pydantic

name	pydantic
description	Pydantic is a Python data validation and serialization library, based on type hints. Use this skill whenever you need to do relatively complex data modeling using Pydantic, e.g. when adding constraints, defining a model hierarchy with subclasses, etc.

Pydantic Validation

In a nutshell, Pydantic is dataclasses with runtime validation. It leverages type hints to understand how validation (and serialization) should be performed. It is mostly useful when dealing with external untrusted data, for example when defining an HTTP API.

It is generally not recommended to use Pydantic to define classes that are instantiated within the user code. By doing so, you will lose flexibility (e.g. can't use types not supported by Pydantic, harder to perform post init changes). It is usually better to use vanilla classes (or standard library dataclasses) in this case, as a static type checker will already catch type mismatches.

Basic usage

Here is a simple example of using a Pydantic model:

from datetime import date

from pydantic import BaseModel, Field


class Person(BaseModel):
    name: str
    age: int = Field(description='The age of the person')
    birthdate: date | None = None


p = Person(name='John', age=20, birthdate='1970-01-01')

Constraints and field metadata

The Field() function is used to provide metadata and constraints. You need to distinguish two types of of metadata:

• field specific metadata: metadata such as deprecated, alias, that only has a meaning when attached to a field.
• type specific metadata: this includes constraints such as gt, max_length, and also metadata that affects the JSON Schema (e.g. description, title).

The Field() function can be attached to model fields using the assignment form:

class User(BaseModel):
    first_name: str = Field(alias='name')

or using the annotated pattern:

class Model(BaseModel):
    value: Annotated[int, Field(deprecated=True)] = 1

The annotated pattern has some advantages:

• Using the f: <type> = Field(...) form can be confusing and might trick users into thinking f has a default value, while in reality it is still required.
• You can provide an arbitrary amount of metadata elements for a field. As shown in the example above. the Field() function only supports a limited set of constraints/metadata, and you may have to use different Pydantic utilities such as WithJsonSchema in some cases.

But note that:

• You should use the assignment form for metadata that has a meaning for static type checkers. This includes: alias, default and default_factory.

• field specific metadata can only be used on the "top-level" type. A common pitfall is to do the following:

  class Model(BaseModel):
      field_bad: Annotated[int, Field(deprecated=True)] | None = None
      field_ok: Annotated[int | None, Field(deprecated=True)] = None
  

  field specific metadata should apply to the whole union in this example.

Constraints

As much as possible, use the "built-in" validation constraints, instead of defining custom validators:

from annotated_types import Gt  # annotated_types is an alternative to the `Field()` function.

class Model(BaseModel):
    constrained_int_ok: Annotated[int, Gt(1)]  # This is good

    constrained_int_bad: int

    @field_validator('constrained_int_bad')  # This is bad
    @classmethod
    def validate(cls, v: int):
        if not v > 1:
            raise ValueError('Value is not greater than 1')

Sometimes, constraints can't be expressed using the Field() function. For example, string constraints such as strip_whitespace, to_upper, to_lower and ascii_only can only be specified using pydantic.StringConstraints:

from typing import Annotated

from pydantic import BaseModel, StringConstraints


class Model(BaseModel):
    # Do this instead of a validator calling s.strip():
    a: Annotated[str, StringConstraints(strip_whitespace=True)]

https://pydantic.dev/docs/validation/latest/api/pydantic/standard_library_types/ is the canonical documentation for all supported standard library types and their constraints.

Validators

In some cases, you may have to use custom validators. As much as possible, use after validators. Because they run after the Pydantic validation, you are guaranteed to work with the type of the field being validated. If you use before validators, the input data can literally be anything, so it is more error-prone (especially for model validators, the input isn't necessarily a dict, it can also be an arbitrary object).

If possible, prefer using the annotated pattern for validators:

from pydantic import BaseModel, ValidationError, field_validator


def is_even(value: int) -> int:
    if value % 2 == 1:
          raise ValueError(f'{value} is not an even number')
      return value


class Model(BaseModel):
    # Prefer this form: the validator is right next to the field, making it easy to understand
    even: Annotated[int, AfterValidator(is_even)]
    odd: int

    # If you define a validator as decorator, make sure to define it as classmethod.
    @field_validator('odd', mode='after')
    @classmethod
    def is_odd(cls, value: int) -> int:
        if value % 2 == 0:
            raise ValueError(f'{value} is not an odd number')
        return value

Using the decorator pattern can lead to unclear behavior, especially when considering the order in which they run (in particular when using subclasses).

Type coercion, collections and unions

Unless you are using strict mode, Pydantic applies type coercion in most cases. For instance, for a field typed as int, strings like '123' will be accepted. This also applies to collections types: list[str] also accepts tuples, sets etc.

This is way you should avoid:

• using unions such as int | str, if your goal is to coerce the str to an int via a validator.
• using abstract collections such as collections.abc.Sequence, if your goal is to accept both list and tuples. Using these abstract collections is inefficient.

In the general case, unions are best avoided because every use of the field will need to check for each type before doing anything with it.

Forward annotations

Python has the ability to write annotations as forward references, by using strings. This can cause challenges for Pydantic to evaluate them, so they are best avoided if possible.

If you are defining Pydantic models in a module, avoid using from __future__ import annotations if possible (which stringifies all annotations by default). Only add explicit quotes to annotations that aren't defined yet, e.g.:

class Model(BaseModel):
    self_ref: 'Model'

Also note that in Python >= 3.14, annotations evaluation is deferred, so you should not use string annotations at all.

Recursive type aliases

You might be tempted to define aliases like this:

JsonValue: TypeAlias = 'list[JsonValue] | dict[str, JsonValue] | str | bool | int | float | None'

The alias needs to be quoted because it is a recursive one. Pydantic will generally not be able to evaluate the alias. Instead, use an explicit type alias:

type JsonValue = list[JsonValue] | dict[str, JsonValue] | str | bool | int | float | None
# Or, if not on Python >= 3.12:
from typing_extensions import TypeAliasType

JsonValue = TypeAliasType('JsonValue', 'list[JsonValue] | dict[str, JsonValue] | str | bool | int | float | None')

Model subclasses, discriminated unions

Subclassing is a really common Python pattern, but can be a footgun in Pydantic. You might be tempted to do:

class Base(BaseModel):
    base_field: int

    def common_method(self): ...


class Sub1(Base):
    sub1_field: str


class Sub2(Base):
    sub2_field: bool


class Main(BaseModel):
    model: Base


m = Main(model=Sub1(base_field=1, sub1_field='test'))

This example works, but will not behave as expected when serializing m:

m.model_dump()
#> {'model': {'base_field': 1}} -> sub1_field missing

This is because Pydantic serializes the model according to the defined type (Base), not the runtime value. Validation will also be unexpected if doing Main(model={'base_field': 1, 'sub1_field': 'test'}).

Instead, try to use discriminated unions (provided that you can set a type field to distinguish models):

class Sub1(Base):
    type: Literal['sub1']
    sub1_field: str


class Sub2(Base):
    type: Literal['sub2']
    sub2_field: bool

Subs = Annotated[Sub1 | Sub2, Field(discriminator='type')]

class Main(BaseModel):
    model: Subs

or generics:

class Main[BaseT: Base](BaseModel):
    model: BaseT

m = Main[Sub1](model={'base_field': 1, 'sub1_field': 'test'})  # Will work

using polymorphic serialization (in Pydantic >=2.13) or serialize as any (in Pydantic <2.13) can be used as last resort.