Merge pull request #1094 from CNMrSunshine/dev

修复配置文件中禁用LPMM无效的问题 一个工作记忆问题 和麦麦重复回复的问题

src/chat/focus_chat/info_processors/working_memory_processor.py

@@ -71,6 +71,7 @@ class WorkingMemoryProcessor(BaseProcessor):
         """
         working_memory = None
         chat_info = ""
+        chat_obs = None
         try:
             for observation in observations:
                 if isinstance(observation, WorkingMemoryObservation):
@@ -79,10 +80,15 @@ class WorkingMemoryProcessor(BaseProcessor):
                     chat_info = observation.get_observe_info()
                     chat_obs = observation
                     # 检查是否有待压缩内容
-            if chat_obs.compressor_prompt:
+            if chat_obs and chat_obs.compressor_prompt:
                 logger.debug(f"{self.log_prefix} 压缩聊天记忆")
                 await self.compress_chat_memory(working_memory, chat_obs)
 
+            # 检查working_memory是否为None
+            if working_memory is None:
+                logger.debug(f"{self.log_prefix} 没有找到工作记忆观察，跳过处理")
+                return []
+
             all_memory = working_memory.get_all_memories()
             if not all_memory:
                 logger.debug(f"{self.log_prefix} 目前没有工作记忆，跳过提取")
@@ -183,6 +189,11 @@ class WorkingMemoryProcessor(BaseProcessor):
             working_memory: 工作记忆对象
             obs: 聊天观察对象
         """
+        # 检查working_memory是否为None
+        if working_memory is None:
+            logger.warning(f"{self.log_prefix} 工作记忆对象为None，无法压缩聊天记忆")
+            return
+            
         try:
             summary_result, _ = await self.llm_model.generate_response_async(obs.compressor_prompt)
             if not summary_result:
@@ -235,6 +246,11 @@ class WorkingMemoryProcessor(BaseProcessor):
             memory_id1: 第一个记忆ID
             memory_id2: 第二个记忆ID
         """
+        # 检查working_memory是否为None
+        if working_memory is None:
+            logger.warning(f"{self.log_prefix} 工作记忆对象为None，无法合并记忆")
+            return
+            
         try:
             merged_memory = await working_memory.merge_memory(memory_id1, memory_id2)
             logger.debug(f"{self.log_prefix} 合并后的记忆梗概: {merged_memory.brief}")

src/chat/focus_chat/planners/planner_simple.py

@@ -29,6 +29,7 @@ def init_prompt():
 {chat_context_description}，以下是具体的聊天内容：
 {chat_content_block}
 {moderation_prompt}
+
 现在请你根据聊天内容选择合适的action:
 
 {action_options_text}

src/chat/knowledge/knowledge_lib.py

@@ -5,11 +5,14 @@ from src.chat.knowledge.mem_active_manager import MemoryActiveManager
 from src.chat.knowledge.qa_manager import QAManager
 from src.chat.knowledge.kg_manager import KGManager
 from src.chat.knowledge.global_logger import logger
+from src.config.config import global_config as bot_global_config
 # try:
 #     import quick_algo
 # except ImportError:
 #     print("quick_algo not found, please install it first")
 
+# 检查LPMM知识库是否启用
+if bot_global_config.lpmm_knowledge.enable:
     logger.info("正在初始化Mai-LPMM\n")
     logger.info("创建LLM客户端")
     llm_client_list = dict()
@@ -62,3 +65,8 @@ inspire_manager = MemoryActiveManager(
         embed_manager,
         llm_client_list[global_config["embedding"]["provider"]],
     )
+else:
+    logger.info("LPMM知识库已禁用，跳过初始化")
+    # 创建空的占位符对象，避免导入错误
+    qa_manager = None
+    inspire_manager = None

src/chat/replyer/default_generator.py

@@ -956,6 +956,11 @@ async def get_prompt_info(message: str, threshold: float):
     logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
     # 从LPMM知识库获取知识
     try:
+        # 检查LPMM知识库是否启用
+        if qa_manager is None:
+            logger.debug("LPMM知识库已禁用，跳过知识获取")
+            return ""
+            
         found_knowledge_from_lpmm = qa_manager.get_knowledge(message)
 
         end_time = time.time()

src/experimental/PFC/pfc_KnowledgeFetcher.py

@@ -35,6 +35,11 @@ class KnowledgeFetcher:
 
         logger.debug(f"[私聊][{self.private_name}]正在从LPMM知识库中获取知识")
         try:
+            # 检查LPMM知识库是否启用
+            if qa_manager is None:
+                logger.debug(f"[私聊][{self.private_name}]LPMM知识库已禁用，跳过知识获取")
+                return "未找到匹配的知识"
+                
             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

src/tools/not_using/lpmm_get_knowledge.py

@@ -36,6 +36,11 @@ class SearchKnowledgeFromLPMMTool(BaseTool):
             query = function_args.get("query")
             # threshold = function_args.get("threshold", 0.4)
 
+            # 检查LPMM知识库是否启用
+            if qa_manager is None:
+                logger.debug("LPMM知识库已禁用，跳过知识获取")
+                return {"type": "info", "id": query, "content": "LPMM知识库已禁用"}
+
             # 调用知识库搜索
 
             knowledge_info = qa_manager.get_knowledge(query)