64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
"""Models for multi-agent analysis system."""
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from sqlalchemy import Column, String, Text, Float, DateTime, ForeignKey, JSON
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from sqlalchemy.orm import relationship
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from sqlalchemy.types import TypeDecorator, CHAR
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from sqlalchemy.dialects.postgresql import UUID
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import uuid
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from datetime import datetime
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from backend.models.base import Model
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class GUID(TypeDecorator):
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"""Platform-independent GUID type for SQLite and PostgreSQL compatibility."""
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impl = CHAR
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cache_ok = True
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def load_dialect_impl(self, dialect):
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if dialect.name == 'postgresql':
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return dialect.type_descriptor(UUID())
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else:
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return dialect.type_descriptor(CHAR(32))
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def process_bind_param(self, value, dialect):
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if value is None:
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return value
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elif dialect.name == 'postgresql':
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return str(value)
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else:
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if not isinstance(value, uuid.UUID):
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return "%.32x" % uuid.UUID(value).int
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else:
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return "%.32x" % value.int
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def process_result_value(self, value, dialect):
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if value is None:
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return value
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else:
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if not isinstance(value, uuid.UUID):
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return uuid.UUID(value)
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return value
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class AgentSummary(Model):
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"""Multi-agent analysis results."""
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__tablename__ = "agent_summaries"
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__table_args__ = {'extend_existing': True}
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id = Column(GUID, primary_key=True, default=uuid.uuid4)
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summary_id = Column(GUID, ForeignKey("summaries.id", ondelete='CASCADE'))
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agent_type = Column(String(20), nullable=False) # technical, business, user, synthesis
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agent_summary = Column(Text, nullable=True)
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key_insights = Column(JSON, nullable=True)
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focus_areas = Column(JSON, nullable=True)
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recommendations = Column(JSON, nullable=True)
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confidence_score = Column(Float, nullable=True)
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processing_time_seconds = Column(Float, nullable=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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# Relationship
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summary = relationship("backend.models.summary.Summary", back_populates="agent_analyses")
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def __repr__(self):
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return f"<AgentSummary(id={self.id}, type={self.agent_type}, summary_id={self.summary_id})>" |