File size: 3,491 Bytes
7c6ffa6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | from __future__ import annotations
from datetime import datetime
from typing import TYPE_CHECKING
from sqlalchemy import DateTime, ForeignKey, Integer, JSON, String, Text, func
from sqlalchemy.orm import Mapped, mapped_column, relationship
from app.core.database import Base
from app.utils.ids import prefixed_id
if TYPE_CHECKING:
from app.models.document_chunk import DocumentChunk
class Document(Base):
__tablename__ = "documents"
id: Mapped[str] = mapped_column(
String(40),
primary_key=True,
default=lambda: prefixed_id("doc"),
)
user_id: Mapped[str] = mapped_column(
String(40),
ForeignKey("users.id"),
index=True,
nullable=False,
)
title: Mapped[str] = mapped_column(String(255), nullable=False)
file_name: Mapped[str] = mapped_column(String(255), nullable=False)
file_type: Mapped[str] = mapped_column(String(80), nullable=False)
file_path: Mapped[str] = mapped_column(String(500), nullable=False)
subject: Mapped[str | None] = mapped_column(String(120), nullable=True)
chapter: Mapped[str | None] = mapped_column(String(160), nullable=True)
syllabus: Mapped[str | None] = mapped_column(String(120), nullable=True)
status: Mapped[str] = mapped_column(String(30), default="uploaded", nullable=False)
extracted_text: Mapped[str | None] = mapped_column(Text, nullable=True)
extraction_error: Mapped[str | None] = mapped_column(Text, nullable=True)
chunk_count: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
source_type: Mapped[str] = mapped_column(
String(40),
default="pdf",
nullable=False,
)
# Semantic material type set by the source classifier on upload.
# Possible values: notes | chapter | textbook | question_paper |
# handwritten_notes | syllabus | resume_or_personal_doc | unknown
material_type: Mapped[str] = mapped_column(
String(40),
default="unknown",
nullable=False,
)
education_extraction_status: Mapped[str] = mapped_column(
String(30),
default="uploaded",
nullable=False,
)
education_extraction_error: Mapped[str | None] = mapped_column(Text, nullable=True)
education_warnings_json: Mapped[list[str]] = mapped_column(
JSON,
default=list,
nullable=False,
)
syllabus_items_count: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
pyq_questions_count: Mapped[int] = mapped_column(Integer, default=0, nullable=False)
pyq_years_json: Mapped[list[int]] = mapped_column(JSON, default=list, nullable=False)
extracted_text_length: Mapped[int | None] = mapped_column(Integer, nullable=True)
processing_started_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
processing_completed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True),
server_default=func.now(),
nullable=False,
)
updated_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True),
server_default=func.now(),
onupdate=func.now(),
nullable=False,
)
chunks: Mapped[list["DocumentChunk"]] = relationship(
"DocumentChunk",
back_populates="document",
cascade="all, delete-orphan",
order_by="DocumentChunk.chunk_index",
)
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