84 lines
3.5 KiB
Python
84 lines
3.5 KiB
Python
"""日志回调处理器模块"""
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import logging
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from typing import Any, Optional, Dict, List
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from langchain_core.callbacks import BaseCallbackHandler
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from langchain_core.messages import BaseMessage
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class LoggingCallbackHandler(BaseCallbackHandler):
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"""自定义的 CallbackHandler，使用项目的 logger 来记录日志"""
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def __init__(self, logger_name: str = 'app'):
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self.logger = logging.getLogger(logger_name)
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# def on_chat_model_start(
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# self,
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# serialized: Dict[str, Any],
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# messages: List[List[BaseMessage]],
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# **kwargs: Any,
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# ) -> None:
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# """当 Chat 模型开始时调用"""
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# self.logger.info("✅ Chat Model Start - Messages:")
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# for msg_list in messages:
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# for msg in msg_list:
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# msg_type = msg.__class__.__name__
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# content = msg.content if hasattr(msg, 'content') else str(msg)
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# self.logger.info(f"[{msg_type}] {content}")
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# def on_llm_start(self, serialized: Dict[str, Any], prompts: Any, **kwargs: Any) -> None:
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# """当 LLM 开始时调用（用于普通 LLM，非 Chat 模型）"""
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# self.logger.info("✅ LLM Start - Input:")
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# for prompt in prompts:
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# self.logger.info(str(prompt))
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def on_llm_end(self, response, **kwargs: Any) -> None:
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"""当 LLM 结束时调用"""
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self.logger.info("✅ LLM End - Output:")
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# 打印生成的文本
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if hasattr(response, 'generations') and response.generations:
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for gen_idx, generation_list in enumerate(response.generations):
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for msg_idx, generation in enumerate(generation_list):
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# ChatGeneration: 使用 text 属性获取内容
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if hasattr(generation, 'text') and generation.text:
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for line in generation.text.split("\n"):
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if line.strip():
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self.logger.info(f" {line}")
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# 如果有 message 属性，输出额外信息（tool_calls 等）
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if hasattr(generation, 'message') and generation.message:
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msg = generation.message
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content = msg.content if hasattr(msg, 'content') else ''
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if not content or (isinstance(content, str) and not content.strip()):
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self.logger.info(f" [EMPTY content]")
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tool_calls = msg.tool_calls if hasattr(msg, 'tool_calls') else []
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if tool_calls:
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self.logger.info(f" [tool_calls: {[tc.get('name', '') for tc in tool_calls]}")
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def on_llm_error(
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self, error: Exception, **kwargs: Any
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) -> None:
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"""当 LLM 出错时调用"""
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self.logger.error(f"❌ LLM Error: {error}")
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def on_tool_start(
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self, serialized: Optional[Dict[str, Any]], input_str: str, **kwargs: Any
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) -> None:
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"""当工具开始调用时调用"""
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if serialized is None:
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tool_name = 'unknown_tool'
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else:
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tool_name = serialized.get('name', 'unknown_tool')
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self.logger.info(f"🔧 Tool Start - {tool_name} with input: {str(input_str)[:1000]}")
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def on_tool_end(self, output: str, **kwargs: Any) -> None:
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"""当工具调用结束时调用"""
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self.logger.info(f"✅ Tool End Output: {output}")
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def on_tool_error(
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self, error: Exception, **kwargs: Any
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) -> None:
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"""当工具调用出错时调用"""
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self.logger.error(f"❌ Tool Error: {error}")
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