feat(expression): 重构表达学习配置，引入基于规则的结构化定义
本次更新旨在提高表达学习配置的可读性和灵活性。旧的基于列表的 `expression_learning` 和 `expression_groups` 配置方式难以理解且容易出错。 通过引入新的 `ExpressionRule` Pydantic模型，我们将所有相关设置（如聊天流ID、是否学习、学习强度、共享组等）整合到一个统一的、自描述的结构中。现在，所有规则都在一个新的 `[[expression.rules]]` 表中进行配置，使得逻辑更加清晰和易于维护。 相关模块，如 `ExpressionSelector`，已更新以适配新的配置结构。同时，数据库中的 `Expression` 模型也已更新为现代的 SQLAlchemy 2.0 风格。 BREAKING CHANGE: 表达学习的配置文件格式已完全改变。旧的 `expression_learning` 和 `expression_groups` 配置不再受支持，用户需要根据新的 `bot_config_template.toml` 文件迁移到 `[[expression.rules]]` 格式。
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minecraft1024a
@@ -114,16 +114,27 @@ class ExpressionSelector:
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return None
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def get_related_chat_ids(self, chat_id: str) -> List[str]:
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"""根据expression_groups配置，获取与当前chat_id相关的所有chat_id（包括自身）"""
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groups = global_config.expression.expression_groups
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for group in groups:
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group_chat_ids = []
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for stream_config_str in group:
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if chat_id_candidate := self._parse_stream_config_to_chat_id(stream_config_str):
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group_chat_ids.append(chat_id_candidate)
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if chat_id in group_chat_ids:
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return group_chat_ids
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return [chat_id]
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"""根据expression.rules配置，获取与当前chat_id相关的所有chat_id（包括自身）"""
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rules = global_config.expression.rules
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current_group = None
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# 找到当前chat_id所在的组
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for rule in rules:
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if rule.chat_stream_id and self._parse_stream_config_to_chat_id(rule.chat_stream_id) == chat_id:
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current_group = rule.group
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break
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if not current_group:
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return [chat_id]
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# 找出同一组的所有chat_id
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related_chat_ids = []
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for rule in rules:
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if rule.group == current_group and rule.chat_stream_id:
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if chat_id_candidate := self._parse_stream_config_to_chat_id(rule.chat_stream_id):
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related_chat_ids.append(chat_id_candidate)
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return related_chat_ids if related_chat_ids else [chat_id]
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def get_random_expressions(
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self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float
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@@ -5,12 +5,12 @@
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from sqlalchemy import Column, String, Float, Integer, Boolean, Text, Index, create_engine, DateTime
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import sessionmaker, Session
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from sqlalchemy.orm import sessionmaker, Session, Mapped, mapped_column
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from sqlalchemy.pool import QueuePool
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import os
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import datetime
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import time
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from typing import Iterator, Optional
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from typing import Iterator, Optional, Any, Dict
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from src.common.logger import get_logger
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from contextlib import contextmanager
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@@ -306,14 +306,14 @@ class Expression(Base):
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"""表达风格模型"""
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__tablename__ = 'expression'
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id = Column(Integer, primary_key=True, autoincrement=True)
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situation = Column(Text, nullable=False)
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style = Column(Text, nullable=False)
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count = Column(Float, nullable=False)
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last_active_time = Column(Float, nullable=False)
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chat_id = Column(get_string_field(64), nullable=False, index=True)
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type = Column(Text, nullable=False)
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create_date = Column(Float, nullable=True)
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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situation: Mapped[str] = mapped_column(Text, nullable=False)
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style: Mapped[str] = mapped_column(Text, nullable=False)
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count: Mapped[float] = mapped_column(Float, nullable=False)
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last_active_time: Mapped[float] = mapped_column(Float, nullable=False)
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chat_id: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
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type: Mapped[str] = mapped_column(Text, nullable=False)
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create_date: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
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__table_args__ = (
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Index('idx_expression_chat_id', 'chat_id'),
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@@ -589,7 +589,7 @@ def initialize_database():
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config = global_config.database
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# 配置引擎参数
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engine_kwargs = {
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engine_kwargs: Dict[str, Any] = {
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'echo': False, # 生产环境关闭SQL日志
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'future': True,
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}
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@@ -642,7 +642,9 @@ def get_db_session() -> Iterator[Session]:
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"""数据库会话上下文管理器 - 推荐使用这个而不是get_session()"""
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session: Optional[Session] = None
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try:
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_, SessionLocal = initialize_database()
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engine, SessionLocal = initialize_database()
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if not SessionLocal:
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raise RuntimeError("Database session not initialized")
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session = SessionLocal()
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yield session
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#session.commit()
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@@ -263,11 +263,20 @@ class NormalChatConfig(ValidatedConfigBase):
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class ExpressionRule(ValidatedConfigBase):
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"""表达学习规则"""
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chat_stream_id: str = Field(..., description="聊天流ID，空字符串表示全局")
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use_expression: bool = Field(default=True, description="是否使用学到的表达")
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learn_expression: bool = Field(default=True, description="是否学习表达")
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learning_strength: float = Field(default=1.0, description="学习强度")
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group: Optional[str] = Field(default=None, description="表达共享组")
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class ExpressionConfig(ValidatedConfigBase):
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"""表达配置类"""
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expression_learning: list[list] = Field(default_factory=lambda: [], description="表达学习")
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expression_groups: list[list[str]] = Field(default_factory=list, description="表达组")
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rules: List[ExpressionRule] = Field(default_factory=list, description="表达学习规则")
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def _parse_stream_config_to_chat_id(self, stream_config_str: str) -> Optional[str]:
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"""
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@@ -314,86 +323,23 @@ class ExpressionConfig(ValidatedConfigBase):
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Returns:
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tuple: (是否使用表达, 是否学习表达, 学习间隔)
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"""
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if not self.expression_learning:
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# 如果没有配置，使用默认值：启用表达，启用学习，300秒间隔
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return True, True, 300
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if not self.rules:
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# 如果没有配置，使用默认值：启用表达，启用学习，强度1.0
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return True, True, 1.0
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# 优先检查聊天流特定的配置
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if chat_stream_id:
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specific_config = self._get_stream_specific_config(chat_stream_id)
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if specific_config is not None:
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return specific_config
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for rule in self.rules:
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if rule.chat_stream_id and self._parse_stream_config_to_chat_id(rule.chat_stream_id) == chat_stream_id:
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return rule.use_expression, rule.learn_expression, rule.learning_strength
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# 检查全局配置（第一个元素为空字符串的配置）
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global_config = self._get_global_config()
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if global_config is not None:
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return global_config
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# 检查全局配置（chat_stream_id为空字符串的配置）
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for rule in self.rules:
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if rule.chat_stream_id == "":
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return rule.use_expression, rule.learn_expression, rule.learning_strength
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# 如果都没有匹配，返回默认值
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return True, True, 300
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def _get_stream_specific_config(self, chat_stream_id: str) -> Optional[tuple[bool, bool, float]]:
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"""
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获取特定聊天流的表达配置
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Args:
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chat_stream_id: 聊天流ID（哈希值）
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Returns:
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tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None
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"""
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for config_item in self.expression_learning:
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if not config_item or len(config_item) < 4:
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continue
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stream_config_str = config_item[0] # 例如 "qq:1026294844:group"
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# 如果是空字符串，跳过（这是全局配置）
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if stream_config_str == "":
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continue
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# 解析配置字符串并生成对应的 chat_id
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config_chat_id = self._parse_stream_config_to_chat_id(stream_config_str)
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if config_chat_id is None:
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continue
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# 比较生成的 chat_id
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if config_chat_id != chat_stream_id:
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continue
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# 解析配置
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try:
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use_expression = config_item[1].lower() == "enable"
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enable_learning = config_item[2].lower() == "enable"
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learning_intensity = float(config_item[3])
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return use_expression, enable_learning, learning_intensity
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except (ValueError, IndexError):
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continue
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return None
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def _get_global_config(self) -> Optional[tuple[bool, bool, float]]:
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"""
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获取全局表达配置
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Returns:
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tuple: (是否使用表达, 是否学习表达, 学习间隔)，如果没有配置则返回 None
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"""
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for config_item in self.expression_learning:
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if not config_item or len(config_item) < 4:
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continue
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# 检查是否为全局配置（第一个元素为空字符串）
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if config_item[0] == "":
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try:
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use_expression = config_item[1].lower() == "enable"
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enable_learning = config_item[2].lower() == "enable"
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learning_intensity = float(config_item[3])
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return use_expression, enable_learning, learning_intensity
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except (ValueError, IndexError):
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continue
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return None
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return True, True, 1.0
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class ToolHistoryConfig(ValidatedConfigBase):
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@@ -44,9 +44,7 @@ connection_timeout = 10 # 连接超时时间（秒）
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# Master用户配置（拥有最高权限，无视所有权限节点）
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# 格式：[[platform, user_id], ...]
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# 示例：[["qq", "123456"], ["telegram", "user789"]]
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master_users = [
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# ["qq", "123456789"], # 示例：QQ平台的Master用户
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]
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master_users = []# ["qq", "123456789"], # 示例：QQ平台的Master用户
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[bot]
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platform = "qq"
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@@ -74,23 +72,37 @@ compress_identity = true # 是否压缩身份，压缩后会精简身份信息
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[expression]
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# 表达学习配置
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expression_learning = [ # 表达学习配置列表，支持按聊天流配置
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["", "enable", "enable", 1.0], # 全局配置：使用表达，启用学习，学习强度1.0
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["qq:1919810:group", "enable", "enable", 1.5], # 特定群聊配置：使用表达，启用学习，学习强度1.5
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["qq:114514:private", "enable", "disable", 0.5], # 特定私聊配置：使用表达，禁用学习，学习强度0.5
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# 格式说明：
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# 第一位: chat_stream_id，空字符串表示全局配置
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# 第二位: 是否使用学到的表达 ("enable"/"disable")
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# 第三位: 是否学习表达 ("enable"/"disable")
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# 第四位: 学习强度（浮点数），影响学习频率，最短学习时间间隔 = 300/学习强度（秒）
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# 学习强度越高，学习越频繁；学习强度越低，学习越少
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]
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# rules是一个列表，每个元素都是一个学习规则
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# chat_stream_id: 聊天流ID，格式为 "platform:id:type"，例如 "qq:123456:private"。空字符串""表示全局配置
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# use_expression: 是否使用学到的表达 (true/false)
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# learn_expression: 是否学习新的表达 (true/false)
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# learning_strength: 学习强度（浮点数），影响学习频率
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# group: 表达共享组的名称（字符串），相同组的聊天会共享学习到的表达方式
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[[expression.rules]]
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chat_stream_id = ""
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use_expression = true
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learn_expression = true
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learning_strength = 1.0
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expression_groups = [
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["qq:1919810:private","qq:114514:private","qq:1111111:group"], # 在这里设置互通组，相同组的chat_id会共享学习到的表达方式
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# 格式：["qq:123456:private","qq:654321:group"]
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# 注意：如果为群聊，则需要设置为group，如果设置为私聊，则需要设置为private
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]
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[[expression.rules]]
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chat_stream_id = "qq:1919810:group"
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use_expression = true
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learn_expression = true
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learning_strength = 1.5
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[[expression.rules]]
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chat_stream_id = "qq:114514:private"
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group = "group_A"
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use_expression = true
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learn_expression = false
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learning_strength = 0.5
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[[expression.rules]]
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chat_stream_id = "qq:1919810:private"
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group = "group_A"
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use_expression = true
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learn_expression = true
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learning_strength = 1.0
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