refactor(database): 阶段二 - 完成核心层重构

- models.py: 迁移25个模型类，使用统一的Mapped类型注解
  * 包含: ChatStreams, Messages, PersonInfo, LLMUsage等
  * 新增: PermissionNodes, UserPermissions, UserRelationships
  * 654行纯模型定义代码，无初始化逻辑

- migration.py: 重构数据库迁移逻辑
  * check_and_migrate_database: 自动检查和迁移表结构
  * create_all_tables: 快速创建所有表
  * drop_all_tables: 测试用删除所有表
  * 使用新架构的engine和models

- __init__.py: 完善导出清单
  * 导出所有25个模型类
  * 导出迁移函数
  * 导出Base和工具函数

- 辅助脚本:
  * extract_models.py: 自动提取模型定义
  * cleanup_models.py: 清理非模型代码

核心层现已完整，下一步进入优化层实现

scripts/cleanup_models.py

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+"""清理 core/models.py，只保留模型定义"""
+
+import os
+
+# 文件路径
+models_file = os.path.join(
+    os.path.dirname(os.path.dirname(__file__)),
+    "src",
+    "common",
+    "database",
+    "core",
+    "models.py"
+)
+
+print(f"正在清理文件: {models_file}")
+
+# 读取文件
+with open(models_file, "r", encoding="utf-8") as f:
+    lines = f.readlines()
+
+# 找到最后一个模型类的结束位置（MonthlyPlan的 __table_args__ 结束）
+# 我们要保留到第593行（包含）
+keep_lines = []
+found_end = False
+
+for i, line in enumerate(lines, 1):
+    keep_lines.append(line)
+    
+    # 检查是否到达 MonthlyPlan 的 __table_args__ 结束
+    if i > 580 and line.strip() == ")":
+        # 再检查前一行是否有 Index 相关内容
+        if "idx_monthlyplan" in "".join(lines[max(0, i-5):i]):
+            print(f"找到模型定义结束位置: 第 {i} 行")
+            found_end = True
+            break
+
+if not found_end:
+    print("❌ 未找到模型定义结束标记")
+    exit(1)
+
+# 写回文件
+with open(models_file, "w", encoding="utf-8") as f:
+    f.writelines(keep_lines)
+
+print(f"✅ 文件清理完成")
+print(f"保留行数: {len(keep_lines)}")
+print(f"原始行数: {len(lines)}")
+print(f"删除行数: {len(lines) - len(keep_lines)}")

scripts/extract_models.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""提取models.py中的模型定义"""
+
+import re
+
+# 读取原始文件
+with open('src/common/database/sqlalchemy_models.py', 'r', encoding='utf-8') as f:
+    content = f.read()
+
+# 找到get_string_field函数的开始和结束
+get_string_field_start = content.find('# MySQL兼容的字段类型辅助函数')
+get_string_field_end = content.find('\n\nclass ChatStreams(Base):')
+get_string_field = content[get_string_field_start:get_string_field_end]
+
+# 找到第一个class定义开始
+first_class_pos = content.find('class ChatStreams(Base):')
+
+# 找到所有class定义，直到遇到非class的def
+# 简单策略：找到所有以"class "开头且继承Base的类
+classes_pattern = r'class \w+\(Base\):.*?(?=\nclass \w+\(Base\):|$)'
+matches = list(re.finditer(classes_pattern, content[first_class_pos:], re.DOTALL))
+
+if matches:
+    # 取最后一个匹配的结束位置
+    models_content = content[first_class_pos:first_class_pos + matches[-1].end()]
+else:
+    # 备用方案：从第一个class到文件的85%位置
+    models_end = int(len(content) * 0.85)
+    models_content = content[first_class_pos:models_end]
+
+# 创建新文件内容
+header = '''"""SQLAlchemy数据库模型定义
+
+本文件只包含纯模型定义，使用SQLAlchemy 2.0的Mapped类型注解风格。
+引擎和会话管理已移至core/engine.py和core/session.py。
+
+所有模型使用统一的类型注解风格：
+    field_name: Mapped[PyType] = mapped_column(Type, ...)
+
+这样IDE/Pylance能正确推断实例属性类型。
+"""
+
+import datetime
+import time
+
+from sqlalchemy import Boolean, DateTime, Float, Index, Integer, String, Text
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import Mapped, mapped_column
+
+# 创建基类
+Base = declarative_base()
+
+
+'''
+
+new_content = header + get_string_field + '\n\n' + models_content
+
+# 写入新文件
+with open('src/common/database/core/models.py', 'w', encoding='utf-8') as f:
+    f.write(new_content)
+
+print('✅ Models file rewritten successfully')
+print(f'File size: {len(new_content)} characters')
+pattern = r"^class \w+\(Base\):"
+model_count = len(re.findall(pattern, models_content, re.MULTILINE))
+print(f'Number of model classes: {model_count}')

src/common/database/core/__init__.py

@@ -8,14 +8,79 @@
 """
 
 from .engine import close_engine, get_engine, get_engine_info
+from .migration import check_and_migrate_database, create_all_tables, drop_all_tables
+from .models import (
+    ActionRecords,
+    AntiInjectionStats,
+    BanUser,
+    Base,
+    BotPersonalityInterests,
+    CacheEntries,
+    ChatStreams,
+    Emoji,
+    Expression,
+    get_string_field,
+    GraphEdges,
+    GraphNodes,
+    ImageDescriptions,
+    Images,
+    LLMUsage,
+    MaiZoneScheduleStatus,
+    Memory,
+    Messages,
+    MonthlyPlan,
+    OnlineTime,
+    PermissionNodes,
+    PersonInfo,
+    Schedule,
+    ThinkingLog,
+    UserPermissions,
+    UserRelationships,
+    Videos,
+)
 from .session import get_db_session, get_db_session_direct, get_session_factory, reset_session_factory
 
 __all__ = [
+    # Engine
     "get_engine",
     "close_engine",
     "get_engine_info",
+    # Session
     "get_db_session",
     "get_db_session_direct",
     "get_session_factory",
     "reset_session_factory",
+    # Migration
+    "check_and_migrate_database",
+    "create_all_tables",
+    "drop_all_tables",
+    # Models - Base
+    "Base",
+    "get_string_field",
+    # Models - Tables (按字母顺序)
+    "ActionRecords",
+    "AntiInjectionStats",
+    "BanUser",
+    "BotPersonalityInterests",
+    "CacheEntries",
+    "ChatStreams",
+    "Emoji",
+    "Expression",
+    "GraphEdges",
+    "GraphNodes",
+    "ImageDescriptions",
+    "Images",
+    "LLMUsage",
+    "MaiZoneScheduleStatus",
+    "Memory",
+    "Messages",
+    "MonthlyPlan",
+    "OnlineTime",
+    "PermissionNodes",
+    "PersonInfo",
+    "Schedule",
+    "ThinkingLog",
+    "UserPermissions",
+    "UserRelationships",
+    "Videos",
 ]

src/common/database/core/migration.py

@@ -0,0 +1,230 @@
+"""数据库迁移模块
+
+此模块负责数据库结构的自动检查和迁移：
+- 自动创建不存在的表
+- 自动为现有表添加缺失的列
+- 自动为现有表创建缺失的索引
+
+使用新架构的 engine 和 models
+"""
+
+from sqlalchemy import inspect
+from sqlalchemy.sql import text
+
+from src.common.database.core.engine import get_engine
+from src.common.database.core.models import Base
+from src.common.logger import get_logger
+
+logger = get_logger("db_migration")
+
+
+async def check_and_migrate_database(existing_engine=None):
+    """异步检查数据库结构并自动迁移
+    
+    自动执行以下操作：
+    - 创建不存在的表
+    - 为现有表添加缺失的列
+    - 为现有表创建缺失的索引
+    
+    Args:
+        existing_engine: 可选的已存在的数据库引擎。如果提供，将使用该引擎；否则获取全局引擎
+        
+    Note:
+        此函数是幂等的，可以安全地多次调用
+    """
+    logger.info("正在检查数据库结构并执行自动迁移...")
+    engine = existing_engine if existing_engine is not None else await get_engine()
+
+    async with engine.connect() as connection:
+        # 在同步上下文中运行inspector操作
+        def get_inspector(sync_conn):
+            return inspect(sync_conn)
+
+        inspector = await connection.run_sync(get_inspector)
+
+        # 获取数据库中已存在的表名
+        db_table_names = await connection.run_sync(
+            lambda conn: set(inspector.get_table_names())
+        )
+
+        # 1. 首先处理表的创建
+        tables_to_create = []
+        for table_name, table in Base.metadata.tables.items():
+            if table_name not in db_table_names:
+                tables_to_create.append(table)
+
+        if tables_to_create:
+            logger.info(f"发现 {len(tables_to_create)} 个不存在的表，正在创建...")
+            try:
+                # 一次性创建所有缺失的表
+                await connection.run_sync(
+                    lambda sync_conn: Base.metadata.create_all(
+                        sync_conn, tables=tables_to_create
+                    )
+                )
+                for table in tables_to_create:
+                    logger.info(f"表 '{table.name}' 创建成功。")
+                    db_table_names.add(table.name)  # 将新创建的表添加到集合中
+                    
+                # 提交表创建事务
+                await connection.commit()
+            except Exception as e:
+                logger.error(f"创建表时失败: {e}", exc_info=True)
+                await connection.rollback()
+
+        # 2. 然后处理现有表的列和索引的添加
+        for table_name, table in Base.metadata.tables.items():
+            if table_name not in db_table_names:
+                logger.warning(
+                    f"跳过检查表 '{table_name}'，因为它在创建步骤中可能已失败。"
+                )
+                continue
+
+            logger.debug(f"正在检查表 '{table_name}' 的列和索引...")
+
+            try:
+                # 检查并添加缺失的列
+                db_columns = await connection.run_sync(
+                    lambda conn: {
+                        col["name"] for col in inspector.get_columns(table_name)
+                    }
+                )
+                model_columns = {col.name for col in table.c}
+                missing_columns = model_columns - db_columns
+
+                if missing_columns:
+                    logger.info(
+                        f"在表 '{table_name}' 中发现缺失的列: {', '.join(missing_columns)}"
+                    )
+
+                    def add_columns_sync(conn):
+                        dialect = conn.dialect
+                        compiler = dialect.ddl_compiler(dialect, None)
+
+                        for column_name in missing_columns:
+                            column = table.c[column_name]
+                            column_type = compiler.get_column_specification(column)
+                            sql = f"ALTER TABLE {table.name} ADD COLUMN {column.name} {column_type}"
+
+                            if column.default:
+                                # 手动处理不同方言的默认值
+                                default_arg = column.default.arg
+                                if dialect.name == "sqlite" and isinstance(
+                                    default_arg, bool
+                                ):
+                                    # SQLite 将布尔值存储为 0 或 1
+                                    default_value = "1" if default_arg else "0"
+                                elif hasattr(compiler, "render_literal_value"):
+                                    try:
+                                        # 尝试使用 render_literal_value
+                                        default_value = compiler.render_literal_value(
+                                            default_arg, column.type
+                                        )
+                                    except AttributeError:
+                                        # 如果失败，则回退到简单的字符串转换
+                                        default_value = (
+                                            f"'{default_arg}'"
+                                            if isinstance(default_arg, str)
+                                            else str(default_arg)
+                                        )
+                                else:
+                                    # 对于没有 render_literal_value 的旧版或特定方言
+                                    default_value = (
+                                        f"'{default_arg}'"
+                                        if isinstance(default_arg, str)
+                                        else str(default_arg)
+                                    )
+
+                                sql += f" DEFAULT {default_value}"
+
+                            if not column.nullable:
+                                sql += " NOT NULL"
+
+                            conn.execute(text(sql))
+                            logger.info(f"成功向表 '{table_name}' 添加列 '{column_name}'。")
+
+                    await connection.run_sync(add_columns_sync)
+                    # 提交列添加事务
+                    await connection.commit()
+                else:
+                    logger.info(f"表 '{table_name}' 的列结构一致。")
+
+                # 检查并创建缺失的索引
+                db_indexes = await connection.run_sync(
+                    lambda conn: {
+                        idx["name"] for idx in inspector.get_indexes(table_name)
+                    }
+                )
+                model_indexes = {idx.name for idx in table.indexes}
+                missing_indexes = model_indexes - db_indexes
+
+                if missing_indexes:
+                    logger.info(
+                        f"在表 '{table_name}' 中发现缺失的索引: {', '.join(missing_indexes)}"
+                    )
+
+                    def add_indexes_sync(conn):
+                        for index_name in missing_indexes:
+                            index_obj = next(
+                                (idx for idx in table.indexes if idx.name == index_name),
+                                None,
+                            )
+                            if index_obj is not None:
+                                index_obj.create(conn)
+                                logger.info(
+                                    f"成功为表 '{table_name}' 创建索引 '{index_name}'。"
+                                )
+
+                    await connection.run_sync(add_indexes_sync)
+                    # 提交索引创建事务
+                    await connection.commit()
+                else:
+                    logger.debug(f"表 '{table_name}' 的索引一致。")
+
+            except Exception as e:
+                logger.error(f"在处理表 '{table_name}' 时发生意外错误: {e}", exc_info=True)
+                await connection.rollback()
+                continue
+
+    logger.info("数据库结构检查与自动迁移完成。")
+
+
+async def create_all_tables(existing_engine=None):
+    """创建所有表（不进行迁移检查）
+    
+    直接创建所有在 Base.metadata 中定义的表。
+    如果表已存在，将被跳过。
+    
+    Args:
+        existing_engine: 可选的已存在的数据库引擎
+        
+    Note:
+        生产环境建议使用 check_and_migrate_database()
+    """
+    logger.info("正在创建所有数据库表...")
+    engine = existing_engine if existing_engine is not None else await get_engine()
+    
+    async with engine.begin() as connection:
+        await connection.run_sync(Base.metadata.create_all)
+    
+    logger.info("数据库表创建完成。")
+
+
+async def drop_all_tables(existing_engine=None):
+    """删除所有表（危险操作！）
+    
+    删除所有在 Base.metadata 中定义的表。
+    
+    Args:
+        existing_engine: 可选的已存在的数据库引擎
+        
+    Warning:
+        此操作将删除所有数据，不可恢复！仅用于测试环境！
+    """
+    logger.warning("⚠️  正在删除所有数据库表...")
+    engine = existing_engine if existing_engine is not None else await get_engine()
+    
+    async with engine.begin() as connection:
+        await connection.run_sync(Base.metadata.drop_all)
+    
+    logger.warning("所有数据库表已删除。")

src/common/database/core/models.py

@@ -0,0 +1,652 @@
+"""SQLAlchemy数据库模型定义
+
+本文件只包含纯模型定义，使用SQLAlchemy 2.0的Mapped类型注解风格。
+引擎和会话管理已移至core/engine.py和core/session.py。
+
+所有模型使用统一的类型注解风格：
+    field_name: Mapped[PyType] = mapped_column(Type, ...)
+
+这样IDE/Pylance能正确推断实例属性类型。
+"""
+
+import datetime
+import time
+
+from sqlalchemy import Boolean, DateTime, Float, Index, Integer, String, Text
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import Mapped, mapped_column
+
+# 创建基类
+Base = declarative_base()
+
+
+# MySQL兼容的字段类型辅助函数
+def get_string_field(max_length=255, **kwargs):
+    """
+    根据数据库类型返回合适的字符串字段
+    MySQL需要指定长度的VARCHAR用于索引，SQLite可以使用Text
+    """
+    from src.config.config import global_config
+
+    if global_config.database.database_type == "mysql":
+        return String(max_length, **kwargs)
+    else:
+        return Text(**kwargs)
+
+
+class ChatStreams(Base):
+    """聊天流模型"""
+
+    __tablename__ = "chat_streams"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    stream_id: Mapped[str] = mapped_column(get_string_field(64), nullable=False, unique=True, index=True)
+    create_time: Mapped[float] = mapped_column(Float, nullable=False)
+    group_platform: Mapped[str | None] = mapped_column(Text, nullable=True)
+    group_id: Mapped[str | None] = mapped_column(get_string_field(100), nullable=True, index=True)
+    group_name: Mapped[str | None] = mapped_column(Text, nullable=True)
+    last_active_time: Mapped[float] = mapped_column(Float, nullable=False)
+    platform: Mapped[str] = mapped_column(Text, nullable=False)
+    user_platform: Mapped[str] = mapped_column(Text, nullable=False)
+    user_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    user_nickname: Mapped[str] = mapped_column(Text, nullable=False)
+    user_cardname: Mapped[str | None] = mapped_column(Text, nullable=True)
+    energy_value: Mapped[float | None] = mapped_column(Float, nullable=True, default=5.0)
+    sleep_pressure: Mapped[float | None] = mapped_column(Float, nullable=True, default=0.0)
+    focus_energy: Mapped[float | None] = mapped_column(Float, nullable=True, default=0.5)
+    # 动态兴趣度系统字段
+    base_interest_energy: Mapped[float | None] = mapped_column(Float, nullable=True, default=0.5)
+    message_interest_total: Mapped[float | None] = mapped_column(Float, nullable=True, default=0.0)
+    message_count: Mapped[int | None] = mapped_column(Integer, nullable=True, default=0)
+    action_count: Mapped[int | None] = mapped_column(Integer, nullable=True, default=0)
+    reply_count: Mapped[int | None] = mapped_column(Integer, nullable=True, default=0)
+    last_interaction_time: Mapped[float | None] = mapped_column(Float, nullable=True, default=None)
+    consecutive_no_reply: Mapped[int | None] = mapped_column(Integer, nullable=True, default=0)
+    # 消息打断系统字段
+    interruption_count: Mapped[int | None] = mapped_column(Integer, nullable=True, default=0)
+    # 聊天流印象字段
+    stream_impression_text: Mapped[str | None] = mapped_column(Text, nullable=True)  # 对聊天流的主观印象描述
+    stream_chat_style: Mapped[str | None] = mapped_column(Text, nullable=True)  # 聊天流的总体风格
+    stream_topic_keywords: Mapped[str | None] = mapped_column(Text, nullable=True)  # 话题关键词，逗号分隔
+    stream_interest_score: Mapped[float | None] = mapped_column(Float, nullable=True, default=0.5)  # 对聊天流的兴趣程度(0-1)
+
+    __table_args__ = (
+        Index("idx_chatstreams_stream_id", "stream_id"),
+        Index("idx_chatstreams_user_id", "user_id"),
+        Index("idx_chatstreams_group_id", "group_id"),
+    )
+
+
+class LLMUsage(Base):
+    """LLM使用记录模型"""
+
+    __tablename__ = "llm_usage"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    model_name: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    model_assign_name: Mapped[str] = mapped_column(get_string_field(100), index=True)
+    model_api_provider: Mapped[str] = mapped_column(get_string_field(100), index=True)
+    user_id: Mapped[str] = mapped_column(get_string_field(50), nullable=False, index=True)
+    request_type: Mapped[str] = mapped_column(get_string_field(50), nullable=False, index=True)
+    endpoint: Mapped[str] = mapped_column(Text, nullable=False)
+    prompt_tokens: Mapped[int] = mapped_column(Integer, nullable=False)
+    completion_tokens: Mapped[int] = mapped_column(Integer, nullable=False)
+    time_cost: Mapped[float | None] = mapped_column(Float, nullable=True)
+    total_tokens: Mapped[int] = mapped_column(Integer, nullable=False)
+    cost: Mapped[float] = mapped_column(Float, nullable=False)
+    status: Mapped[str] = mapped_column(Text, nullable=False)
+    timestamp: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, index=True, default=datetime.datetime.now)
+
+    __table_args__ = (
+        Index("idx_llmusage_model_name", "model_name"),
+        Index("idx_llmusage_model_assign_name", "model_assign_name"),
+        Index("idx_llmusage_model_api_provider", "model_api_provider"),
+        Index("idx_llmusage_time_cost", "time_cost"),
+        Index("idx_llmusage_user_id", "user_id"),
+        Index("idx_llmusage_request_type", "request_type"),
+        Index("idx_llmusage_timestamp", "timestamp"),
+    )
+
+
+class Emoji(Base):
+    """表情包模型"""
+
+    __tablename__ = "emoji"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    full_path: Mapped[str] = mapped_column(get_string_field(500), nullable=False, unique=True, index=True)
+    format: Mapped[str] = mapped_column(Text, nullable=False)
+    emoji_hash: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    description: Mapped[str] = mapped_column(Text, nullable=False)
+    query_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    is_registered: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    is_banned: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    emotion: Mapped[str | None] = mapped_column(Text, nullable=True)
+    record_time: Mapped[float] = mapped_column(Float, nullable=False)
+    register_time: Mapped[float | None] = mapped_column(Float, nullable=True)
+    usage_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    last_used_time: Mapped[float | None] = mapped_column(Float, nullable=True)
+
+    __table_args__ = (
+        Index("idx_emoji_full_path", "full_path"),
+        Index("idx_emoji_hash", "emoji_hash"),
+    )
+
+
+class Messages(Base):
+    """消息模型"""
+
+    __tablename__ = "messages"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    message_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    time: Mapped[float] = mapped_column(Float, nullable=False)
+    chat_id: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    reply_to: Mapped[str | None] = mapped_column(Text, nullable=True)
+    interest_value: Mapped[float | None] = mapped_column(Float, nullable=True)
+    key_words: Mapped[str | None] = mapped_column(Text, nullable=True)
+    key_words_lite: Mapped[str | None] = mapped_column(Text, nullable=True)
+    is_mentioned: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
+
+    # 从 chat_info 扁平化而来的字段
+    chat_info_stream_id: Mapped[str] = mapped_column(Text, nullable=False)
+    chat_info_platform: Mapped[str] = mapped_column(Text, nullable=False)
+    chat_info_user_platform: Mapped[str] = mapped_column(Text, nullable=False)
+    chat_info_user_id: Mapped[str] = mapped_column(Text, nullable=False)
+    chat_info_user_nickname: Mapped[str] = mapped_column(Text, nullable=False)
+    chat_info_user_cardname: Mapped[str | None] = mapped_column(Text, nullable=True)
+    chat_info_group_platform: Mapped[str | None] = mapped_column(Text, nullable=True)
+    chat_info_group_id: Mapped[str | None] = mapped_column(Text, nullable=True)
+    chat_info_group_name: Mapped[str | None] = mapped_column(Text, nullable=True)
+    chat_info_create_time: Mapped[float] = mapped_column(Float, nullable=False)
+    chat_info_last_active_time: Mapped[float] = mapped_column(Float, nullable=False)
+
+    # 从顶层 user_info 扁平化而来的字段
+    user_platform: Mapped[str | None] = mapped_column(Text, nullable=True)
+    user_id: Mapped[str | None] = mapped_column(get_string_field(100), nullable=True, index=True)
+    user_nickname: Mapped[str | None] = mapped_column(Text, nullable=True)
+    user_cardname: Mapped[str | None] = mapped_column(Text, nullable=True)
+
+    processed_plain_text: Mapped[str | None] = mapped_column(Text, nullable=True)
+    display_message: Mapped[str | None] = mapped_column(Text, nullable=True)
+    memorized_times: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    priority_mode: Mapped[str | None] = mapped_column(Text, nullable=True)
+    priority_info: Mapped[str | None] = mapped_column(Text, nullable=True)
+    additional_config: Mapped[str | None] = mapped_column(Text, nullable=True)
+    is_emoji: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    is_picid: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    is_command: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    is_notify: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    is_public_notice: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    notice_type: Mapped[str | None] = mapped_column(String(50), nullable=True)
+
+    # 兴趣度系统字段
+    actions: Mapped[str | None] = mapped_column(Text, nullable=True)
+    should_reply: Mapped[bool | None] = mapped_column(Boolean, nullable=True, default=False)
+    should_act: Mapped[bool | None] = mapped_column(Boolean, nullable=True, default=False)
+
+    __table_args__ = (
+        Index("idx_messages_message_id", "message_id"),
+        Index("idx_messages_chat_id", "chat_id"),
+        Index("idx_messages_time", "time"),
+        Index("idx_messages_user_id", "user_id"),
+        Index("idx_messages_should_reply", "should_reply"),
+        Index("idx_messages_should_act", "should_act"),
+    )
+
+
+class ActionRecords(Base):
+    """动作记录模型"""
+
+    __tablename__ = "action_records"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    action_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    time: Mapped[float] = mapped_column(Float, nullable=False)
+    action_name: Mapped[str] = mapped_column(Text, nullable=False)
+    action_data: Mapped[str] = mapped_column(Text, nullable=False)
+    action_done: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    action_build_into_prompt: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    action_prompt_display: Mapped[str] = mapped_column(Text, nullable=False)
+    chat_id: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    chat_info_stream_id: Mapped[str] = mapped_column(Text, nullable=False)
+    chat_info_platform: Mapped[str] = mapped_column(Text, nullable=False)
+
+    __table_args__ = (
+        Index("idx_actionrecords_action_id", "action_id"),
+        Index("idx_actionrecords_chat_id", "chat_id"),
+        Index("idx_actionrecords_time", "time"),
+    )
+
+
+class Images(Base):
+    """图像信息模型"""
+
+    __tablename__ = "images"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    image_id: Mapped[str] = mapped_column(Text, nullable=False, default="")
+    emoji_hash: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    description: Mapped[str | None] = mapped_column(Text, nullable=True)
+    path: Mapped[str] = mapped_column(get_string_field(500), nullable=False, unique=True)
+    count: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
+    timestamp: Mapped[float] = mapped_column(Float, nullable=False)
+    type: Mapped[str] = mapped_column(Text, nullable=False)
+    vlm_processed: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+
+    __table_args__ = (
+        Index("idx_images_emoji_hash", "emoji_hash"),
+        Index("idx_images_path", "path"),
+    )
+
+
+class ImageDescriptions(Base):
+    """图像描述信息模型"""
+
+    __tablename__ = "image_descriptions"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    type: Mapped[str] = mapped_column(Text, nullable=False)
+    image_description_hash: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    description: Mapped[str] = mapped_column(Text, nullable=False)
+    timestamp: Mapped[float] = mapped_column(Float, nullable=False)
+
+    __table_args__ = (Index("idx_imagedesc_hash", "image_description_hash"),)
+
+
+class Videos(Base):
+    """视频信息模型"""
+
+    __tablename__ = "videos"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    video_id: Mapped[str] = mapped_column(Text, nullable=False, default="")
+    video_hash: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True, unique=True)
+    description: Mapped[str | None] = mapped_column(Text, nullable=True)
+    count: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
+    timestamp: Mapped[float] = mapped_column(Float, nullable=False)
+    vlm_processed: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+
+    # 视频特有属性
+    duration: Mapped[float | None] = mapped_column(Float, nullable=True)
+    frame_count: Mapped[int | None] = mapped_column(Integer, nullable=True)
+    fps: Mapped[float | None] = mapped_column(Float, nullable=True)
+    resolution: Mapped[str | None] = mapped_column(Text, nullable=True)
+    file_size: Mapped[int | None] = mapped_column(Integer, nullable=True)
+
+    __table_args__ = (
+        Index("idx_videos_video_hash", "video_hash"),
+        Index("idx_videos_timestamp", "timestamp"),
+    )
+
+
+class OnlineTime(Base):
+    """在线时长记录模型"""
+
+    __tablename__ = "online_time"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    timestamp: Mapped[str] = mapped_column(Text, nullable=False, default=str(datetime.datetime.now))
+    duration: Mapped[int] = mapped_column(Integer, nullable=False)
+    start_timestamp: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+    end_timestamp: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, index=True)
+
+    __table_args__ = (Index("idx_onlinetime_end_timestamp", "end_timestamp"),)
+
+
+class PersonInfo(Base):
+    """人物信息模型"""
+
+    __tablename__ = "person_info"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    person_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, unique=True, index=True)
+    person_name: Mapped[str | None] = mapped_column(Text, nullable=True)
+    name_reason: Mapped[str | None] = mapped_column(Text, nullable=True)
+    platform: Mapped[str] = mapped_column(Text, nullable=False)
+    user_id: Mapped[str] = mapped_column(get_string_field(50), nullable=False, index=True)
+    nickname: Mapped[str | None] = mapped_column(Text, nullable=True)
+    impression: Mapped[str | None] = mapped_column(Text, nullable=True)
+    short_impression: Mapped[str | None] = mapped_column(Text, nullable=True)
+    points: Mapped[str | None] = mapped_column(Text, nullable=True)
+    forgotten_points: Mapped[str | None] = mapped_column(Text, nullable=True)
+    info_list: Mapped[str | None] = mapped_column(Text, nullable=True)
+    know_times: Mapped[float | None] = mapped_column(Float, nullable=True)
+    know_since: Mapped[float | None] = mapped_column(Float, nullable=True)
+    last_know: Mapped[float | None] = mapped_column(Float, nullable=True)
+    attitude: Mapped[int | None] = mapped_column(Integer, nullable=True, default=50)
+
+    __table_args__ = (
+        Index("idx_personinfo_person_id", "person_id"),
+        Index("idx_personinfo_user_id", "user_id"),
+    )
+
+
+class BotPersonalityInterests(Base):
+    """机器人人格兴趣标签模型"""
+
+    __tablename__ = "bot_personality_interests"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    personality_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    personality_description: Mapped[str] = mapped_column(Text, nullable=False)
+    interest_tags: Mapped[str] = mapped_column(Text, nullable=False)
+    embedding_model: Mapped[str] = mapped_column(get_string_field(100), nullable=False, default="text-embedding-ada-002")
+    version: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
+    last_updated: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now, index=True)
+
+    __table_args__ = (
+        Index("idx_botpersonality_personality_id", "personality_id"),
+        Index("idx_botpersonality_version", "version"),
+        Index("idx_botpersonality_last_updated", "last_updated"),
+    )
+
+
+class Memory(Base):
+    """记忆模型"""
+
+    __tablename__ = "memory"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    memory_id: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    chat_id: Mapped[str | None] = mapped_column(Text, nullable=True)
+    memory_text: Mapped[str | None] = mapped_column(Text, nullable=True)
+    keywords: Mapped[str | None] = mapped_column(Text, nullable=True)
+    create_time: Mapped[float | None] = mapped_column(Float, nullable=True)
+    last_view_time: Mapped[float | None] = mapped_column(Float, nullable=True)
+
+    __table_args__ = (Index("idx_memory_memory_id", "memory_id"),)
+
+
+class Expression(Base):
+    """表达风格模型"""
+
+    __tablename__ = "expression"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    situation: Mapped[str] = mapped_column(Text, nullable=False)
+    style: Mapped[str] = mapped_column(Text, nullable=False)
+    count: Mapped[float] = mapped_column(Float, nullable=False)
+    last_active_time: Mapped[float] = mapped_column(Float, nullable=False)
+    chat_id: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    type: Mapped[str] = mapped_column(Text, nullable=False)
+    create_date: Mapped[float | None] = mapped_column(Float, nullable=True)
+
+    __table_args__ = (Index("idx_expression_chat_id", "chat_id"),)
+
+
+class ThinkingLog(Base):
+    """思考日志模型"""
+
+    __tablename__ = "thinking_logs"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    chat_id: Mapped[str] = mapped_column(get_string_field(64), nullable=False, index=True)
+    trigger_text: Mapped[str | None] = mapped_column(Text, nullable=True)
+    response_text: Mapped[str | None] = mapped_column(Text, nullable=True)
+    trigger_info_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    response_info_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    timing_results_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    chat_history_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    chat_history_in_thinking_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    chat_history_after_response_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    heartflow_data_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    reasoning_data_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+
+    __table_args__ = (Index("idx_thinkinglog_chat_id", "chat_id"),)
+
+
+class GraphNodes(Base):
+    """记忆图节点模型"""
+
+    __tablename__ = "graph_nodes"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    concept: Mapped[str] = mapped_column(get_string_field(255), nullable=False, unique=True, index=True)
+    memory_items: Mapped[str] = mapped_column(Text, nullable=False)
+    hash: Mapped[str] = mapped_column(Text, nullable=False)
+    weight: Mapped[float] = mapped_column(Float, nullable=False, default=1.0)
+    created_time: Mapped[float] = mapped_column(Float, nullable=False)
+    last_modified: Mapped[float] = mapped_column(Float, nullable=False)
+
+    __table_args__ = (Index("idx_graphnodes_concept", "concept"),)
+
+
+class GraphEdges(Base):
+    """记忆图边模型"""
+
+    __tablename__ = "graph_edges"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    source: Mapped[str] = mapped_column(get_string_field(255), nullable=False, index=True)
+    target: Mapped[str] = mapped_column(get_string_field(255), nullable=False, index=True)
+    strength: Mapped[int] = mapped_column(Integer, nullable=False)
+    hash: Mapped[str] = mapped_column(Text, nullable=False)
+    created_time: Mapped[float] = mapped_column(Float, nullable=False)
+    last_modified: Mapped[float] = mapped_column(Float, nullable=False)
+
+    __table_args__ = (
+        Index("idx_graphedges_source", "source"),
+        Index("idx_graphedges_target", "target"),
+    )
+
+
+class Schedule(Base):
+    """日程模型"""
+
+    __tablename__ = "schedule"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    date: Mapped[str] = mapped_column(get_string_field(10), nullable=False, unique=True, index=True)
+    schedule_data: Mapped[str] = mapped_column(Text, nullable=False)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+    updated_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now, onupdate=datetime.datetime.now)
+
+    __table_args__ = (Index("idx_schedule_date", "date"),)
+
+
+class MaiZoneScheduleStatus(Base):
+    """麦麦空间日程处理状态模型"""
+
+    __tablename__ = "maizone_schedule_status"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    datetime_hour: Mapped[str] = mapped_column(get_string_field(13), nullable=False, unique=True, index=True)
+    activity: Mapped[str] = mapped_column(Text, nullable=False)
+    is_processed: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    processed_at: Mapped[datetime.datetime | None] = mapped_column(DateTime, nullable=True)
+    story_content: Mapped[str | None] = mapped_column(Text, nullable=True)
+    send_success: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+    updated_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now, onupdate=datetime.datetime.now)
+
+    __table_args__ = (
+        Index("idx_maizone_datetime_hour", "datetime_hour"),
+        Index("idx_maizone_is_processed", "is_processed"),
+    )
+
+
+class BanUser(Base):
+    """被禁用用户模型
+
+    使用 SQLAlchemy 2.0 类型标注写法，方便静态类型检查器识别实际字段类型，
+    避免在业务代码中对属性赋值时报 `Column[...]` 不可赋值的告警。
+    """
+
+    __tablename__ = "ban_users"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    platform: Mapped[str] = mapped_column(Text, nullable=False)
+    user_id: Mapped[str] = mapped_column(get_string_field(50), nullable=False, index=True)
+    violation_num: Mapped[int] = mapped_column(Integer, nullable=False, default=0, index=True)
+    reason: Mapped[str] = mapped_column(Text, nullable=False)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+
+    __table_args__ = (
+        Index("idx_violation_num", "violation_num"),
+        Index("idx_banuser_user_id", "user_id"),
+        Index("idx_banuser_platform", "platform"),
+        Index("idx_banuser_platform_user_id", "platform", "user_id"),
+    )
+
+
+class AntiInjectionStats(Base):
+    """反注入系统统计模型"""
+
+    __tablename__ = "anti_injection_stats"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    total_messages: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    """总处理消息数"""
+
+    detected_injections: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    """检测到的注入攻击数"""
+
+    blocked_messages: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    """被阻止的消息数"""
+
+    shielded_messages: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    """被加盾的消息数"""
+
+    processing_time_total: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
+    """总处理时间"""
+
+    total_process_time: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
+    """累计总处理时间"""
+
+    last_process_time: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
+    """最近一次处理时间"""
+
+    error_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    """错误计数"""
+
+    start_time: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+    """统计开始时间"""
+
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+    """记录创建时间"""
+
+    updated_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now, onupdate=datetime.datetime.now)
+    """记录更新时间"""
+
+    __table_args__ = (
+        Index("idx_anti_injection_stats_created_at", "created_at"),
+        Index("idx_anti_injection_stats_updated_at", "updated_at"),
+    )
+
+
+class CacheEntries(Base):
+    """工具缓存条目模型"""
+
+    __tablename__ = "cache_entries"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    cache_key: Mapped[str] = mapped_column(get_string_field(500), nullable=False, unique=True, index=True)
+    """缓存键，包含工具名、参数和代码哈希"""
+
+    cache_value: Mapped[str] = mapped_column(Text, nullable=False)
+    """缓存的数据，JSON格式"""
+
+    expires_at: Mapped[float] = mapped_column(Float, nullable=False, index=True)
+    """过期时间戳"""
+
+    tool_name: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    """工具名称"""
+
+    created_at: Mapped[float] = mapped_column(Float, nullable=False, default=lambda: time.time())
+    """创建时间戳"""
+
+    last_accessed: Mapped[float] = mapped_column(Float, nullable=False, default=lambda: time.time())
+    """最后访问时间戳"""
+
+    access_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    """访问次数"""
+
+    __table_args__ = (
+        Index("idx_cache_entries_key", "cache_key"),
+        Index("idx_cache_entries_expires_at", "expires_at"),
+        Index("idx_cache_entries_tool_name", "tool_name"),
+        Index("idx_cache_entries_created_at", "created_at"),
+    )
+
+
+class MonthlyPlan(Base):
+    """月度计划模型"""
+
+    __tablename__ = "monthly_plans"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    plan_text: Mapped[str] = mapped_column(Text, nullable=False)
+    target_month: Mapped[str] = mapped_column(String(7), nullable=False, index=True)
+    status: Mapped[str] = mapped_column(get_string_field(20), nullable=False, default="active", index=True)
+    usage_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    last_used_date: Mapped[str | None] = mapped_column(String(10), nullable=True, index=True)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, nullable=False, default=datetime.datetime.now)
+    is_deleted: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False, index=True)
+
+    __table_args__ = (
+        Index("idx_monthlyplan_target_month_status", "target_month", "status"),
+        Index("idx_monthlyplan_last_used_date", "last_used_date"),
+        Index("idx_monthlyplan_usage_count", "usage_count"),
+    )
+
+
+class PermissionNodes(Base):
+    """权限节点模型"""
+
+    __tablename__ = "permission_nodes"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    node_name: Mapped[str] = mapped_column(get_string_field(255), nullable=False, unique=True, index=True)
+    description: Mapped[str] = mapped_column(Text, nullable=False)
+    plugin_name: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    default_granted: Mapped[bool] = mapped_column(Boolean, default=False, nullable=False)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, default=datetime.datetime.utcnow, nullable=False)
+
+    __table_args__ = (
+        Index("idx_permission_plugin", "plugin_name"),
+        Index("idx_permission_node", "node_name"),
+    )
+
+
+class UserPermissions(Base):
+    """用户权限模型"""
+
+    __tablename__ = "user_permissions"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    platform: Mapped[str] = mapped_column(get_string_field(50), nullable=False, index=True)
+    user_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, index=True)
+    permission_node: Mapped[str] = mapped_column(get_string_field(255), nullable=False, index=True)
+    granted: Mapped[bool] = mapped_column(Boolean, default=True, nullable=False)
+    granted_at: Mapped[datetime.datetime] = mapped_column(DateTime, default=datetime.datetime.utcnow, nullable=False)
+    granted_by: Mapped[str | None] = mapped_column(get_string_field(100), nullable=True)
+
+    __table_args__ = (
+        Index("idx_user_platform_id", "platform", "user_id"),
+        Index("idx_user_permission", "platform", "user_id", "permission_node"),
+        Index("idx_permission_granted", "permission_node", "granted"),
+    )
+
+
+class UserRelationships(Base):
+    """用户关系模型 - 存储用户与bot的关系数据"""
+
+    __tablename__ = "user_relationships"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    user_id: Mapped[str] = mapped_column(get_string_field(100), nullable=False, unique=True, index=True)
+    user_name: Mapped[str | None] = mapped_column(get_string_field(100), nullable=True)
+    user_aliases: Mapped[str | None] = mapped_column(Text, nullable=True)  # 用户别名，逗号分隔
+    relationship_text: Mapped[str | None] = mapped_column(Text, nullable=True)
+    preference_keywords: Mapped[str | None] = mapped_column(Text, nullable=True)  # 用户偏好关键词，逗号分隔
+    relationship_score: Mapped[float] = mapped_column(Float, nullable=False, default=0.3)  # 关系分数(0-1)
+    last_updated: Mapped[float] = mapped_column(Float, nullable=False, default=time.time)
+    created_at: Mapped[datetime.datetime] = mapped_column(DateTime, default=datetime.datetime.utcnow, nullable=False)
+
+    __table_args__ = (
+        Index("idx_user_relationship_id", "user_id"),
+        Index("idx_relationship_score", "relationship_score"),
+        Index("idx_relationship_updated", "last_updated"),
+    )