P06NFYvXo1HILGHZFFe+Dn06HQadzsvTdERETOz90d+gEDEPfUU2qdiIiIiJyXzYNShw8fRqNGjeDr66uuS7BJsqUys3fvXly+fNkUlBoxYoTKuDI+X+vWrU33bdCgAQ4cOACH4OaFiApz8PbfNaHTeNp7b4jIyV29Cly+bLgkclleXtCvXIm7H3yg1omIiIjIedm8izEyMhLlypUzXZf6UG5ubmpIntSYSo8Mx2vatClmzpyJO3fuqOF68+fPx5QpUx54vgIFCuDKlSsZvr4Es4wBLeP+CJ1Op5a8JvWy5BghdbP0ef/8Wbz4/QU2fu3cunfe5Pzl5O8ij8npY52dPY/dru93F3jP16+vQViYBqGhely8qHec8+4ocnLsufys4Wd8/vuMd8XvDSIiIiKXCUq5u7vDK03Ppre3t6oblVFQasmSJamuS92oBQsWqKBU2uczPldGZs+ejVmzZj1we0REBOLj45HXoqKiUK50KfikxEETdQu2pYcmLgrQyLr6x2nI+ZLzJudPaonl5EeFDBOVHy1aF5sy3J7Hbt/3e/5/z+t0QZKCqf7G4eERptujo6MRHFTUjufdAQJRel2Ojj23nzX8jM9/n/GyT85EyhhIu+bmzZsqW/yTTz5B+fLlVc3NwYMH4/Tp06rEwZw5cwzBawDbt2/H8OHDVdtn6tSpmCAzKJhNHjNx4kQkJSVh7ty56NOnjx2PjoiIiPI7mwelChcurBpKaRuAnp6WD28LDg5GWFiY6fmkUWXpc7388supGl+SKVWqVCk1NFCyrPJUcgyCtrfAgsZxWK19BvqAwrAplTUB6P0LS0oanEncrUicu3gJAQEB6u+dkx8s0viWv6srBqXsdewSHJG/W203HxS09fvdBd7zWq3hmOTvan4f9cM+4gZK2Ou825Pm3ntco83RZ2xuP2vs+p7P5+/3DMXEQFOqFEIkU+riRWgDAvJ036Rzy1nIJDCvvfYaNm3ahKJFi6rg1KBBg/Drr7+qSWXatWuHdevWYezYsWpSGQlSSZupa9euKvAkAafevXujdu3aqj6ncfKYhQsXomHDhujRowfq1KmDSpUq2ftQiYiIKJ+yeVCqfv36WLZsmen6uXPn1HA6CS5lpHHjxvjqq69U8Ejs2rULZcqUMT2fXB8yZIipKHpoaGiGzyVZVWkztYw/8vL8B7w8X0oU/FWMTHP/x5PN6Aw/VNTiZIGZe8M6JLiS07+L8bGuFpSy57Ebh+PY7z3nKu95uY/Ggc67g8jJsefys8a+595V3u9pyP3v3jXkQlrhc86ZvjOkzSN1OiVwJJ599lk8+eSTqSaVkRqeMqnMqFGjVFDqiy++QIkSJdSsxXL+JftcJpyRoJT55DHCOHnMG2+8YecjJSIiovzK5kGp5s2bq+yklStXqsaRNJTatm2r6kpJvSjpNZV1c1WrVsXzzz+PGTNm4MSJEyqdXHrxRM+ePdGkSROMGzdO1ZaSYX39+/eHQ3DzwcWav+Hlma+j0XAWYyUiIso1Hx/oTpxQw9WK3JuJ11VVqVIFW7duxaFDh1QbaNGiRXjssccynVRGtkngyTiUT4b8STkE47YOHTqYnl+2SSaWo9TpJCLHZ/5/n58FRK5NZ2FbwC41paQnTlLGJ0+erHokt23bprZJTSnp9atVq1aqx7z33nsqgCWNKEnzf/fddzFw4EC1rWbNmiogVa9ePZVyX7FiRYwcORIOQaNFkndZhEX5OV8vNhERkSOSTKaKFZFSsKBh3cWDUr169VLD74QEpvbs2YO33347w0llJHAkj0tvgpjsTh5j6zqdROT4zGv7ymdBTEyMXfeHiOzH0jqdNg9KCallIHUQDhw4oHryihQpom5XQyDSERgYiI0bN2b4fG+++aaqgSB1plq0aJGt+lREREREzmjv3r347rvvsHv3bjzyyCOqmHnHjh3RunXrDCeVyWyCmOxOHmPTOp1E5BTMg1DyWeDn52fX/SEi+7G0TqddglIiJCQEnTp1yrPnk14/854/h6BLQoFrq9G90nlo9Un23hsiIiLnl5QEfPwxfKX3TQIi6dSJdBVr165VhcqlKLmQ2k+LFy9W2VMZTSqT2QQx2Z08xqZ1OonIKZj/3+dnAZFr01rYFmCLwZp0iQi6MAsvNDwGrT7Zqi9FRETkEhIToR0zBgWmTlXrrl6rITw8PFUQyZgNJZPApDepjHGCGCPzCWIy20ZERERkDQxKWZPGDdGF22Pb+RDoWVOKiIgo99zcoO/ZE/GdO6t1V9asWTN88803mD9/PtasWYPu3burTPSxY8eaJpUR5pPKSAmFnTt34rfffkNSUpIa8teuXTvT5DHr1q3DkSNHEB0drSaPMW4jIiIisga7Dd9zCW7euF7xI8x8fyx6NnLd4QVElDd+/x1ITpa6LzyjRtPbfgtd1P/buxPwKqr7/+Ofm42QkAABwi6ERRQFWURQkEX0wYVFwZ+i0B8uCJRSbFXq0r8irq0LUPwhUlFpQUBFUVTAqrgUixUQ2cGCiGySQFiyQpI7/+ccTJpgEiLJ3PX9ep6bO3Pnzr1zZiZzZ75zzvccUkTCydyECAOxsXJef11HUlOVXMFcBaHKBJG2bNmiqVOnav/+/Tr//PNtDs7o6OgyO5WpW7euDWKZ3FM1atSweTtnz54d+J3HAACAkMSlDQAEiTZt/L0EgadRYmt5PHXkJCT5e1EAnzO96j344IP2UdFOZYwxY8bYGlBbt261ta1McKoQnccAAABfIigFAAAQgsrrVCYlJcU+gqbzGAAAEJLIKeWm/Gw1+7q73rj+Y0U5Oa5+FQAAYSE7W56mTVWvY0c7DAAAgOBFTSlXOYrKO6B6cXYQACpl3ryT1+BxcdLNN7MyjRU7F+pEVppi4uupR4sbWCnhwHHk2bdPJsW51+HHFQAAIJgRlHJTRKx2n79Yk558Sh1Gxrj6VQBC3x/+IO3dK5ke2glKnTR37USl5+xXUvWGBKXCRWysvGvWKD09XUlhnugcAAAg2NF8z9W1G6kT8W21/XCiHE94d1sNAECViIyUOnRQ/vnnnxwGAABA0KKmFAAgaA3vOKmo+R4AAACA4EJQyk3ePCWkvakrW+5RhJPn6lcBQDjqkXK9PBnpchKS/L0o8JW8PGnOHFXPyJDGjJGqVWPdAwAABCma77nJe0LJ392r+7qvV4ST7+pXAQAQFk6cUMTtt6vm735nhwEAABC8qCnlJk+ksmr11vqNm+TUJ/4HAEClRUbKueoqHT9xQjHklAIAAAhqRErcFBmrH9vM0v3Lu6jAQ/MCAKhq+479R7uPfWufESZiY+W8956OzJ1rhwEAABC8qCkFAAhaj3x0rdJz9iupekO9MGSLvxcHAAAAwC9ATSkAAAAAAAD4HDWl3JSfrabf9NXca9P0rpPj6lcBCH0NGpR8BsJSdrY8F1ygugUF0vr1Uo0a/l4iAAAAnCGCUq5yFHN8l5ok2kEAqJTVq1mBgBxHnu3b7QmM1+HHFQAAIJjRfM/VtRurvW1f07il3VTgiXH1qwAACAuxsfJ+/rkOvfMOic4BAACCHDWl3BQRqdyEztqYlqQ2nkhXvwoAgLAQGSl176681NSTwwAAAAha1JQCAAAAAACAz1FTyk3efMUfWqJezfbL4+S7+lUAQt/o0VJ6upSUJM2c6e+lAfwkP196801VO3ZMGjFCiqF5PAAAQLAiKOUm73E12D5ek3pJM5w8V78KQOh7/31p716pcWN/LwngR8ePK2LoUNU2P7NDhxKUAgAACGIEpVwVoZyEi7TtP9vl1Pe4+1UAAISDiAg5vXop78QJRUWQhQAAACCYcTbnpqjq2td2nn73D9P7XqyrXwUAQFioXl3O8uVKf+stOwwAAIDgRVAKAAAAAAAAPkdQCgAAAAAAAD5HUMpN+TlqsmGAZvX/pyKdXFe/CgCAsJCTI0+nTqpz+eV2GAAAAMGLROeu8qpa9ha1SpI+dBx3vwoAgHDg9cqzbp2i7aDX30sDAACASqCmlJsiYrXvnNm6+8MuKvDEuPpVAACEhdhYeZctU/qCBXYYAAAAwYuglKtrN1I5NXtozf56cjyRrn4VAABhITJSuuIKnejV6+QwAAAAghbN9wAgSNx0k3T4sFS7tr+XJHB0bz5EWVkHFB9f39+LAgAAAOAXIijlJm++4g5/om6NU+Vx8l39KgCh7+mn/b0EgedXnR6RJyNdTkKSvxcFvpKfLy1dqmpHj0o33CDF0DweAAAgWNF8z03e42r47R36U9/VinTyXP0qAADCwvHjihg4ULV/9Ss7DAAAgOBFTSlXRSg3vp2+3/WDnPoed78KAIBwEBEh58ILlZ+Xp8gI7q0BAAAEM87m3BRVXXvPX6QxS7qrwEMPQQAAVFr16nL+/W8dWrbMDgMAACB4UVMKAILEOedI+/ZJjRpJW7f6e2kCw+/e7arD2ftUO66Rpg5c5e/FAQAAABDoNaU2btyoLl26qHbt2powYYIcxzntPJMmTVJSUpKqVaum6667ThkZGUXT2rdvL4/HU/QYOXKkyyUAAN/LzJTMoc8846TcvEzl5GfZZwAAAADBxedBqePHj2vAgAHq3LmzVq9erc2bN2v27NnlzvPqq6/ax7Jly7Rp0yZt2bJFf/rTn+y07Oxs7dixQ6mpqTp8+LB9PPfccwoI+TlqtOkGPXflSkU6uf5eGgAIOQ0TW6lJQiv7jDCRkyPPpZcqaeBAOwwAAIDg5fPme0uXLtXRo0c1efJkxcXF6YknntBvfvMb3XrrrWXOs3v3bv3tb3/TRRddZMdvvPFGrVp1spnG2rVrbU2pevXqKfB4VT3za7VLllZUoDYYAOCXmXj5O/JkpMtJSGLVhQuvV55//UsxdtDr76UBAABAMAWl1q1bp27dutmAlGECSqa2VHnuu+++EuPbtm1T69at7fBXX32lPXv22KBUXl6ebrrpJk2dOtU28yurppZ5FDp27Jh9Nie2VX9yG60DrZ/X9BdfUoMboiTHxyfPJhBW+FCQnbg7jm2KaZp2nsl2MfOc6bzBzp9ld37abif3Oz+s+5Df5z0/Pcx7nMBZ7/5U2W1eyWONX9d9yO/vZYiOlrNwob3BlRgdbYNUVSkcfzcAAADCJihlgkApKSlF4+akNDIy0ja7MzmmTufbb7/VokWL9PXXXxcFqHr06KGHH35YR44c0bBhwzRlypSfBbIKPfnkkzY/1anS0tKUm1v1Tex+jOqmfZ71au7Ns3fzfcuRJyfj5DXsyT9Bo3pBjlLOampzh5mmmWdyUWEuWMwFT0SYdRnuz7Kb7WW2m9l+vt/fQ3+f93pNjdBIu41TU9OKXs/MzFRyvbp+XO/+VLltXtljjX/3+dDe38vjvfhie5zLTU+v8uNc8ZyVAAAACLGgVFRU1M9qMcXGxtrcUKcLSpkLsdtuu80mMj/vvPPsay+88EKJ9zz00EOaNm1amUGp+++/X3fddVeJIFnTpk1tTavExERVNXOxuPOH3eoYWV01fd28xN5Bl5waSSb6p2CSk37MrreEhAQlJyf/4vnNvmICnma7hmNQyl9l9+v+Hgb7fETEyTKZ7Vr8PfbCPu2gGvlrvftTJbd5ZY81HOND7xhvzkmC0b333mtrnr/77rtFncqY1Ajbt2+3501PPfXUyVp9kj777DONGTPG3pB74IEHSpwXLVy4UHfffbetff7ss8/aGugAAAAhE5QyPeiZE6XizAVVTIzJDlG+Rx99VOnp6Xr66afLfI85ud27d2+Z001ArLSmfeaktsov4L0Fqp7xb12QfFAe07TC4+vgiPlOz0+PIAvM/NSsw5xAn+l2KZw33IJS/ix7YXMc/+1z4bLPm/d4Ami9+8+0L0YqI+tHJcQ30Pges3x+rPHvug+X/f0UBQXS55+r2pEjihgwoMqPc8H4m7F+/Xo9//zzNkVC8U5l+vXrpwULFmj8+PG2UxkTpDKBqIEDB9rAkwk4DR06VB07dlSfPn3s+ZmpcT59+nR17dpVgwcPVqdOndSmTRt/FxEAAIQon595denSRStXriwa37lzpz15MsGq8pg7fyY5+ptvvlmUj8q4+OKLbSL0QuazmzVrpoDgzVXjLcM1td+/Femc8PfSAEDI2XzgC61LW2GfESZycxXRt6+Shgyxw+HO1BobNWqUfv/736tFixY/61SmZcuWtlOZl156yU4zvRk3atRIDz74oM3PaWqYF06bNWuWDU6ZmlXt2rXTuHHjNGfOHL+WDwAAhDafB6V69uxpm8y98sordtycKF1++eU2r5TJCVVg7oCeYsuWLfZu3nPPPWeb2pnmEqa5n2Ga8Y0ePVr//ve/bQ99pqr5r3/9awUGj05Ub6WdR2oEW7oPAAACt5ZV27bKO/vsoGum6waTxmDDhg1q3ry5Fi9erBMnTpTbqYyZZgJPhU35TM/Ga9asKZp22WWXFX128WkAAAAhk1PK3IkzQaYJEybYavKffvqpnWZySq1du1YdOnQoMc9f//pXZWVlacSIEfZhmNpQ33//vZ555hlbHd2cYJmme6ZpX+F7/C4qTrvbL9PtM8dryB+q+3tpAAQ5k0IvJ0eqzuEE4SwuTs6GDTqUmqrkYjWnw5G5STdx4kRbQ2rXrl22VtNjjz2mSy+9tMxOZcyNwbZt2xZNM/k09+3bV2pnNMWn+b9HYwDBoPj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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "分析结果：\n", "好的！所有分析已顺利完成，下面是完整的分析报告。\n", "\n", "---\n", "\n", "## 📊 员工薪资分布分析报告\n", "\n", "### 一、核心统计指标\n", "\n", "| 指标 | 数值 | 说明 |\n", "|------|------|------|\n", "| **员工总数** | 5 人 | — |\n", "| **工资总额** | **69,000 元** | — |\n", "| **平均薪资（均值）** | **13,800 元** | 整体薪酬水平 |\n", "| **薪资中位数** | **16,000 元** | 比均值高 2,200 元 |\n", "| **最高薪资** | **20,000 元**（张三 - 技术部） | — |\n", "| **最低薪资** | **5,000 元**（赵六 - 人力资源） | — |\n", "| **薪资极差** | **15,000 元** | 差距较大，达 4 倍 |\n", "| **标准差** | **5,891 元** | 离散程度较高 |\n", "| **变异系数** | **42.69%** | 薪资分布不均衡 |\n", "\n", "### 二、分布形态分析\n", "\n", "- **偏度 = -0.860（左偏）**：薪资分布呈现左偏态，说明**低薪员工（赵六 5,000元）拉低了整体均值**，导致均值（13,800）小于中位数（16,000）\n", "- **峰度 ≈ 0（接近正态）**：分布形态接近正态，没有特别极端的异常值\n", "\n", "### 三、薪资分位值\n", "\n", "| 分位 | 薪资 | 解读 |\n", "|------|------|------|\n", "| 10% 分位 | 7,400 元 | 底层 10% 的员工薪资低于此值 |\n", "| 25% 分位 | 11,000 元 | 四分之一员工低于此 |\n", "| **50% 分位（中位数）** | **16,000 元** | 半数员工在此之上 |\n", "| 75% 分位 | 17,000 元 | 四分之三员工低于此 |\n", "| 90% 分位 | 18,800 元 | 顶尖 10% |\n", "\n", "### 四、部门薪资对比\n", "\n", "| 部门 | 平均薪资 | 中位数 | 人数 | 特点 |\n", "|------|---------|-------|:----:|------|\n", "| 🥇 **技术部** | **18,000 元** | 18,000 元 | 2人 | 薪资最高，内部差距较小（1.6万~2万） |\n", "| 🥈 **销售部** | **14,000 元** | 14,000 元 | 2人 | 居中，内部有差距（1.1万~1.7万） |\n", "| 🥉 **人力资源** | **5,000 元** | 5,000 元 | 1人 | 最低，仅为技术部的 28% |\n", "\n", "### 五、排名一览\n", "\n", "| 排名 | 员工 | 部门 | 薪资 | 与均值比较 |\n", "|:---:|:----:|:----:|:----:|:---------:|\n", "| 1 | 张三 | 技术部 | 20,000 元 | ✅ 高于均值（+44.9%） |\n", "| 2 | 钱七 | 销售部 | 17,000 元 | ✅ 高于均值（+23.2%） |\n", "| 3 | 王五 | 技术部 | 16,000 元 | ✅ 高于均值（+15.9%） |\n", "| 4 | 李四 | 销售部 | 11,000 元 | ❌ 低于均值（-20.3%） |\n", "| 5 | 赵六 | 人力资源 | 5,000 元 | ❌ 低于均值（-63.8%） |\n", "\n", "### 六、业务洞察与建议\n", "\n", "1. **🏢 部门薪酬差异显著**：技术部的平均薪资（1.8万）是人力资源部（5千）的 **3.6 倍**，存在较大部门间薪酬不均衡。\n", "\n", "2. **📉 人力资源岗位薪资偏低**：赵六的 5,000 元薪资远低于整体平均水平，需关注该岗位的薪酬竞争力，可能存在**招聘困难和流失风险**。\n", "\n", "3. **💡 中位数高于均值**：中位数（16,000）高于均值（13,800），说明多数员工薪资处于中高水平，个别低薪岗位拉低了平均值，整体薪酬结构相对健康。\n", "\n", "4. **🎯 建议**：\n", " - 评估人力资源岗位的市场薪酬水平，适当调薪以增强岗位竞争力\n", " - 销售部内部差距较大（1.1万 ~ 1.7万），可以建立更透明的绩效薪酬机制\n", " - 技术部薪资水平具有竞争力，可作为招聘宣传的亮点\n", "=== 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": [ "import warnings\n", "warnings.filterwarnings(\"ignore\") # 抑制 matplotlib 的字体警告\n", "\n", "response = visualization_agent.invoke({\n", " \"messages\": [{\"role\": \"user\", \"content\": \"分析一下员工薪资的分布情况，包括均值、中位数、最大最小值等统计量\"}]\n", "})\n", "final_msg = response[\"messages\"][-1]\n", "print(f\"\\n分析结果：\\n{final_msg.content}\")\n", "\n", "# 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": 24, "id": "b4d9cc30", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "分析结果：\n", "---\n", "\n", "## 📊 各部门平均薪资分析结果\n", "\n", "### 核心数据\n", "\n", "| 部门 | 平均薪资（元） |\n", "|------|:----------:|\n", "| 🥇 **技术部** | **¥18,000** |\n", "| 🥈 **销售部** | **¥14,000** |\n", "| 🥉 **人力资源** | **¥5,000** |\n", "\n", "### 📌 业务洞察\n", "\n", "1. **技术部薪资最高（¥18,000）** — 技术岗位作为公司核心产出部门，薪资领先优势明显，高出销售部约 **28.6%**，符合市场规律。\n", "\n", "2. **人力资源部薪资最低（¥5,000）** — 与最高部门差距高达 **¥13,000**，薪资极差比达到 **3.6 倍**，说明公司内部各职能部门的薪酬分配存在较大差异。\n", "\n", "3. **销售部处于中游（¥14,000）** — 考虑到销售通常还有业绩提成，实际总收入可能会更高，建议结合绩效数据做进一步分析。\n", "\n", "> 💡 **建议：** 可以进一步分析各部门的人均产出、工龄分布等数据，评估当前薪资结构是否合理，是否需要适当调整人力资源等支持部门的薪资水平以提升留任率。\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": 25, "id": "a7b31809", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "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": 26, "id": "eff7bd04", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "分析结果：\n", "## 📊 分析结果：各产品类别库存总量\n", "\n", "### 数据概览\n", "\n", "| 产品类别 | 库存总量 |\n", "|---------|:-------:|\n", "| 🖥️ **电子产品** | **1,900** 件 |\n", "| 🪑 **办公用品** | **300** 件 |\n", "| **合计** | **2,200** 件 |\n", "\n", "### 业务洞察\n", "\n", "1. **电子产品占据绝对主导**：电子产品库存总量为 **1,900 件**，占总库存的 **86.4%**，是办公用品的 6 倍以上。这主要是因为电子品类下包含笔记本电脑、机械键盘和显示器 3 个产品，而办公用品仅有办公椅 1 个产品。\n", "\n", "2. **库存结构差异明显**：\n", " - 虽然电子产品中机械键盘单品库存高达 **1,000 件**，但笔记本电脑（500 件）和显示器（400 件）的库存也较为充足，产品组合丰富。\n", " - 办公用品仅有办公椅（300 件），品类单一，建议适当扩充办公用品产品线。\n", "\n", "3. **建议**：如果库存周转数据支持，可考虑增加办公用品品类（如打印机、文件柜等），丰富产品结构，降低对电子产品的过度依赖。\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": null, "id": "d7c034ef", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "Agent 生成的第 2 段代码：\n", "============================================================\n", "# 1. 先探索产品表的数据结构\n", "print(\"===== 产品表基本信息 =====\")\n", "products_df.info()\n", "\n", "print(\"\\n===== 产品表前5行 =====\")\n", "print(products_df.head())\n", "\n", "print(\"\\n===== 类别列唯一值 =====\")\n", "print(products_df['category'].value_counts())\n", "\n", "print(\"\\n===== 价格描述统计 =====\")\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", "\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 object \n", " 2 category 4 non-null object \n", " 3 price 4 non-null float64\n", " 4 stock 4 non-null int64 \n", "dtypes: float64(1), int64(2), object(2)\n", "memory usage: 288.0+ bytes\n", "\n", "=====\n", "\n", "============================================================\n", "Agent 生成的第 4 段代码：\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", "avg_price_by_category = products_df.groupby('category')['price'].mean()\n", "print(\"===== 各产品类别平均价格 =====\")\n", "print(avg_price_by_category)\n", "print(f\"\\n总平均价格: {products_df['price'].mean():.2f}\")\n", "\n", "# 饼图可视化\n", "plt.figure(figsize=(8, 8))\n", "colors = ['#ff9999', '#66b3ff', '#99ff99', '#ffcc99']\n", "explode = [0.05] * len(avg_price_by_category) # 轻微分离\n", "\n", "wedges, texts, autotexts = plt.pie(\n", " avg_price_by_category.values,\n", " labels=avg_price_by_category.index,\n", " autopct='%1.1f%%',\n", " startangle=90,\n", " colors=colors[:len(avg_price_by_category)],\n", " explode=explode,\n", " shadow=True,\n", " textprops={'fontsize': 14}\n", ")\n", "\n", "# 在每个切片上添加平均价格数值\n", "for i, (category, avg_price) in enumerate(avg_price_by_category.items()):\n", " print(f\" {category}: 平均价格 ¥{avg_price:.2f}\")\n", "\n", "plt.title('Average Price Distribution by Product Category', fontsize=16, fontweight='bold', pad=20)\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", "总平均价格: 2274.25\n", " 办公用品: 平均价格 ¥499.00\n", " 电子产品: 平均价格 ¥2866.00\n", "\n", "\n", "============================================================\n", "最终回答：\n", "---\n", "\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 | 产品数量 | 平均价格 |\n", "|---------|:-------:|:--------:|\n", "| 📱 **电子产品** | 3 个 | **¥2,866.00** |\n", "| 🪑 **办公用品** | 1 个 | **¥499.00** |\n", "| **整体平均** | 4 个 | **¥2,274.25** |\n", "\n", "### 🎯 核心洞察\n", "\n", "1. **品类价格差距悬殊** — 电子产品的平均价格（¥2,866）是办公用品（¥499）的 **5.7 倍**，说明电子产品单价远高于办公用品。\n", "\n", "2. **电子产品贡献了绝大部分的\"价格价值\"** — 从饼图上看，电子产品类占据了 **85.2%** 的平均价格份额，而办公用品仅占 **14.8%**。\n", "\n", "3. **品类内部差异明显** — 电子产品类中包含了 ¥6,999 的笔记本电脑和 ¥399 的机械键盘，价格跨度很大（标准差约 ¥3,170），而办公用品目前仅有一款产品，代表性还不够。\n", "\n", "### 💡 业务建议\n", "\n", "- ✅ **产品线扩展**：办公用品目前只有 1 款产品，建议补充更多办公品类（如打印机、书桌、文具等），以便更准确地评估该品类的价格定位。\n", "- ✅ **电子产品分层定价**：电子类产品价格跨度大，可以考虑进一步细分（如\"高端数码\"和\"外设配件\"），做更有针对性的定价策略。\n" ] }, { "ename": "", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n", "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n", "\u001b[1;31mClick here for more info. \n", "\u001b[1;31mView Jupyter log for further details." ] } ], "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}\")" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.20" } }, "nbformat": 4, "nbformat_minor": 5 }