pydantic/docs/usage/schema.md

Pydantic allows auto creation of JSON Schemas from models:

{!./examples/schema1.py!}

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

Outputs:

{!./examples/schema1.json!}

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.

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 the docstring of the class or the argument description to the Field class.

Field customisation

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

• default (positional argument), since the Field is replacing the field's default, its first argument is used to set the default, use ellipsis (...) to indicate the field is required
• 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
• const this field must take it's default value if it is 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, adds a validation of "greater than or equal" and an annotation of minimum to the JSON Schema
• lt for numeric values, adds a validation of "less than" and an annotation of exclusiveMaximum to the JSON Schema
• le for numeric values, adds a validation of "less than or equal" and an annotation of maximum to the JSON Schema
• multiple_of for numeric values, adds a validation of "a multiple of" and an annotation of multipleOf to the JSON Schema
• min_items for list values, adds a corresponding validation and an annotation of minItems to the JSON Schema
• max_items for list values, adds a corresponding validation and an annotation of maxItems to the JSON Schema
• min_length for string values, adds a corresponding validation and an annotation of minLength to the JSON Schema
• max_length for string values, adds a corresponding validation and an annotation of maxLength to the JSON Schema
• regex for string values, adds a Regular Expression validation generated from the passed string and an annotation of pattern to the JSON Schema
• ** any other keyword arguments (eg. 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 the arguments above except default.

The schema is generated by default using aliases as keys, it can also be generated using model property names not aliases with MainModel.schema/schema_json(by_alias=False).

JSON Schema Types

Types, custom field types, and constraints (as max_length) are mapped to the corresponding JSON Schema Core spec format when there's an equivalent available, next to JSON Schema Validation, OpenAPI Data Types (which are based on JSON Schema), or otherwise use the standard format JSON field to define Pydantic extensions for more complex string sub-types.

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

{!./.tmp_schema_mappings.html!}

Top-level schema generation

You can also generate a top-level JSON Schema that only includes a list of models and all their related submodules in its definitions:

{!./examples/schema2.py!}

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

Outputs:

{!./examples/schema2.json!}

Schema customization

You can customize the generated $ref JSON location, the definitions will still be in the key definitions and you can still get them from there, but the references will point to your defined prefix instead of the default.

This is useful if you need to extend or modify JSON Schema default definitions location, e.g. with OpenAPI:

{!./examples/schema3.py!}

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

Outputs:

{!./examples/schema3.json!}

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:

{!./examples/schema4.py!}

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

Outputs:

{!./examples/schema4.json!}