总之就是成了！😋😋😋主动思考终于成了
This commit is contained in:
tt-P607
Windpicker-owo
@@ -122,53 +122,67 @@ class ProactiveThinker:
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try:
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# 如果是提醒事件，跳过规划器，直接构建默认动作
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if trigger_event.source == "reminder_system":
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# 1. 获取原始消息上下文
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action_message = {}
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if trigger_event.related_message_id:
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# 直接将从数据库获取的完整消息记录作为 action_message
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action_message = await db_get(
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Messages, {"message_id": trigger_event.related_message_id}, single_result=True
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) or {}
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# 1. 获取上下文信息
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metadata = trigger_event.metadata or {}
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action_message = metadata
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reminder_content = trigger_event.reason.replace("定时提醒：", "").strip()
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# 2. 智能确定@对象
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reason_text = trigger_event.reason.replace("定时提醒：", "").strip()
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user_name_match = re.search(r"艾特一下(\S+)", reason_text)
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if user_name_match:
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user_name = user_name_match.group(1)
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at_message = reason_text.replace(f"艾特一下{user_name}", "").strip()
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elif action_message.get("user_nickname"):
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user_name = action_message.get("user_nickname")
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at_message = reason_text
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# 2. 确定目标用户名
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target_user_name = None
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match = re.search(r"艾特一下([^,，\s]+)", reminder_content)
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if match:
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target_user_name = match.group(1)
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else:
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user_name = "我"
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at_message = reason_text
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from src.person_info.person_info import get_person_info_manager
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user_id = metadata.get("user_id")
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platform = metadata.get("platform")
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if user_id and platform:
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person_id = get_person_info_manager().get_person_id(platform, user_id)
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target_user_name = await get_person_info_manager().get_value(person_id, "person_name")
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if not target_user_name:
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logger.warning(f"无法从提醒 '{reminder_content}' 中确定目标用户，回退")
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raise Exception("无法确定目标用户")
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# 3. 构建动作
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action_result = {
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"action_type": "at_user",
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"reasoning": "执行定时提醒",
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"action_data": {
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"user_name": user_name,
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"at_message": at_message or "时间到啦！"
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"user_name": target_user_name,
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"at_message": reminder_content
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},
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"action_message": action_message
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}
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# 4. 执行或回退
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try:
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success, _, _ = await self.cycle_processor._handle_action(
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original_chat_id = metadata.get("chat_id")
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if not original_chat_id:
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if trigger_event.related_message_id:
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db_message = await db_get(Messages, {"message_id": trigger_event.related_message_id}, single_result=True) or {}
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original_chat_id = db_message.get("chat_id")
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if not original_chat_id:
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raise Exception("提醒事件中缺少chat_id")
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from src.chat.heart_flow.heartflow import heartflow
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subflow = await heartflow.get_or_create_subheartflow(original_chat_id)
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if not subflow:
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raise Exception(f"无法为chat_id {original_chat_id} 获取subflow")
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success, _, _ = await subflow.heart_fc_instance.cycle_processor._handle_action(
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action=action_result["action_type"],
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reasoning=action_result["reasoning"],
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action_data=action_result["action_data"],
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cycle_timers={},
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thinking_id="",
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action_message=action_result["action_message"]
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action_message=action_result["action_message"],
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)
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if not success:
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raise Exception("at_user action failed")
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except Exception:
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logger.warning(f"{self.context.log_prefix} at_user动作执行失败，回退到proactive_reply")
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except Exception as e:
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logger.warning(f"{self.context.log_prefix} at_user动作执行失败: {e}，回退到proactive_reply")
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fallback_action = {
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"action_type": "proactive_reply",
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"action_data": {"topic": trigger_event.reason},
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@@ -100,7 +100,7 @@ class SmartReminderAnalyzer:
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请判断用户是否想要设置提醒，如果是，请提取：
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1. 是否包含提醒请求 (has_reminder: true/false)
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2. 置信度 (confidence: 0.0-1.0)
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3. 相对时间表达 (relative_time: 如"3分钟后", "2小时后")
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3. 相对时间表达 (relative_time: 标准化的时间表达，例如将'半小时后'转换为'30分钟后', '明天下午三点'转换为'明天15点')
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4. 提醒内容 (content: 提醒的具体内容)
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5. 分析原因 (reasoning: 判断理由)
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@@ -108,7 +108,7 @@ class SmartReminderAnalyzer:
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{{
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"has_reminder": true/false,
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"confidence": 0.0-1.0,
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"relative_time": "时间表达",
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"relative_time": "标准化的时间表达 (例如 '30分钟后', '2小时后')",
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"content": "提醒内容",
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"reasoning": "判断理由"
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}}"""
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@@ -152,7 +152,7 @@ class SmartReminderAnalyzer:
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logger.info(f"备用解析成功: {result}")
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return result
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except Exception as fallback_error:
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logger.error(f"备用JSON解析也失败: {fallback_error}")
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logger.error(f"备用JSON解析也失败了: {fallback_error}")
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except Exception as e:
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logger.error(f"LLM分析失败: {e}")
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@@ -130,9 +130,9 @@ class HeartFCMessageReceiver:
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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(chat.stream_id)
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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: {chat.stream_id}")
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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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@@ -142,11 +142,9 @@ class HeartFCMessageReceiver:
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event = ProactiveTriggerEvent(
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source="reminder_system",
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reason=f"定时提醒：{reminder_content}",
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metadata={
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"reminder_text": reminder_content,
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"trigger_time": datetime.now().isoformat()
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}
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)
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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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@@ -160,10 +158,11 @@ class HeartFCMessageReceiver:
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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=chat.stream_id,
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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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@@ -175,6 +174,7 @@ class HeartFCMessageReceiver:
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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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"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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@@ -50,6 +50,7 @@ def init_prompt():
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{time_block}
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{identity_block}
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{users_in_chat}
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{custom_prompt_block}
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{chat_context_description}，以下是具体的聊天内容。
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{chat_content_block}
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@@ -154,7 +155,6 @@ def init_prompt():
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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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@@ -165,7 +165,6 @@ class ActionPlanner:
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self.planner_llm = LLMRequest(
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model_set=model_config.model_task_config.planner, request_type="planner"
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)
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self.last_obs_time_mark = 0.0
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async def _get_long_term_memory_context(self) -> str:
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@@ -268,14 +267,14 @@ class ActionPlanner:
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# 假设消息列表是按时间顺序排列的，最后一个是最新的
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return message_id_list[-1].get("message")
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def _parse_single_action(
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async def _parse_single_action(
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self,
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action_json: dict,
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message_id_list: list, # 使用 planner.py 的 list of dict
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current_available_actions: list, # 使用 planner.py 的 list of tuple
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) -> List[Dict[str, Any]]:
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"""
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[注释] 解析单个小脑LLM返回的action JSON，并将其转换为标准化的字典。
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[注释] 解析单个LLM返回的action JSON，并将其转换为标准化的字典。
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"""
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parsed_actions = []
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try:
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@@ -312,6 +311,16 @@ class ActionPlanner:
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"available_actions": available_actions_dict,
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}
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)
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# 如果是at_user动作且只有user_name，尝试转换为user_id
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if action == "at_user" and "user_name" in action_data and "user_id" not in action_data:
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user_name = action_data["user_name"]
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from src.person_info.person_info import get_person_info_manager
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user_info = await get_person_info_manager().get_person_info_by_name(user_name)
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if user_info and user_info.get("user_id"):
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action_data["user_id"] = user_info["user_id"]
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logger.info(f"成功将用户名 '{user_name}' 解析为 user_id '{user_info['user_id']}'")
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else:
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logger.warning(f"无法将用户名 '{user_name}' 解析为 user_id")
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except Exception as e:
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logger.error(f"{self.log_prefix}解析单个action时出错: {e}")
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parsed_actions.append(
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@@ -349,22 +358,6 @@ class ActionPlanner:
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统一决策是否进行聊天回复(reply)以及执行哪些actions。
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"""
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# --- 1. 准备上下文信息 ---
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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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timestamp=time.time(),
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limit=int(global_config.chat.max_context_size * 0.6),
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)
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chat_content_block, message_id_list = build_readable_messages_with_id(
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messages=message_list_before_now,
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timestamp_mode="normal",
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read_mark=self.last_obs_time_mark,
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truncate=True,
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show_actions=True,
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)
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if pseudo_message:
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chat_content_block += f"\n[m99] 刚刚, 用户: {pseudo_message}"
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self.last_obs_time_mark = time.time()
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is_group_chat, chat_target_info, current_available_actions = self.get_necessary_info()
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if available_actions is None:
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available_actions = current_available_actions
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@@ -377,8 +370,6 @@ class ActionPlanner:
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chat_target_info=chat_target_info,
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current_available_actions=available_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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)
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llm_content, _ = await self.planner_llm.generate_response_async(prompt=prompt)
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@@ -392,7 +383,7 @@ class ActionPlanner:
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if isinstance(parsed_json, list):
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for item in parsed_json:
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if isinstance(item, dict):
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final_actions.extend(self._parse_single_action(item, used_message_id_list, list(available_actions.items())))
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final_actions.extend(await self._parse_single_action(item, used_message_id_list, list(available_actions.items())))
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# 如果是私聊且开启了强制回复，并且没有任何回复性action，则强制添加reply
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if not is_group_chat and global_config.chat.force_reply_private:
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@@ -402,7 +393,7 @@ class ActionPlanner:
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"action_type": "reply",
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"reasoning": "私聊强制回复",
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"action_data": {},
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"action_message": self.get_latest_message(message_id_list),
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"action_message": self.get_latest_message(used_message_id_list),
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"available_actions": available_actions,
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})
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logger.info(f"{self.log_prefix}私聊强制回复已触发，添加 'reply' 动作")
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@@ -444,8 +435,6 @@ class ActionPlanner:
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chat_target_info: Optional[dict],
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current_available_actions: Dict[str, ActionInfo],
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mode: ChatMode = ChatMode.FOCUS,
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chat_content_block_override: Optional[str] = None,
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message_id_list_override: Optional[List] = None,
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refresh_time: bool = False, # 添加缺失的参数
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) -> tuple[str, list]:
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"""构建 Planner LLM 的提示词 (获取模板并填充数据)"""
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@@ -479,7 +468,7 @@ class ActionPlanner:
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timestamp=time.time(),
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limit=int(global_config.chat.max_context_size * 0.2), # 主动思考时只看少量最近消息
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)
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chat_content_block, _ = build_readable_messages_with_id(
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chat_content_block, message_id_list = build_readable_messages_with_id(
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messages=message_list_short,
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timestamp_mode="normal",
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truncate=False,
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@@ -505,7 +494,7 @@ class ActionPlanner:
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chat_content_block=chat_content_block or "最近没有聊天内容。",
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actions_before_now_block=actions_before_now_block,
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)
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return prompt, []
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return prompt, message_id_list
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# --- FOCUS 和 NORMAL 模式的逻辑 ---
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message_list_before_now = get_raw_msg_before_timestamp_with_chat(
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@@ -513,7 +502,6 @@ class ActionPlanner:
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timestamp=time.time(),
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limit=int(global_config.chat.max_context_size * 0.6),
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)
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chat_content_block, message_id_list = build_readable_messages_with_id(
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messages=message_list_before_now,
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timestamp_mode="normal",
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@@ -580,6 +568,14 @@ class ActionPlanner:
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custom_prompt_block = ""
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if global_config.custom_prompt.planner_custom_prompt_content:
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custom_prompt_block = global_config.custom_prompt.planner_custom_prompt_content
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from src.person_info.person_info import get_person_info_manager
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users_in_chat_str = ""
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if is_group_chat and chat_target_info and chat_target_info.get("group_id"):
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user_list = await get_person_info_manager().get_specific_value_list("person_name", lambda x: x is not None)
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if user_list:
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users_in_chat_str = "当前聊天中的用户列表（用于@）：\n" + "\n".join([f"- {name} (ID: {pid})" for pid, name in user_list.items()]) + "\n"
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planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt")
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prompt = planner_prompt_template.format(
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@@ -597,6 +593,7 @@ class ActionPlanner:
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identity_block=identity_block,
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custom_prompt_block=custom_prompt_block,
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bot_name=bot_name,
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users_in_chat=users_in_chat_str
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)
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return prompt, message_id_list
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except Exception as e:
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Block a user