Add optional field argument to __modify_schema__() (#3434)

Co-authored-by: Samuel Colvin <s@muelcolvin.com>

changes/3434-jasujm.md

@@ -0,0 +1,2 @@
+When generating field schema, pass optional `field` argument (of type
+`pydantic.fields.ModelField`) to `__modify_schema__()` if present

docs/examples/schema_with_field.py

@@ -0,0 +1,31 @@
+# output-json
+from typing import Optional
+
+from pydantic import BaseModel, Field
+from pydantic.fields import ModelField
+
+
+class RestrictedAlphabetStr(str):
+    @classmethod
+    def __get_validators__(cls):
+        yield cls.validate
+
+    @classmethod
+    def validate(cls, value, 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, 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))

docs/usage/schema.md

@@ -150,6 +150,21 @@ For versions of Python prior to 3.9, `typing_extensions.Annotated` can be used.
 Custom field types can customise the schema generated for them using the `__modify_schema__` class method;
 see [Custom Data Types](types.md#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.
+
+```py
+{!.tmp_examples/schema_with_field.py!}
+```
+_(This script is complete, it should run "as is")_
+
+Outputs:
+
+```json
+{!.tmp_examples/schema_with_field.json!}
+```
+
 ## JSON Schema Types
 
 Types, custom field types, and constraints (like `max_length`) are mapped to the corresponding spec formats in the

pydantic/schema.py

@@ -90,6 +90,19 @@
 TypeModelSet = Set[TypeModelOrEnum]
 
 
+def _apply_modify_schema(
+    modify_schema: Callable[..., None], field: Optional[ModelField], field_schema: Dict[str, Any]
+) -> None:
+    from inspect import signature
+
+    sig = signature(modify_schema)
+    args = set(sig.parameters.keys())
+    if 'field' in args or 'kwargs' in args:
+        modify_schema(field_schema, field=field)
+    else:
+        modify_schema(field_schema)
+
+
 def schema(
     models: Sequence[Union[Type['BaseModel'], Type['Dataclass']]],
     *,
@@ -335,7 +348,7 @@ def get_field_schema_validations(field: ModelField) -> Dict[str, Any]:
         f_schema.update(field.field_info.extra)
     modify_schema = getattr(field.outer_type_, '__modify_schema__', None)
     if modify_schema:
-        modify_schema(f_schema)
+        _apply_modify_schema(modify_schema, field, f_schema)
     return f_schema
 
 
@@ -567,7 +580,7 @@ def field_type_schema(
             field_type = field.outer_type_
         modify_schema = getattr(field_type, '__modify_schema__', None)
         if modify_schema:
-            modify_schema(f_schema)
+            _apply_modify_schema(modify_schema, field, f_schema)
     return f_schema, definitions, nested_models
 
 
@@ -579,6 +592,7 @@ def model_process_schema(
     ref_prefix: Optional[str] = None,
     ref_template: str = default_ref_template,
     known_models: TypeModelSet = None,
+    field: Optional[ModelField] = None,
 ) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]:
     """
     Used by ``model_schema()``, you probably should be using that function.
@@ -592,7 +606,7 @@ def model_process_schema(
     known_models = known_models or set()
     if lenient_issubclass(model, Enum):
         model = cast(Type[Enum], model)
-        s = enum_process_schema(model)
+        s = enum_process_schema(model, field=field)
         return s, {}, set()
     model = cast(Type['BaseModel'], model)
     s = {'title': model.__config__.title or model.__name__}
@@ -674,7 +688,7 @@ def model_type_schema(
     return out_schema, definitions, nested_models
 
 
-def enum_process_schema(enum: Type[Enum]) -> Dict[str, Any]:
+def enum_process_schema(enum: Type[Enum], *, field: Optional[ModelField] = None) -> Dict[str, Any]:
     """
     Take a single `enum` and generate its schema.
 
@@ -695,7 +709,7 @@ def enum_process_schema(enum: Type[Enum]) -> Dict[str, Any]:
 
     modify_schema = getattr(enum, '__modify_schema__', None)
     if modify_schema:
-        modify_schema(schema_)
+        _apply_modify_schema(modify_schema, field, schema_)
 
     return schema_
 
@@ -871,7 +885,7 @@ def field_singleton_schema(  # noqa: C901 (ignore complexity)
         enum_name = model_name_map[field_type]
         f_schema, schema_overrides = get_field_info_schema(field, schema_overrides)
         f_schema.update(get_schema_ref(enum_name, ref_prefix, ref_template, schema_overrides))
-        definitions[enum_name] = enum_process_schema(field_type)
+        definitions[enum_name] = enum_process_schema(field_type, field=field)
     elif is_namedtuple(field_type):
         sub_schema, *_ = model_process_schema(
             field_type.__pydantic_model__,
@@ -880,6 +894,7 @@ def field_singleton_schema(  # noqa: C901 (ignore complexity)
             ref_prefix=ref_prefix,
             ref_template=ref_template,
             known_models=known_models,
+            field=field,
         )
         items_schemas = list(sub_schema['properties'].values())
         f_schema.update(
@@ -895,7 +910,7 @@ def field_singleton_schema(  # noqa: C901 (ignore complexity)
 
         modify_schema = getattr(field_type, '__modify_schema__', None)
         if modify_schema:
-            modify_schema(f_schema)
+            _apply_modify_schema(modify_schema, field, f_schema)
 
     if f_schema:
         return f_schema, definitions, nested_models
@@ -914,6 +929,7 @@ def field_singleton_schema(  # noqa: C901 (ignore complexity)
                 ref_prefix=ref_prefix,
                 ref_template=ref_template,
                 known_models=known_models,
+                field=field,
             )
             definitions.update(sub_definitions)
             definitions[model_name] = sub_schema

tests/test_schema.py

@@ -32,6 +32,7 @@
 from pydantic import BaseModel, Extra, Field, ValidationError, confrozenset, conlist, conset, validator
 from pydantic.color import Color
 from pydantic.dataclasses import dataclass
+from pydantic.fields import ModelField
 from pydantic.generics import GenericModel
 from pydantic.networks import AnyUrl, EmailStr, IPvAnyAddress, IPvAnyInterface, IPvAnyNetwork, NameEmail, stricturl
 from pydantic.schema import (
@@ -2628,6 +2629,27 @@ def resolve(self) -> 'Model':  # noqa
     }
 
 
+def test_schema_with_field_parameter():
+    class RestrictedAlphabetStr(str):
+        @classmethod
+        def __modify_schema__(cls, field_schema, field: Optional[ModelField]):
+            assert isinstance(field, ModelField)
+            alphabet = field.field_info.extra['alphabet']
+            field_schema['examples'] = [c * 3 for c in alphabet]
+
+    class MyModel(BaseModel):
+        value: RestrictedAlphabetStr = Field(alphabet='ABC')
+
+    assert MyModel.schema() == {
+        'title': 'MyModel',
+        'type': 'object',
+        'properties': {
+            'value': {'title': 'Value', 'alphabet': 'ABC', 'examples': ['AAA', 'BBB', 'CCC'], 'type': 'string'}
+        },
+        'required': ['value'],
+    }
+
+
 def test_discriminated_union():
     class BlackCat(BaseModel):
         pet_type: Literal['cat']