✨ Add autocomplete support for VS Code, via dataclass_transform

changes/2721-tiangolo.md

@@ -0,0 +1 @@
+Add support for autocomplete in VS Code via `__dataclass_transform__`

docs/visual_studio_code.md

@@ -0,0 +1,266 @@
+*pydantic* works well with any editor or IDE out of the box because it's made on top of standard Python type annotations.
+
+When using [Visual Studio Code (VS Code)](https://code.visualstudio.com/), there are some **additional editor features** supported, comparable to the ones provided by the [PyCharm plugin](./pycharm_plugin.md).
+
+This means that you will have **autocompletion** (or "IntelliSense") and **error checks** for types and required arguments even while creating new *pydantic* model instances.
+
+![pydantic autocompletion in VS Code](./img/vs_code_01.png)
+
+## Configure VS Code
+
+To take advantage of these features, you need to make sure you configure VS Code correctly, using the recommended settings.
+
+In case you have a different configuration, here's a short overview of the steps.
+
+### Install Pylance
+
+You should use the [Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance) extension for VS Code. It is the recommended, next-generation, official VS Code plug-in for Python.
+
+Make sure you install and enable it in your editor.
+
+### Configure your environment
+
+Then you need to make sure your editor knows the [Python environment](https://code.visualstudio.com/docs/python/python-tutorial#_install-and-use-packages) (probably a virtual environment) for your Python project.
+
+This would be the environment in where you installed *pydantic*.
+
+### Configure Pylance and Pyright
+
+Pylance is the VS Code extension. Underneath, it uses an open source tool called [Pyright](https://github.com/microsoft/pyright) that does all the heavy lifting.
+
+With the default configurations, you will get support for autocompletion, but Pyright (and Pylance) might not check for type errors.
+
+You can enable type error checks from Pylance/Pyright with these steps:
+
+* Open the "User Settings"
+* Search for `Type Checking Mode`
+* You will find an option under `Python › Analysis: Type Checking Mode`
+* Set it to `basic` or `strict` (by default it's `off`)
+
+![Type Checking Mode set to strict in VS Code](./img/vs_code_02.png)
+
+Now you will not only get autocompletion when creating new *pydantic* model instances but also error checks for **required arguments**.
+
+![Required arguments error checks in VS Code](./img/vs_code_03.png)
+
+And you will also get error checks for **invalid data types**.
+
+![Invalid data types error checks in VS Code](./img/vs_code_04.png)
+
+### Configure mypy
+
+You might also want to configure mypy in VS Code to get mypy error checks inline in your editor (alternatively/additionally to Pyright).
+
+This would include the errors detected by the [*pydantic* mypy plugin](./mypy_plugin.md), if you configured it.
+
+To enable mypy in VS Code, do the following:
+
+* Open the "User Settings"
+* Search for `Mypy Enabled`
+* You will find an option under `Python › Linting: Mypy Enabled`
+* Check the box (by default it's unchecked)
+
+![mypy enabled in VS Code](./img/vs_code_05.png)
+
+## Tips and tricks
+
+Here are some additional tips and tricks to improve your developer experience when using VS Code with *pydantic*.
+
+### Strict errors
+
+The way this additional editor support works is that Pylance/Pyright will treat your *pydantic* models as if they were Python's pure `dataclasses`.
+
+And it will show **strict type error checks** about the data types passed in arguments when creating a new *pydantic* model instance.
+
+In this example you can see that it shows that a `str` of `'23'` is not a valid `int` for the argument `age`.
+
+![VS Code strict type errors](./img/vs_code_06.png)
+
+It would expect `age=23` instead of `age='23'`.
+
+Nevertheless, the design, and one of the main features of *pydantic*, is that it is very **lenient with data types**.
+
+It will actually accept the `str` with value `'23'` and will convert it to an `int` with value `23`.
+
+These strict error checks are **very useful** most of the time and can help you **detect many bugs early**. But there are cases, like with `age='23'`, where they could be inconvenient by reporting a "false positive" error.
+
+---
+
+This example above with `age='23'` is intentionally simple, to show the error and the differences in types.
+
+But more common cases where these strict errors would be inconvenient would be when using more sophisticated data types, like `int` values for `datetime` fields, or `dict` values for *pydantic* sub-models.
+
+For example, this is valid for *pydantic*:
+
+```Python hl_lines="12 17"
+from pydantic import BaseModel
+
+
+class Knight(BaseModel):
+    title: str
+    age: int
+    color: str = 'blue'
+
+
+class Quest(BaseModel):
+    title: str
+    knight: Knight
+
+
+quest = Quest(
+    title='To seek the Holy Grail',
+    knight={'title': 'Sir Lancelot', 'age': 23}
+)
+```
+
+The type of the field `knight` is declared with the class `Knight` (a *pydantic* model) and the code is passing a literal `dict` instead. This is still valid for *pydantic*, and the `dict` would be automatically converted to a `Knight` instance.
+
+Nevertheless, it would be detected as a type error:
+
+![VS Code strict type errors with model](./img/vs_code_07.png)
+
+In those cases, there are several ways to disable or ignore strict errors in very specific places, while still preserving them in the rest of the code.
+
+Below are several techniques to achieve it.
+
+#### Disable type checks in a line
+
+You can disable the errors for a specific line using a comment of:
+
+```
+# type: ignore
+```
+
+coming back to the example with `age='23'`, it would be:
+
+```Python hl_lines="10"
+from pydantic import BaseModel
+
+
+class Knight(BaseModel):
+    title: str
+    age: int
+    color: str = 'blue'
+
+
+lancelot = Knight(title='Sir Lancelot', age='23')  # type: ignore
+```
+
+that way Pylance/Pyright and mypy will ignore errors in that line.
+
+**Pros**: it's a simple change in that line to remove errors there.
+
+**Cons**: any other error in that line will also be omitted, including type checks, misspelled arguments, required arguments not provided, etc.
+
+#### Override the type of a variable
+
+You can also create a variable with the value you want to use and declare it's type explicitly with `Any`.
+
+```Python hl_lines="1 11-12"
+from typing import Any
+from pydantic import BaseModel
+
+
+class Knight(BaseModel):
+    title: str
+    age: int
+    color: str = 'blue'
+
+
+age_str: Any = '23'
+lancelot = Knight(title='Sir Lancelot', age=age_str)
+```
+
+that way Pylance/Pyright and mypy will interpret the variable `age_str` as if they didn't know its type, instead of knowing it has a type of `str` when an `int` was expected (and then showing the corresponding error).
+
+**Pros**: errors will be ignored only for a specific value, and you will still see any additional errors for the other arguments.
+
+**Cons**: it requires importing `Any` and a new variable in a new line for each argument that needs ignoring errors.
+
+#### Override the type of a value with `cast`
+
+The same idea from the previous example can be put on the same line with the help of `cast()`.
+
+This way, the type declaration of the value is overriden inline, without requiring another variable.
+
+```Python hl_lines="1 11"
+from typing import Any, cast
+from pydantic import BaseModel
+
+
+class Knight(BaseModel):
+    title: str
+    age: int
+    color: str = 'blue'
+
+
+lancelot = Knight(title='Sir Lancelot', age=cast(Any, '23'))
+```
+
+`cast(Any, '23')` doesn't affect the value, it's still just `'23'`, but now Pylance/Pyright and mypy will assume it is of type `Any`, which means, they will act as if they didn't know the type of the value.
+
+So, this is the equivalent of the previous example, without the additional variable.
+
+**Pros**: errors will be ignored only for a specific value, and you will still see any additional errors for the other arguments. There's no need for additional variables.
+
+**Cons**: it requires importing `Any` and `cast`, and if you are not used to using `cast()`, it could seem strange at first.
+
+### Config in class arguments
+
+*pydantic* has a rich set of [Model Configurations](./usage/model_config.md) available.
+
+These configurations can be set in an internal `class Config` on each model:
+
+```Python hl_lines="9-10"
+from pydantic import BaseModel
+
+
+class Knight(BaseModel):
+    title: str
+    age: int
+    color: str = 'blue'
+
+    class Config:
+        frozen = True
+```
+
+or passed as keyword arguments when defining the model class:
+
+```Python hl_lines="4"
+from pydantic import BaseModel
+
+
+class Knight(BaseModel, frozen=True):
+    title: str
+    age: int
+    color: str = 'blue'
+```
+
+The specific configuration **`frozen`** (in beta) has a special meaning.
+
+It prevents other code from changing a model instance once it's created, keeping it **"frozen"**.
+
+When using the second version to declare `frozen=True` (with **keyword arguments** in the class definition), Pylance/Pyright can use it to help you check in your code and **detect errors** when something is trying to set values in a model that is "frozen".
+
+![VS Code strict type errors with model](./img/vs_code_08.png)
+
+## Technical Details
+
+!!! warning
+    As a *pydantic* user, you don't need the details below. Feel free to skip the rest of this section.
+
+    These details are only useful for other library authors, etc.
+
+This additional editor support works by implementing the proposed draft standard for [Dataclass Transform](https://github.com/microsoft/pyright/blob/master/specs/dataclass_transforms.md).
+
+The proposed draft standard is written by Eric Traut, from the Microsoft team, the same author of the open source package Pyright (used by Pylance to provide Python support in VS Code).
+
+The intention of the standard is to provide a way for libraries like *pydantic* and others to tell editors and tools that they (the editors) should treat these libraries (e.g. *pydantic*) as if they were `dataclasses`, providing autocompletion, type checks, etc.
+
+The draft standard also includes an [Alternate Form](https://github.com/microsoft/pyright/blob/master/specs/dataclass_transforms.md#alternate-form) for early adopters, like *pydantic*, to add support for it right away, even before the new draft standard is finished and approved.
+
+This new draft standard, with the Alternate Form, is already supported by Pyright, so it can be used via Pylance in VS Code.
+
+As it is being proposed as an official standard for Python, other editors can also easily add support for it.
+
+And authors of other libraries similar to *pydantic* can also easily adopt the standard right away (using the "Alternate Form") and get the benefits of these additional editor features.

mkdocs.yml

@@ -45,6 +45,7 @@ nav:
 - benchmarks.md
 - 'Mypy plugin': mypy_plugin.md
 - 'PyCharm plugin': pycharm_plugin.md
+- 'Visual Studio Code': visual_studio_code.md
 - 'Hypothesis plugin': hypothesis_plugin.md
 - 'Code Generation': datamodel_code_generator.md
 - changelog.md

pydantic/env_settings.py

@@ -1,7 +1,7 @@
 import os
 import warnings
 from pathlib import Path
-from typing import AbstractSet, Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
+from typing import TYPE_CHECKING, AbstractSet, Any, Callable, Dict, List, Mapping, Optional, Tuple, Type, Union
 
 from .fields import ModelField
 from .main import BaseConfig, BaseModel, Extra
@@ -113,7 +113,10 @@ def customise_sources(
         ) -> Tuple[SettingsSourceCallable, ...]:
             return init_settings, env_settings, file_secret_settings
 
-    __config__: Config  # type: ignore
+    if TYPE_CHECKING:
+        # populated by the metaclass using the Config class defined above, annotated here to help IDEs only
+        # inside a TYPE_CHECKING block to avoid showing it in __init__ by IDEs supporting dataclass_transform
+        __config__: Type[Config]
 
 
 class InitSettingsSource:
@@ -169,7 +172,7 @@ def __call__(self, settings: BaseSettings) -> Dict[str, Any]:
 
             if field.is_complex():
                 try:
-                    env_val = settings.__config__.json_loads(env_val)  # type: ignore
+                    env_val = settings.__config__.json_loads(env_val)
                 except ValueError as e:
                     raise SettingsError(f'error parsing JSON for "{env_name}"') from e
             d[field.alias] = env_val

pydantic/main.py

@@ -28,7 +28,7 @@
 from .class_validators import ValidatorGroup, extract_root_validators, extract_validators, inherit_validators
 from .error_wrappers import ErrorWrapper, ValidationError
 from .errors import ConfigError, DictError, ExtraError, MissingError
-from .fields import MAPPING_LIKE_SHAPES, ModelField, ModelPrivateAttr, PrivateAttr, Undefined
+from .fields import MAPPING_LIKE_SHAPES, Field, FieldInfo, ModelField, ModelPrivateAttr, PrivateAttr, Undefined
 from .json import custom_pydantic_encoder, pydantic_encoder
 from .parse import Protocol, load_file, load_str_bytes
 from .schema import default_ref_template, model_schema
@@ -102,6 +102,20 @@ def __call__(self, schema: Dict[str, Any], model_class: Type['Model']) -> None:
     except AttributeError:
         compiled = False
 
+
+_T = TypeVar('_T')
+
+
+def __dataclass_transform__(
+    *,
+    eq_default: bool = True,
+    order_default: bool = False,
+    kw_only_default: bool = False,
+    field_descriptors: Tuple[Union[type, Callable[..., Any]], ...] = (()),
+) -> Callable[[_T], _T]:
+    return lambda a: a
+
+
 __all__ = 'BaseConfig', 'BaseModel', 'Extra', 'compiled', 'create_model', 'validate_model'
 
 
@@ -223,6 +237,7 @@ def hash_function(self_: Any) -> int:
 _is_base_model_class_defined = False
 
 
+@__dataclass_transform__(kw_only_default=True, field_descriptors=(Field, FieldInfo))
 class ModelMetaclass(ABCMeta):
     @no_type_check  # noqa C901
     def __new__(mcs, name, bases, namespace, **kwargs):  # noqa C901