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115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
"""
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RAG Document Model
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Represents documents within RAG collections
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"""
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from sqlalchemy import (
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Column,
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Integer,
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String,
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Text,
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DateTime,
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Boolean,
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BigInteger,
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ForeignKey,
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JSON,
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)
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from sqlalchemy.sql import func
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from sqlalchemy.orm import relationship
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from app.db.database import Base
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class RagDocument(Base):
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__tablename__ = "rag_documents"
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id = Column(Integer, primary_key=True, index=True)
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# Collection relationship
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collection_id = Column(
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Integer,
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ForeignKey("rag_collections.id", ondelete="CASCADE"),
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nullable=False,
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index=True,
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)
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collection = relationship("RagCollection", back_populates="documents")
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# File information
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filename = Column(String(255), nullable=False) # sanitized filename for storage
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original_filename = Column(String(255), nullable=False) # user's original filename
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file_path = Column(String(500), nullable=False) # path to stored file
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file_type = Column(String(50), nullable=False) # pdf, docx, txt, etc.
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file_size = Column(BigInteger, nullable=False) # file size in bytes
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mime_type = Column(String(100), nullable=True)
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# Processing status
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status = Column(
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String(50), default="processing", nullable=False
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) # 'processing', 'processed', 'error', 'indexed'
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processing_error = Column(Text, nullable=True)
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# Content information
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converted_content = Column(Text, nullable=True) # markdown converted content
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word_count = Column(Integer, default=0, nullable=False)
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character_count = Column(Integer, default=0, nullable=False)
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# Vector information
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vector_count = Column(
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Integer, default=0, nullable=False
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) # number of chunks/vectors created
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chunk_size = Column(
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Integer, default=1000, nullable=False
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) # chunk size used for vectorization
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# Metadata extracted from document
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document_metadata = Column(
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JSON, nullable=True
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) # language, entities, keywords, etc.
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# Processing timestamps
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created_at = Column(
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DateTime(timezone=True), server_default=func.now(), nullable=False
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)
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processed_at = Column(DateTime(timezone=True), nullable=True)
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indexed_at = Column(DateTime(timezone=True), nullable=True)
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updated_at = Column(
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DateTime(timezone=True),
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server_default=func.now(),
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onupdate=func.now(),
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nullable=False,
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)
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# Soft delete
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is_deleted = Column(Boolean, default=False, nullable=False)
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deleted_at = Column(DateTime(timezone=True), nullable=True)
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def to_dict(self):
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"""Convert model to dictionary for API responses"""
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return {
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"id": str(self.id),
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"collection_id": str(self.collection_id),
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"collection_name": self.collection.name if self.collection else None,
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"filename": self.filename,
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"original_filename": self.original_filename,
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"file_type": self.file_type,
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"size": self.file_size,
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"mime_type": self.mime_type,
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"status": self.status,
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"processing_error": self.processing_error,
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"converted_content": self.converted_content,
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"word_count": self.word_count,
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"character_count": self.character_count,
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"vector_count": self.vector_count,
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"chunk_size": self.chunk_size,
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"metadata": self.document_metadata or {},
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"created_at": self.created_at.isoformat() if self.created_at else None,
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"processed_at": self.processed_at.isoformat()
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if self.processed_at
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else None,
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"indexed_at": self.indexed_at.isoformat() if self.indexed_at else None,
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"updated_at": self.updated_at.isoformat() if self.updated_at else None,
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"is_deleted": self.is_deleted,
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}
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def __repr__(self):
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return f"<RagDocument(id={self.id}, filename='{self.original_filename}', status='{self.status}')>"
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