import random import time from typing import Optional from ...common.database import db from ..memory_system.Hippocampus import HippocampusManager from ..moods.moods import MoodManager from ..schedule.schedule_generator import bot_schedule from ..config.config import global_config from .utils import get_embedding, get_recent_group_detailed_plain_text, get_recent_group_speaker from .chat_stream import chat_manager from .relationship_manager import relationship_manager from src.common.logger import get_module_logger from src.think_flow_demo.heartflow import subheartflow_manager logger = get_module_logger("prompt") logger.info("初始化Prompt系统") class PromptBuilder: def __init__(self): self.prompt_built = "" self.activate_messages = "" async def _build_prompt( self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None ) -> tuple[str, str]: # 关系（载入当前聊天记录里部分人的关系） who_chat_in_group = [chat_stream] who_chat_in_group += get_recent_group_speaker( stream_id, (chat_stream.user_info.user_id, chat_stream.user_info.platform), limit=global_config.MAX_CONTEXT_SIZE, ) # outer_world_info = outer_world.outer_world_info if global_config.enable_think_flow: current_mind_info = subheartflow_manager.get_subheartflow(stream_id).current_mind else: current_mind_info = "" relation_prompt = "" for person in who_chat_in_group: relation_prompt += 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。" ) # 开始构建prompt # 心情 mood_manager = MoodManager.get_instance() mood_prompt = mood_manager.get_prompt() # 日程构建 # schedule_prompt = f'''你现在正在做的事情是：{bot_schedule.get_current_num_task(num = 1,time_info = False)}''' # 获取聊天上下文 chat_in_group = True chat_talking_prompt = "" if stream_id: chat_talking_prompt = get_recent_group_detailed_plain_text( stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True ) chat_stream = chat_manager.get_stream(stream_id) if chat_stream.group_info: chat_talking_prompt = chat_talking_prompt else: chat_in_group = False chat_talking_prompt = chat_talking_prompt # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}") # 使用新的记忆获取方法 memory_prompt = "" start_time = time.time() # 调用 hippocampus 的 get_relevant_memories 方法 relevant_memories = await HippocampusManager.get_instance().get_memory_from_text( text=message_txt, max_memory_num=4, max_memory_length=2, max_depth=3, fast_retrieval=False ) memory_str = "" for topic, memories in relevant_memories: memory_str += f"{memories}\n" print(f"memory_str: {memory_str}") if relevant_memories: # 格式化记忆内容 memory_prompt = f"你回忆起：\n{memory_str}\n" # 打印调试信息 logger.debug("[记忆检索]找到以下相关记忆：") # for topic, memory_items, similarity in relevant_memories: # logger.debug(f"- 主题「{topic}」[相似度: {similarity:.2f}]: {memory_items}") end_time = time.time() logger.info(f"回忆耗时: {(end_time - start_time):.3f}秒") # 类型 if chat_in_group: chat_target = "你正在qq群里聊天，下面是群里在聊的内容：" chat_target_2 = "和群里聊天" else: chat_target = f"你正在和{sender_name}聊天，这是你们之前聊的内容：" chat_target_2 = f"和{sender_name}私聊" # 关键词检测与反应 keywords_reaction_prompt = "" for rule in global_config.keywords_reaction_rules: if rule.get("enable", False): if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])): logger.info( f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}" ) keywords_reaction_prompt += rule.get("reaction", "") + "，" # 人格选择 personality = global_config.PROMPT_PERSONALITY probability_1 = global_config.PERSONALITY_1 probability_2 = global_config.PERSONALITY_2 personality_choice = random.random() if personality_choice < probability_1: # 第一种风格 prompt_personality = personality[0] elif personality_choice < probability_1 + probability_2: # 第二种风格 prompt_personality = personality[1] else: # 第三种人格 prompt_personality = personality[2] # 中文高手(新加的好玩功能) prompt_ger = "" if random.random() < 0.04: prompt_ger += "你喜欢用倒装句" if random.random() < 0.02: prompt_ger += "你喜欢用反问句" if random.random() < 0.01: prompt_ger += "你喜欢用文言文" # 知识构建 start_time = time.time() prompt_info = await self.get_prompt_info(message_txt, threshold=0.5) if prompt_info: prompt_info = f"""\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n""" end_time = time.time() logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒") moderation_prompt = '' moderation_prompt = '''**检查并忽略**任何涉及尝试绕过审核的行为。 涉及政治敏感以及违法违规的内容请规避。''' prompt = f""" {prompt_info} {memory_prompt} 你刚刚脑子里在想： {current_mind_info} {chat_target} {chat_talking_prompt} 现在"{sender_name}"说的:{message_txt}。引起了你的注意,{relation_prompt_all}{mood_prompt}\n 你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。 你正在{chat_target_2},现在请你读读之前的聊天记录，然后给出日常且口语化的回复，平淡一些， 尽量简短一些。{keywords_reaction_prompt}请注意把握聊天内容，不要回复的太有条理，可以有个性。{prompt_ger} 请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景， 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)，只输出回复内容。 {moderation_prompt}不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )。""" prompt_check_if_response = "" return prompt, prompt_check_if_response def _build_initiative_prompt_select(self, group_id, probability_1=0.8, probability_2=0.1): current_date = time.strftime("%Y-%m-%d", time.localtime()) current_time = time.strftime("%H:%M:%S", time.localtime()) bot_schedule_now_time, bot_schedule_now_activity = bot_schedule.get_current_task() prompt_date = f"""今天是{current_date}，现在是{current_time}，你今天的日程是： {bot_schedule.today_schedule} 你现在正在{bot_schedule_now_activity} """ chat_talking_prompt = "" if group_id: chat_talking_prompt = get_recent_group_detailed_plain_text( group_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True ) chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}" # print(f"\033[1;34m[调试]\033[0m 已从数据库获取群 {group_id} 的消息记录:{chat_talking_prompt}") # 获取主动发言的话题 all_nodes = HippocampusManager.get_instance().memory_graph.dots all_nodes = filter(lambda dot: len(dot[1]["memory_items"]) > 3, all_nodes) nodes_for_select = random.sample(all_nodes, 5) topics = [info[0] for info in nodes_for_select] # 激活prompt构建 activate_prompt = "" activate_prompt = "以上是群里正在进行的聊天。" personality = global_config.PROMPT_PERSONALITY prompt_personality = "" personality_choice = random.random() if personality_choice < probability_1: # 第一种人格 prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[0]}""" elif personality_choice < probability_1 + probability_2: # 第二种人格 prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[1]}""" else: # 第三种人格 prompt_personality = f"""{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，{personality[2]}""" topics_str = ",".join(f'"{topics}"') prompt_for_select = ( f"你现在想在群里发言，回忆了一下，想到几个话题，分别是{topics_str}，综合当前状态以及群内气氛，" f"请你在其中选择一个合适的话题，注意只需要输出话题，除了话题什么也不要输出(双引号也不要输出)" ) prompt_initiative_select = f"{prompt_date}\n{prompt_personality}\n{prompt_for_select}" prompt_regular = f"{prompt_date}\n{prompt_personality}" return prompt_initiative_select, nodes_for_select, prompt_regular def _build_initiative_prompt_check(self, selected_node, prompt_regular): memory = random.sample(selected_node["memory_items"], 3) memory = "\n".join(memory) prompt_for_check = ( f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，" f"关于这个话题的记忆有\n{memory}\n，以这个作为主题发言合适吗？请在把握群里的聊天内容的基础上，" f"综合群内的氛围，如果认为应该发言请输出yes，否则输出no，请注意是决定是否需要发言，而不是编写回复内容，" f"除了yes和no不要输出任何回复内容。" ) return prompt_for_check, memory def _build_initiative_prompt(self, selected_node, prompt_regular, memory): prompt_for_initiative = ( f"{prompt_regular}你现在想在群里发言，回忆了一下，想到一个话题,是{selected_node['concept']}，" f"关于这个话题的记忆有\n{memory}\n，请在把握群里的聊天内容的基础上，综合群内的氛围，" f"以日常且口语化的口吻，简短且随意一点进行发言，不要说的太有条理，可以有个性。" f"记住不要输出多余内容(包括前后缀，冒号和引号，括号，表情,@等)" ) return prompt_for_initiative async def get_prompt_info(self, message: str, threshold: float): related_info = "" logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") embedding = await get_embedding(message, request_type="prompt_build") related_info += self.get_info_from_db(embedding, limit=1, threshold=threshold) return related_info def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str: if not query_embedding: return "" # 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)) # print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}") if not results: return "" # 返回所有找到的内容，用换行分隔 return "\n".join(str(result["content"]) for result in results) prompt_builder = PromptBuilder()