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"""集中导入所有模型，确保 Base.metadata 完整（Alembic autogenerate 需要）。"""
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from app.models.image import Image
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from app.models.job import JobStatus, ProcessingJob
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from app.models.practice import AiGeneration, JudgedBy, PracticeRecord
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from app.models.question import (
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OBJECTIVE_TYPES,
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Question,
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QuestionTag,
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QuestionType,
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Tag,
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)
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from app.models.user import User, UserSettings
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__all__ = [
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"User",
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"UserSettings",
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"Question",
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"QuestionType",
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"OBJECTIVE_TYPES",
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"Tag",
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"QuestionTag",
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"Image",
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"ProcessingJob",
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"JobStatus",
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"PracticeRecord",
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"AiGeneration",
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"JudgedBy",
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]
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"""模型共用的小工具。"""
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import uuid
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def new_uuid() -> str:
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"""主键默认值：UUID4 字符串。"""
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return str(uuid.uuid4())
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"""上传图片元数据（文件本体存 S3）。"""
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from sqlalchemy import BigInteger, ForeignKey, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from app.models.base import new_uuid
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from app.db import Base, TimestampMixin
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class Image(Base, TimestampMixin):
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__tablename__ = "images"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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user_id: Mapped[str] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), index=True
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)
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object_key: Mapped[str] = mapped_column(String(512)) # S3 中的 key
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mime: Mapped[str] = mapped_column(String(64))
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size_bytes: Mapped[int] = mapped_column(BigInteger)
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sha256: Mapped[str] = mapped_column(String(64), index=True) # 去重
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# 缓存原始 OCR 文本：一张图可能拆成多条笔记，留着便于追溯与重新拆分
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ocr_markdown: Mapped[str | None] = mapped_column(Text, nullable=True)
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"""OCR / AI 后台任务状态，前端轮询。"""
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import enum
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from sqlalchemy import ForeignKey, String, Text
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from sqlalchemy import Enum as SAEnum
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from sqlalchemy.orm import Mapped, mapped_column
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from sqlalchemy.types import JSON
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from app.models.base import new_uuid
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from app.db import Base, TimestampMixin
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class JobStatus(str, enum.Enum):
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pending = "pending"
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ocr_running = "ocr_running"
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ai_running = "ai_running"
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done = "done"
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failed = "failed"
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class ProcessingJob(Base, TimestampMixin):
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__tablename__ = "processing_jobs"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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user_id: Mapped[str] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), index=True
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)
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image_id: Mapped[str | None] = mapped_column(
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ForeignKey("images.id", ondelete="SET NULL"), nullable=True
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)
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kind: Mapped[str] = mapped_column(String(32), default="ocr_extract")
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status: Mapped[JobStatus] = mapped_column(
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SAEnum(JobStatus), default=JobStatus.pending
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)
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# 一张图可能拆成多条笔记
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result_question_ids: Mapped[list | None] = mapped_column(JSON, nullable=True)
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error_message: Mapped[str | None] = mapped_column(Text, nullable=True)
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engine_used: Mapped[str | None] = mapped_column(String(32), nullable=True)
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"""答题 / 练习记录，以及 AI 调用审计。"""
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import enum
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from sqlalchemy import Boolean, ForeignKey, Integer, String, Text
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from sqlalchemy import Enum as SAEnum
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from sqlalchemy.orm import Mapped, mapped_column
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from sqlalchemy.types import JSON
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from app.models.base import new_uuid
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from app.db import Base, TimestampMixin
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class JudgedBy(str, enum.Enum):
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auto = "auto" # 客观题本地判分
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ai = "ai" # 主观题 AI 判分
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self_ = "self" # 用户自评
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class PracticeRecord(Base, TimestampMixin):
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__tablename__ = "practice_records"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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user_id: Mapped[str] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), index=True
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)
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question_id: Mapped[str] = mapped_column(
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ForeignKey("questions.id", ondelete="CASCADE"), index=True
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)
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user_answer: Mapped[list | None] = mapped_column(JSON, nullable=True)
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is_correct: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
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judged_by: Mapped[JudgedBy] = mapped_column(SAEnum(JudgedBy), default=JudgedBy.auto)
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ai_feedback_markdown: Mapped[str | None] = mapped_column(Text, nullable=True)
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class AiGeneration(Base, TimestampMixin):
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"""记录每次 AI 调用，便于复用缓存、成本观察、排查。"""
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__tablename__ = "ai_generations"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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user_id: Mapped[str] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), index=True
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)
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question_id: Mapped[str | None] = mapped_column(String(36), nullable=True)
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task: Mapped[str] = mapped_column(String(32)) # extract/judge/summarize/explain
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model: Mapped[str | None] = mapped_column(String(128), nullable=True)
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request_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)
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response_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)
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content_markdown: Mapped[str | None] = mapped_column(Text, nullable=True)
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"""题目、标签、题目-标签关联。"""
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import enum
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from sqlalchemy import (
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Boolean,
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ForeignKey,
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Integer,
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String,
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Text,
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UniqueConstraint,
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)
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from sqlalchemy import Enum as SAEnum
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from sqlalchemy.types import JSON
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from app.models.base import new_uuid
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from app.db import Base, TimestampMixin
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class QuestionType(str, enum.Enum):
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# 拍照入库的默认值：先收进来，想练习时再补题型/答案
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unclassified = "unclassified"
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single_choice = "single_choice"
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multiple_choice = "multiple_choice"
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true_false = "true_false"
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fill_blank = "fill_blank"
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short_answer = "short_answer"
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# 客观题：有标准答案时可本地精确判分；其余交给 AI 判
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OBJECTIVE_TYPES = {
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QuestionType.single_choice,
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QuestionType.multiple_choice,
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QuestionType.true_false,
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}
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class Tag(Base, TimestampMixin):
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__tablename__ = "tags"
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__table_args__ = (UniqueConstraint("user_id", "name", name="uq_tag_user_name"),)
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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user_id: Mapped[str] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), index=True
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)
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name: Mapped[str] = mapped_column(String(128))
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questions: Mapped[list["Question"]] = relationship(
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secondary="question_tags", back_populates="tags"
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)
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class QuestionTag(Base):
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__tablename__ = "question_tags"
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question_id: Mapped[str] = mapped_column(
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ForeignKey("questions.id", ondelete="CASCADE"), primary_key=True
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)
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tag_id: Mapped[str] = mapped_column(
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ForeignKey("tags.id", ondelete="CASCADE"), primary_key=True
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)
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class Question(Base, TimestampMixin):
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"""一条笔记。
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只有 type 和 stem_markdown 是必填，其中 type 可以是 unclassified，
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所以"拍照识别完直接存"不需要用户补任何东西。
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"""
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__tablename__ = "questions"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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user_id: Mapped[str] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), index=True
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)
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type: Mapped[QuestionType] = mapped_column(
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SAEnum(QuestionType), default=QuestionType.unclassified
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)
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stem_markdown: Mapped[str] = mapped_column(Text)
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# 选项：[{"key": "A", "text_markdown": "..."}]，选择/判断题用
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options: Mapped[list | None] = mapped_column(JSON, nullable=True)
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# 标准答案：选择题 ["A","C"]，填空/简答 ["文本"]
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correct_answer: Mapped[list | None] = mapped_column(JSON, nullable=True)
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# AI 生成的讲解（每次 /ai/explain 会覆写，别往这里写用户内容）
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explanation_markdown: Mapped[str | None] = mapped_column(Text, nullable=True)
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# 用户自己写的笔记 / 错因反思，AI 永不触碰
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my_note_markdown: Mapped[str | None] = mapped_column(Text, nullable=True)
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# 手动标记"待复习"；复习清单 = 这个标记 ∪ 最近一次作答判错
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needs_review: Mapped[bool] = mapped_column(Boolean, default=False, index=True)
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difficulty: Mapped[int | None] = mapped_column(Integer, nullable=True) # 1-5
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source_image_id: Mapped[str | None] = mapped_column(
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ForeignKey("images.id", ondelete="SET NULL"), nullable=True, index=True
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)
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ocr_engine: Mapped[str | None] = mapped_column(String(32), nullable=True)
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tags: Mapped[list["Tag"]] = relationship(
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secondary="question_tags", back_populates="questions"
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)
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# 便于详情页把原图和笔记一起展示
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source_image: Mapped["Image | None"] = relationship("Image")
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"""用户与用户 AI 配置。"""
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from sqlalchemy import Boolean, ForeignKey, String, Text
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from app.models.base import new_uuid
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from app.db import Base, TimestampMixin
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class User(Base, TimestampMixin):
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__tablename__ = "users"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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username: Mapped[str] = mapped_column(String(64), unique=True, index=True)
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email: Mapped[str | None] = mapped_column(String(255), unique=True, nullable=True)
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password_hash: Mapped[str] = mapped_column(String(255))
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is_active: Mapped[bool] = mapped_column(Boolean, default=True)
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settings: Mapped["UserSettings"] = relationship(
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back_populates="user", uselist=False, cascade="all, delete-orphan"
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)
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class UserSettings(Base, TimestampMixin):
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"""每个用户可在 UI 里配置自己的 OpenAI 兼容 AI 接口。api_key 用 Fernet 加密存储。"""
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__tablename__ = "user_settings"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=new_uuid)
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user_id: Mapped[str] = mapped_column(
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ForeignKey("users.id", ondelete="CASCADE"), unique=True, index=True
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)
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llm_base_url: Mapped[str | None] = mapped_column(String(512), nullable=True)
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llm_api_key_encrypted: Mapped[str | None] = mapped_column(Text, nullable=True)
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llm_text_model: Mapped[str | None] = mapped_column(String(128), nullable=True)
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llm_vision_model: Mapped[str | None] = mapped_column(String(128), nullable=True)
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user: Mapped["User"] = relationship(back_populates="settings")
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