diff --git a/src/chat/focus_chat/info_processors/working_memory_processor.py b/src/chat/focus_chat/info_processors/working_memory_processor.py index 2de0bcfab..abe9786d4 100644 --- a/src/chat/focus_chat/info_processors/working_memory_processor.py +++ b/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}") diff --git a/src/chat/focus_chat/planners/planner_simple.py b/src/chat/focus_chat/planners/planner_simple.py index 20f41c711..3d044d6e7 100644 --- a/src/chat/focus_chat/planners/planner_simple.py +++ b/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} diff --git a/src/chat/knowledge/knowledge_lib.py b/src/chat/knowledge/knowledge_lib.py index 6a4fcd4ea..5540d95e2 100644 --- a/src/chat/knowledge/knowledge_lib.py +++ b/src/chat/knowledge/knowledge_lib.py @@ -5,60 +5,67 @@ 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") -logger.info("正在初始化Mai-LPMM\n") -logger.info("创建LLM客户端") -llm_client_list = dict() -for key in global_config["llm_providers"]: - llm_client_list[key] = LLMClient( - global_config["llm_providers"][key]["base_url"], - global_config["llm_providers"][key]["api_key"], +# 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() + for key in global_config["llm_providers"]: + llm_client_list[key] = LLMClient( + global_config["llm_providers"][key]["base_url"], + global_config["llm_providers"][key]["api_key"], + ) + + # 初始化Embedding库 + embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]]) + logger.info("正在从文件加载Embedding库") + try: + embed_manager.load_from_file() + except Exception as e: + logger.warning("此消息不会影响正常使用：从文件加载Embedding库时，{}".format(e)) + # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误") + logger.info("Embedding库加载完成") + # 初始化KG + kg_manager = KGManager() + logger.info("正在从文件加载KG") + try: + kg_manager.load_from_file() + except Exception as e: + logger.warning("此消息不会影响正常使用：从文件加载KG时，{}".format(e)) + # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误") + 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 + if key not in embed_manager.stored_pg_hashes: + logger.warning(f"KG中存在Embedding库中不存在的段落：{key}") + + # 问答系统（用于知识库） + qa_manager = QAManager( + embed_manager, + kg_manager, + llm_client_list[global_config["embedding"]["provider"]], + llm_client_list[global_config["qa"]["llm"]["provider"]], + llm_client_list[global_config["qa"]["llm"]["provider"]], ) -# 初始化Embedding库 -embed_manager = EmbeddingManager(llm_client_list[global_config["embedding"]["provider"]]) -logger.info("正在从文件加载Embedding库") -try: - embed_manager.load_from_file() -except Exception as e: - logger.warning("此消息不会影响正常使用：从文件加载Embedding库时，{}".format(e)) - # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误") -logger.info("Embedding库加载完成") -# 初始化KG -kg_manager = KGManager() -logger.info("正在从文件加载KG") -try: - kg_manager.load_from_file() -except Exception as e: - logger.warning("此消息不会影响正常使用：从文件加载KG时，{}".format(e)) - # logger.warning("如果你是第一次导入知识，或者还未导入知识，请忽略此错误") -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 - if key not in embed_manager.stored_pg_hashes: - logger.warning(f"KG中存在Embedding库中不存在的段落：{key}") - -# 问答系统（用于知识库） -qa_manager = QAManager( - embed_manager, - kg_manager, - llm_client_list[global_config["embedding"]["provider"]], - llm_client_list[global_config["qa"]["llm"]["provider"]], - llm_client_list[global_config["qa"]["llm"]["provider"]], -) - -# 记忆激活（用于记忆库） -inspire_manager = MemoryActiveManager( - embed_manager, - llm_client_list[global_config["embedding"]["provider"]], -) + # 记忆激活（用于记忆库） + 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 diff --git a/src/chat/message_receive/message.py b/src/chat/message_receive/message.py index ef68d7852..091570839 100644 --- a/src/chat/message_receive/message.py +++ b/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 """ { diff --git a/src/chat/normal_chat/normal_chat.py b/src/chat/normal_chat/normal_chat.py index a737d5bec..38bc1076e 100644 --- a/src/chat/normal_chat/normal_chat.py +++ b/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) diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index da9d9a584..1fec8646e 100644 --- a/src/chat/replyer/default_generator.py +++ b/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() diff --git a/src/chat/utils/utils.py b/src/chat/utils/utils.py index a147846ca..edfb9f31c 100644 --- a/src/chat/utils/utils.py +++ b/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 diff --git a/src/experimental/PFC/pfc_KnowledgeFetcher.py b/src/experimental/PFC/pfc_KnowledgeFetcher.py index 38a6dafb9..a1d161a70 100644 --- a/src/experimental/PFC/pfc_KnowledgeFetcher.py +++ b/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 diff --git a/src/tools/not_using/lpmm_get_knowledge.py b/src/tools/not_using/lpmm_get_knowledge.py index df4fa6a4f..80b9b617b 100644 --- a/src/tools/not_using/lpmm_get_knowledge.py +++ b/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)