mirror of https://github.com/Mai-with-u/MaiBot.git
typing 修复
UnCLAS-Prommer
parent
a196c940bf
commit
9d78cbb1f1
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@ -108,9 +108,9 @@ class HeartFChatting:
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self._current_cycle_detail: CycleDetail = None # type: ignore
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self.last_read_time = time.time() - 10
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self.talk_threshold = global_config.chat.talk_value
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self.no_reply_until_call = False
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async def start(self):
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@ -172,7 +172,7 @@ class HeartFChatting:
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f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒" # type: ignore
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+ (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "")
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)
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def get_talk_threshold(self):
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talk_value = global_config.chat.talk_value
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# 处理talk_value：取整数部分和小数部分
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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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@ -207,8 +211,7 @@ class HeartFChatting:
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# logger.info(f"{self.log_prefix} 没有提到，继续保持沉默")
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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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@ -283,7 +286,7 @@ class HeartFChatting:
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async with global_prompt_manager.async_message_scope(self.chat_stream.context.get_template_name()):
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await self.expression_learner.trigger_learning_for_chat()
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cycle_timers, thinking_id = self.start_cycle()
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logger.info(f"{self.log_prefix} 开始第{self._cycle_counter}次思考")
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@ -326,27 +329,25 @@ class HeartFChatting:
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return False
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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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if (message.is_mentioned or message.is_at) and global_config.chat.mentioned_bot_reply:
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metioned_message = message
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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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action_to_use_info.append(
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ActionPlannerInfo(
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@ -357,7 +358,6 @@ class HeartFChatting:
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available_actions=available_actions,
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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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@ -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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@ -574,17 +573,18 @@ class HeartFChatting:
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action_name="no_action",
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)
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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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elif action_planner_info.action_type == "no_reply_until_call":
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logger.info(f"{self.log_prefix} 保持沉默，直到有人直接叫的名字")
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self.no_reply_until_call = True
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return {"action_type": "no_reply_until_call", "success": True, "reply_text": "", "command": ""}
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elif action_planner_info.action_type == "reply":
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try:
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success, llm_response = await generator_api.generate_reply(
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@ -624,7 +624,7 @@ class HeartFChatting:
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"reply_text": reply_text,
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"loop_info": loop_info,
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}
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# 其他动作
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else:
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# 执行普通动作
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@ -643,7 +643,7 @@ class HeartFChatting:
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"reply_text": reply_text,
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"command": command,
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}
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except Exception as e:
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logger.error(f"{self.log_prefix} 执行动作时出错: {e}")
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logger.error(f"{self.log_prefix} 错误信息: {traceback.format_exc()}")
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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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@ -168,7 +167,7 @@ class ActionPlanner:
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action_data = {key: value for key, value in action_json.items() if key not in ["action", "reason"]}
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# 非no_action动作需要target_message_id
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target_message = None
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if target_message_id := action_json.get("target_message_id"):
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# 根据target_message_id查找原始消息
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target_message = self.find_message_by_id(target_message_id, message_id_list)
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@ -179,12 +178,11 @@ class ActionPlanner:
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else:
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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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if action not in internal_action_names and action not in available_action_names:
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logger.warning(
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f"{self.log_prefix}LLM 返回了当前不可用或无效的动作: '{action}' (可用: {available_action_names})，将强制使用 'no_reply'"
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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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@ -249,7 +246,7 @@ class ActionPlanner:
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truncate=True,
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show_actions=True,
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)
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message_list_before_now_short = message_list_before_now[-int(global_config.chat.max_context_size * 0.3) :]
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chat_content_block_short, message_id_list_short = build_readable_messages_with_id(
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messages=message_list_before_now_short,
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@ -257,17 +254,15 @@ class ActionPlanner:
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truncate=False,
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show_actions=False,
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)
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self.last_obs_time_mark = time.time()
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# 获取必要信息
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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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# 构建包含所有动作的提示词
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@ -279,21 +274,21 @@ class ActionPlanner:
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message_id_list=message_id_list,
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interest=global_config.personality.interest,
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)
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# 调用LLM获取决策
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actions = await self._execute_main_planner(
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prompt=prompt,
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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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non_no_actions = [a for a in actions if a.action_type != "no_reply"]
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if non_no_actions:
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target_message = non_no_actions[0].action_message
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return actions, target_message
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async def build_planner_prompt(
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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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@ -379,15 +376,12 @@ 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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for action_name, action_info in available_actions.items():
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if action_info.activation_type == ActionActivationType.NEVER:
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logger.debug(f"{self.log_prefix}动作 {action_name} 设置为 NEVER 激活类型，跳过")
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@ -405,14 +399,15 @@ class ActionPlanner:
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break
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else:
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logger.warning(f"{self.log_prefix}未知的激活类型: {action_info.activation_type}，跳过处理")
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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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action_options_block = ""
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for action_name, action_info in current_available_actions.items():
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# 构建参数文本
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@ -422,13 +417,13 @@ class ActionPlanner:
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for param_name, param_description in action_info.action_parameters.items():
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param_text += f' "{param_name}":"{param_description}"\n'
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param_text = param_text.rstrip("\n")
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# 构建要求文本
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require_text = ""
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for require_item in action_info.action_require:
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require_text += f"- {require_item}\n"
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require_text = require_text.rstrip("\n")
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# 获取动作提示模板并填充
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using_action_prompt = await global_prompt_manager.get_prompt_async("action_prompt")
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using_action_prompt = using_action_prompt.format(
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@ -437,30 +432,30 @@ class ActionPlanner:
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action_parameters=param_text,
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action_require=require_text,
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)
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action_options_block += using_action_prompt
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return action_options_block
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async def _execute_main_planner(
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self,
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prompt: str,
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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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actions: List[ActionPlannerInfo] = []
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||||
|
||||
|
||||
try:
|
||||
# 调用LLM
|
||||
llm_content, (reasoning_content, _, _) = await self.planner_llm.generate_response_async(prompt=prompt)
|
||||
|
||||
|
||||
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
|
||||
|
||||
if global_config.debug.show_prompt:
|
||||
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
|
||||
logger.info(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
|
|
@ -471,7 +466,7 @@ class ActionPlanner:
|
|||
logger.debug(f"{self.log_prefix}规划器原始响应: {llm_content}")
|
||||
if reasoning_content:
|
||||
logger.debug(f"{self.log_prefix}规划器推理: {reasoning_content}")
|
||||
|
||||
|
||||
except Exception as req_e:
|
||||
logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
|
||||
return [
|
||||
|
|
@ -483,41 +478,38 @@ class ActionPlanner:
|
|||
available_actions=available_actions,
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
# 解析LLM响应
|
||||
if llm_content:
|
||||
try:
|
||||
# 处理新的格式：多个```json包裹的JSON对象
|
||||
json_objects = self._extract_json_from_markdown(llm_content)
|
||||
|
||||
if json_objects:
|
||||
if json_objects := self._extract_json_from_markdown(llm_content):
|
||||
logger.info(f"{self.log_prefix}从响应中提取到{len(json_objects)}个JSON对象")
|
||||
filtered_actions_list = list(filtered_actions.items())
|
||||
for json_obj in json_objects:
|
||||
actions.extend(
|
||||
self._parse_single_action(json_obj, message_id_list, filtered_actions_list)
|
||||
)
|
||||
actions.extend(self._parse_single_action(json_obj, message_id_list, filtered_actions_list))
|
||||
else:
|
||||
# 尝试解析为直接的JSON
|
||||
logger.warning(f"{self.log_prefix}LLM没有返回可用动作: {llm_content}")
|
||||
actions = self._create_no_reply("LLM没有返回可用动作", available_actions)
|
||||
|
||||
|
||||
except Exception as json_e:
|
||||
logger.warning(f"{self.log_prefix}解析LLM响应JSON失败 {json_e}. LLM原始输出: '{llm_content}'")
|
||||
actions = self._create_no_reply(f"解析LLM响应JSON失败: {json_e}", available_actions)
|
||||
traceback.print_exc()
|
||||
else:
|
||||
actions = self._create_no_reply("规划器没有获得LLM响应", available_actions)
|
||||
|
||||
|
||||
|
||||
# 添加循环开始时间到所有非no_action动作
|
||||
for action in actions:
|
||||
action.action_data = action.action_data or {}
|
||||
action.action_data["loop_start_time"] = loop_start_time
|
||||
|
||||
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])}")
|
||||
|
||||
|
||||
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])}"
|
||||
)
|
||||
|
||||
return actions
|
||||
|
||||
|
||||
def _create_no_reply(self, reasoning: str, available_actions: Dict[str, ActionInfo]) -> List[ActionPlannerInfo]:
|
||||
"""创建no_action"""
|
||||
return [
|
||||
|
|
@ -529,23 +521,22 @@ class ActionPlanner:
|
|||
available_actions=available_actions,
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
def _extract_json_from_markdown(self, content: str) -> List[dict]:
|
||||
# sourcery skip: for-append-to-extend
|
||||
"""从Markdown格式的内容中提取JSON对象"""
|
||||
json_objects = []
|
||||
|
||||
|
||||
# 使用正则表达式查找```json包裹的JSON内容
|
||||
json_pattern = r'```json\s*(.*?)\s*```'
|
||||
json_pattern = r"```json\s*(.*?)\s*```"
|
||||
matches = re.findall(json_pattern, content, re.DOTALL)
|
||||
|
||||
|
||||
for match in matches:
|
||||
try:
|
||||
# 清理可能的注释和格式问题
|
||||
json_str = re.sub(r'//.*?\n', '\n', match) # 移除单行注释
|
||||
json_str = re.sub(r'/\*.*?\*/', '', json_str, flags=re.DOTALL) # 移除多行注释
|
||||
json_str = json_str.strip()
|
||||
|
||||
if json_str:
|
||||
json_str = re.sub(r"//.*?\n", "\n", match) # 移除单行注释
|
||||
json_str = re.sub(r"/\*.*?\*/", "", json_str, flags=re.DOTALL) # 移除多行注释
|
||||
if json_str := json_str.strip():
|
||||
json_obj = json.loads(repair_json(json_str))
|
||||
if isinstance(json_obj, dict):
|
||||
json_objects.append(json_obj)
|
||||
|
|
@ -556,7 +547,7 @@ class ActionPlanner:
|
|||
except Exception as e:
|
||||
logger.warning(f"解析JSON块失败: {e}, 块内容: {match[:100]}...")
|
||||
continue
|
||||
|
||||
|
||||
return json_objects
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -23,3 +23,4 @@ class ActionPlannerInfo(BaseDataModel):
|
|||
action_data: Optional[Dict] = None
|
||||
action_message: Optional["DatabaseMessages"] = None
|
||||
available_actions: Optional[Dict[str, "ActionInfo"]] = None
|
||||
loop_start_time: Optional[float] = None
|
||||
|
|
|
|||
|
|
@ -72,9 +72,6 @@ class ChatConfig(ConfigBase):
|
|||
interest_rate_mode: Literal["fast", "accurate"] = "fast"
|
||||
"""兴趣值计算模式，fast为快速计算，accurate为精确计算"""
|
||||
|
||||
mentioned_bot_reply: float = 1
|
||||
"""提及 bot 必然回复，1为100%回复，0为不额外增幅"""
|
||||
|
||||
planner_size: float = 1.5
|
||||
"""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"""
|
||||
|
||||
|
|
|
|||
14
src/main.py
14
src/main.py
|
|
@ -142,13 +142,13 @@ class MainSystem:
|
|||
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
async def forget_memory_task(self):
|
||||
"""记忆遗忘任务"""
|
||||
while True:
|
||||
await asyncio.sleep(global_config.memory.forget_memory_interval)
|
||||
logger.info("[记忆遗忘] 开始遗忘记忆...")
|
||||
await self.hippocampus_manager.forget_memory(percentage=global_config.memory.memory_forget_percentage) # type: ignore
|
||||
logger.info("[记忆遗忘] 记忆遗忘完成")
|
||||
# async def forget_memory_task(self):
|
||||
# """记忆遗忘任务"""
|
||||
# while True:
|
||||
# await asyncio.sleep(global_config.memory.forget_memory_interval)
|
||||
# logger.info("[记忆遗忘] 开始遗忘记忆...")
|
||||
# await self.hippocampus_manager.forget_memory(percentage=global_config.memory.memory_forget_percentage) # type: ignore
|
||||
# logger.info("[记忆遗忘] 记忆遗忘完成")
|
||||
|
||||
|
||||
async def main():
|
||||
|
|
|
|||
|
|
@ -179,7 +179,7 @@ class BuildRelationAction(BaseAction):
|
|||
chat_model_config = models.get("utils")
|
||||
success, update_memory, _, _ = await llm_api.generate_with_model(
|
||||
prompt,
|
||||
model_config=chat_model_config,
|
||||
model_config=chat_model_config, # type: ignore
|
||||
request_type="relation.category.update", # type: ignore
|
||||
)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue