Validation and Serialization

Validation and serialization of the data is done using Pydantic v2 models. It validates data type, required fields, default values, etc. When a validation fails, it raises a ValidationError exception and returns a 422 (Unprocessable Entity) response with the description in the response in the detail key.

Examples

For a SQLAlchemy model with the following definition

class User(Base):
    __tablename__ = "user"
    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(nullable=False)
    age: Mapped[int] = mapped_column()
    date_of_birth: Mapped[date] = mapped_column(nullable=False)
    address: Mapped[str] = mapped_column(nullable=False)

Try to create a new record with invalid field

Request to POST method with the following data:

{
    "name": "John",
    "age": "foo",
    "date_of_birth": "2000-01-01",
    "address": "Street 1"
}

Will return an error like this:

{
    "detail": [
        {
            "type": "int_parsing",
            "loc": [
                "age"
            ],
            "msg": "Input should be a valid integer, unable to parse string as an integer",
            "input": "foo",
            "url": "https://errors.pydantic.dev/2.0.3/v/int_parsing"
        }
    ]
}

Try to create a new record with missing required field

Request to POST method with the following data:

{
    "name": "John",
    "age": 18,
    "address": "Street 1"
}

Will return an error like this:

{
    "detail": [
        {
            "type": "missing",
            "loc": [
                "date_of_birth"
            ],
            "msg": "Field required",
            "input": {
                "name": "John",
                "age": 18,
                "address": "Street 1"
            },
            "url": "https://errors.pydantic.dev/2.0.3/v/missing"
        }
    ]
}