初始化
This commit is contained in:
雅诺狐
648
src/chat/express/expression_learner.py
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648
src/chat/express/expression_learner.py
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import time
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import random
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import json
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import os
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from datetime import datetime
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from typing import List, Dict, Optional, Any, Tuple
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from src.common.logger import get_logger
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from src.common.database.sqlalchemy_database_api import get_session
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from sqlalchemy import select
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from src.common.database.sqlalchemy_models import Expression
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from src.llm_models.utils_model import LLMRequest
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from src.config.config import model_config, global_config
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from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_with_chat_inclusive, build_anonymous_messages
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from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
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from src.chat.message_receive.chat_stream import get_chat_manager
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MAX_EXPRESSION_COUNT = 300
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DECAY_DAYS = 30 # 30天衰减到0.01
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DECAY_MIN = 0.01 # 最小衰减值
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logger = get_logger("expressor")
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session = get_session()
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def format_create_date(timestamp: float) -> str:
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"""
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将时间戳格式化为可读的日期字符串
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"""
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try:
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return datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")
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except (ValueError, OSError):
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return "未知时间"
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def init_prompt() -> None:
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learn_style_prompt = """
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{chat_str}
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请从上面这段群聊中概括除了人名为"SELF"之外的人的语言风格
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1. 只考虑文字，不要考虑表情包和图片
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2. 不要涉及具体的人名，只考虑语言风格
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3. 语言风格包含特殊内容和情感
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4. 思考有没有特殊的梗，一并总结成语言风格
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5. 例子仅供参考，请严格根据群聊内容总结!!!
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注意：总结成如下格式的规律，总结的内容要详细，但具有概括性：
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例如：当"AAAAA"时，可以"BBBBB", AAAAA代表某个具体的场景，不超过20个字。BBBBB代表对应的语言风格，特定句式或表达方式，不超过20个字。
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例如：
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当"对某件事表示十分惊叹，有些意外"时，使用"我嘞个xxxx"
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当"表示讽刺的赞同，不想讲道理"时，使用"对对对"
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当"想说明某个具体的事实观点，但懒得明说，或者不便明说，或表达一种默契"，使用"懂的都懂"
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当"当涉及游戏相关时，表示意外的夸赞，略带戏谑意味"时，使用"这么强！"
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请注意：不要总结你自己（SELF）的发言
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现在请你概括
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"""
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Prompt(learn_style_prompt, "learn_style_prompt")
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learn_grammar_prompt = """
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{chat_str}
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请从上面这段群聊中概括除了人名为"SELF"之外的人的语法和句法特点，只考虑纯文字，不要考虑表情包和图片
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1.不要总结【图片】，【动画表情】，[图片]，[动画表情]，不总结 表情符号 at @ 回复 和[回复]
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2.不要涉及具体的人名，只考虑语法和句法特点,
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3.语法和句法特点要包括，句子长短（具体字数），有何种语病，如何拆分句子。
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4. 例子仅供参考，请严格根据群聊内容总结!!!
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总结成如下格式的规律，总结的内容要简洁，不浮夸：
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当"xxx"时，可以"xxx"
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例如：
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当"表达观点较复杂"时，使用"省略主语(3-6个字)"的句法
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当"不用详细说明的一般表达"时，使用"非常简洁的句子"的句法
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当"需要单纯简单的确认"时，使用"单字或几个字的肯定(1-2个字)"的句法
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注意不要总结你自己（SELF）的发言
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现在请你概括
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"""
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Prompt(learn_grammar_prompt, "learn_grammar_prompt")
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class ExpressionLearner:
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def __init__(self, chat_id: str) -> None:
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self.express_learn_model: LLMRequest = LLMRequest(
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model_set=model_config.model_task_config.replyer_1, request_type="expressor.learner"
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)
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self.chat_id = chat_id
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self.chat_name = get_chat_manager().get_stream_name(chat_id) or chat_id
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# 维护每个chat的上次学习时间
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self.last_learning_time: float = time.time()
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# 学习参数
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self.min_messages_for_learning = 25 # 触发学习所需的最少消息数
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self.min_learning_interval = 300 # 最短学习时间间隔（秒）
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def can_learn_for_chat(self) -> bool:
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"""
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检查指定聊天流是否允许学习表达
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Args:
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chat_id: 聊天流ID
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Returns:
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bool: 是否允许学习
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"""
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try:
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use_expression, enable_learning, _ = global_config.expression.get_expression_config_for_chat(self.chat_id)
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return enable_learning
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except Exception as e:
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logger.error(f"检查学习权限失败: {e}")
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return False
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def should_trigger_learning(self) -> bool:
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"""
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检查是否应该触发学习
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Args:
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chat_id: 聊天流ID
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Returns:
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bool: 是否应该触发学习
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"""
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current_time = time.time()
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# 获取该聊天流的学习强度
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try:
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use_expression, enable_learning, learning_intensity = global_config.expression.get_expression_config_for_chat(self.chat_id)
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except Exception as e:
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logger.error(f"获取聊天流 {self.chat_id} 的学习配置失败: {e}")
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return False
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# 检查是否允许学习
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if not enable_learning:
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return False
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# 根据学习强度计算最短学习时间间隔
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min_interval = self.min_learning_interval / learning_intensity
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# 检查时间间隔
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time_diff = current_time - self.last_learning_time
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if time_diff < min_interval:
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return False
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# 检查消息数量（只检查指定聊天流的消息）
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recent_messages = get_raw_msg_by_timestamp_with_chat_inclusive(
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chat_id=self.chat_id,
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timestamp_start=self.last_learning_time,
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timestamp_end=time.time(),
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)
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if not recent_messages or len(recent_messages) < self.min_messages_for_learning:
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return False
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return True
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async def trigger_learning_for_chat(self) -> bool:
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"""
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为指定聊天流触发学习
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Args:
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chat_id: 聊天流ID
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Returns:
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bool: 是否成功触发学习
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"""
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if not self.should_trigger_learning():
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return False
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try:
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logger.info(f"为聊天流 {self.chat_name} 触发表达学习")
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# 学习语言风格
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learnt_style = await self.learn_and_store(type="style", num=25)
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# 学习句法特点
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learnt_grammar = await self.learn_and_store(type="grammar", num=10)
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# 更新学习时间
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self.last_learning_time = time.time()
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if learnt_style or learnt_grammar:
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logger.info(f"聊天流 {self.chat_name} 表达学习完成")
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return True
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else:
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logger.warning(f"聊天流 {self.chat_name} 表达学习未获得有效结果")
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return False
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except Exception as e:
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logger.error(f"为聊天流 {self.chat_name} 触发学习失败: {e}")
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return False
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def get_expression_by_chat_id(self) -> Tuple[List[Dict[str, float]], List[Dict[str, float]]]:
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"""
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获取指定chat_id的style和grammar表达方式
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返回的每个表达方式字典中都包含了source_id, 用于后续的更新操作
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"""
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learnt_style_expressions = []
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learnt_grammar_expressions = []
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# 直接从数据库查询
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style_query = session.execute(select(Expression).where((Expression.chat_id == self.chat_id) & (Expression.type == "style")))
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for expr in style_query.scalars():
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# 确保create_date存在，如果不存在则使用last_active_time
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create_date = expr.create_date if expr.create_date is not None else expr.last_active_time
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learnt_style_expressions.append(
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{
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"situation": expr.situation,
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"style": expr.style,
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"count": expr.count,
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"last_active_time": expr.last_active_time,
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"source_id": self.chat_id,
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"type": "style",
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"create_date": create_date,
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}
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)
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grammar_query = session.execute(select(Expression).where((Expression.chat_id == self.chat_id) & (Expression.type == "grammar")))
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for expr in grammar_query.scalars():
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# 确保create_date存在，如果不存在则使用last_active_time
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create_date = expr.create_date if expr.create_date is not None else expr.last_active_time
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learnt_grammar_expressions.append(
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{
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"situation": expr.situation,
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"style": expr.style,
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"count": expr.count,
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"last_active_time": expr.last_active_time,
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"source_id": self.chat_id,
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"type": "grammar",
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"create_date": create_date,
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}
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)
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return learnt_style_expressions, learnt_grammar_expressions
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def _apply_global_decay_to_database(self, current_time: float) -> None:
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"""
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对数据库中的所有表达方式应用全局衰减
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"""
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try:
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# 获取所有表达方式
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all_expressions = session.execute(select(Expression)).scalars()
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updated_count = 0
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deleted_count = 0
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for expr in all_expressions:
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# 计算时间差
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last_active = expr.last_active_time
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time_diff_days = (current_time - last_active) / (24 * 3600) # 转换为天
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# 计算衰减值
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decay_value = self.calculate_decay_factor(time_diff_days)
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new_count = max(0.01, expr.count - decay_value)
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if new_count <= 0.01:
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# 如果count太小，删除这个表达方式
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session.delete(expr)
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deleted_count += 1
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else:
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# 更新count
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expr.count = new_count
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updated_count += 1
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session.commit()
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if updated_count > 0 or deleted_count > 0:
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logger.info(f"全局衰减完成：更新了 {updated_count} 个表达方式，删除了 {deleted_count} 个表达方式")
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except Exception as e:
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session.rollback()
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logger.error(f"数据库全局衰减失败: {e}")
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def calculate_decay_factor(self, time_diff_days: float) -> float:
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"""
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计算衰减值
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当时间差为0天时，衰减值为0（最近活跃的不衰减）
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当时间差为7天时，衰减值为0.002（中等衰减）
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当时间差为30天或更长时，衰减值为0.01（高衰减）
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使用二次函数进行曲线插值
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"""
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if time_diff_days <= 0:
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return 0.0 # 刚激活的表达式不衰减
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if time_diff_days >= DECAY_DAYS:
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return 0.01 # 长时间未活跃的表达式大幅衰减
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# 使用二次函数插值：在0-30天之间从0衰减到0.01
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# 使用简单的二次函数：y = a * x^2
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# 当x=30时，y=0.01，所以 a = 0.01 / (30^2) = 0.01 / 900
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a = 0.01 / (DECAY_DAYS**2)
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decay = a * (time_diff_days**2)
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return min(0.01, decay)
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async def learn_and_store(self, type: str, num: int = 10) -> List[Tuple[str, str, str]]:
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# sourcery skip: use-join
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"""
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学习并存储表达方式
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type: "style" or "grammar"
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"""
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if type == "style":
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type_str = "语言风格"
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elif type == "grammar":
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type_str = "句法特点"
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else:
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raise ValueError(f"Invalid type: {type}")
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# 检查是否允许在此聊天流中学习（在函数最前面检查）
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if not self.can_learn_for_chat():
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logger.debug(f"聊天流 {self.chat_name} 不允许学习表达，跳过学习")
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return []
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res = await self.learn_expression(type, num)
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if res is None:
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return []
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learnt_expressions, chat_id = res
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chat_stream = get_chat_manager().get_stream(chat_id)
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if chat_stream is None:
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group_name = f"聊天流 {chat_id}"
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elif chat_stream.group_info:
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group_name = chat_stream.group_info.group_name
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else:
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group_name = f"{chat_stream.user_info.user_nickname}的私聊"
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learnt_expressions_str = ""
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for _chat_id, situation, style in learnt_expressions:
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learnt_expressions_str += f"{situation}->{style}\n"
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logger.info(f"在 {group_name} 学习到{type_str}:\n{learnt_expressions_str}")
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if not learnt_expressions:
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logger.info(f"没有学习到{type_str}")
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return []
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# 按chat_id分组
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chat_dict: Dict[str, List[Dict[str, Any]]] = {}
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for chat_id, situation, style in learnt_expressions:
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if chat_id not in chat_dict:
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chat_dict[chat_id] = []
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chat_dict[chat_id].append({"situation": situation, "style": style})
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current_time = time.time()
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# 存储到数据库 Expression 表
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for chat_id, expr_list in chat_dict.items():
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for new_expr in expr_list:
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# 查找是否已存在相似表达方式
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query = session.execute(select(Expression).where(
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(Expression.chat_id == chat_id)
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& (Expression.type == type)
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& (Expression.situation == new_expr["situation"])
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& (Expression.style == new_expr["style"])
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)).scalar()
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if query:
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expr_obj = query
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# 50%概率替换内容
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if random.random() < 0.5:
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expr_obj.situation = new_expr["situation"]
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expr_obj.style = new_expr["style"]
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expr_obj.count = expr_obj.count + 1
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expr_obj.last_active_time = current_time
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else:
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new_expression = Expression(
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situation=new_expr["situation"],
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style=new_expr["style"],
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count=1,
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last_active_time=current_time,
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chat_id=chat_id,
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type=type,
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create_date=current_time, # 手动设置创建日期
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)
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session.add(new_expression)
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# 限制最大数量
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exprs = list(
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session.execute(select(Expression)
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.where((Expression.chat_id == chat_id) & (Expression.type == type))
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.order_by(Expression.count.asc())).scalars()
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)
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if len(exprs) > MAX_EXPRESSION_COUNT:
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# 删除count最小的多余表达方式
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for expr in exprs[: len(exprs) - MAX_EXPRESSION_COUNT]:
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session.delete(expr)
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session.commit()
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return learnt_expressions
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async def learn_expression(self, type: str, num: int = 10) -> Optional[Tuple[List[Tuple[str, str, str]], str]]:
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"""从指定聊天流学习表达方式
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Args:
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type: "style" or "grammar"
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"""
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if type == "style":
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type_str = "语言风格"
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prompt = "learn_style_prompt"
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elif type == "grammar":
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type_str = "句法特点"
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prompt = "learn_grammar_prompt"
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else:
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raise ValueError(f"Invalid type: {type}")
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current_time = time.time()
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# 获取上次学习时间
|
||||
random_msg: Optional[List[Dict[str, Any]]] = get_raw_msg_by_timestamp_with_chat_inclusive(
|
||||
chat_id=self.chat_id,
|
||||
timestamp_start=self.last_learning_time,
|
||||
timestamp_end=current_time,
|
||||
limit=num,
|
||||
)
|
||||
|
||||
# print(random_msg)
|
||||
if not random_msg or random_msg == []:
|
||||
return None
|
||||
# 转化成str
|
||||
chat_id: str = random_msg[0]["chat_id"]
|
||||
# random_msg_str: str = build_readable_messages(random_msg, timestamp_mode="normal")
|
||||
random_msg_str: str = await build_anonymous_messages(random_msg)
|
||||
# print(f"random_msg_str:{random_msg_str}")
|
||||
|
||||
prompt: str = await global_prompt_manager.format_prompt(
|
||||
prompt,
|
||||
chat_str=random_msg_str,
|
||||
)
|
||||
|
||||
logger.debug(f"学习{type_str}的prompt: {prompt}")
|
||||
|
||||
try:
|
||||
response, _ = await self.express_learn_model.generate_response_async(prompt, temperature=0.3)
|
||||
except Exception as e:
|
||||
logger.error(f"学习{type_str}失败: {e}")
|
||||
return None
|
||||
|
||||
logger.debug(f"学习{type_str}的response: {response}")
|
||||
|
||||
expressions: List[Tuple[str, str, str]] = self.parse_expression_response(response, chat_id)
|
||||
|
||||
return expressions, chat_id
|
||||
|
||||
def parse_expression_response(self, response: str, chat_id: str) -> List[Tuple[str, str, str]]:
|
||||
"""
|
||||
解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组
|
||||
"""
|
||||
expressions: List[Tuple[str, str, str]] = []
|
||||
for line in response.splitlines():
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
# 查找"当"和下一个引号
|
||||
idx_when = line.find('当"')
|
||||
if idx_when == -1:
|
||||
continue
|
||||
idx_quote1 = idx_when + 1
|
||||
idx_quote2 = line.find('"', idx_quote1 + 1)
|
||||
if idx_quote2 == -1:
|
||||
continue
|
||||
situation = line[idx_quote1 + 1 : idx_quote2]
|
||||
# 查找"使用"
|
||||
idx_use = line.find('使用"', idx_quote2)
|
||||
if idx_use == -1:
|
||||
continue
|
||||
idx_quote3 = idx_use + 2
|
||||
idx_quote4 = line.find('"', idx_quote3 + 1)
|
||||
if idx_quote4 == -1:
|
||||
continue
|
||||
style = line[idx_quote3 + 1 : idx_quote4]
|
||||
expressions.append((chat_id, situation, style))
|
||||
return expressions
|
||||
|
||||
|
||||
init_prompt()
|
||||
|
||||
class ExpressionLearnerManager:
|
||||
def __init__(self):
|
||||
self.expression_learners = {}
|
||||
|
||||
self._ensure_expression_directories()
|
||||
self._auto_migrate_json_to_db()
|
||||
self._migrate_old_data_create_date()
|
||||
|
||||
def get_expression_learner(self, chat_id: str) -> ExpressionLearner:
|
||||
if chat_id not in self.expression_learners:
|
||||
self.expression_learners[chat_id] = ExpressionLearner(chat_id)
|
||||
return self.expression_learners[chat_id]
|
||||
|
||||
def _ensure_expression_directories(self):
|
||||
"""
|
||||
确保表达方式相关的目录结构存在
|
||||
"""
|
||||
base_dir = os.path.join("data", "expression")
|
||||
directories_to_create = [
|
||||
base_dir,
|
||||
os.path.join(base_dir, "learnt_style"),
|
||||
os.path.join(base_dir, "learnt_grammar"),
|
||||
]
|
||||
|
||||
for directory in directories_to_create:
|
||||
try:
|
||||
os.makedirs(directory, exist_ok=True)
|
||||
logger.debug(f"确保目录存在: {directory}")
|
||||
except Exception as e:
|
||||
logger.error(f"创建目录失败 {directory}: {e}")
|
||||
|
||||
|
||||
def _auto_migrate_json_to_db(self):
|
||||
"""
|
||||
自动将/data/expression/learnt_style 和 learnt_grammar 下所有expressions.json迁移到数据库。
|
||||
迁移完成后在/data/expression/done.done写入标记文件，存在则跳过。
|
||||
"""
|
||||
base_dir = os.path.join("data", "expression")
|
||||
done_flag = os.path.join(base_dir, "done.done")
|
||||
|
||||
# 确保基础目录存在
|
||||
try:
|
||||
os.makedirs(base_dir, exist_ok=True)
|
||||
logger.debug(f"确保目录存在: {base_dir}")
|
||||
except Exception as e:
|
||||
logger.error(f"创建表达方式目录失败: {e}")
|
||||
return
|
||||
|
||||
if os.path.exists(done_flag):
|
||||
logger.info("表达方式JSON已迁移，无需重复迁移。")
|
||||
return
|
||||
|
||||
logger.info("开始迁移表达方式JSON到数据库...")
|
||||
migrated_count = 0
|
||||
|
||||
for type in ["learnt_style", "learnt_grammar"]:
|
||||
type_str = "style" if type == "learnt_style" else "grammar"
|
||||
type_dir = os.path.join(base_dir, type)
|
||||
if not os.path.exists(type_dir):
|
||||
logger.debug(f"目录不存在，跳过: {type_dir}")
|
||||
continue
|
||||
|
||||
try:
|
||||
chat_ids = os.listdir(type_dir)
|
||||
logger.debug(f"在 {type_dir} 中找到 {len(chat_ids)} 个聊天ID目录")
|
||||
except Exception as e:
|
||||
logger.error(f"读取目录失败 {type_dir}: {e}")
|
||||
continue
|
||||
|
||||
for chat_id in chat_ids:
|
||||
expr_file = os.path.join(type_dir, chat_id, "expressions.json")
|
||||
if not os.path.exists(expr_file):
|
||||
continue
|
||||
try:
|
||||
with open(expr_file, "r", encoding="utf-8") as f:
|
||||
expressions = json.load(f)
|
||||
|
||||
if not isinstance(expressions, list):
|
||||
logger.warning(f"表达方式文件格式错误，跳过: {expr_file}")
|
||||
continue
|
||||
|
||||
for expr in expressions:
|
||||
if not isinstance(expr, dict):
|
||||
continue
|
||||
|
||||
situation = expr.get("situation")
|
||||
style_val = expr.get("style")
|
||||
count = expr.get("count", 1)
|
||||
last_active_time = expr.get("last_active_time", time.time())
|
||||
|
||||
if not situation or not style_val:
|
||||
logger.warning(f"表达方式缺少必要字段，跳过: {expr}")
|
||||
continue
|
||||
|
||||
# 查重：同chat_id+type+situation+style
|
||||
|
||||
query = session.execute(select(Expression).where(
|
||||
(Expression.chat_id == chat_id)
|
||||
& (Expression.type == type_str)
|
||||
& (Expression.situation == situation)
|
||||
& (Expression.style == style_val)
|
||||
)).scalar()
|
||||
if query:
|
||||
expr_obj = query
|
||||
expr_obj.count = max(expr_obj.count, count)
|
||||
expr_obj.last_active_time = max(expr_obj.last_active_time, last_active_time)
|
||||
else:
|
||||
new_expression = Expression(
|
||||
situation=situation,
|
||||
style=style_val,
|
||||
count=count,
|
||||
last_active_time=last_active_time,
|
||||
chat_id=chat_id,
|
||||
type=type_str,
|
||||
create_date=last_active_time, # 迁移时使用last_active_time作为创建时间
|
||||
)
|
||||
session.add(new_expression)
|
||||
migrated_count += 1
|
||||
logger.info(f"已迁移 {expr_file} 到数据库，包含 {len(expressions)} 个表达方式")
|
||||
except json.JSONDecodeError as e:
|
||||
logger.error(f"JSON解析失败 {expr_file}: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"迁移表达方式 {expr_file} 失败: {e}")
|
||||
|
||||
# 标记迁移完成
|
||||
try:
|
||||
# 确保done.done文件的父目录存在
|
||||
done_parent_dir = os.path.dirname(done_flag)
|
||||
if not os.path.exists(done_parent_dir):
|
||||
os.makedirs(done_parent_dir, exist_ok=True)
|
||||
logger.debug(f"为done.done创建父目录: {done_parent_dir}")
|
||||
|
||||
with open(done_flag, "w", encoding="utf-8") as f:
|
||||
f.write("done\n")
|
||||
logger.info(f"表达方式JSON迁移已完成，共迁移 {migrated_count} 个表达方式，已写入done.done标记文件")
|
||||
except PermissionError as e:
|
||||
logger.error(f"权限不足，无法写入done.done标记文件: {e}")
|
||||
except OSError as e:
|
||||
logger.error(f"文件系统错误，无法写入done.done标记文件: {e}")
|
||||
except Exception as e:
|
||||
logger.error(f"写入done.done标记文件失败: {e}")
|
||||
|
||||
def _migrate_old_data_create_date(self):
|
||||
"""
|
||||
为没有create_date的老数据设置创建日期
|
||||
使用last_active_time作为create_date的默认值
|
||||
"""
|
||||
try:
|
||||
# 查找所有create_date为空的表达方式
|
||||
old_expressions = session.execute(select(Expression).where(Expression.create_date.is_(None))).scalars()
|
||||
updated_count = 0
|
||||
|
||||
for expr in old_expressions:
|
||||
# 使用last_active_time作为create_date
|
||||
expr.create_date = expr.last_active_time
|
||||
updated_count += 1
|
||||
|
||||
session.commit()
|
||||
|
||||
if updated_count > 0:
|
||||
logger.info(f"已为 {updated_count} 个老的表达方式设置创建日期")
|
||||
except Exception as e:
|
||||
session.rollback()
|
||||
logger.error(f"迁移老数据创建日期失败: {e}")
|
||||
|
||||
|
||||
expression_learner_manager = ExpressionLearnerManager()
|
||||
Reference in New Issue
Block a user