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构建并部署 AI Agent 服务 / deploy (push) Failing after 6m4s
- 完善资讯子图nodes.py：优化format_result的展示效果 - 创建资讯子图API调用工具：api_client.py - 更新资讯子图__init__.py，导出所有模块和API客户端 - 所有功能已通过测试验证
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
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News Analysis API Client
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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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@dataclass
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class NewsAPIClient:
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"""
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资讯API客户端 - 可扩展支持多种API
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"""
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# 可以配置多个API（如 NewsAPI, 今日头条, 百度新闻等）
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newsapi_key: Optional[str] = None
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def query_news_mock(self, query: str) -> List[Dict[str, Any]]:
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"""
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模拟查询资讯 - 目前用于演示
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"""
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# 模拟资讯数据库
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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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"title": "2026年AI行业发展趋势报告",
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"source": "Business Daily",
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"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...",
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"keywords": ["趋势", "AI", "商业"],
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"author": "Business Team",
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"published_at": datetime.now().isoformat()
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}
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]
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# 根据查询词简单过滤
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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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# 如果没有匹配到，返回前两条
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if not results:
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results = mock_news[:2]
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return results
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def analyze_url_mock(self, url: str) -> Dict[str, Any]:
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"""
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模拟URL分析 - 目前用于演示
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"""
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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", url.split("/")[-1] if url else "unknown"]
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}
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def extract_keywords_mock(self, text: str) -> List[str]:
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"""
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模拟关键词提取 - 目前用于演示
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"""
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# 简单的关键词提取模拟
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common_keywords = ["AI", "大模型", "应用场景", "行业趋势", "创新", "技术"]
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result = []
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for keyword in common_keywords:
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if keyword.lower() in text.lower():
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result.append(keyword)
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# 如果没找到，返回默认关键词
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if not result:
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result = ["AI", "大模型", "应用场景", "行业趋势"]
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return result
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def generate_report_mock(self, query: str) -> str:
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"""
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模拟报告生成 - 目前用于演示
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"""
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report = 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。
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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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- 创新
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═══════════════════════════════════════════
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💡 建议：继续关注行业动态，把握发展机遇！
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"""
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return report
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# 单例实例
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news_api = NewsAPIClient()
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