mirror of https://github.com/Mai-with-u/MaiBot.git
Merge branch 'PFC-test' of https://github.com/smartmita/MaiBot into G-Test
Bakadax
commit
cb9b4423d8
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@ -1,5 +1,14 @@
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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
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from src.plugins.memory_system.Hippocampus import HippocampusManager
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# --- NEW IMPORT ---
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# 从 heartflow 导入知识检索和数据库查询函数/实例
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from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder
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# --- END NEW IMPORT ---
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# import jieba # 如果需要旧版知识库的回退，可能需要
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# import re # 如果需要旧版知识库的回退，可能需要
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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 ...config.config import global_config
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@ -21,20 +30,21 @@ 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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{knowledge_info_str}
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【最近行动历史概要】
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{action_history_summary}
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【你想起来的相关知识】
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{retrieved_knowledge_str}
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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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【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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【你的的回忆】
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{retrieved_memory_str}
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------
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可选行动类型以及解释：
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fetch_knowledge: 需要调取知识或记忆，当需要专业知识或特定信息时选择，对方若提到你不太认识的人名或实体也可以尝试选择
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listening: 倾听对方发言，当你认为对方话才说到一半，发言明显未结束时选择
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direct_reply: 直接回复对方
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rethink_goal: 思考一个对话目标，当你觉得目前对话需要目标，或当前目标不再适用，或话题卡住时选择。注意私聊的环境是灵活的，有可能需要经常选择
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@ -50,24 +60,24 @@ 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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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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# 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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{knowledge_info_str}
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【最近行动历史概要】
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{action_history_summary}
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【你想起来的相关知识】
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{retrieved_knowledge_str}
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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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【你的的回忆】
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{retrieved_memory_str}
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------
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可选行动类型以及解释：
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fetch_knowledge: 需要调取知识，当需要专业知识或特定信息时选择，对方若提到你不太认识的人名或实体也可以尝试选择
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wait: 暂时不说话，留给对方交互空间，等待对方回复（尤其是在你刚发言后、或上次发言因重复、发言过多被拒时、或不确定做什么时，这是不错的选择）
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listening: 倾听对方发言（虽然你刚发过言，但如果对方立刻回复且明显话没说完，可以选择这个）
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send_new_message: 发送一条新消息继续对话，允许适当的追问、补充、深入话题，或开启相关新话题。**但是避免在因重复被拒后立即使用，也不要在对方没有回复的情况下过多的“消息轰炸”或重复发言**
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@ -117,7 +127,41 @@ class ActionPlanner:
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self.name = global_config.BOT_NICKNAME
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self.private_name = private_name
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self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
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# self.action_planner_info = ActionPlannerInfo() # 移除未使用的变量
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# _get_memory_info 保持不变
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async def _get_memory_info(self, text: str) -> str:
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"""根据文本自动检索相关记忆"""
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memory_prompt = ""
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related_memory_info = ""
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try:
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related_memory = await HippocampusManager.get_instance().get_memory_from_text(
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text=text,
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max_memory_num=2, # 最多获取 2 条记忆
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max_memory_length=2, # 每条记忆长度限制（这个参数含义可能需确认）
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max_depth=3, # 搜索深度
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fast_retrieval=False, # 是否快速检索
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)
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if related_memory:
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for memory in related_memory:
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# memory[0] 是记忆ID, memory[1] 是记忆内容
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related_memory_info += memory[1] + "\n" # 将记忆内容拼接起来
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if related_memory_info:
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memory_prompt = f"你回忆起：\n{related_memory_info.strip()}\n(以上是你的回忆，供参考)\n"
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logger.debug(
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f"[私聊]决策层[{self.private_name}]自动检索到记忆: {related_memory_info.strip()[:100]}..."
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)
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else:
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logger.debug(f"[私聊]决策层[{self.private_name}]自动检索记忆返回为空。")
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else:
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logger.debug(f"[私聊]决策层[{self.private_name}]未自动检索到相关记忆。")
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except Exception as e:
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logger.error(f"[私聊]决策层[{self.private_name}]自动检索记忆时出错: {e}")
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# memory_prompt = "检索记忆时出错。\n" # 可以选择是否提示错误
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return memory_prompt
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# --- REMOVED _get_prompt_info_old ---
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# --- REMOVED _get_prompt_info ---
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# 修改 plan 方法签名，增加 last_successful_reply_action 参数
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async def plan(
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@ -226,41 +270,6 @@ class ActionPlanner:
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logger.error(f"[私聊][{self.private_name}]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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# --- 知识信息字符串构建开始 ---
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knowledge_info_str = "【已获取的相关知识和记忆】\n"
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try:
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# 检查 conversation_info 是否有 knowledge_list 并且不为空
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if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
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# 最多只显示最近的 5 条知识，防止 Prompt 过长
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recent_knowledge = conversation_info.knowledge_list[-5:]
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for i, knowledge_item in enumerate(recent_knowledge):
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if isinstance(knowledge_item, dict):
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query = knowledge_item.get("query", "未知查询")
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knowledge = knowledge_item.get("knowledge", "无知识内容")
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source = knowledge_item.get("source", "未知来源")
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# 只取知识内容的前 2000 个字，避免太长
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knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
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knowledge_info_str += (
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f"{i + 1}. 关于 '{query}' 的知识 (来源: {source}):\n {knowledge_snippet}\n"
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)
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else:
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# 处理列表里不是字典的异常情况
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knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
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if not recent_knowledge: # 如果 knowledge_list 存在但为空
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knowledge_info_str += "- 暂无相关知识和记忆。\n"
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else:
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# 如果 conversation_info 没有 knowledge_list 属性，或者列表为空
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knowledge_info_str += "- 暂无相关知识记忆。\n"
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except AttributeError:
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logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
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knowledge_info_str += "- 获取知识列表时出错。\n"
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except Exception as e:
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logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
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knowledge_info_str += "- 处理知识列表时出错。\n"
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# --- 知识信息字符串构建结束 ---
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# 获取聊天历史记录 (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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@ -368,6 +377,39 @@ class ActionPlanner:
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last_action_context += f"- 该行动当前状态: {status}\n"
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# self.last_successful_action_type = None # 非完成状态，清除记录
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retrieved_memory_str_planner = ""
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retrieved_knowledge_str_planner = ""
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retrieval_context = chat_history_text # 使用聊天记录作为检索上下文
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if retrieval_context and retrieval_context != "还没有聊天记录。" and retrieval_context != "[构建聊天记录出错]":
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try:
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# 调用本地的 _get_memory_info
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logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 开始自动检索记忆...")
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retrieved_memory_str_planner = await self._get_memory_info(text=retrieval_context)
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logger.info(
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f"[私聊][{self.private_name}] (ActionPlanner) 自动检索记忆 {'完成' if retrieved_memory_str_planner else '无结果'}。"
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)
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# --- MODIFIED KNOWLEDGE RETRIEVAL ---
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# 调用导入的 prompt_builder.get_prompt_info
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logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 开始自动检索知识 (使用导入函数)...")
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# 使用导入的 prompt_builder 实例及其方法
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retrieved_knowledge_str_planner = await prompt_builder.get_prompt_info(
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message=retrieval_context, threshold=0.38
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)
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# --- END MODIFIED KNOWLEDGE RETRIEVAL ---
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logger.info(
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f"[私聊][{self.private_name}] (ActionPlanner) 自动检索知识 {'完成' if retrieved_knowledge_str_planner else '无结果'}。"
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)
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except Exception as retrieval_err:
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logger.error(f"[私聊][{self.private_name}] (ActionPlanner) 自动检索时出错: {retrieval_err}")
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retrieved_memory_str_planner = "检索记忆时出错。\n"
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retrieved_knowledge_str_planner = "检索知识时出错。\n"
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else:
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logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 无有效聊天记录，跳过自动检索。")
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retrieved_memory_str_planner = "无聊天记录无法检索记忆。\n"
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retrieved_knowledge_str_planner = "无聊天记录无法检索知识。\n"
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# --- 选择 Prompt ---
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if last_successful_reply_action in ["direct_reply", "send_new_message"]:
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prompt_template = PROMPT_FOLLOW_UP
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@ -385,7 +427,11 @@ class ActionPlanner:
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time_since_last_bot_message_info=time_since_last_bot_message_info,
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timeout_context=timeout_context,
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chat_history_text=chat_history_text if chat_history_text.strip() else "还没有聊天记录。",
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knowledge_info_str=knowledge_info_str,
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# knowledge_info_str=knowledge_info_str, # 移除了旧知识展示方式
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retrieved_memory_str=retrieved_memory_str_planner if retrieved_memory_str_planner else "无相关记忆。",
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retrieved_knowledge_str=retrieved_knowledge_str_planner
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if retrieved_knowledge_str_planner
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else "无相关知识。",
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)
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|
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logger.debug(f"[私聊][{self.private_name}]发送到LLM的最终提示词:\n------\n{prompt}\n------")
|
||||
|
|
@ -468,7 +514,6 @@ class ActionPlanner:
|
|||
valid_actions = [
|
||||
"direct_reply",
|
||||
"send_new_message",
|
||||
"fetch_knowledge",
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"wait",
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"listening",
|
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"rethink_goal",
|
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|
|
|
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|
@ -508,31 +508,31 @@ class Conversation:
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|||
}
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conversation_info.done_action.append(wait_action_record)
|
||||
|
||||
elif action == "fetch_knowledge":
|
||||
self.state = ConversationState.FETCHING
|
||||
knowledge_query = reason
|
||||
try:
|
||||
# 检查 knowledge_fetcher 是否存在
|
||||
if not hasattr(self, "knowledge_fetcher"):
|
||||
logger.error(f"[私聊][{self.private_name}]KnowledgeFetcher 未初始化，无法获取知识。")
|
||||
raise AttributeError("KnowledgeFetcher not initialized")
|
||||
# elif action == "fetch_knowledge":
|
||||
# self.state = ConversationState.FETCHING
|
||||
# knowledge_query = reason
|
||||
# try:
|
||||
# 检查 knowledge_fetcher 是否存在
|
||||
# if not hasattr(self, "knowledge_fetcher"):
|
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# logger.error(f"[私聊][{self.private_name}]KnowledgeFetcher 未初始化，无法获取知识。")
|
||||
# raise AttributeError("KnowledgeFetcher not initialized")
|
||||
|
||||
knowledge, source = await self.knowledge_fetcher.fetch(knowledge_query, observation_info.chat_history)
|
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logger.info(f"[私聊][{self.private_name}]获取到知识: {knowledge[:100]}..., 来源: {source}")
|
||||
if knowledge:
|
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# 确保 knowledge_list 存在
|
||||
if not hasattr(conversation_info, "knowledge_list"):
|
||||
conversation_info.knowledge_list = []
|
||||
conversation_info.knowledge_list.append(
|
||||
{"query": knowledge_query, "knowledge": knowledge, "source": source}
|
||||
)
|
||||
action_successful = True
|
||||
except Exception as fetch_err:
|
||||
logger.error(f"[私聊][{self.private_name}]获取知识时出错: {str(fetch_err)}")
|
||||
conversation_info.done_action[action_index].update(
|
||||
{"status": "recall", "final_reason": f"获取知识失败: {str(fetch_err)}"}
|
||||
)
|
||||
self.conversation_info.last_successful_reply_action = None # 重置状态
|
||||
# knowledge, source = await self.knowledge_fetcher.fetch(knowledge_query, observation_info.chat_history)
|
||||
# logger.info(f"[私聊][{self.private_name}]获取到知识: {knowledge[:100]}..., 来源: {source}")
|
||||
# if knowledge:
|
||||
# 确保 knowledge_list 存在
|
||||
# if not hasattr(conversation_info, "knowledge_list"):
|
||||
# conversation_info.knowledge_list = []
|
||||
# conversation_info.knowledge_list.append(
|
||||
# {"query": knowledge_query, "knowledge": knowledge, "source": source}
|
||||
# )
|
||||
# action_successful = True
|
||||
# except Exception as fetch_err:
|
||||
# logger.error(f"[私聊][{self.private_name}]获取知识时出错: {str(fetch_err)}")
|
||||
# conversation_info.done_action[action_index].update(
|
||||
# {"status": "recall", "final_reason": f"获取知识失败: {str(fetch_err)}"}
|
||||
# )
|
||||
# self.conversation_info.last_successful_reply_action = None # 重置状态
|
||||
|
||||
elif action == "rethink_goal":
|
||||
self.state = ConversationState.RETHINKING
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ from ..chat.message import Message
|
|||
from maim_message import UserInfo, Seg
|
||||
from src.plugins.chat.message import MessageSending, MessageSet
|
||||
from src.plugins.chat.message_sender import message_manager
|
||||
from ..storage.storage import MessageStorage
|
||||
from ...config.config import global_config
|
||||
from rich.traceback import install
|
||||
|
||||
|
|
@ -21,7 +20,6 @@ class DirectMessageSender:
|
|||
|
||||
def __init__(self, private_name: str):
|
||||
self.private_name = private_name
|
||||
self.storage = MessageStorage()
|
||||
|
||||
async def send_message(
|
||||
self,
|
||||
|
|
@ -73,7 +71,6 @@ class DirectMessageSender:
|
|||
message_set = MessageSet(chat_stream, message_id)
|
||||
message_set.add_message(message)
|
||||
await message_manager.add_message(message_set)
|
||||
await self.storage.store_message(message, chat_stream)
|
||||
logger.info(f"[私聊][{self.private_name}]PFC消息已发送: {content}")
|
||||
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,14 @@
|
|||
# 用于访问记忆系统
|
||||
from src.plugins.memory_system.Hippocampus import HippocampusManager
|
||||
|
||||
# --- NEW IMPORT ---
|
||||
# 从 heartflow 导入知识检索和数据库查询函数/实例
|
||||
from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder
|
||||
|
||||
# --- END NEW IMPORT ---
|
||||
# 可能用于旧知识库提取主题 (如果需要回退到旧方法)
|
||||
# import jieba # 如果报错说找不到 jieba，可能需要安装: pip install jieba
|
||||
# import re # 正则表达式库，通常 Python 自带
|
||||
from typing import Tuple, List, Dict, Any
|
||||
from src.common.logger import get_module_logger
|
||||
from ..models.utils_model import LLMRequest
|
||||
|
|
@ -18,17 +29,21 @@ PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请
|
|||
|
||||
当前对话目标：{goals_str}
|
||||
|
||||
{knowledge_info_str}
|
||||
你有以下这些知识：
|
||||
{retrieved_knowledge_str}
|
||||
请你**记住上面的知识**，在回复中有可能会用到。
|
||||
|
||||
最近的聊天记录：
|
||||
{chat_history_text}
|
||||
|
||||
{retrieved_memory_str}
|
||||
|
||||
|
||||
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！）
|
||||
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. 可以适当利用相关知识，但不要生硬引用
|
||||
4. 可以适当利用相关知识和回忆，但**不要生硬引用**，若无必要，也可以不利用
|
||||
5. 自然、得体，结合聊天记录逻辑合理，且没有重复表达同质内容
|
||||
|
||||
请注意把握聊天内容，不要回复的太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。
|
||||
|
|
@ -39,21 +54,24 @@ PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请
|
|||
请直接输出回复内容，不需要任何额外格式。"""
|
||||
|
||||
# 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}
|
||||
|
||||
{knowledge_info_str}
|
||||
你有以下这些知识：
|
||||
{retrieved_knowledge_str}
|
||||
请你**记住上面的知识**，在发消息时有可能会用到。
|
||||
|
||||
最近的聊天记录：
|
||||
{chat_history_text}
|
||||
|
||||
{retrieved_memory_str}
|
||||
|
||||
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：
|
||||
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！）
|
||||
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. 可以适当利用相关知识，但不要生硬引用
|
||||
4. 可以适当利用相关知识和回忆，但**不要生硬引用**，若无必要，也可以不利用
|
||||
5. 跟之前你发的消息自然的衔接，逻辑合理，且没有重复表达同质内容或部分重叠内容
|
||||
|
||||
请注意把握聊天内容，不用太有条理，可以有个性。请分清"你"和对方说的话，不要把"你"说的话当做对方说的话，这是你自己说的话。
|
||||
|
|
@ -98,6 +116,39 @@ class ReplyGenerator:
|
|||
self.chat_observer = ChatObserver.get_instance(stream_id, private_name)
|
||||
self.reply_checker = ReplyChecker(stream_id, private_name)
|
||||
|
||||
# _get_memory_info 保持不变，因为它不是与 heartflow 重复的部分
|
||||
async def _get_memory_info(self, text: str) -> str:
|
||||
"""根据文本自动检索相关记忆"""
|
||||
memory_prompt = ""
|
||||
related_memory_info = ""
|
||||
try:
|
||||
related_memory = await HippocampusManager.get_instance().get_memory_from_text(
|
||||
text=text,
|
||||
max_memory_num=2, # 最多获取 2 条记忆
|
||||
max_memory_length=2, # 每条记忆长度限制（这个参数含义可能需确认）
|
||||
max_depth=3, # 搜索深度
|
||||
fast_retrieval=False, # 是否快速检索
|
||||
)
|
||||
if related_memory:
|
||||
for memory in related_memory:
|
||||
# memory[0] 是记忆ID, memory[1] 是记忆内容
|
||||
related_memory_info += memory[1] + "\n" # 将记忆内容拼接起来
|
||||
if related_memory_info:
|
||||
memory_prompt = f"你回忆起：\n{related_memory_info.strip()}\n(以上是你的回忆，不一定是目前聊天里的人说的，回忆中别人说的事情也不一定是准确的，请记住)\n"
|
||||
logger.debug(f"[私聊][{self.private_name}]自动检索到记忆: {related_memory_info.strip()[:100]}...")
|
||||
else:
|
||||
logger.debug(f"[私聊][{self.private_name}]自动检索记忆返回为空。")
|
||||
else:
|
||||
logger.debug(f"[私聊][{self.private_name}]未自动检索到相关记忆。")
|
||||
except Exception as e:
|
||||
logger.error(f"[私聊][{self.private_name}]自动检索记忆时出错: {e}")
|
||||
# memory_prompt = "检索记忆时出错。\n" # 可以选择是否提示错误
|
||||
return memory_prompt
|
||||
|
||||
# --- REMOVED _get_prompt_info_old ---
|
||||
|
||||
# --- REMOVED _get_prompt_info ---
|
||||
|
||||
# 修改 generate 方法签名，增加 action_type 参数
|
||||
async def generate(
|
||||
self, observation_info: ObservationInfo, conversation_info: ConversationInfo, action_type: str
|
||||
|
|
@ -137,38 +188,6 @@ class ReplyGenerator:
|
|||
else:
|
||||
goals_str = "- 目前没有明确对话目标\n" # 简化无目标情况
|
||||
|
||||
# --- 新增：构建知识信息字符串 ---
|
||||
knowledge_info_str = "【供参考的相关知识和记忆】\n" # 稍微改下标题，表明是供参考
|
||||
try:
|
||||
# 检查 conversation_info 是否有 knowledge_list 并且不为空
|
||||
if hasattr(conversation_info, "knowledge_list") and conversation_info.knowledge_list:
|
||||
# 最多只显示最近的 5 条知识
|
||||
recent_knowledge = conversation_info.knowledge_list[-5:]
|
||||
for i, knowledge_item in enumerate(recent_knowledge):
|
||||
if isinstance(knowledge_item, dict):
|
||||
query = knowledge_item.get("query", "未知查询")
|
||||
knowledge = knowledge_item.get("knowledge", "无知识内容")
|
||||
source = knowledge_item.get("source", "未知来源")
|
||||
# 只取知识内容的前 2000 个字
|
||||
knowledge_snippet = knowledge[:2000] + "..." if len(knowledge) > 2000 else knowledge
|
||||
knowledge_info_str += (
|
||||
f"{i + 1}. 关于 '{query}' (来源: {source}): {knowledge_snippet}\n" # 格式微调，更简洁
|
||||
)
|
||||
else:
|
||||
knowledge_info_str += f"{i + 1}. 发现一条格式不正确的知识记录。\n"
|
||||
|
||||
if not recent_knowledge:
|
||||
knowledge_info_str += "- 暂无。\n" # 更简洁的提示
|
||||
|
||||
else:
|
||||
knowledge_info_str += "- 暂无。\n"
|
||||
except AttributeError:
|
||||
logger.warning(f"[私聊][{self.private_name}]ConversationInfo 对象可能缺少 knowledge_list 属性。")
|
||||
knowledge_info_str += "- 获取知识列表时出错。\n"
|
||||
except Exception as e:
|
||||
logger.error(f"[私聊][{self.private_name}]构建知识信息字符串时出错: {e}")
|
||||
knowledge_info_str += "- 处理知识列表时出错。\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:
|
||||
|
|
@ -186,6 +205,42 @@ class ReplyGenerator:
|
|||
|
||||
# 构建 Persona 文本 (persona_text)
|
||||
persona_text = f"你的名字是{self.name}，{self.personality_info}。"
|
||||
retrieved_memory_str = ""
|
||||
retrieved_knowledge_str = ""
|
||||
# 使用 chat_history_text 作为检索的上下文，因为它包含了最近的对话和新消息
|
||||
retrieval_context = chat_history_text
|
||||
if retrieval_context and retrieval_context != "还没有聊天记录。" and retrieval_context != "[构建聊天记录出错]":
|
||||
try:
|
||||
# 提取记忆 (调用本地的 _get_memory_info)
|
||||
logger.debug(f"[私聊][{self.private_name}]开始自动检索记忆...")
|
||||
retrieved_memory_str = await self._get_memory_info(text=retrieval_context)
|
||||
if retrieved_memory_str:
|
||||
logger.info(f"[私聊][{self.private_name}]自动检索到记忆片段。")
|
||||
else:
|
||||
logger.info(f"[私聊][{self.private_name}]未自动检索到相关记忆。")
|
||||
|
||||
# --- MODIFIED KNOWLEDGE RETRIEVAL ---
|
||||
# 提取知识 (调用导入的 prompt_builder.get_prompt_info)
|
||||
logger.debug(f"[私聊][{self.private_name}]开始自动检索知识 (使用导入函数)...")
|
||||
# 使用导入的 prompt_builder 实例及其方法
|
||||
retrieved_knowledge_str = await prompt_builder.get_prompt_info(
|
||||
message=retrieval_context, threshold=0.38
|
||||
)
|
||||
# --- END MODIFIED KNOWLEDGE RETRIEVAL ---
|
||||
|
||||
if retrieved_knowledge_str:
|
||||
logger.info(f"[私聊][{self.private_name}]自动检索到相关知识。")
|
||||
else:
|
||||
logger.info(f"[私聊][{self.private_name}]未自动检索到相关知识。")
|
||||
|
||||
except Exception as retrieval_err:
|
||||
logger.error(f"[私聊][{self.private_name}]在自动检索记忆/知识时发生错误: {retrieval_err}")
|
||||
retrieved_memory_str = "检索记忆时出错。\n"
|
||||
retrieved_knowledge_str = "检索知识时出错。\n"
|
||||
else:
|
||||
logger.debug(f"[私聊][{self.private_name}]聊天记录为空或无效，跳过自动记忆/知识检索。")
|
||||
retrieved_memory_str = "无聊天记录，无法自动检索记忆。\n"
|
||||
retrieved_knowledge_str = "无聊天记录，无法自动检索知识。\n"
|
||||
|
||||
# --- 选择 Prompt ---
|
||||
if action_type == "send_new_message":
|
||||
|
|
@ -203,7 +258,11 @@ class ReplyGenerator:
|
|||
persona_text=persona_text,
|
||||
goals_str=goals_str,
|
||||
chat_history_text=chat_history_text,
|
||||
knowledge_info_str=knowledge_info_str,
|
||||
# knowledge_info_str=knowledge_info_str, # 移除了这个旧的知识展示方式
|
||||
retrieved_memory_str=retrieved_memory_str if retrieved_memory_str else "无相关记忆。", # 如果为空则提示无
|
||||
retrieved_knowledge_str=retrieved_knowledge_str
|
||||
if retrieved_knowledge_str
|
||||
else "无相关知识。", # 如果为空则提示无
|
||||
)
|
||||
|
||||
# --- 调用 LLM 生成 ---
|
||||
|
|
|
|||
Loading…
Reference in New Issue