mirror of https://github.com/Mai-with-u/MaiBot.git
typing 修复
UnCLAS-Prommer
parent
a196c940bf
commit
9d78cbb1f1
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@ -183,7 +183,7 @@ class HeartFChatting:
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self.talk_threshold = think_len
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logger.info(f"{self.log_prefix} 思考频率阈值: {self.talk_threshold}")
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async def _loopbody(self):
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async def _loopbody(self): # sourcery skip: hoist-if-from-if
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recent_messages_list = message_api.get_messages_by_time_in_chat(
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chat_id=self.stream_id,
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start_time=self.last_read_time,
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@ -195,11 +195,15 @@ class HeartFChatting:
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)
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if len(recent_messages_list) >= self.talk_threshold:
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# !处理no_reply_until_call逻辑
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if self.no_reply_until_call:
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for message in recent_messages_list:
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if message.is_mentioned or message.is_at or len(recent_messages_list) >= 8 or time.time() - self.last_read_time > 600:
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if (
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message.is_mentioned
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or message.is_at
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or len(recent_messages_list) >= 8
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or time.time() - self.last_read_time > 600
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):
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self.no_reply_until_call = False
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break
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# 没有提到，继续保持沉默
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@ -208,7 +212,6 @@ class HeartFChatting:
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await asyncio.sleep(1)
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return True
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self.last_read_time = time.time()
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await self._observe(
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recent_messages_list=recent_messages_list,
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@ -271,9 +274,9 @@ class HeartFChatting:
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return loop_info, reply_text, cycle_timers
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async def _observe(
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self, # interest_value: float = 0.0,
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recent_messages_list: Optional[List["DatabaseMessages"]] = None
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) -> bool:
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self, # interest_value: float = 0.0,
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recent_messages_list: Optional[List["DatabaseMessages"]] = None,
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) -> bool: # sourcery skip: merge-else-if-into-elif, remove-redundant-if
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if recent_messages_list is None:
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recent_messages_list = []
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reply_text = "" # 初始化reply_text变量，避免UnboundLocalError
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@ -327,14 +330,12 @@ class HeartFChatting:
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if modified_message and modified_message._modify_flags.modify_llm_prompt:
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prompt_info = (modified_message.llm_prompt, prompt_info[1])
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with Timer("规划器", cycle_timers):
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action_to_use_info, _ = await self.action_planner.plan(
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loop_start_time=self.last_read_time,
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available_actions=available_actions,
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)
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# !此处使at或者提及必定回复
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metioned_message = None
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for message in recent_messages_list:
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@ -344,7 +345,7 @@ class HeartFChatting:
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has_reply = False
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for action in action_to_use_info:
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if action.action_type == "reply":
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has_reply =True
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has_reply = True
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break
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if not has_reply and metioned_message:
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@ -358,7 +359,6 @@ class HeartFChatting:
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)
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)
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# 3. 并行执行所有动作
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action_tasks = [
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asyncio.create_task(
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@ -521,10 +521,9 @@ class HeartFChatting:
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reply_text = ""
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first_replied = False
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for reply_content in reply_set.reply_data:
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if reply_content.content_type != ReplyContentType.TEXT:
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continue
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data: str = reply_content.content # type: ignore
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data: str = reply_content.content # type: ignore
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if not first_replied:
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await send_api.text_to_stream(
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text=data,
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@ -576,6 +575,7 @@ class HeartFChatting:
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return {"action_type": "no_action", "success": True, "reply_text": "", "command": ""}
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elif action_planner_info.action_type == "wait_time":
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action_planner_info.action_data = action_planner_info.action_data or {}
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logger.info(f"{self.log_prefix} 等待{action_planner_info.action_data['time']}秒后回复")
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await asyncio.sleep(action_planner_info.action_data["time"])
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return {"action_type": "wait_time", "success": True, "reply_text": "", "command": ""}
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@ -10,7 +10,6 @@ from src.chat.message_receive.message import MessageRecv
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from src.chat.message_receive.storage import MessageStorage
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from src.chat.heart_flow.heartflow import heartflow
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from src.chat.utils.utils import is_mentioned_bot_in_message
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from src.chat.utils.timer_calculator import Timer
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from src.chat.utils.chat_message_builder import replace_user_references
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from src.common.logger import get_logger
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from src.mood.mood_manager import mood_manager
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@ -36,7 +35,7 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, list[str]]:
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return 0.0, []
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is_mentioned, is_at, reply_probability_boost = is_mentioned_bot_in_message(message)
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interested_rate = 0.0
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# interested_rate = 0.0
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keywords = []
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# with Timer("记忆激活"):
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# interested_rate, keywords, keywords_lite = await hippocampus_manager.get_activate_from_text(
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@ -153,10 +152,10 @@ class HeartFCMessageReceiver:
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logger.info(f"[{mes_name}]{userinfo.user_nickname}:{processed_plain_text}[{interested_rate:.2f}]") # type: ignore
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_ = Person.register_person(
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platform=message.message_info.platform,
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user_id=message.message_info.user_info.user_id,
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nickname=userinfo.user_nickname,
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) # type: ignore
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platform=message.message_info.platform, # type: ignore
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user_id=message.message_info.user_info.user_id, # type: ignore
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nickname=userinfo.user_nickname, # type: ignore
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)
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except Exception as e:
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logger.error(f"消息处理失败: {e}")
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@ -22,12 +22,12 @@ from src.chat.utils.chat_message_builder import (
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from src.chat.utils.utils import get_chat_type_and_target_info
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from src.chat.planner_actions.action_manager import ActionManager
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from src.chat.message_receive.chat_stream import get_chat_manager
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from src.plugin_system.base.component_types import ActionInfo, ChatMode, ComponentType, ActionActivationType
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from src.plugin_system.base.component_types import ActionInfo, ComponentType, ActionActivationType
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from src.plugin_system.core.component_registry import component_registry
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if TYPE_CHECKING:
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from src.common.data_models.info_data_model import TargetPersonInfo
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from src.common.data_models.database_data_model import DatabaseMessages, DatabaseActionRecords
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from src.common.data_models.database_data_model import DatabaseMessages
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logger = get_logger("planner")
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@ -121,7 +121,6 @@ no_reply_until_call
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)
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class ActionPlanner:
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def __init__(self, chat_id: str, action_manager: ActionManager):
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self.chat_id = chat_id
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@ -180,7 +179,6 @@ class ActionPlanner:
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target_message = message_id_list[-1][1]
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logger.info(f"{self.log_prefix}动作'{action}'缺少target_message_id，使用最新消息作为target_message")
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# 验证action是否可用
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available_action_names = [action_name for action_name, _ in current_available_actions]
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internal_action_names = ["no_reply", "reply", "wait_time", "no_reply_until_call"]
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@ -223,18 +221,17 @@ class ActionPlanner:
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return action_planner_infos
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async def plan(
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self,
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available_actions: Dict[str, ActionInfo],
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loop_start_time: float = 0.0,
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) -> Tuple[List[ActionPlannerInfo], Optional["DatabaseMessages"]]:
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# sourcery skip: use-named-expression
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"""
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规划器 (Planner): 使用LLM根据上下文决定做出什么动作。
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"""
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target_message: Optional["DatabaseMessages"] = None
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# 获取聊天上下文
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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chat_id=self.chat_id,
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@ -264,9 +261,7 @@ class ActionPlanner:
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is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
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# 应用激活类型过滤
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filtered_actions = self._filter_actions_by_activation_type(
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available_actions, chat_content_block_short
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)
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filtered_actions = self._filter_actions_by_activation_type(available_actions, chat_content_block_short)
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logger.info(f"{self.log_prefix}过滤后有{len(filtered_actions)}个可用动作")
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@ -286,7 +281,7 @@ class ActionPlanner:
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message_id_list=message_id_list,
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filtered_actions=filtered_actions,
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available_actions=available_actions,
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loop_start_time=loop_start_time
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loop_start_time=loop_start_time,
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)
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# 获取target_message（如果有非no_action的动作）
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@ -333,7 +328,9 @@ class ActionPlanner:
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moderation_prompt_block = "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。"
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time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
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bot_name = global_config.bot.nickname
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bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
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bot_nickname = (
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f",也有人叫你{','.join(global_config.bot.alias_names)}" if global_config.bot.alias_names else ""
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)
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name_block = f"你的名字是{bot_name}{bot_nickname}，请注意哪些是你自己的发言。"
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# 获取主规划器模板并填充
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@ -379,11 +376,8 @@ class ActionPlanner:
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return is_group_chat, chat_target_info, current_available_actions
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def _filter_actions_by_activation_type(
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self,
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available_actions: Dict[str, ActionInfo],
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chat_content_block: str
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self, available_actions: Dict[str, ActionInfo], chat_content_block: str
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) -> Dict[str, ActionInfo]:
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"""根据激活类型过滤动作"""
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filtered_actions = {}
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@ -409,6 +403,7 @@ class ActionPlanner:
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return filtered_actions
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async def _build_action_options_block(self, current_available_actions: Dict[str, ActionInfo]) -> str:
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# sourcery skip: use-join
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"""构建动作选项块"""
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if not current_available_actions:
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return ""
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@ -448,7 +443,7 @@ class ActionPlanner:
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message_id_list: List[Tuple[str, "DatabaseMessages"]],
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filtered_actions: Dict[str, ActionInfo],
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available_actions: Dict[str, ActionInfo],
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loop_start_time: float
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loop_start_time: float,
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) -> List[ActionPlannerInfo]:
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"""执行主规划器"""
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llm_content = None
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@ -487,16 +482,11 @@ class ActionPlanner:
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# 解析LLM响应
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if llm_content:
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try:
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# 处理新的格式：多个```json包裹的JSON对象
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json_objects = self._extract_json_from_markdown(llm_content)
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if json_objects:
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if json_objects := self._extract_json_from_markdown(llm_content):
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logger.info(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
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filtered_actions_list = list(filtered_actions.items())
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for json_obj in json_objects:
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actions.extend(
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self._parse_single_action(json_obj, message_id_list, filtered_actions_list)
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)
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actions.extend(self._parse_single_action(json_obj, message_id_list, filtered_actions_list))
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else:
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# 尝试解析为直接的JSON
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logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
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@ -509,12 +499,14 @@ class ActionPlanner:
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else:
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actions = self._create_no_reply("规划器没有获得LLM响应", available_actions)
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# 添加循环开始时间到所有非no_action动作
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for action in actions:
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action.action_data = action.action_data or {}
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action.action_data["loop_start_time"] = loop_start_time
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logger.info(f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}")
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logger.info(
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f"{self.log_prefix}规划器决定执行{len(actions)}个动作: {' '.join([a.action_type for a in actions])}"
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)
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return actions
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@ -531,21 +523,20 @@ class ActionPlanner:
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]
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def _extract_json_from_markdown(self, content: str) -> List[dict]:
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# sourcery skip: for-append-to-extend
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"""从Markdown格式的内容中提取JSON对象"""
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json_objects = []
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# 使用正则表达式查找```json包裹的JSON内容
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json_pattern = r'```json\s*(.*?)\s*```'
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json_pattern = r"```json\s*(.*?)\s*```"
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matches = re.findall(json_pattern, content, re.DOTALL)
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for match in matches:
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try:
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# 清理可能的注释和格式问题
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json_str = re.sub(r'//.*?\n', '\n', match) # 移除单行注释
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json_str = re.sub(r'/\*.*?\*/', '', json_str, flags=re.DOTALL) # 移除多行注释
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json_str = json_str.strip()
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if json_str:
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json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
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json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
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if json_str := json_str.strip():
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json_obj = json.loads(repair_json(json_str))
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if isinstance(json_obj, dict):
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json_objects.append(json_obj)
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@ -23,3 +23,4 @@ class ActionPlannerInfo(BaseDataModel):
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action_data: Optional[Dict] = None
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action_message: Optional["DatabaseMessages"] = None
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available_actions: Optional[Dict[str, "ActionInfo"]] = None
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loop_start_time: Optional[float] = None
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@ -72,9 +72,6 @@ class ChatConfig(ConfigBase):
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interest_rate_mode: Literal["fast", "accurate"] = "fast"
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"""兴趣值计算模式，fast为快速计算，accurate为精确计算"""
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mentioned_bot_reply: float = 1
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"""提及 bot 必然回复，1为100%回复，0为不额外增幅"""
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planner_size: float = 1.5
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"""副规划器大小，越小，麦麦的动作执行能力越精细，但是消耗更多token，调大可以缓解429类错误"""
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14
src/main.py
14
src/main.py
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@ -142,13 +142,13 @@ class MainSystem:
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await asyncio.gather(*tasks)
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async def forget_memory_task(self):
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"""记忆遗忘任务"""
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while True:
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await asyncio.sleep(global_config.memory.forget_memory_interval)
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logger.info("[记忆遗忘] 开始遗忘记忆...")
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await self.hippocampus_manager.forget_memory(percentage=global_config.memory.memory_forget_percentage) # type: ignore
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logger.info("[记忆遗忘] 记忆遗忘完成")
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# async def forget_memory_task(self):
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# """记忆遗忘任务"""
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# while True:
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# await asyncio.sleep(global_config.memory.forget_memory_interval)
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# logger.info("[记忆遗忘] 开始遗忘记忆...")
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# await self.hippocampus_manager.forget_memory(percentage=global_config.memory.memory_forget_percentage) # type: ignore
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# logger.info("[记忆遗忘] 记忆遗忘完成")
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async def main():
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@ -179,7 +179,7 @@ class BuildRelationAction(BaseAction):
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chat_model_config = models.get("utils")
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success, update_memory, _, _ = await llm_api.generate_with_model(
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prompt,
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model_config=chat_model_config,
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model_config=chat_model_config, # type: ignore
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request_type="relation.category.update", # type: ignore
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)
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