Merge branch 'dev' of https://github.com/MaiM-with-u/MaiBot into dev

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()
@@ -41,7 +44,6 @@ logger.info("KG加载完成")
     logger.info(f"KG节点数量：{len(kg_manager.graph.get_node_list())}")
     logger.info(f"KG边数量：{len(kg_manager.graph.get_edge_list())}")
 
-
     # 数据比对：Embedding库与KG的段落hash集合
     for pg_hash in kg_manager.stored_paragraph_hashes:
         key = PG_NAMESPACE + "-" + pg_hash
@@ -62,3 +64,8 @@ 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/message_receive/message.py

@@ -108,7 +108,7 @@ class MessageRecv(Message):
         self.detailed_plain_text = message_dict.get("detailed_plain_text", "")
         self.is_emoji = False
         self.is_picid = False
-        self.is_mentioned = 0.0
+        self.is_mentioned = None
         self.priority_mode = "interest"
         self.priority_info = None
 
@@ -152,14 +152,10 @@ class MessageRecv(Message):
             elif segment.type == "mention_bot":
                 self.is_mentioned = float(segment.data)
                 return ""
-            elif segment.type == "set_priority_mode":
-                # 处理设置优先级模式的消息段
-                if isinstance(segment.data, str):
-                    self.priority_mode = segment.data
-                return ""
             elif segment.type == "priority_info":
                 if isinstance(segment.data, dict):
                     # 处理优先级信息
+                    self.priority_mode = "priority"
                     self.priority_info = segment.data
                     """
                     {

src/chat/normal_chat/normal_chat.py

@@ -492,7 +492,11 @@ class NormalChat:
                         # 检查是否有用户满足关系构建条件
                         asyncio.create_task(self._check_relation_building_conditions(message))
 
-                        await self.reply_one_message(message)
+                        do_reply = await self.reply_one_message(message)
+                        response_set = do_reply if do_reply else []
+                        factor = 0.5
+                        cnt = sum([len(r) for r in response_set])
+                        await asyncio.sleep(max(1, factor * cnt - 3))  # 等待tts
 
                 # 等待一段时间再检查队列
                 await asyncio.sleep(1)

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/chat/utils/utils.py

@@ -47,7 +47,8 @@ def is_mentioned_bot_in_message(message: MessageRecv) -> tuple[bool, float]:
     reply_probability = 0.0
     is_at = False
     is_mentioned = False
-
+    if message.is_mentioned is not None:
+        return bool(message.is_mentioned), message.is_mentioned
     if (
         message.message_info.additional_config is not None
         and message.message_info.additional_config.get("is_mentioned") is not None

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)