feat: v1.2.0 - 图片去重与管理、微信机器人优化、搜索设置可配置

主要功能:
- 图片上传时 OCR 内容去重（3个上传端点统一使用公共函数 _check_ocr_duplicate）
- 图片管理 Tab：展示所有图片、手动删除、一键去重
- 搜索结果详情弹窗增加删除按钮（带确认弹窗）
- 图片管理卡片点击查看详情（复用 showOcrDetailModal）
- 搜索限制和 LLM 批量判断数量可通过网站设置
- MiniMax API 调用添加 reasoning_split=True
- 企业微信机器人：WebSocket 长连接、图片搜索、配置化搜索数量
- 版本号升级至 1.2.0

app/models/base.py

@@ -0,0 +1,123 @@
+"""
+SQLAlchemy 基础模型 + 通用 Mixin
+提供 DeclarativeBase、时间戳 Mixin 和公共序列化方法
+兼容 PostgreSQL（pgvector）和 SQLite（本地开发）
+"""
+
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any, Dict
+
+from sqlalchemy import DateTime, Float, Integer, String, Text, func
+from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
+
+from app.database import IS_SQLITE
+
+# SQLite 下不使用 pgvector，用 Text 存储向量（JSON 格式）
+if IS_SQLITE:
+    VectorType = Text
+else:
+    from pgvector.sqlalchemy import Vector
+    VectorType = Vector
+
+
+class Base(DeclarativeBase):
+    """所有 ORM 模型的基类"""
+    pass
+
+
+class TimestampMixin:
+    """
+    时间戳 Mixin，为模型自动添加 created_at / updated_at 字段。
+    """
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        server_default=func.now(),
+        comment="创建时间",
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True),
+        server_default=func.now(),
+        onupdate=func.now(),
+        comment="更新时间",
+    )
+
+    def to_dict(self) -> Dict[str, Any]:
+        """将模型实例转换为字典"""
+        result: Dict[str, Any] = {}
+        for column in self.__table__.columns:  # type: ignore[attr-defined]
+            value = getattr(self, column.name, None)
+            if isinstance(value, datetime):
+                value = value.isoformat()
+            elif hasattr(value, "tolist"):
+                value = value.tolist()
+            result[column.name] = value
+        return result
+
+
+# ──────────────────────────── 业务模型 ────────────────────────────
+
+class KnowledgePage(Base, TimestampMixin):
+    """知识页面模型"""
+    __tablename__ = "knowledge_pages"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    title: Mapped[str] = mapped_column(String(500), nullable=False, comment="页面标题")
+    content: Mapped[str] = mapped_column(Text, nullable=False, comment="页面正文内容")
+    source_file: Mapped[str | None] = mapped_column(String(500), nullable=True, comment="来源文件名")
+    course_name: Mapped[str | None] = mapped_column(String(200), nullable=True, comment="课程名称")
+    teacher_name: Mapped[str | None] = mapped_column(String(100), nullable=True, comment="讲师名称")
+    live_date: Mapped[str | None] = mapped_column(String(20), nullable=True, comment="直播日期")
+    page_number: Mapped[int | None] = mapped_column(Integer, nullable=True, comment="原始页码")
+    metadata_json: Mapped[str | None] = mapped_column(Text, nullable=True, comment="额外元数据")
+
+
+class KnowledgeChunk(Base, TimestampMixin):
+    """知识分块模型"""
+    __tablename__ = "knowledge_chunks"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    page_id: Mapped[int] = mapped_column(Integer, nullable=False, index=True, comment="关联页面 ID")
+    chunk_index: Mapped[int] = mapped_column(Integer, nullable=False, comment="分块序号")
+    content: Mapped[str] = mapped_column(Text, nullable=False, comment="分块文本内容")
+    embedding: Mapped[list | None] = mapped_column(
+        VectorType(768) if not IS_SQLITE else Text,
+        nullable=True,
+        comment="嵌入向量",
+    )
+    search_vector: Mapped[Any | None] = mapped_column(
+        Text, nullable=True, comment="全文搜索向量（仅 PG）",
+    )
+
+    def to_dict(self) -> Dict[str, Any]:
+        result = super().to_dict()
+        result.pop("embedding", None)
+        result.pop("search_vector", None)
+        return result
+
+
+class OCRImage(Base, TimestampMixin):
+    """OCR 图片记录模型"""
+    __tablename__ = "ocr_images"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
+    file_path: Mapped[str] = mapped_column(String(500), nullable=False, comment="图片文件路径")
+    ocr_text: Mapped[str | None] = mapped_column(Text, nullable=True, comment="OCR 识别全文")
+    confidence: Mapped[float | None] = mapped_column(Float, nullable=True, comment="平均置信度")
+    provider: Mapped[str | None] = mapped_column(String(50), nullable=True, comment="OCR 提供商")
+    status: Mapped[str] = mapped_column(String(20), default="pending", comment="处理状态")
+    error_message: Mapped[str | None] = mapped_column(Text, nullable=True, comment="错误信息")
+    tags: Mapped[str | None] = mapped_column(Text, nullable=True, comment="LLM 提取的标签，JSON 数组格式")
+    story_summary: Mapped[str | None] = mapped_column(Text, nullable=True, comment="Qwen3-8B 提炼的故事摘要")
+
+    def to_dict(self) -> Dict[str, Any]:
+        result = super().to_dict()
+        # 将 tags 从 JSON 字符串解析为列表返回
+        if result.get("tags") and isinstance(result["tags"], str):
+            try:
+                import json
+                result["tags"] = json.loads(result["tags"])
+            except (json.JSONDecodeError, TypeError):
+                pass
+        return result