You will learn more about adding extra information later in the docs, when learning to declare examples.

!!! warning
    Extra keys passed to `Field` will also be present in the resulting OpenAPI schema for your application.

After you have tests, then you can upgrade the **FastAPI** version to a more recent one, and make sure that all your code is working correctly by running your tests.

If everything is working, or after you make the necessary changes, and all your tests are passing, then you can pin your `fastapi` to that new recent version.

## About Starlette

You shouldn't pin the version of `starlette`.

!!! info
    <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Enumerations (or enums) are available in Python</a> since version 3.4.

!!! tip
    If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

### Declare a *path parameter*

```

!!! info "说明"

    Python 3.4 及之后版本支持<a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">枚举（即 enums）</a>。

!!! tip "提示"

    **AlexNet**、**ResNet**、**LeNet** 是机器学习<abbr title="技术上来说是深度学习模型架构">模型</abbr>。

### 声明*路径参数*

使用 Enum 类（`ModelName`）创建使用类型注解的*路径参数*：

```Python hl_lines="16"
{!../../../docs_src/path_params/tutorial005.py!}
```

    assert response.status_code == 200
    assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}


def test_get_enums_resnet():
    response = client.get("/models/resnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "resnet", "message": "Have some residuals"}


def test_get_enums_invalid():
    response = client.get("/models/foo")
    assert response.status_code == 422

async def set_up_request_state_dependency():
    request_state = {"user": "deadpond"}
    contextvar_token = legacy_request_state_context_var.set(request_state)
    yield request_state
    legacy_request_state_context_var.reset(contextvar_token)


@app.middleware("http")
async def custom_middleware(
    request: Request, call_next: Callable[[Request], Awaitable[Response]]
):
    response = await call_next(request)

database.db.connect()
database.db.create_tables([models.User, models.Item])
database.db.close()

app = FastAPI()

sleep_time = 10


async def reset_db_state():
    database.db._state._state.set(db_state_default.copy())
    database.db._state.reset()


def get_db(db_state=Depends(reset_db_state)):
    try:
        database.db.connect()
        yield
    finally:
        if not database.db.is_closed():

!!! tip "Consejo"
    Si lo estás dudando, "AlexNet", "ResNet", y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

### Declara un *parámetro de path*

    <a href="https://docs.python.org/3/library/enum.html" class="external-link" target="_blank">Enumerations (ou enums) estão disponíveis no Python</a> desde a versão 3.4.

!!! tip "Dica"
	Se você está se perguntando, "AlexNet", "ResNet", e "LeNet" são apenas nomes de <abbr title="técnicamente, modelos de arquitetura de Deep Learning">modelos</abbr> de Machine Learning (aprendizado de máquina).

### Declare um *parâmetro de rota*

from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":