fix:修复llm爆炸小问题
This commit is contained in:
SengokuCola
@@ -81,10 +81,10 @@ class BackgroundTaskManager:
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),
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(
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self._hf_judge_state_update_task,
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lambda: self._run_hf_judge_state_update_cycle(300),
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lambda: self._run_hf_judge_state_update_cycle(60),
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"hf_judge_state_update",
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"debug",
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f"状态评估任务已启动 间隔:{300}s",
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f"状态评估任务已启动 间隔:{60}s",
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"_hf_judge_state_update_task",
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),
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(
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@@ -12,12 +12,14 @@ from src.plugins.chat.chat_stream import chat_manager
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# 导入心流相关类
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from src.heart_flow.sub_heartflow import SubHeartflow, ChatState
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from src.heart_flow.mai_state_manager import MaiStateInfo
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from .observation import ChattingObservation, Observation
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from .observation import ChattingObservation
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# 导入LLM请求工具
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from src.plugins.models.utils_model import LLMRequest
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from src.config.config import global_config
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import traceback
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# 初始化日志记录器
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subheartflow_manager_log_config = LogConfig(
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@@ -44,7 +46,7 @@ class SubHeartflowManager:
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model=global_config.llm_heartflow, # 与 Heartflow 一致
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temperature=0.6, # 与 Heartflow 一致
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max_tokens=1000, # 与 Heartflow 一致 (虽然可能不需要这么多)
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request_type="subheartflow_state_eval" # 保留特定的请求类型
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request_type="subheartflow_state_eval", # 保留特定的请求类型
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)
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def get_all_subheartflows(self) -> List["SubHeartflow"]:
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@@ -87,7 +89,7 @@ class SubHeartflowManager:
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# 注册子心流
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self.subheartflows[subheartflow_id] = new_subflow
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heartflow_name = chat_manager.get_stream_name(subheartflow_id) or subheartflow_id
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logger.info(f"[{heartflow_name}] 开始看消息")
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logger.info(f"[{heartflow_name}] 开始接收消息")
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# 启动后台任务
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asyncio.create_task(new_subflow.subheartflow_start_working())
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@@ -180,7 +182,6 @@ class SubHeartflowManager:
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else:
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logger.debug(f"[限制] 无需停止, 当前总数:{len(self.subheartflows)}")
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async def deactivate_all_subflows(self):
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"""将所有子心流的状态更改为 ABSENT (例如主状态变为OFFLINE时调用)"""
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# logger.info("[停用] 开始将所有子心流状态设置为 ABSENT")
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@@ -343,7 +344,6 @@ class SubHeartflowManager:
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else:
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logger.debug(f"{log_prefix_manager} 随机停用周期结束, 未停用任何子心流。")
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async def evaluate_and_transition_subflows_by_llm(self):
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"""
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使用LLM评估每个子心流的状态，并根据LLM的判断执行状态转换（ABSENT <-> CHAT）。
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@@ -375,7 +375,7 @@ class SubHeartflowManager:
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# --- 获取观察内容 ---
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# 从 sub_hf.observations 获取 ChattingObservation 并提取信息
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observation_summary = "没有可用的观察信息。" # 默认值
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_observation_summary = "没有可用的观察信息。" # 默认值
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try:
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# 检查 observations 列表是否存在且不为空
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@@ -388,13 +388,11 @@ class SubHeartflowManager:
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else:
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logger.warning(f"{log_prefix} [{stream_name}] 第一个观察者不是 ChattingObservation 类型。")
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except Exception as e:
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logger.warning(f"{log_prefix} [{stream_name}] 获取观察信息失败: {e}", exc_info=True)
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# 保留默认值或错误信息
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combined_summary = f"获取观察信息时出错: {e}"
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# --- 获取麦麦状态 ---
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mai_state_description = f"麦麦当前状态: {current_mai_state.value}。"
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@@ -417,7 +415,9 @@ class SubHeartflowManager:
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if should_activate:
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# 检查CHAT限额
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if current_chat_count < chat_limit:
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logger.info(f"{log_prefix} [{stream_name}] LLM建议激活到CHAT状态，且未达上限({current_chat_count}/{chat_limit})。正在尝试转换...")
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logger.info(
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f"{log_prefix} [{stream_name}] LLM建议激活到CHAT状态，且未达上限({current_chat_count}/{chat_limit})。正在尝试转换..."
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)
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await sub_hf.change_chat_state(ChatState.CHAT)
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if sub_hf.chat_state.chat_status == ChatState.CHAT:
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transitioned_to_chat += 1
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@@ -425,10 +425,13 @@ class SubHeartflowManager:
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else:
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logger.warning(f"{log_prefix} [{stream_name}] 尝试激活到CHAT失败。")
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else:
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logger.info(f"{log_prefix} [{stream_name}] LLM建议激活到CHAT状态，但已达到上限({current_chat_count}/{chat_limit})。跳过转换。")
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logger.info(
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f"{log_prefix} [{stream_name}] LLM建议激活到CHAT状态，但已达到上限({current_chat_count}/{chat_limit})。跳过转换。"
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)
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except Exception as e:
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logger.error(f"{log_prefix} [{stream_name}] LLM评估或状态转换(ABSENT->CHAT)时出错: {e}", exc_info=True)
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logger.error(
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f"{log_prefix} [{stream_name}] LLM评估或状态转换(ABSENT->CHAT)时出错: {e}", exc_info=True
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)
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# --- 针对 CHAT 状态 ---
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elif current_subflow_state == ChatState.CHAT:
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@@ -455,7 +458,9 @@ class SubHeartflowManager:
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else:
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logger.warning(f"{log_prefix} [{stream_name}] 尝试转换为ABSENT失败。")
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except Exception as e:
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logger.error(f"{log_prefix} [{stream_name}] LLM评估或状态转换(CHAT->ABSENT)时出错: {e}", exc_info=True)
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logger.error(
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f"{log_prefix} [{stream_name}] LLM评估或状态转换(CHAT->ABSENT)时出错: {e}", exc_info=True
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)
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# 可以选择性地为 FOCUSED 状态添加评估逻辑，例如判断是否降级回 CHAT 或 ABSENT
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@@ -465,7 +470,6 @@ class SubHeartflowManager:
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f" 成功转换到ABSENT: {transitioned_to_absent}."
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)
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async def _llm_evaluate_state_transition(self, prompt: str) -> bool:
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"""
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使用 LLM 评估是否应进行状态转换。
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@@ -479,8 +483,8 @@ class SubHeartflowManager:
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log_prefix = "[LLM状态评估]"
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try:
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# --- 真实的 LLM 调用 ---
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response_text, _, model_name = await self.llm_state_evaluator.generate_response_async(prompt)
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logger.debug(f"{log_prefix} 使用模型 {model_name} 评估，原始响应: {response_text}")
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response_text, _ = await self.llm_state_evaluator.generate_response_async(prompt)
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logger.debug(f"{log_prefix} 使用模型 {self.llm_state_evaluator.model_name} 评估，原始响应: {response_text}")
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# 解析响应 - 这里需要根据你的LLM的确切输出来调整逻辑
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# 假设 LLM 会明确回答 "是" 或 "否"
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if response_text and "是" in response_text.strip():
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@@ -493,19 +497,11 @@ class SubHeartflowManager:
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logger.warning(f"{log_prefix} LLM 未明确回答 '是' 或 '否'，响应: {response_text}")
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# 可以设定一个默认行为，例如默认不转换
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return False
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# --- 真实的 LLM 调用结束 ---
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# # --- 占位符逻辑：随机返回 True/False ---
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# # 请在接入真实 LLM 后移除此部分
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# await asyncio.sleep(0.1) # 模拟LLM调用延迟
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# result = random.choice([True, False])
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# logger.debug(f"{log_prefix} (占位符) LLM评估结果: {'建议转换' if result else '建议不转换'}")
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# return result
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# # --- 占位符逻辑结束 ---
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except Exception as e:
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logger.error(f"{log_prefix} 调用 LLM 进行状态评估时出错: {e}", exc_info=True)
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traceback.print_exc()
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return False
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def count_subflows_by_state(self, state: ChatState) -> int:
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"""统计指定状态的子心流数量"""
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@@ -404,7 +404,7 @@ class Hippocampus:
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# logger.info("没有找到有效的关键词节点")
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return []
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logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
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logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
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# 从每个关键词获取记忆
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all_memories = []
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@@ -576,7 +576,7 @@ class Hippocampus:
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# logger.info("没有找到有效的关键词节点")
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return []
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logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
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logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
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# 从每个关键词获取记忆
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all_memories = []
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@@ -761,7 +761,7 @@ class Hippocampus:
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# logger.info("没有找到有效的关键词节点")
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return 0
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logger.info(f"有效的关键词: {', '.join(valid_keywords)}")
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logger.debug(f"有效的关键词: {', '.join(valid_keywords)}")
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# 从每个关键词获取记忆
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activate_map = {} # 存储每个词的累计激活值
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