Function model_process_schema

pydantic/v1/schema.py:551–598

Used by ``model_schema()``, you probably should be using that function. Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The sub-models of the returned schema will be referenced, but their definitions will not be included in t

(
    model: TypeModelOrEnum,
    *,
    by_alias: bool = True,
    model_name_map: Dict[TypeModelOrEnum, str],
    ref_prefix: Optional[str] = None,
    ref_template: str = default_ref_template,
    known_models: Optional[TypeModelSet] = None,
    field: Optional[ModelField] = None,
)

def model_process_schema(
    model: TypeModelOrEnum,
    *,
    by_alias: bool = True,
    model_name_map: Dict[TypeModelOrEnum, str],
    ref_prefix: Optional[str] = None,
    ref_template: str = default_ref_template,
    known_models: Optional[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.

    Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The
    sub-models of the returned schema will be referenced, but their definitions will not be included in the schema. All
    the definitions are returned as the second value.
    """
    from inspect import getdoc, signature

    known_models = known_models or set()
    if lenient_issubclass(model, Enum):
        model = cast(Type[Enum], model)
        s = enum_process_schema(model, field=field)
        return s, {}, set()
    model = cast(Type['BaseModel'], model)
    s = {'title': model.__config__.title or model.__name__}
    doc = getdoc(model)
    if doc:
        s['description'] = doc
    known_models.add(model)
    m_schema, m_definitions, nested_models = model_type_schema(
        model,
        by_alias=by_alias,
        model_name_map=model_name_map,
        ref_prefix=ref_prefix,
        ref_template=ref_template,
        known_models=known_models,
    )
    s.update(m_schema)
    schema_extra = model.__config__.schema_extra
    if callable(schema_extra):
        if len(signature(schema_extra).parameters) == 1:
            schema_extra(s)
        else:
            schema_extra(s, model)
    else:
        s.update(schema_extra)
    return s, m_definitions, nested_models


def model_type_schema(