import sys import os import time import asyncio import re import json import gc from typing import List, Optional # 并发控制：防止OOM，保证每个请求都能完成 MAX_CONCURRENT_REQUESTS = 3 # 最大同时处理请求数 _request_semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS) # 添加项目根目录到 Python 路径 sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) # Load environment variables from .env file from dotenv import load_dotenv load_dotenv() from fastapi import FastAPI, HTTPException, Request from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse from pydantic import BaseModel from slowapi.errors import RateLimitExceeded from slowapi.middleware import SlowAPIMiddleware # WhiteNoise将通过StaticFiles中间件集成，不需要ASGI↔WSGI转换 from api.routers import auth, seo_pages, miniprogram # 导入应用模块 try: from app.config import config from app.tool.meetspot_recommender import CafeRecommender from app.logger import logger from app.db.database import init_db print("✅ 成功导入所有必要模块") config_available = True except ImportError as e: print(f"⚠️ 导入模块警告: {e}") config = None config_available = False # 创建 fallback logger（当 app.logger 导入失败时） import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger("meetspot") # Fallback for init_db async def init_db(): logger.warning("Database module not available, skipping init") # 导入 Agent 模块（高内存消耗，暂时禁用以保证稳定性） agent_available = False # 禁用 Agent 模式，节省内存 # try: # from app.agent import MeetSpotAgent, create_meetspot_agent # agent_available = True # print("✅ 成功导入 Agent 模块") # except ImportError as e: # print(f"⚠️ Agent 模块导入失败: {e}") print("ℹ️ Agent 模块已禁用（节省内存）") def create_meetspot_agent(): """Stub function - Agent模式已禁用，此函数不应被调用""" raise RuntimeError("Agent模式已禁用，请使用规则模式") # 导入 LLM 模块 llm_available = False llm_instance = None try: from app.llm import LLM from app.schema import Message llm_available = True print("✅ 成功导入 LLM 模块") except ImportError as e: print(f"⚠️ LLM 模块导入失败: {e}") # 在Vercel环境下创建最小化配置类 class MinimalConfig: class AMapSettings: def __init__(self, api_key): self.api_key = api_key def __init__(self): amap_key = os.getenv("AMAP_API_KEY", "") if amap_key: self.amap = self.AMapSettings(amap_key) else: self.amap = None if os.getenv("AMAP_API_KEY"): config = MinimalConfig() config_available = True print("✅ 创建最小化配置（仅高德地图）") else: print("❌ 未找到AMAP_API_KEY环境变量") # 在Vercel环境下导入最小化推荐器 if not config_available and os.getenv("AMAP_API_KEY"): try: # 创建最小化推荐器 import asyncio import httpx import json import hashlib import time from datetime import datetime class MinimalCafeRecommender: """最小化推荐器，专为Vercel环境设计""" def __init__(self): self.api_key = os.getenv("AMAP_API_KEY") self.base_url = "https://restapi.amap.com/v3" async def execute(self, locations, keywords="咖啡馆", place_type="", user_requirements=""): """执行推荐""" try: # 简化的推荐逻辑 result_html = await self._generate_recommendations( locations, keywords, user_requirements ) # 生成HTML文件 html_filename = f"place_recommendation_{datetime.now().strftime('%Y%m%d%H%M%S')}_{hashlib.md5(str(time.time()).encode()).hexdigest()[:8]}.html" html_path = f"workspace/js_src/{html_filename}" # 确保目录存在 os.makedirs("workspace/js_src", exist_ok=True) # 写入HTML文件 with open(html_path, 'w', encoding='utf-8') as f: f.write(result_html) # 返回结果对象 class Result: def __init__(self, output): self.output = output return Result(f"生成的推荐页面：{html_path}\nHTML页面: {html_filename}") except Exception as e: return Result(f"推荐失败: {str(e)}") async def _generate_recommendations(self, locations, keywords, user_requirements): """生成推荐HTML""" # 简化的HTML模板 html_content = f""" MeetSpot 推荐结果

🎯 MeetSpot 推荐结果

为您推荐最佳会面地点

📍 您的位置信息

位置: {', '.join(locations)}

需求: {keywords}

{f'

特殊要求: {user_requirements}

' if user_requirements else ''}

💡 推荐建议

由于在Vercel环境下运行，推荐功能已简化。建议您:

⚠️ 注意事项

当前运行在简化模式下。如需完整功能，请在本地环境运行或配置完整的环境变量。

""" return html_content CafeRecommender = MinimalCafeRecommender print("✅ 创建最小化推荐器") except Exception as e: print(f"❌ 创建最小化推荐器失败: {e}") CafeRecommender = None # 请求模型定义 class LocationRequest(BaseModel): locations: List[str] venue_types: Optional[List[str]] = ["咖啡馆"] user_requirements: Optional[str] = "" class LocationCoord(BaseModel): """预解析的地址坐标信息（来自前端 Autocomplete 选择）""" name: str # 用户选择的地点名称 address: str # 完整地址 lng: float # 经度 lat: float # 纬度 city: Optional[str] = "" # 城市名 class MeetSpotRequest(BaseModel): locations: List[str] keywords: Optional[str] = "咖啡馆" place_type: Optional[str] = "" user_requirements: Optional[str] = "" # 筛选条件 min_rating: Optional[float] = 0.0 # 最低评分 (0-5) max_distance: Optional[int] = 100000 # 最大距离 (米) price_range: Optional[str] = "" # 价格区间: economy/mid/high # 预解析坐标（可选，由前端 Autocomplete 提供） location_coords: Optional[List[LocationCoord]] = None class AIChatRequest(BaseModel): message: str conversation_history: Optional[List[dict]] = [] # MeetSpot AI客服系统提示词 MEETSPOT_SYSTEM_PROMPT = """你是MeetSpot（聚点）的AI Agent智能助手。MeetSpot是一款多人会面地点推荐的AI Agent，核心解决"在哪见面最公平"的问题。 ## 核心定位 MeetSpot不是简单的搜索工具，而是一个完整的AI Agent： - 高德地图搜"我附近"，MeetSpot搜"我们中间" - 大众点评帮你找"好店"，MeetSpot帮你找"对所有人都公平的好店" ## 技术特点 1. **球面几何算法**：使用Haversine公式计算地球曲面真实中点，比平面算法精确15-20% 2. **GPT-4o智能评分**：AI对候选场所进行多维度评分（距离、评分、停车、环境、交通便利度） 3. **5步透明推理**：解析地址 -> 计算中点 -> 搜索周边 -> GPT-4o评分 -> 生成推荐 4. **可解释AI**：用户可以看到Agent每一步是怎么"思考"的，完全透明 ## 产品能力 - **覆盖范围**：350+城市，基于高德地图数据 - **场景类型**：12种主题（咖啡馆、餐厅、图书馆、KTV、健身房、密室逃脱等） - **智能识别**：60+高校简称预置，"北大"自动识别为"北京市海淀区北京大学" - **参与人数**：支持2-10人，满足团队与家人聚会需求 ## 响应时间 - 单场景推荐：5-8秒 - 双场景推荐：8-12秒 - Agent复杂模式：15-30秒 （包含完整流程：地理编码、POI搜索、GPT-4o智能评分、交通建议） ## 使用方法 1. 输入2个以上参与者地点（支持地址、地标、简称如"北大"） 2. 选择场景类型（可多选，如"咖啡馆 餐厅"） 3. 可选：设置特殊需求（停车方便、环境安静等） 4. 点击推荐，5-30秒后获取AI Agent推荐结果 ## 常见问题 - **和高德有什么区别？** 高德搜"我附近"，MeetSpot搜"我们中间"，是高德/百度都没有的功能 - **支持哪些城市？** 350+城市，覆盖全国主要城市 - **推荐速度如何？** 单场景5-8秒，双场景8-12秒，复杂Agent模式15-30秒 - **是否收费？** 完全免费，无需注册，直接使用 ## 回答规范 - 用友好、专业的语气回答问题 - 强调MeetSpot是AI Agent，不是简单搜索工具 - 突出"公平"、"透明可解释"、"GPT-4o智能评分"等核心价值 - 回答简洁明了，使用中文 - 如果用户问无关问题，礼貌引导了解产品功能""" # 预设问题列表 PRESET_QUESTIONS = [ {"id": 1, "question": "MeetSpot是什么？", "category": "产品介绍"}, {"id": 2, "question": "AI Agent怎么工作的？", "category": "技术"}, {"id": 3, "question": "支持哪些场景？", "category": "功能"}, {"id": 4, "question": "推荐需要多久？", "category": "性能"}, {"id": 5, "question": "和高德地图有什么区别？", "category": "对比"}, {"id": 6, "question": "是否收费？", "category": "其他"}, ] # 环境变量配置（用于 Vercel） AMAP_API_KEY = os.getenv("AMAP_API_KEY", "") AMAP_JS_API_KEY = os.getenv("AMAP_JS_API_KEY", "") # JS API key for frontend map AMAP_SECURITY_JS_CODE = os.getenv("AMAP_SECURITY_JS_CODE", "") # 创建 FastAPI 应用 app = FastAPI( title="MeetSpot", description="MeetSpot会面点推荐服务 - 完整功能版", version="1.0.0" ) # ============================================================================ # 应用启动事件 - 生成设计token CSS文件 # ============================================================================ @app.on_event("startup") async def startup_event(): """应用启动时生成设计token CSS文件""" try: from app.design_tokens import generate_design_tokens_css generate_design_tokens_css() logger.info("✅ Design tokens CSS generated successfully") except Exception as e: logger.error(f"❌ Failed to generate design tokens CSS: {e}") # 不阻止应用启动,即使CSS生成失败 @app.on_event("startup") async def startup_database(): """确保MVP所需的数据库表已创建。""" try: await init_db() logger.info("✅ Database initialized") except Exception as e: logger.error(f"❌ Database init failed: {e}") raise # 配置CORS app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # 缓存中间件 - 为静态资源添加 Cache-Control 头 @app.middleware("http") async def add_cache_headers(request: Request, call_next): """Add Cache-Control headers for static assets to improve performance.""" response = await call_next(request) path = request.url.path # 静态资源长期缓存 (1 year for immutable assets) if any(path.endswith(ext) for ext in ['.css', '.js', '.woff2', '.woff', '.ttf']): response.headers["Cache-Control"] = "public, max-age=31536000, immutable" # 图片缓存 (30 days) elif any(path.endswith(ext) for ext in ['.png', '.jpg', '.jpeg', '.gif', '.webp', '.svg', '.ico']): response.headers["Cache-Control"] = "public, max-age=2592000" # HTML 页面短期缓存 (10 minutes, revalidate) elif path.endswith('.html') or path == '/' or path in ['/about', '/faq', '/how-it-works']: response.headers["Cache-Control"] = "public, max-age=600, stale-while-revalidate=86400" # sitemap/robots - long cache with stale-while-revalidate for Render cold starts # This ensures CDN can serve cached content when origin is cold (fixes GSC "Couldn't fetch") elif path in ['/sitemap.xml', '/robots.txt']: response.headers["Cache-Control"] = "public, max-age=86400, stale-while-revalidate=604800" return response async def _rate_limit_handler(request: Request, exc: RateLimitExceeded): """全局限流处理器.""" return JSONResponse( status_code=429, content={"detail": "请求过于频繁, 请稍后再试"}, ) app.state.limiter = seo_pages.limiter app.add_exception_handler(RateLimitExceeded, _rate_limit_handler) app.add_middleware(SlowAPIMiddleware) app.include_router(auth.router) app.include_router(seo_pages.router) app.include_router(miniprogram.router) @app.get("/health") async def health_check(): """健康检查和配置状态""" return { "status": "healthy", "timestamp": time.time(), "config": { "amap_configured": bool(AMAP_API_KEY or (config and hasattr(config, 'amap') and config.amap)), "full_features": config_available, "minimal_mode": not config_available and bool(AMAP_API_KEY) } } @app.api_route("/google48ac1a797739b7b0.html", methods=["GET", "HEAD"]) async def google_verification(): """返回Google Search Console验证文件（支持GET和HEAD请求）""" google_file = "public/google48ac1a797739b7b0.html" if os.path.exists(google_file): response = FileResponse( google_file, media_type="text/html", headers={ "Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0" } ) return response # 如果文件不存在，返回404 raise HTTPException(status_code=404, detail="Google verification file not found") @app.api_route("/BingSiteAuth.xml", methods=["GET", "HEAD"]) async def bing_verification(): """返回Bing站点验证文件（支持GET和HEAD请求）""" bing_file = "public/BingSiteAuth.xml" if os.path.exists(bing_file): response = FileResponse( bing_file, media_type="application/xml", headers={ "Cache-Control": "no-cache, no-store, must-revalidate", "Pragma": "no-cache", "Expires": "0" } ) return response # 如果文件不存在，返回404 raise HTTPException(status_code=404, detail="Bing verification file not found") @app.api_route("/sitemap.xml", methods=["GET", "HEAD"]) async def sitemap(): """返回站点地图（支持GET和HEAD请求）""" sitemap_file = "public/sitemap.xml" if os.path.exists(sitemap_file): return FileResponse( sitemap_file, media_type="application/xml", headers={ "Cache-Control": "public, max-age=3600", "Content-Type": "application/xml; charset=utf-8" } ) raise HTTPException(status_code=404, detail="Sitemap not found") @app.api_route("/robots.txt", methods=["GET", "HEAD"]) async def robots(): """返回robots.txt（支持GET和HEAD请求）""" robots_file = "public/robots.txt" if os.path.exists(robots_file): return FileResponse( robots_file, media_type="text/plain", headers={ "Cache-Control": "public, max-age=3600", "Content-Type": "text/plain; charset=utf-8" } ) raise HTTPException(status_code=404, detail="robots.txt not found") @app.api_route("/favicon.ico", methods=["GET", "HEAD"]) async def favicon_ico(): """返回网站图标（支持GET和HEAD请求）""" # 优先返回SVG favicon（现代浏览器支持） svg_file = "public/favicon.svg" if os.path.exists(svg_file): return FileResponse( svg_file, media_type="image/svg+xml", headers={ "Cache-Control": "public, max-age=31536000, immutable", "Content-Type": "image/svg+xml" } ) raise HTTPException(status_code=404, detail="Favicon not found") @app.api_route("/favicon.svg", methods=["GET", "HEAD"]) async def favicon_svg(): """返回SVG网站图标（支持GET和HEAD请求）""" svg_file = "public/favicon.svg" if os.path.exists(svg_file): return FileResponse( svg_file, media_type="image/svg+xml", headers={ "Cache-Control": "public, max-age=31536000, immutable", "Content-Type": "image/svg+xml" } ) raise HTTPException(status_code=404, detail="Favicon not found") @app.get("/config") async def get_config(): """获取当前配置状态（不暴露敏感信息）""" amap_key = "" if config: amap_key = config.amap.api_key else: amap_key = AMAP_API_KEY return { "amap_api_key_configured": bool(amap_key), "amap_api_key_length": len(amap_key) if amap_key else 0, "config_loaded": bool(config), "full_features_available": bool(config) } # ==================== AI 客服接口 ==================== @app.get("/api/ai_chat/preset_questions") async def get_preset_questions(): """获取预设问题列表""" return { "success": True, "questions": PRESET_QUESTIONS } @app.post("/api/ai_chat") async def ai_chat(request: AIChatRequest): """AI客服聊天接口""" start_time = time.time() try: print(f"🤖 [AI客服] 收到消息: {request.message[:50]}...") if not llm_available: # LLM不可用时返回预设回复 print("⚠️ LLM模块不可用，使用预设回复") return { "success": True, "response": "抱歉，AI客服暂时不可用。您可以直接使用我们的会面点推荐功能，或查看页面上的使用说明。如有问题请稍后再试。", "processing_time": time.time() - start_time, "mode": "fallback" } # 获取LLM API配置 llm_api_key = os.getenv("LLM_API_KEY", "") llm_api_base = os.getenv("LLM_API_BASE", "https://newapi.deepwisdom.ai/v1") llm_model = os.getenv("LLM_MODEL", "deepseek-chat") # 默认使用deepseek，中文能力强 if not llm_api_key: print("⚠️ LLM_API_KEY未配置") return { "success": True, "response": "AI客服配置中，请稍后再试。您也可以直接体验我们的会面点推荐功能！", "processing_time": time.time() - start_time, "mode": "fallback" } # 使用openai库直接调用（兼容DeepWisdom API） from openai import AsyncOpenAI client = AsyncOpenAI( api_key=llm_api_key, base_url=llm_api_base ) # 构建消息列表 messages = [ {"role": "system", "content": MEETSPOT_SYSTEM_PROMPT} ] # 添加历史对话（最多保留最近5轮） if request.conversation_history: recent_history = request.conversation_history[-10:] # 最多10条消息 messages.extend(recent_history) # 添加当前用户消息 messages.append({"role": "user", "content": request.message}) print(f"🚀 [AI客服] 调用LLM ({llm_model})，消息数: {len(messages)}") # 调用LLM response = await client.chat.completions.create( model=llm_model, messages=messages, max_tokens=500, temperature=0.7 ) ai_response = response.choices[0].message.content processing_time = time.time() - start_time print(f"✅ [AI客服] 回复生成成功，耗时: {processing_time:.2f}秒") return { "success": True, "response": ai_response, "processing_time": processing_time, "mode": "llm" } except Exception as e: print(f"💥 [AI客服] 错误: {str(e)}") return { "success": False, "response": f"抱歉，AI客服遇到了问题。您可以直接使用会面点推荐功能，或稍后再试。", "error": str(e), "processing_time": time.time() - start_time, "mode": "error" } # ==================== 智能路由逻辑 ==================== def assess_request_complexity(request: MeetSpotRequest) -> dict: """评估请求复杂度，决定使用哪种模式 Returns: dict: { "use_agent": bool, # 是否使用Agent模式 "complexity_score": int, # 复杂度分数 (0-100) "reasons": list, # 判断原因 "mode_name": str # 模式名称（用于日志） } """ score = 0 reasons = [] # 1. 地点数量 (权重: 30分) location_count = len(request.locations) if location_count >= 4: score += 30 reasons.append(f"{location_count}个地点，需要复杂的中心点计算") elif location_count >= 3: score += 15 reasons.append(f"{location_count}个地点") # 2. 场所类型数量 (权重: 25分) keywords = request.keywords or "" keyword_count = len(keywords.split()) if keywords else 0 if keyword_count >= 3: score += 25 reasons.append(f"{keyword_count}种场所类型，需要智能平衡") elif keyword_count >= 2: score += 12 reasons.append(f"{keyword_count}种场所类型") # 3. 特殊需求复杂度 (权重: 25分) requirements = request.user_requirements or "" if requirements: req_keywords = ["商务", "安静", "停车", "Wi-Fi", "包间", "儿童", "24小时", "久坐"] matched_reqs = sum(1 for kw in req_keywords if kw in requirements) if matched_reqs >= 3: score += 25 reasons.append(f"{matched_reqs}个特殊需求，需要综合权衡") elif matched_reqs >= 2: score += 15 reasons.append(f"{matched_reqs}个特殊需求") elif len(requirements) > 50: score += 20 reasons.append("详细的自定义需求描述") # 4. 筛选条件 (权重: 20分) has_filters = False if request.min_rating and request.min_rating > 0: has_filters = True score += 5 if request.max_distance and request.max_distance < 10000: has_filters = True score += 5 if request.price_range: has_filters = True score += 5 if has_filters: reasons.append("有精确筛选条件") # 决定模式 (阈值: 40分) use_agent = score >= 40 and agent_available # 如果Agent不可用，降级到规则模式 if score >= 40 and not agent_available: reasons.append("Agent模块不可用，使用增强规则模式") mode_name = "Agent智能模式" if use_agent else "快速规则模式" return { "use_agent": use_agent, "complexity_score": min(score, 100), "reasons": reasons, "mode_name": mode_name } # ==================== 会面点推荐接口 ==================== @app.post("/api/find_meetspot") async def find_meetspot(request: MeetSpotRequest): """统一的会面地点推荐入口 - 智能路由 根据请求复杂度自动选择最优模式： - 简单请求: 规则+LLM模式 (快速，0.3-0.8秒) - 复杂请求: Agent模式 (深度分析，3-8秒) """ start_time = time.time() # 并发控制：排队处理，保证每个请求都能完成 async with _request_semaphore: return await _process_meetspot_request(request, start_time) async def _process_meetspot_request(request: MeetSpotRequest, start_time: float): """实际处理推荐请求的内部函数""" # 评估请求复杂度 complexity = assess_request_complexity(request) print(f"🧠 [智能路由] 复杂度评估: {complexity['complexity_score']}分, 模式: {complexity['mode_name']}") if complexity['reasons']: print(f" 原因: {', '.join(complexity['reasons'])}") try: print(f"📝 收到请求: {request.model_dump()}") # 检查配置 if config: api_key = config.amap.api_key print(f"✅ 使用配置文件中的API密钥: {api_key[:10]}...") else: api_key = AMAP_API_KEY print(f"✅ 使用环境变量中的API密钥: {api_key[:10]}...") if not api_key: raise HTTPException( status_code=500, detail="高德地图API密钥未配置，请设置AMAP_API_KEY环境变量或配置config.toml文件" ) # ========== 智能路由：根据复杂度选择模式 ========== if complexity['use_agent']: print(f"🤖 [Agent模式] 复杂请求，启用Agent智能分析...") try: agent = create_meetspot_agent() # 添加15秒超时，确保Agent模式不会无限等待 AGENT_TIMEOUT = 15 # 秒 agent_result = await asyncio.wait_for( agent.recommend( locations=request.locations, keywords=request.keywords or "咖啡馆", requirements=request.user_requirements or "" ), timeout=AGENT_TIMEOUT ) processing_time = time.time() - start_time print(f"⏱️ [Agent] 推荐完成，耗时: {processing_time:.2f}秒") # Agent模式返回格式 return { "success": agent_result.get("success", False), "html_url": None, # Agent模式暂不生成HTML "locations_count": len(request.locations), "processing_time": processing_time, "message": "Agent智能推荐完成", "output": agent_result.get("recommendation", ""), "mode": "agent", "complexity_score": complexity['complexity_score'], "complexity_reasons": complexity['reasons'], "agent_data": { "geocode_results": agent_result.get("geocode_results", []), "center_point": agent_result.get("center_point"), "search_results": agent_result.get("search_results", []), "steps_executed": agent_result.get("steps_executed", 0) } } except asyncio.TimeoutError: print(f"⚠️ [Agent] 执行超时({AGENT_TIMEOUT}秒)，降级到规则模式") except Exception as agent_error: print(f"⚠️ [Agent] 执行失败，降级到规则模式: {agent_error}") # 降级到规则模式，继续执行下面的代码 # ========== 规则+LLM模式（默认/降级） ========== if config: print("🔧 开始初始化推荐工具...") recommender = CafeRecommender() print("🚀 开始执行推荐...") # 转换 location_coords 为推荐器期望的格式 pre_resolved_coords = None if request.location_coords: pre_resolved_coords = [ { "name": coord.name, "address": coord.address, "lng": coord.lng, "lat": coord.lat, "city": coord.city or "" } for coord in request.location_coords ] print(f"📍 使用前端预解析坐标: {len(pre_resolved_coords)} 个") # 调用推荐工具 result = await recommender.execute( locations=request.locations, keywords=request.keywords or "咖啡馆", place_type=request.place_type or "", user_requirements=request.user_requirements or "", min_rating=request.min_rating or 0.0, max_distance=request.max_distance or 100000, price_range=request.price_range or "", pre_resolved_coords=pre_resolved_coords ) processing_time = time.time() - start_time print(f"⏱️ 推荐完成，耗时: {processing_time:.2f}秒") # 解析工具输出，提取HTML文件路径 output_text = result.output html_url = None print(f"📄 工具输出预览: {output_text[:200]}...") # 从输出中提取HTML文件路径 - 修复的正则表达式 html_match = re.search(r'HTML页面:\s*([^\s\n]+\.html)', output_text) if html_match: html_filename = html_match.group(1) print(f"🔍 找到HTML文件名: {html_filename}") html_url = f"/workspace/js_src/{html_filename}" print(f"🌐 转换为URL: {html_url}") else: print("❌ 未找到'HTML页面:'模式，尝试其他模式...") # 尝试匹配生成的推荐页面格式 html_match2 = re.search(r'生成的推荐页面：\s*([^\s\n]+\.html)', output_text) if html_match2: html_path = html_match2.group(1) if html_path.startswith('workspace/'): html_url = f"/{html_path}" else: html_url = f"/workspace/{html_path}" print(f"🔍 备用匹配1找到: {html_url}") else: # 尝试匹配任何place_recommendation格式的文件名 html_match3 = re.search(r'(place_recommendation_\d{14}_[a-f0-9]+\.html)', output_text) if html_match3: html_filename = html_match3.group(1) html_url = f"/workspace/js_src/{html_filename}" print(f"🔍 备用匹配2找到: {html_url}") else: print("❌ 所有匹配模式都失败了") html_url = None # 返回前端期望的格式（包含模式信息） response_data = { "success": True, "html_url": html_url, "locations_count": len(request.locations), "processing_time": processing_time, "message": "推荐生成成功", "output": output_text, "mode": "rule_llm", # 规则+LLM增强模式 "complexity_score": complexity['complexity_score'], "complexity_reasons": complexity['reasons'] } print(f"📤 返回响应: success={response_data['success']}, html_url={response_data['html_url']}") # 主动释放内存 gc.collect() return response_data else: # Fallback：如果无法加载完整模块，返回错误 print("❌ 配置未加载") raise HTTPException( status_code=500, detail="服务配置错误：无法加载推荐模块，请确保在本地环境运行或正确配置Vercel环境变量" ) except Exception as e: print(f"💥 异常发生: {str(e)}") print(f"异常类型: {type(e)}") import traceback traceback.print_exc() processing_time = time.time() - start_time # 主动释放内存 gc.collect() # 返回错误响应，但保持前端期望的格式 error_response = { "success": False, "error": str(e), "processing_time": processing_time, "message": f"推荐失败: {str(e)}" } print(f"📤 返回错误响应: {error_response['message']}") return error_response @app.post("/api/find_meetspot_agent") async def find_meetspot_agent(request: MeetSpotRequest): """Agent 模式的会面地点推荐功能 使用 AI Agent 进行智能推荐，支持： - 自主规划推荐流程 - 智能分析场所特点 - 生成个性化推荐理由 """ start_time = time.time() try: print(f"🤖 [Agent] 收到请求: {request.model_dump()}") # 检查 Agent 是否可用 if not agent_available: print("⚠️ Agent 模块不可用，回退到规则模式") return await find_meetspot(request) # 检查配置 if not config or not config.amap or not config.amap.api_key: print("❌ API 密钥未配置") raise HTTPException( status_code=500, detail="高德地图API密钥未配置" ) print("🔧 [Agent] 初始化 MeetSpotAgent...") agent = create_meetspot_agent() print("🚀 [Agent] 开始执行推荐任务...") result = await agent.recommend( locations=request.locations, keywords=request.keywords or "咖啡馆", requirements=request.user_requirements or "" ) processing_time = time.time() - start_time print(f"⏱️ [Agent] 推荐完成，耗时: {processing_time:.2f}秒") # 构建响应 response_data = { "success": result.get("success", False), "mode": "agent", "recommendation": result.get("recommendation", ""), "geocode_results": result.get("geocode_results", []), "center_point": result.get("center_point"), "search_results": result.get("search_results", []), "steps_executed": result.get("steps_executed", 0), "locations_count": len(request.locations), "processing_time": processing_time, "message": "Agent 推荐生成成功" if result.get("success") else "推荐失败" } print(f"📤 [Agent] 返回响应: success={response_data['success']}") return response_data except Exception as e: print(f"💥 [Agent] 异常发生: {str(e)}") import traceback traceback.print_exc() processing_time = time.time() - start_time # 尝试回退到规则模式 print("⚠️ [Agent] 尝试回退到规则模式...") try: fallback_result = await find_meetspot(request) fallback_result["mode"] = "rule_fallback" fallback_result["agent_error"] = str(e) return fallback_result except Exception as fallback_error: return { "success": False, "mode": "agent", "error": str(e), "processing_time": processing_time, "message": f"Agent 推荐失败: {str(e)}" } @app.post("/recommend") async def get_recommendations(request: LocationRequest): """兼容性API端点 - 统一响应格式""" # 转换请求格式 meetspot_request = MeetSpotRequest( locations=request.locations, keywords=request.venue_types[0] if request.venue_types else "咖啡馆", user_requirements=request.user_requirements ) # 直接调用主端点并返回相同格式 return await find_meetspot(meetspot_request) @app.get("/api/config/amap") async def get_amap_config(): """返回 AMap 配置（用于前端地图和 Autocomplete） Note: 前端需要 JS API key，与后端 geocoding 使用的 Web服务 key 不同 """ # 优先使用 JS API key（前端地图专用） js_api_key = AMAP_JS_API_KEY security_js_code = AMAP_SECURITY_JS_CODE # 从 config.toml 获取（如果存在） if config and hasattr(config, "amap") and config.amap: if not js_api_key: js_api_key = getattr(config.amap, "js_api_key", "") or getattr(config.amap, "api_key", "") if not security_js_code: security_js_code = getattr(config.amap, "security_js_code", "") # 最后回退到 Web服务 key（不推荐，可能无法加载地图） if not js_api_key: js_api_key = AMAP_API_KEY return { "api_key": js_api_key, "security_js_code": security_js_code } @app.get("/api/status") async def api_status(): """API状态检查""" return { "status": "healthy", "service": "MeetSpot", "version": "1.0.0", "platform": "Multi-platform", "features": "Complete" if config else "Limited", "timestamp": time.time() } # 静态文件服务（替代WhiteNoise，使用FastAPI原生StaticFiles） # StaticFiles自带gzip压缩和缓存控制 if os.path.exists("static"): app.mount("/static", StaticFiles(directory="static"), name="static") if os.path.exists("public"): app.mount("/public", StaticFiles(directory="public", html=True), name="public") # 添加缓存控制头（用于静态资源） @app.middleware("http") async def add_cache_headers(request: Request, call_next): response = await call_next(request) # 对静态资源添加长期缓存 if request.url.path.startswith(("/static/", "/public/")): response.headers["Cache-Control"] = "public, max-age=31536000" # 1年 return response # Vercel 处理函数 app_instance = app # 如果直接运行此文件（本地测试） if __name__ == "__main__": import uvicorn print("🚀 启动 MeetSpot 完整功能服务器...") uvicorn.run(app, host="0.0.0.0", port=8000)