274 lines
12 KiB
Python
274 lines
12 KiB
Python
import time
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import random
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from dotenv import load_dotenv
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from ..schedule.schedule_generator import bot_schedule
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import os
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from .utils import get_embedding, combine_messages, get_recent_group_detailed_plain_text
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from ...common.database import Database
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from .config import global_config
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from .topic_identifier import topic_identifier
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from ..memory_system.memory import memory_graph
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from random import choice
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# 获取当前文件的绝对路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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root_dir = os.path.abspath(os.path.join(current_dir, '..', '..', '..'))
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load_dotenv(os.path.join(root_dir, '.env'))
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class PromptBuilder:
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def __init__(self):
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self.prompt_built = ''
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self.activate_messages = ''
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self.db = Database.get_instance()
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def _build_prompt(self,
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message_txt: str,
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sender_name: str = "某人",
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relationship_value: float = 0.0,
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group_id: int = None) -> str:
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"""构建prompt
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Args:
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message_txt: 消息文本
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sender_name: 发送者昵称
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relationship_value: 关系值
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group_id: 群组ID
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Returns:
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str: 构建好的prompt
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"""
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memory_prompt = ''
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start_time = time.time() # 记录开始时间
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topic = topic_identifier.identify_topic_jieba(message_txt)
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# print(f"\033[1;32m[pb主题识别]\033[0m 主题: {topic}")
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all_first_layer_items = [] # 存储所有第一层记忆
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all_second_layer_items = {} # 用字典存储每个topic的第二层记忆
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overlapping_second_layer = set() # 存储重叠的第二层记忆
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if topic:
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# 遍历所有topic
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for current_topic in topic:
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first_layer_items, second_layer_items = memory_graph.get_related_item(current_topic, depth=2)
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# if first_layer_items:
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# print(f"\033[1;32m[前额叶]\033[0m 主题 '{current_topic}' 的第一层记忆: {first_layer_items}")
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# 记录第一层数据
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all_first_layer_items.extend(first_layer_items)
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# 记录第二层数据
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all_second_layer_items[current_topic] = second_layer_items
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# 检查是否有重叠的第二层数据
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for other_topic, other_second_layer in all_second_layer_items.items():
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if other_topic != current_topic:
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# 找到重叠的记忆
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overlap = set(second_layer_items) & set(other_second_layer)
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if overlap:
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# print(f"\033[1;32m[前额叶]\033[0m 发现主题 '{current_topic}' 和 '{other_topic}' 有共同的第二层记忆: {overlap}")
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overlapping_second_layer.update(overlap)
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# 合并所有需要的记忆
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if all_first_layer_items:
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print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆1: {all_first_layer_items}")
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if overlapping_second_layer:
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print(f"\033[1;32m[前额叶]\033[0m 合并所有需要的记忆2: {list(overlapping_second_layer)}")
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all_memories = all_first_layer_items + list(overlapping_second_layer)
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if all_memories: # 只在列表非空时选择随机项
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random_item = choice(all_memories)
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memory_prompt = f"看到这些聊天，你想起来{random_item}\n"
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else:
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memory_prompt = "" # 如果没有记忆，则返回空字符串
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end_time = time.time() # 记录结束时间
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print(f"\033[1;32m[回忆耗时]\033[0m 耗时: {(end_time - start_time):.3f}秒") # 输出耗时
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#先禁用关系
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if 0 > 30:
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relation_prompt = "关系特别特别好，你很喜欢喜欢他"
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relation_prompt_2 = "热情发言或者回复"
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elif 0 <-20:
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relation_prompt = "关系很差，你很讨厌他"
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relation_prompt_2 = "骂他"
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else:
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relation_prompt = "关系一般"
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relation_prompt_2 = "发言或者回复"
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#开始构建prompt
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#日程构建
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current_date = time.strftime("%Y-%m-%d", time.localtime())
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current_time = time.strftime("%H:%M:%S", time.localtime())
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bot_schedule_now_time,bot_schedule_now_activity = bot_schedule.get_current_task()
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prompt_date = f'''今天是{current_date}，现在是{current_time}，你今天的日程是：\n{bot_schedule.today_schedule}\n你现在正在{bot_schedule_now_activity}\n'''
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#知识构建
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start_time = time.time()
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prompt_info = ''
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promt_info_prompt = ''
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prompt_info = self.get_prompt_info(message_txt,threshold=0.5)
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if prompt_info:
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prompt_info = f'''\n----------------------------------------------------\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----------------------------------------------------\n'''
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promt_info_prompt = '你有一些[知识]，在上面可以参考。'
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end_time = time.time()
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print(f"\033[1;32m[知识检索]\033[0m 耗时: {(end_time - start_time):.3f}秒")
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# print(f"\033[1;34m[调试]\033[0m 获取知识库内容结果: {prompt_info}")
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# print(f"\033[1;34m[调试信息]\033[0m 正在构建聊天上下文")
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chat_talking_prompt = ''
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if group_id:
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chat_talking_prompt = get_recent_group_detailed_plain_text(self.db, group_id, limit=global_config.MAX_CONTEXT_SIZE,combine = True)
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chat_talking_prompt = f"以下是群里正在聊天的内容：\n{chat_talking_prompt}"
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# 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}")
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#激活prompt构建
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activate_prompt = ''
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activate_prompt = f"以上是群里正在进行的聊天，{memory_prompt} 现在昵称为 '{sender_name}' 的用户说的:{message_txt}。引起了你的注意,你和他{relation_prompt}，你想要{relation_prompt_2}。"
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#检测机器人相关词汇
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bot_keywords = ['人机', 'bot', '机器', '入机', 'robot', '机器人']
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is_bot = any(keyword in message_txt.lower() for keyword in bot_keywords)
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if is_bot:
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is_bot_prompt = '有人说你是人机或者机器人，否定这一事实，攻击或反问他。'
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else:
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is_bot_prompt = ''
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#人格选择
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prompt_personality = ''
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personality_choice = random.random()
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if personality_choice < 4/6: # 第一种人格
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prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，曾经是一个学习地质的女大学生，现在学习心理学和脑科学，你会刷贴吧，你正在浏览qq群,{promt_info_prompt},
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现在请你给出日常且口语化的回复，平淡一些，尽量简短一些。{is_bot_prompt}
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请注意把握群里的聊天内容，不要回复的太有条理，可以有个性。'''
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elif personality_choice < 1: # 第二种人格
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prompt_personality = f'''{activate_prompt}你的网名叫{global_config.BOT_NICKNAME}，是一个女大学生，你有黑色头发，你会刷小红书, 你正在浏览qq群,{promt_info_prompt},
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现在请你给出日常且口语化的回复，请表现你自己的见解，不要一昧迎合，尽量简短一些。{is_bot_prompt}
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请你表达自己的见解和观点。可以有个性。'''
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#中文高手(新加的好玩功能)
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prompt_ger = ''
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if random.random() < 0.04:
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prompt_ger += '你喜欢用倒装句'
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if random.random() < 0.02:
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prompt_ger += '你喜欢用反问句'
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if random.random() < 0.01:
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prompt_ger += '你喜欢用文言文'
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#额外信息要求
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extra_info = '''但是记得回复平淡一些，简短一些,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),只需要输出回复内容就好，不要输出其他任何内容'''
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#合并prompt
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prompt = ""
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prompt += f"{prompt_info}\n"
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prompt += f"{prompt_date}\n"
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prompt += f"{chat_talking_prompt}\n"
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# prompt += f"{memory_prompt}\n"
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# prompt += f"{activate_prompt}\n"
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prompt += f"{prompt_personality}\n"
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prompt += f"{prompt_ger}\n"
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prompt += f"{extra_info}\n"
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return prompt
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def get_prompt_info(self,message:str,threshold:float):
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related_info = ''
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if len(message) > 10:
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message_segments = [message[i:i+10] for i in range(0, len(message), 10)]
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for segment in message_segments:
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embedding = get_embedding(segment)
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related_info += self.get_info_from_db(embedding,threshold=threshold)
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else:
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embedding = get_embedding(message)
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related_info += self.get_info_from_db(embedding,threshold=threshold)
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return related_info
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def get_info_from_db(self, query_embedding: list, limit: int = 1, threshold: float = 0.5) -> str:
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if not query_embedding:
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return ''
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# 使用余弦相似度计算
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pipeline = [
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{
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"$addFields": {
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"dotProduct": {
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"$reduce": {
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"input": {"$range": [0, {"$size": "$embedding"}]},
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"initialValue": 0,
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"in": {
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"$add": [
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"$$value",
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{"$multiply": [
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{"$arrayElemAt": ["$embedding", "$$this"]},
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{"$arrayElemAt": [query_embedding, "$$this"]}
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]}
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]
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}
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}
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},
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"magnitude1": {
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"$sqrt": {
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"$reduce": {
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"input": "$embedding",
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"initialValue": 0,
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"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
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}
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}
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},
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"magnitude2": {
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"$sqrt": {
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"$reduce": {
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"input": query_embedding,
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"initialValue": 0,
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"in": {"$add": ["$$value", {"$multiply": ["$$this", "$$this"]}]}
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}
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}
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}
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}
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},
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{
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"$addFields": {
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"similarity": {
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"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]
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}
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}
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},
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{
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"$match": {
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"similarity": {"$gte": threshold} # 只保留相似度大于等于阈值的结果
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}
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},
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{"$sort": {"similarity": -1}},
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{"$limit": limit},
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{"$project": {"content": 1, "similarity": 1}}
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]
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results = list(self.db.db.knowledges.aggregate(pipeline))
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# print(f"\033[1;34m[调试]\033[0m获取知识库内容结果: {results}")
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if not results:
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return ''
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# 返回所有找到的内容，用换行分隔
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return '\n'.join(str(result['content']) for result in results)
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prompt_builder = PromptBuilder() |