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66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
"""
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RAG Collection Model
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Represents document collections for the RAG system
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"""
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from sqlalchemy import Column, Integer, String, Text, DateTime, Boolean, BigInteger
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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 RagCollection(Base):
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__tablename__ = "rag_collections"
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id = Column(Integer, primary_key=True, index=True)
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name = Column(String(255), nullable=False, index=True)
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description = Column(Text, nullable=True)
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qdrant_collection_name = Column(
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String(255), nullable=False, unique=True, index=True
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)
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# Metadata
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document_count = Column(Integer, default=0, nullable=False)
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size_bytes = Column(BigInteger, default=0, nullable=False)
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vector_count = Column(Integer, default=0, nullable=False)
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# Status tracking
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status = Column(
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String(50), default="active", nullable=False
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) # 'active', 'indexing', 'error'
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is_active = Column(Boolean, default=True, nullable=False)
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# 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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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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# Relationships
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documents = relationship(
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"RagDocument", back_populates="collection", cascade="all, delete-orphan"
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)
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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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"name": self.name,
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"description": self.description or "",
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"document_count": self.document_count,
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"size_bytes": self.size_bytes,
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"vector_count": self.vector_count,
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"status": self.status,
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"created_at": self.created_at.isoformat() if self.created_at else None,
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"updated_at": self.updated_at.isoformat() if self.updated_at else None,
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"is_active": self.is_active,
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}
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def __repr__(self):
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return f"<RagCollection(id={self.id}, name='{self.name}', documents={self.document_count})>"
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