repository·master·Indexed 23 days ago
https://github.com/tiangolo/pydantic-sqlalchemyAn experimental tool to dynamically generate Pydantic models from SQLAlchemy models using the sqlalchemy_to_pydantic function. It allows for the creation of Pydantic schemas that mirror database structures and includes OrmConfig to enable orm_mode for instantiating models from SQLAlchemy ORM objects.
You can dynamically generate Pydantic models from existing SQLAlchemy models using the sqlalchemy_to_pydantic function. This allows you to create Pydantic schemas that mirror your database structure. You can then extend these generated models using standard Pydantic inheritance to include relationships or additional fields.
from typing import List
from pydantic_sqlalchemy import sqlalchemy_to_pydantic
from sqlalchemy import Column, ForeignKey, Integer, String, create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import Session, relationship, sessionmaker
Base = declarative_base()
engine = create_engine("sqlite://", echo=True)
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True)
name = Column(String)
fullname = Column(String)
nickname = Column(String)
addresses = relationship(
"Address", back_populates="user", cascade="all, delete, delete-orphan"
)
class Address(Base):
__tablename__ = "addresses"
id = Column(Integer, primary_key=True)
email_address = Column(String, nullable=False)
user_id = Column(Integer, ForeignKey("users.id"))
user = relationship("User", back_populates="addresses")
# Generate Pydantic models
PydanticUser = sqlalchemy_to_pydantic(User)
PydanticAddress = sqlalchemy_to_pydantic(Address)
# Extend generated models to include relationships
class PydanticUserWithAddresses(PydanticUser):
addresses: List[PydanticAddress] = []
# Usage with SQLAlchemy ORM objects
# ... (setup engine and session) ...
user = db.query(User).first()
# Convert ORM object to Pydantic model
pydantic_user = PydanticUser.from_orm(user)
data = pydantic_user.dict()
# Convert ORM object with relationships to extended Pydantic model
pydantic_user_with_addresses = PydanticUserWithAddresses.from_orm(user)
data_with_addresses = pydantic_user_with_addresses.dict()sqlalchemy_to_pydantic function takes a SQLAlchemy model class as an argument and returns a new Pydantic model class that represents the fields defined in that SQLAlchemy model.The sqlalchemy_to_pydantic function has the following signature:
sqlalchemy_to_pydantic(db_model: Type, *, config: Type = OrmConfig, exclude: Container[str] = []) -> Type[BaseModel]
Parameters:
db_model (Type): The SQLAlchemy model class to convert.config (Type, optional): A Pydantic configuration class. Defaults to OrmConfig (which enables orm_mode = True).exclude (Container[str], optional): A container of strings representing field names to be excluded from the generated Pydantic model. Defaults to an empty list [].sqlalchemy_to_pydantic function to automatically create a Pydantic model class from an existing SQLAlchemy model. This is useful for generating Pydantic schemas that match your database structure without manual re-definition.The sqlalchemy_to_pydantic function automates the creation of a Pydantic BaseModel from a SQLAlchemy model. It inspects the SQLAlchemy model's columns, infers their Python types, and handles default values and nullability.
To use it, pass your SQLAlchemy model class to the function. You can optionally provide a custom configuration class or a list of field names to exclude from the resulting Pydantic model.
OrmConfig class is a specialized Pydantic BaseConfig that sets orm_mode = True. This is required when you want to use Pydantic's .from_orm() method to instantiate models directly from SQLAlchemy ORM objects.