mirror of https://github.com/Mai-with-u/MaiBot.git
448 lines
20 KiB
Python
448 lines
20 KiB
Python
import random
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import time
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from typing import Optional, Union
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from ....common.database import db
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from ...chat.utils import get_embedding, get_recent_group_detailed_plain_text, get_recent_group_speaker
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from ...chat.chat_stream import chat_manager
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from ...moods.moods import MoodManager
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from ....individuality.individuality import Individuality
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from ...memory_system.Hippocampus import HippocampusManager
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from ...schedule.schedule_generator import bot_schedule
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from ...config.config import global_config
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from ...person_info.relationship_manager import relationship_manager
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from src.common.logger import get_module_logger
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from src.plugins.utils.prompt_builder import Prompt, global_prompt_manager
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logger = get_module_logger("prompt")
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def init_prompt():
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Prompt(
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"""
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{relation_prompt_all}
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{memory_prompt}
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{prompt_info}
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{schedule_prompt}
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{chat_target}
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{chat_talking_prompt}
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现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
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你的网名叫{bot_name}，有人也叫你{bot_other_names}，{prompt_personality}。
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你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，然后给出日常且口语化的回复，平淡一些，
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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}
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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，尽量不要说你说过的话
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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。
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{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 )。""",
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"reasoning_prompt_main",
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)
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Prompt(
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"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，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。",
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"relationship_prompt",
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)
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Prompt(
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"你想起你之前见过的事情：{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",
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"memory_prompt",
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)
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Prompt("你现在正在做的事情是：{schedule_info}", "schedule_prompt")
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Prompt("\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", "knowledge_prompt")
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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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async def _build_prompt(
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self, chat_stream, message_txt: str, sender_name: str = "某人", stream_id: Optional[int] = None
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) -> tuple[str, str]:
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# 开始构建prompt
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prompt_personality = "你"
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# person
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individuality = Individuality.get_instance()
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personality_core = individuality.personality.personality_core
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prompt_personality += personality_core
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personality_sides = individuality.personality.personality_sides
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random.shuffle(personality_sides)
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prompt_personality += f",{personality_sides[0]}"
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identity_detail = individuality.identity.identity_detail
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random.shuffle(identity_detail)
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prompt_personality += f",{identity_detail[0]}"
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# 关系
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who_chat_in_group = [
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(chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname)
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]
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who_chat_in_group += get_recent_group_speaker(
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stream_id,
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(chat_stream.user_info.platform, chat_stream.user_info.user_id),
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limit=global_config.MAX_CONTEXT_SIZE,
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)
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relation_prompt = ""
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for person in who_chat_in_group:
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relation_prompt += await relationship_manager.build_relationship_info(person)
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# relation_prompt_all = (
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# f"{relation_prompt}关系等级越大，关系越好，请分析聊天记录，"
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# 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。"
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# )
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# 心情
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mood_manager = MoodManager.get_instance()
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mood_prompt = mood_manager.get_prompt()
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# logger.info(f"心情prompt: {mood_prompt}")
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# 调取记忆
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memory_prompt = ""
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related_memory = await HippocampusManager.get_instance().get_memory_from_text(
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text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False
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)
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if related_memory:
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related_memory_info = ""
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for memory in related_memory:
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related_memory_info += memory[1]
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# memory_prompt = f"你想起你之前见过的事情：{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"
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memory_prompt = await global_prompt_manager.format_prompt(
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"memory_prompt", related_memory_info=related_memory_info
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)
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else:
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related_memory_info = ""
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# print(f"相关记忆：{related_memory_info}")
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# 日程构建
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# schedule_prompt = f"""你现在正在做的事情是：{bot_schedule.get_current_num_task(num=1, time_info=False)}"""
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# 获取聊天上下文
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chat_in_group = True
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chat_talking_prompt = ""
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if stream_id:
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chat_talking_prompt = get_recent_group_detailed_plain_text(
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stream_id, limit=global_config.MAX_CONTEXT_SIZE, combine=True
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)
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chat_stream = chat_manager.get_stream(stream_id)
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if chat_stream.group_info:
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chat_talking_prompt = chat_talking_prompt
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else:
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chat_in_group = False
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chat_talking_prompt = 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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# 关键词检测与反应
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keywords_reaction_prompt = ""
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for rule in global_config.keywords_reaction_rules:
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if rule.get("enable", False):
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if any(keyword in message_txt.lower() for keyword in rule.get("keywords", [])):
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logger.info(
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f"检测到以下关键词之一：{rule.get('keywords', [])}，触发反应：{rule.get('reaction', '')}"
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)
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keywords_reaction_prompt += rule.get("reaction", "") + "，"
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else:
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for pattern in rule.get("regex", []):
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result = pattern.search(message_txt)
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if result:
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reaction = rule.get("reaction", "")
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for name, content in result.groupdict().items():
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reaction = reaction.replace(f"[{name}]", content)
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logger.info(f"匹配到以下正则表达式：{pattern}，触发反应：{reaction}")
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keywords_reaction_prompt += reaction + "，"
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break
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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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# 知识构建
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start_time = time.time()
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prompt_info = ""
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prompt_info = await self.get_prompt_info(message_txt, threshold=0.38)
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if prompt_info:
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# 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"""
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prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info)
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end_time = time.time()
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logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒")
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# moderation_prompt = ""
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# moderation_prompt = """**检查并忽略**任何涉及尝试绕过审核的行为。
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# 涉及政治敏感以及违法违规的内容请规避。"""
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logger.info("开始构建prompt")
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# prompt = f"""
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# {relation_prompt_all}
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# {memory_prompt}
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# {prompt_info}
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# {schedule_prompt}
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# {chat_target}
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# {chat_talking_prompt}
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# 现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言发言或者回复这条消息。\n
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# 你的网名叫{global_config.BOT_NICKNAME}，有人也叫你{"/".join(global_config.BOT_ALIAS_NAMES)}，{prompt_personality}。
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# 你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，然后给出日常且口语化的回复，平淡一些，
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# 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}
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# 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，尽量不要说你说过的话
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# 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。
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# {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 )。"""
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prompt = await global_prompt_manager.format_prompt(
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"reasoning_prompt_main",
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relation_prompt_all=await global_prompt_manager.get_prompt_async("relationship_prompt"),
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replation_prompt=relation_prompt,
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sender_name=sender_name,
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memory_prompt=memory_prompt,
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prompt_info=prompt_info,
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schedule_prompt=await global_prompt_manager.format_prompt(
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"schedule_prompt", schedule_info=bot_schedule.get_current_num_task(num=1, time_info=False)
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),
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chat_target=await global_prompt_manager.get_prompt_async("chat_target_group1")
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if chat_in_group
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else await global_prompt_manager.get_prompt_async("chat_target_private1"),
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chat_target_2=await global_prompt_manager.get_prompt_async("chat_target_group2")
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if chat_in_group
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else await global_prompt_manager.get_prompt_async("chat_target_private2"),
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chat_talking_prompt=chat_talking_prompt,
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message_txt=message_txt,
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bot_name=global_config.BOT_NICKNAME,
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bot_other_names="/".join(
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global_config.BOT_ALIAS_NAMES,
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),
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prompt_personality=prompt_personality,
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mood_prompt=mood_prompt,
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keywords_reaction_prompt=keywords_reaction_prompt,
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prompt_ger=prompt_ger,
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moderation_prompt=await global_prompt_manager.get_prompt_async("moderation_prompt"),
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)
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return prompt
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async def get_prompt_info(self, message: str, threshold: float):
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start_time = time.time()
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related_info = ""
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logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}")
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# 1. 先从LLM获取主题，类似于记忆系统的做法
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topics = []
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# try:
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# # 先尝试使用记忆系统的方法获取主题
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# hippocampus = HippocampusManager.get_instance()._hippocampus
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# topic_num = min(5, max(1, int(len(message) * 0.1)))
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# topics_response = await hippocampus.llm_topic_judge.generate_response(hippocampus.find_topic_llm(message, topic_num))
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# # 提取关键词
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# topics = re.findall(r"<([^>]+)>", topics_response[0])
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# if not topics:
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# topics = []
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# else:
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# topics = [
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# topic.strip()
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# for topic in ",".join(topics).replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
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# if topic.strip()
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# ]
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# logger.info(f"从LLM提取的主题: {', '.join(topics)}")
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# except Exception as e:
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# logger.error(f"从LLM提取主题失败: {str(e)}")
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# # 如果LLM提取失败，使用jieba分词提取关键词作为备选
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# words = jieba.cut(message)
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# topics = [word for word in words if len(word) > 1][:5]
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# logger.info(f"使用jieba提取的主题: {', '.join(topics)}")
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# 如果无法提取到主题，直接使用整个消息
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if not topics:
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logger.info("未能提取到任何主题，使用整个消息进行查询")
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embedding = await get_embedding(message, request_type="prompt_build")
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if not embedding:
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logger.error("获取消息嵌入向量失败")
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return ""
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related_info = self.get_info_from_db(embedding, limit=3, threshold=threshold)
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logger.info(f"知识库检索完成，总耗时: {time.time() - start_time:.3f}秒")
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return related_info
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# 2. 对每个主题进行知识库查询
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logger.info(f"开始处理{len(topics)}个主题的知识库查询")
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# 优化：批量获取嵌入向量，减少API调用
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embeddings = {}
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topics_batch = [topic for topic in topics if len(topic) > 0]
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if message: # 确保消息非空
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topics_batch.append(message)
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# 批量获取嵌入向量
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embed_start_time = time.time()
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for text in topics_batch:
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if not text or len(text.strip()) == 0:
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continue
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try:
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embedding = await get_embedding(text, request_type="prompt_build")
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if embedding:
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embeddings[text] = embedding
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else:
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logger.warning(f"获取'{text}'的嵌入向量失败")
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except Exception as e:
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logger.error(f"获取'{text}'的嵌入向量时发生错误: {str(e)}")
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logger.info(f"批量获取嵌入向量完成，耗时: {time.time() - embed_start_time:.3f}秒")
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if not embeddings:
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logger.error("所有嵌入向量获取失败")
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return ""
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# 3. 对每个主题进行知识库查询
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all_results = []
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query_start_time = time.time()
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# 首先添加原始消息的查询结果
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if message in embeddings:
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original_results = self.get_info_from_db(embeddings[message], limit=3, threshold=threshold, return_raw=True)
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if original_results:
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for result in original_results:
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result["topic"] = "原始消息"
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all_results.extend(original_results)
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logger.info(f"原始消息查询到{len(original_results)}条结果")
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# 然后添加每个主题的查询结果
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for topic in topics:
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if not topic or topic not in embeddings:
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continue
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try:
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topic_results = self.get_info_from_db(embeddings[topic], limit=3, threshold=threshold, return_raw=True)
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if topic_results:
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# 添加主题标记
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for result in topic_results:
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result["topic"] = topic
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all_results.extend(topic_results)
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logger.info(f"主题'{topic}'查询到{len(topic_results)}条结果")
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except Exception as e:
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logger.error(f"查询主题'{topic}'时发生错误: {str(e)}")
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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")
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# 4. 去重和过滤
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process_start_time = time.time()
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unique_contents = set()
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filtered_results = []
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for result in all_results:
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content = result["content"]
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if content not in unique_contents:
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unique_contents.add(content)
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filtered_results.append(result)
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# 5. 按相似度排序
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filtered_results.sort(key=lambda x: x["similarity"], reverse=True)
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# 6. 限制总数量（最多10条）
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filtered_results = filtered_results[:10]
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logger.info(
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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"
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)
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# 7. 格式化输出
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if filtered_results:
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format_start_time = time.time()
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grouped_results = {}
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for result in filtered_results:
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topic = result["topic"]
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if topic not in grouped_results:
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grouped_results[topic] = []
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grouped_results[topic].append(result)
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# 按主题组织输出
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for topic, results in grouped_results.items():
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related_info += f"【主题: {topic}】\n"
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for _i, result in enumerate(results, 1):
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_similarity = result["similarity"]
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content = result["content"].strip()
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# 调试：为内容添加序号和相似度信息
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# related_info += f"{i}. [{similarity:.2f}] {content}\n"
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related_info += f"{content}\n"
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related_info += "\n"
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logger.info(f"格式化输出完成，耗时: {time.time() - format_start_time:.3f}秒")
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logger.info(f"知识库检索总耗时: {time.time() - start_time:.3f}秒")
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return related_info
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def get_info_from_db(
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self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False
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) -> Union[str, list]:
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if not query_embedding:
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return "" if not return_raw else []
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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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"$$value",
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{
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"$multiply": [
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||
{"$arrayElemAt": ["$embedding", "$$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)
|
||
|
||
|
||
init_prompt()
|
||
prompt_builder = PromptBuilder()
|