"""
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