refactor(chat): 重构消息管理器以使用集中式上下文管理和能量系统
- 将流上下文管理从MessageManager迁移到专门的ContextManager - 使用统一的能量系统计算focus_energy和分发间隔 - 重构ChatStream的消息数据转换逻辑，支持更完整的数据字段 - 更新数据库模型，移除interest_degree字段，统一使用interest_value - 集成新的兴趣度管理系统替代原有的评分系统 - 添加消息存储的interest_value修复功能
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Windpicker-owo
@@ -368,41 +368,30 @@ class ChatterPlanFilter:
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interest_scores = {}
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try:
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from src.plugins.built_in.affinity_flow_chatter.interest_scoring import (
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chatter_interest_scoring_system as interest_scoring_system,
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)
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from src.common.data_models.database_data_model import DatabaseMessages
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from src.chat.interest_system import interest_manager
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# 转换消息格式
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db_messages = []
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# 使用新的兴趣度管理系统计算评分
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for msg_dict in messages:
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try:
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db_msg = DatabaseMessages(
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message_id=msg_dict.get("message_id", ""),
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time=msg_dict.get("time", time.time()),
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chat_id=msg_dict.get("chat_id", ""),
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processed_plain_text=msg_dict.get("processed_plain_text", ""),
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user_id=msg_dict.get("user_id", ""),
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user_nickname=msg_dict.get("user_nickname", ""),
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user_platform=msg_dict.get("platform", "qq"),
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chat_info_group_id=msg_dict.get("group_id", ""),
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chat_info_group_name=msg_dict.get("group_name", ""),
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chat_info_group_platform=msg_dict.get("platform", "qq"),
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# 构建计算上下文
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calc_context = {
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"stream_id": msg_dict.get("chat_id", ""),
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"user_id": msg_dict.get("user_id"),
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}
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# 计算消息兴趣度
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interest_score = interest_manager.calculate_message_interest(
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message=msg_dict,
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context=calc_context
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)
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db_messages.append(db_msg)
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# 构建兴趣度字典
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interest_scores[msg_dict.get("message_id", "")] = interest_score
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except Exception as e:
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logger.warning(f"转换消息格式失败: {e}")
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logger.warning(f"计算消息兴趣度失败: {e}")
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continue
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# 计算兴趣度评分
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if db_messages:
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bot_nickname = global_config.bot.nickname or "麦麦"
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scores = await interest_scoring_system.calculate_interest_scores(db_messages, bot_nickname)
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# 构建兴趣度字典
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for score in scores:
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interest_scores[score.message_id] = score.total_score
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except Exception as e:
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logger.warning(f"获取兴趣度评分失败: {e}")
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@@ -4,13 +4,13 @@
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"""
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from dataclasses import asdict
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import time
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from typing import TYPE_CHECKING, Dict, List, Optional, Tuple
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from src.plugins.built_in.affinity_flow_chatter.plan_executor import ChatterPlanExecutor
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from src.plugins.built_in.affinity_flow_chatter.plan_filter import ChatterPlanFilter
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from src.plugins.built_in.affinity_flow_chatter.plan_generator import ChatterPlanGenerator
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from src.plugins.built_in.affinity_flow_chatter.interest_scoring import ChatterInterestScoringSystem
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from src.plugins.built_in.affinity_flow_chatter.relationship_tracker import ChatterRelationshipTracker
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from src.chat.interest_system import interest_manager
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from src.mood.mood_manager import mood_manager
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@@ -52,14 +52,7 @@ class ChatterActionPlanner:
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self.generator = ChatterPlanGenerator(chat_id)
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self.executor = ChatterPlanExecutor(action_manager)
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# 初始化兴趣度评分系统
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self.interest_scoring = ChatterInterestScoringSystem()
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# 创建新的关系追踪器
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self.relationship_tracker = ChatterRelationshipTracker(self.interest_scoring)
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# 设置执行器的关系追踪器
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self.executor.set_relationship_tracker(self.relationship_tracker)
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# 使用新的统一兴趣度管理系统
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# 规划器统计
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self.planner_stats = {
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@@ -107,51 +100,39 @@ class ChatterActionPlanner:
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initial_plan.available_actions = self.action_manager.get_using_actions()
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unread_messages = context.get_unread_messages() if context else []
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# 2. 兴趣度评分 - 只对未读消息进行评分
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# 2. 使用新的兴趣度管理系统进行评分
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score = 0.0
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should_reply = False
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reply_not_available = False
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if unread_messages:
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bot_nickname = global_config.bot.nickname
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interest_scores = await self.interest_scoring.calculate_interest_scores(unread_messages, bot_nickname)
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# 获取用户ID
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user_id = None
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if unread_messages[0].user_id:
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user_id = unread_messages[0].user_id
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# 3. 根据兴趣度调整可用动作
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if interest_scores:
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latest_score = max(interest_scores, key=lambda s: s.total_score)
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latest_message = next(
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(msg for msg in unread_messages if msg.message_id == latest_score.message_id), None
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)
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should_reply, score = self.interest_scoring.should_reply(latest_score, latest_message)
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# 构建计算上下文
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calc_context = {
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"stream_id": self.chat_id,
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"user_id": user_id,
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}
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reply_not_available = False
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if not should_reply and "reply" in initial_plan.available_actions:
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logger.info(f"兴趣度不足 ({latest_score.total_score:.2f})，移除回复")
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reply_not_available = True
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# 更新ChatStream的兴趣度数据
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from src.plugin_system.apis.chat_api import get_chat_manager
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chat_stream = get_chat_manager().get_stream(self.chat_id)
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logger.debug(f"已更新聊天 {self.chat_id} 的ChatStream兴趣度，分数: {score:.3f}")
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# 更新情绪状态和ChatStream兴趣度数据
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if latest_message and score > 0:
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chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
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await chat_mood.update_mood_by_message(latest_message, score)
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logger.debug(f"已更新聊天 {self.chat_id} 的情绪状态，兴趣度: {score:.3f}")
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elif latest_message:
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# 即使不更新情绪状态，也要更新ChatStream的兴趣度数据
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chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
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if hasattr(chat_mood, 'chat_stream') and chat_mood.chat_stream:
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chat_mood.chat_stream.add_message_interest(score)
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# 在这里同步更新 focus_energy
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chat_mood.chat_stream.update_focus_energy()
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logger.debug(f"已更新聊天 {self.chat_id} 的ChatStream兴趣度和Focus Energy，分数: {score:.3f}")
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# 为所有未读消息记录兴趣度信息
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# 为每条消息计算兴趣度
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for message in unread_messages:
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# 查找对应的兴趣度评分
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message_score = next((s for s in interest_scores if s.message_id == message.message_id), None)
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if message_score:
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message.interest_degree = message_score.total_score
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message.should_reply = self.interest_scoring.should_reply(message_score, message)[0]
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logger.debug(f"已记录消息 {message.message_id} - 兴趣度: {message_score.total_score:.3f}, 应回复: {message.should_reply}")
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try:
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# 使用新的兴趣度管理器计算
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message_interest = interest_manager.calculate_message_interest(
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message=message.__dict__,
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context=calc_context
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)
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# 更新消息的兴趣度
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message.interest_value = message_interest
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# 简单的回复决策逻辑：兴趣度超过阈值则回复
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message.should_reply = message_interest > global_config.affinity_flow.non_reply_action_interest_threshold
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logger.debug(f"消息 {message.message_id} 兴趣度: {message_interest:.3f}, 应回复: {message.should_reply}")
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# 更新StreamContext中的消息信息并刷新focus_energy
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if context:
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@@ -159,25 +140,35 @@ class ChatterActionPlanner:
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message_manager.update_message_and_refresh_energy(
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stream_id=self.chat_id,
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message_id=message.message_id,
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interest_degree=message_score.total_score,
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interest_value=message_interest,
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should_reply=message.should_reply
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)
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else:
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# 如果没有找到评分，设置默认值
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message.interest_degree = 0.0
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# 更新数据库中的消息记录
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try:
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from src.chat.message_receive.storage import MessageStorage
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MessageStorage.update_message_interest_value(message.message_id, message_interest)
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logger.debug(f"已更新数据库中消息 {message.message_id} 的兴趣度为: {message_interest:.3f}")
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except Exception as e:
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logger.warning(f"更新数据库消息兴趣度失败: {e}")
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# 更新话题兴趣度
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interest_manager.update_topic_interest(message.__dict__, message_interest)
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# 记录最高分
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if message_interest > score:
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score = message_interest
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if message.should_reply:
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should_reply = True
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else:
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reply_not_available = True
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except Exception as e:
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logger.warning(f"计算消息 {message.message_id} 兴趣度失败: {e}")
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# 设置默认值
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message.interest_value = 0.0
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message.should_reply = False
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# 更新StreamContext中的消息信息并刷新focus_energy
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if context:
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from src.chat.message_manager.message_manager import message_manager
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message_manager.update_message_and_refresh_energy(
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stream_id=self.chat_id,
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message_id=message.message_id,
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interest_degree=0.0,
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should_reply=False
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)
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# base_threshold = self.interest_scoring.reply_threshold
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# 检查兴趣度是否达到非回复动作阈值
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non_reply_action_interest_threshold = global_config.affinity_flow.non_reply_action_interest_threshold
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if score < non_reply_action_interest_threshold:
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@@ -199,26 +190,16 @@ class ChatterActionPlanner:
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plan_filter = ChatterPlanFilter(self.chat_id, available_actions)
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filtered_plan = await plan_filter.filter(reply_not_available, initial_plan)
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# 检查filtered_plan是否有reply动作，以便记录reply action
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has_reply_action = False
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for decision in filtered_plan.decided_actions:
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if decision.action_type == "reply":
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has_reply_action = True
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self.interest_scoring.record_reply_action(has_reply_action)
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# 检查filtered_plan是否有reply动作，用于统计
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has_reply_action = any(decision.action_type == "reply" for decision in filtered_plan.decided_actions)
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# 5. 使用 PlanExecutor 执行 Plan
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execution_result = await self.executor.execute(filtered_plan)
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# 6. 动作记录现在由ChatterActionManager统一处理
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# 动作记录逻辑已移至ChatterActionManager.execute_action方法中
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# 7. 根据执行结果更新统计信息
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# 6. 根据执行结果更新统计信息
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self._update_stats_from_execution_result(execution_result)
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# 8. 检查关系更新
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await self.relationship_tracker.check_and_update_relationships()
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# 8. 返回结果
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# 7. 返回结果
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return self._build_return_result(filtered_plan)
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except Exception as e:
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@@ -267,37 +248,10 @@ class ChatterActionPlanner:
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return final_actions_dict, final_target_message_dict
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def get_user_relationship(self, user_id: str) -> float:
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"""获取用户关系分"""
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return self.interest_scoring.get_user_relationship(user_id)
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def update_interest_keywords(self, new_keywords: Dict[str, List[str]]):
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"""更新兴趣关键词（已弃用，仅保留用于兼容性）"""
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logger.info("传统关键词匹配已移除，此方法仅保留用于兼容性")
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# 此方法已弃用，因为现在完全使用embedding匹配
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def get_planner_stats(self) -> Dict[str, any]:
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"""获取规划器统计"""
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return self.planner_stats.copy()
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def get_interest_scoring_stats(self) -> Dict[str, any]:
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"""获取兴趣度评分统计"""
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return {
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"no_reply_count": self.interest_scoring.no_reply_count,
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"max_no_reply_count": self.interest_scoring.max_no_reply_count,
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"reply_threshold": self.interest_scoring.reply_threshold,
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"mention_threshold": self.interest_scoring.mention_threshold,
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"user_relationships": len(self.interest_scoring.user_relationships),
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}
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def get_relationship_stats(self) -> Dict[str, any]:
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"""获取用户关系统计"""
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return {
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"tracking_users": len(self.relationship_tracker.tracking_users),
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"relationship_history": len(self.relationship_tracker.relationship_history),
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"max_tracking_users": self.relationship_tracker.max_tracking_users,
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}
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def get_current_mood_state(self) -> str:
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"""获取当前聊天的情绪状态"""
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chat_mood = mood_manager.get_mood_by_chat_id(self.chat_id)
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