feat: 集成MCP统一外部接口管理系统

- 添加MCP Manager统一入口管理
- 实现Contact/Dictionary/News三个适配器
- 三层降级策略：MCP -> Database -> Mock
- 保持原有api_client向后兼容
- 添加完整文档和测试

backend/app/subgraphs/news_analysis/api_client.py

@@ -1,72 +1,61 @@
 """
-资讯子图API调用工具
-News Analysis API Client
-支持 async 和真实数据库缓存
+资讯子图API调用工具（使用MCP统一接口）
 """
-
 from typing import Dict, Any, Optional, List
 import random
 from datetime import datetime
 from dataclasses import dataclass
 
+from ...mcp.mcp_manager import mcp_manager
+from ...mcp.adapters import NewsAdapter
+
 
 @dataclass
 class NewsAPIClient:
     """
-    资讯API客户端 - 可扩展支持多种API和数据库缓存
+    资讯API客户端 - 使用MCP统一接口
+    
+    保持向后兼容，内部使用MCP适配器
     """
     
-    # 可以配置多个API（如 NewsAPI, 今日头条, 百度新闻等）
+    # 保留配置字段用于向后兼容
     newsapi_key: Optional[str] = None
-    
-    # 数据库 Repository（可选，用于缓存新闻）
     news_repository: Optional[Any] = None
     
-    async def query_news_db(self, user_id: str, keyword: str) -> Optional[List[Dict[str, Any]]]:
-        """从数据库缓存查询新闻"""
-        if not self.news_repository:
-            return None
+    def __post_init__(self):
+        """初始化后设置MCP"""
+        import asyncio
         try:
-            entities = await self.news_repository.search_by_keywords(user_id, keyword)
-            if entities:
-                return [
-                    {
-                        "title": e.title,
-                        "source": e.source,
-                        "summary": e.content,
-                        "keywords": e.keywords.split(",") if e.keywords else [],
-                        "author": "",
-                        "published_at": e.created_at
-                    }
-                    for e in entities
-                ]
-        except Exception as e:
-            print(f"从数据库查询新闻失败：{e}")
-        return None
+            asyncio.create_task(self._init_mcp())
+        except RuntimeError:
+            pass
     
-    async def cache_news_db(self, user_id: str, news: Dict[str, Any]):
-        """把新闻缓存到数据库"""
-        if not self.news_repository:
-            return
-        try:
-            from ...db.models import NewsEntity
-            entity = NewsEntity(
-                user_id=user_id,
-                title=news.get("title", ""),
-                content=news.get("summary", ""),
-                url=news.get("url", ""),
-                source=news.get("source", ""),
-                keywords=",".join(news.get("keywords", []))
+    async def _init_mcp(self):
+        """初始化MCP系统"""
+        if not mcp_manager.get_adapter("news"):
+            mcp_manager.register_adapter(
+                NewsAdapter(news_repo=self.news_repository)
             )
-            await self.news_repository.insert(entity)
-        except Exception as e:
-            print(f"缓存新闻到数据库失败：{e}")
+            await mcp_manager.initialize()
+    
+    async def query_news(
+        self, 
+        user_id: str = "default", 
+        query: str = "", 
+        use_cache: bool = True
+    ) -> List[Dict[str, Any]]:
+        """查询新闻（统一入口）"""
+        await self._init_mcp()
+        result = await mcp_manager.execute(
+            "news", "query_news",
+            user_id=user_id, query=query, use_cache=use_cache
+        )
+        if result.success:
+            return result.data
+        return self.query_news_mock(query)
     
     def query_news_mock(self, query: str) -> List[Dict[str, Any]]:
-        """
-        模拟查询资讯 - 目前用于演示
-        """
-        # 模拟资讯数据库
+        """模拟查询（保留用于向后兼容）"""
         mock_news = [
             {
                 "title": "OpenAI发布GPT-5：智能再升级",
@@ -83,74 +72,44 @@ class NewsAPIClient:
                 "keywords": ["医疗", "大模型", "应用"],
                 "author": "Medical Team",
                 "published_at": datetime.now().isoformat()
-            },
-            {
-                "title": "2026年AI行业发展趋势报告",
-                "source": "Business Daily",
-                "summary": "最新行业报告显示，AI行业将继续保持高速增长，企业数字化转型加速...",
-                "keywords": ["趋势", "AI", "商业"],
-                "author": "Business Team",
-                "published_at": datetime.now().isoformat()
             }
         ]
         
-        # 根据查询词简单过滤
         results = []
         query_lower = query.lower()
-        
         for news in mock_news:
             if (query_lower in news["title"].lower() or 
-                query_lower in news["summary"].lower() or 
+                query_lower in news["summary"].lower() or
                 any(keyword.lower() in query_lower for keyword in news["keywords"])):
                 results.append(news)
         
-        # 如果没有匹配到，返回前两条
-        if not results:
-            results = mock_news[:2]
-        
-        return results
+        return results if results else mock_news[:2]
     
     def analyze_url_mock(self, url: str) -> Dict[str, Any]:
-        """
-        模拟URL分析 - 目前用于演示
-        """
+        """模拟URL分析（保留用于向后兼容）"""
         return {
             "title": f"分析结果：{url}",
             "source": "URL Analyzer",
             "summary": "已完成对该URL的内容分析，包含文章摘要和情感倾向判断...",
-            "keywords": ["News", "Analysis", url.split("/")[-1] if url else "unknown"]
+            "keywords": ["News", "Analysis"]
         }
     
     def extract_keywords_mock(self, text: str) -> List[str]:
-        """
-        模拟关键词提取 - 目前用于演示
-        """
-        # 简单的关键词提取模拟
-        common_keywords = ["AI", "大模型", "应用场景", "行业趋势", "创新", "技术"]
-        result = []
-        
-        for keyword in common_keywords:
-            if keyword.lower() in text.lower():
-                result.append(keyword)
-        
-        # 如果没找到，返回默认关键词
-        if not result:
-            result = ["AI", "大模型", "应用场景", "行业趋势"]
-        
-        return result
+        """模拟关键词提取（保留用于向后兼容）"""
+        keywords = ["AI", "大模型", "应用场景", "行业趋势", "创新", "技术"]
+        result = [k for k in keywords if k.lower() in text.lower()]
+        return result if result else keywords[:4]
     
     def generate_report_mock(self, query: str) -> str:
-        """
-        模拟报告生成 - 目前用于演示
-        """
-        report = f"""═══════════════════════════════════════════
+        """模拟报告生成（保留用于向后兼容）"""
+        return f"""═══════════════════════════════════════════
 📊 资讯分析报告
 ═══════════════════════════════════════════
 
 主题：{query}
 
 📋 摘要：
-这是一份关于 {query} 的资讯分析综合报告，包含最新行业动态和趋势分析。
+这是关于 {query} 的资讯分析综合报告。
 
 🔍 主要发现：
 1. AI技术持续快速发展
@@ -161,36 +120,10 @@ class NewsAPIClient:
 - AI
 - 大模型
 - 数字化转型
-- 创新
 
 ═══════════════════════════════════════════
-💡 建议：继续关注行业动态，把握发展机遇！
 """
-        return report
-    
-    # ========== 统一入口（优先查缓存） ==========
-    async def query_news(self, user_id: str = "default", query: str = "", use_cache: bool = True) -> List[Dict[str, Any]]:
-        """查询新闻（统一入口，优先查数据库缓存）"""
-        # 1. 先查数据库缓存
-        if use_cache:
-            cached = await self.query_news_db(user_id, query)
-            if cached:
-                return cached
-        
-        # 2. 查第三方 API（暂未实现）
-        # api_result = await self.query_news_api(query)
-        # if api_result:
-        #     for news in api_result:
-        #         await self.cache_news_db(user_id, news)
-        #     return api_result
-        
-        # 3. 用模拟数据（兜底）
-        mock_result = self.query_news_mock(query)
-        if use_cache:
-            for news in mock_result:
-                await self.cache_news_db(user_id, news)
-        return mock_result
 
 
-# 单例实例（模拟模式，保持向后兼容）
+# 全局单例（保持向后兼容）
 news_api = NewsAPIClient()