feat: PFC谈话模式，可选择启用，实验性功能

src/plugins/PFC/pfc_KnowledgeFetcher.py

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+from typing import List, Tuple
+from src.common.logger import get_module_logger
+from src.plugins.memory_system.Hippocampus import HippocampusManager
+from ..models.utils_model import LLM_request
+from ..config.config import global_config
+from ..chat.message import Message
+
+logger = get_module_logger("knowledge_fetcher")
+
+class KnowledgeFetcher:
+    """知识调取器"""
+    
+    def __init__(self):
+        self.llm = LLM_request(
+            model=global_config.llm_normal,
+            temperature=0.7,
+            max_tokens=1000,
+            request_type="knowledge_fetch"
+        )
+        
+    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 = ""
+        for msg in chat_history:
+            # sender = msg.message_info.user_info.user_nickname or f"用户{msg.message_info.user_info.user_id}"
+            chat_history_text += f"{msg.detailed_plain_text}\n"
+        
+        # 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
+        )
+        
+        if related_memory:
+            knowledge = ""
+            sources = []
+            for memory in related_memory:
+                knowledge += memory[1] + "\n"
+                sources.append(f"记忆片段{memory[0]}")
+            return knowledge.strip(), "，".join(sources)
+        
+        return "未找到相关知识", "无记忆匹配"