diff --git a/src/plugins/PFC/action_planner.py b/src/plugins/PFC/action_planner.py index 5cba1bb1..faf13bd0 100644 --- a/src/plugins/PFC/action_planner.py +++ b/src/plugins/PFC/action_planner.py @@ -1,6 +1,7 @@ import time from typing import Tuple, Optional 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 @@ -336,10 +337,13 @@ 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 = await retrieve_contextual_info(chat_history_text, self.private_name) + 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'}") - + 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 a7a412c1..b0f3f841 100644 --- a/src/plugins/PFC/pfc_utils.py +++ b/src/plugins/PFC/pfc_utils.py @@ -2,9 +2,9 @@ import traceback import json import re from typing import Dict, Any, Optional, Tuple, List, Union -from src.common.logger_manager import get_logger # 确认 logger 的导入路径 +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 +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") @@ -47,33 +47,36 @@ async def retrieve_contextual_info(text: str, private_name: str) -> Tuple[str, s 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。" + 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()}") + 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 + 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。" + 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()}") + 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 - - def get_items_from_json( content: str, private_name: str, diff --git a/src/plugins/PFC/reply_generator.py b/src/plugins/PFC/reply_generator.py index 1dc564de..892e881b 100644 --- a/src/plugins/PFC/reply_generator.py +++ b/src/plugins/PFC/reply_generator.py @@ -1,8 +1,10 @@ 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 @@ -114,7 +116,6 @@ class ReplyGenerator: self.chat_observer = ChatObserver.get_instance(stream_id, private_name) self.reply_checker = ReplyChecker(stream_id, private_name) - # 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 @@ -171,11 +172,15 @@ class ReplyGenerator: # 构建 Persona 文本 (persona_text) persona_text = f"你的名字是{self.name}，{self.personality_info}。" - retrieval_context = chat_history_text # 使用前面构建好的 chat_history_text + 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'}") - + 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