JSON Schema

Pydantic allows automatic creation of JSON schemas from models.

Using Pydantic, there are several ways to generate JSON schemas or JSON representations from fields or models:

BaseModel.model_json_schema returns a dict of the schema.
BaseModel.model_dump_json returns a JSON string representation of the dict of the schema.
TypeAdapter.dump_json serializes an instance of the adapted type to JSON.
TypeAdapter.json_schema generates a JSON schema for the adapted type.

The generated JSON schemas are compliant with the following specifications:

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,
    )


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

JSON output:

{
  "$defs": {
    "FooBar": {
      "properties": {
        "count": {
          "title": "Count",
          "type": "integer"
        },
        "size": {
          "anyOf": [
            {
              "type": "number"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "title": "Size"
        }
      },
      "required": [
        "count"
      ],
      "title": "FooBar",
      "type": "object"
    },
    "Gender": {
      "enum": [
        "male",
        "female",
        "other",
        "not_given"
      ],
      "title": "Gender",
      "type": "string"
    }
  },
  "description": "\n    This is the description of the main model\n    ",
  "properties": {
    "foo_bar": {
      "$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"
}

General 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.
The JSON schema does not preserve namedtuples as namedtuples.
When they differ, you can specify whether you want the JSON schema to represent the inputs to validation or the outputs from serialization.
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.
The format of $refs can be altered by calling model_json_schema() or model_dump_json() with the ref_template keyword argument.

Getting schema of a specified type¶

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 (which is now deprecated).

from typing import List

from pydantic import TypeAdapter

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

Field customization¶

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

See Customizing JSON Schema for details on field parameters that are used exclusively to customize the generated JSON schema.

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

title
use_enum_values
json_schema_extra
ser_json_timedelta
ser_json_bytes

See ConfigDict for details on these options.

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',
}
"""

typing.Annotated Fields¶

Rather than assigning a Field value, it can be specified in the type hint with typing.Annotated:

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)] = 'Bar'

Note

Field can only be supplied once per field; an error will be raised if used in Annotated and as the assigned value.

Defaults can be set outside Annotated as the assigned value or with Field.default_factory inside Annotated. The Field.default argument is not supported inside Annotated.

For versions of Python prior to 3.9, typing_extensions.Annotated can be used.

Modifying the 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:

The type annotation that corresponds to this type (so in the case of TheType[T][int] it would be TheType[int]).
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]
    """

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]
    """

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):

JSON Schema Core
JSON Schema Validation
OpenAPI Data Types
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"
}

Schema customization¶

You can customize the generated $ref JSON location: 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"
}

It's also possible to extend or override the generated JSON schema in a model by implementing __get_pydantic_json_schema__ on your model.

For example, you could add examples to the JSON schema:

import json

from pydantic_core import CoreSchema

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


class Person(BaseModel):
    name: str
    age: int

    @classmethod
    def __get_pydantic_json_schema__(
        cls, 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,
            }
        ]
        return json_schema


print(json.dumps(Person.model_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"
}

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

JSON Schema

General notes on JSON schema generation¶

Getting schema of a specified type¶

Field customization¶

Unenforced Field constraints¶

typing.Annotated Fields¶

Modifying the schema¶

JSON schema types¶

Top-level schema generation¶

Schema customization¶

Unenforced `Field` constraints¶