3fa26debb9
为了解决 `focus_energy` 更新不及时，导致其无法准确反映当前对话兴趣度的问题，本次提交引入了一套新的实时更新与同步机制。 这确保了每当消息的兴趣度发生变化时，代表机器人注意力的 `focus_energy` 也能被立即重新计算和更新，使决策更加精准。 主要变更： 1. **手动更新**: 在 `ChatStream` 中新增 `update_focus_energy` 方法，允许外部逻辑在需要时手动触发 `focus_energy` 的重新计算。 2. **实时计算**: `ChatterActionPlanner` 在评估并更新消息兴趣度后，会立即调用 `update_focus_energy`，确保了兴趣度到注意力的即时传导。 3. **状态同步**: `ChatterManager` 在完成一次执行后，会主动将 `mood_manager` 中可能已更新的 `chat_stream` 同步回当前的 `StreamContext`，保证了整个处理流中数据的一致性。
145 lines
6.4 KiB
Python
145 lines
6.4 KiB
Python
from typing import Dict, List, Optional, Any
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import time
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from src.plugin_system.base.base_chatter import BaseChatter
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from src.common.data_models.message_manager_data_model import StreamContext
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from src.plugins.built_in.affinity_flow_chatter.planner import ChatterActionPlanner as ActionPlanner
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from src.chat.planner_actions.action_manager import ChatterActionManager
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from src.plugin_system.base.component_types import ChatType, ComponentType
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from src.common.logger import get_logger
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logger = get_logger("chatter_manager")
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class ChatterManager:
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def __init__(self, action_manager: ChatterActionManager):
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self.action_manager = action_manager
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self.chatter_classes: Dict[ChatType, List[type]] = {}
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self.instances: Dict[str, BaseChatter] = {}
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# 管理器统计
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self.stats = {
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"chatters_registered": 0,
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"streams_processed": 0,
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"successful_executions": 0,
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"failed_executions": 0,
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}
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def _auto_register_from_component_registry(self):
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"""从组件注册表自动注册已注册的chatter组件"""
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try:
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from src.plugin_system.core.component_registry import component_registry
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# 获取所有CHATTER类型的组件
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chatter_components = component_registry.get_enabled_chatter_registry()
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for chatter_name, chatter_class in chatter_components.items():
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self.register_chatter(chatter_class)
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logger.info(f"自动注册chatter组件: {chatter_name}")
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except Exception as e:
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logger.warning(f"自动注册chatter组件时发生错误: {e}")
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def register_chatter(self, chatter_class: type):
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"""注册聊天处理器类"""
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for chat_type in chatter_class.chat_types:
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if chat_type not in self.chatter_classes:
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self.chatter_classes[chat_type] = []
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self.chatter_classes[chat_type].append(chatter_class)
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logger.info(f"注册聊天处理器 {chatter_class.__name__} 支持 {chat_type.value} 聊天类型")
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self.stats["chatters_registered"] += 1
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def get_chatter_class(self, chat_type: ChatType) -> Optional[type]:
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"""获取指定聊天类型的聊天处理器类"""
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if chat_type in self.chatter_classes:
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return self.chatter_classes[chat_type][0]
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return None
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def get_supported_chat_types(self) -> List[ChatType]:
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"""获取支持的聊天类型列表"""
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return list(self.chatter_classes.keys())
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def get_registered_chatters(self) -> Dict[ChatType, List[type]]:
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"""获取已注册的聊天处理器"""
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return self.chatter_classes.copy()
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def get_stream_instance(self, stream_id: str) -> Optional[BaseChatter]:
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"""获取指定流的聊天处理器实例"""
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return self.instances.get(stream_id)
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def cleanup_inactive_instances(self, max_inactive_minutes: int = 60):
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"""清理不活跃的实例"""
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current_time = time.time()
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max_inactive_seconds = max_inactive_minutes * 60
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inactive_streams = []
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for stream_id, instance in self.instances.items():
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if hasattr(instance, 'get_activity_time'):
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activity_time = instance.get_activity_time()
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if (current_time - activity_time) > max_inactive_seconds:
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inactive_streams.append(stream_id)
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for stream_id in inactive_streams:
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del self.instances[stream_id]
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logger.info(f"清理不活跃聊天流实例: {stream_id}")
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async def process_stream_context(self, stream_id: str, context: StreamContext) -> dict:
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"""处理流上下文"""
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chat_type = context.chat_type
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logger.debug(f"处理流 {stream_id}，聊天类型: {chat_type.value}")
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if not self.chatter_classes:
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self._auto_register_from_component_registry()
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# 获取适合该聊天类型的chatter
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chatter_class = self.get_chatter_class(chat_type)
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if not chatter_class:
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# 如果没有找到精确匹配，尝试查找支持ALL类型的chatter
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from src.plugin_system.base.component_types import ChatType
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all_chatter_class = self.get_chatter_class(ChatType.ALL)
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if all_chatter_class:
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chatter_class = all_chatter_class
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logger.info(f"流 {stream_id} 使用通用chatter (类型: {chat_type.value})")
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else:
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raise ValueError(f"No chatter registered for chat type {chat_type}")
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if stream_id not in self.instances:
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self.instances[stream_id] = chatter_class(stream_id=stream_id, action_manager=self.action_manager)
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logger.info(f"创建新的聊天流实例: {stream_id} 使用 {chatter_class.__name__} (类型: {chat_type.value})")
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self.stats["streams_processed"] += 1
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try:
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result = await self.instances[stream_id].execute(context)
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self.stats["successful_executions"] += 1
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# 从 mood_manager 获取最新的 chat_stream 并同步回 StreamContext
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try:
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from src.mood.mood_manager import mood_manager
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mood = mood_manager.get_mood_by_chat_id(stream_id)
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if mood and mood.chat_stream:
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context.chat_stream = mood.chat_stream
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logger.debug(f"已将最新的 chat_stream 同步回流 {stream_id} 的 StreamContext")
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except Exception as sync_e:
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logger.error(f"同步 chat_stream 回 StreamContext 失败: {sync_e}")
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# 记录处理结果
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success = result.get("success", False)
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actions_count = result.get("actions_count", 0)
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logger.debug(f"流 {stream_id} 处理完成: 成功={success}, 动作数={actions_count}")
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return result
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except Exception as e:
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self.stats["failed_executions"] += 1
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logger.error(f"处理流 {stream_id} 时发生错误: {e}")
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raise
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def get_stats(self) -> Dict[str, Any]:
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"""获取管理器统计信息"""
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stats = self.stats.copy()
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stats["active_instances"] = len(self.instances)
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stats["registered_chatter_types"] = len(self.chatter_classes)
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return stats
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def reset_stats(self):
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"""重置统计信息"""
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self.stats = {
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"chatters_registered": 0,
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"streams_processed": 0,
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"successful_executions": 0,
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"failed_executions": 0,
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} |