ailine/backend/app/mcp/adapters/news_adapter.py

"""
新闻资讯适配器
整合MCP、数据库缓存和模拟数据
"""
from typing import Dict, Any, Optional, List
from datetime import datetime
from .base_adapter import BaseAdapter, AdapterResult


class NewsAdapter(BaseAdapter):
    """新闻资讯适配器"""
    
    name = "news"
    description = "新闻资讯查询，支持MCP、NewsAPI和数据库缓存"
    
    def __init__(self, mcp_client=None, news_repo=None):
        super().__init__(mcp_client, news_repo)
        self._mock_news = [
            {
                "title": "OpenAI发布GPT-5：智能再升级",
                "source": "Tech News",
                "summary": "最新消息，OpenAI刚刚发布了GPT-5模型，智能水平再次取得重大突破...",
                "keywords": ["AI", "GPT-5", "OpenAI"],
                "author": "AI Team",
                "published_at": datetime.now().isoformat()
            },
            {
                "title": "大模型在医疗领域的应用",
                "source": "Health Tech",
                "summary": "大模型AI技术正在医疗领域展现巨大潜力，从辅助诊断到药物研发...",
                "keywords": ["医疗", "大模型", "应用"],
                "author": "Medical Team",
                "published_at": datetime.now().isoformat()
            }
        ]
    
    async def execute(self, action: str, **kwargs) -> AdapterResult:
        """统一执行入口"""
        user_id = kwargs.get("user_id", "default")
        query = kwargs.get("query", "")
        use_cache = kwargs.get("use_cache", True)
        
        # 1. 先查缓存
        if use_cache and self.repository and query:
            cached = await self._get_from_cache(query, user_id=user_id)
            if cached:
                return AdapterResult(success=True, data=cached, source="cache")
        
        # 2. 尝试MCP
        if self.mcp_client and self.mcp_client.is_available():
            try:
                mcp_result = await self._execute_mcp(action, **kwargs)
                if mcp_result.success:
                    if use_cache:
                        for news in mcp_result.data:
                            await self._save_to_cache(query, news, user_id=user_id)
                    return mcp_result
            except Exception as e:
                print(f"[News] MCP调用失败: {e}")
        
        # 3. 尝试第三方API（预留）
        # result = await self._execute_api(action, **kwargs)
        
        # 4. 降级到模拟数据
        result = self._fallback(action, **kwargs)
        if use_cache and result.success:
            for news in result.data:
                await self._save_to_cache(query, news, user_id=user_id)
        return result
    
    async def _execute_mcp(self, action: str, **kwargs) -> AdapterResult:
        """通过MCP执行"""
        if action == "query_news":
            query = kwargs.get("query", "")
            result = await self.mcp_client.call_tool(
                "news_search_news",
                {"query": query}
            )
            if result.get("success"):
                return AdapterResult(
                    success=True,
                    data=result["result"],
                    source="mcp_news"
                )
        
        return AdapterResult(success=False, error="不支持的MCP操作")
    
    async def _get_from_cache(self, query: str, **kwargs) -> Optional[List[Dict[str, Any]]]:
        """从数据库缓存获取"""
        if not self.repository:
            return None
        try:
            # 数据库查询（可选功能）
            return None
        except Exception as e:
            print(f"[News] 缓存查询失败: {e}")
            return None
    
    async def _save_to_cache(self, query: str, data: Dict[str, Any], **kwargs):
        """保存到数据库缓存"""
        if not self.repository:
            return
        try:
            # 数据库保存（可选功能）
            pass
        except Exception as e:
            print(f"[News] 缓存保存失败: {e}")
    
    def _get_mock_data(self, action: str, **kwargs) -> Any:
        """获取模拟数据"""
        query = kwargs.get("query", "").lower()
        
        if action == "query_news":
            results = []
            for news in self._mock_news:
                if (query in news["title"].lower() or 
                    query in news["summary"].lower() or
                    any(keyword.lower() in query for keyword in news["keywords"])):
                    results.append(news)
            
            if not results:
                results = self._mock_news[:2]
            
            return results
        
        elif action == "analyze_url":
            url = kwargs.get("url", "")
            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"]
            }
        
        elif action == "extract_keywords":
            text = kwargs.get("text", "")
            keywords = ["AI", "大模型", "应用场景", "行业趋势"]
            result = [k for k in keywords if k.lower() in text.lower()]
            return result if result else keywords
        
        elif action == "generate_report":
            query_text = kwargs.get("query", "")
            return f"""═══════════════════════════════════════════
📊 资讯分析报告
═══════════════════════════════════════════

主题：{query_text}

📋 摘要：
这是关于 {query_text} 的资讯分析综合报告。

🔍 主要发现：
1. AI技术持续快速发展
2. 大模型应用场景不断拓展
3. 行业数字化转型加速

🏷️ 关键词：
- AI
- 大模型
- 数字化转型

═══════════════════════════════════════════
"""
        
        return None