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JSON Schema

API Documentation

pydantic.json_schema

Pydantic allows automatic creation and customization of JSON schemas from models. The generated JSON schemas are compliant with the following specifications:

Generating JSON Schema

Use the following functions to generate JSON schema:

Note

These methods are not to be confused with BaseModel.model_dump_json and TypeAdapter.dump_json, which serialize instances of the model or adapted type, respectively. These methods return JSON strings. In comparison, BaseModel.model_json_schema and TypeAdapter.json_schema return a jsonable dict representing the JSON schema of the model or adapted type, respectively.

on the "jsonable" nature of JSON schema

Regarding the "jsonable" nature of the model_json_schema results, calling json.dumps(m.model_json_schema())on some BaseModel m returns a valid JSON string. Similarly, for TypeAdapter.json_schema, calling json.dumps(TypeAdapter(<some_type>).json_schema()) returns a valid JSON string.

Tip

Pydantic offers support for both of:

  1. Customizing JSON Schema
  2. Customizing the JSON Schema Generation Process

The first approach generally has a more narrow scope, allowing for customization of the JSON schema for more specific cases and types. The second approach generally has a more broad scope, allowing for customization of the JSON schema generation process overall. The same effects can be achieved with either approach, but depending on your use case, one approach might offer a more simple solution than the other.

Here's an example of generating JSON schema from a BaseModel:

import json
from enum import Enum
from typing import Union

from typing_extensions import Annotated

from pydantic import BaseModel, Field
from pydantic.config import ConfigDict


class FooBar(BaseModel):
    count: int
    size: Union[float, None] = None


class Gender(str, Enum):
    male = 'male'
    female = 'female'
    other = 'other'
    not_given = 'not_given'


class MainModel(BaseModel):
    """
    This is the description of the main model
    """

    model_config = ConfigDict(title='Main')

    foo_bar: FooBar
    gender: Annotated[Union[Gender, None], Field(alias='Gender')] = None
    snap: int = Field(
        42,
        title='The Snap',
        description='this is the value of snap',
        gt=30,
        lt=50,
    )


main_model_schema = MainModel.model_json_schema()  # (1)!
print(json.dumps(main_model_schema, indent=2))  # (2)!

JSON output:

{
  "$defs": {
    "FooBar": {
      "properties": {
        "count": {
          "title": "Count",
          "type": "integer"
        },
        "size": {
          "anyOf": [
            {
              "type": "number"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "title": "Size"
        }
      },
      "required": [
        "count"
      ],
      "title": "FooBar",
      "type": "object"
    },
    "Gender": {
      "enum": [
        "male",
        "female",
        "other",
        "not_given"
      ],
      "title": "Gender",
      "type": "string"
    }
  },
  "description": "This is the description of the main model",
  "properties": {
    "foo_bar": {
      "$ref": "#/$defs/FooBar"
    },
    "Gender": {
      "anyOf": [
        {
          "$ref": "#/$defs/Gender"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "snap": {
      "default": 42,
      "description": "this is the value of snap",
      "exclusiveMaximum": 50,
      "exclusiveMinimum": 30,
      "title": "The Snap",
      "type": "integer"
    }
  },
  "required": [
    "foo_bar"
  ],
  "title": "Main",
  "type": "object"
}
  1. This produces a "jsonable" dict of MainModel's schema.
  2. Calling json.dumps on the schema dict produces a JSON string.
import json
from enum import Enum
from typing import Union

from typing import Annotated

from pydantic import BaseModel, Field
from pydantic.config import ConfigDict


class FooBar(BaseModel):
    count: int
    size: Union[float, None] = None


class Gender(str, Enum):
    male = 'male'
    female = 'female'
    other = 'other'
    not_given = 'not_given'


class MainModel(BaseModel):
    """
    This is the description of the main model
    """

    model_config = ConfigDict(title='Main')

    foo_bar: FooBar
    gender: Annotated[Union[Gender, None], Field(alias='Gender')] = None
    snap: int = Field(
        42,
        title='The Snap',
        description='this is the value of snap',
        gt=30,
        lt=50,
    )


main_model_schema = MainModel.model_json_schema()  # (1)!
print(json.dumps(main_model_schema, indent=2))  # (2)!

JSON output:

{
  "$defs": {
    "FooBar": {
      "properties": {
        "count": {
          "title": "Count",
          "type": "integer"
        },
        "size": {
          "anyOf": [
            {
              "type": "number"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "title": "Size"
        }
      },
      "required": [
        "count"
      ],
      "title": "FooBar",
      "type": "object"
    },
    "Gender": {
      "enum": [
        "male",
        "female",
        "other",
        "not_given"
      ],
      "title": "Gender",
      "type": "string"
    }
  },
  "description": "This is the description of the main model",
  "properties": {
    "foo_bar": {
      "$ref": "#/$defs/FooBar"
    },
    "Gender": {
      "anyOf": [
        {
          "$ref": "#/$defs/Gender"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "snap": {
      "default": 42,
      "description": "this is the value of snap",
      "exclusiveMaximum": 50,
      "exclusiveMinimum": 30,
      "title": "The Snap",
      "type": "integer"
    }
  },
  "required": [
    "foo_bar"
  ],
  "title": "Main",
  "type": "object"
}
  1. This produces a "jsonable" dict of MainModel's schema.
  2. Calling json.dumps on the schema dict produces a JSON string.
import json
from enum import Enum

from typing import Annotated

from pydantic import BaseModel, Field
from pydantic.config import ConfigDict


class FooBar(BaseModel):
    count: int
    size: float | None = None


class Gender(str, Enum):
    male = 'male'
    female = 'female'
    other = 'other'
    not_given = 'not_given'


class MainModel(BaseModel):
    """
    This is the description of the main model
    """

    model_config = ConfigDict(title='Main')

    foo_bar: FooBar
    gender: Annotated[Gender | None, Field(alias='Gender')] = None
    snap: int = Field(
        42,
        title='The Snap',
        description='this is the value of snap',
        gt=30,
        lt=50,
    )


main_model_schema = MainModel.model_json_schema()  # (1)!
print(json.dumps(main_model_schema, indent=2))  # (2)!

JSON output:

{
  "$defs": {
    "FooBar": {
      "properties": {
        "count": {
          "title": "Count",
          "type": "integer"
        },
        "size": {
          "anyOf": [
            {
              "type": "number"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "title": "Size"
        }
      },
      "required": [
        "count"
      ],
      "title": "FooBar",
      "type": "object"
    },
    "Gender": {
      "enum": [
        "male",
        "female",
        "other",
        "not_given"
      ],
      "title": "Gender",
      "type": "string"
    }
  },
  "description": "This is the description of the main model",
  "properties": {
    "foo_bar": {
      "$ref": "#/$defs/FooBar"
    },
    "Gender": {
      "anyOf": [
        {
          "$ref": "#/$defs/Gender"
        },
        {
          "type": "null"
        }
      ],
      "default": null
    },
    "snap": {
      "default": 42,
      "description": "this is the value of snap",
      "exclusiveMaximum": 50,
      "exclusiveMinimum": 30,
      "title": "The Snap",
      "type": "integer"
    }
  },
  "required": [
    "foo_bar"
  ],
  "title": "Main",
  "type": "object"
}
  1. This produces a "jsonable" dict of MainModel's schema.
  2. Calling json.dumps on the schema dict produces a JSON string.

The TypeAdapter class lets you create an object with methods for validating, serializing, and producing JSON schemas for arbitrary types. This serves as a complete replacement for schema_of in Pydantic V1 (which is now deprecated).

Here's an example of generating JSON schema from a TypeAdapter:

from typing import List

from pydantic import TypeAdapter

adapter = TypeAdapter(List[int])
print(adapter.json_schema())
#> {'items': {'type': 'integer'}, 'type': 'array'}

You can also generate JSON schemas for combinations of BaseModels and TypeAdapters, as shown in this example:

import json
from typing import Union

from pydantic import BaseModel, TypeAdapter


class Cat(BaseModel):
    name: str
    color: str


class Dog(BaseModel):
    name: str
    breed: str


ta = TypeAdapter(Union[Cat, Dog])
ta_schema = ta.json_schema()
print(json.dumps(ta_schema, indent=2))

JSON output:

{
  "$defs": {
    "Cat": {
      "properties": {
        "name": {
          "title": "Name",
          "type": "string"
        },
        "color": {
          "title": "Color",
          "type": "string"
        }
      },
      "required": [
        "name",
        "color"
      ],
      "title": "Cat",
      "type": "object"
    },
    "Dog": {
      "properties": {
        "name": {
          "title": "Name",
          "type": "string"
        },
        "breed": {
          "title": "Breed",
          "type": "string"
        }
      },
      "required": [
        "name",
        "breed"
      ],
      "title": "Dog",
      "type": "object"
    }
  },
  "anyOf": [
    {
      "$ref": "#/$defs/Cat"
    },
    {
      "$ref": "#/$defs/Dog"
    }
  ]
}

Configuring the JsonSchemaMode

Specify the mode of JSON schema generation via the mode parameter in the model_json_schema and TypeAdapter.json_schema methods. By default, the mode is set to 'validation', which produces a JSON schema corresponding to the model's validation schema.

The JsonSchemaMode is a type alias that represents the available options for the mode parameter:

  • 'validation'
  • 'serialization'

Here's an example of how to specify the mode parameter, and how it affects the generated JSON schema:

from decimal import Decimal

from pydantic import BaseModel


class Model(BaseModel):
    a: Decimal = Decimal('12.34')


print(Model.model_json_schema(mode='validation'))
"""
{
    'properties': {
        'a': {
            'anyOf': [{'type': 'number'}, {'type': 'string'}],
            'default': '12.34',
            'title': 'A',
        }
    },
    'title': 'Model',
    'type': 'object',
}
"""

print(Model.model_json_schema(mode='serialization'))
"""
{
    'properties': {'a': {'default': '12.34', 'title': 'A', 'type': 'string'}},
    'title': 'Model',
    'type': 'object',
}
"""

Customizing JSON Schema

The generated JSON schema can be customized at both the field level and model level via:

  1. Field-level customization with the Field constructor
  2. Model-level customization with model_config

At both the field and model levels, you can use the json_schema_extra option to add extra information to the JSON schema. The Using json_schema_extra section below provides more details on this option.

For custom types, Pydantic offers other tools for customizing JSON schema generation:

  1. WithJsonSchema annotation
  2. SkipJsonSchema annotation
  3. Implementing __get_pydantic_core_schema__
  4. Implementing __get_pydantic_json_schema__

Field-Level Customization

Optionally, the Field function can be used to provide extra information about the field and validations.

Some field parameters are used exclusively to customize the generated JSON Schema:

  • title: The title of the field.
  • description: The description of the field.
  • examples: The examples of the field.
  • json_schema_extra: Extra JSON Schema properties to be added to the field.

Here's an example:

import json

from pydantic import BaseModel, EmailStr, Field, SecretStr


class User(BaseModel):
    age: int = Field(description='Age of the user')
    email: EmailStr = Field(examples=['[email protected]'])
    name: str = Field(title='Username')
    password: SecretStr = Field(
        json_schema_extra={
            'title': 'Password',
            'description': 'Password of the user',
            'examples': ['123456'],
        }
    )


print(json.dumps(User.model_json_schema(), indent=2))

JSON output:

{
  "properties": {
    "age": {
      "description": "Age of the user",
      "title": "Age",
      "type": "integer"
    },
    "email": {
      "examples": [
        "[email protected]"
      ],
      "format": "email",
      "title": "Email",
      "type": "string"
    },
    "name": {
      "title": "Username",
      "type": "string"
    },
    "password": {
      "description": "Password of the user",
      "examples": [
        "123456"
      ],
      "format": "password",
      "title": "Password",
      "type": "string",
      "writeOnly": true
    }
  },
  "required": [
    "age",
    "email",
    "name",
    "password"
  ],
  "title": "User",
  "type": "object"
}

Unenforced Field constraints

If Pydantic finds constraints which are not being enforced, an error will be raised. If you want to force the constraint to appear in the schema, even though it's not being checked upon parsing, you can use variadic arguments to Field with the raw schema attribute name:

from pydantic import BaseModel, Field, PositiveInt

try:
    # this won't work since `PositiveInt` takes precedence over the
    # constraints defined in `Field`, meaning they're ignored
    class Model(BaseModel):
        foo: PositiveInt = Field(..., lt=10)

except ValueError as e:
    print(e)


# if you find yourself needing this, an alternative is to declare
# the constraints in `Field` (or you could use `conint()`)
# here both constraints will be enforced:
class ModelB(BaseModel):
    # Here both constraints will be applied and the schema
    # will be generated correctly
    foo: int = Field(..., gt=0, lt=10)


print(ModelB.model_json_schema())
"""
{
    'properties': {
        'foo': {
            'exclusiveMaximum': 10,
            'exclusiveMinimum': 0,
            'title': 'Foo',
            'type': 'integer',
        }
    },
    'required': ['foo'],
    'title': 'ModelB',
    'type': 'object',
}
"""

You can specify JSON schema modifications via the Field constructor via typing.Annotated as well:

import json
from uuid import uuid4

from typing_extensions import Annotated

from pydantic import BaseModel, Field


class Foo(BaseModel):
    id: Annotated[str, Field(default_factory=lambda: uuid4().hex)]
    name: Annotated[str, Field(max_length=256)] = Field(
        'Bar', title='CustomName'
    )


print(json.dumps(Foo.model_json_schema(), indent=2))

JSON output:

{
  "properties": {
    "id": {
      "title": "Id",
      "type": "string"
    },
    "name": {
      "default": "Bar",
      "maxLength": 256,
      "title": "CustomName",
      "type": "string"
    }
  },
  "title": "Foo",
  "type": "object"
}

Model-Level Customization

You can also use model config to customize JSON schema generation on a model. Specifically, the following config options are relevant:

Using json_schema_extra

The json_schema_extra option can be used to add extra information to the JSON schema, either at the Field level or at the Model level. You can pass a dict or a Callable to json_schema_extra.

Using json_schema_extra with a dict

You can pass a dict to json_schema_extra to add extra information to the JSON schema:

import json

from pydantic import BaseModel, ConfigDict


class Model(BaseModel):
    a: str

    model_config = ConfigDict(json_schema_extra={'examples': [{'a': 'Foo'}]})


print(json.dumps(Model.model_json_schema(), indent=2))

JSON output:

{
  "examples": [
    {
      "a": "Foo"
    }
  ],
  "properties": {
    "a": {
      "title": "A",
      "type": "string"
    }
  },
  "required": [
    "a"
  ],
  "title": "Model",
  "type": "object"
}

Using json_schema_extra with a Callable

You can pass a Callable to json_schema_extra to modify the JSON schema with a function:

import json

from pydantic import BaseModel, Field


def pop_default(s):
    s.pop('default')


class Model(BaseModel):
    a: int = Field(default=1, json_schema_extra=pop_default)


print(json.dumps(Model.model_json_schema(), indent=2))

JSON output:

{
  "properties": {
    "a": {
      "title": "A",
      "type": "integer"
    }
  },
  "title": "Model",
  "type": "object"
}

WithJsonSchema annotation

API Documentation

pydantic.json_schema.WithJsonSchema

Tip

Using WithJsonSchema] is preferred over implementing __get_pydantic_json_schema__ for custom types, as it's more simple and less error-prone.

The WithJsonSchema annotation can be used to override the generated (base) JSON schema for a given type without the need to implement __get_pydantic_core_schema__ or __get_pydantic_json_schema__ on the type itself.

This provides a way to set a JSON schema for types that would otherwise raise errors when producing a JSON schema, such as Callable, or types that have an is-instance core schema.

For example, the use of a PlainValidator in the following example would otherwise raise an error when producing a JSON schema because the PlainValidator is a Callable. However, by using the WithJsonSchema annotation, we can override the generated JSON schema for the custom MyInt type:

import json

from typing_extensions import Annotated

from pydantic import BaseModel, PlainValidator, WithJsonSchema

MyInt = Annotated[
    int,
    PlainValidator(lambda v: int(v) + 1),
    WithJsonSchema({'type': 'integer', 'examples': [1, 0, -1]}),
]


class Model(BaseModel):
    a: MyInt


print(Model(a='1').a)
#> 2

print(json.dumps(Model.model_json_schema(), indent=2))

JSON output:

{
  "properties": {
    "a": {
      "examples": [
        1,
        0,
        -1
      ],
      "title": "A",
      "type": "integer"
    }
  },
  "required": [
    "a"
  ],
  "title": "Model",
  "type": "object"
}

Note

As discussed in this issue, in the future, it's likely that Pydantic will add builtin support for JSON schema generation for types like PlainValidator, but the WithJsonSchema annotation will still be useful for other custom types.

SkipJsonSchema annotation

API Documentation

pydantic.json_schema.SkipJsonSchema

The SkipJsonSchema annotation can be used to skip a including field (or part of a field's specifications) from the generated JSON schema. See the API docs for more details.

Implementing __get_pydantic_core_schema__

Custom types (used as field_name: TheType or field_name: Annotated[TheType, ...]) as well as Annotated metadata (used as field_name: Annotated[int, SomeMetadata]) can modify or override the generated schema by implementing __get_pydantic_core_schema__. This method receives two positional arguments:

  1. The type annotation that corresponds to this type (so in the case of TheType[T][int] it would be TheType[int]).
  2. A handler/callback to call the next implementer of __get_pydantic_core_schema__.

The handler system works just like mode='wrap' validators. In this case the input is the type and the output is a core_schema.

Here is an example of a custom type that overrides the generated core_schema:

from dataclasses import dataclass
from typing import Any, Dict, List, Type

from pydantic_core import core_schema

from pydantic import BaseModel, GetCoreSchemaHandler


@dataclass
class CompressedString:
    dictionary: Dict[int, str]
    text: List[int]

    def build(self) -> str:
        return ' '.join([self.dictionary[key] for key in self.text])

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source: Type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        assert source is CompressedString
        return core_schema.no_info_after_validator_function(
            cls._validate,
            core_schema.str_schema(),
            serialization=core_schema.plain_serializer_function_ser_schema(
                cls._serialize,
                info_arg=False,
                return_schema=core_schema.str_schema(),
            ),
        )

    @staticmethod
    def _validate(value: str) -> 'CompressedString':
        inverse_dictionary: Dict[str, int] = {}
        text: List[int] = []
        for word in value.split(' '):
            if word not in inverse_dictionary:
                inverse_dictionary[word] = len(inverse_dictionary)
            text.append(inverse_dictionary[word])
        return CompressedString(
            {v: k for k, v in inverse_dictionary.items()}, text
        )

    @staticmethod
    def _serialize(value: 'CompressedString') -> str:
        return value.build()


class MyModel(BaseModel):
    value: CompressedString


print(MyModel.model_json_schema())
"""
{
    'properties': {'value': {'title': 'Value', 'type': 'string'}},
    'required': ['value'],
    'title': 'MyModel',
    'type': 'object',
}
"""
print(MyModel(value='fox fox fox dog fox'))
"""
value = CompressedString(dictionary={0: 'fox', 1: 'dog'}, text=[0, 0, 0, 1, 0])
"""

print(MyModel(value='fox fox fox dog fox').model_dump(mode='json'))
#> {'value': 'fox fox fox dog fox'}

Since Pydantic would not know how to generate a schema for CompressedString, if you call handler(source) in its __get_pydantic_core_schema__ method you would get a pydantic.errors.PydanticSchemaGenerationError error. This will be the case for most custom types, so you almost never want to call into handler for custom types.

The process for Annotated metadata is much the same except that you can generally call into handler to have Pydantic handle generating the schema.

from dataclasses import dataclass
from typing import Any, Sequence, Type

from pydantic_core import core_schema
from typing_extensions import Annotated

from pydantic import BaseModel, GetCoreSchemaHandler, ValidationError


@dataclass
class RestrictCharacters:
    alphabet: Sequence[str]

    def __get_pydantic_core_schema__(
        self, source: Type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        if not self.alphabet:
            raise ValueError('Alphabet may not be empty')
        schema = handler(
            source
        )  # get the CoreSchema from the type / inner constraints
        if schema['type'] != 'str':
            raise TypeError('RestrictCharacters can only be applied to strings')
        return core_schema.no_info_after_validator_function(
            self.validate,
            schema,
        )

    def validate(self, value: str) -> str:
        if any(c not in self.alphabet for c in value):
            raise ValueError(
                f'{value!r} is not restricted to {self.alphabet!r}'
            )
        return value


class MyModel(BaseModel):
    value: Annotated[str, RestrictCharacters('ABC')]


print(MyModel.model_json_schema())
"""
{
    'properties': {'value': {'title': 'Value', 'type': 'string'}},
    'required': ['value'],
    'title': 'MyModel',
    'type': 'object',
}
"""
print(MyModel(value='CBA'))
#> value='CBA'

try:
    MyModel(value='XYZ')
except ValidationError as e:
    print(e)
    """
    1 validation error for MyModel
    value
      Value error, 'XYZ' is not restricted to 'ABC' [type=value_error, input_value='XYZ', input_type=str]
    """

So far we have been wrapping the schema, but if you just want to modify it or ignore it you can as well.

To modify the schema, first call the handler, then mutate the result:

from typing import Any, Type

from pydantic_core import ValidationError, core_schema
from typing_extensions import Annotated

from pydantic import BaseModel, GetCoreSchemaHandler


class SmallString:
    def __get_pydantic_core_schema__(
        self,
        source: Type[Any],
        handler: GetCoreSchemaHandler,
    ) -> core_schema.CoreSchema:
        schema = handler(source)
        assert schema['type'] == 'str'
        schema['max_length'] = 10  # modify in place
        return schema


class MyModel(BaseModel):
    value: Annotated[str, SmallString()]


try:
    MyModel(value='too long!!!!!')
except ValidationError as e:
    print(e)
    """
    1 validation error for MyModel
    value
      String should have at most 10 characters [type=string_too_long, input_value='too long!!!!!', input_type=str]
    """

Tip

Note that you must return a schema, even if you are just mutating it in place.

To override the schema completely, do not call the handler and return your own CoreSchema:

from typing import Any, Type

from pydantic_core import ValidationError, core_schema
from typing_extensions import Annotated

from pydantic import BaseModel, GetCoreSchemaHandler


class AllowAnySubclass:
    def __get_pydantic_core_schema__(
        self, source: Type[Any], handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        # we can't call handler since it will fail for arbitrary types
        def validate(value: Any) -> Any:
            if not isinstance(value, source):
                raise ValueError(
                    f'Expected an instance of {source}, got an instance of {type(value)}'
                )

        return core_schema.no_info_plain_validator_function(validate)


class Foo:
    pass


class Model(BaseModel):
    f: Annotated[Foo, AllowAnySubclass()]


print(Model(f=Foo()))
#> f=None


class NotFoo:
    pass


try:
    Model(f=NotFoo())
except ValidationError as e:
    print(e)
    """
    1 validation error for Model
    f
      Value error, Expected an instance of <class '__main__.Foo'>, got an instance of <class '__main__.NotFoo'> [type=value_error, input_value=<__main__.NotFoo object at 0x0123456789ab>, input_type=NotFoo]
    """

As seen above, annotating a field with a BaseModel type can be used to modify or override the generated json schema. However, if you want to take advantage of storing metadata via Annotated, but you don't want to override the generated JSON schema, you can use the following approach with a no-op version of __get_pydantic_core_schema__ implemented on the metadata class:

from typing import Type

from pydantic_core import CoreSchema
from typing_extensions import Annotated

from pydantic import BaseModel, GetCoreSchemaHandler


class Metadata(BaseModel):
    foo: str = 'metadata!'
    bar: int = 100

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source_type: Type[BaseModel], handler: GetCoreSchemaHandler
    ) -> CoreSchema:
        if cls is not source_type:
            return handler(source_type)
        return super().__get_pydantic_core_schema__(source_type, handler)


class Model(BaseModel):
    state: Annotated[int, Metadata()]


m = Model.model_validate({'state': 2})
print(repr(m))
#> Model(state=2)
print(m.model_fields)
"""
{
    'state': FieldInfo(
        annotation=int,
        required=True,
        metadata=[Metadata(foo='metadata!', bar=100)],
    )
}
"""

Implementing __get_pydantic_json_schema__

You can also implement __get_pydantic_json_schema__ to modify or override the generated json schema. Modifying this method only affects the JSON schema - it doesn't affect the core schema, which is used for validation and serialization.

Here's an example of modifying the generated JSON schema:

import json
from typing import Any

from pydantic_core import core_schema

from pydantic import GetCoreSchemaHandler, GetJsonSchemaHandler, TypeAdapter
from pydantic.json_schema import JsonSchemaValue


class Person:
    name: str
    age: int

    def __init__(self, name: str, age: int):
        self.name = name
        self.age = age

    @classmethod
    def __get_pydantic_core_schema__(
        cls, source_type: Any, handler: GetCoreSchemaHandler
    ) -> core_schema.CoreSchema:
        return core_schema.typed_dict_schema(
            {
                'name': core_schema.typed_dict_field(core_schema.str_schema()),
                'age': core_schema.typed_dict_field(core_schema.int_schema()),
            },
        )

    @classmethod
    def __get_pydantic_json_schema__(
        cls, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler
    ) -> JsonSchemaValue:
        json_schema = handler(core_schema)
        json_schema = handler.resolve_ref_schema(json_schema)
        json_schema['examples'] = [
            {
                'name': 'John Doe',
                'age': 25,
            }
        ]
        json_schema['title'] = 'Person'
        return json_schema


print(json.dumps(TypeAdapter(Person).json_schema(), indent=2))

JSON output:

{
  "examples": [
    {
      "age": 25,
      "name": "John Doe"
    }
  ],
  "properties": {
    "name": {
      "title": "Name",
      "type": "string"
    },
    "age": {
      "title": "Age",
      "type": "integer"
    }
  },
  "required": [
    "name",
    "age"
  ],
  "title": "Person",
  "type": "object"
}

JSON schema types

Types, custom field types, and constraints (like max_length) are mapped to the corresponding spec formats in the following priority order (when there is an equivalent available):

  1. JSON Schema Core
  2. JSON Schema Validation
  3. OpenAPI Data Types
  4. The standard format JSON field is used to define Pydantic extensions for more complex string sub-types.

The field schema mapping from Python or Pydantic to JSON schema is done as follows:

Top-level schema generation

You can also generate a top-level JSON schema that only includes a list of models and related sub-models in its $defs:

import json

from pydantic import BaseModel
from pydantic.json_schema import models_json_schema


class Foo(BaseModel):
    a: str = None


class Model(BaseModel):
    b: Foo


class Bar(BaseModel):
    c: int


_, top_level_schema = models_json_schema(
    [(Model, 'validation'), (Bar, 'validation')], title='My Schema'
)
print(json.dumps(top_level_schema, indent=2))

JSON output:

{
  "$defs": {
    "Bar": {
      "properties": {
        "c": {
          "title": "C",
          "type": "integer"
        }
      },
      "required": [
        "c"
      ],
      "title": "Bar",
      "type": "object"
    },
    "Foo": {
      "properties": {
        "a": {
          "default": null,
          "title": "A",
          "type": "string"
        }
      },
      "title": "Foo",
      "type": "object"
    },
    "Model": {
      "properties": {
        "b": {
          "$ref": "#/$defs/Foo"
        }
      },
      "required": [
        "b"
      ],
      "title": "Model",
      "type": "object"
    }
  },
  "title": "My Schema"
}

Customizing the JSON Schema Generation Process

API Documentation

pydantic.json_schema

If you need custom schema generation, you can use a schema_generator, modifying the GenerateJsonSchema class as necessary for your application.

The various methods that can be used to produce JSON schema accept a keyword argument schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, and you can pass your custom subclass to these methods in order to use your own approach to generating JSON schema.

GenerateJsonSchema implements the translation of a type's pydantic-core schema into a JSON schema. By design, this class breaks the JSON schema generation process into smaller methods that can be easily overridden in subclasses to modify the "global" approach to generating JSON schema.

from pydantic import BaseModel
from pydantic.json_schema import GenerateJsonSchema


class MyGenerateJsonSchema(GenerateJsonSchema):
    def generate(self, schema, mode='validation'):
        json_schema = super().generate(schema, mode=mode)
        json_schema['title'] = 'Customize title'
        json_schema['$schema'] = self.schema_dialect
        return json_schema


class MyModel(BaseModel):
    x: int


print(MyModel.model_json_schema(schema_generator=MyGenerateJsonSchema))
"""
{
    'properties': {'x': {'title': 'X', 'type': 'integer'}},
    'required': ['x'],
    'title': 'Customize title',
    'type': 'object',
    '$schema': 'https://json-schema.org/draft/2020-12/schema',
}
"""

Below is an approach you can use to exclude any fields from the schema that don't have valid json schemas:

from typing import Callable

from pydantic_core import PydanticOmit, core_schema

from pydantic import BaseModel
from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue


class MyGenerateJsonSchema(GenerateJsonSchema):
    def handle_invalid_for_json_schema(
        self, schema: core_schema.CoreSchema, error_info: str
    ) -> JsonSchemaValue:
        raise PydanticOmit


def example_callable():
    return 1


class Example(BaseModel):
    name: str = 'example'
    function: Callable = example_callable


instance_example = Example()

validation_schema = instance_example.model_json_schema(
    schema_generator=MyGenerateJsonSchema, mode='validation'
)
print(validation_schema)
"""
{
    'properties': {
        'name': {'default': 'example', 'title': 'Name', 'type': 'string'}
    },
    'title': 'Example',
    'type': 'object',
}
"""

Customizing the $refs in JSON Schema

The format of $refs can be altered by calling model_json_schema() or model_dump_json() with the ref_template keyword argument. The definitions are always stored under the key $defs, but a specified prefix can be used for the references.

This is useful if you need to extend or modify the JSON schema default definitions location. For example, with OpenAPI:

import json

from pydantic import BaseModel
from pydantic.type_adapter import TypeAdapter


class Foo(BaseModel):
    a: int


class Model(BaseModel):
    a: Foo


adapter = TypeAdapter(Model)

print(
    json.dumps(
        adapter.json_schema(ref_template='#/components/schemas/{model}'),
        indent=2,
    )
)

JSON output:

{
  "$defs": {
    "Foo": {
      "properties": {
        "a": {
          "title": "A",
          "type": "integer"
        }
      },
      "required": [
        "a"
      ],
      "title": "Foo",
      "type": "object"
    }
  },
  "properties": {
    "a": {
      "$ref": "#/components/schemas/Foo"
    }
  },
  "required": [
    "a"
  ],
  "title": "Model",
  "type": "object"
}

Miscellaneous Notes on JSON Schema Generation

  • The JSON schema for Optional fields indicates that the value null is allowed.
  • The Decimal type is exposed in JSON schema (and serialized) as a string.
  • Since the namedtuple type doesn't exist in JSON, a model's JSON schema does not preserve namedtuples as namedtuples.
  • Sub-models used are added to the $defs JSON attribute and referenced, as per the spec.
  • Sub-models with modifications (via the Field class) like a custom title, description, or default value, are recursively included instead of referenced.
  • The description for models is taken from either the docstring of the class or the argument description to the Field class.
  • The schema is generated by default using aliases as keys, but it can be generated using model property names instead by calling model_json_schema() or model_dump_json() with the by_alias=False keyword argument.