Hug tinha um incomum, interessante recurso: usando o mesmo framework, é possível criar tanto APIs como CLIs.

Como é baseado nos padrões anteriores de frameworks web síncronos (WSGI), ele não pode controlar _Websockets_ e outras coisas, embora ele ainda tenha uma alta performance também.

/// info

				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>reindex-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>mapper-extras-client</artifactId>
				</exclusion>
				<exclusion>
					<groupId>org.opensearch.plugin</groupId>
					<artifactId>aggs-matrix-stats-client</artifactId>
				</exclusion>
				<exclusion>

* [Forbid Extra Query Parameters](https://fastapi.tiangolo.com/tutorial/query-param-models/#forbid-extra-query-parameters)
* [Forbid Extra Headers](https://fastapi.tiangolo.com/tutorial/header-param-models/#forbid-extra-headers)
* [Forbid Extra Cookies](https://fastapi.tiangolo.com/tutorial/cookie-param-models/#forbid-extra-cookies)

### Features

				Content:  []byte("This small file is in ZIP64 format.\n"),
				Modified: time.Date(2012, 8, 10, 14, 33, 32, 0, time.UTC),
				Mode:     0644,
			},
		},
	},
	// Another zip64 file with different Extras fields. (golang.org/issue/7069)
	{
		Name: "zip64-2.zip",
		File: []ZipTestFile{
			{
				Name:     "README",
				Content:  []byte("This small file is in ZIP64 format.\n"),

runAsUser: 1000 runAsGroup: 1000 resources: requests: memory: 128Mi ## Additional volumes to add to the post-job. extraVolumes: [] # - name: extra-policies # configMap: # name: my-extra-policies-cm ## Additional volumeMounts to add to the custom commands container when ## running the post-job. extraVolumeMounts: [] # - name: extra-policies # mountPath: /mnt/extras/ # Command to run after the main command on exit exitCommand: "" ## Merge jobs postJob: podAnnotations: {} annotations: {} securityContext:...

# Declare Request Example Data

You can declare examples of the data your app can receive.

Here are several ways to do it.

## Extra JSON Schema data in Pydantic models

You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema.

//// tab | Python 3.10+ Pydantic v2

```Python hl_lines="13-24"
{!> ../../docs_src/schema_extra_example/tutorial001_py310.py!}
```

////

So, we get a Pydantic model from the data in another Pydantic model.

#### Unwrapping a `dict` and extra keywords

And then adding the extra keyword argument `hashed_password=hashed_password`, like in:

```Python
UserInDB(**user_in.dict(), hashed_password=hashed_password)
```

...ends up being like:

```Python

# Extra Data Types

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

## Other data types

Especificamente, observa-se Uvicorn, Starlette e FastAPI comparados juntos (entre muitas outras ferramentas).

Quanto mais simples o problema resolvido pela ferramenta, melhor a performance que ela terá. E a maioria dos _benchmarks_ não testam as características adicionais fornecidas pela ferramenta.

A hierarquia segue assim:

* **Uvicorn**: um servidor ASGI

Sebastián Ramírez <******@****.***> 1728247014 +0200