feat(planner): 实现大小脑规划器分离以优化决策流程
将规划器(Planner)拆分为“大脑”和“小脑”两个部分,以实现更精细化的决策控制。 - **大脑(BIG_BRAIN)**: 负责宏观决策,如是否回复、是否需要@人等高层级意图。 - **小脑(SMALL_BRAIN)**: 负责具体的功能性动作执行。 此重构引入了 `PlannerType` 枚举,并更新了动作(Action)定义,允许将动作明确分配给大脑或小脑,从而提升了AI回复的逻辑性和条理性。同时,新增了 `no_action` 类型,用于在规划阶段明确表示“无动作”,提高了处理流程的清晰度。
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committed by
Windpicker-owo
parent
cfffc69e20
commit
a30652b0bc
@@ -228,6 +228,8 @@ class CycleProcessor:
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async def execute_action(action_info):
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"""执行单个动作的通用函数"""
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try:
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if action_info["action_type"] == "no_action":
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return {"action_type": "no_action", "success": True, "reply_text": "", "command": ""}
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if action_info["action_type"] == "no_reply":
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# 直接处理no_reply逻辑,不再通过动作系统
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reason = action_info.get("reasoning", "选择不回复")
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@@ -245,7 +247,7 @@ class CycleProcessor:
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)
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return {"action_type": "no_reply", "success": True, "reply_text": "", "command": ""}
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elif action_info["action_type"] != "reply":
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elif action_info["action_type"] != "reply" and action_info["action_type"] != "no_action":
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# 执行普通动作
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with Timer("动作执行", cycle_timers):
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success, reply_text, command = await self._handle_action(
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@@ -420,7 +422,7 @@ class CycleProcessor:
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if "reply" in available_actions:
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fallback_action = "reply"
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elif available_actions:
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fallback_action = list(available_actions.keys())
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fallback_action = list(available_actions.keys())[0]
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if fallback_action and fallback_action != action:
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logger.info(f"{self.context.log_prefix} 使用回退动作: {fallback_action}")
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@@ -23,7 +23,13 @@ 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 (
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ActionInfo,
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ChatMode,
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ComponentType,
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ActionActivationType,
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PlannerType,
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)
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from src.plugin_system.core.component_registry import component_registry
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from src.schedule.schedule_manager import schedule_manager
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from src.mood.mood_manager import mood_manager
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@@ -503,6 +509,9 @@ class ActionPlanner:
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try:
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sub_planner_actions: Dict[str, ActionInfo] = {}
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for action_name, action_info in available_actions.items():
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if action_info.planner_type not in [PlannerType.SMALL_BRAIN, PlannerType.ALL]:
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continue
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if action_info.activation_type in [ActionActivationType.LLM_JUDGE, ActionActivationType.ALWAYS]:
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sub_planner_actions[action_name] = action_info
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elif action_info.activation_type == ActionActivationType.RANDOM:
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@@ -550,10 +559,15 @@ class ActionPlanner:
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# --- 3. 大脑独立思考是否回复 ---
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action, reasoning, action_data, target_message = "no_reply", "大脑初始化默认", {}, None
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try:
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big_brain_actions = {
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name: info
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for name, info in available_actions.items()
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if info.planner_type in [PlannerType.BIG_BRAIN, PlannerType.ALL]
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}
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prompt, _ = await self.build_planner_prompt(
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is_group_chat=is_group_chat,
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chat_target_info=chat_target_info,
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current_available_actions={}, # 大脑不考虑具体action
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current_available_actions=big_brain_actions,
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mode=mode,
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chat_content_block_override=chat_content_block,
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message_id_list_override=message_id_list,
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@@ -6,7 +6,7 @@ from typing import Tuple, Optional, Dict, Any
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from src.common.logger import get_logger
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from src.chat.message_receive.chat_stream import ChatStream
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from src.plugin_system.base.component_types import ActionActivationType, ChatMode, ActionInfo, ComponentType, ChatType
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from src.plugin_system.base.component_types import ActionActivationType, ChatMode, ActionInfo, ComponentType, ChatType,PlannerType
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from src.plugin_system.apis import send_api, database_api, message_api
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@@ -92,6 +92,8 @@ class BaseAction(ABC):
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self.parallel_action: bool = getattr(self.__class__, "parallel_action", True)
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self.associated_types: list[str] = getattr(self.__class__, "associated_types", []).copy()
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self.chat_type_allow: ChatType = getattr(self.__class__, "chat_type_allow", ChatType.ALL)
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self.planner_type: PlannerType = getattr(self.__class__, "planner_type", ChatType.ALL)
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# =============================================================================
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# 便捷属性 - 直接在初始化时获取常用聊天信息(带类型注解)
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@@ -51,6 +51,17 @@ class ChatMode(Enum):
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# 聊天类型枚举
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class PlannerType(Enum):
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"""规划器类型枚举"""
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BIG_BRAIN = "big_brain" # 大脑,负责宏观决策
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SMALL_BRAIN = "small_brain" # 小脑,负责具体动作
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ALL = "all" # 通用
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def __str__(self):
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return self.value
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class ChatType(Enum):
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"""聊天类型枚举,用于限制插件在不同聊天环境中的使用"""
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@@ -140,6 +151,7 @@ class ActionInfo(ComponentInfo):
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mode_enable: ChatMode = ChatMode.ALL
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parallel_action: bool = False
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chat_type_allow: ChatType = ChatType.ALL # 允许的聊天类型
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planner_type: PlannerType = PlannerType.ALL
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def __post_init__(self):
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super().__post_init__()
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@@ -215,27 +227,7 @@ class EventInfo(ComponentInfo):
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def __post_init__(self):
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super().__post_init__()
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self.component_type = ComponentType.EVENT
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# 事件类型枚举
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class EventType(Enum):
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"""
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事件类型枚举类
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"""
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ON_START = "on_start" # 启动事件,用于调用按时任务
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ON_STOP = "on_stop" # 停止事件,用于调用按时任务
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ON_MESSAGE = "on_message"
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ON_PLAN = "on_plan"
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POST_LLM = "post_llm"
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AFTER_LLM = "after_llm"
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POST_SEND = "post_send"
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AFTER_SEND = "after_send"
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UNKNOWN = "unknown" # 未知事件类型
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def __str__(self) -> str:
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return self.value
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self.component_type = ComponentType.EVENT_HANDLER
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@dataclass
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@@ -10,7 +10,7 @@ from src.plugin_system import (
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)
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from src.person_info.person_info import get_person_info_manager
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from src.common.logger import get_logger
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from src.plugin_system.base.component_types import ChatType
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from src.plugin_system.base.component_types import ChatType,PlannerType
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logger = get_logger(__name__)
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@@ -24,6 +24,7 @@ class AtAction(BaseAction):
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activation_type = ActionActivationType.LLM_JUDGE # 消息接收时激活(?)
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parallel_action = False
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chat_type_allow = ChatType.GROUP
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planner_type = PlannerType.BIG_BRAIN
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# === 功能描述(必须填写)===
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action_parameters = {"user_name": "需要艾特用户的名字", "at_message": "艾特用户时要发送的消,注意消息里不要有@"}
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