Schema

Pydantic allows auto creation of JSON Schemas from models:

Python 3.7 and above

from enum import Enum
from pydantic import BaseModel, Field


class FooBar(BaseModel):
    count: int
    size: float = 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
    """

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

    class Config:
        title = 'Main'


# this is equivalent to json.dumps(MainModel.schema(), indent=2):
print(MainModel.schema_json(indent=2))

(This script is complete, it should run "as is") Outputs:

{
  "title": "Main",
  "description": "This is the description of the main model",
  "type": "object",
  "properties": {
    "foo_bar": {
      "$ref": "#/definitions/FooBar"
    },
    "Gender": {
      "$ref": "#/definitions/Gender"
    },
    "snap": {
      "title": "The Snap",
      "description": "this is the value of snap",
      "default": 42,
      "exclusiveMinimum": 30,
      "exclusiveMaximum": 50,
      "type": "integer"
    }
  },
  "required": [
    "foo_bar"
  ],
  "definitions": {
    "FooBar": {
      "title": "FooBar",
      "type": "object",
      "properties": {
        "count": {
          "title": "Count",
          "type": "integer"
        },
        "size": {
          "title": "Size",
          "type": "number"
        }
      },
      "required": [
        "count"
      ]
    },
    "Gender": {
      "title": "Gender",
      "description": "An enumeration.",
      "enum": [
        "male",
        "female",
        "other",
        "not_given"
      ],
      "type": "string"
    }
  }
}

The generated schemas are compliant with the specifications: JSON Schema Core, JSON Schema Validation and OpenAPI.

BaseModel.schema will return a dict of the schema, while BaseModel.schema_json will return a JSON string representation of that dict.

Sub-models used are added to the definitions JSON attribute and referenced, as per the spec.

All sub-models' (and their sub-models') schemas are put directly in a top-level definitions JSON key for easy re-use and reference.

"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 MainModel.schema/schema_json(by_alias=False).

The format of $refs ("#/definitions/FooBar" above) can be altered by calling schema() or schema_json() with the ref_template keyword argument, e.g. ApplePie.schema(ref_template='/schemas/{model}.json#/'), here {model} will be replaced with the model naming using str.format().

Getting schema of a specified type

Pydantic includes two standalone utility functions schema_of and schema_json_of that can be used to apply the schema generation logic used for pydantic models in a more ad-hoc way. These functions behave similarly to BaseModel.schema and BaseModel.schema_json, but work with arbitrary pydantic-compatible types.

Python 3.7 and abovePython 3.9 and above

from typing import Literal, Union

from typing_extensions import Annotated

from pydantic import BaseModel, Field, schema_json_of


class Cat(BaseModel):
    pet_type: Literal['cat']
    cat_name: str


class Dog(BaseModel):
    pet_type: Literal['dog']
    dog_name: str


Pet = Annotated[Union[Cat, Dog], Field(discriminator='pet_type')]

print(schema_json_of(Pet, title='The Pet Schema', indent=2))
"""
{
  "title": "The Pet Schema",
  "discriminator": {
    "propertyName": "pet_type",
    "mapping": {
      "cat": "#/definitions/Cat",
      "dog": "#/definitions/Dog"
    }
  },
  "oneOf": [
    {
      "$ref": "#/definitions/Cat"
    },
    {
      "$ref": "#/definitions/Dog"
    }
  ],
  "definitions": {
    "Cat": {
      "title": "Cat",
      "type": "object",
      "properties": {
        "pet_type": {
          "title": "Pet Type",
          "enum": [
            "cat"
          ],
          "type": "string"
        },
        "cat_name": {
          "title": "Cat Name",
          "type": "string"
        }
      },
      "required": [
        "pet_type",
        "cat_name"
      ]
    },
    "Dog": {
      "title": "Dog",
      "type": "object",
      "properties": {
        "pet_type": {
          "title": "Pet Type",
          "enum": [
            "dog"
          ],
          "type": "string"
        },
        "dog_name": {
          "title": "Dog Name",
          "type": "string"
        }
      },
      "required": [
        "pet_type",
        "dog_name"
      ]
    }
  }
}
"""

from typing import Literal, Union

from typing import Annotated

from pydantic import BaseModel, Field, schema_json_of


class Cat(BaseModel):
    pet_type: Literal['cat']
    cat_name: str


class Dog(BaseModel):
    pet_type: Literal['dog']
    dog_name: str


Pet = Annotated[Union[Cat, Dog], Field(discriminator='pet_type')]

print(schema_json_of(Pet, title='The Pet Schema', indent=2))
"""
{
  "title": "The Pet Schema",
  "discriminator": {
    "propertyName": "pet_type",
    "mapping": {
      "cat": "#/definitions/Cat",
      "dog": "#/definitions/Dog"
    }
  },
  "oneOf": [
    {
      "$ref": "#/definitions/Cat"
    },
    {
      "$ref": "#/definitions/Dog"
    }
  ],
  "definitions": {
    "Cat": {
      "title": "Cat",
      "type": "object",
      "properties": {
        "pet_type": {
          "title": "Pet Type",
          "enum": [
            "cat"
          ],
          "type": "string"
        },
        "cat_name": {
          "title": "Cat Name",
          "type": "string"
        }
      },
      "required": [
        "pet_type",
        "cat_name"
      ]
    },
    "Dog": {
      "title": "Dog",
      "type": "object",
      "properties": {
        "pet_type": {
          "title": "Pet Type",
          "enum": [
            "dog"
          ],
          "type": "string"
        },
        "dog_name": {
          "title": "Dog Name",
          "type": "string"
        }
      },
      "required": [
        "pet_type",
        "dog_name"
      ]
    }
  }
}
"""

(This script is complete, it should run "as is")

Field customization

Optionally, the Field function can be used to provide extra information about the field and validations. It has the following arguments:

• default: (a positional argument) the default value of the field. Since the Field replaces the field's default, this first argument can be used to set the default. Use ellipsis (...) to indicate the field is required.
• default_factory: a zero-argument callable that will be called when a default value is needed for this field. Among other purposes, this can be used to set dynamic default values. It is forbidden to set both default and default_factory.
• alias: the public name of the field
• title: if omitted, field_name.title() is used
• description: if omitted and the annotation is a sub-model, the docstring of the sub-model will be used
• exclude: exclude this field when dumping (.dict and .json) the instance. The exact syntax and configuration options are described in details in the exporting models section.
• include: include (only) this field when dumping (.dict and .json) the instance. The exact syntax and configuration options are described in details in the exporting models section.
• const: this argument must be the same as the field's default value if present.
• gt: for numeric values (int, float, Decimal), adds a validation of "greater than" and an annotation of exclusiveMinimum to the JSON Schema
• ge: for numeric values, this adds a validation of "greater than or equal" and an annotation of minimum to the JSON Schema
• lt: for numeric values, this adds a validation of "less than" and an annotation of exclusiveMaximum to the JSON Schema
• le: for numeric values, this adds a validation of "less than or equal" and an annotation of maximum to the JSON Schema
• multiple_of: for numeric values, this adds a validation of "a multiple of" and an annotation of multipleOf to the JSON Schema
• max_digits: for Decimal values, this adds a validation to have a maximum number of digits within the decimal. It does not include a zero before the decimal point or trailing decimal zeroes.
• decimal_places: for Decimal values, this adds a validation to have at most a number of decimal places allowed. It does not include trailing decimal zeroes.
• min_items: for list values, this adds a corresponding validation and an annotation of minItems to the JSON Schema
• max_items: for list values, this adds a corresponding validation and an annotation of maxItems to the JSON Schema
• unique_items: for list values, this adds a corresponding validation and an annotation of uniqueItems to the JSON Schema
• min_length: for string values, this adds a corresponding validation and an annotation of minLength to the JSON Schema
• max_length: for string values, this adds a corresponding validation and an annotation of maxLength to the JSON Schema
• allow_mutation: a boolean which defaults to True. When False, the field raises a TypeError if the field is assigned on an instance. The model config must set validate_assignment to True for this check to be performed.

• regex: for string values, this adds a Regular Expression validation generated from the passed string and an annotation of pattern to the JSON Schema

  Note

  pydantic validates strings using re.match, which treats regular expressions as implicitly anchored at the beginning. On the contrary, JSON Schema validators treat the pattern keyword as implicitly unanchored, more like what re.search does.

  For interoperability, depending on your desired behavior, either explicitly anchor your regular expressions with ^ (e.g. ^foo to match any string starting with foo), or explicitly allow an arbitrary prefix with .*? (e.g. .*?foo to match any string containing the substring foo).

  See #1631 for a discussion of possible changes to pydantic behavior in v2.

• repr: a boolean which defaults to True. When False, the field shall be hidden from the object representation.
• ** any other keyword arguments (e.g. examples) will be added verbatim to the field's schema

Instead of using Field, the fields property of the Config class can be used to set all of the arguments above except default.

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:

Python 3.7 and above

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)
    """
    On field "foo" the following field constraints are set but not enforced:
    lt.
    For more details see https://pydantic-
    docs.helpmanual.io/usage/schema/#unenforced-field-constraints
    """


# but you can set the schema attribute directly:
# (Note: here exclusiveMaximum will not be enforce)
class Model(BaseModel):
    foo: PositiveInt = Field(..., exclusiveMaximum=10)


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


# 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 Model(BaseModel):
    # Here both constraints will be applied and the schema
    # will be generated correctly
    foo: int = Field(..., gt=0, lt=10)


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

(This script is complete, it should run "as is")

typing.Annotated Fields

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

Python 3.7 and abovePython 3.9 and above

from uuid import uuid4

from pydantic import BaseModel, Field
from typing_extensions import Annotated


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

from uuid import uuid4

from pydantic import BaseModel, Field
from typing import Annotated


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

(This script is complete, it should run "as is")

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 schema in custom fields

Custom field types can customise the schema generated for them using the __modify_schema__ class method; see Custom Data Types for more details.

__modify_schema__ can also take a field argument which will have type Optional[ModelField]. pydantic will inspect the signature of __modify_schema__ to determine whether the field argument should be included.

Python 3.7 and abovePython 3.9 and abovePython 3.10 and above

from typing import Any, Callable, Dict, Generator, Optional

from pydantic import BaseModel, Field
from pydantic.fields import ModelField


class RestrictedAlphabetStr(str):
    @classmethod
    def __get_validators__(cls) -> Generator[Callable, None, None]:
        yield cls.validate

    @classmethod
    def validate(cls, value: str, field: ModelField):
        alphabet = field.field_info.extra['alphabet']
        if any(c not in alphabet for c in value):
            raise ValueError(f'{value!r} is not restricted to {alphabet!r}')
        return cls(value)

    @classmethod
    def __modify_schema__(
        cls, field_schema: Dict[str, Any], field: Optional[ModelField]
    ):
        if field:
            alphabet = field.field_info.extra['alphabet']
            field_schema['examples'] = [c * 3 for c in alphabet]


class MyModel(BaseModel):
    value: RestrictedAlphabetStr = Field(alphabet='ABC')


print(MyModel.schema_json(indent=2))

from typing import Any, Callable, Optional
from collections.abc import Generator

from pydantic import BaseModel, Field
from pydantic.fields import ModelField


class RestrictedAlphabetStr(str):
    @classmethod
    def __get_validators__(cls) -> Generator[Callable, None, None]:
        yield cls.validate

    @classmethod
    def validate(cls, value: str, field: ModelField):
        alphabet = field.field_info.extra['alphabet']
        if any(c not in alphabet for c in value):
            raise ValueError(f'{value!r} is not restricted to {alphabet!r}')
        return cls(value)

    @classmethod
    def __modify_schema__(
        cls, field_schema: dict[str, Any], field: Optional[ModelField]
    ):
        if field:
            alphabet = field.field_info.extra['alphabet']
            field_schema['examples'] = [c * 3 for c in alphabet]


class MyModel(BaseModel):
    value: RestrictedAlphabetStr = Field(alphabet='ABC')


print(MyModel.schema_json(indent=2))

from typing import Any
from collections.abc import Callable, Generator

from pydantic import BaseModel, Field
from pydantic.fields import ModelField


class RestrictedAlphabetStr(str):
    @classmethod
    def __get_validators__(cls) -> Generator[Callable, None, None]:
        yield cls.validate

    @classmethod
    def validate(cls, value: str, field: ModelField):
        alphabet = field.field_info.extra['alphabet']
        if any(c not in alphabet for c in value):
            raise ValueError(f'{value!r} is not restricted to {alphabet!r}')
        return cls(value)

    @classmethod
    def __modify_schema__(
        cls, field_schema: dict[str, Any], field: ModelField | None
    ):
        if field:
            alphabet = field.field_info.extra['alphabet']
            field_schema['examples'] = [c * 3 for c in alphabet]


class MyModel(BaseModel):
    value: RestrictedAlphabetStr = Field(alphabet='ABC')


print(MyModel.schema_json(indent=2))

(This script is complete, it should run "as is") Outputs:

{
  "title": "MyModel",
  "type": "object",
  "properties": {
    "value": {
      "title": "Value",
      "alphabet": "ABC",
      "examples": [
        "AAA",
        "BBB",
        "CCC"
      ],
      "type": "string"
    }
  },
  "required": [
    "value"
  ]
}

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 / pydantic to JSON Schema is done as follows:

Python type	JSON Schema Type	Additional JSON Schema	Defined in
None	null		JSON Schema Core
Same for type(None) or Literal[None]
bool	boolean		JSON Schema Core
str	string		JSON Schema Core
float	number		JSON Schema Core
int	integer		JSON Schema Validation
dict	object		JSON Schema Core
list	array	{"items": {}}	JSON Schema Core
tuple	array	{"items": {}}	JSON Schema Core
set	array	{"items": {}, "uniqueItems": true}	JSON Schema Validation
frozenset	array	{"items": {}, "uniqueItems": true}	JSON Schema Validation
List[str]	array	{"items": {"type": "string"}}	JSON Schema Validation
And equivalently for any other sub type, e.g. List[int].
Tuple[str, ...]	array	{"items": {"type": "string"}}	JSON Schema Validation
And equivalently for any other sub type, e.g. Tuple[int, ...].
Tuple[str, int]	array	{"items": [{"type": "string"}, {"type": "integer"}], "minItems": 2, "maxItems": 2}	JSON Schema Validation
And equivalently for any other set of subtypes. Note: If using schemas for OpenAPI, you shouldn't use this declaration, as it would not be valid in OpenAPI (although it is valid in JSON Schema).
Dict[str, int]	object	{"additionalProperties": {"type": "integer"}}	JSON Schema Validation
And equivalently for any other subfields for dicts. Have in mind that although you can use other types as keys for dicts with Pydantic, only strings are valid keys for JSON, and so, only str is valid as JSON Schema key types.
Union[str, int]	anyOf	{"anyOf": [{"type": "string"}, {"type": "integer"}]}	JSON Schema Validation
And equivalently for any other subfields for unions.
Enum	enum	{"enum": [...]}	JSON Schema Validation
All the literal values in the enum are included in the definition.
SecretStr	string	{"writeOnly": true}	JSON Schema Validation
SecretBytes	string	{"writeOnly": true}	JSON Schema Validation
EmailStr	string	{"format": "email"}	JSON Schema Validation
NameEmail	string	{"format": "name-email"}	Pydantic standard "format" extension
AnyUrl	string	{"format": "uri"}	JSON Schema Validation
Pattern	string	{"format": "regex"}	JSON Schema Validation
bytes	string	{"format": "binary"}	OpenAPI
Decimal	number		JSON Schema Core
UUID1	string	{"format": "uuid1"}	Pydantic standard "format" extension
UUID3	string	{"format": "uuid3"}	Pydantic standard "format" extension
UUID4	string	{"format": "uuid4"}	Pydantic standard "format" extension
UUID5	string	{"format": "uuid5"}	Pydantic standard "format" extension
UUID	string	{"format": "uuid"}	Pydantic standard "format" extension
Suggested in OpenAPI.
FilePath	string	{"format": "file-path"}	Pydantic standard "format" extension
DirectoryPath	string	{"format": "directory-path"}	Pydantic standard "format" extension
Path	string	{"format": "path"}	Pydantic standard "format" extension
datetime	string	{"format": "date-time"}	JSON Schema Validation
date	string	{"format": "date"}	JSON Schema Validation
time	string	{"format": "time"}	JSON Schema Validation
timedelta	number	{"format": "time-delta"}	Difference in seconds (a float), with Pydantic standard "format" extension
Suggested in JSON Schema repository's issues by maintainer.
Json	string	{"format": "json-string"}	Pydantic standard "format" extension
IPv4Address	string	{"format": "ipv4"}	JSON Schema Validation
IPv6Address	string	{"format": "ipv6"}	JSON Schema Validation
IPvAnyAddress	string	{"format": "ipvanyaddress"}	Pydantic standard "format" extension
IPv4 or IPv6 address as used in ipaddress module
IPv4Interface	string	{"format": "ipv4interface"}	Pydantic standard "format" extension
IPv4 interface as used in ipaddress module
IPv6Interface	string	{"format": "ipv6interface"}	Pydantic standard "format" extension
IPv6 interface as used in ipaddress module
IPvAnyInterface	string	{"format": "ipvanyinterface"}	Pydantic standard "format" extension
IPv4 or IPv6 interface as used in ipaddress module
IPv4Network	string	{"format": "ipv4network"}	Pydantic standard "format" extension
IPv4 network as used in ipaddress module
IPv6Network	string	{"format": "ipv6network"}	Pydantic standard "format" extension
IPv6 network as used in ipaddress module
IPvAnyNetwork	string	{"format": "ipvanynetwork"}	Pydantic standard "format" extension
IPv4 or IPv6 network as used in ipaddress module
StrictBool	boolean		JSON Schema Core
StrictStr	string		JSON Schema Core
ConstrainedStr	string		JSON Schema Core
If the type has values declared for the constraints, they are included as validations. See the mapping for constr below.
constr(regex='^text$', min_length=2, max_length=10)	string	{"pattern": "^text$", "minLength": 2, "maxLength": 10}	JSON Schema Validation
Any argument not passed to the function (not defined) will not be included in the schema.
ConstrainedInt	integer		JSON Schema Core
If the type has values declared for the constraints, they are included as validations. See the mapping for conint below.
conint(gt=1, ge=2, lt=6, le=5, multiple_of=2)	integer	{"maximum": 5, "exclusiveMaximum": 6, "minimum": 2, "exclusiveMinimum": 1, "multipleOf": 2}
Any argument not passed to the function (not defined) will not be included in the schema.
PositiveInt	integer	{"exclusiveMinimum": 0}	JSON Schema Validation
NegativeInt	integer	{"exclusiveMaximum": 0}	JSON Schema Validation
NonNegativeInt	integer	{"minimum": 0}	JSON Schema Validation
NonPositiveInt	integer	{"maximum": 0}	JSON Schema Validation
ConstrainedFloat	number		JSON Schema Core
If the type has values declared for the constraints, they are included as validations. See the mapping for confloat below.
confloat(gt=1, ge=2, lt=6, le=5, multiple_of=2)	number	{"maximum": 5, "exclusiveMaximum": 6, "minimum": 2, "exclusiveMinimum": 1, "multipleOf": 2}	JSON Schema Validation
Any argument not passed to the function (not defined) will not be included in the schema.
PositiveFloat	number	{"exclusiveMinimum": 0}	JSON Schema Validation
NegativeFloat	number	{"exclusiveMaximum": 0}	JSON Schema Validation
NonNegativeFloat	number	{"minimum": 0}	JSON Schema Validation
NonPositiveFloat	number	{"maximum": 0}	JSON Schema Validation
ConstrainedDecimal	number		JSON Schema Core
If the type has values declared for the constraints, they are included as validations. See the mapping for condecimal below.
condecimal(gt=1, ge=2, lt=6, le=5, multiple_of=2)	number	{"maximum": 5, "exclusiveMaximum": 6, "minimum": 2, "exclusiveMinimum": 1, "multipleOf": 2}	JSON Schema Validation
Any argument not passed to the function (not defined) will not be included in the schema.
BaseModel	object		JSON Schema Core
All the properties defined will be defined with standard JSON Schema, including submodels.
Color	string	{"format": "color"}	Pydantic standard "format" extension

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 definitions:

Python 3.7 and above

import json
from pydantic import BaseModel
from pydantic.schema import schema


class Foo(BaseModel):
    a: str = None


class Model(BaseModel):
    b: Foo


class Bar(BaseModel):
    c: int


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

(This script is complete, it should run "as is") Outputs:

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

Schema customization

You can customize the generated $ref JSON location: the definitions are always stored under the key definitions, 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. E.g. with OpenAPI:

Python 3.7 and above

import json
from pydantic import BaseModel
from pydantic.schema import schema


class Foo(BaseModel):
    a: int


class Model(BaseModel):
    a: Foo


# Default location for OpenAPI
top_level_schema = schema([Model], ref_prefix='#/components/schemas/')
print(json.dumps(top_level_schema, indent=2))

(This script is complete, it should run "as is") Outputs:

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

It's also possible to extend/override the generated JSON schema in a model.

To do it, use the Config sub-class attribute schema_extra.

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

Python 3.7 and above

from pydantic import BaseModel


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

    class Config:
        schema_extra = {
            'examples': [
                {
                    'name': 'John Doe',
                    'age': 25,
                }
            ]
        }


print(Person.schema_json(indent=2))

(This script is complete, it should run "as is") Outputs:

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

For more fine-grained control, you can alternatively set schema_extra to a callable and post-process the generated schema. The callable can have one or two positional arguments. The first will be the schema dictionary. The second, if accepted, will be the model class. The callable is expected to mutate the schema dictionary in-place; the return value is not used.

For example, the title key can be removed from the model's properties:

Python 3.7 and abovePython 3.9 and above

from typing import Dict, Any, Type
from pydantic import BaseModel


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

    class Config:
        @staticmethod
        def schema_extra(schema: Dict[str, Any], model: Type['Person']) -> None:
            for prop in schema.get('properties', {}).values():
                prop.pop('title', None)


print(Person.schema_json(indent=2))

from typing import Any
from pydantic import BaseModel


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

    class Config:
        @staticmethod
        def schema_extra(schema: dict[str, Any], model: type['Person']) -> None:
            for prop in schema.get('properties', {}).values():
                prop.pop('title', None)


print(Person.schema_json(indent=2))

(This script is complete, it should run "as is") Outputs:

{
  "title": "Person",
  "type": "object",
  "properties": {
    "name": {
      "type": "string"
    },
    "age": {
      "type": "integer"
    }
  },
  "required": [
    "name",
    "age"
  ]
}

```