From c45ac99fd59bc16cb383bb2ef84278c36c1e786d Mon Sep 17 00:00:00 2001 From: 114514 <2514624910@qq.com> Date: Sun, 4 May 2025 18:20:07 +0800 Subject: [PATCH] =?UTF-8?q?PFC=E5=90=8C=E6=AD=A5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/plugins/PFC/action_planner.py | 79 ++--------------- src/plugins/PFC/pfc_utils.py | 72 +++++++++++++++- src/plugins/PFC/reply_generator.py | 134 ++++++++++------------------- 3 files changed, 121 insertions(+), 164 deletions(-) diff --git a/src/plugins/PFC/action_planner.py b/src/plugins/PFC/action_planner.py index 51d9bff2..5cba1bb1 100644 --- a/src/plugins/PFC/action_planner.py +++ b/src/plugins/PFC/action_planner.py @@ -1,12 +1,6 @@ import time from typing import Tuple, Optional -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 --- +from .pfc_utils import retrieve_contextual_info # import jieba # 如果需要旧版知识库的回退，可能需要 # import re # 如果需要旧版知识库的回退，可能需要 from src.common.logger_manager import get_logger @@ -128,41 +122,6 @@ class ActionPlanner: self.private_name = private_name self.chat_observer = ChatObserver.get_instance(stream_id, private_name) - # _get_memory_info 保持不变 - 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，供参考)\n" - logger.debug( - f"[私聊]决策层[{self.private_name}]自动检索到记忆: {related_memory_info.strip()[:100]}..." - ) - else: - logger.debug(f"[私聊]决策层[{self.private_name}]自动检索记忆返回为空。") - else: - logger.debug(f"[私聊]决策层[{self.private_name}]未自动检索到相关记忆。") - except Exception as e: - logger.error(f"[私聊]决策层[{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 plan 方法签名，增加 last_successful_reply_action 参数 async def plan( self, @@ -377,38 +336,10 @@ class ActionPlanner: 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 - retrieved_memory_str_planner = "" - retrieved_knowledge_str_planner = "" - retrieval_context = chat_history_text # 使用聊天记录作为检索上下文 - 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: - # 调用本地的 _get_memory_info - logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 开始自动检索记忆...") - retrieved_memory_str_planner = await self._get_memory_info(text=retrieval_context) - logger.info( - f"[私聊][{self.private_name}] (ActionPlanner) 自动检索记忆 {'完成' if retrieved_memory_str_planner else '无结果'}。" - ) - - # --- MODIFIED KNOWLEDGE RETRIEVAL --- - # 调用导入的 prompt_builder.get_prompt_info - logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 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_planner = await prompt_builder.get_prompt_info( - message=retrieval_context, threshold=0.38 - ) - # --- END MODIFIED KNOWLEDGE RETRIEVAL --- - logger.info( - f"[私聊][{self.private_name}] (ActionPlanner) 自动检索知识 {'完成' if retrieved_knowledge_str_planner else '无结果'}。" - ) - - except Exception as retrieval_err: - logger.error(f"[私聊][{self.private_name}] (ActionPlanner) 自动检索时出错: {retrieval_err}") - retrieved_memory_str_planner = "检索记忆时出错。\n" - retrieved_knowledge_str_planner = "检索知识时出错。\n" - else: - logger.debug(f"[私聊][{self.private_name}] (ActionPlanner) 无有效聊天记录，跳过自动检索。") - retrieved_memory_str_planner = "无聊天记录无法检索记忆。\n" - retrieved_knowledge_str_planner = "无聊天记录无法检索知识。\n" + retrieved_memory_str_planner, retrieved_knowledge_str_planner = await retrieve_contextual_info(chat_history_text, self.private_name) + # Optional: 可以加一行日志确认结果，方便调试 + logger.info(f"[私聊][{self.private_name}] (ActionPlanner) 统一检索完成。记忆: {'有' if '回忆起' in retrieved_memory_str_planner else '无'} / 知识: {'有' if '出错' not in retrieved_knowledge_str_planner and '无相关知识' not in retrieved_knowledge_str_planner else '无'}") + # --- 选择 Prompt --- if last_successful_reply_action in ["direct_reply", "send_new_message"]: diff --git a/src/plugins/PFC/pfc_utils.py b/src/plugins/PFC/pfc_utils.py index 2f7bd5e0..a7a412c1 100644 --- a/src/plugins/PFC/pfc_utils.py +++ b/src/plugins/PFC/pfc_utils.py @@ -1,9 +1,77 @@ +import traceback import json import re from typing import Dict, Any, Optional, Tuple, List, Union -from src.common.logger import get_module_logger +from src.common.logger_manager import get_logger # 确认 logger 的导入路径 +from src.plugins.memory_system.Hippocampus import HippocampusManager +from src.plugins.heartFC_chat.heartflow_prompt_builder import prompt_builder # 确认 prompt_builder 的导入路径 + +logger = get_logger("pfc_utils") + + +async def retrieve_contextual_info(text: str, private_name: str) -> Tuple[str, str]: + """ + 根据输入文本检索相关的记忆和知识。 + + Args: + text: 用于检索的上下文文本 (例如聊天记录)。 + private_name: 私聊对象的名称，用于日志记录。 + + Returns: + Tuple[str, str]: (检索到的记忆字符串, 检索到的知识字符串) + """ + retrieved_memory_str = "无相关记忆。" + retrieved_knowledge_str = "无相关知识。" + memory_log_msg = "未自动检索到相关记忆。" + knowledge_log_msg = "未自动检索到相关知识。" + + if not text or text == "还没有聊天记录。" or text == "[构建聊天记录出错]": + logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 无有效上下文，跳过检索。") + return retrieved_memory_str, retrieved_knowledge_str + + # 1. 检索记忆 (逻辑来自原 _get_memory_info) + try: + related_memory = await HippocampusManager.get_instance().get_memory_from_text( + text=text, + max_memory_num=2, + max_memory_length=2, + max_depth=3, + fast_retrieval=False, + ) + if related_memory: + related_memory_info = "" + for memory in related_memory: + related_memory_info += memory[1] + "\n" + if related_memory_info: + # 注意：原版提示信息可以根据需要调整 + retrieved_memory_str = f"你回忆起：\n{related_memory_info.strip()}\n(以上是你的回忆，供参考)\n" + memory_log_msg = f"自动检索到记忆: {related_memory_info.strip()[:100]}..." + else: + memory_log_msg = "自动检索记忆返回为空。" + logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 记忆检索: {memory_log_msg}") + + except Exception as e: + logger.error(f"[私聊][{private_name}] (retrieve_contextual_info) 自动检索记忆时出错: {e}\n{traceback.format_exc()}") + retrieved_memory_str = "检索记忆时出错。\n" + + # 2. 检索知识 (逻辑来自原 action_planner 和 reply_generator) + try: + # 使用导入的 prompt_builder 实例及其方法 + knowledge_result = await prompt_builder.get_prompt_info( + message=text, threshold=0.38 # threshold 可以根据需要调整 + ) + if knowledge_result: + retrieved_knowledge_str = knowledge_result # 直接使用返回结果 + knowledge_log_msg = "自动检索到相关知识。" + logger.debug(f"[私聊][{private_name}] (retrieve_contextual_info) 知识检索: {knowledge_log_msg}") + + except Exception as e: + logger.error(f"[私聊][{private_name}] (retrieve_contextual_info) 自动检索知识时出错: {e}\n{traceback.format_exc()}") + retrieved_knowledge_str = "检索知识时出错。\n" + + return retrieved_memory_str, retrieved_knowledge_str + -logger = get_module_logger("pfc_utils") def get_items_from_json( diff --git a/src/plugins/PFC/reply_generator.py b/src/plugins/PFC/reply_generator.py index b9d2c00e..646e0ee9 100644 --- a/src/plugins/PFC/reply_generator.py +++ b/src/plugins/PFC/reply_generator.py @@ -1,16 +1,9 @@ -# 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 --- +from .pfc_utils import retrieve_contextual_info # 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 src.common.logger_manager import get_logger from ..models.utils_model import LLMRequest from ...config.config import global_config from .chat_observer import ChatObserver @@ -20,7 +13,7 @@ from .observation_info import ObservationInfo from .conversation_info import ConversationInfo from src.plugins.utils.chat_message_builder import build_readable_messages -logger = get_module_logger("reply_generator") +logger = get_logger("reply_generator") # --- 定义 Prompt 模板 --- @@ -38,6 +31,8 @@ PROMPT_DIRECT_REPLY = """{persona_text}。现在你在参与一场QQ私聊，请 {retrieved_memory_str} +{last_rejection_info} + 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！） @@ -67,6 +62,8 @@ PROMPT_SEND_NEW_MESSAGE = """{persona_text}。现在你在参与一场QQ私聊 {retrieved_memory_str} +{last_rejection_info} + 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 @@ -116,39 +113,7 @@ 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 @@ -205,42 +170,29 @@ 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。") + retrieval_context = chat_history_text # 使用前面构建好的 chat_history_text + # 调用共享函数进行检索 + retrieved_memory_str, retrieved_knowledge_str = await retrieve_contextual_info(retrieval_context, self.private_name) + logger.info(f"[私聊][{self.private_name}] (ReplyGenerator) 统一检索完成。记忆: {'有' if '回忆起' in retrieved_memory_str else '无'} / 知识: {'有' if '出错' not in retrieved_knowledge_str and '无相关知识' not in retrieved_knowledge_str else '无'}") + + # --- 修改：构建上次回复失败原因和内容提示 --- + last_rejection_info_str = "" + # 检查 conversation_info 是否有上次拒绝的原因和内容，并且它们都不是 None + last_reason = getattr(conversation_info, 'last_reply_rejection_reason', None) + last_content = getattr(conversation_info, 'last_rejected_reply_content', None) - # --- 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" + if last_reason and last_content: + last_rejection_info_str = ( + f"\n------\n" + f"【重要提示：你上一次尝试回复时失败了，以下是详细信息】\n" + f"上次试图发送的消息内容： “{last_content}”\n" # <-- 显示上次内容 + f"失败原因： “{last_reason}”\n" + f"请根据【消息内容】和【失败原因】调整你的新回复，避免重复之前的错误。\n" + f"------\n" + ) + logger.info(f"[私聊][{self.private_name}]检测到上次回复失败信息，将加入 Prompt:\n" + f" 内容: {last_content}\n" + f" 原因: {last_reason}") # --- 选择 Prompt --- if action_type == "send_new_message": @@ -254,16 +206,22 @@ class ReplyGenerator: logger.info(f"[私聊][{self.private_name}]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, - # 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 - ) + try: # <--- 增加 try-except 块处理可能的 format 错误 + prompt = prompt_template.format( + persona_text=persona_text, + goals_str=goals_str, + chat_history_text=chat_history_text, + retrieved_memory_str=retrieved_memory_str if retrieved_memory_str else "无相关记忆。", + retrieved_knowledge_str=retrieved_knowledge_str if retrieved_knowledge_str else "无相关知识。", + last_rejection_info=last_rejection_info_str # <--- 新增传递上次拒绝原因 + ) + except KeyError as e: + logger.error(f"[私聊][{self.private_name}]格式化 Prompt 时出错，缺少键: {e}。请检查 Prompt 模板和传递的参数。") + # 返回错误信息或默认回复 + return "抱歉，准备回复时出了点问题，请检查一下我的代码..." + except Exception as fmt_err: + logger.error(f"[私聊][{self.private_name}]格式化 Prompt 时发生未知错误: {fmt_err}") + return "抱歉，准备回复时出了点内部错误，请检查一下我的代码..." # --- 调用 LLM 生成 --- logger.debug(f"[私聊][{self.private_name}]发送到LLM的生成提示词:\n------\n{prompt}\n------")