mirror of https://github.com/Mai-with-u/MaiBot.git
麦麦联网
Voyager1
parent
af9194f3a2
commit
dd8e40f2bd
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@ -0,0 +1,397 @@
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import random
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import time
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from typing import Optional, Union
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import re
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import jieba
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import numpy as np
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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 ...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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logger = get_module_logger("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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search_result: Optional[str] = None
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) -> tuple[str, str]:
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# 开始构建prompt
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# 关系
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who_chat_in_group = [(chat_stream.user_info.platform,
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chat_stream.user_info.user_id,
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chat_stream.user_info.user_nickname)]
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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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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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# 类型
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if chat_in_group:
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chat_target = "你正在qq群里聊天，下面是群里在聊的内容："
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chat_target_2 = "和群里聊天"
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else:
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chat_target = f"你正在和{sender_name}聊天，这是你们之前聊的内容："
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chat_target_2 = f"和{sender_name}私聊"
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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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# 人格选择
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personality = global_config.PROMPT_PERSONALITY
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probability_1 = global_config.PERSONALITY_1
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probability_2 = global_config.PERSONALITY_2
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personality_choice = random.random()
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if personality_choice < probability_1: # 第一种风格
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prompt_personality = personality[0]
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elif personality_choice < probability_1 + probability_2: # 第二种风格
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prompt_personality = personality[1]
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else: # 第三种人格
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prompt_personality = personality[2]
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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=0.4)
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# 添加搜索结果到知识中
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search_prompt = ""
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if search_result:
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search_prompt = f"""【搜索结果】
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我通过搜索引擎查询了用户问题，获取以下最新信息：
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{search_result}
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请将上述搜索结果作为回答的主要依据。这些是权威且最新的信息，请优先使用这些内容，并确保内容的完整性和准确性。回答时保持自然友好的对话风格，不要直接说"根据搜索结果"，而是像日常交谈那样回答。"""
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# 如果已有知识库内容，合并搜索结果
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if prompt_info:
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prompt_info = f"{prompt_info}\n\n{search_prompt}"
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else:
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prompt_info = search_prompt
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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**，之后会用到。\n"""
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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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{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。{relation_prompt_all}\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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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(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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# 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(self, query_embedding: list, limit: int = 1, threshold: float = 0.5, return_raw: bool = False) -> 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"]},
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{"$arrayElemAt": [query_embedding, "$$this"]},
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]
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},
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]
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},
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}
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},
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"magnitude1": {
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"$sqrt": {
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"$reduce": {
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"input": "$embedding",
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"initialValue": 0,
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"in": {"$add": ["$$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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{"$addFields": {"similarity": {"$divide": ["$dotProduct", {"$multiply": ["$magnitude1", "$magnitude2"]}]}}},
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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(db.knowledges.aggregate(pipeline))
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logger.debug(f"知识库查询结果数量: {len(results)}")
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if not results:
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return "" if not return_raw else []
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if return_raw:
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return results
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else:
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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()
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|
|
@ -0,0 +1,3 @@
|
|||
from .search_manager import SearchManager
|
||||
|
||||
__all__ = ["SearchManager"]
|
||||
|
|
@ -0,0 +1,324 @@
|
|||
import time
|
||||
import asyncio
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
import os
|
||||
import re
|
||||
|
||||
from tavily import TavilyClient
|
||||
from ....models.utils_model import LLM_request
|
||||
from ....config.config import global_config
|
||||
from src.common.logger import get_module_logger, LogConfig, LLM_STYLE_CONFIG
|
||||
|
||||
# 定义日志配置
|
||||
llm_config = LogConfig(
|
||||
console_format=LLM_STYLE_CONFIG["console_format"],
|
||||
file_format=LLM_STYLE_CONFIG["file_format"],
|
||||
)
|
||||
|
||||
logger = get_module_logger("tavily_search", config=llm_config)
|
||||
|
||||
class SearchManager:
|
||||
_instance = None
|
||||
|
||||
@classmethod
|
||||
def get_instance(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = SearchManager()
|
||||
return cls._instance
|
||||
|
||||
def __init__(self):
|
||||
"""初始化搜索管理器"""
|
||||
# 确保单例模式
|
||||
if SearchManager._instance is not None:
|
||||
raise RuntimeError("SearchManager是单例类，请使用SearchManager.get_instance()获取实例")
|
||||
|
||||
# 初始化搜索判断模型
|
||||
self.search_judge_model = LLM_request(
|
||||
model=global_config.llm_search_judge,
|
||||
temperature=0.2,
|
||||
max_tokens=100,
|
||||
request_type="search_judge"
|
||||
)
|
||||
|
||||
# 配置tavily客户端
|
||||
self.tavily_api_key = os.environ.get("SEARCH_API_KEY", "")
|
||||
if not self.tavily_api_key:
|
||||
logger.warning("未找到Tavily API密钥，搜索功能将不可用")
|
||||
self.tavily_client = None
|
||||
if self.tavily_api_key:
|
||||
self.tavily_client = TavilyClient(self.tavily_api_key)
|
||||
|
||||
# 初始化搜索配置
|
||||
self.enable_search = global_config.tavily_search.get("enable", False)
|
||||
self.search_probability = global_config.tavily_search.get("search_probability", 0.7)
|
||||
self.max_search_results = global_config.tavily_search.get("max_search_results", 3)
|
||||
|
||||
logger.info(f"搜索管理器初始化完成，搜索功能启用状态: {self.enable_search}")
|
||||
|
||||
async def should_search_and_extract_keywords(self, message_text: str) -> Tuple[bool, str, float, str]:
|
||||
"""判断是否需要搜索并提取搜索关键词及主题
|
||||
|
||||
Args:
|
||||
message_text: 用户消息文本
|
||||
|
||||
Returns:
|
||||
(是否搜索, 搜索关键词, 置信度, 搜索主题)
|
||||
"""
|
||||
if not self.enable_search or not self.tavily_client:
|
||||
return False, "", 0.0, "general"
|
||||
|
||||
# 构建组合提示词，同时判断搜索需求并提取关键词
|
||||
prompt = f"""
|
||||
请同时完成以下三个任务：
|
||||
|
||||
任务1：判断用户消息是否需要进行搜索引擎查询获取信息
|
||||
任务2：提取适合搜索的关键词或短语
|
||||
任务3：确定搜索主题类型
|
||||
|
||||
用户消息: "{message_text}"
|
||||
|
||||
判断标准:
|
||||
需要搜索的情况：询问最新消息、新闻、事实数据、专业知识、明确要求查找信息
|
||||
不需要搜索的情况：日常问候、情感表达、基于已有对话的提问、简单常识问题
|
||||
|
||||
关键词提取要求:
|
||||
1. 提取核心问题相关的关键词或短语
|
||||
2. 保留重要的专有名词、人名、地名、时间等
|
||||
3. 去除闲聊内容和修饰性词语
|
||||
4. 使用完整的问句或关键词组合
|
||||
5. 控制在100个字符以内
|
||||
|
||||
主题类型判断:
|
||||
- "news": 与新闻、时事、最新事件相关的查询
|
||||
- "finance": 与金融、投资、经济、股市相关的查询
|
||||
- "general": 其他类型的一般性查询
|
||||
|
||||
返回格式：
|
||||
搜索评分：[0-1之间的数值]
|
||||
搜索关键词：[提取的关键词]
|
||||
搜索主题：[news/finance/general]
|
||||
|
||||
请注意：搜索评分0表示完全不需要搜索，1表示非常需要搜索。
|
||||
"""
|
||||
|
||||
try:
|
||||
# 调用模型进行判断
|
||||
start_time = time.time()
|
||||
result, _, _ = await self.search_judge_model.generate_response(prompt)
|
||||
|
||||
# 解析结果
|
||||
search_score = 0.0
|
||||
keywords = ""
|
||||
topic = "general" # 默认主题
|
||||
|
||||
for line in result.strip().split('\n'):
|
||||
line = line.strip()
|
||||
if line.startswith("搜索评分：") or line.startswith("搜索评分:"):
|
||||
score_text = line.split('：' if '：' in line else ':', 1)[1].strip()
|
||||
search_score = self._extract_score(score_text)
|
||||
elif line.startswith("搜索关键词：") or line.startswith("搜索关键词:"):
|
||||
keywords = line.split('：' if '：' in line else ':', 1)[1].strip()
|
||||
elif line.startswith("搜索主题：") or line.startswith("搜索主题:"):
|
||||
topic_text = line.split('：' if '：' in line else ':', 1)[1].strip().lower()
|
||||
if topic_text in ["news", "finance", "general"]:
|
||||
topic = topic_text
|
||||
|
||||
# 如果关键词提取失败，使用原始消息
|
||||
if not keywords or len(keywords) < 5:
|
||||
keywords = message_text
|
||||
|
||||
# 判断是否执行搜索
|
||||
should_search = search_score >= self.search_probability
|
||||
|
||||
# 记录结果
|
||||
logger.info(f"搜索判断：分数={search_score:.2f}, 阈值={self.search_probability}, "
|
||||
f"关键词='{keywords}', 主题='{topic}', 用时={time.time() - start_time:.2f}秒")
|
||||
|
||||
return should_search, keywords, search_score, topic
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"搜索判断和关键词提取出错: {e}")
|
||||
return False, "", 0.0, "general"
|
||||
|
||||
def _extract_score(self, result: str) -> float:
|
||||
"""从模型结果中提取搜索分数"""
|
||||
try:
|
||||
# 清理结果文本
|
||||
cleaned_result = result.strip()
|
||||
# 尝试直接解析为浮点数
|
||||
score = float(cleaned_result)
|
||||
# 确保分数在0-1范围内
|
||||
return max(0.0, min(1.0, score))
|
||||
except ValueError:
|
||||
# 如果无法直接解析，尝试通过正则表达式提取
|
||||
score_match = re.search(r'(\d+(\.\d+)?)', cleaned_result)
|
||||
if score_match:
|
||||
try:
|
||||
score = float(score_match.group(1))
|
||||
return max(0.0, min(1.0, score))
|
||||
except ValueError:
|
||||
pass
|
||||
# 默认返回0，表示不搜索
|
||||
logger.warning(f"无法从结果中提取搜索分数: {result}")
|
||||
return 0.0
|
||||
|
||||
async def perform_search(self, query: str, topic: str = "general") -> Optional[str]:
|
||||
"""执行搜索并整理结果
|
||||
|
||||
Args:
|
||||
query: 搜索查询文本
|
||||
topic: 搜索主题，可以是"general"、"news"或"finance"
|
||||
|
||||
Returns:
|
||||
整理后的搜索结果，如果搜索失败则返回None
|
||||
"""
|
||||
if not self.tavily_client:
|
||||
return None
|
||||
|
||||
try:
|
||||
# 执行搜索
|
||||
logger.info(f"开始搜索: 关键词='{query}', 主题='{topic}'")
|
||||
start_time = time.time()
|
||||
|
||||
# 异步搜索
|
||||
loop = asyncio.get_event_loop()
|
||||
response = await loop.run_in_executor(
|
||||
None,
|
||||
lambda: self.tavily_client.search(
|
||||
query=query,
|
||||
topic=topic,
|
||||
max_results=self.max_search_results
|
||||
)
|
||||
)
|
||||
# response = response['title']+'\n'+response['content']
|
||||
search_time = time.time() - start_time
|
||||
logger.info(f"搜索完成，耗时: {search_time:.2f}秒，找到{len(response.get('results', []))}条结果")
|
||||
|
||||
# 整理搜索结果
|
||||
return await self._process_search_results(query, response, topic)
|
||||
except Exception as e:
|
||||
logger.error(f"搜索出错: {e}")
|
||||
return None
|
||||
|
||||
async def _process_search_results(self, query: str, response: Dict, topic: str = "general") -> Optional[str]:
|
||||
"""处理和整理搜索结果
|
||||
|
||||
Args:
|
||||
query: 原始搜索查询
|
||||
response: Tavily API的响应
|
||||
topic: 搜索主题
|
||||
|
||||
Returns:
|
||||
整理后的知识文本
|
||||
"""
|
||||
if not response or "results" not in response or not response["results"]:
|
||||
return None
|
||||
|
||||
# 提取搜索结果
|
||||
results = response["results"]
|
||||
|
||||
# 记录原始搜索结果
|
||||
logger.info(f"原始搜索结果({len(results)}条), 主题={topic}:")
|
||||
for i, result in enumerate(results, 1):
|
||||
title = result.get('title', '无标题')
|
||||
url = result.get('url', '未知来源')
|
||||
content_preview = result.get('content', '无内容')[:150].replace("\n", " ")
|
||||
published_date = result.get('published_date', '') if topic == "news" else ""
|
||||
date_info = f" | 发布日期: {published_date}" if published_date else ""
|
||||
logger.info(f"结果[{i}] 标题: {title} | 来源: {url}{date_info} | 内容预览: {content_preview}...")
|
||||
|
||||
# 根据主题类型，处理搜索结果
|
||||
processed_results = []
|
||||
for result in results:
|
||||
title = result.get('title', '无标题')
|
||||
content = result.get('content', '无内容')
|
||||
|
||||
# 不再限制内容长度，保留完整内容
|
||||
# 构建单条结果
|
||||
processed_result = f"标题: {title}\n"
|
||||
|
||||
# 对于新闻主题，添加发布日期
|
||||
if topic == "news" and "published_date" in result:
|
||||
published_date = result.get('published_date', '')
|
||||
if published_date:
|
||||
processed_result += f"发布日期: {published_date}\n"
|
||||
|
||||
processed_result += f"内容: {content}"
|
||||
processed_results.append(processed_result)
|
||||
|
||||
# 将所有结果合并为一个字符串
|
||||
raw_content = "\n\n---\n\n".join(processed_results)
|
||||
|
||||
prompt = f"""
|
||||
请将以下搜索结果整理为详细、完整的知识摘要，以便聊天机器人能够提供全面信息。
|
||||
|
||||
搜索查询: "{query}"
|
||||
搜索主题: "{topic}"
|
||||
|
||||
搜索结果:
|
||||
{raw_content}
|
||||
|
||||
要求:
|
||||
1. 保持客观准确，不要添加不在原始结果中的信息
|
||||
2. 去除冗余和重复内容，但保留所有重要细节
|
||||
3. 按逻辑顺序组织信息
|
||||
4. 保留所有重要的数字、日期、名称、地点等关键事实
|
||||
5. 使用清晰的标题和小标题结构化信息
|
||||
6. 确保内容完整，不要截断或简化重要信息
|
||||
7. 如果有多个来源的信息，请按主题组织，而不是按来源
|
||||
8. 如果存在不同来源的矛盾信息，请指出这些差异
|
||||
|
||||
格式要求:
|
||||
- 使用markdown格式，用标题和小标题组织内容
|
||||
- 对复杂概念提供简短解释
|
||||
- 确保输出信息丰富且完整
|
||||
|
||||
请输出一个全面且完整的知识摘要:
|
||||
"""
|
||||
|
||||
try:
|
||||
# 创建一个新的搜索结果整理模型，使用更大的token限制
|
||||
start_time = time.time()
|
||||
|
||||
# 创建一个新的搜索结果整理模型，使用更大的token限制
|
||||
try:
|
||||
# 创建一个全新的高容量结果整理模型
|
||||
summary_model = LLM_request(
|
||||
model=global_config.llm_summary_by_topic, # 使用摘要模型代替搜索判断模型
|
||||
temperature=0.3,
|
||||
max_tokens=1500, # 大幅增加token输出限制
|
||||
request_type="search_summary"
|
||||
)
|
||||
|
||||
# 使用新模型进行结果整理
|
||||
result, reasoning_content, _ = await summary_model.generate_response(prompt)
|
||||
logger.info(f"使用摘要模型整理搜索结果完成，耗时: {time.time() - start_time:.2f}秒")
|
||||
except Exception as inner_e:
|
||||
logger.error(f"摘要模型调用失败: {inner_e}，尝试使用原始搜索模型")
|
||||
try:
|
||||
# 如果摘要模型失败，回退到使用原始搜索判断模型
|
||||
result, reasoning_content, _ = await self.search_judge_model.generate_response(prompt)
|
||||
logger.info(f"使用原始搜索模型整理结果完成，耗时: {time.time() - start_time:.2f}秒")
|
||||
except Exception as fallback_e:
|
||||
logger.error(f"所有模型整理失败: {fallback_e}")
|
||||
# 如果所有模型都失败，返回原始内容
|
||||
result = raw_content
|
||||
|
||||
# 如果结果显得不完整（如以逗号、省略号结尾或中间有明显截断），补充说明
|
||||
result = result.strip()
|
||||
if result.endswith((",", ".", ":", "...", "…")) or "..." in result[-20:]:
|
||||
result += "\n\n(注：由于内容较长，摘要可能不完整，但已包含主要信息)"
|
||||
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"搜索结果整理出错: {e}")
|
||||
# 如果整理失败，返回原始内容的简化版本
|
||||
simplified_results = []
|
||||
for i, result in enumerate(results, 1):
|
||||
title = result.get('title', '无标题')
|
||||
content_summary = result.get('content', '无内容')
|
||||
if len(content_summary) > 150:
|
||||
content_summary = content_summary[:147] + "..."
|
||||
simplified_results.append(f"{i}. {title}: {content_summary}")
|
||||
|
||||
return "搜索结果摘要:\n\n" + "\n\n".join(simplified_results)
|
||||
|
|
@ -0,0 +1,250 @@
|
|||
[inner]
|
||||
version = "1.1.3"
|
||||
|
||||
|
||||
#以下是给开发人员阅读的，一般用户不需要阅读
|
||||
#如果你想要修改配置文件，请在修改后将version的值进行变更
|
||||
#如果新增项目，请在BotConfig类下新增相应的变量
|
||||
#1.如果你修改的是[]层级项目，例如你新增了 [memory],那么请在config.py的 load_config函数中的include_configs字典中新增"内容":{
|
||||
#"func":memory,
|
||||
#"support":">=0.0.0", #新的版本号
|
||||
#"necessary":False #是否必须
|
||||
#}
|
||||
#2.如果你修改的是[]下的项目，例如你新增了[memory]下的 memory_ban_words ,那么请在config.py的 load_config函数中的 memory函数下新增版本判断:
|
||||
# if config.INNER_VERSION in SpecifierSet(">=0.0.2"):
|
||||
# config.memory_ban_words = set(memory_config.get("memory_ban_words", []))
|
||||
|
||||
# 版本格式：主版本号.次版本号.修订号，版本号递增规则如下：
|
||||
# 主版本号：当你做了不兼容的 API 修改，
|
||||
# 次版本号：当你做了向下兼容的功能性新增，
|
||||
# 修订号：当你做了向下兼容的问题修正。
|
||||
# 先行版本号及版本编译信息可以加到“主版本号.次版本号.修订号”的后面，作为延伸。
|
||||
|
||||
[bot]
|
||||
qq = 114514
|
||||
nickname = "麦麦"
|
||||
alias_names = ["麦叠", "牢麦"]
|
||||
|
||||
[groups]
|
||||
talk_allowed = [
|
||||
123,
|
||||
123,
|
||||
] #可以回复消息的群号码
|
||||
talk_frequency_down = [] #降低回复频率的群号码
|
||||
ban_user_id = [] #禁止回复和读取消息的QQ号
|
||||
|
||||
[personality]
|
||||
prompt_personality = [
|
||||
"用一句话或几句话描述性格特点和其他特征",
|
||||
"例如，是一个热爱国家热爱党的新时代好青年",
|
||||
"例如，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"
|
||||
]
|
||||
personality_1_probability = 0.7 # 第一种人格出现概率
|
||||
personality_2_probability = 0.2 # 第二种人格出现概率，可以为0
|
||||
personality_3_probability = 0.1 # 第三种人格出现概率，请确保三个概率相加等于1
|
||||
|
||||
[schedule]
|
||||
enable_schedule_gen = true # 是否启用日程表(尚未完成)
|
||||
prompt_schedule_gen = "用几句话描述描述性格特点或行动规律，这个特征会用来生成日程表"
|
||||
schedule_doing_update_interval = 900 # 日程表更新间隔 单位秒
|
||||
schedule_temperature = 0.3 # 日程表温度，建议0.3-0.6
|
||||
time_zone = "Asia/Shanghai" # 给你的机器人设置时区，可以解决运行电脑时区和国内时区不同的情况，或者模拟国外留学生日程
|
||||
|
||||
[platforms] # 必填项目，填写每个平台适配器提供的链接
|
||||
nonebot-qq="http://127.0.0.1:18002/api/message"
|
||||
|
||||
[response] #使用哪种回复策略
|
||||
response_mode = "heart_flow" # 回复策略，可选值：heart_flow（心流），reasoning（推理）
|
||||
|
||||
#推理回复参数
|
||||
model_r1_probability = 0.7 # 麦麦回答时选择主要回复模型1 模型的概率
|
||||
model_v3_probability = 0.3 # 麦麦回答时选择次要回复模型2 模型的概率
|
||||
|
||||
[heartflow] # 注意：可能会消耗大量token，请谨慎开启
|
||||
sub_heart_flow_update_interval = 60 # 子心流更新频率，间隔 单位秒
|
||||
sub_heart_flow_freeze_time = 120 # 子心流冻结时间，超过这个时间没有回复，子心流会冻结，间隔 单位秒
|
||||
sub_heart_flow_stop_time = 600 # 子心流停止时间，超过这个时间没有回复，子心流会停止，间隔 单位秒
|
||||
heart_flow_update_interval = 300 # 心流更新频率，间隔 单位秒
|
||||
|
||||
|
||||
[message]
|
||||
max_context_size = 12 # 麦麦获得的上文数量，建议12，太短太长都会导致脑袋尖尖
|
||||
emoji_chance = 0.2 # 麦麦使用表情包的概率
|
||||
thinking_timeout = 60 # 麦麦最长思考时间，超过这个时间的思考会放弃
|
||||
max_response_length = 256 # 麦麦回答的最大token数
|
||||
ban_words = [
|
||||
# "403","张三"
|
||||
]
|
||||
|
||||
ban_msgs_regex = [
|
||||
# 需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤（支持CQ码），若不了解正则表达式请勿修改
|
||||
#"https?://[^\\s]+", # 匹配https链接
|
||||
#"\\d{4}-\\d{2}-\\d{2}", # 匹配日期
|
||||
# "\\[CQ:at,qq=\\d+\\]" # 匹配@
|
||||
]
|
||||
|
||||
[willing]
|
||||
willing_mode = "classical" # 回复意愿模式 经典模式
|
||||
# willing_mode = "dynamic" # 动态模式(可能不兼容)
|
||||
# willing_mode = "custom" # 自定义模式（可自行调整
|
||||
response_willing_amplifier = 1 # 麦麦回复意愿放大系数，一般为1
|
||||
response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数
|
||||
down_frequency_rate = 3 # 降低回复频率的群组回复意愿降低系数 除法
|
||||
emoji_response_penalty = 0.1 # 表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率
|
||||
|
||||
|
||||
[emoji]
|
||||
max_emoji_num = 120 # 表情包最大数量
|
||||
max_reach_deletion = true # 开启则在达到最大数量时删除表情包，关闭则达到最大数量时不删除，只是不会继续收集表情包
|
||||
check_interval = 30 # 检查表情包（注册，破损，删除）的时间间隔(分钟)
|
||||
auto_save = true # 是否保存表情包和图片
|
||||
enable_check = false # 是否启用表情包过滤
|
||||
check_prompt = "符合公序良俗" # 表情包过滤要求
|
||||
|
||||
[memory]
|
||||
build_memory_interval = 2000 # 记忆构建间隔 单位秒 间隔越低，麦麦学习越多，但是冗余信息也会增多
|
||||
build_memory_distribution = [4.0,2.0,0.6,24.0,8.0,0.4] # 记忆构建分布，参数：分布1均值，标准差，权重，分布2均值，标准差，权重
|
||||
build_memory_sample_num = 10 # 采样数量，数值越高记忆采样次数越多
|
||||
build_memory_sample_length = 20 # 采样长度，数值越高一段记忆内容越丰富
|
||||
memory_compress_rate = 0.1 # 记忆压缩率 控制记忆精简程度 建议保持默认,调高可以获得更多信息，但是冗余信息也会增多
|
||||
|
||||
forget_memory_interval = 1000 # 记忆遗忘间隔 单位秒 间隔越低，麦麦遗忘越频繁，记忆更精简，但更难学习
|
||||
memory_forget_time = 24 #多长时间后的记忆会被遗忘 单位小时
|
||||
memory_forget_percentage = 0.01 # 记忆遗忘比例 控制记忆遗忘程度 越大遗忘越多 建议保持默认
|
||||
|
||||
memory_ban_words = [ #不希望记忆的词
|
||||
# "403","张三"
|
||||
]
|
||||
|
||||
[mood]
|
||||
mood_update_interval = 1.0 # 情绪更新间隔 单位秒
|
||||
mood_decay_rate = 0.95 # 情绪衰减率
|
||||
mood_intensity_factor = 1.0 # 情绪强度因子
|
||||
|
||||
[keywords_reaction] # 针对某个关键词作出反应
|
||||
enable = true # 关键词反应功能的总开关
|
||||
|
||||
[[keywords_reaction.rules]] # 如果想要新增多个关键词，直接复制本条，修改keywords和reaction即可
|
||||
enable = true # 是否启用此条（为了人类在未来AI战争能更好地识别AI（bushi），默认开启）
|
||||
keywords = ["人机", "bot", "机器", "入机", "robot", "机器人","ai","AI"] # 会触发反应的关键词
|
||||
reaction = "有人说你是人机或者机器人，否定这一事实，攻击或反问他。注意，只有认为你是机器人才回复，否则不要否认" # 触发之后添加的提示词
|
||||
|
||||
[[keywords_reaction.rules]] # 就像这样复制
|
||||
enable = false # 仅作示例，不会触发
|
||||
keywords = ["测试关键词回复","test",""]
|
||||
reaction = "回答“测试成功”"
|
||||
|
||||
[chinese_typo]
|
||||
enable = true # 是否启用中文错别字生成器
|
||||
error_rate=0.001 # 单字替换概率
|
||||
min_freq=9 # 最小字频阈值
|
||||
tone_error_rate=0.1 # 声调错误概率
|
||||
word_replace_rate=0.006 # 整词替换概率
|
||||
|
||||
[response_spliter]
|
||||
enable_response_spliter = true # 是否启用回复分割器
|
||||
response_max_length = 100 # 回复允许的最大长度
|
||||
response_max_sentence_num = 4 # 回复允许的最大句子数
|
||||
|
||||
[remote] #发送统计信息，主要是看全球有多少只麦麦
|
||||
enable = true
|
||||
|
||||
[experimental]
|
||||
enable_friend_chat = false # 是否启用好友聊天
|
||||
pfc_chatting = false # 是否启用PFC聊天
|
||||
|
||||
#下面的模型若使用硅基流动则不需要更改，使用ds官方则改成.env自定义的宏，使用自定义模型则选择定位相似的模型自己填写
|
||||
#推理模型
|
||||
|
||||
# 额外字段
|
||||
# 下面的模型有以下额外字段可以添加：
|
||||
|
||||
# stream = <true|false> : 用于指定模型是否是使用流式输出
|
||||
# 如果不指定，则该项是 False
|
||||
|
||||
[model.llm_reasoning] #只在回复模式为reasoning时启用
|
||||
name = "Pro/deepseek-ai/DeepSeek-R1"
|
||||
# name = "Qwen/QwQ-32B"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 4 #模型的输入价格（非必填，可以记录消耗）
|
||||
pri_out = 16 #模型的输出价格（非必填，可以记录消耗）
|
||||
|
||||
#非推理模型
|
||||
|
||||
[model.llm_normal] #V3 回复模型1 主要回复模型
|
||||
name = "Pro/deepseek-ai/DeepSeek-V3"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 2 #模型的输入价格（非必填，可以记录消耗）
|
||||
pri_out = 8 #模型的输出价格（非必填，可以记录消耗）
|
||||
|
||||
[model.llm_emotion_judge] #表情包判断
|
||||
name = "Qwen/Qwen2.5-14B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0.7
|
||||
pri_out = 0.7
|
||||
|
||||
[model.llm_topic_judge] #记忆主题判断：建议使用qwen2.5 7b
|
||||
name = "Pro/Qwen/Qwen2.5-7B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0
|
||||
pri_out = 0
|
||||
|
||||
[model.llm_summary_by_topic] #概括模型，建议使用qwen2.5 32b 及以上
|
||||
name = "Qwen/Qwen2.5-32B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.26
|
||||
pri_out = 1.26
|
||||
|
||||
[model.moderation] #内容审核，开发中
|
||||
name = ""
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.0
|
||||
pri_out = 2.0
|
||||
|
||||
# 识图模型
|
||||
|
||||
[model.vlm] #图像识别
|
||||
name = "Pro/Qwen/Qwen2.5-VL-7B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0.35
|
||||
pri_out = 0.35
|
||||
|
||||
#嵌入模型
|
||||
|
||||
[model.embedding] #嵌入
|
||||
name = "BAAI/bge-m3"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0
|
||||
pri_out = 0
|
||||
|
||||
[model.llm_observation] #观察模型，建议用免费的：建议使用qwen2.5 7b
|
||||
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"
|
||||
name = "Qwen/Qwen2.5-7B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0
|
||||
pri_out = 0
|
||||
|
||||
[model.llm_sub_heartflow] #心流：建议使用qwen2.5 7b
|
||||
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"
|
||||
name = "Qwen/Qwen2.5-32B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.26
|
||||
pri_out = 1.26
|
||||
|
||||
[model.llm_heartflow] #心流：建议使用qwen2.5 32b
|
||||
# name = "Pro/Qwen/Qwen2.5-7B-Instruct"
|
||||
name = "Qwen/Qwen2.5-32B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 1.26
|
||||
pri_out = 1.26
|
||||
|
||||
[model.llm_search_judge] #搜索判断模型
|
||||
name = "Qwen/Qwen2.5-7B-Instruct"
|
||||
provider = "SILICONFLOW"
|
||||
pri_in = 0.7
|
||||
pri_out = 0.7
|
||||
|
||||
[tavily_search]
|
||||
enable = true # 是否启用Tavily搜索功能
|
||||
search_probability = 0.7 # 触发搜索的概率阈值
|
||||
max_search_results = 3 # 最多返回的搜索结果数量
|
||||
|
|
@ -33,4 +33,7 @@ SIMPLE_OUTPUT=true # 精简控制台输出格式
|
|||
CONSOLE_LOG_LEVEL=INFO # 自定义日志的默认控制台输出日志级别
|
||||
FILE_LOG_LEVEL=DEBUG # 自定义日志的默认文件输出日志级别
|
||||
DEFAULT_CONSOLE_LOG_LEVEL=SUCCESS # 原生日志的控制台输出日志级别（nonebot就是这一类）
|
||||
DEFAULT_FILE_LOG_LEVEL=DEBUG # 原生日志的默认文件输出日志级别（nonebot就是这一类）
|
||||
DEFAULT_FILE_LOG_LEVEL=DEBUG # 原生日志的默认文件输出日志级别（nonebot就是这一类）
|
||||
|
||||
# 这里是搜索工具的key,申请网站：https://app.tavily.com
|
||||
SEARCH_API_KEY=
|
||||
Loading…
Reference in New Issue