{ "cells": [ { "cell_type": "markdown", "id": "38b007c8", "metadata": {}, "source": [ "\n", "## GiftGeniusAgent——你的送礼智能Agent\n", "\n", "### 项目简介\n", "本项目演示一个基于HelloAgents框架的智能送礼Agent\n", "\n", "### 作者信息\n", "- 姓名：张善祺\n", "- GitHub:@jack6249\n", "- 日期：2025-11-21\n" ] }, { "cell_type": "markdown", "id": "62a3b6a3", "metadata": {}, "source": [ "### 第1部分：环境配置" ] }, { "cell_type": "code", "execution_count": null, "id": "dd9b8a20", "metadata": {}, "outputs": [], "source": [ "#导入库和参数配置\n", "from hello_agents import SimpleAgent, HelloAgentsLLM, ReflectionAgent, ToolRegistry\n", "from hello_agents.tools import Tool, ToolParameter\n", "from typing import Dict, Any, List\n", "from tavily import TavilyClient\n", "import os\n", "import json\n", "import re\n", "import numpy as np \n", "from dotenv import load_dotenv\n", "import asyncio\n", "import nest_asyncio\n", "from mcp.client.sse import sse_client\n", "from mcp.client.session import ClientSession\n", "\n", "load_dotenv()\n", "\n", "#LLM参数\n", "LLM_MODEL_ID = os.getenv(\"LLM_MODEL_ID\")\n", "LLM_API_KEY = os.getenv(\"LLM_API_KEY\")\n", "LLM_BASE_URL = os.getenv(\"LLM_BASE_URL\")\n", "#Tavily参数\n", "TAVILY_API_KEY = os.getenv(\"TAVILY_API_KEY\",\"\")\n", "#百度MCP参数\n", "BAIDU_TOKEN = os.getenv(\"BAIDU_MCP_TOKEN\",\"\")\n", "#输入json路径配置\n", "INPUT_FILENAME = \"data/test_cases.json\"\n", "\n", "# 搜索源配置\n", "# 可选值: \"tavily\" (通用/海外) 或 \"baidu\" (电商/国内)\n", "os.environ[\"SEARCH_PROVIDER\"] = \"baidu\" \n", "\n", "print(\"✅ 环境配置完成\")\n" ] }, { "cell_type": "markdown", "id": "9404c299", "metadata": {}, "source": [ "### 第2部分：定义工具" ] }, { "cell_type": "code", "execution_count": null, "id": "37b994bc", "metadata": {}, "outputs": [], "source": [ "# [Cell 2 终极版] 定义统一搜索工具 (兼容 Tavily 和 Baidu)\n", "# 允许 Jupyter 运行异步\n", "nest_asyncio.apply()\n", "\n", "class BatchSearchTool(Tool):\n", " def __init__(self):\n", " super().__init__(\n", " name=\"batch_search\",\n", " description=\"统一搜索工具，支持 Tavily 和 Baidu 切换。\"\n", " )\n", " self.provider = os.environ.get(\"SEARCH_PROVIDER\", \"tavily\").lower()\n", "\n", " def run(self, parameters: Any) -> str:\n", " return \"请使用 Python 代码直接调用 search_raw 方法获取数据。\"\n", "\n", " def search_raw(self, query: str) -> List[Dict]:\n", " if self.provider == \"baidu\":\n", " return self._search_baidu(query)\n", " else:\n", " return self._search_tavily(query)\n", "\n", " # --- 引擎 A: Tavily ---\n", " def _search_tavily(self, query: str) -> List[Dict]:\n", " api_key = os.environ.get(\"TAVILY_API_KEY\")\n", " if not api_key: return []\n", " print(f\" 🚀 [Tavily] 正在搜索: {query} ...\")\n", " try:\n", " tavily = TavilyClient(api_key=api_key)\n", " response = tavily.search(query, max_results=5, include_images=True)\n", " results = []\n", " if 'results' in response:\n", " for r in response['results']:\n", " results.append({\n", " \"title\": r['title'], \"url\": r['url'], \"content\": r['content'], \n", " \"type\": \"text\", \"img\": \"\" # Tavily 文本通常不带图\n", " })\n", " if 'images' in response and response['images']:\n", " results.append({\"images\": response['images'][:3], \"type\": \"image\"})\n", " return results\n", " except Exception as e:\n", " print(f\" ⚠️ Tavily 异常: {e}\")\n", " return []\n", "\n", " # --- 引擎 B: Baidu MCP ---\n", " def _search_baidu(self, query: str) -> List[Dict]:\n", " token = os.environ.get(\"BAIDU_MCP_TOKEN\")\n", " if not token: return []\n", " print(f\" 🐼 [百度优选] 正在搜索: {query} ...\")\n", " try:\n", " raw_json_str = asyncio.run(self._async_baidu_call(query, token))\n", " print(f\" 🔍 原始 JSON 响应: {raw_json_str}\")\n", " return self._parse_baidu_response(raw_json_str)\n", " except Exception as e:\n", " print(f\" ⚠️ 百度 MCP 异常: {e}\")\n", " return []\n", "\n", " async def _async_baidu_call(self, query: str, token: str) -> str:\n", " sse_url = f\"https://mcp-youxuan.baidu.com/mcp/sse?key={token}\"\n", " async with sse_client(sse_url) as (read, write):\n", " async with ClientSession(read, write) as session:\n", " await session.initialize()\n", " result = await session.call_tool(\"goods_search\", arguments={\"query\": query})\n", " return result.content[0].text if result.content else \"\"\n", "\n", " def _parse_baidu_response(self, json_str: str) -> List[Dict]:\n", " results = []; images = []\n", " try:\n", " data = json.loads(json_str)\n", " items = data if isinstance(data, list) else []\n", " \n", " for item in items[:5]:\n", " title = item.get(\"goodsName\") or item.get(\"title\") or \"未知商品\"\n", " price = item.get(\"price\") or item.get(\"minPrice\") or \"\"\n", " shop = item.get(\"shopName\") or item.get(\"mall\") or \"\"\n", " url = item.get(\"detailUrl\") or item.get(\"url\") or item.get(\"ori_url\") or \"#\"\n", " img = item.get(\"imgUrl\") or item.get(\"picUrl\") or item.get(\"img\")\n", " \n", " content = f\"价格: {price}元。店铺: {shop}。商品详情: {title}\"\n", " \n", " # 📝【修复点】直接在 text 类型结果里绑定 img\n", " results.append({\n", " \"title\": title, \"url\": url, \"content\": content, \n", " \"type\": \"text\", \"img\": img \n", " })\n", " if img: images.append(img)\n", " \n", " if images: results.append({\"images\": images[:3], \"type\": \"image\"})\n", " \n", " except json.JSONDecodeError:\n", " print(\" ⚠️ 百度返回非 JSON 数据\")\n", " return results\n", "\n", " def get_parameters(self):\n", " return [ToolParameter(name=\"query\", type=\"string\", description=\"关键词\")]\n", "\n", "tool_registry = ToolRegistry()\n", "tool_registry.register_tool(BatchSearchTool())\n", "\n", "print(\"✅ 统一搜索工具已加载！\")\n", "print(f\"当前模式: {'百度优选 (电商)' if os.environ.get('SEARCH_PROVIDER') == 'baidu' else 'Tavily (通用)'}\")" ] }, { "cell_type": "markdown", "id": "e8ffecb0", "metadata": {}, "source": [ "### 第3部分：创建智能体\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6c5a2e82", "metadata": {}, "outputs": [], "source": [ "# 初始化大模型\n", "llm = HelloAgentsLLM()\n", "\n", "# --- 1. 军师 (Profiler) - 已升级支持多维度画像 ---\n", "PROFILER_PROMPT = \"\"\"\n", "你是一个精通 MBTI 人格分析与消费市场趋势的 \"送礼军师\"。\n", "你的任务是根据用户提供的多维度画像，制定 3 个**极度精准**的搜索关键词。\n", "\n", "【⚠️ 时效性死命令 (CRITICAL)】\n", "当前时间视作 **2025年11月**。\n", "1. **严禁过时**：绝对不要推荐 2024 年或更早的旧款（除非是经典恒久款如黑胶唱片）。\n", "2. **价格尺度**：给出的价格单位是人民币元。请严格遵照范围进行联想，禁止超出预算范围。\n", "\n", "为了确保推荐质量，请参考以下的【优秀思考范例】：\n", "\n", "### 范例 1\n", "**用户画像**: \n", "- 女, 26岁, ISFP (探险家), 金牛座\n", "- 预算: 500-1000元\n", "- 场景: 情人节\n", "- 自定义: 喜欢有质感的生活小物\n", "**军师分析**: \n", "ISFP 重视审美和感官体验，金牛座喜欢实实在在的质感。情人节需要浪漫。\n", "**生成策略**:\n", "1. 观夏 (To Summer) 昆仑煮雪 晶石香薰 (符合质感与审美)\n", "2. 野兽派 2025 情人节限定 睡衣礼盒 (金牛座喜欢的舒适)\n", "3. 富士 Instax mini Evo 拍立得 (记录生活瞬间)\n", "\n", "### 范例 2\n", "**用户画像**: \n", "- 男, 30岁, INTJ (建筑师), 处女座\n", "- 预算: 1000元以上\n", "- 场景: 生日\n", "- 自定义: 程序员，喜欢整洁\n", "**军师分析**: \n", "INTJ 追求极致的逻辑和效率，处女座有洁癖，喜欢桌面整洁。\n", "**生成策略**:\n", "1. Keychron Q1 Pro 机械键盘 铝坨坨 (符合极客对工具的追求)\n", "2. 明基 (BenQ) ScreenBar Halo 屏幕挂灯 (极致护眼与桌面美学)\n", "3. 赫曼米勒 (Herman Miller) 显示器支架 (人体工学)\n", "\n", "### 范例 3\n", "**用户画像**: \n", "- 女, 20岁, ENFP (竞选者), 狮子座\n", "- 预算: 300元以内\n", "- 场景: 圣诞节\n", "- 自定义: 喜欢二次元，痛包\n", "**军师分析**: \n", "ENFP 热情奔放，狮子座喜欢张扬、闪亮的东西。预算有限但要素多。\n", "**生成策略**:\n", "1. 泡泡玛特 圣诞系列 盲盒整端 (符合节日气氛和二次元)\n", "2. WEGO 痛包 镭射款 (符合自定义需求，狮子座喜欢的亮眼)\n", "3. Chiikawa 吉伊卡哇 圣诞公仔 (当下顶流二次元IP)\n", "\n", "---\n", "\n", "**现在的任务**：\n", "请根据以下【当前用户画像】进行分析，模仿上述范例的深度，制定搜索策略。\n", "\n", "【当前用户画像】\n", "{user_profile_text}\n", "\n", "【关键词生成要求】\n", "1. **必须具体**：格式为 `[品牌] + [产品名/系列] + [限定/属性]`。\n", "2. **拒绝大词**：严禁搜索 \"礼物\"、\"口红\"、\"玩具\" 这种泛词。\n", "3. **必须包含品牌**：根据预算推断合适的品牌（如：预算低选名创优品/泡泡玛特，预算高选Dior/索尼）。\n", "\n", "【输出格式】\n", "只输出 3 行搜索关键词，每行一个。不要输出分析过程，不要序号。\n", "\"\"\"\n", "\n", "profiler_agent = SimpleAgent(\n", " llm=llm,\n", " name=\"Agent_Profiler\",\n", " system_prompt=PROFILER_PROMPT\n", ")\n", "\n", "# ==============================================================================\n", "# 2. 种草达人 (Pitcher) - 文案创作 (加入风格指导)\n", "# ==============================================================================\n", "PITCHER_PROMPT = \"\"\"\n", "你是一个 **金牌种草文案**。\n", "用户会给你一个 **【商品名称】**。\n", "\n", "### 🎯 关键要点 (Few Points)\n", "1. **痛点直击**: 一句话说清楚为什么买它（限定？显白？绝美？）。\n", "2. **情绪价值**: 使用 \"绝绝子\", \"氛围感\", \"心动\" 等高频热词。\n", "3. **字数限制**: 严格控制在 **40字以内**，短小精悍。\n", "4. **Emoji**: 必须包含 1-2 个 emoji。\n", "\n", "### 🌟 创作范例 (Few-Shot)\n", "**输入**: Dior 999 烈艳蓝金\n", "**输出**: 💄本宫不死终是妃！Dior 999 传奇正红，显白更有气场，送女友绝对没错！\n", "\n", "**输入**: 泡泡玛特 Labubu 坐坐派对\n", "**输出**: ✨太可爱了吧！Labubu 坐坐派对系列，每一个都丑萌到心巴上，摆在桌上超治愈~\n", "\n", "**输入**: 罗技 MX Master 3S\n", "**输出**: 🖱️打工人本命！罗技 Master 3S 静音又顺滑，人体工学设计，手腕再也不累了。\n", "\n", "---\n", "\n", "**当前任务**：\n", "请为【{input}】写一句朋友圈风格种草语。\n", "\"\"\"\n", "pitcher_agent = SimpleAgent(\n", " llm=llm,\n", " name=\"Agent_Pitcher\",\n", " system_prompt=PITCHER_PROMPT\n", ")\n", "\n", "print(\"✅ 智能体初始化完成！\")" ] }, { "cell_type": "markdown", "id": "25907c14", "metadata": {}, "source": [ "### 第4部分:读取数据" ] }, { "cell_type": "code", "execution_count": null, "id": "58bd39dd", "metadata": {}, "outputs": [], "source": [ "def load_user_profile(filename):\n", " # 1. 检查文件是否存在\n", " if not os.path.exists(filename):\n", " print(f\"⚠️ 未找到配置文件: {filename}\")\n", " # 如果没有文件，将默认数据写入文件\n", " default_data = {\n", " \"性别\": \"女\",\n", " \"年龄\": \"24岁\",\n", " \"MBTI\": \"ENFP\",\n", " \"星座\": \"天秤座\",\n", " \"预算\": \"500元以内\",\n", " \"节日\": \"恋爱一周年纪念日\",\n", " \"自定义\": \"喜欢二次元，平时喜欢喝咖啡，不要送太实用的家电\"\n", " }\n", " with open(filename, \"w\", encoding=\"utf-8\") as f:\n", " json.dump(default_data, f, ensure_ascii=False, indent=4)\n", " print(f\"✅ 已自动生成默认配置文件，请修改 {filename} 后再次运行。\")\n", " return default_data\n", "\n", " # 2. 读取文件内容\n", " try:\n", " with open(filename, \"r\", encoding=\"utf-8\") as f:\n", " data = json.load(f)\n", " print(f\"✅ 成功加载用户画像: {filename}\")\n", " print(f\"📋 内容预览: {json.dumps(data, ensure_ascii=False)}\")\n", " return data\n", " except Exception as e:\n", " print(f\"❌ 读取 JSON 失败: {e}\")\n", " return {}\n", "\n", "# 加载数据\n", "user_input_data = load_user_profile(INPUT_FILENAME)\n" ] }, { "cell_type": "markdown", "id": "0fdcab5a", "metadata": {}, "source": [ "### 第5部分：生成礼物计划" ] }, { "cell_type": "code", "execution_count": null, "id": "cdaef6b1", "metadata": {}, "outputs": [], "source": [ "def parse_budget_range(budget_str):\n", " \"\"\"解析用户预算字符串，返回 (min, max)\"\"\"\n", " nums = [float(x) for x in re.findall(r'\\d+', str(budget_str).replace(',', ''))]\n", " if not nums: return 0, 999999 \n", " if \"以内\" in budget_str or \"以下\" in budget_str: return 0, nums[0]\n", " if \"以上\" in budget_str: return nums[0], 999999\n", " if len(nums) >= 2: return min(nums), max(nums)\n", " return 0, nums[0]\n", "\n", "def extract_all_prices(raw_results):\n", " \"\"\"从搜索结果列表中提取所有有效的价格\"\"\"\n", " prices = []\n", " for res in raw_results:\n", " # 只处理文本类型的结果\n", " if res.get('type') == 'text':\n", " text = res.get('title', '') + \" \" + res.get('content', '')\n", " # 匹配 ¥, $, 元 等格式\n", " matches = re.findall(r'(?:¥|￥|\\$|HK\\$|NT\\$)\\s*(\\d+(?:,\\d{3})*(?:\\.\\d+)?)', text)\n", " for m in matches:\n", " val = float(m.replace(',', ''))\n", " # 过滤掉像年份(2025)或过小/过大的异常值\n", " if 10 < val < 100000 and val not in [2024, 2025, 2026]:\n", " prices.append(val)\n", " # 备用正则：匹配 \"xxx元\"\n", " matches_yuan = re.findall(r'(\\d+(?:,\\d{3})*(?:\\.\\d+)?)\\s*元', text)\n", " for m in matches_yuan:\n", " val = float(m.replace(',', ''))\n", " if 10 < val < 100000 and val not in [2024, 2025, 2026]:\n", " prices.append(val)\n", " return prices\n", "\n", "\n", "def find_best_product(hunter, profiler_agent, keyword, budget_min, budget_max):\n", " limit_upper = budget_max * 1.2\n", " limit_lower = budget_min * 0.8\n", " \n", " all_candidates = []\n", " current_kw = keyword\n", " \n", " # --- Round 1: 首次搜索 ---\n", " print(f\" 🕵️ 第1次搜索: {current_kw} 价格\")\n", " results_1 = hunter.search_raw(f\"{current_kw} 价格\")\n", " \n", " fallback_img = \"\"\n", " for r in results_1:\n", " if r.get('images'): \n", " fallback_img = r['images'][0]\n", " break\n", "\n", " has_valid_info = False\n", " for res in results_1:\n", " if res.get('type') == 'text':\n", " has_valid_info = True\n", " p_vals = extract_all_prices([res])\n", " if p_vals:\n", " res['price_val'] = p_vals[0]\n", " # 📝【核心修复点1】记录当前结果所属的关键词\n", " res['source_kw'] = current_kw \n", " all_candidates.append(res)\n", " \n", " if limit_lower <= p_vals[0] <= limit_upper:\n", " if not res.get('img') and fallback_img: res['img'] = fallback_img\n", " return res, f\"约 {p_vals[0]}元\", current_kw\n", "\n", " # --- 机制 3: 无数据防御 ---\n", " if not has_valid_info:\n", " print(f\" ⚠️ [机制3触发] 首次搜索无有效信息。\")\n", " correction_prompt = f\"原策略 '{current_kw}' 搜索结果为空，请推荐一个同品类但更热门的具体商品型号。只输出关键词。\"\n", " new_kw = profiler_agent.run(correction_prompt).strip()\n", " print(f\" 🔄 军师换词: {new_kw}\")\n", " current_kw = new_kw\n", " \n", " results = hunter.search_raw(f\"{current_kw} 价格\")\n", " \n", " # 更新 fallback_img\n", " fallback_img = \"\" \n", " for r in results:\n", " if r.get('images'): \n", " fallback_img = r['images'][0]\n", " break\n", " \n", " for res in results:\n", " if res.get('type') == 'text':\n", " p_vals = extract_all_prices([res])\n", " if p_vals:\n", " res['price_val'] = p_vals[0]\n", " # 📝【核心修复点1】记录关键词\n", " res['source_kw'] = current_kw\n", " all_candidates.append(res)\n", "\n", " # --- 机制 1 & 2: 价格修正 ---\n", " avg_price = np.mean([c['price_val'] for c in all_candidates]) if all_candidates else 0\n", " \n", " if avg_price > 0:\n", " correction_prompt = \"\"\n", " if avg_price > limit_upper:\n", " print(f\" 💸 [机制1触发] 均价 {int(avg_price)} > 上限 {int(limit_upper)}，找平替...\")\n", " correction_prompt = f\"原策略 '{current_kw}' 均价约 {int(avg_price)}元，超预算 ({budget_max}元)。请推荐一个同品类更便宜的具体型号（平替）。只输出关键词。\"\n", " elif avg_price < limit_lower:\n", " print(f\" 📉 [机制2触发] 均价 {int(avg_price)} < 下限 {int(limit_lower)}，找升级款...\")\n", " correction_prompt = f\"原策略 '{current_kw}' 均价约 {int(avg_price)}元，低于预算下限 ({budget_min}元)。请推荐一个同品类更高端的型号。只输出关键词。\"\n", " \n", " if correction_prompt:\n", " new_kw = profiler_agent.run(correction_prompt).strip()\n", " print(f\" 🔄 军师修正: {new_kw}\")\n", " current_kw = new_kw\n", " \n", " results_2 = hunter.search_raw(f\"{new_kw} 价格\")\n", " \n", " # 更新 fallback_img\n", " fallback_img = \"\" \n", " for r in results_2:\n", " if r.get('images'): \n", " fallback_img = r['images'][0]\n", " break\n", " \n", " for res in results_2:\n", " if res.get('type') == 'text':\n", " p_vals = extract_all_prices([res])\n", " if p_vals:\n", " res['price_val'] = p_vals[0]\n", " # 📝【核心修复点1】记录关键词\n", " res['source_kw'] = current_kw\n", " all_candidates.append(res) \n", " \n", " if limit_lower <= p_vals[0] <= limit_upper:\n", " if not res.get('img') and fallback_img: res['img'] = fallback_img\n", " tag = \"(平替)\" if avg_price > limit_upper else \"(升级)\"\n", " return res, f\"约 {p_vals[0]}元 {tag}\", current_kw\n", "\n", " # --- 机制 4: 兜底防御 ---\n", " print(\" ⚠️ [机制4触发] 启用强制兜底模式...\")\n", " best_fallback = None\n", " status_msg = \"暂无报价\"\n", " \n", " if all_candidates:\n", " # 选离预算最近的\n", " target = (budget_min + budget_max) / 2\n", " best_fallback = sorted(all_candidates, key=lambda x: abs(x['price_val'] - target))[0]\n", " p = best_fallback['price_val']\n", " \n", " if p > limit_upper: status_msg = f\"约 {p}元 (⚠️超预算)\"\n", " elif p < limit_lower: status_msg = f\"约 {p}元 (📉低于预算)\"\n", " else: status_msg = f\"约 {p}元\"\n", " \n", " elif results_1:\n", " # 实在没数据，硬取第一条\n", " for res in results_1:\n", " if res.get('type') == 'text': \n", " best_fallback = res\n", " # 兜底时如果也没价格，就用原始关键词\n", " best_fallback['source_kw'] = keyword \n", " break\n", " \n", " if best_fallback:\n", " if not best_fallback.get('img') and fallback_img:\n", " best_fallback['img'] = fallback_img\n", " \n", " # 📝【核心修复点2】返回结果里记录的那个 source_kw，而不是当前的 current_kw\n", " final_name_to_use = best_fallback.get('source_kw', current_kw)\n", " \n", " return best_fallback, status_msg, final_name_to_use\n", " \n", " return None, \"搜索失败\", keyword" ] }, { "cell_type": "code", "execution_count": null, "id": "4ec82cc7", "metadata": {}, "outputs": [], "source": [ "if not user_input_data:\n", " print(\"❌ 未加载用户数据\")\n", "else:\n", " # 0. 解析预算\n", " b_min, b_max = parse_budget_range(user_input_data.get('预算', ''))\n", " print(f\"\\n💰 预算范围: {b_min} - {b_max}元\")\n", "\n", " # 1. 军师制定策略\n", " profile_text = \"\\n\".join([f\"- {k}: {v if v else '未知/不限'}\" for k, v in user_input_data.items()])\n", " print(f\"\\n🚀 任务启动...\\n{'-'*40}\")\n", " print(\"\\n🧠 [1/3] 军师正在制定初步策略...\")\n", " search_strategy = profiler_agent.run(f\"请根据以下用户画像制定搜索策略：\\n\\n{profile_text}\")\n", " print(f\"📝 策略: \\n{search_strategy}\")\n", "\n", " # 2. 准备循环\n", " keywords = [k.strip() for k in search_strategy.replace(\"，\", \",\").replace(\"\\n\", \",\").split(',') if k.strip()]\n", " final_items = []\n", " hunter = BatchSearchTool()\n", "\n", " print(f\"\\n🔄 进入处理流程 (共 {len(keywords)} 个商品)...\")\n", "\n", " for index, kw in enumerate(keywords):\n", " print(f\"\\n 👉 [商品 {index+1}/{len(keywords)}] 正在处理: {kw}\")\n", " \n", " # 调用智能搜索函数 (传入 min 和 max)\n", " valid_result, price_status, final_kw = find_best_product(hunter, profiler_agent, kw, b_min, b_max)\n", " \n", " if not valid_result:\n", " print(\" ❌ 彻底无数据，跳过。\")\n", " continue\n", " \n", " # === 生成文案 ===\n", " product_name = valid_result.get('title', final_kw)\n", " \n", " print(f\" ✍️ 正在撰写文案: {product_name[:30]}...\")\n", " pitch_prompt = f\"\"\"\n", " 商品：{product_name}\n", " 价格：{price_status}\n", " 卖点片段：{valid_result.get('content', '')[:200]}...\n", " \n", " 请写一句30字以内的种草文案。\n", " \"\"\"\n", " pitch = pitcher_agent.run(pitch_prompt)\n", " \n", " final_items.append({\n", " \"name\": final_kw, \n", " \"title_full\": product_name,\n", " \"price\": price_status,\n", " \"desc\": pitch.replace(\"\\n\", \" \").strip(),\n", " \"img\": valid_result.get('img', ''),\n", " \"link\": valid_result.get('url', '')\n", " })\n", " print(f\" ✅ 已收录 (状态: {price_status})\")" ] }, { "cell_type": "markdown", "id": "0197ef43", "metadata": {}, "source": [ "\n", "### 第6部分：输出礼物计划" ] }, { "cell_type": "code", "execution_count": null, "id": "8426fb13", "metadata": {}, "outputs": [], "source": [ "# --- 4. 渲染与保存 ---\n", "print(f\"\\n💾 正在生成最终报告...\")\n", "\n", "if not final_items:\n", " final_md = \"很抱歉，网络搜索似乎出现了问题，未能获取到任何商品信息。\"\n", "else:\n", " table_header = \"| 🎁 礼物名称 | 💰 价格 | ✨ 种草理由 | 🖼️ 图片/链接 |\\n| :--- | :--- | :--- | :--- |\\n\"\n", " table_rows = []\n", " \n", " for item in final_items:\n", " # 1. 清洗文本字段 (防止 | 破坏表格)\n", " name = item.get('name', '未知').replace(\"|\", \"/\")\n", " price = item.get('price', '暂无').replace(\"|\", \"/\")\n", " desc = item.get('desc', '').replace(\"|\", \"/\")\n", " \n", " # 2. 🚨【核心修复】清洗链接中的竖线\n", " # 百度/京东链接常包含 '|'，必须替换为 '%7C'，否则 Markdown 表格会炸\n", " raw_link = item.get('link', '#')\n", " safe_link = raw_link.replace(\"|\", \"%7C\")\n", " \n", " raw_img = item.get('img', '')\n", " safe_img = raw_img.replace(\"|\", \"%7C\")\n", " \n", " # 3. 构建媒体列\n", " if safe_img and safe_img.startswith(\"http\"):\n", " # 图片链接套购买链接\n", " media = f\"[![图]({safe_img})]({safe_link})\"\n", " else:\n", " media = f\"[点击购买]({safe_link})\"\n", " \n", " # 4. 组装行 (注意名字上的链接也要用 safe_link)\n", " # 使用 strip() 去除可能的首尾空格\n", " row = f\"| [{name}]({safe_link}) | {price} | {desc} | {media} |\"\n", " table_rows.append(row)\n", " \n", " final_md = table_header + \"\\n\".join(table_rows)\n", "filename = \"outputs/gift_plan_output.md\"\n", "# 确保输出目录存在\n", "os.makedirs(os.path.dirname(filename), exist_ok=True)\n", "\n", "with open(filename, \"w\", encoding=\"utf-8\") as f:\n", " f.write(final_md)\n", "print(f\"🎉 任务完成！文件已保存: {os.path.abspath(filename)}\")" ] }, { "cell_type": "markdown", "id": "d8b884b5", "metadata": {}, "source": [ "### 第7部分：总结与展望" ] }, { "cell_type": "markdown", "id": "08b0b8e8", "metadata": {}, "source": [ "#### 实现的功能\n", "- 基于用户输入的个人信息，生成符合预算的礼物建议\n", "- 支持用户自定义预算范围、节日、个人喜好等\n", "- 支持百度MCP和Tavily API双数据源，利用搜索引擎获取最新的商品信息和价格\n", "- 提供可视化的建议结果展示\n", "#### 遇到的挑战与解决方案\n", "- 大模型的“幻觉”问题（JSON格式错误/编造数据）\n", " - 解决方案：放弃让 LLM 直接生成最终数据。改为使用 Python 正则表达式 从搜索结果中暴力提取硬数据（价格、图片），仅让 LLM 负责生成文案。代码逻辑负责准确性，模型负责创造性。\n", "- 上下文过长导致提取失败\n", " - 解决方案：结合实际业务场景，分析各个阶段对上下文的要求，在搜索阶段限制返回长度。同时通过拆分 “硬数据流”（找参数）和 “软数据流”（找卖点），大幅降低单次上下文长度，提升响应速度。\n", "- 大模型推荐的礼品价格超出预算\n", " - 解决方案：引入检核机制。如果搜到的商品均价超预算，系统会自动呼叫“军师”重新制定“平替”策略，直到找到合适商品为止。\n", "- Agent传入的参数格式问题\n", " - 解决方案：在工具层兼容 Agent 传入的各种参数格式（JSON/字符串、逗号/换行符分隔），确保搜索指令不丢失。\n", "#### 未来改进方向\n", "- 前端交互：开发前端页面，替代目前的Notebook交互，提供更好的用户交互体验\n", "- 数据源深度集成：完全接入百度优选MCP 的比价与历史价格接口，获取更精准的实时价格和库存信息，实现“全网比价”\n", "- 丰富选项：增加更多的个人喜好选项，如喜欢的商品类型、品牌等\n" ] } ], "metadata": { "kernelspec": { "display_name": "ai_3.10", "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": "3.10.19" } }, "nbformat": 4, "nbformat_minor": 5 }