fix：模块化PFC

src/plugins/PFC/action_planner.py

@@ -0,0 +1,157 @@
+import datetime
+import asyncio
+from typing import List, Optional, Dict, Any, Tuple, Literal, Set
+from enum import Enum
+from src.common.logger import get_module_logger
+from ..chat.chat_stream import ChatStream
+from ..message.message_base import UserInfo, Seg
+from ..chat.message import Message
+from ..models.utils_model import LLM_request
+from ..config.config import global_config
+from src.plugins.chat.message import MessageSending
+from ..message.api import global_api
+from ..storage.storage import MessageStorage
+from .chat_observer import ChatObserver
+from .reply_checker import ReplyChecker
+from .pfc_utils import get_items_from_json
+from src.individuality.individuality import Individuality
+from .chat_states import NotificationHandler, Notification, NotificationType
+import time
+from dataclasses import dataclass, field
+from .pfc import DecisionInfo, DecisionInfoType
+
+logger = get_module_logger("action_planner")
+
+class ActionPlanner:
+    """行动规划器"""
+    
+    def __init__(self, stream_id: str):
+        self.llm = LLM_request(
+            model=global_config.llm_normal,
+            temperature=0.7,
+            max_tokens=1000,
+            request_type="action_planning"
+        )
+        self.personality_info = Individuality.get_instance().get_prompt(type = "personality", x_person = 2, level = 2)
+        self.name = global_config.BOT_NICKNAME
+        self.chat_observer = ChatObserver.get_instance(stream_id)
+        
+    async def plan(
+        self, 
+        goal: str, 
+        method: str, 
+        reasoning: str,
+        action_history: List[Dict[str, str]] = None,
+        decision_info: DecisionInfoType = None  # Use DecisionInfoType here
+    ) -> Tuple[str, str]:
+        """规划下一步行动
+        
+        Args:
+            goal: 对话目标
+            method: 实现方法
+            reasoning: 目标原因
+            action_history: 行动历史记录
+            decision_info: 决策信息
+            
+        Returns:
+            Tuple[str, str]: (行动类型, 行动原因)
+        """
+        # 构建提示词
+        logger.debug(f"开始规划行动：当前目标: {goal}")
+        
+        # 获取最近20条消息
+        messages = self.chat_observer.get_message_history(limit=20)
+        chat_history_text = ""
+        for msg in messages:
+            time_str = datetime.datetime.fromtimestamp(msg["time"]).strftime("%H:%M:%S")
+            user_info = UserInfo.from_dict(msg.get("user_info", {}))
+            sender = user_info.user_nickname or f"用户{user_info.user_id}"
+            if sender == self.name:
+                sender = "你说"
+            chat_history_text += f"{time_str},{sender}:{msg.get('processed_plain_text', '')}\n"
+            
+        personality_text = f"你的名字是{self.name}，{self.personality_info}"
+        
+        # 构建action历史文本
+        action_history_text = ""
+        if action_history and action_history[-1]['action'] == "direct_reply":
+            action_history_text = "你刚刚发言回复了对方"
+            
+        # 构建决策信息文本
+        decision_info_text = ""
+        if decision_info:
+            decision_info_text = "当前对话状态：\n"
+            if decision_info.is_cold_chat:
+                decision_info_text += f"对话处于冷场状态，已持续{int(decision_info.cold_chat_duration)}秒\n"
+            
+            if decision_info.new_messages_count > 0:
+                decision_info_text += f"有{decision_info.new_messages_count}条新消息未处理\n"
+                
+            user_response_time = decision_info.get_user_response_time()
+            if user_response_time:
+                decision_info_text += f"距离用户上次发言已过去{int(user_response_time)}秒\n"
+                
+            bot_response_time = decision_info.get_bot_response_time()
+            if bot_response_time:
+                decision_info_text += f"距离你上次发言已过去{int(bot_response_time)}秒\n"
+                
+            if decision_info.active_users:
+                decision_info_text += f"当前活跃用户数: {len(decision_info.active_users)}\n"
+
+        prompt = f"""{personality_text}。现在你在参与一场QQ聊天，请分析以下内容，根据信息决定下一步行动：
+
+当前对话目标：{goal}
+实现该对话目标的方式：{method}
+产生该对话目标的原因：{reasoning}
+
+{decision_info_text}
+{action_history_text}
+
+最近的对话记录：
+{chat_history_text}
+
+请你接下去想想要你要做什么，可以发言，可以等待，可以倾听，可以调取知识。注意不同行动类型的要求，不要重复发言：
+行动类型：
+fetch_knowledge: 需要调取知识，当需要专业知识或特定信息时选择
+wait: 当你做出了发言,对方尚未回复时等待对方的回复
+listening: 倾听对方发言，当你认为对方发言尚未结束时采用
+direct_reply: 不符合上述情况，回复对方，注意不要过多或者重复发言
+rethink_goal: 重新思考对话目标，当发现对话目标不合适时选择，会重新思考对话目标
+judge_conversation: 判断对话是否结束，当发现对话目标已经达到或者希望停止对话时选择，会判断对话是否结束
+
+请以JSON格式输出，包含以下字段：
+1. action: 行动类型，注意你之前的行为
+2. reason: 选择该行动的原因，注意你之前的行为（简要解释）
+
+注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
+
+        logger.debug(f"发送到LLM的提示词: {prompt}")
+        try:
+            content, _ = await self.llm.generate_response_async(prompt)
+            logger.debug(f"LLM原始返回内容: {content}")
+            
+            # 使用简化函数提取JSON内容
+            success, result = get_items_from_json(
+                content,
+                "action", "reason",
+                default_values={"action": "direct_reply", "reason": "默认原因"}
+            )
+            
+            if not success:
+                return "direct_reply", "JSON解析失败，选择直接回复"
+            
+            action = result["action"]
+            reason = result["reason"]
+            
+            # 验证action类型
+            if action not in ["direct_reply", "fetch_knowledge", "wait", "listening", "rethink_goal", "judge_conversation"]:
+                logger.warning(f"未知的行动类型: {action}，默认使用listening")
+                action = "listening"
+                
+            logger.info(f"规划的行动: {action}")
+            logger.info(f"行动原因: {reason}")
+            return action, reason
+            
+        except Exception as e:
+            logger.error(f"规划行动时出错: {str(e)}")
+            return "direct_reply", "发生错误，选择直接回复"