crawler.py 3.9 KB

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  1. """
  2. 爬虫工具 - 集成抖音、快手等平台的爬虫能力
  3. """
  4. from typing import List, Dict, Any, Optional
  5. from datetime import datetime
  6. from agent.tools import tool, ToolResult, ToolContext
  7. @tool(description="从抖音搜索视频内容")
  8. async def douyin_search(
  9. keywords: str,
  10. max_results: int = 20,
  11. min_views: Optional[int] = None,
  12. min_likes: Optional[int] = None,
  13. ctx: ToolContext = None,
  14. ) -> ToolResult:
  15. """
  16. 从抖音搜索视频内容
  17. Args:
  18. keywords: 搜索关键词
  19. max_results: 最大结果数
  20. min_views: 最小播放量
  21. min_likes: 最小点赞数
  22. ctx: 工具上下文
  23. """
  24. # 伪代码:实际实现需要调用抖音爬虫API
  25. results = await _call_douyin_crawler(
  26. keywords=keywords,
  27. max_results=max_results,
  28. min_views=min_views,
  29. min_likes=min_likes,
  30. )
  31. return ToolResult(
  32. title=f"抖音搜索结果",
  33. output=f"找到 {len(results)} 条内容",
  34. data={"items": results},
  35. )
  36. @tool(description="从快手搜索视频内容")
  37. async def kuaishou_search(
  38. keywords: str,
  39. max_results: int = 20,
  40. min_views: Optional[int] = None,
  41. min_likes: Optional[int] = None,
  42. ctx: ToolContext = None,
  43. ) -> ToolResult:
  44. """
  45. 从快手搜索视频内容
  46. Args:
  47. keywords: 搜索关键词
  48. max_results: 最大结果数
  49. min_views: 最小播放量
  50. min_likes: 最小点赞数
  51. ctx: 工具上下文
  52. """
  53. # 伪代码:实际实现需要调用快手爬虫API
  54. results = await _call_kuaishou_crawler(
  55. keywords=keywords,
  56. max_results=max_results,
  57. min_views=min_views,
  58. min_likes=min_likes,
  59. )
  60. return ToolResult(
  61. title=f"快手搜索结果",
  62. output=f"找到 {len(results)} 条内容",
  63. data={"items": results},
  64. )
  65. # ===== 爬虫实现(伪代码)=====
  66. async def _call_douyin_crawler(
  67. keywords: str,
  68. max_results: int,
  69. min_views: Optional[int],
  70. min_likes: Optional[int],
  71. ) -> List[Dict[str, Any]]:
  72. """
  73. 调用抖音爬虫
  74. 实际实现需要:
  75. 1. 调用抖音API或爬虫服务
  76. 2. 解析返回数据
  77. 3. 应用筛选条件
  78. 4. 格式化为标准ContentItem格式
  79. """
  80. # 伪代码示例
  81. results = [
  82. {
  83. "content_id": "dy_123456",
  84. "platform": "douyin",
  85. "title": "示例视频标题",
  86. "author": "作者名",
  87. "url": "https://douyin.com/video/123456",
  88. "cover_url": "https://douyin.com/cover/123456.jpg",
  89. "description": "视频描述",
  90. "stats": {
  91. "views": 100000,
  92. "likes": 5000,
  93. "comments": 200,
  94. "shares": 300,
  95. },
  96. "tags": ["标签1", "标签2"],
  97. "publish_time": datetime.now().isoformat(),
  98. }
  99. ]
  100. return results
  101. async def _call_kuaishou_crawler(
  102. keywords: str,
  103. max_results: int,
  104. min_views: Optional[int],
  105. min_likes: Optional[int],
  106. ) -> List[Dict[str, Any]]:
  107. """
  108. 调用快手爬虫
  109. 实际实现需要:
  110. 1. 调用快手API或爬虫服务
  111. 2. 解析返回数据
  112. 3. 应用筛选条件
  113. 4. 格式化为标准ContentItem格式
  114. """
  115. # 伪代码示例
  116. results = [
  117. {
  118. "content_id": "ks_789012",
  119. "platform": "kuaishou",
  120. "title": "示例视频标题",
  121. "author": "作者名",
  122. "url": "https://kuaishou.com/video/789012",
  123. "cover_url": "https://kuaishou.com/cover/789012.jpg",
  124. "description": "视频描述",
  125. "stats": {
  126. "views": 80000,
  127. "likes": 4000,
  128. "comments": 150,
  129. "shares": 250,
  130. },
  131. "tags": ["标签1", "标签2"],
  132. "publish_time": datetime.now().isoformat(),
  133. }
  134. ]
  135. return results