core.py 6.6 KB

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  1. """
  2. 内容寻找 Agent - 核心执行逻辑
  3. 提供可复用的 agent 执行函数,供 run.py 和 server.py 调用。
  4. """
  5. import asyncio
  6. import logging
  7. import sys
  8. import os
  9. from pathlib import Path
  10. from typing import Optional, Dict, Any
  11. sys.path.insert(0, str(Path(__file__).parent.parent.parent))
  12. from dotenv import load_dotenv
  13. load_dotenv()
  14. from agent import (
  15. AgentRunner,
  16. RunConfig,
  17. FileSystemTraceStore,
  18. Trace,
  19. Message,
  20. )
  21. from agent.llm import create_openrouter_llm_call
  22. from agent.llm.prompts import SimplePrompt
  23. from agent.tools.builtin.knowledge import KnowledgeConfig
  24. # 导入工具(确保工具被注册)
  25. from tools import (
  26. douyin_search,
  27. douyin_user_videos,
  28. get_content_fans_portrait,
  29. get_account_fans_portrait,
  30. )
  31. logger = logging.getLogger(__name__)
  32. # 默认 query
  33. DEFAULT_QUERY = """找10个和"养老服务与政策扶持"相关的,老年人感兴趣的视频。
  34. 要求:
  35. - 适合老年人分享观看
  36. - 热度要高,质量要好"""
  37. async def run_agent(query: Optional[str] = None, stream_output: bool = True) -> Dict[str, Any]:
  38. """
  39. 执行 agent 任务
  40. Args:
  41. query: 查询内容,None 则使用默认值
  42. stream_output: 是否流式输出到 stdout(run.py 需要,server.py 不需要)
  43. Returns:
  44. {
  45. "trace_id": "20260317_103046_xyz789",
  46. "status": "completed" | "failed",
  47. "error": "错误信息" # 失败时
  48. }
  49. """
  50. query = query or DEFAULT_QUERY
  51. # 加载 prompt
  52. prompt_path = Path(__file__).parent / "content_finder.prompt"
  53. prompt = SimplePrompt(prompt_path)
  54. # 构建消息(替换 %query%)
  55. messages = prompt.build_messages(query=query)
  56. # 初始化配置
  57. api_key = os.getenv("OPEN_ROUTER_API_KEY")
  58. if not api_key:
  59. raise ValueError("OPEN_ROUTER_API_KEY 未设置")
  60. model_name = prompt.config.get("model", "sonnet-4.6")
  61. model = os.getenv("MODEL", f"anthropic/claude-{model_name}")
  62. temperature = float(prompt.config.get("temperature", 0.3))
  63. max_iterations = int(os.getenv("MAX_ITERATIONS", "30"))
  64. trace_dir = os.getenv("TRACE_DIR", ".cache/traces")
  65. output_dir = os.getenv("OUTPUT_DIR", ".cache/output")
  66. skills_dir = str(Path(__file__).parent / "skills")
  67. Path(trace_dir).mkdir(parents=True, exist_ok=True)
  68. store = FileSystemTraceStore(base_path=trace_dir)
  69. allowed_tools = [
  70. "douyin_search",
  71. "douyin_user_videos",
  72. "get_content_fans_portrait",
  73. "get_account_fans_portrait",
  74. ]
  75. runner = AgentRunner(
  76. llm_call=create_openrouter_llm_call(model=model),
  77. trace_store=store,
  78. skills_dir=skills_dir,
  79. )
  80. config = RunConfig(
  81. name="内容寻找",
  82. model=model,
  83. temperature=temperature,
  84. max_iterations=max_iterations,
  85. tools=allowed_tools,
  86. extra_llm_params={"max_tokens": 8192},
  87. knowledge=KnowledgeConfig(
  88. enable_extraction=True,
  89. enable_completion_extraction=True,
  90. enable_injection=True,
  91. owner="content_finder_agent",
  92. default_tags={"project": "content_finder"},
  93. default_scopes=["com.piaoquantv.supply"],
  94. default_search_types=["tool", "usecase", "definition"],
  95. default_search_owner="content_finder_agent"
  96. )
  97. )
  98. # 执行
  99. trace_id = None
  100. try:
  101. async for item in runner.run(messages=messages, config=config):
  102. if isinstance(item, Trace):
  103. trace_id = item.trace_id
  104. if item.status == "completed":
  105. logger.info(f"Agent 执行完成: trace_id={trace_id}")
  106. return {
  107. "trace_id": trace_id,
  108. "status": "completed"
  109. }
  110. elif item.status == "failed":
  111. logger.error(f"Agent 执行失败: {item.error_message}")
  112. return {
  113. "trace_id": trace_id,
  114. "status": "failed",
  115. "error": item.error_message
  116. }
  117. elif isinstance(item, Message) and stream_output:
  118. # 流式输出(仅 run.py 需要)
  119. if item.role == "assistant":
  120. content = item.content
  121. if isinstance(content, dict):
  122. text = content.get("text", "")
  123. tool_calls = content.get("tool_calls", [])
  124. if text:
  125. # 如果有推荐结果,完整输出
  126. if len(text) > 500 and ("推荐结果" in text or "推荐内容" in text or "🎯" in text):
  127. print(f"\n{text}")
  128. # 如果有工具调用且文本较短,只输出摘要
  129. elif tool_calls and len(text) > 100:
  130. print(f"[思考] {text[:100]}...")
  131. # 其他情况输出完整文本
  132. else:
  133. print(f"\n{text}")
  134. # 输出工具调用信息
  135. if tool_calls:
  136. for tc in tool_calls:
  137. tool_name = tc.get("function", {}).get("name", "unknown")
  138. # 跳过 goal 工具的输出,减少噪音
  139. if tool_name != "goal":
  140. print(f"[工具] {tool_name}")
  141. elif isinstance(content, str) and content:
  142. print(f"\n{content}")
  143. elif item.role == "tool":
  144. content = item.content
  145. if isinstance(content, dict):
  146. tool_name = content.get("tool_name", "unknown")
  147. print(f"[结果] {tool_name} ✓")
  148. # 如果循环结束但没有返回,说明异常退出
  149. return {
  150. "trace_id": trace_id,
  151. "status": "failed",
  152. "error": "Agent 异常退出"
  153. }
  154. except KeyboardInterrupt:
  155. logger.info("用户中断")
  156. if stream_output:
  157. print("\n用户中断")
  158. return {
  159. "trace_id": trace_id,
  160. "status": "failed",
  161. "error": "用户中断"
  162. }
  163. except Exception as e:
  164. logger.error(f"Agent 执行异常: {e}", exc_info=True)
  165. if stream_output:
  166. print(f"\n执行失败: {e}")
  167. return {
  168. "trace_id": trace_id,
  169. "status": "failed",
  170. "error": str(e)
  171. }