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构建并部署 AI Agent 服务 / deploy (push) Successful in 5m38s
- 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
166 lines
6.1 KiB
Python
166 lines
6.1 KiB
Python
"""
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新闻资讯适配器
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整合MCP、数据库缓存和模拟数据
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"""
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from typing import Dict, Any, Optional, List
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from datetime import datetime
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from .base_adapter import BaseAdapter, AdapterResult
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class NewsAdapter(BaseAdapter):
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"""新闻资讯适配器"""
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name = "news"
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description = "新闻资讯查询，支持MCP、NewsAPI和数据库缓存"
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def __init__(self, mcp_client=None, news_repo=None):
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super().__init__(mcp_client, news_repo)
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self._mock_news = [
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{
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"title": "OpenAI发布GPT-5：智能再升级",
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"source": "Tech News",
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"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...",
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"keywords": ["AI", "GPT-5", "OpenAI"],
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"author": "AI Team",
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"published_at": datetime.now().isoformat()
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},
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{
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"title": "大模型在医疗领域的应用",
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"source": "Health Tech",
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"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...",
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"keywords": ["医疗", "大模型", "应用"],
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"author": "Medical Team",
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"published_at": datetime.now().isoformat()
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}
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]
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async def execute(self, action: str, **kwargs) -> AdapterResult:
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"""统一执行入口"""
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user_id = kwargs.get("user_id", "default")
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query = kwargs.get("query", "")
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use_cache = kwargs.get("use_cache", True)
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# 1. 先查缓存
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if use_cache and self.repository and query:
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cached = await self._get_from_cache(query, user_id=user_id)
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if cached:
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return AdapterResult(success=True, data=cached, source="cache")
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# 2. 尝试MCP
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if self.mcp_client and self.mcp_client.is_available():
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try:
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mcp_result = await self._execute_mcp(action, **kwargs)
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if mcp_result.success:
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if use_cache:
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for news in mcp_result.data:
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await self._save_to_cache(query, news, user_id=user_id)
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return mcp_result
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except Exception as e:
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print(f"[News] MCP调用失败: {e}")
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# 3. 尝试第三方API（预留）
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# result = await self._execute_api(action, **kwargs)
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# 4. 降级到模拟数据
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result = self._fallback(action, **kwargs)
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if use_cache and result.success:
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for news in result.data:
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await self._save_to_cache(query, news, user_id=user_id)
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return result
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async def _execute_mcp(self, action: str, **kwargs) -> AdapterResult:
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"""通过MCP执行"""
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if action == "query_news":
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query = kwargs.get("query", "")
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result = await self.mcp_client.call_tool(
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"news_search_news",
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{"query": query}
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)
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if result.get("success"):
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return AdapterResult(
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success=True,
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data=result["result"],
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source="mcp_news"
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)
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return AdapterResult(success=False, error="不支持的MCP操作")
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async def _get_from_cache(self, query: str, **kwargs) -> Optional[List[Dict[str, Any]]]:
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"""从数据库缓存获取"""
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if not self.repository:
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return None
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try:
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# 数据库查询（可选功能）
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return None
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except Exception as e:
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print(f"[News] 缓存查询失败: {e}")
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return None
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async def _save_to_cache(self, query: str, data: Dict[str, Any], **kwargs):
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"""保存到数据库缓存"""
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if not self.repository:
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return
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try:
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# 数据库保存（可选功能）
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pass
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except Exception as e:
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print(f"[News] 缓存保存失败: {e}")
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def _get_mock_data(self, action: str, **kwargs) -> Any:
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"""获取模拟数据"""
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query = kwargs.get("query", "").lower()
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if action == "query_news":
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results = []
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for news in self._mock_news:
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if (query in news["title"].lower() or
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query in news["summary"].lower() or
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any(keyword.lower() in query for keyword in news["keywords"])):
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results.append(news)
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if not results:
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results = self._mock_news[:2]
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return results
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elif action == "analyze_url":
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url = kwargs.get("url", "")
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return {
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"title": f"分析结果：{url}",
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"source": "URL Analyzer",
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"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...",
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"keywords": ["News", "Analysis"]
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}
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elif action == "extract_keywords":
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text = kwargs.get("text", "")
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keywords = ["AI", "大模型", "应用场景", "行业趋势"]
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result = [k for k in keywords if k.lower() in text.lower()]
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return result if result else keywords
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elif action == "generate_report":
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query_text = kwargs.get("query", "")
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return f"""═══════════════════════════════════════════
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📊 资讯分析报告
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═══════════════════════════════════════════
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主题：{query_text}
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📋 摘要：
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这是关于 {query_text} 的资讯分析综合报告。
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🔍 主要发现：
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1. AI技术持续快速发展
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2. 大模型应用场景不断拓展
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3. 行业数字化转型加速
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🏷️ 关键词：
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- AI
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- 大模型
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- 数字化转型
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═══════════════════════════════════════════
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"""
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return None
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