310491a557
- Updated python_coding_microproject.csv to use NVIDIA NIM Kimi K2 - Updated kali_linux_shell_simulator.csv to use NVIDIA NIM Kimi K2 - Made more general-purpose (flexible targets, expanded tools) - Added nemotron-mini-agent.csv for fast local inference via Ollama - Added nemotron-agent.csv for advanced reasoning via Ollama - Added wiki page: Projects for NVIDIA NIMs and Nemotron
126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
"""
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GT 2.0 Base Model Classes - Service-Based Architecture
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Provides Pydantic models for data serialization with the DuckDB service.
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No SQLAlchemy ORM dependency - pure Python/Pydantic models.
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"""
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from typing import Any, Dict, Optional, List, Type, TypeVar
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from datetime import datetime
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import uuid
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from pydantic import BaseModel, Field, ConfigDict
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# Generic type for model classes
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T = TypeVar('T', bound='BaseServiceModel')
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class BaseServiceModel(BaseModel):
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"""
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Base model for all GT 2.0 entities using service-based architecture.
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Replaces SQLAlchemy models with Pydantic models + DuckDB service.
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"""
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# Pydantic v2 configuration
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model_config = ConfigDict(
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from_attributes=True,
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validate_assignment=True,
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arbitrary_types_allowed=True,
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use_enum_values=True
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)
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# Standard fields for all models
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id: str = Field(default_factory=lambda: str(uuid.uuid4()), description="Unique identifier")
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created_at: datetime = Field(default_factory=datetime.utcnow, description="Creation timestamp")
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updated_at: datetime = Field(default_factory=datetime.utcnow, description="Last update timestamp")
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def to_dict(self) -> Dict[str, Any]:
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"""Convert model to dictionary"""
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return self.model_dump()
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@classmethod
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def from_dict(cls: Type[T], data: Dict[str, Any]) -> T:
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"""Create model instance from dictionary"""
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return cls(**data)
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@classmethod
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def from_row(cls: Type[T], row: Dict[str, Any]) -> T:
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"""Create model instance from database row"""
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# Convert database row to model, handling type conversions
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model_data = {}
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for field_name, field_info in cls.model_fields.items():
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if field_name in row:
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value = row[field_name]
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# Handle datetime conversion
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if field_info.annotation == datetime and isinstance(value, str):
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try:
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value = datetime.fromisoformat(value)
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except ValueError:
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value = datetime.utcnow()
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model_data[field_name] = value
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return cls(**model_data)
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def update_timestamp(self):
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"""Update the updated_at timestamp"""
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self.updated_at = datetime.utcnow()
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class BaseCreateModel(BaseModel):
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"""Base model for creation requests"""
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model_config = ConfigDict(from_attributes=True)
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class BaseUpdateModel(BaseModel):
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"""Base model for update requests"""
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model_config = ConfigDict(from_attributes=True)
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updated_at: datetime = Field(default_factory=datetime.utcnow)
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class BaseResponseModel(BaseServiceModel):
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"""Base model for API responses"""
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pass
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# Legacy compatibility - some files might still import Base
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Base = BaseServiceModel # For backwards compatibility during migration
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# Database service integration helpers
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class DatabaseMixin:
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"""Mixin providing database service integration methods"""
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@classmethod
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async def get_table_name(cls) -> str:
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"""Get the database table name for this model"""
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# Convert CamelCase to snake_case and pluralize
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name = cls.__name__.lower()
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if name.endswith('y'):
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name = name[:-1] + 'ies'
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elif name.endswith('s'):
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name = name + 'es'
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else:
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name = name + 's'
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return name
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@classmethod
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async def create_sql(cls) -> str:
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"""Generate CREATE TABLE SQL for this model"""
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# This would generate SQL based on Pydantic field types
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# For now, return placeholder - actual schemas are in DuckDB service
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table_name = await cls.get_table_name()
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return f"-- CREATE TABLE {table_name} generated by DuckDB service"
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async def to_sql_values(self) -> Dict[str, Any]:
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"""Convert model to SQL-safe values"""
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data = self.to_dict()
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# Convert datetime objects to ISO strings
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for key, value in data.items():
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if isinstance(value, datetime):
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data[key] = value.isoformat()
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return data |