from .observation import Observation import asyncio from src.plugins.moods.moods import MoodManager from src.plugins.models.utils_model import LLM_request from src.plugins.config.config import global_config import re import time from src.plugins.schedule.schedule_generator import bot_schedule from src.plugins.memory_system.Hippocampus import HippocampusManager from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402 from src.plugins.chat.utils import get_embedding from src.common.database import db from typing import Union from src.individuality.individuality import Individuality import random from src.plugins.chat.chat_stream import ChatStream from src.plugins.person_info.relationship_manager import relationship_manager from src.plugins.chat.utils import get_recent_group_speaker subheartflow_config = LogConfig( # 使用海马体专用样式 console_format=SUB_HEARTFLOW_STYLE_CONFIG["console_format"], file_format=SUB_HEARTFLOW_STYLE_CONFIG["file_format"], ) logger = get_module_logger("subheartflow", config=subheartflow_config) class CurrentState: def __init__(self): self.willing = 0 self.current_state_info = "" self.mood_manager = MoodManager() self.mood = self.mood_manager.get_prompt() def update_current_state_info(self): self.current_state_info = self.mood_manager.get_current_mood() class SubHeartflow: def __init__(self, subheartflow_id): self.subheartflow_id = subheartflow_id self.current_mind = "" self.past_mind = [] self.current_state: CurrentState = CurrentState() self.llm_model = LLM_request( model=global_config.llm_sub_heartflow, temperature=0.2, max_tokens=600, request_type="sub_heart_flow" ) self.main_heartflow_info = "" self.last_reply_time = time.time() self.last_active_time = time.time() # 添加最后激活时间 if not self.current_mind: self.current_mind = "你什么也没想" self.is_active = False self.observations: list[Observation] = [] self.running_knowledges = [] self.bot_name = global_config.BOT_NICKNAME def add_observation(self, observation: Observation): """添加一个新的observation对象到列表中，如果已存在相同id的observation则不添加""" # 查找是否存在相同id的observation for existing_obs in self.observations: if existing_obs.observe_id == observation.observe_id: # 如果找到相同id的observation，直接返回 return # 如果没有找到相同id的observation，则添加新的 self.observations.append(observation) def remove_observation(self, observation: Observation): """从列表中移除一个observation对象""" if observation in self.observations: self.observations.remove(observation) def get_all_observations(self) -> list[Observation]: """获取所有observation对象""" return self.observations def clear_observations(self): """清空所有observation对象""" self.observations.clear() async def subheartflow_start_working(self): while True: current_time = time.time() if ( current_time - self.last_reply_time > global_config.sub_heart_flow_freeze_time ): # 120秒无回复/不在场，冻结 self.is_active = False await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 每60秒检查一次 else: self.is_active = True self.last_active_time = current_time # 更新最后激活时间 self.current_state.update_current_state_info() # await self.do_a_thinking() # await self.judge_willing() await asyncio.sleep(global_config.sub_heart_flow_update_interval) # 检查是否超过10分钟没有激活 if ( current_time - self.last_active_time > global_config.sub_heart_flow_stop_time ): # 5分钟无回复/不在场，销毁 logger.info(f"子心流 {self.subheartflow_id} 已经5分钟没有激活，正在销毁...") break # 退出循环，销毁自己 async def do_observe(self): observation = self.observations[0] await observation.observe() async def do_thinking_before_reply(self, message_txt:str, sender_name:str, chat_stream:ChatStream): current_thinking_info = self.current_mind mood_info = self.current_state.mood # mood_info = "你很生气，很愤怒" observation = self.observations[0] chat_observe_info = observation.observe_info # print(f"chat_observe_info：{chat_observe_info}") # 开始构建prompt prompt_personality = f"你的名字是{self.bot_name},你" # person individuality = Individuality.get_instance() personality_core = individuality.personality.personality_core prompt_personality += personality_core personality_sides = individuality.personality.personality_sides random.shuffle(personality_sides) prompt_personality += f",{personality_sides[0]}" identity_detail = individuality.identity.identity_detail random.shuffle(identity_detail) prompt_personality += f",{identity_detail[0]}" # 关系 who_chat_in_group = [ (chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname) ] who_chat_in_group += get_recent_group_speaker( chat_stream.stream_id, (chat_stream.user_info.platform, chat_stream.user_info.user_id), limit=global_config.MAX_CONTEXT_SIZE, ) relation_prompt = "" for person in who_chat_in_group: relation_prompt += await relationship_manager.build_relationship_info(person) relation_prompt_all = ( f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，" f"根据你和说话者{sender_name}的关系和态度进行回复，明确你的立场和情感。" ) # 调取记忆 related_memory = await HippocampusManager.get_instance().get_memory_from_text( text=chat_observe_info, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False ) if related_memory: related_memory_info = "" for memory in related_memory: related_memory_info += memory[1] else: related_memory_info = "" related_info, grouped_results = await self.get_prompt_info(chat_observe_info + message_txt, 0.4) # print(related_info) for _topic, results in grouped_results.items(): for result in results: # print(result) self.running_knowledges.append(result) # print(f"相关记忆：{related_memory_info}") schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False) prompt = "" # prompt += f"麦麦的总体想法是：{self.main_heartflow_info}\n\n" prompt += f"{relation_prompt_all}\n" prompt += f"{prompt_personality}\n" # prompt += f"你刚刚在做的事情是：{schedule_info}\n" # if related_memory_info: # prompt += f"你想起来你之前见过的回忆：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n" # if related_info: # prompt += f"你想起你知道：{related_info}\n" prompt += f"刚刚你的想法是{current_thinking_info}。如果有新的内容，记得转换话题\n" prompt += "-----------------------------------\n" prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n" prompt += f"你现在{mood_info}\n" prompt += f"你注意到{sender_name}刚刚说：{message_txt}\n" prompt += "现在你接下去继续思考，产生新的想法，不要分点输出，输出连贯的内心独白，不要太长，" prompt += "思考时可以想想如何对群聊内容进行回复。回复的要求是：平淡一些，简短一些，说中文，不要刻意突出自身学科背景，尽量不要说你说过的话 ，注意只输出回复内容。" prompt += "请注意不要输出多余内容(包括前后缀，冒号和引号，括号，表情等)，" prompt += f"记得结合上述的消息，生成符合内心想法的内心独白，文字不要浮夸，注意你就是{self.bot_name}，{self.bot_name}指的就是你。" try: response, reasoning_content = await self.llm_model.generate_response_async(prompt) except Exception as e: logger.error(f"回复前内心独白获取失败: {e}") response = "" self.update_current_mind(response) self.current_mind = response logger.info(f"prompt:\n{prompt}\n") logger.info(f"麦麦的思考前脑内状态：{self.current_mind}") return self.current_mind ,self.past_mind async def do_thinking_after_reply(self, reply_content, chat_talking_prompt): # print("麦麦回复之后脑袋转起来了") # 开始构建prompt prompt_personality = f"你的名字是{self.bot_name},你" # person individuality = Individuality.get_instance() personality_core = individuality.personality.personality_core prompt_personality += personality_core personality_sides = individuality.personality.personality_sides random.shuffle(personality_sides) prompt_personality += f",{personality_sides[0]}" identity_detail = individuality.identity.identity_detail random.shuffle(identity_detail) prompt_personality += f",{identity_detail[0]}" current_thinking_info = self.current_mind mood_info = self.current_state.mood observation = self.observations[0] chat_observe_info = observation.observe_info message_new_info = chat_talking_prompt reply_info = reply_content # schedule_info = bot_schedule.get_current_num_task(num=1, time_info=False) prompt = "" # prompt += f"你现在正在做的事情是：{schedule_info}\n" prompt += f"{prompt_personality}\n" prompt += f"现在你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：{chat_observe_info}\n" prompt += f"刚刚你的想法是{current_thinking_info}。" prompt += f"你现在看到了网友们发的新消息:{message_new_info}\n" prompt += f"你刚刚回复了群友们:{reply_info}" prompt += f"你现在{mood_info}" prompt += "现在你接下去继续思考，产生新的想法，记得保留你刚刚的想法，不要分点输出，输出连贯的内心独白" prompt += "不要太长，但是记得结合上述的消息，要记得你的人设，关注聊天和新内容，关注你回复的内容，不要思考太多:" try: response, reasoning_content = await self.llm_model.generate_response_async(prompt) except Exception as e: logger.error(f"回复后内心独白获取失败: {e}") response = "" self.update_current_mind(response) self.current_mind = response logger.info(f"麦麦回复后的脑内状态：{self.current_mind}") self.last_reply_time = time.time() async def judge_willing(self): # 开始构建prompt prompt_personality = "你" # person individuality = Individuality.get_instance() personality_core = individuality.personality.personality_core prompt_personality += personality_core personality_sides = individuality.personality.personality_sides random.shuffle(personality_sides) prompt_personality += f",{personality_sides[0]}" identity_detail = individuality.identity.identity_detail random.shuffle(identity_detail) prompt_personality += f",{identity_detail[0]}" # print("麦麦闹情绪了1") current_thinking_info = self.current_mind mood_info = self.current_state.mood # print("麦麦闹情绪了2") prompt = "" prompt += f"{prompt_personality}\n" prompt += "现在你正在上网，和qq群里的网友们聊天" prompt += f"你现在的想法是{current_thinking_info}。" prompt += f"你现在{mood_info}。" prompt += "现在请你思考，你想不想发言或者回复，请你输出一个数字，1-10，1表示非常不想，10表示非常想。" prompt += "请你用<>包裹你的回复意愿，输出<1>表示不想回复，输出<10>表示非常想回复。请你考虑，你完全可以不回复" try: response, reasoning_content = await self.llm_model.generate_response_async(prompt) # 解析willing值 willing_match = re.search(r"<(\d+)>", response) except Exception as e: logger.error(f"意愿判断获取失败: {e}") willing_match = None if willing_match: self.current_state.willing = int(willing_match.group(1)) else: self.current_state.willing = 0 return self.current_state.willing def update_current_mind(self, response): self.past_mind.append(self.current_mind) self.current_mind = response async def get_prompt_info(self, message: str, threshold: float): start_time = time.time() related_info = "" logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") # 1. 先从LLM获取主题，类似于记忆系统的做法 topics = [] # try: # # 先尝试使用记忆系统的方法获取主题 # hippocampus = HippocampusManager.get_instance()._hippocampus # topic_num = min(5, max(1, int(len(message) * 0.1))) # topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num)) # # 提取关键词 # topics = re.findall(r"<([^>]+)>", topics_response[0]) # if not topics: # topics = [] # else: # topics = [ # topic.strip() # for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",") # if topic.strip() # ] # logger.info(f"从LLM提取的主题: {', '.join(topics)}") # except Exception as e: # logger.error(f"从LLM提取主题失败: {str(e)}") # # 如果LLM提取失败，使用jieba分词提取关键词作为备选 # words = jieba.cut(message) # topics = [word for word in words if len(word) > 1][:5] # logger.info(f"使用jieba提取的主题: {', '.join(topics)}") # 如果无法提取到主题，直接使用整个消息 if not topics: logger.debug("未能提取到任何主题，使用整个消息进行查询") embedding = await get_embedding(message, request_type="info_retrieval") if not embedding: logger.error("获取消息嵌入向量失败") return "" related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold) logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒") return related_info, {} # 2. 对每个主题进行知识库查询 logger.info(f"开始处理{len(topics)}个主题的知识库查询") # 优化：批量获取嵌入向量，减少API调用 embeddings = {} topics_batch = [topic for topic in topics if len(topic) > 0] if message: # 确保消息非空 topics_batch.append(message) # 批量获取嵌入向量 embed_start_time = time.time() for text in topics_batch: if not text or len(text.strip()) == 0: continue try: embedding = await get_embedding(text, request_type="info_retrieval") if embedding: embeddings[text] = embedding else: logger.warning(f"获取'{text}'的嵌入向量失败") except Exception as e: logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}") logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒") if not embeddings: logger.error("所有嵌入向量获取失败") return "" # 3. 对每个主题进行知识库查询 all_results = [] query_start_time = time.time() # 首先添加原始消息的查询结果 if message in embeddings: original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True) if original_results: for result in original_results: result["topic"] = "原始消息" all_results.extend(original_results) logger.info(f"原始消息查询到{len(original_results)}条结果") # 然后添加每个主题的查询结果 for topic in topics: if not topic or topic not in embeddings: continue try: topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True) if topic_results: # 添加主题标记 for result in topic_results: result["topic"] = topic all_results.extend(topic_results) logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果") except Exception as e: logger.error(f"查询主题'{topic}'时发生错误: {str(e)}") logger.info(f"知识库查询完成，耗时: {time.time() - query_start_time:.3f}秒，共获取{len(all_results)}条结果") # 4. 去重和过滤 process_start_time = time.time() unique_contents = set() filtered_results = [] for result in all_results: content = result["content"] if content not in unique_contents: unique_contents.add(content) filtered_results.append(result) # 5. 按相似度排序 filtered_results.sort(key=lambda x: x["similarity"], reverse=True) # 6. 限制总数量（最多10条） filtered_results = filtered_results[:10] logger.info( f"结果处理完成，耗时: {time.time() - process_start_time:.3f}秒，过滤后剩余{len(filtered_results)}条结果" ) # 7. 格式化输出 if filtered_results: format_start_time = time.time() grouped_results = {} for result in filtered_results: topic = result["topic"] if topic not in grouped_results: grouped_results[topic] = [] grouped_results[topic].append(result) # 按主题组织输出 for topic, results in grouped_results.items(): related_info += f"【主题: {topic}】\n" for _i, result in enumerate(results, 1): _similarity = result["similarity"] content = result["content"].strip() # 调试：为内容添加序号和相似度信息 # related_info += f"{i}. [{similarity:.2f}] {content}\n" related_info += f"{content}\n" related_info += "\n" logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒") logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒") return related_info, grouped_results def get_info_from_db( self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False ) -> Union[str, list]: if not query_embedding: return "" if not return_raw else [] # 使用余弦相似度计算 pipeline = [ { "$addFields": { "dotProduct": { "$reduce": { "input": {"$range": [0, {"$size": "$embedding"}]}, "initialValue": 0, "in": { "$add": [ "$$value", { "$multiply": [ {"$arrayElemAt": ["$embedding", "$$this"]}, {"$arrayElemAt": [query_embedding, "$$this"]}, ] }, ] }, } }, "magnitude1": { "$sqrt": { "$reduce": { "input": "$embedding", "initialValue": 0, "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}, } } }, "magnitude2": { "$sqrt": { "$reduce": { "input": query_embedding, "initialValue": 0, "in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}, } } }, } }, {"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}}, { "$match": { "similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果 } }, {"$sort": {"similarity": -1}}, {"$limit": limit}, {"$project": {"content": 1, "similarity": 1}}, ] results = list(db.knowledges.aggregate(pipeline)) logger.debug(f"知识库查询结果数量: {len(results)}") if not results: return "" if not return_raw else [] if return_raw: return results else: # 返回所有找到的内容，用换行分隔 return "\n".join(str(result["content"]) for result in results) # subheartflow = SubHeartflow()