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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
130 lines
4.4 KiB
Python
130 lines
4.4 KiB
Python
"""
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资讯子图API调用工具（使用MCP统一接口）
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"""
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from typing import Dict, Any, Optional, List
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import random
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from datetime import datetime
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from dataclasses import dataclass
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from ...mcp.mcp_manager import mcp_manager
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from ...mcp.adapters import NewsAdapter
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@dataclass
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class NewsAPIClient:
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"""
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资讯API客户端 - 使用MCP统一接口
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保持向后兼容，内部使用MCP适配器
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"""
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# 保留配置字段用于向后兼容
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newsapi_key: Optional[str] = None
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news_repository: Optional[Any] = None
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def __post_init__(self):
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"""初始化后设置MCP"""
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import asyncio
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try:
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asyncio.create_task(self._init_mcp())
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except RuntimeError:
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pass
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async def _init_mcp(self):
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"""初始化MCP系统"""
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if not mcp_manager.get_adapter("news"):
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mcp_manager.register_adapter(
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NewsAdapter(news_repo=self.news_repository)
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)
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await mcp_manager.initialize()
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async def query_news(
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self,
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user_id: str = "default",
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query: str = "",
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use_cache: bool = True
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) -> List[Dict[str, Any]]:
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"""查询新闻（统一入口）"""
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await self._init_mcp()
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result = await mcp_manager.execute(
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"news", "query_news",
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user_id=user_id, query=query, use_cache=use_cache
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)
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if result.success:
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return result.data
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return self.query_news_mock(query)
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def query_news_mock(self, query: str) -> List[Dict[str, Any]]:
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"""模拟查询（保留用于向后兼容）"""
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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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results = []
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query_lower = query.lower()
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for news in mock_news:
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if (query_lower in news["title"].lower() or
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query_lower in news["summary"].lower() or
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any(keyword.lower() in query_lower for keyword in news["keywords"])):
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results.append(news)
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return results if results else mock_news[:2]
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def analyze_url_mock(self, url: str) -> Dict[str, Any]:
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"""模拟URL分析（保留用于向后兼容）"""
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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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def extract_keywords_mock(self, text: str) -> List[str]:
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"""模拟关键词提取（保留用于向后兼容）"""
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keywords = ["AI", "大模型", "应用场景", "行业趋势", "创新", "技术"]
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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[:4]
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def generate_report_mock(self, query: str) -> str:
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"""模拟报告生成（保留用于向后兼容）"""
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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}
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📋 摘要：
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这是关于 {query} 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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# 全局单例（保持向后兼容）
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news_api = NewsAPIClient()
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