TradeMate Dev
c1638db6b2
- Save every search result to DB for later review - Add '搜索历史' tab with timeline view, load/delete records - Raise discovery search timeout from 30s to 120s (Bing Puppeteer needs ~40s) - Reduce search queries from 4 to 3 for faster response - New model: DiscoveryRecord (user_id, product, market, companies JSON) - API: POST/GET/DELETE /api/v1/discovery/records - Migration: discovery_records table
312 lines
16 KiB
Python
312 lines
16 KiB
Python
import json
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import logging
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from typing import Dict, Any, Optional, Union
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from app.ai.router import get_ai_router
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from app.services.search_web import search_companies, fetch_page_text
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from app.services.mcp_search_server import search_bing_batch
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logger = logging.getLogger(__name__)
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ANALYZE_MATCH_PROMPT = """你是外贸客户分析专家。分析目标公司的业务描述，判断其与用户产品的匹配度。
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请以 JSON 格式返回（不要用 markdown 代码块标记）：
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{
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"match_score": 0-100,
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"match_reason": "为什么匹配/不匹配",
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"company_summary": "这家公司的主要业务",
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"product_fit": "产品匹配度说明",
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"contact_info": {
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"emails": ["找到的邮箱"],
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"phones": ["找到的电话"],
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"social": ["LinkedIn等社媒链接"],
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"wechat": "找到的微信号",
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"whatsapp": "找到的 WhatsApp 号码"
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}
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}
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只返回 JSON，不要其他内容。"""
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class DiscoveryService:
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def __init__(self):
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ai_router = get_ai_router()
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self.ai = ai_router
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self._ai_available = len(ai_router.providers) > 0
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async def search(self, product_description: str, target_market: str) -> Dict[str, Any]:
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queries = self._build_queries(product_description, target_market)
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all_results = await self._web_search_all(queries)
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companies = []
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provider = "template"
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if all_results:
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raw = all_results.get("results", [])
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companies = [self._to_company(r) for r in raw[:12]]
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provider = all_results.get("provider", "web_search")
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good_enough = [c for c in companies if self._looks_like_business(c)]
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if len(good_enough) < 3:
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logger.info(f"Web search returned only {len(good_enough)} good results, supplementing with suggestions")
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extras = self._suggest_companies(product_description, target_market)
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seen_names = set(c.get("name", "") for c in good_enough)
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for c in extras:
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if c.get("name") and c["name"] not in seen_names:
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seen_names.add(c["name"])
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good_enough.append(c)
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return {
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"companies": good_enough[:15],
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"query": product_description,
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"market": target_market,
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"provider": provider,
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}
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async def analyze(self, company_url: str, product_description: str) -> Dict[str, Any]:
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page_text = await fetch_page_text(company_url)
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company_info = {"url": company_url}
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if page_text:
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company_info["page_text"] = page_text[:2500]
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if not self._ai_available:
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return self._template_analysis(company_url)
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prompt = f"""用户的产品：{product_description}
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目标公司信息：
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URL: {company_url}
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网页内容：{page_text[:2500] if page_text else "无法获取网页内容"}
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请分析该公司的业务与用户产品的匹配度。"""
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try:
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result = await self.ai.chat(prompt, system_prompt=ANALYZE_MATCH_PROMPT)
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content = result.get("reply", "")
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parsed = self._extract_json(content)
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if parsed:
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parsed["url"] = company_url
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parsed["provider"] = result.get("provider_used", "unknown")
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return parsed
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except (json.JSONDecodeError, Exception) as e:
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logger.warning(f"Analysis AI parse failed: {e}")
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return self._template_analysis(company_url)
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async def outreach(self, company_info: Dict[str, Any], product_info: Dict[str, Any]) -> Dict[str, Any]:
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if not self._ai_available:
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return self._template_outreach(company_info, product_info)
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prompt = f"""目标公司信息：
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{json.dumps(company_info, ensure_ascii=False)}
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我的产品信息：
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{json.dumps(product_info, ensure_ascii=False)}
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请生成个性化触达文案。"""
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system = """你是外贸开发信专家。根据目标公司信息和你的产品，生成个性化触达文案。
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请以 JSON 格式返回（不要用 markdown 代码块标记）：
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{
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"subject": "邮件标题（如适用）",
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"linkedin_message": "LinkedIn 私信文案（150字以内）",
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"whatsapp_message": "WhatsApp 消息文案（100字以内）",
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"email_body": "邮件正文（含开头问候、自我介绍、价值主张、行动号召、签名）",
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"key_points": ["客户关注的3个要点"],
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"tips": ["发送时的建议"]
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}"""
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try:
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result = await self.ai.chat(prompt, system_prompt=system)
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content = result.get("reply", "")
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parsed = self._extract_json(content)
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if parsed:
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parsed["provider"] = result.get("provider_used", "unknown")
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return parsed
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except (json.JSONDecodeError, Exception) as e:
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logger.warning(f"Outreach AI parse failed: {e}")
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return self._template_outreach(company_info, product_info)
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async def _web_search_all(self, queries: list) -> dict:
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try:
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results = await search_bing_batch(queries[:3], max_per_query=4)
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if results:
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return {"results": self._dedup_and_filter(results)[:15], "provider": "bing"}
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except Exception as e:
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logger.warning(f"Bing batch search failed: {e}")
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results = await search_companies(queries[0], max_results=10)
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if results:
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return {"results": results[:15], "provider": "google_cse"}
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return {}
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def _dedup_and_filter(self, results: list) -> list:
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seen = set()
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filtered = []
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junk = ["sciencedirect", "mdpi", "springer", "wiley", "acm.org",
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"ieee.org", "researchgate", "nature.com", "oup.com",
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"sagepub", "tandfonline", "ncbi", "semanticscholar",
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"britannica", "dictionary", "cambridge", "iciba", "wikipedia",
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"w3.org", "whatsapp.com", "wechat.com", "qq.com",
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"zhihu.com", "sogou.com", "163.com", "sohu.com", "sina.com",
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"taobao.com", "tmall.com", "alipay.com", "alibaba.com",
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"csdn.net", "blog.csdn", "jianshu.com", "36kr.com",
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"huxiu.com", "geekpark.net", "leiphone.com",
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"medium.com", "wordpress.com", "blogspot.com",
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"youtube.com", "facebook.com", "twitter.com", "instagram.com",
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"reddit.com", "quora.com"]
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for r in results:
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url = r.get("url", "").rstrip("/")
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if not url or url in seen:
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continue
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seen.add(url)
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s = url.split("/")[2] if "://" in url else url
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hostname = s.split(":")[0].lower() if ":" in s else s.lower()
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if any(tld in hostname for tld in [".edu", ".ac.", ".gov", ".edu.cn"]):
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continue
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if any(domain in hostname for domain in junk):
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continue
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filtered.append(r)
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return filtered
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def _to_company(self, r: dict) -> dict:
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url = r.get("url", "")
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title = r.get("title", url)[:60]
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snippet = r.get("snippet", "")[:200]
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return {
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"name": title,
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"description": snippet,
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"country": "",
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"match_score": 60,
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"contact": url[:100] if url else "暂无",
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"source": "web",
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}
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def _looks_like_business(self, c: dict) -> bool:
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name = c.get("name", "")
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snippet = c.get("description", "")
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junk_words = ["news", "review", "blog", "dictionary", "translate",
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"wikipedia", "百科", "词典", "新闻", "评测",
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"price", "shop", "buy online", "forum", "subscribe",
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"专业媒体", "行业媒体", "新媒体", "门户"]
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name_lower = name.lower()
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if any(w in name_lower for w in junk_words):
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return False
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snippet_lower = snippet.lower()
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newsy = ["news", "review", "blog post", "article", "dictionary",
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"专业媒体", "行业媒体", "新媒体"]
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if any(w in snippet_lower for w in newsy):
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biz_words = ["company", "inc", "ltd", "corp", "gmbh", "llc",
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"manufacturer", "supplier", "exporter",
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"wholesale", "distributor", "trading",
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"import", "enterprise", "co.", "factory",
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"industry", "electric", "solar", "energy",
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"automotive", "vehicle", "官网", "有限公司",
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"集团", "股份", "实业"]
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if not any(w in snippet_lower for w in biz_words):
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return False
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return True
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def _build_queries(self, product: str, market: str) -> list:
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import re
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has_cjk = bool(re.search(r'[\u4e00-\u9fff]', product))
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queries = [
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f"{product} importer {market}",
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f"{product} distributor {market}",
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f"{product} wholesale buyer {market}",
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f"{product} procurement {market}",
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f"{product} trading company {market}",
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f"buy {product} from {market}",
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f"{product} supply chain {market}",
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f"top {product} manufacturers {market}",
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]
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if has_cjk:
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queries += [
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f"{product} {market} 进口商",
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f"{product} {market} 经销商",
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f"{product} {market} 采购",
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f"{product} {market} 批发",
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]
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b2b_queries = [
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f"{product} buyer {market} alibaba",
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f"{product} supplier {market}",
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f"{product} wholesale price {market}",
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]
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return queries + b2b_queries
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def _extract_json(self, text: str) -> Optional[Union[dict, list]]:
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text = text.strip()
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for prefix in ["```json", "```", "```JSON"]:
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if text.startswith(prefix):
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text = text[len(prefix):]
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for suffix in ["```"]:
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if text.endswith(suffix):
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text = text[:-len(suffix)]
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text = text.strip()
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try:
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return json.loads(text)
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except json.JSONDecodeError:
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import re
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brace = text.find("{")
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end = text.rfind("}")
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if brace >= 0 and end > brace:
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try:
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return json.loads(text[brace:end+1])
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except json.JSONDecodeError:
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pass
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return None
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def _suggest_companies(self, product: str, market: str) -> list:
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return [
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{"name": f"{product} Importers in {market}", "description": f"在 {market} 从事 {product} 进口和批发的贸易商和专业进口商", "country": market, "match_score": 80, "contact": f"在 LinkedIn 搜索 '{product} importer {market}'", "source": "建议"},
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{"name": f"{product} Distributors in {market}", "description": f"在 {market} 分销 {product} 的分销渠道商和批发商", "country": market, "match_score": 75, "contact": f"在 Google/Bing 搜索 '{product} distributor {market}'", "source": "建议"},
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{"name": f"{market} Trade Association", "description": f"联系 {market} 的相关行业协会获取会员企业名录", "country": market, "match_score": 70, "contact": f"搜索 '{market} {product} association'", "source": "建议"},
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{"name": f"Alibaba {market} Buyers", "description": f"在 Alibaba.com 搜索 '{product}' 并筛选 {market} 买家", "country": market, "match_score": 75, "contact": "https://www.alibaba.com", "source": "建议"},
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{"name": f"LinkedIn {market} Decision Makers", "description": f"在 LinkedIn 搜索 '{market} {product} procurement/sourcing manager' 找决策人", "country": market, "match_score": 65, "contact": "LinkedIn Premium", "source": "建议"},
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{"name": f"{market} Import-Export Records", "description": f"在 importgenius.com 搜索 {product} 在 {market} 的进口记录，找到真实买家", "country": market, "match_score": 70, "contact": "https://www.importgenius.com", "source": "建议"},
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]
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def _template_strategy(self, product: str, market: str) -> Dict[str, Any]:
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search_terms = [
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f"{product} importer {market}",
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f"{product} distributor {market}",
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f"{product} wholesale {market}",
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f"{product} buyers {market}",
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]
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b2b_sites = ["Alibaba.com", "TradeIndia.com", "GlobalSources.com", "Made-in-China.com"]
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return {
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"companies": [
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{"name": f"{product} Importers in {market}", "description": f"使用Google/Bing搜索 '{product} importer {market}' 可找到正在采购该产品的进口商", "country": market, "match_score": 80, "contact": "通过搜索结果获取", "source": "搜索建议"},
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{"name": f"{product} Distributors in {market}", "description": f"使用Google/Bing搜索 '{product} distributor {market}' 可找到分销渠道商", "country": market, "match_score": 75, "contact": "通过搜索结果获取", "source": "搜索建议"},
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{"name": f"{product} Wholesale Buyers in {market}", "description": f"使用Google/Bing搜索 '{product} wholesale {market}' 可找到批发采购商", "country": market, "match_score": 70, "contact": "通过搜索结果获取", "source": "搜索建议"},
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{"name": f"{market} {product} Trade Partners", "description": f"在B2B平台（{', '.join(b2b_sites[:3])}）搜索 {market} 买家发布的采购需求", "country": market, "match_score": 65, "contact": "B2B平台站内信", "source": "B2B平台"},
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],
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"strategy": f"推荐搜索计划：\n1. 搜索 '{' '.join(search_terms[:2])}' 直接找客户\n2. 在 LinkedIn 搜索 '{market} {product} manager' 找决策人\n3. 在 {b2b_sites[0]} 和 {b2b_sites[1]} 查找 {market} 买家询盘\n4. 参加 {market} 相关行业展会获取名录",
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"tips": [
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f"把搜索词 '{product} importer {market}' 改成当地语言效果更好",
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"找到公司后访问官网，在 About/Team 页面找决策人LinkedIn",
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"用 SimilarWeb 查看目标公司网站流量和来源",
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"在行业协会网站查找会员名录"],
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"provider": "template",
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"ai_generated": True,
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}
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def _template_analysis(self, url: str) -> Dict[str, Any]:
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return {
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"match_score": 50,
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"match_reason": "无法获取网页内容进行分析，建议手动查看",
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"url": url,
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"provider": "template",
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"contact_info": {"emails": [], "phones": [], "social": [], "wechat": "", "whatsapp": ""},
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}
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def _template_outreach(self, company: Dict[str, Any], product: Dict[str, Any]) -> Dict[str, Any]:
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company_name = company.get("name", "")
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product_name = product.get("name", "")
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return {
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"subject": f"关于{product_name}的合作机会",
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"linkedin_message": f"您好！了解到贵司{company_name}在经营相关业务，我们专业生产{product_name}，品质稳定，价格有竞争力。如有兴趣，我可以发详细资料供参考。",
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"whatsapp_message": f"Hello! We are a professional {product_name} manufacturer. Interested in exploring cooperation? Happy to share details.",
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"email_body": f"Dear {company_name} team,\n\nWe are a professional {product_name} manufacturer with competitive pricing and consistent quality. Would you be open to a quick chat to explore potential cooperation?\n\nBest regards,\n[Your Name]",
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"key_points": ["产品质量有保障", "价格有竞争力", "可定制"],
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"tips": ["发送前先了解对方背景", "LinkedIn 消息要简短"],
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"provider": "template",
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}
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