refactor(heart_flow): 移除智能提醒分析逻辑
从消息处理流程中移除旧的智能提醒分析模块。 该功能已被一个新的、更可靠的提醒系统取代，因此旧的实现被移除以简化代码库并消除冗余。
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minecraft1024a
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Windpicker-owo
Windpicker-owo
parent
7d06dd1bce
commit
38c94ecb73
@@ -96,8 +96,7 @@ class HeartFCMessageReceiver:
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主要流程:
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主要流程:
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1. 消息解析与初始化
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1. 消息解析与初始化
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2. 智能提醒分析
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2. 消息缓冲处理
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3. 消息缓冲处理
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4. 过滤检查
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4. 过滤检查
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5. 兴趣度计算
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5. 兴趣度计算
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6. 关系处理
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6. 关系处理
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@@ -110,99 +109,7 @@ class HeartFCMessageReceiver:
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userinfo = message.message_info.user_info
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userinfo = message.message_info.user_info
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chat = message.chat_stream
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chat = message.chat_stream
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# 2. 智能提醒分析 - 检查用户是否请求提醒
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# 2. 兴趣度计算与更新
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from src.chat.chat_loop.proactive.smart_reminder_analyzer import smart_reminder_analyzer
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from src.chat.chat_loop.proactive.event_scheduler import event_scheduler
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try:
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reminder_event = await smart_reminder_analyzer.analyze_message(
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userinfo.user_id, # type: ignore
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message.processed_plain_text
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)
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if reminder_event:
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logger.info(f"检测到提醒请求: {reminder_event}")
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# 创建提醒回调函数
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async def reminder_callback(metadata):
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"""提醒执行回调函数 - 触发完整的主动思考流程"""
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try:
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# 获取对应的subheartflow实例
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from src.chat.heart_flow.heartflow import heartflow
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subflow = await heartflow.get_or_create_subheartflow(metadata.get("chat_id"))
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if not subflow:
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logger.error(f"无法获取subheartflow实例: {metadata.get('chat_id')}")
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return
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# 创建主动思考事件，触发完整的思考流程
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from src.chat.chat_loop.proactive.events import ProactiveTriggerEvent
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reminder_content = metadata.get('content', '提醒时间到了')
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# 使用原始消息内容作为reason，如果没有则使用处理后的内容
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original_message = metadata.get('original_message', '')
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reason_content = original_message if original_message else reminder_content
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event = ProactiveTriggerEvent(
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source="reminder_system",
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reason=f"定时提醒：{reason_content}",
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metadata=metadata,
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related_message_id=metadata.get("original_message_id")
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)
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# 通过subflow的HeartFChatting实例触发主动思考
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await subflow.heart_fc_instance.proactive_thinker.think(event)
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logger.info(f"已触发提醒的主动思考，内容: {reminder_content}")
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except Exception as callback_error:
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logger.error(f"执行提醒回调失败: {callback_error}")
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import traceback
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logger.error(traceback.format_exc())
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# Fallback: 如果主动思考失败，直接发送提醒消息
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try:
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from src.plugin_system.apis.send_api import text_to_stream
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reminder_content = metadata.get('content', '提醒时间到了')
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await text_to_stream(
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text=f"⏰ 提醒：{reminder_content}",
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stream_id=metadata.get("chat_id"),
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typing=False
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)
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logger.info(f"Fallback提醒消息已发送: {reminder_content}")
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except Exception as fallback_error:
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logger.error(f"Fallback提醒也失败了: {fallback_error}")
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# 调度提醒事件
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event_id = f"reminder_{reminder_event.user_id}_{int(reminder_event.reminder_time.timestamp())}"
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metadata = {
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"type": "reminder",
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"user_id": reminder_event.user_id,
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"sender_name": userinfo.user_nickname, # 添加发送者昵称
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"platform": chat.platform,
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"chat_id": chat.stream_id,
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"content": reminder_event.content,
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"confidence": reminder_event.confidence,
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"created_at": datetime.now().isoformat(),
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"original_message_id": message.message_info.message_id,
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"original_message": message.processed_plain_text # 保存完整的原始消息
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}
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success = await event_scheduler.schedule_event(
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event_id=event_id,
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trigger_time=reminder_event.reminder_time,
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callback=reminder_callback,
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metadata=metadata
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)
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if success:
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logger.info(f"提醒事件调度成功: {event_id}")
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else:
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logger.error(f"提醒事件调度失败: {event_id}")
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except Exception as e:
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logger.error(f"智能提醒分析失败: {e}")
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# 3. 兴趣度计算与更新
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interested_rate, is_mentioned, keywords = await _calculate_interest(message)
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interested_rate, is_mentioned, keywords = await _calculate_interest(message)
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message.interest_value = interested_rate
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message.interest_value = interested_rate
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message.is_mentioned = is_mentioned
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message.is_mentioned = is_mentioned
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