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Pydantic allows auto creation of JSON Schemas from models:

{!.tmp_examples/schema_main.py!}

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

Outputs:

{!.tmp_examples/schema_main.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 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).

Field customisation

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

  • 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

  • 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

  • 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

  • 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 containig the substring `foo`).

  See [#1631](https://github.com/samuelcolvin/pydantic/issues/1631)
  for a discussion of possible changes to *pydantic* behavior in **v2**.

  • ** 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:

{!.tmp_examples/schema_unenforced_constraints.py!}

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

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.

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:

{!.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 related sub-models in its definitions:

{!.tmp_examples/schema_top_level.py!}

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

Outputs:

{!.tmp_examples/schema_top_level.json!}

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:

{!.tmp_examples/schema_custom.py!}

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

Outputs:

{!.tmp_examples/schema_custom.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:

{!.tmp_examples/schema_with_example.py!}

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

Outputs:

{!.tmp_examples/schema_with_example.json!}

For more fine-grained control, you can alternatively set schema_extra to a callable and post-process the generated schema. The callable is passed the schema dictionary as the first and only argument, and is expected to mutate it in-place; the return value is not used.

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

{!.tmp_examples/schema_extra_callable.py!}

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

Outputs:

{!.tmp_examples/schema_extra_callable.json!}