mirror of https://github.com/Mai-with-u/MaiBot.git
fix：截断Log优化
SengokuCola
parent
e0aa745ba4
commit
c6dadc2872
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@ -237,15 +237,8 @@ def _build_stream_api_resp(
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resp.tool_calls.append(ToolCall(call_id, function_name, arguments))
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# 检查 max_tokens 截断
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if finish_reason == "length":
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if resp.content and resp.content.strip():
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logger.warning(
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"⚠ OpenAI 响应因达到 max_tokens 限制被部分截断，\n"
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" 可能会对回复内容造成影响，建议修改模型 max_tokens 配置！"
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)
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else:
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logger.warning("⚠ OpenAI 响应因达到 max_tokens 限制被截断，\n 请修改模型 max_tokens 配置！")
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# 检查 max_tokens 截断（流式的告警改由处理函数统一输出，这里不再输出）
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# 保留 finish_reason 仅用于上层判断
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if not resp.content and not resp.tool_calls:
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raise EmptyResponseException()
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@ -270,6 +263,7 @@ async def _default_stream_response_handler(
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_tool_calls_buffer: list[tuple[str, str, io.StringIO]] = [] # 工具调用缓冲区，用于存储接收到的工具调用
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_usage_record = None # 使用情况记录
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finish_reason: str | None = None # 记录最后的 finish_reason
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_model_name: str | None = None # 记录模型名
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def _insure_buffer_closed():
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# 确保缓冲区被关闭
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@ -300,6 +294,9 @@ async def _default_stream_response_handler(
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if hasattr(event.choices[0], "finish_reason") and event.choices[0].finish_reason:
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finish_reason = event.choices[0].finish_reason
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if hasattr(event, "model") and event.model and not _model_name:
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_model_name = event.model # 记录模型名
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if hasattr(delta, "reasoning_content") and delta.reasoning_content: # type: ignore
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# 标记：有独立的推理内容块
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_has_rc_attr_flag = True
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@ -322,12 +319,34 @@ async def _default_stream_response_handler(
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)
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try:
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return _build_stream_api_resp(
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resp = _build_stream_api_resp(
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_fc_delta_buffer,
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_rc_delta_buffer,
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_tool_calls_buffer,
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finish_reason=finish_reason,
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), _usage_record
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)
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# 统一在这里输出 max_tokens 截断的警告，并从 resp 中读取
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if finish_reason == "length":
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# 把模型名塞到 resp.raw_data，后续严格“从 resp 提取”
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try:
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if _model_name:
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resp.raw_data = {"model": _model_name}
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except Exception:
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pass
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model_dbg = None
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try:
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if isinstance(resp.raw_data, dict):
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model_dbg = resp.raw_data.get("model")
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except Exception:
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model_dbg = None
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# 统一日志格式
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logger.info(
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"模型%s因为超过最大max_token限制，可能仅输出部分内容，可视情况调整"
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% (model_dbg or "")
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)
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return resp, _usage_record
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except Exception:
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# 确保缓冲区被关闭
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_insure_buffer_closed()
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@ -402,14 +421,13 @@ def _default_normal_response_parser(
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choice0 = resp.choices[0]
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reason = getattr(choice0, "finish_reason", None)
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if reason and reason == "length":
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has_real_output = bool(api_response.content and api_response.content.strip())
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if has_real_output:
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logger.warning(
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"⚠ OpenAI 响应因达到 max_tokens 限制被部分截断，\n"
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" 可能会对回复内容造成影响，建议修改模型 max_tokens 配置！"
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)
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else:
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logger.warning("⚠ OpenAI 响应因达到 max_tokens 限制被截断，\n 请修改模型 max_tokens 配置！")
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print(resp)
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_model_name = resp.model
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# 统一日志格式
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logger.info(
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"模型%s因为超过最大max_token限制，可能仅输出部分内容，可视情况调整"
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% (_model_name or "")
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)
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return api_response, _usage_record
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except Exception as e:
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logger.debug(f"检查 MAX_TOKENS 截断时异常: {e}")
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@ -522,7 +540,7 @@ class OpenaiClient(BaseClient):
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await asyncio.sleep(0.1) # 等待0.5秒后再次检查任务&中断信号量状态
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# logger.
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logger.debug(f"OpenAI API响应(非流式): {req_task.result()}")
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# logger.debug(f"OpenAI API响应(非流式): {req_task.result()}")
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# logger.info(f"OpenAI请求时间: {model_info.model_identifier} {time.time() - start_time} \n{messages}")
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@ -105,10 +105,12 @@ class MemoryChest:
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return 0.60
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elif percentage < 1.0:
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return 0.50
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elif percentage < 1.2:
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elif percentage < 1.5:
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return 0.40
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elif percentage < 2:
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return 0.30
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else:
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return 0.35
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return 0.25
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except Exception:
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# 发生异常时使用保守阈值
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return 0.70
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@ -58,16 +58,16 @@ class MemoryManagementTask(AsyncTask):
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if percentage < 0.6:
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# 小于50%，每600秒执行一次
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return 3600
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elif percentage < 0.8:
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elif percentage < 1:
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# 大于等于50%，每300秒执行一次
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return 1800
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elif percentage < 1.0:
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elif percentage < 1.5:
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# 大于等于100%，每120秒执行一次
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return 300
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elif percentage < 1.2:
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return 30
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return 600
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elif percentage < 1.8:
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return 120
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else:
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return 10
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return 30
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except Exception as e:
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logger.error(f"[记忆管理] 计算执行间隔时出错: {e}")
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@ -93,10 +93,10 @@ class MemoryManagementTask(AsyncTask):
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logger.info(f"当前记忆数量: {current_count}/{self.max_memory_number} ({percentage:.1%})")
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# 当占比 > 1.6 时，持续删除直到占比 <= 1.6（越老/越新更易被删）
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if percentage > 1.6:
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if percentage > 2:
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logger.info("记忆过多，开始遗忘记忆")
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while True:
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if percentage <= 1.6:
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if percentage <= 1.8:
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break
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removed = global_memory_chest.remove_one_memory_by_age_weight()
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if not removed:
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