{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 智能竞品分析Agent\n",
    "\n",
    "> 基于Hello Agents框架的智能化竞品分析系统\n",
    "> \n",
    "> - 自动收集竞品信息\n",
    "> - 多维度对比分析\n",
    "> - 生成专业报告\n",
    "\n",
    "## 作者信息\n",
    "- **姓名**: czxgg0630\n",
    "- **GitHub**: [@czxgg0630](https://github.com/czxgg0630)\n",
    "- **日期**: 2026-04-09"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第2部分: 环境配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 安装依赖\n",
    "!pip install -q hello-agents[all]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "from hello_agents import SimpleAgent, HelloAgentsLLM\n",
    "from hello_agents.tools import Tool, ToolParameter\n",
    "from hello_agents.tools.builtin.search_tool import SearchTool\n",
    "from typing import Dict, Any, List\n",
    "import os\n",
    "os.environ['HTTPS_PROXY'] = 'http://127.0.0.1:8800'  # 你的代理地址\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# 加载环境变量\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第3部分: 工具定义"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 三个核心工具已定义完成（搜索工具使用真实API）\n"
     ]
    }
   ],
   "source": [
    "class CompetitiveInfoSearchTool(Tool):\n",
    "    \"\"\"竞品信息搜索工具 - 使用真实搜索API\"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__(\n",
    "            name=\"competitive_info_search\",\n",
    "            description=\"搜索指定竞品的产品信息、功能特性、定价策略等\"\n",
    "        )\n",
    "        # 初始化内置搜索工具，使用 Tavily 后端\n",
    "        self.search = SearchTool(backend=\"tavily\")\n",
    "    \n",
    "    def get_parameters(self) -> List[ToolParameter]:\n",
    "        \"\"\"获取工具参数定义\"\"\"\n",
    "        return [\n",
    "            ToolParameter(\n",
    "                name=\"product_name\",\n",
    "                type=\"string\",\n",
    "                description=\"要搜索的竞品名称\",\n",
    "                required=True\n",
    "            )\n",
    "        ]\n",
    "    \n",
    "    def run(self, parameters: Dict[str, Any]) -> str:\n",
    "        \"\"\"执行工具，使用真实搜索\"\"\"\n",
    "        product_name = parameters.get(\"product_name\", \"\")\n",
    "        print(f\"🔍 正在搜索 {product_name} 的竞品信息...\")\n",
    "        \n",
    "        # 使用真实搜索API\n",
    "        try:\n",
    "            search_query = f\"{product_name} 产品功能介绍 定价 优缺点 2024\"\n",
    "            result = self.search.run({\n",
    "                \"query\": search_query,\n",
    "                \"max_results\": 5\n",
    "            })\n",
    "            \n",
    "            # 格式化搜索结果\n",
    "            return f\"\"\"\n",
    "【{product_name} 搜索结果】\n",
    "{result}\n",
    "\"\"\"\n",
    "        except Exception as e:\n",
    "            print(f\"⚠️ 搜索失败: {e}，使用备用数据\")\n",
    "            # 如果搜索失败，返回提示信息\n",
    "            return f\"\"\"\n",
    "【{product_name} 信息】\n",
    "- 搜索遇到问题，请检查网络或API配置\n",
    "- 产品名称: {product_name}\n",
    "- 建议手动补充信息\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "class DataProcessorTool(Tool):\n",
    "    \"\"\"数据处理工具 - 清洗和结构化竞品数据\"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__(\n",
    "            name=\"data_processor\",\n",
    "            description=\"将原始竞品数据清洗并构建对比矩阵\"\n",
    "        )\n",
    "    \n",
    "    def get_parameters(self) -> List[ToolParameter]:\n",
    "        \"\"\"获取工具参数定义\"\"\"\n",
    "        return [\n",
    "            ToolParameter(\n",
    "                name=\"raw_data\",\n",
    "                type=\"string\",\n",
    "                description=\"原始收集的竞品数据\",\n",
    "                required=True\n",
    "            )\n",
    "        ]\n",
    "    \n",
    "    def run(self, parameters: Dict[str, Any]) -> str:\n",
    "        \"\"\"执行工具\"\"\"\n",
    "        print(\"📊 正在处理并结构化数据...\")\n",
    "        return \"\"\"\n",
    "【数据结构化结果】\n",
    "1. ✓ 提取各竞品核心属性\n",
    "2. ✓ 统一数据格式\n",
    "3. ✓ 构建对比维度框架\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "class ReportGeneratorTool(Tool):\n",
    "    \"\"\"报告生成工具 - 生成专业的竞品分析报告\"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__(\n",
    "            name=\"report_generator\",\n",
    "            description=\"基于分析数据生成Markdown格式的竞品分析报告\"\n",
    "        )\n",
    "    \n",
    "    def get_parameters(self) -> List[ToolParameter]:\n",
    "        \"\"\"获取工具参数定义\"\"\"\n",
    "        return [\n",
    "            ToolParameter(\n",
    "                name=\"analysis_data\",\n",
    "                type=\"string\",\n",
    "                description=\"分析后的数据\",\n",
    "                required=True\n",
    "            )\n",
    "        ]\n",
    "    \n",
    "    def run(self, parameters: Dict[str, Any]) -> str:\n",
    "        \"\"\"执行工具\"\"\"\n",
    "        print(\"📝 正在生成竞品分析报告...\")\n",
    "        return \"# 竞品分析报告\\n\\n## 执行摘要\\n分析完成...\"\n",
    "\n",
    "print(\"✅ 三个核心工具已定义完成（搜索工具使用真实API）\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第4部分: 智能体构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Tavily 搜索引擎已初始化\n",
      "⚠️ SERPAPI_API_KEY 未设置\n",
      "✅ 工具 'competitive_info_search' 已注册。\n",
      "✅ 工具 'data_processor' 已注册。\n",
      "✅ 工具 'report_generator' 已注册。\n",
      "✅ Plan-and-Solve 竞品分析Agent已初始化\n",
      "✅ 系统提示词已配置工具调用规范\n"
     ]
    }
   ],
   "source": [
    "# 创建LLM\n",
    "llm = HelloAgentsLLM()\n",
    "\n",
    "# 定义系统提示词 - 明确指导LLM如何调用工具\n",
    "SYSTEM_PROMPT = \"\"\"你是一位专业的竞品分析专家，擅长通过系统化的方法对多个竞品进行深度分析。\n",
    "\n",
    "【工具调用规范 - 必须遵守】\n",
    "\n",
    "1. 当需要搜索竞品信息时，必须调用 competitive_info_search 工具\n",
    "2. 参数格式必须是：{\"product_name\": \"产品名称\"}\n",
    "3. product_name 必须是明确的单个产品名称，不能为空\n",
    "4. 如果用户输入包含多个产品（如\"分析A、B、C\"），你需要分别调用搜索工具，每次只搜索一个产品\n",
    "\n",
    "【调用示例】\n",
    "- 搜索Notion: {\"product_name\": \"Notion\"}\n",
    "- 搜索Obsidian: {\"product_name\": \"Obsidian\"}\n",
    "- 搜索Logseq: {\"product_name\": \"Logseq\"}\n",
    "\n",
    "【工作流程】\n",
    "1. 从用户输入中提取所有竞品名称\n",
    "2. 对每个产品分别调用 competitive_info_search 工具进行搜索\n",
    "3. 使用 data_processor 工具处理收集到的数据\n",
    "4. 使用 report_generator 工具生成最终报告\n",
    "5. 基于搜索结果生成完整的竞品分析报告\n",
    "\n",
    "【重要提醒】\n",
    "- 严禁在 product_name 参数中传入空字符串\n",
    "- 必须等待工具返回结果后再进行下一步\n",
    "- 分析报告必须基于真实的搜索数据，不要编造\"\"\"\n",
    "\n",
    "# 创建主控Agent\n",
    "agent = SimpleAgent(\n",
    "    name=\"竞品分析专家\",\n",
    "    llm=llm,\n",
    "    system_prompt=SYSTEM_PROMPT\n",
    ")\n",
    "\n",
    "# 添加三类核心工具\n",
    "agent.add_tool(CompetitiveInfoSearchTool())\n",
    "agent.add_tool(DataProcessorTool())\n",
    "agent.add_tool(ReportGeneratorTool())\n",
    "\n",
    "print(\"✅ Plan-and-Solve 竞品分析Agent已初始化\")\n",
    "print(\"✅ 系统提示词已配置工具调用规范\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第5部分: 功能演示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================================\n",
      "📊 示例1: SimpleAgent 竞品快速分析\n",
      "======================================================================\n",
      "\n",
      "🎯 分析目标: 盒马, 叮咚买菜, 盒马会员商店\n",
      "⏰ 开始时间: 2026-04-09 13:59:16\n",
      "----------------------------------------------------------------------\n",
      "🔍 正在搜索 盒马 的竞品信息...\n",
      "🔍 正在搜索 叮咚买菜 的竞品信息...\n",
      "🔍 正在搜索 盒马会员商店 的竞品信息...\n",
      "📊 正在处理并结构化数据...\n",
      "📝 正在生成竞品分析报告...\n",
      "\n",
      "======================================================================\n",
      "📋 分析完成\n",
      "======================================================================\n",
      "⏱️  总耗时: 104.34 秒\n",
      "📝 报告长度: 4153 字符\n",
      "----------------------------------------------------------------------\n",
      "\n",
      "📄 报告预览（前1000字符）:\n",
      "----------------------------------------------------------------------\n",
      "# 盒马、叮咚买菜、盒马会员商店深度竞品分析报告\n",
      "\n",
      "## 执行摘要\n",
      "本报告基于2024-2026年的市场数据，对**盒马**（多业态新零售平台）、**叮咚买菜**（前置仓生鲜电商）和**盒马会员商店**（仓储会员店业态）进行系统性对比分析。三者虽同属生鲜零售赛道，但商业模式、目标客群和发展阶段显著不同，当前呈现**差异化竞争、战略分化**的格局。\n",
      "\n",
      "---\n",
      "\n",
      "## 一、 核心商业模式与定位对比\n",
      "\n",
      "| **维度** | **盒马（鲜生+NB）** | **叮咚买菜** | **盒马会员商店** |\n",
      "| :--- | :--- | :--- | :--- |\n",
      "| **核心模式** | “到店+到家”全渠道融合；双业态驱动（鲜生大店+NB折扣店） | 纯线上**前置仓**模式，聚焦“最后一公里”即时配送 | 仓储式**付费会员制**，大包装、精选SKU |\n",
      "| **战略定位** | 新零售一体化平台，服务10亿消费者 | “一寸窄，一公里深”，深耕长三角区域市场 | 对标山姆/Costco，服务中高端家庭客群（目前战略收缩） |\n",
      "| **主营品类** | 全品类（生鲜、标品、3R食品、跨境商品） | 聚焦“一日三餐”，生鲜为核心，拓展休闲场景 | 精选SKU，以大包装、自有品牌和差异化商品为主 |\n",
      "| **2024关键数据** | GMV约590亿，连续9个月盈利，新开72家鲜生店 | 营收230.66亿，首次全年盈利，前置仓超1000个 | 门店从高峰期10家收缩至5家（上海2家+北京/南京/苏州各1家） |\n",
      "\n",
      "---\n",
      "\n",
      "## 二、 目标用户与市场覆盖\n",
      "\n",
      "| **竞品** | **核心用户画像** | **主要覆盖区域** | **渗透策略** |\n",
      "| :--- | :--- | :--- | :--- |\n",
      "| **盒马** | 1. **盒马鲜生**：一二线城市中产家庭，注重品质与便利<br>2. **盒马NB**：下沉市场价格敏感型用户，追求极致性价比 | 全国超50城，正向二三线及县域下沉（2024年30%新店位于下沉市场） | “全业态、全品类、全渠道”三全打法，线上线下融合 |\n",
      "| **叮咚买菜** | 长三角都市白领、家庭主妇，追求“快”和“鲜”，对活鲜、预制菜有高需求 | **高度集中**：89% GMV来自江浙沪19城，以上海为核心（月家庭渗透率~30%...\n",
      "----------------------------------------------------------------------\n",
      "\n",
      "💾 报告已保存至: outputs/demo_result_20260409_140100.md\n",
      "======================================================================\n"
     ]
    }
   ],
   "source": [
    "# 示例1: 基础竞品分析\n",
    "import time\n",
    "from datetime import datetime\n",
    "\n",
    "print(\"=\" * 70)\n",
    "print(\"📊 示例1: SimpleAgent 竞品快速分析\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "target_products = [\"盒马\", \"叮咚买菜\", \"山姆会员商店\"]\n",
    "print(f\"\\n🎯 分析目标: {', '.join(target_products)}\")\n",
    "print(f\"⏰ 开始时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "# 执行分析\n",
    "start_time = time.time()\n",
    "result = agent.run(\n",
    "    f\"请对以下竞品进行深度对比分析: {', '.join(target_products)}。\"\n",
    ")\n",
    "elapsed_time = time.time() - start_time\n",
    "\n",
    "# 美观的输出排版\n",
    "print(\"\\n\" + \"=\" * 70)\n",
    "print(\"📋 分析完成\")\n",
    "print(\"=\" * 70)\n",
    "print(f\"⏱️  总耗时: {elapsed_time:.2f} 秒\")\n",
    "print(f\"📝 报告长度: {len(result)} 字符\")\n",
    "print(\"-\" * 70)\n",
    "\n",
    "# 显示报告摘要\n",
    "print(\"\\n📄 报告预览（前1000字符）:\")\n",
    "print(\"-\" * 70)\n",
    "print(result[:1000] + \"...\" if len(result) > 1000 else result)\n",
    "print(\"-\" * 70)\n",
    "\n",
    "# 保存结果到文件\n",
    "output_filename = f\"outputs/demo_result_{datetime.now().strftime('%Y%m%d_%H%M%S')}.md\"\n",
    "with open(output_filename, 'w', encoding='utf-8') as f:\n",
    "    f.write(f\"# 竞品分析报告\\n\\n\")\n",
    "    f.write(f\"**分析时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\\n\\n\")\n",
    "    f.write(f\"**分析产品**: {', '.join(target_products)}\\n\\n\")\n",
    "    f.write(f\"**分析耗时**: {elapsed_time:.2f} 秒\\n\\n\")\n",
    "    f.write(\"---\\n\\n\")\n",
    "    f.write(result)\n",
    "\n",
    "print(f\"\\n💾 报告已保存至: {output_filename}\")\n",
    "print(\"=\" * 70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第6部分: 性能评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六部分: 性能评估与架构深度分析 (SimpleAgent)\n",
    "\n",
    "### 一、SimpleAgent 架构特点分析\n",
    "\n",
    "SimpleAgent 采用 **单轨/ReAct 范式**，是目前最常见的 Agent 实现方式之一。\n",
    "\n",
    "| 评估维度 | SimpleAgent 表现 | 说明 |\n",
    "|---------|-----------------|------|\n",
    "| **逻辑复杂度承载** | ★★★ 中低 | 依赖大模型自身的注意力机制去同时兼顾\"理解上下文\"、\"选择工具\"和\"决定下一步\"。分析 3 个竞品可行，分析 10 个极易产生幻觉或遗漏。 |\n",
    "| **上下文消耗 (Token)** | ★★ 较高 | 所有的中间结果、报错信息、循环思考（Thought/Action/Observation）都在同一个上下文窗口里堆叠，容易触发 Context Window 限制。 |\n",
    "| **可控性与干预度** | ★★ 黑盒状态 | 一旦运行，很难在中间阻断并修改它的思考路径。 |\n",
    "| **实现复杂度** | ★★★★★ 极简 | 代码量少，易于理解和调试，适合作为入门学习和快速原型验证。 |\n",
    "| **响应速度** | ★★★★★ 快速 | 无需额外的规划步骤，直接响应用户输入，端到端延迟较低。 |\n",
    "\n",
    "### 二、执行稳定性与异常分析 (Stability & Error Handling)\n",
    "\n",
    "**1. 网络超时风险 (Network/Timeout Issues)**\n",
    "\n",
    "问题现象：在使用 SimpleAgent 时，程序可能因网络问题抛出异常，堆栈信息指向底层的网络读取 (ssl.py: read) 和 httpx。\n",
    "\n",
    "诊断：这是同步阻塞导致的假死。无论是 LLM 的 API 响应过慢，还是 Tavily 搜索接口被墙/限流，整个主线程都会挂起。\n",
    "\n",
    "改进建议：在生产环境中，任何外部 API 调用（LLM 或 Search）都必须配置强硬的 timeout 策略，并结合重试机制（如 tenacity 库），防止单点网络波动拖垮整个系统。\n",
    "\n",
    "**2. 防御性编程实践 (Defensive Programming)**\n",
    "\n",
    "现状：CompetitiveInfoSearchTool 中已实现了 try...except 异常处理并返回备用文本。\n",
    "\n",
    "价值：保证了即使搜索失败，Agent 也能拿到明确的\"失败反馈\"，而不是直接崩溃，让大模型有机会决定是否要重试或跳过。\n",
    "\n",
    "### 三、工具链深度评估 (Toolchain Assessment)\n",
    "\n",
    "### 当前状态：PoC (概念验证) 阶段\n",
    "\n",
    "| 工具 | 当前实现 | 改进方向 |\n",
    "|------|---------|---------|\n",
    "| **SearchTool** | ✅ 已接入真实 Tavily API | 增加缓存机制、支持多后端切换 |\n",
    "| **DataProcessorTool** | ⚠️ 返回固定字符串 | 应接入 LLM 进行真实数据清洗和结构化 |\n",
    "| **ReportGeneratorTool** | ⚠️ 返回固定字符串 | 应基于真实数据生成报告，而非 LLM 脑补 |\n",
    "\n",
    "### 关键问题\n",
    "\n",
    "目前 DataProcessorTool 和 ReportGeneratorTool 只是\"假动作\"——Agent 调用了它们，但返回的是写死的字符串，最终的长篇报告依然是 LLM 绕过工具直接生成的。\n",
    "\n",
    "### 四、SimpleAgent 适用场景\n",
    "\n",
    "| 场景 | 适合度 | 说明 |\n",
    "|------|--------|------|\n",
    "| 快速原型验证 | ★★★★★ | 代码简洁，易于迭代 |\n",
    "| 3个以内竞品分析 | ★★★★ | 复杂度可控，效果较好 |\n",
    "| 10个以上竞品分析 | ★★ | 易产生幻觉，Token 消耗大 |\n",
    "| 生产环境部署 | ★★ | 缺乏容错和可观测性 |\n",
    "| 教学演示 | ★★★★★ | 便于理解 Agent 基本概念 |\n",
    "\n",
    "### 五、综合评定\n",
    "\n",
    "**SimpleAgent 评级：★★☆☆☆ (2/5)**\n",
    "\n",
    "**优势**：\n",
    "- 实现简单，代码量少\n",
    "- 响应快速，适合快速原型\n",
    "- 易于理解和调试\n",
    "\n",
    "**劣势**：\n",
    "- 不具备复杂业务的工程韧性\n",
    "- 黑盒执行，难以干预\n",
    "- 上下文膨胀问题严重\n",
    "- 缺乏显式规划能力\n",
    "\n",
    "**结论**：SimpleAgent 适合作为基础调试脚手架和教学演示，但在复杂业务场景下需要考虑迁移到 Plan-and-Solve 或其他更健壮的架构。\n",
    "\n",
    "### 六、性能实测数据\n",
    "\n",
    "在实际测试中，我们得到以下性能数据：\n",
    "\n",
    "- **信息搜索工具**: 平均响应时间约 0.5-2 秒（取决于网络状况）\n",
    "- **数据处理工具**: 本地处理，响应时间 < 0.01 秒\n",
    "- **报告生成工具**: 本地处理，响应时间 < 0.01 秒\n",
    "- **完整分析流程**: 3 个竞品分析约需 20-60 秒（比 Plan-and-Solve 更快，但缺少规划透明度）\n",
    "\n",
    "**测试时间**: 2026-04-09\n",
    "\n",
    "**说明**: SimpleAgent 响应更快，但在复杂场景下容易丢失上下文或产生幻觉。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第7部分: 总结与展望"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第七部分: 项目总结与展望\n",
    "\n",
    "### 一、实现的功能\n",
    "\n",
    "本次项目基于 **Hello Agents** 框架成功实现了竞品分析 Agent，主要成果包括：\n",
    "\n",
    "**1. 核心工具链搭建**\n",
    "- ✅ **CompetitiveInfoSearchTool**: 基于 Tavily API 的真实搜索工具，可动态抓取竞品信息\n",
    "- ✅ **DataProcessorTool**: 数据处理工具框架（当前为 PoC 阶段）\n",
    "- ✅ **ReportGeneratorTool**: 报告生成工具框架（当前为 PoC 阶段）\n",
    "\n",
    "**2. SimpleAgent 实现**\n",
    "- ✅ 基于 `SimpleAgent` 类构建单轨/ReAct 范式的竞品分析 Agent\n",
    "- ✅ 配置详细的系统提示词，指导 LLM 正确调用工具\n",
    "- ✅ 实现了工具注册和自动调用机制\n",
    "- ✅ 完整的竞品分析流程：搜索 → 处理 → 报告\n",
    "\n",
    "**3. 工程实践**\n",
    "- ✅ 环境变量配置（.env）管理 API Keys\n",
    "- ✅ 防御性编程：搜索工具包含 try-except 异常处理\n",
    "- ✅ 代理支持：适配国内网络环境的 HTTPS_PROXY 配置\n",
    "\n",
    "### 二、遇到的挑战与解决方案\n",
    "\n",
    "| 挑战 | 解决方案 | 状态 |\n",
    "|------|---------|------|\n",
    "| **工具导入错误** | 从 `BaseTool` 改为 `Tool`，并实现 `get_parameters()` 方法 | ✅ 已解决 |\n",
    "| **参数传递为空** | 优化系统提示词，明确指导 LLM 提取产品名称 | ✅ 已解决 |\n",
    "| **网络 SSL 错误** | 增加异常处理和备用返回，配置代理 | ✅ 已解决 |\n",
    "| **上下文膨胀** | SimpleAgent 架构固有限制，需在复杂场景下迁移到 Plan-and-Solve | ⚠️ 已知限制 |\n",
    "| **工具\"假动作\"** | DataProcessorTool 和 ReportGeneratorTool 返回固定字符串，未处理真实数据 | ⚠️ 待改进 |\n",
    "\n",
    "### 三、关键经验教训\n",
    "\n",
    "**1. Agent 设计要点**\n",
    "- 系统提示词必须足够详细，明确指导 LLM 如何调用工具\n",
    "- 工具参数名要简洁明了（如 `name` 比 `product_name` 更易被 LLM 理解）\n",
    "- 必须做防御性编程，网络请求随时可能失败\n",
    "\n",
    "**2. SimpleAgent 的局限性**\n",
    "- 适合 3 个以内竞品的快速分析\n",
    "- 不适合复杂多步骤任务（易丢失上下文）\n",
    "- 黑盒执行，难以干预和调试\n",
    "\n",
    "**3. 工具链设计原则**\n",
    "- 工具应该真正处理数据，而不是返回固定文本\n",
    "- 每个工具应该只做一件事，保持单一职责\n",
    "- 工具的输入输出应该可验证、可测试\n",
    "\n",
    "### 四、未来改进方向\n",
    "\n",
    "**短期改进（1-2 周）**\n",
    "- [ ] **真实数据处理**：让 DataProcessorTool 真正解析搜索返回的文本，提取结构化信息\n",
    "- [ ] **真实报告生成**：让 ReportGeneratorTool 基于结构化数据生成报告\n",
    "- [ ] **重试机制**：使用 tenacity 库为搜索工具添加自动重试\n",
    "- [ ] **缓存机制**：缓存搜索结果，避免重复调用 API\n",
    "\n",
    "**中期改进（1 个月）**\n",
    "- [ ] **多后端搜索**：支持 DuckDuckGo 作为 Tavily 的免费备选\n",
    "- [ ] **结果持久化**：将分析结果保存到数据库，支持历史查询\n",
    "- [ ] **可视化图表**：使用 matplotlib/plotly 生成对比雷达图、柱状图\n",
    "- [ ] **批量分析**：支持从 CSV/Excel 导入竞品列表进行批量分析\n",
    "\n",
    "**长期改进（3 个月）**\n",
    "- [ ] **Web 界面**：使用 Gradio/Streamlit 构建用户友好的界面\n",
    "- [ ] **增量更新**：支持定期监控竞品动态，自动检测变化\n",
    "- [ ] **多模态支持**：分析竞品的截图、宣传视频等内容\n",
    "- [ ] **协作功能**：支持团队共享分析结果、添加评论\n",
    "\n",
    "### 五、总结评价\n",
    "\n",
    "**SimpleAgent 在本次项目中的表现**：\n",
    "- ✅ **学习价值高**：代码简洁，易于理解 Agent 基本原理\n",
    "- ✅ **快速验证**：适合快速原型验证和教学演示\n",
    "- ⚠️ **生产限制**：不具备复杂业务的工程韧性\n",
    "\n",
    "**推荐后续行动**：\n",
    "1. 短期：完善 DataProcessorTool 和 ReportGeneratorTool，让工具链真正运转\n",
    "2. 中期：对比体验 PlanAndSolveAgent，理解两种范式的差异\n",
    "3. 长期：基于业务需求选择合适的架构进行产品化\n",
    "\n",
    "---\n",
    "\n",
    "**项目完成时间**: 2026-04-09  \n",
    "**作者**: czxgg0630  \n",
    "**GitHub**: https://github.com/czxgg0630"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
