fix：调整目录结构，优化hfc prompt，移除日程，移除动态和llm判断willing模式，

src/experimental/PFC/pfc_KnowledgeFetcher.py

@@ -0,0 +1,85 @@
+from typing import List, Tuple
+from src.common.logger import get_module_logger
+from src.chat.memory_system.Hippocampus import HippocampusManager
+from src.chat.models.utils_model import LLMRequest
+from src.config.config import global_config
+from src.chat.message_receive.message import Message
+from src.chat.knowledge.knowledge_lib import qa_manager
+from src.chat.utils.chat_message_builder import build_readable_messages
+
+logger = get_module_logger("knowledge_fetcher")
+
+
+class KnowledgeFetcher:
+    """知识调取器"""
+
+    def __init__(self, private_name: str):
+        self.llm = LLMRequest(
+            model=global_config.llm_normal,
+            temperature=global_config.llm_normal["temp"],
+            max_tokens=1000,
+            request_type="knowledge_fetch",
+        )
+        self.private_name = private_name
+
+    def _lpmm_get_knowledge(self, query: str) -> str:
+        """获取相关知识
+
+        Args:
+            query: 查询内容
+
+        Returns:
+            str: 构造好的,带相关度的知识
+        """
+
+        logger.debug(f"[私聊][{self.private_name}]正在从LPMM知识库中获取知识")
+        try:
+            knowledge_info = qa_manager.get_knowledge(query)
+            logger.debug(f"[私聊][{self.private_name}]LPMM知识库查询结果: {knowledge_info:150}")
+            return knowledge_info
+        except Exception as e:
+            logger.error(f"[私聊][{self.private_name}]LPMM知识库搜索工具执行失败: {str(e)}")
+            return "未找到匹配的知识"
+
+    async def fetch(self, query: str, chat_history: List[Message]) -> Tuple[str, str]:
+        """获取相关知识
+
+        Args:
+            query: 查询内容
+            chat_history: 聊天历史
+
+        Returns:
+            Tuple[str, str]: (获取的知识, 知识来源)
+        """
+        # 构建查询上下文
+        chat_history_text = await build_readable_messages(
+            chat_history,
+            replace_bot_name=True,
+            merge_messages=False,
+            timestamp_mode="relative",
+            read_mark=0.0,
+        )
+
+        # 从记忆中获取相关知识
+        related_memory = await HippocampusManager.get_instance().get_memory_from_text(
+            text=f"{query}\n{chat_history_text}",
+            max_memory_num=3,
+            max_memory_length=2,
+            max_depth=3,
+            fast_retrieval=False,
+        )
+        knowledge_text = ""
+        sources_text = "无记忆匹配"  # 默认值
+        if related_memory:
+            sources = []
+            for memory in related_memory:
+                knowledge_text += memory[1] + "\n"
+                sources.append(f"记忆片段{memory[0]}")
+            knowledge_text = knowledge_text.strip()
+            sources_text = "，".join(sources)
+
+        knowledge_text += "\n现在有以下**知识**可供参考：\n "
+        knowledge_text += self._lpmm_get_knowledge(query)
+        knowledge_text += "\n请记住这些**知识**，并根据**知识**回答问题。\n"
+
+        return knowledge_text or "未找到相关知识", sources_text or "无记忆匹配"