feat:把CHAT状态判断交给LLM
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SengokuCola
@@ -12,7 +12,11 @@ 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
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from .observation import ChattingObservation, Observation
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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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# 初始化日志记录器
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@@ -34,6 +38,15 @@ class SubHeartflowManager:
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self._lock = asyncio.Lock() # 用于保护 self.subheartflows 的访问
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self.mai_state_info: MaiStateInfo = mai_state_info # 存储传入的 MaiStateInfo 实例
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# 为 LLM 状态评估创建一个 LLMRequest 实例
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# 使用与 Heartflow 相同的模型和参数
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self.llm_state_evaluator = LLMRequest(
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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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)
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def get_all_subheartflows(self) -> List["SubHeartflow"]:
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"""获取所有当前管理的 SubHeartflow 实例列表 (快照)。"""
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return list(self.subheartflows.values())
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@@ -103,24 +116,6 @@ class SubHeartflowManager:
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except Exception as e:
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logger.error(f"[子心流管理] 设置ABSENT状态失败: {e}")
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# 停止子心流内部循环
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subheartflow.should_stop = True
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# 取消后台任务
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task = subheartflow.task
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if task and not task.done():
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task.cancel()
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logger.debug(f"[子心流管理] 已取消 {stream_name} 的后台任务")
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# 从管理字典中移除
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if subheartflow_id in self.subheartflows:
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del self.subheartflows[subheartflow_id]
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logger.debug(f"[子心流管理] 已移除 {stream_name}")
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return True
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else:
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logger.warning(f"[子心流管理] {stream_name} 已被提前移除")
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return False
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def get_inactive_subheartflows(self, max_age_seconds=INACTIVE_THRESHOLD_SECONDS):
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"""识别并返回需要清理的不活跃(处于ABSENT状态超过一小时)子心流(id, 原因)"""
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current_time = time.time()
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@@ -185,52 +180,6 @@ 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 activate_random_subflows_to_chat(self):
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"""主状态激活时，随机选择ABSENT子心流进入CHAT状态"""
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# 使用 self.mai_state_info 获取当前状态和限制
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current_mai_state = self.mai_state_info.get_current_state()
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limit = current_mai_state.get_normal_chat_max_num()
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if limit <= 0:
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logger.info("[激活] 当前状态不允许CHAT子心流")
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return
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# 获取所有ABSENT状态的子心流
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absent_flows = [flow for flow in self.subheartflows.values() if flow.chat_state.chat_status == ChatState.ABSENT]
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num_to_activate = min(limit, len(absent_flows))
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if num_to_activate <= 0:
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logger.info(f"[激活] 无可用ABSENT子心流(限额:{limit}, 可用:{len(absent_flows)})")
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return
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logger.info(f"[激活] 随机选择{num_to_activate}个ABSENT子心流进入CHAT状态")
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activated_count = 0
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for flow in random.sample(absent_flows, num_to_activate):
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flow_id = flow.subheartflow_id
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stream_name = chat_manager.get_stream_name(flow_id) or flow_id
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if flow_id not in self.subheartflows:
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logger.warning(f"[激活] 跳过{stream_name}, 子心流已不存在")
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continue
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logger.debug(f"[激活] 正在激活子心流{stream_name}")
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# --- 限额检查 --- #
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current_chat_count = self.count_subflows_by_state(ChatState.CHAT)
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if current_chat_count >= limit:
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logger.warning(f"[激活] 跳过{stream_name}, 普通聊天已达上限 ({current_chat_count}/{limit})")
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continue # 跳过此子心流，继续尝试激活下一个
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# --- 结束限额检查 --- #
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# 移除 states_num 参数
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await flow.change_chat_state(ChatState.CHAT)
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if flow.chat_state.chat_status == ChatState.CHAT:
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activated_count += 1
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else:
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logger.warning(f"[激活] {stream_name}状态设置失败")
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logger.info(f"[激活] 完成, 成功激活{activated_count}个子心流")
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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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@@ -394,15 +343,172 @@ 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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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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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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注意：此函数包含对假设的LLM函数的调用。
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"""
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log_prefix = "[LLM状态评估]"
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logger.info(f"{log_prefix} 开始基于LLM评估子心流状态...")
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# 获取当前状态和限制，用于CHAT激活检查
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current_mai_state = self.mai_state_info.get_current_state()
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chat_limit = current_mai_state.get_normal_chat_max_num()
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transitioned_to_chat = 0
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transitioned_to_absent = 0
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async with self._lock: # 在锁内获取快照并迭代
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subflows_snapshot = list(self.subheartflows.values())
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# 使用不上锁的版本，因为我们已经在锁内
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current_chat_count = self.count_subflows_by_state_nolock(ChatState.CHAT)
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if not subflows_snapshot:
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logger.info(f"{log_prefix} 当前没有子心流需要评估。")
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return
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for sub_hf in subflows_snapshot:
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flow_id = sub_hf.subheartflow_id
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stream_name = chat_manager.get_stream_name(flow_id) or flow_id
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current_subflow_state = sub_hf.chat_state.chat_status
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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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try:
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# 检查 observations 列表是否存在且不为空
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# 假设第一个观察者是 ChattingObservation
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first_observation = sub_hf.observations[0]
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if isinstance(first_observation, ChattingObservation):
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# 组合中期记忆和当前聊天内容
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current_chat = first_observation.talking_message_str or "当前无聊天内容。"
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combined_summary = f"当前聊天内容:\n{current_chat}"
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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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# --- 针对 ABSENT 状态 ---
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if current_subflow_state == ChatState.ABSENT:
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# 构建Prompt
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prompt = (
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f"子心流 [{stream_name}] 当前处于非活跃(ABSENT)状态。\n"
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f"{mai_state_description}\n"
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f"最近观察到的内容摘要:\n---\n{combined_summary}\n---\n"
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f"基于以上信息，该子心流是否表现出足够的活跃迹象或重要性，"
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f"值得将其唤醒并进入常规聊天(CHAT)状态？"
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f"请回答 '是' 或 '否'。"
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)
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# 调用LLM评估
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try:
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# 使用 self._llm_evaluate_state_transition
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should_activate = await self._llm_evaluate_state_transition(prompt)
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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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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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current_chat_count += 1 # 更新计数器
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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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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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# --- 针对 CHAT 状态 ---
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elif current_subflow_state == ChatState.CHAT:
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# 构建Prompt
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prompt = (
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f"子心流 [{stream_name}] 当前处于常规聊天(CHAT)状态。\n"
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f"{mai_state_description}\n"
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f"最近观察到的内容摘要:\n---\n{combined_summary}\n---\n"
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f"基于以上信息，该子心流是否表现出不活跃、对话结束或不再需要关注的迹象，"
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f"应该让其进入休眠(ABSENT)状态？"
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f"请回答 '是' 或 '否'。"
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)
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# 调用LLM评估
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try:
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# 使用 self._llm_evaluate_state_transition
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should_deactivate = await self._llm_evaluate_state_transition(prompt)
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if should_deactivate:
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logger.info(f"{log_prefix} [{stream_name}] LLM建议进入ABSENT状态。正在尝试转换...")
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await sub_hf.change_chat_state(ChatState.ABSENT)
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if sub_hf.chat_state.chat_status == ChatState.ABSENT:
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transitioned_to_absent += 1
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current_chat_count -= 1 # 更新计数器
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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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# 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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logger.info(
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f"{log_prefix} LLM评估周期结束。"
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f" 成功转换到CHAT: {transitioned_to_chat}."
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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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Args:
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state: 要统计的聊天状态枚举值
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prompt: 提供给 LLM 的提示信息。
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Returns:
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int: 处于该状态的子心流数量
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bool: True 表示应该转换，False 表示不应该转换。
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"""
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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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# 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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logger.debug(f"{log_prefix} LLM评估结果: 建议转换 (响应包含 '是')")
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return True
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elif response_text and "否" in response_text.strip():
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logger.debug(f"{log_prefix} LLM评估结果: 建议不转换 (响应包含 '否')")
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return False
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else:
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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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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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count = 0
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# 遍历所有子心流实例
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for subheartflow in self.subheartflows.values():
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@@ -411,12 +517,19 @@ class SubHeartflowManager:
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count += 1
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return count
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def get_active_subflow_minds(self) -> List[str]:
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"""获取所有活跃(非ABSENT)子心流的当前想法
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返回:
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List[str]: 包含所有活跃子心流当前想法的列表
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def count_subflows_by_state_nolock(self, state: ChatState) -> int:
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"""
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统计指定状态的子心流数量 (不上锁版本)。
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警告：仅应在已持有 self._lock 的上下文中使用此方法。
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"""
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count = 0
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for subheartflow in self.subheartflows.values():
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if subheartflow.chat_state.chat_status == state:
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count += 1
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return count
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def get_active_subflow_minds(self) -> List[str]:
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"""获取所有活跃(非ABSENT)子心流的当前想法"""
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minds = []
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for subheartflow in self.subheartflows.values():
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# 检查子心流是否活跃(非ABSENT状态)
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