# Dynamic models

Models can be [created dynamically](/guides/concepts-models#dynamic-model-creation) using the [`create_model()`](/guides/pydantic-base-model)
factory function.

In this example, we will show how to dynamically derive a model from an existing one, making every field optional. To achieve this,
we will make use of the [`model_fields`](/guides/pydantic-base-model) model class attribute, and derive new annotations
from the field definitions to be passed to the [`create_model()`](/guides/pydantic-base-model) factory. Of course, this example can apply
to any use case where you need to derive a new model from another (remove default values, add aliases, etc).

::::tabs
:::tab{title="Python 3.10"}
```python showLineNumbers
from typing import Annotated

from pydantic import BaseModel, Field, create_model


def make_fields_optional(model_cls: type[BaseModel]) -> type[BaseModel]:
    new_fields = {}

    for f_name, f_info in model_cls.model_fields.items():
        f_dct = f_info.asdict()
        new_fields[f_name] = (
            Annotated[(f_dct['annotation'] | None, *f_dct['metadata'], Field(**f_dct['attributes']))],
            None,
        )

    return create_model(
        f'{model_cls.__name__}Optional',
        __base__=model_cls,  # (1)!
        **new_fields,
    )
```

1. Using the original model as a base will inherit the [validators](/guides/concepts-validators), [computed fields](/guides/concepts-fields#the-computed_field-decorator), etc.
   The parent fields are overridden by the ones we define.
:::

:::tab{title="Python 3.11 and above"}
```python showLineNumbers
from typing import Annotated

from pydantic import BaseModel, Field, create_model


def make_fields_optional(model_cls: type[BaseModel]) -> type[BaseModel]:
    new_fields = {}

    for f_name, f_info in model_cls.model_fields.items():
        f_dct = f_info.asdict()
        new_fields[f_name] = (
            Annotated[f_dct['annotation'] | None, *f_dct['metadata'], Field(**f_dct['attributes'])],
            None,
        )

    return create_model(
        f'{model_cls.__name__}Optional',
        __base__=model_cls,  # (1)!
        **new_fields,
    )
```

1. Using the original model as a base will inherit the [validators](/guides/concepts-validators), [computed fields](/guides/concepts-fields#the-computed_field-decorator), etc.
   The parent fields are overridden by the ones we define.
:::
::::

For each field, we generate a dictionary representation of the [`FieldInfo`](/guides/pydantic-fields) instance
using the [`asdict()`](/guides/pydantic-fields) method, containing the annotation, metadata and attributes.

With the following model:

```python
class Model(BaseModel):
    f: Annotated[int, Field(gt=1), WithJsonSchema({'extra': 'data'}), Field(title='F')] = 1
```

The [`FieldInfo`](/guides/pydantic-fields) instance of `f` will have three items in its dictionary representation:

- `annotation`: `int`.
- `metadata`: A list containing the type-specific constraints and other metadata: `[Gt(1), WithJsonSchema({'extra': 'data'})]`.
- `attributes`: The remaining field-specific attributes: `{'title': 'F'}`.

With that in mind, we can recreate an annotation that "simulates" the one from the original model:

::::tabs
:::tab{title="Python 3.10 and above"}
```python
new_annotation = Annotated[(
    f_dct['annotation'] | None,  # (1)!
    *f_dct['metadata'],  # (2)!
    Field(**f_dct['attributes']),  # (3)!
)]
```

1. We create a new annotation from the existing one, but adding `None` as an allowed value
   (in our previous example, this is equivalent to `int | None`).

2. We unpack the metadata to be reused (in our previous example, this is equivalent to
   specifying `Field(gt=1)` and `WithJsonSchema({'extra': 'data'})` as `Annotated`
   metadata).

3. We specify the field-specific attributes by using the `Field()` function
   (in our previous example, this is equivalent to `Field(title='F')`).
:::

:::tab{title="Python 3.11 and above"}
```python
new_annotation = Annotated[
    f_dct['annotation'] | None,  # (1)!
    *f_dct['metadata'],  # (2)!
    Field(**f_dct['attributes']),  # (3)!
]
```

1. We create a new annotation from the existing one, but adding `None` as an allowed value
   (in our previous example, this is equivalent to `int | None`).

2. We unpack the metadata to be reused (in our previous example, this is equivalent to
   specifying `Field(gt=1)` and `WithJsonSchema({'extra': 'data'})` as `Annotated`
   metadata).

3. We specify the field-specific attributes by using the `Field()` function
   (in our previous example, this is equivalent to `Field(title='F')`).
:::
::::

and specify `None` as a default value (the second element of the tuple for the field definition accepted by [`create_model()`](/guides/pydantic-base-model)).

Here is a demonstration of our factory function:

```python
from pydantic import BaseModel, Field


class Model(BaseModel):
    a: Annotated[int, Field(gt=1)]


ModelOptional = make_fields_optional(Model)

m = ModelOptional()
print(m.a)
#> None
```

A couple notes on the implementation:

- Our `make_fields_optional()` function is defined as returning an arbitrary Pydantic model class (`-> type[BaseModel]`).
  An alternative solution can be to use a type variable to preserve the input class:

  \=== "Python 3.10 and above"

  ````
    ```python
    ModelTypeT = TypeVar('ModelTypeT', bound=type[BaseModel])

    def make_fields_optional(model_cls: ModelTypeT) -> ModelTypeT:
        ...
    ```
  ````

  \=== "Python 3.12 and above"

  ````
    ```python
    def make_fields_optional[ModelTypeT: type[BaseModel]](model_cls: ModelTypeT) -> ModelTypeT:
        ...
    ```
  ````

  However, note that static type checkers _won't_ be able to understand that all fields are now optional.

- The [`MISSING` sentinel](/guides/concepts-types#missing-sentinel) can be used as an alternative to `None`
  for the default values. Simply replace `None` by `MISSING` in the new annotation and default value.

- You might be tempted to make a copy of the original [`FieldInfo`](/guides/pydantic-fields) instances, add a
  default and/or perform other mutations, to then reuse it as `Annotated` metadata. While this
  may work in some cases, it is **not** a supported pattern, and could break or be deprecated at any point. We strongly
  encourage using the pattern from this example instead.

## Related pages

- [API Documentation](./api-documentation-index.md)
- [Concepts](./concepts-index.md)
- [Dev Tools](./dev-tools-index.md)
- [Error Messages](./error-messages-index.md)
- [Examples](./examples-index.md)
- [Integrations](./integrations-index.md)
- [Internals](./internals-index.md)
- [Production Tools](./production-tools-index.md)
- [Pydantic](./pydantic-index.md)
- [Pydantic Core](./pydantic-core-index.md)

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