Instead of a WildValue, the JSON/Sentry webhook expect the request body to be a
dict.
For the JSON webhook, json.dumps accepts other types of input as well and the
constraint is not necessary, but this serve as a good example of an alternative
use of WebhookPayload to describe a payload that is intended to be parsed from
the entire request body from JSON, into a type other than WildValue.
Transifex has parameters that need to be parsed from JSON and converted
to int. Note that we use Optional[Json[int]] instead of
Json[Optional[int]] to replicate the behavior of json_validator. This
caveat is explained in a new test called test_json_optional.
These webhooks have some URL query params that do not need additional
validation or parsing from JSON. So WebhookPaylaod is not applicable to
these webhooks.
This converts most webhook integration views to use @typed_endpoint instead
of @has_request_variables, rewriting REQ parameters. For these
webhooks, it simply requires switching the decorator, rewriting the
type annotation of payload/message to WebhookPayload[WildValue], and
removing the REQ default that defines the to_wild_value converter.
This function is used by almost all webhooks.
To support it, we use the "api_ignore_parameter" flag so that positional
arguments like topic and body that are not intended to be parsed from
the request can be ignored.
This demonstrates the use of BaseModel to replace a check_dict_only
validator.
We also add support to referring to $defs in the OpenAPI tests. In the
future, we can descend down each object instead of mapping them to dict
for more accurate checks.
This demonstrates some basic use cases of the Json[...] wrapper with
@typed_endpoint.
Along with this change we extend test_openapi so that schema checking
based on function signatures will still work with this new decorator.
Pydantic's TypeAdapter supports dumping the JSON schema of any given type,
which is leveraged here to validate against our own OpenAPI definitions.
Parts of the implementation will be covered in later commits as we
migrate more functions to use @typed_endpoint.
See also:
https://docs.pydantic.dev/latest/api/type_adapter/#pydantic.type_adapter.TypeAdapter.json_schema
For the OpenAPI schema, we preprocess it mostly the same way. For the
parameter types though, we no longer need to use
get_standardized_argument_type to normalize type annotation, because
Pydantic dumps a JSON schema that is compliant with OpenAPI schema
already, which makes it a lot convenient for us to compare the types
with our OpenAPI definitions.
Do note that there are some exceptions where our definitions do not match
the generated one. For example, we use JSON to parse int and bool parameters,
but we don't mark them to use "application/json" in our definitions.
We want to reject ambiguous type annotations that set ApiParamConfig
inside a Union. If a parameter is Optional and has a default of None, we
prefer Annotated[Optional[T], ...] over Optional[Annotated[T, ...]].
This implements a check that detects Optional[Annotated[T, ...]] and
raise an assertion error if ApiParamConfig is in the annotation. It also
checks if the type annotation contains any ApiParamConfig objects that
are ignored, which can happen if the Annotated type is nested inside
another type like List, Union, etc.
Note that because
param: Annotated[Optional[T], ...] = None
and
param: Optional[Annotated[Optional[T], ...]] = None
are equivalent in runtime prior to Python 3.11, there is no way for us
to distinguish the two. So we cannot detect that in runtime.
See also: https://github.com/python/cpython/issues/90353
The goal of typed_endpoint is to replicate most features supported by
has_request_variables, and to improve on top of it. There are some
unresolved issues that we don't plan to work on currently. For example,
typed_endpoint does not support ignored_parameters_supported for 400
responses, and it does not run validators on path-only arguments.
Unlike has_request_variables, typed_endpoint supports error handling by
processing validation errors from Pydantic.
Most features supported by has_request_variables are supported by
typed_endpoint in various ways.
To define a function, use a syntax like this with Annotated if there is
any metadata you want to associate with a parameter, do note that
parameters that are not keyword-only are ignored from the request:
```
@typed_endpoint
def view(
request: HttpRequest,
user_profile: UserProfile,
*,
foo: Annotated[int, ApiParamConfig(path_only=True)],
bar: Json[int],
other: Annotated[
Json[int],
ApiParamConfig(
whence="lorem",
documentation_status=NTENTIONALLY_UNDOCUMENTED
)
] = 10,
) -> HttpResponse:
....
```
There are also some shorthands for the commonly used annotated types,
which are encouraged when applicable for better readability and less
typing:
```
WebhookPayload = Annotated[Json[T], ApiParamConfig(argument_type_is_body=True)]
PathOnly = Annotated[T, ApiParamConfig(path_only=True)]
```
Then the view function above can be rewritten as:
```
@typed_endpoint
def view(
request: HttpRequest,
user_profile: UserProfile,
*,
foo: PathOnly[int],
bar: Json[int],
other: Annotated[
Json[int],
ApiParamConfig(
whence="lorem",
documentation_status=INTENTIONALLY_UNDOCUMENTED
)
] = 10,
) -> HttpResponse:
....
```
There are some intentional restrictions:
- A single parameter cannot have more than one ApiParamConfig
- Path-only parameters cannot have default values
- argument_type_is_body is incompatible with whence
- Arguments of name "request", "user_profile", "args", and "kwargs" and
etc. are ignored by typed_endpoint.
- positional-only arguments are not supported by typed_endpoint. Only
keyword-only parameters are expected to be parsed from the request.
- Pydantic's strict mode is always enabled, because we don't want to
coerce input parsed from JSON into other types unnecessarily.
- Using strict mode all the time also means that we should always use
Json[int] instead of int, because it is only possible for the request
to have data of type str, and a type annotation of int will always
reject such data.
typed_endpoint's handling of ignored_parameters_unsupported is mostly
identical to that of has_request_variables.
Along with pydantic we add annotated_types for Annotated utils that can
be used for more specific validation constraints.
Signed-off-by: Zixuan James Li <p359101898@gmail.com>
We should avoid adding extra fields directly on the server data because
it makes it hard to infer the types for the functions such as
`format_attachment_data`.
_default_manager is the same as objects on most of our models. But
when a model class is stored in a variable, the type system doesn’t
know which model the variable is referring to, so it can’t know that
objects even exists (Django doesn’t add it if the user added a custom
manager of a different name). django-stubs used to incorrectly assume
it exists unconditionally, but it no longer does.
Signed-off-by: Anders Kaseorg <anders@zulip.com>
Zixuan James Li
David Rosa
evykassirer
Lalit Kumar Singh
Mateusz Mandera
Anders Kaseorg