mirror of https://github.com/Mai-with-u/MaiBot.git
死亡性的试图加入心流 爆炸概率90%
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@ -1,7 +1,10 @@
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# PFC/action_planner.py
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import traceback
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import time
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from typing import Tuple, Optional # 增加了 Optional
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from typing import Tuple, Optional, Dict, Any # 增加了 Optional, Dict, Any
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from src.common.logger_manager import get_logger
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from ..models.utils_model import LLMRequest
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# from ..models.utils_model import LLMRequest # Assuming LLMRequest is needed, ensure import path is correct
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from src.common.utils_llm import LLMRequest # Using updated common location assumption
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from ...config.config import global_config
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from .chat_observer import ChatObserver
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from .pfc_utils import get_items_from_json
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@ -21,6 +24,8 @@ PROMPT_INITIAL_REPLY = """{persona_text}。现在你在参与一场QQ私聊，
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【当前对话目标】
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{goals_str}
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【你现在的想法】
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{pfc_heartflow}
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【最近行动历史概要】
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{action_history_summary}
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@ -43,23 +48,25 @@ block_and_ignore: 更加极端的结束对话方式，直接结束对话并在
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请以JSON格式输出你的决策：
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{{
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"action": "选择的行动类型 (必须是上面列表中的一个)",
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"reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的)"
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"reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”、你的想法和自身设定人设做出合理判断的)"
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}}
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注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
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注意：请严格按照JSON格式输出，不要包含任何其他内容。""" # Updated reason instruction
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# Prompt(2): 上一次成功回复后，决定继续发言时的决策 Prompt
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PROMPT_FOLLOW_UP = """{persona_text}。现在你在参与一场QQ私聊，刚刚你已经回复了对方，请根据以下【所有信息】审慎且灵活的决策下一步行动，可以继续发送新消息，可以等待，可以倾听，可以调取知识，甚至可以屏蔽对方：
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PROMPT_FOLLOW_UP = """{persona_text}。现在你在参与一场QQ私聊，刚刚你已经回复了对方，请根据以下【所有信息】审慎且灵活的决策下一步行动，可以继续发送新消息，可以等待，可以倾听，可以调取知识，甚至可以屏蔽对方：
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【当前对话目标】
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{goals_str}
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【你现在的想法】
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{pfc_heartflow}
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【最近行动历史概要】
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{action_history_summary}
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【上一次行动的详细情况和结果】
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{last_action_context}
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【时间和超时提示】
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{time_since_last_bot_message_info}{timeout_context}
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{time_since_last_bot_message_info}{timeout_context}
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【最近的对话记录】(包括你已成功发送的消息 和 新收到的消息)
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{chat_history_text}
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@ -76,10 +83,10 @@ block_and_ignore: 更加极端的结束对话方式，直接结束对话并在
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请以JSON格式输出你的决策：
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{{
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"action": "选择的行动类型 (必须是上面列表中的一个)",
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"reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”和自身设定人设做出合理判断的。请说明你为什么选择继续发言而不是等待，以及打算发送什么类型的新消息连续发言，必须记录已经发言了几次)"
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"reason": "选择该行动的详细原因 (必须有解释你是如何根据“上一次行动结果”、“对话记录”、你的想法和自身设定人设做出合理判断的。请说明你为什么选择继续发言而不是等待，以及打算发送什么类型的新消息连续发言，必须记录已经发言了几次)"
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}}
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注意：请严格按照JSON格式输出，不要包含任何其他内容。"""
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注意：请严格按照JSON格式输出，不要包含任何其他内容。""" # Updated reason instruction
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# ActionPlanner 类定义，顶格
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@ -87,70 +94,80 @@ class ActionPlanner:
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"""行动规划器"""
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def __init__(self, stream_id: str):
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self.llm = LLMRequest(
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model=global_config.llm_PFC_action_planner,
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temperature=global_config.llm_PFC_action_planner["temp"],
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max_tokens=1500,
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request_type="action_planning",
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)
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self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=3)
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self.identity_detail_info = Individuality.get_instance().get_prompt(type="identity", x_person=2, level=2)
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# Ensure correct LLM config path/structure
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try:
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self.llm = LLMRequest(
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model=global_config.llm_PFC_action_planner,
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temperature=global_config.llm_PFC_action_planner.get("temp", 0.7), # Use .get for safety
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max_tokens=global_config.llm_PFC_action_planner.get("max_tokens", 1500), # Use .get for safety
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request_type="action_planning",
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)
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except AttributeError:
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logger.error("Config error: llm_PFC_action_planner not found or missing keys ('temp'/'max_tokens'). Using fallback.")
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# Fallback or raise error
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self.llm = LLMRequest(model=global_config.llm_normal, temperature=0.7, max_tokens=1000, request_type="action_planning_fallback")
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# Load personality/identity prompts
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self.individuality = Individuality.get_instance() # Store instance
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self.personality_info = self.individuality.get_prompt(type="personality", x_person=2, level=3)
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self.identity_detail_info = self.individuality.get_prompt(type="identity", x_person=2, level=2)
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self.name = global_config.BOT_NICKNAME
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self.chat_observer = ChatObserver.get_instance(stream_id)
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# self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量
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# 修改 plan 方法签名，增加 last_successful_reply_action 参数
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# 修改 plan 方法签名，增加 pfc_heartflow 参数
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async def plan(
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self,
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observation_info: ObservationInfo,
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conversation_info: ConversationInfo,
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last_successful_reply_action: Optional[str],
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pfc_heartflow: Optional[str], # <--- 新增参数
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) -> Tuple[str, str]:
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"""规划下一步行动
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Args:
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observation_info: 决策信息
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conversation_info: 对话信息
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last_successful_reply_action: 上一次成功的回复动作类型 ('direct_reply' 或 'send_new_message' 或 None)
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last_successful_reply_action: 上一次成功的回复动作类型
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pfc_heartflow: 当前的心流文本 # <--- 新增参数说明
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Returns:
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Tuple[str, str]: (行动类型, 行动原因)
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"""
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# --- 获取 Bot 上次发言时间信息 ---
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# (这部分逻辑不变)
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time_since_last_bot_message_info = ""
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try:
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bot_id = str(global_config.BOT_QQ)
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if hasattr(observation_info, "chat_history") and observation_info.chat_history:
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for i in range(len(observation_info.chat_history) - 1, -1, -1):
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msg = observation_info.chat_history[i]
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if not isinstance(msg, dict):
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continue
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sender_info = msg.get("user_info", {})
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sender_id = str(sender_info.get("user_id")) if isinstance(sender_info, dict) else None
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msg_time = msg.get("time")
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if sender_id == bot_id and msg_time:
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time_diff = time.time() - msg_time
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if time_diff < 60.0:
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time_since_last_bot_message_info = (
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f"提示：你上一条成功发送的消息是在 {time_diff:.1f} 秒前。\n"
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)
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break
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else:
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logger.debug("Observation info chat history is empty or not available for bot time check.")
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except AttributeError:
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logger.warning("ObservationInfo object might not have chat_history attribute yet for bot time check.")
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except Exception as e:
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logger.warning(f"获取 Bot 上次发言时间时出错: {e}")
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bot_last_speak_time = observation_info.last_bot_speak_time # Use ObservationInfo directly
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if bot_last_speak_time:
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time_diff = time.time() - bot_last_speak_time
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if time_diff < 3600: # Show within an hour
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time_since_last_bot_message_info = f"提示：你上一条成功发送的消息是在 {time_diff:.1f} 秒前。\n"
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# --- 获取超时提示信息 ---
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# (这部分逻辑不变)
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timeout_context = ""
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<<<<<<< HEAD
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# Check for timeout goal added by Waiter
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if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
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last_goal_item = conversation_info.goal_list[-1]
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goal_text = ""
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reason_text = ""
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if isinstance(last_goal_item, dict):
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goal_text = last_goal_item.get("goal", "")
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reason_text = last_goal_item.get("reason", "") # Get reason from dict
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elif isinstance(last_goal_item, tuple) and len(last_goal_item) > 0:
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goal_text = last_goal_item[0]
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if len(last_goal_item) > 1:
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reason_text = last_goal_item[1] # Get reason from tuple
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# Check if goal indicates a wait timeout
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if "分钟，思考接下来要做什么" in goal_text or "对方话说一半消失了" in goal_text:
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timeout_context = f"重要提示：检测到等待超时。({reason_text}) 请基于此情况规划下一步。\n"
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=======
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try:
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if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
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last_goal_tuple = conversation_info.goal_list[-1]
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if isinstance(last_goal_tuple, tuple) and len(last_goal_tuple) > 0:
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last_goal_text = last_goal_tuple[0]
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last_goal_dict = conversation_info.goal_list[-1]
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if isinstance(last_goal_dict, dict) and "goal" in last_goal_dict:
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last_goal_text = last_goal_dict["goal"]
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if isinstance(last_goal_text, str) and "分钟，思考接下来要做什么" in last_goal_text:
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try:
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timeout_minutes_text = last_goal_text.split("，")[0].replace("你等待了", "")
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@ -163,28 +180,52 @@ class ActionPlanner:
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logger.warning("ConversationInfo object might not have goal_list attribute yet for timeout check.")
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except Exception as e:
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logger.warning(f"检查超时目标时出错: {e}")
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>>>>>>> 3cfa1e6b17340f82f2937a2243b8e99030196294
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# --- 构建通用 Prompt 参数 ---
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logger.debug(f"开始规划行动：当前目标: {getattr(conversation_info, 'goal_list', '不可用')}")
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# 构建对话目标 (goals_str)
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<<<<<<< HEAD
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goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n" # Default
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if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
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temp_goals_str = ""
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for goal_reason in conversation_info.goal_list:
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goal = "目标内容缺失"
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reasoning = "没有明确原因"
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if isinstance(goal_reason, tuple) and len(goal_reason) > 0:
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goal = goal_reason[0]
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if len(goal_reason) > 1: reasoning = goal_reason[1]
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elif isinstance(goal_reason, dict):
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goal = goal_reason.get("goal", "目标内容缺失")
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reasoning = goal_reason.get("reason", "没有明确原因") # Use 'reason' key based on Waiter
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else:
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goal = str(goal_reason)
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goal = str(goal) if goal is not None else "目标内容缺失"
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reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
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temp_goals_str += f"- 目标：{goal}\n 原因：{reasoning}\n"
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if temp_goals_str: # Only overwrite default if goals were found
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goals_str = temp_goals_str
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=======
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goals_str = ""
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try:
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if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
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for goal_reason in conversation_info.goal_list:
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if isinstance(goal_reason, tuple) and len(goal_reason) > 0:
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goal = goal_reason[0]
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reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
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elif isinstance(goal_reason, dict):
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if isinstance(goal_reason, dict):
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goal = goal_reason.get("goal", "目标内容缺失")
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reasoning = goal_reason.get("reasoning", "没有明确原因")
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else:
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goal = str(goal_reason)
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reasoning = "没有明确原因"
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goal = str(goal) if goal is not None else "目标内容缺失"
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reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
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goals_str += f"- 目标：{goal}\n 原因：{reasoning}\n"
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if not goals_str:
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if not goals_str:
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goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n"
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else:
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goals_str = "- 目前没有明确对话目标，请考虑设定一个。\n"
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except AttributeError:
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logger.warning("ConversationInfo object might not have goal_list attribute yet.")
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@ -192,43 +233,37 @@ class ActionPlanner:
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except Exception as e:
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logger.error(f"构建对话目标字符串时出错: {e}")
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goals_str = "- 构建对话目标时出错。\n"
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>>>>>>> 3cfa1e6b17340f82f2937a2243b8e99030196294
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# 获取聊天历史记录 (chat_history_text)
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chat_history_text = ""
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try:
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if hasattr(observation_info, "chat_history") and observation_info.chat_history:
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chat_history_text = observation_info.chat_history_str
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if not chat_history_text:
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chat_history_text = "还没有聊天记录。\n"
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else:
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chat_history_text = "还没有聊天记录。\n"
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chat_history_text = "还没有聊天记录。\n" # Default
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if hasattr(observation_info, 'chat_history_str') and observation_info.chat_history_str:
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chat_history_text = observation_info.chat_history_str + "\n" # Ensure newline
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if hasattr(observation_info, "new_messages_count") and observation_info.new_messages_count > 0:
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if hasattr(observation_info, "unprocessed_messages") and observation_info.unprocessed_messages:
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new_messages_list = observation_info.unprocessed_messages
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# Append unprocessed messages if any
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if hasattr(observation_info, 'new_messages_count') and observation_info.new_messages_count > 0:
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if hasattr(observation_info, 'unprocessed_messages') and observation_info.unprocessed_messages:
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new_messages_list = observation_info.unprocessed_messages
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try:
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# Ensure build_readable_messages exists and handles the list format correctly
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new_messages_str = await build_readable_messages(
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new_messages_list,
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replace_bot_name=True,
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merge_messages=False,
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timestamp_mode="relative",
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read_mark=0.0,
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read_mark=0.0, # Assuming this param exists
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)
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chat_history_text += (
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f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
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)
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else:
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logger.warning(
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"ObservationInfo has new_messages_count > 0 but unprocessed_messages is empty or missing."
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)
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except AttributeError:
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logger.warning("ObservationInfo object might be missing expected attributes for chat history.")
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chat_history_text = "获取聊天记录时出错。\n"
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except Exception as e:
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logger.error(f"处理聊天记录时发生未知错误: {e}")
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chat_history_text = "处理聊天记录时出错。\n"
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chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新收到的消息 ---\n{new_messages_str}\n"
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except Exception as build_err:
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logger.error(f"Error building readable messages: {build_err}")
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chat_history_text += "\n--- (无法格式化新消息) ---\n"
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else:
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logger.warning("new_messages_count > 0 but unprocessed_messages is empty/missing.")
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chat_history_text += f"\n--- (有 {observation_info.new_messages_count} 条新消息，但无法显示内容) ---\n"
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# 构建 Persona 文本 (persona_text)
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# (这部分逻辑不变)
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# Using stored Individuality instance
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identity_details_only = self.identity_detail_info
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identity_addon = ""
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if isinstance(identity_details_only, str):
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@ -245,71 +280,57 @@ class ActionPlanner:
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persona_text = f"你的名字是{self.name}，{self.personality_info}{identity_addon}。"
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# 构建行动历史和上一次行动结果 (action_history_summary, last_action_context)
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# (这部分逻辑不变)
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action_history_summary = "你最近执行的行动历史：\n"
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last_action_context = "关于你【上一次尝试】的行动：\n"
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action_history_list = []
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try:
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if hasattr(conversation_info, "done_action") and conversation_info.done_action:
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action_history_list = conversation_info.done_action[-5:]
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else:
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logger.debug("Conversation info done_action is empty or not available.")
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except AttributeError:
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logger.warning("ConversationInfo object might not have done_action attribute yet.")
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except Exception as e:
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logger.error(f"访问行动历史时出错: {e}")
|
||||
if hasattr(conversation_info, "done_action") and conversation_info.done_action:
|
||||
action_history_list = conversation_info.done_action[-5:] # Get last 5
|
||||
|
||||
if not action_history_list:
|
||||
action_history_summary += "- 还没有执行过行动。\n"
|
||||
last_action_context += "- 这是你规划的第一个行动。\n"
|
||||
else:
|
||||
for i, action_data in enumerate(action_history_list):
|
||||
action_type = "未知"
|
||||
plan_reason = "未知"
|
||||
status = "未知"
|
||||
# Default values
|
||||
action_type = "未知行动"
|
||||
plan_reason = "未知原因"
|
||||
status = "未知状态"
|
||||
final_reason = ""
|
||||
action_time = ""
|
||||
action_time = "未知时间"
|
||||
|
||||
# Check if action_data is a dictionary (new format)
|
||||
if isinstance(action_data, dict):
|
||||
action_type = action_data.get("action", "未知")
|
||||
plan_reason = action_data.get("plan_reason", "未知规划原因")
|
||||
status = action_data.get("status", "未知")
|
||||
final_reason = action_data.get("final_reason", "")
|
||||
action_time = action_data.get("time", "")
|
||||
elif isinstance(action_data, tuple):
|
||||
# 假设旧格式兼容
|
||||
if len(action_data) > 0:
|
||||
action_type = action_data[0]
|
||||
if len(action_data) > 1:
|
||||
plan_reason = action_data[1] # 可能是规划原因或最终原因
|
||||
if len(action_data) > 2:
|
||||
status = action_data[2]
|
||||
if status == "recall" and len(action_data) > 3:
|
||||
final_reason = action_data[3]
|
||||
elif status == "done" and action_type in ["direct_reply", "send_new_message"]:
|
||||
plan_reason = "成功发送" # 简化显示
|
||||
action_type = action_data.get("action", action_type)
|
||||
plan_reason = action_data.get("plan_reason", plan_reason)
|
||||
status = action_data.get("status", status)
|
||||
final_reason = action_data.get("final_reason", "") # Get final_reason if exists
|
||||
action_time = action_data.get("time", action_time)
|
||||
else:
|
||||
# Handle potential old format or unexpected data gracefully
|
||||
logger.warning(f"Unexpected action history format: {action_data}")
|
||||
action_type = str(action_data) # Basic representation
|
||||
|
||||
# Build summary line
|
||||
reason_text = f", 失败/取消原因: {final_reason}" if final_reason else ""
|
||||
summary_line = f"- 时间:{action_time}, 尝试行动:'{action_type}', 状态:{status}{reason_text}"
|
||||
action_history_summary += summary_line + "\n"
|
||||
|
||||
# Build context for the very last action
|
||||
if i == len(action_history_list) - 1:
|
||||
last_action_context += f"- 上次【规划】的行动是: '{action_type}'\n"
|
||||
last_action_context += f"- 当时规划的【原因】是: {plan_reason}\n"
|
||||
if status == "done":
|
||||
last_action_context += "- 该行动已【成功执行】。\n"
|
||||
# 记录这次成功的行动类型，供下次决策
|
||||
# self.last_successful_action_type = action_type # 不在这里记录，由 conversation 控制
|
||||
elif status == "recall":
|
||||
last_action_context += "- 但该行动最终【未能执行/被取消】。\n"
|
||||
if final_reason:
|
||||
last_action_context += f"- 【重要】失败/取消的具体原因是: “{final_reason}”\n"
|
||||
else:
|
||||
last_action_context += "- 【重要】失败/取消原因未明确记录。\n"
|
||||
# self.last_successful_action_type = None # 行动失败，清除记录
|
||||
else:
|
||||
last_action_context += f"- 该行动当前状态: {status}\n"
|
||||
# self.last_successful_action_type = None # 非完成状态，清除记录
|
||||
# Handle other potential statuses like 'start' or unexpected ones
|
||||
last_action_context += f"- 该行动当前状态: {status} (未完成或状态未知)\n"
|
||||
|
||||
|
||||
# --- 选择 Prompt ---
|
||||
if last_successful_reply_action in ["direct_reply", "send_new_message"]:
|
||||
|
|
@ -320,36 +341,46 @@ class ActionPlanner:
|
|||
logger.debug("使用 PROMPT_INITIAL_REPLY (首次/非连续回复决策)")
|
||||
|
||||
# --- 格式化最终的 Prompt ---
|
||||
prompt = prompt_template.format(
|
||||
persona_text=persona_text,
|
||||
goals_str=goals_str if goals_str.strip() else "- 目前没有明确对话目标，请考虑设定一个。",
|
||||
action_history_summary=action_history_summary,
|
||||
last_action_context=last_action_context,
|
||||
time_since_last_bot_message_info=time_since_last_bot_message_info,
|
||||
timeout_context=timeout_context,
|
||||
chat_history_text=chat_history_text if chat_history_text.strip() else "还没有聊天记录。",
|
||||
)
|
||||
# Provide default for heartflow if None or empty
|
||||
heartflow_for_prompt = pfc_heartflow if pfc_heartflow else "你现在还没有明确的想法，请先思考。"
|
||||
|
||||
try:
|
||||
prompt = prompt_template.format(
|
||||
persona_text=persona_text,
|
||||
goals_str=goals_str.strip(), # Remove leading/trailing whitespace
|
||||
pfc_heartflow=heartflow_for_prompt, # <--- 传入心流
|
||||
action_history_summary=action_history_summary.strip(),
|
||||
last_action_context=last_action_context.strip(),
|
||||
time_since_last_bot_message_info=time_since_last_bot_message_info,
|
||||
timeout_context=timeout_context,
|
||||
chat_history_text=chat_history_text.strip(),
|
||||
)
|
||||
except KeyError as e:
|
||||
logger.error(f"格式化行动规划 Prompt 时出错，缺少键: {e}")
|
||||
# Handle error: maybe return a default action or raise
|
||||
return "wait", f"内部错误：无法格式化行动规划提示（缺少 {e}）"
|
||||
|
||||
logger.debug(f"发送到LLM的最终提示词:\n------\n{prompt}\n------")
|
||||
try:
|
||||
content, _ = await self.llm.generate_response_async(prompt)
|
||||
logger.debug(f"LLM原始返回内容: {content}")
|
||||
|
||||
# Use default_values in get_items_from_json for robustness
|
||||
success, result = get_items_from_json(
|
||||
content,
|
||||
"action",
|
||||
"reason",
|
||||
default_values={"action": "wait", "reason": "LLM返回格式错误或未提供原因，默认等待"},
|
||||
required_types={"action": str, "reason": str} # Add type checking
|
||||
)
|
||||
|
||||
action = result.get("action", "wait")
|
||||
action = result.get("action", "wait") # Ensure default if key somehow missing after get_items
|
||||
reason = result.get("reason", "LLM未提供原因，默认等待")
|
||||
|
||||
# 验证action类型
|
||||
# 更新 valid_actions 列表以包含 send_new_message
|
||||
valid_actions = [
|
||||
"direct_reply",
|
||||
"send_new_message", # 添加新动作
|
||||
"send_new_message",
|
||||
"fetch_knowledge",
|
||||
"wait",
|
||||
"listening",
|
||||
|
|
@ -362,10 +393,14 @@ class ActionPlanner:
|
|||
reason = f"(原始行动'{action}'无效，已强制改为wait) {reason}"
|
||||
action = "wait"
|
||||
|
||||
# Sanitize reason (optional, e.g., remove extra quotes)
|
||||
reason = reason.strip().strip('"')
|
||||
|
||||
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"规划行动时调用 LLM 或处理结果出错: {str(e)}")
|
||||
return "wait", f"行动规划处理中发生错误，暂时等待: {str(e)}"
|
||||
logger.error(traceback.format_exc()) # Log full traceback
|
||||
return "wait", f"行动规划处理中发生错误，暂时等待: {str(e)}"
|
||||
|
|
@ -188,10 +188,7 @@ class Conversation:
|
|||
goal_ended = False
|
||||
if hasattr(self.conversation_info, "goal_list") and self.conversation_info.goal_list:
|
||||
for goal_item in self.conversation_info.goal_list:
|
||||
current_goal = None
|
||||
if isinstance(goal_item, tuple) and len(goal_item) > 0:
|
||||
current_goal = goal_item[0]
|
||||
elif isinstance(goal_item, dict):
|
||||
if isinstance(goal_item, dict):
|
||||
current_goal = goal_item.get("goal")
|
||||
|
||||
if current_goal == "结束对话":
|
||||
|
|
|
|||
|
|
@ -1,10 +1,11 @@
|
|||
from typing import Optional
|
||||
from typing import Optional, List, Dict, Any
|
||||
|
||||
|
||||
class ConversationInfo:
|
||||
def __init__(self):
|
||||
self.done_action = []
|
||||
self.goal_list = []
|
||||
self.knowledge_list = []
|
||||
self.memory_list = []
|
||||
self.done_action: List[Dict[str, Any]] = [] # Added type hint
|
||||
self.goal_list: List[Dict[str, Any]] = [] # Added type hint (assuming goal list contains dicts)
|
||||
self.knowledge_list: List[Dict[str, Any]] = [] # Added type hint
|
||||
self.memory_list = [] # Keep as is if type is unknown or mixed
|
||||
self.last_successful_reply_action: Optional[str] = None
|
||||
self.current_heartflow: Optional[str] = None # <--- 新增：存储当前心流
|
||||
|
|
@ -60,16 +60,10 @@ class GoalAnalyzer:
|
|||
goals_str = ""
|
||||
if conversation_info.goal_list:
|
||||
for goal_reason in conversation_info.goal_list:
|
||||
# 处理字典或元组格式
|
||||
if isinstance(goal_reason, tuple):
|
||||
# 假设元组的第一个元素是目标，第二个元素是原因
|
||||
goal = goal_reason[0]
|
||||
reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
|
||||
elif isinstance(goal_reason, dict):
|
||||
goal = goal_reason.get("goal")
|
||||
if isinstance(goal_reason, dict):
|
||||
goal = goal_reason.get("goal", "目标内容缺失")
|
||||
reasoning = goal_reason.get("reasoning", "没有明确原因")
|
||||
else:
|
||||
# 如果是其他类型，尝试转为字符串
|
||||
goal = str(goal_reason)
|
||||
reasoning = "没有明确原因"
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,266 @@
|
|||
# PFC/pfc_heartflow.py
|
||||
import traceback
|
||||
import time
|
||||
from typing import Optional, Dict, Any
|
||||
from src.individuality.individuality import Individuality
|
||||
from src.common.logger import get_module_logger
|
||||
from src.common.utils_llm import LLMRequest # Assuming utils_model is renamed or refactored
|
||||
from ...config.config import global_config # Make sure path is correct
|
||||
from .observation_info import ObservationInfo
|
||||
from .conversation_info import ConversationInfo
|
||||
from maim_message import UserInfo # Assuming maim_message provides UserInfo
|
||||
from src.plugins.utils.chat_message_builder import build_readable_messages # Make sure path is correct
|
||||
|
||||
logger = get_module_logger("pfc_heartflow")
|
||||
|
||||
# --- Heartflow Generation Prompts ---
|
||||
|
||||
# Prompt (1): Initial Heartflow Generation (When conversation starts or bot receives first message)
|
||||
PROMPT_INITIAL = """{persona_text}
|
||||
现在你正在上网，{sender_name} ({sender_id}) 刚刚给你发来了一条QQ私聊消息。
|
||||
|
||||
【最近的聊天记录】(包括你已成功发送的消息 和 新收到的消息)
|
||||
{chat_history_text}
|
||||
--- 消息结束 ---
|
||||
|
||||
请根据以上信息，结合你的性格，设想一下你收到这条消息时的第一反应和真实想法。
|
||||
要求：
|
||||
- 输出一段连贯、自然、真实的内心独白。
|
||||
- 像一个真人在思考，语气可以随意一些，符合你的性格。
|
||||
- 不要分点输出，不要包含任何动作描写、表情符号、引号或括号。
|
||||
- **只输出内心独白内容，不要任何额外文字或格式。**
|
||||
"""
|
||||
|
||||
# Prompt (2): Heartflow Update After Successfully Sending a Message
|
||||
PROMPT_AFTER_SEND = """{persona_text}
|
||||
你正在和 {sender_name} ({sender_id}) QQ私聊。
|
||||
你刚才的想法是：『{previous_heartflow}』
|
||||
刚刚你成功发送了一条消息给对方："{last_bot_message}"
|
||||
|
||||
【你们最近的聊天记录】(包括你刚发送的消息)
|
||||
{chat_history_text}
|
||||
--- 消息结束 ---
|
||||
|
||||
现在，请结合你刚才的想法、你发送的消息以及聊天记录，继续思考。
|
||||
- 你的想法可以是对刚才发送内容的补充、延伸，或者思考对方可能的反应，或者计划下一步说什么。
|
||||
- 保持想法的连贯性，但也要注意话题的推进，不要停留在完全相同的想法上，除非你觉得有必要强调。
|
||||
- 输出一段连贯、自然、真实的内心独白。
|
||||
- 像一个真人在思考，语气可以随意一些，符合你的性格。
|
||||
- 不要分点输出，不要包含任何动作描写、表情符号、引号或括号。
|
||||
- **只输出内心独白内容，不要任何额外文字或格式。**
|
||||
"""
|
||||
|
||||
# Prompt (3): Heartflow Update After Reply Check Failed (e.g., message rejected)
|
||||
PROMPT_AFTER_FAIL = """{persona_text}
|
||||
你正在和 {sender_name} ({sender_id}) QQ私聊。
|
||||
你之前的想法是：『{previous_heartflow}』
|
||||
你本来想发送一条消息："{failed_message}"
|
||||
但是，这个想法/回复因为『{fail_reason}』被你自己否定了/觉得不合适。
|
||||
|
||||
【你们最近的聊天记录】
|
||||
{chat_history_text}
|
||||
--- 消息结束 ---
|
||||
|
||||
现在，请结合你之前的想法、被否定的消息以及失败原因，重新思考。
|
||||
- 你可能会反思为什么刚才的想法不合适，或者思考替代的说法，或者决定暂时不回复。
|
||||
- 保持想法的连贯性，但要根据失败原因调整思路。
|
||||
- 输出一段连贯、自然、真实的内心独白。
|
||||
- 像一个真人在思考，语气可以随意一些，符合你的性格。
|
||||
- 不要分点输出，不要包含任何动作描写、表情符号、引号或括号。
|
||||
- **只输出内心独白内容，不要任何额外文字或格式。**
|
||||
"""
|
||||
|
||||
# Prompt (4): Heartflow Update After Waiting Timeout
|
||||
PROMPT_AFTER_TIMEOUT = """{persona_text}
|
||||
你正在和 {sender_name} ({sender_id}) QQ私聊。
|
||||
你之前的想法是：『{previous_heartflow}』
|
||||
你上次发言是 {time_since_last_bot_speak:.1f} 秒前。
|
||||
你已经等待了对方 {wait_duration:.1f} 分钟没有回应。 ({timeout_reason})
|
||||
|
||||
【你们最近的聊天记录】
|
||||
{chat_history_text}
|
||||
--- 消息结束 ---
|
||||
|
||||
对方长时间没有回复，你现在的想法是什么？
|
||||
- 你可能会思考对方为什么没回，是在忙吗？还是对话结束了？
|
||||
- 你可能会考虑是否要发点什么打破沉默，或者就此结束对话。
|
||||
- 结合你之前的想法和等待的情况进行思考。
|
||||
- 输出一段连贯、自然、真实的内心独白。
|
||||
- 像一个真人在思考，语气可以随意一些，符合你的性格。
|
||||
- 不要分点输出，不要包含任何动作描写、表情符号、引号或括号。
|
||||
- **只输出内心独白内容，不要任何额外文字或格式。**
|
||||
"""
|
||||
|
||||
# Prompt (5): Heartflow Update When Rethinking Goal
|
||||
PROMPT_WHEN_RETHINKING = """{persona_text}
|
||||
你正在和 {sender_name} ({sender_id}) QQ私聊。
|
||||
你之前的想法是：『{previous_heartflow}』
|
||||
你觉得现在需要重新思考一下对话的目标或方向了。({rethink_reason})
|
||||
|
||||
【你们最近的聊天记录】
|
||||
{chat_history_text}
|
||||
--- 消息结束 ---
|
||||
|
||||
请结合你之前的想法和需要重新思考目标的原因，梳理一下你现在的思路。
|
||||
- 你可能会回顾一下之前的对话，思考当前进展如何。
|
||||
- 你可能会考虑开启新的话题，或者如何引导对话到你期望的方向。
|
||||
- 输出一段连贯、自然、真实的内心独白。
|
||||
- 像一个真人在思考，语气可以随意一些，符合你的性格。
|
||||
- 不要分点输出，不要包含任何动作描写、表情符号、引号或括号。
|
||||
- **只输出内心独白内容，不要任何额外文字或格式。**
|
||||
"""
|
||||
|
||||
|
||||
class HeartflowGenerator:
|
||||
"""心流生成器"""
|
||||
|
||||
def __init__(self, stream_id: str):
|
||||
self.stream_id = stream_id
|
||||
# 这里假设你在 config.py 中定义了名为 'llm_PFC_heartflow' 的新 LLM 配置
|
||||
# 需要确保 global_config 中存在 llm_PFC_heartflow 这个键
|
||||
try:
|
||||
self.llm = LLMRequest(
|
||||
model=global_config.llm_PFC_heartflow, # 使用新的配置
|
||||
temperature=global_config.llm_PFC_heartflow.get("temp", 0.8), # 假设温度设置，可调整
|
||||
max_tokens=global_config.llm_PFC_heartflow.get("max_tokens", 200), # 限制心流长度，可调整
|
||||
request_type="heartflow_generation",
|
||||
)
|
||||
except AttributeError:
|
||||
logger.error("*"*20)
|
||||
logger.error("错误：无法找到 'llm_PFC_heartflow' 配置！")
|
||||
logger.error("请确保在 config.py 中定义了 llm_PFC_heartflow 的 LLM 配置。")
|
||||
logger.error("将使用 llm_normal 作为备用，但这可能不是最佳效果。")
|
||||
logger.error("*"*20)
|
||||
# 使用备用配置，但这可能不是最优选择
|
||||
self.llm = LLMRequest(
|
||||
model=global_config.llm_normal,
|
||||
temperature=0.8,
|
||||
max_tokens=200,
|
||||
request_type="heartflow_generation_fallback",
|
||||
)
|
||||
|
||||
self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=3)
|
||||
self.identity_detail_info = Individuality.get_instance().get_prompt(type="identity", x_person=2, level=2)
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.bot_id = str(global_config.BOT_QQ)
|
||||
|
||||
|
||||
async def generate_heartflow(
|
||||
self,
|
||||
situation: str, # e.g., "initial", "after_send", "after_fail", "after_timeout", "rethinking"
|
||||
observation_info: ObservationInfo,
|
||||
conversation_info: ConversationInfo,
|
||||
context_data: Optional[Dict[str, Any]] = None # To pass specific data like failed_message, reason, etc.
|
||||
) -> str:
|
||||
"""根据不同情境生成心流
|
||||
|
||||
Args:
|
||||
situation: 当前情境标识符
|
||||
observation_info: 观察信息
|
||||
conversation_info: 对话信息
|
||||
context_data: 传递特定情境所需的数据 (e.g., {'last_bot_message': '...', 'previous_heartflow': '...'})
|
||||
|
||||
Returns:
|
||||
str: 生成的心流文本
|
||||
"""
|
||||
if context_data is None:
|
||||
context_data = {}
|
||||
|
||||
logger.info(f"开始生成心流，情境: {situation}")
|
||||
|
||||
# --- 构建通用 Prompt 参数 ---
|
||||
# Persona Text (Character info)
|
||||
identity_details_only = self.identity_detail_info
|
||||
identity_addon = ""
|
||||
if isinstance(identity_details_only, str):
|
||||
pronouns = ["你", "我", "他"]
|
||||
for p in pronouns:
|
||||
if identity_details_only.startswith(p):
|
||||
identity_details_only = identity_details_only[len(p) :]
|
||||
break
|
||||
if identity_details_only.endswith("。"):
|
||||
identity_details_only = identity_details_only[:-1]
|
||||
cleaned_details = identity_details_only.strip(",， ")
|
||||
if cleaned_details:
|
||||
identity_addon = f"并且{cleaned_details}"
|
||||
persona_text = f"你的名字是{self.name}，{self.personality_info}{identity_addon}。"
|
||||
|
||||
# Chat History
|
||||
chat_history_text = "还没有聊天记录。"
|
||||
sender_name = "对方"
|
||||
sender_id = "未知"
|
||||
if hasattr(observation_info, 'chat_history_str') and observation_info.chat_history_str:
|
||||
chat_history_text = observation_info.chat_history_str
|
||||
# Try to get sender info from the last message if available
|
||||
last_msg = observation_info.chat_history[-1] if observation_info.chat_history else None
|
||||
if isinstance(last_msg, dict):
|
||||
user_info_dict = last_msg.get('user_info')
|
||||
if isinstance(user_info_dict, dict):
|
||||
user_info = UserInfo.from_dict(user_info_dict)
|
||||
# Get info of the other person, not the bot itself
|
||||
if str(user_info.user_id) != self.bot_id:
|
||||
sender_name = user_info.user_nickname or "对方"
|
||||
sender_id = str(user_info.user_id)
|
||||
|
||||
# Previous Heartflow (handle None case)
|
||||
previous_heartflow = context_data.get('previous_heartflow', conversation_info.current_heartflow if hasattr(conversation_info, 'current_heartflow') else '')
|
||||
if not previous_heartflow:
|
||||
previous_heartflow = "你之前还没来得及形成具体的想法。" # Default if no previous thought
|
||||
|
||||
# --- 选择并格式化 Prompt ---
|
||||
prompt_template = None
|
||||
format_params = {
|
||||
"persona_text": persona_text,
|
||||
"sender_name": sender_name,
|
||||
"sender_id": sender_id,
|
||||
"chat_history_text": chat_history_text,
|
||||
"previous_heartflow": previous_heartflow,
|
||||
"bot_name": self.name,
|
||||
# Add more common params if needed
|
||||
}
|
||||
|
||||
if situation == "initial":
|
||||
prompt_template = PROMPT_INITIAL
|
||||
# 'initial' specific params (if any, likely none needed beyond common ones)
|
||||
elif situation == "after_send":
|
||||
prompt_template = PROMPT_AFTER_SEND
|
||||
format_params["last_bot_message"] = context_data.get("last_bot_message", "（未能获取到刚发送的消息）")
|
||||
elif situation == "after_fail":
|
||||
prompt_template = PROMPT_AFTER_FAIL
|
||||
format_params["failed_message"] = context_data.get("failed_message", "（未能获取到失败的消息）")
|
||||
format_params["fail_reason"] = context_data.get("fail_reason", "（未知原因）")
|
||||
elif situation == "after_timeout":
|
||||
prompt_template = PROMPT_AFTER_TIMEOUT
|
||||
# Calculate times - requires observation_info to be up-to-date
|
||||
now = time.time()
|
||||
time_since_last_bot_speak = (now - observation_info.last_bot_speak_time) if observation_info.last_bot_speak_time else float('inf')
|
||||
wait_duration_minutes = context_data.get("wait_duration", 0) / 60.0 # Expect duration in seconds from context_data
|
||||
format_params["time_since_last_bot_speak"] = time_since_last_bot_speak
|
||||
format_params["wait_duration"] = wait_duration_minutes
|
||||
format_params["timeout_reason"] = context_data.get("timeout_reason", "长时间未回应")
|
||||
elif situation == "rethinking":
|
||||
prompt_template = PROMPT_WHEN_RETHINKING
|
||||
format_params["rethink_reason"] = context_data.get("rethink_reason", "需要调整对话方向")
|
||||
else:
|
||||
logger.warning(f"未知的的心流生成情境: {situation}，将使用 'initial' 作为默认。")
|
||||
prompt_template = PROMPT_INITIAL
|
||||
|
||||
# --- 调用 LLM 生成 ---
|
||||
try:
|
||||
final_prompt = prompt_template.format(**format_params)
|
||||
logger.debug(f"发送到LLM的心流生成提示词 (情境: {situation}):\n------\n{final_prompt}\n------")
|
||||
|
||||
heartflow_content, _ = await self.llm.generate_response_async(final_prompt)
|
||||
# Clean up potential unwanted prefixes/suffixes if LLM adds them
|
||||
heartflow_content = heartflow_content.strip().strip('\"').strip('\'').strip("内心独白：").strip()
|
||||
logger.info(f"生成的心流 (情境: {situation}): 『{heartflow_content}』")
|
||||
return heartflow_content
|
||||
|
||||
except KeyError as e:
|
||||
logger.error(f"格式化心流 Prompt 时缺少键: {e}")
|
||||
logger.error(f"可用参数: {format_params.keys()}")
|
||||
return f"（生成内心想法时出错：缺少参数 {e}）"
|
||||
except Exception as e:
|
||||
logger.error(f"生成心流时出错 (情境: {situation}): {e}")
|
||||
logger.error(traceback.format_exc())
|
||||
return "（生成内心想法时遇到错误）"
|
||||
|
|
@ -1,6 +1,9 @@
|
|||
from typing import Tuple, List, Dict, Any
|
||||
# PFC/reply_generator.py
|
||||
import traceback
|
||||
from typing import Tuple, List, Dict, Any, Optional # Added Optional
|
||||
from src.common.logger import get_module_logger
|
||||
from ..models.utils_model import LLMRequest
|
||||
# from ..models.utils_model import LLMRequest # Ensure correct path
|
||||
from src.common.utils_llm import LLMRequest # Using updated common location assumption
|
||||
from ...config.config import global_config
|
||||
from .chat_observer import ChatObserver
|
||||
from .reply_checker import ReplyChecker
|
||||
|
|
@ -16,13 +19,17 @@ logger = get_module_logger("reply_generator")
|
|||
# Prompt for direct_reply (首次回复)
|
||||
PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请根据以下信息生成一条回复：
|
||||
|
||||
当前对话目标：{goals_str}
|
||||
最近的聊天记录：
|
||||
【当前对话目标】
|
||||
{goals_str}
|
||||
【你现在的想法】
|
||||
『{pfc_heartflow}』
|
||||
|
||||
【最近的聊天记录】
|
||||
{chat_history_text}
|
||||
--- 消息结束 ---
|
||||
|
||||
|
||||
请根据上述信息，结合聊天记录，回复对方。该回复应该：
|
||||
1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！）
|
||||
请根据上述信息，结合聊天记录和你自己的想法，回复对方。该回复应该：
|
||||
1. 符合对话目标和你的内心想法，以"你"的角度发言（不要自己与自己对话！）
|
||||
2. 符合你的性格特征和身份细节
|
||||
3. 通俗易懂，自然流畅，像正常聊天一样，简短（通常20字以内，除非特殊情况）
|
||||
4. 适当利用相关知识，但不要生硬引用
|
||||
|
|
@ -33,18 +40,22 @@ PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请
|
|||
请你注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出回复内容。
|
||||
不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。
|
||||
|
||||
请直接输出回复内容，不需要任何额外格式。"""
|
||||
请直接输出回复内容，不需要任何额外格式。""" # Added heartflow placeholder and updated instructions
|
||||
|
||||
# Prompt for send_new_message (追问/补充)
|
||||
PROMPT_SEND_NEW_MESSAGE = """{persona_text}。现在你在参与一场QQ私聊，**刚刚你已经发送了一条或多条消息**，现在请根据以下信息再发一条新消息：
|
||||
PROMPT_SEND_NEW_MESSAGE = """{persona_text}。现在你在参与一场QQ私聊，**刚刚你已经发送了一条或多条消息**，现在请根据以下信息再发一条新消息：
|
||||
|
||||
当前对话目标：{goals_str}
|
||||
最近的聊天记录：
|
||||
【当前对话目标】
|
||||
{goals_str}
|
||||
【你现在的想法】
|
||||
『{pfc_heartflow}』
|
||||
|
||||
【最近的聊天记录】
|
||||
{chat_history_text}
|
||||
--- 消息结束 ---
|
||||
|
||||
|
||||
请根据上述信息，结合聊天记录，继续发一条新消息（例如对之前消息的补充，深入话题，或追问等等）。该消息应该：
|
||||
1. 符合对话目标，以"你"的角度发言（不要自己与自己对话！）
|
||||
请根据上述信息，结合聊天记录和你自己的想法，继续发一条新消息（例如对之前消息的补充，深入话题，或追问等等）。该消息应该：
|
||||
1. 符合对话目标和你的内心想法，以"你"的角度发言（不要自己与自己对话！）
|
||||
2. 符合你的性格特征和身份细节
|
||||
3. 通俗易懂，自然流畅，像正常聊天一样，简短（通常20字以内，除非特殊情况）
|
||||
4. 适当利用相关知识，但不要生硬引用
|
||||
|
|
@ -55,28 +66,41 @@ PROMPT_SEND_NEW_MESSAGE = """{persona_text}。现在你在参与一场QQ私聊
|
|||
请你注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，只输出消息内容。
|
||||
不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。
|
||||
|
||||
请直接输出回复内容，不需要任何额外格式。"""
|
||||
请直接输出回复内容，不需要任何额外格式。""" # Added heartflow placeholder and updated instructions
|
||||
|
||||
|
||||
class ReplyGenerator:
|
||||
"""回复生成器"""
|
||||
|
||||
def __init__(self, stream_id: str):
|
||||
self.llm = LLMRequest(
|
||||
model=global_config.llm_PFC_chat,
|
||||
temperature=global_config.llm_PFC_chat["temp"],
|
||||
max_tokens=300,
|
||||
request_type="reply_generation",
|
||||
)
|
||||
self.personality_info = Individuality.get_instance().get_prompt(type="personality", x_person=2, level=3)
|
||||
self.identity_detail_info = Individuality.get_instance().get_prompt(type="identity", x_person=2, level=2)
|
||||
# Ensure correct LLM config path/structure
|
||||
try:
|
||||
self.llm = LLMRequest(
|
||||
model=global_config.llm_PFC_chat,
|
||||
temperature=global_config.llm_PFC_chat.get("temp", 0.7), # Use .get for safety
|
||||
max_tokens=global_config.llm_PFC_chat.get("max_tokens", 300), # Use .get for safety
|
||||
request_type="reply_generation",
|
||||
)
|
||||
except AttributeError:
|
||||
logger.error("Config error: llm_PFC_chat not found or missing keys ('temp'/'max_tokens'). Using fallback.")
|
||||
# Fallback or raise error
|
||||
self.llm = LLMRequest(model=global_config.llm_normal, temperature=0.7, max_tokens=300, request_type="reply_generation_fallback")
|
||||
|
||||
self.individuality = Individuality.get_instance() # Store instance
|
||||
self.personality_info = self.individuality.get_prompt(type="personality", x_person=2, level=3)
|
||||
self.identity_detail_info = self.individuality.get_prompt(type="identity", x_person=2, level=2)
|
||||
|
||||
self.name = global_config.BOT_NICKNAME
|
||||
self.chat_observer = ChatObserver.get_instance(stream_id)
|
||||
self.reply_checker = ReplyChecker(stream_id)
|
||||
self.reply_checker = ReplyChecker(stream_id) # Assuming ReplyChecker exists
|
||||
|
||||
# 修改 generate 方法签名，增加 action_type 参数
|
||||
# 修改 generate 方法签名，增加 action_type 和 pfc_heartflow 参数
|
||||
async def generate(
|
||||
self, observation_info: ObservationInfo, conversation_info: ConversationInfo, action_type: str
|
||||
self,
|
||||
observation_info: ObservationInfo,
|
||||
conversation_info: ConversationInfo,
|
||||
action_type: str,
|
||||
pfc_heartflow: Optional[str], # <--- 新增参数
|
||||
) -> str:
|
||||
"""生成回复
|
||||
|
||||
|
|
@ -84,49 +108,78 @@ class ReplyGenerator:
|
|||
observation_info: 观察信息
|
||||
conversation_info: 对话信息
|
||||
action_type: 当前执行的动作类型 ('direct_reply' 或 'send_new_message')
|
||||
pfc_heartflow: 当前的心流文本 # <--- 新增参数说明
|
||||
|
||||
Returns:
|
||||
str: 生成的回复
|
||||
"""
|
||||
# 构建提示词
|
||||
logger.debug(f"开始生成回复 (动作类型: {action_type})：当前目标: {conversation_info.goal_list}")
|
||||
logger.debug(f"开始生成回复 (动作类型: {action_type})：当前目标: {getattr(conversation_info, 'goal_list', '不可用')}")
|
||||
|
||||
# --- 构建通用 Prompt 参数 ---
|
||||
# (这部分逻辑基本不变)
|
||||
|
||||
# 构建对话目标 (goals_str)
|
||||
goals_str = ""
|
||||
if conversation_info.goal_list:
|
||||
# 构建对话目标 (goals_str) - Robust handling
|
||||
goals_str = "- 目前没有明确对话目标。\n" # Default
|
||||
if hasattr(conversation_info, "goal_list") and conversation_info.goal_list:
|
||||
temp_goals_str = ""
|
||||
for goal_reason in conversation_info.goal_list:
|
||||
if isinstance(goal_reason, tuple):
|
||||
goal = goal_reason[0] if len(goal_reason) > 0 else "目标内容缺失"
|
||||
reasoning = goal_reason[1] if len(goal_reason) > 1 else "没有明确原因"
|
||||
<<<<<<< HEAD
|
||||
<<<<<<< HEAD
|
||||
goal = "目标内容缺失"
|
||||
reasoning = "没有明确原因"
|
||||
if isinstance(goal_reason, tuple) and len(goal_reason) > 0:
|
||||
goal = goal_reason[0]
|
||||
if len(goal_reason) > 1: reasoning = goal_reason[1]
|
||||
elif isinstance(goal_reason, dict):
|
||||
=======
|
||||
if isinstance(goal_reason, dict):
|
||||
>>>>>>> 3cfa1e6b17340f82f2937a2243b8e99030196294
|
||||
=======
|
||||
if isinstance(goal_reason, dict):
|
||||
>>>>>>> 3cfa1e6b17340f82f2937a2243b8e99030196294
|
||||
goal = goal_reason.get("goal", "目标内容缺失")
|
||||
reasoning = goal_reason.get("reasoning", "没有明确原因")
|
||||
reasoning = goal_reason.get("reason", "没有明确原因") # Use 'reason' key
|
||||
else:
|
||||
goal = str(goal_reason)
|
||||
<<<<<<< HEAD
|
||||
=======
|
||||
reasoning = "没有明确原因"
|
||||
<<<<<<< HEAD
|
||||
>>>>>>> 3cfa1e6b17340f82f2937a2243b8e99030196294
|
||||
=======
|
||||
>>>>>>> 3cfa1e6b17340f82f2937a2243b8e99030196294
|
||||
|
||||
goal = str(goal) if goal is not None else "目标内容缺失"
|
||||
reasoning = str(reasoning) if reasoning is not None else "没有明确原因"
|
||||
goals_str += f"- 目标：{goal}\n 原因：{reasoning}\n"
|
||||
else:
|
||||
goals_str = "- 目前没有明确对话目标\n" # 简化无目标情况
|
||||
temp_goals_str += f"- 目标：{goal}\n 原因：{reasoning}\n"
|
||||
if temp_goals_str:
|
||||
goals_str = temp_goals_str
|
||||
|
||||
|
||||
# 获取聊天历史记录 (chat_history_text) - Robust handling
|
||||
chat_history_text = "还没有聊天记录。\n" # Default
|
||||
if hasattr(observation_info, 'chat_history_str') and observation_info.chat_history_str:
|
||||
chat_history_text = observation_info.chat_history_str + "\n" # Ensure newline
|
||||
|
||||
# Append unprocessed messages if any
|
||||
if hasattr(observation_info, 'new_messages_count') and observation_info.new_messages_count > 0:
|
||||
if hasattr(observation_info, 'unprocessed_messages') and observation_info.unprocessed_messages:
|
||||
new_messages_list = observation_info.unprocessed_messages
|
||||
try:
|
||||
new_messages_str = await build_readable_messages(
|
||||
new_messages_list,
|
||||
replace_bot_name=True,
|
||||
merge_messages=False,
|
||||
timestamp_mode="relative",
|
||||
read_mark=0.0,
|
||||
)
|
||||
chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新收到的消息 ---\n{new_messages_str}\n"
|
||||
except Exception as build_err:
|
||||
logger.error(f"Error building readable messages for reply gen: {build_err}")
|
||||
chat_history_text += "\n--- (无法格式化新消息) ---\n"
|
||||
else:
|
||||
logger.warning("Reply gen: new_messages_count > 0 but unprocessed_messages is empty/missing.")
|
||||
chat_history_text += f"\n--- (有 {observation_info.new_messages_count} 条新消息，但无法显示内容) ---\n"
|
||||
|
||||
# 获取聊天历史记录 (chat_history_text)
|
||||
chat_history_text = observation_info.chat_history_str
|
||||
if observation_info.new_messages_count > 0 and observation_info.unprocessed_messages:
|
||||
new_messages_list = observation_info.unprocessed_messages
|
||||
new_messages_str = await build_readable_messages(
|
||||
new_messages_list,
|
||||
replace_bot_name=True,
|
||||
merge_messages=False,
|
||||
timestamp_mode="relative",
|
||||
read_mark=0.0,
|
||||
)
|
||||
chat_history_text += f"\n--- 以下是 {observation_info.new_messages_count} 条新消息 ---\n{new_messages_str}"
|
||||
elif not chat_history_text:
|
||||
chat_history_text = "还没有聊天记录。"
|
||||
|
||||
# 构建 Persona 文本 (persona_text)
|
||||
identity_details_only = self.identity_detail_info
|
||||
|
|
@ -153,20 +206,34 @@ class ReplyGenerator:
|
|||
logger.info("使用 PROMPT_DIRECT_REPLY (首次/非连续回复生成)")
|
||||
|
||||
# --- 格式化最终的 Prompt ---
|
||||
prompt = prompt_template.format(
|
||||
persona_text=persona_text, goals_str=goals_str, chat_history_text=chat_history_text
|
||||
)
|
||||
# Provide default for heartflow if None or empty
|
||||
heartflow_for_prompt = pfc_heartflow if pfc_heartflow else "无（没有内心想法信息）"
|
||||
|
||||
try:
|
||||
prompt = prompt_template.format(
|
||||
persona_text=persona_text,
|
||||
goals_str=goals_str.strip(),
|
||||
pfc_heartflow=heartflow_for_prompt, # <--- 传入心流
|
||||
chat_history_text=chat_history_text.strip(),
|
||||
)
|
||||
except KeyError as e:
|
||||
logger.error(f"格式化回复生成 Prompt 时出错，缺少键: {e}")
|
||||
return f"抱歉，我在组织语言时遇到了点内部问题（缺少参数 {e}），请稍后再试。"
|
||||
|
||||
|
||||
# --- 调用 LLM 生成 ---
|
||||
logger.debug(f"发送到LLM的生成提示词:\n------\n{prompt}\n------")
|
||||
try:
|
||||
content, _ = await self.llm.generate_response_async(prompt)
|
||||
logger.debug(f"生成的回复: {content}")
|
||||
# 移除旧的检查新消息逻辑，这应该由 conversation 控制流处理
|
||||
# Basic cleaning
|
||||
content = content.strip().strip('"')
|
||||
logger.debug(f"生成的原始回复: {content}")
|
||||
# No check for new messages here, handled by Conversation loop
|
||||
return content
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成回复时出错: {e}")
|
||||
logger.error(traceback.format_exc()) # Log traceback
|
||||
return "抱歉，我现在有点混乱，让我重新思考一下..."
|
||||
|
||||
# check_reply 方法保持不变
|
||||
|
|
@ -174,6 +241,11 @@ class ReplyGenerator:
|
|||
self, reply: str, goal: str, chat_history: List[Dict[str, Any]], chat_history_str: str, retry_count: int = 0
|
||||
) -> Tuple[bool, str, bool]:
|
||||
"""检查回复是否合适
|
||||
(此方法逻辑保持不变)
|
||||
(此方法逻辑保持不变, 不接收心流)
|
||||
"""
|
||||
return await self.reply_checker.check(reply, goal, chat_history, chat_history_str, retry_count)
|
||||
# Ensure ReplyChecker exists
|
||||
if not hasattr(self, 'reply_checker'):
|
||||
logger.error("ReplyChecker not initialized in ReplyGenerator!")
|
||||
return False, "内部错误：无法检查回复", True # Assume replan is needed
|
||||
|
||||
return await self.reply_checker.check(reply, goal, chat_history, chat_history_str, retry_count)
|
||||
|
|
@ -39,7 +39,7 @@ class Waiter:
|
|||
logger.info(f"等待超过 {DESIRED_TIMEOUT_SECONDS} 秒...添加思考目标。")
|
||||
wait_goal = {
|
||||
"goal": f"你等待了{elapsed_time / 60:.1f}分钟，注意可能在对方看来聊天已经结束，思考接下来要做什么",
|
||||
"reason": "对方很久没有回复你的消息了",
|
||||
"reasoning": "对方很久没有回复你的消息了",
|
||||
}
|
||||
conversation_info.goal_list.append(wait_goal)
|
||||
logger.info(f"添加目标: {wait_goal}")
|
||||
|
|
@ -66,7 +66,7 @@ class Waiter:
|
|||
wait_goal = {
|
||||
# 保持 goal 文本一致
|
||||
"goal": f"你等待了{elapsed_time / 60:.1f}分钟，对方似乎话说一半突然消失了，可能忙去了？也可能忘记了回复？要问问吗？还是结束对话？或继续等待？思考接下来要做什么",
|
||||
"reason": "对方话说一半消失了，很久没有回复",
|
||||
"reasoning": "对方话说一半消失了，很久没有回复",
|
||||
}
|
||||
conversation_info.goal_list.append(wait_goal)
|
||||
logger.info(f"添加目标: {wait_goal}")
|
||||
|
|
|
|||
Loading…
Reference in New Issue