something(some_argument, some_keyword_argument="foo")
```

これを「呼び出し可能」なものと呼びます。

## 依存関係としてのクラス

Pythonのクラスのインスタンスを作成する際に、同じ構文を使用していることに気づくかもしれません。

例えば:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

この場合、`fluffy`は`Cat`クラスのインスタンスです。

そして`fluffy`を作成するために、`Cat`を「呼び出している」ことになります。

そのため、Pythonのクラスもまた「呼び出し可能」です。

* Automatic data model documentation with <a href="https://json-schema.org/" class="external-link" target="_blank"><strong>JSON Schema</strong></a> (as OpenAPI itself is based on JSON Schema).
* Designed around these standards, after a meticulous study. Instead of an afterthought layer on top.
* This also allows using automatic **client code generation** in many languages.

### Automatic docs

Möglicherweise stellen Sie fest, dass Sie zum Erstellen einer Instanz einer Python-Klasse die gleiche Syntax verwenden.

Zum Beispiel:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name


fluffy = Cat(name="Mr Fluffy")
```

In diesem Fall ist `fluffy` eine Instanz der Klasse `Cat`.

Und um `fluffy` zu erzeugen, rufen Sie `Cat` auf.

And it will be annotated / documented accordingly too.

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

还有许多其他镜像用于不同的需要（例如数据库），例如：


* <a href="https://hub.docker.com/_/postgres" class="external-link" target="_blank">PostgreSQL</a>
* <a href="https://hub.docker.com/_/mysql" class="external-link" target="_blank">MySQL</a>
* <a href="https://hub.docker.com/_/mongo" class="external-link" target="_blank">MongoDB</a>
* <a href="https://hub.docker.com/_/redis" class="external-link" target="_blank">Redis</a>, etc.

Es ist das beliebteste Python-Framework und genießt großes Vertrauen. Es wird zum Aufbau von Systemen wie Instagram verwendet.

Ist relativ eng mit relationalen Datenbanken (wie MySQL oder PostgreSQL) gekoppelt, daher ist es nicht sehr einfach, eine NoSQL-Datenbank (wie Couchbase, MongoDB, Cassandra, usw.) als Hauptspeicherengine zu verwenden.

async def contextmanager_in_threadpool(
    cm: ContextManager[_T],
) -> AsyncGenerator[_T, None]:
    # blocking __exit__ from running waiting on a free thread
    # can create race conditions/deadlocks if the context manager itself
    # has its own internal pool (e.g. a database connection pool)
    # to avoid this we let __exit__ run without a capacity limit
    # since we're creating a new limiter for each call, any non-zero limit

from fastapi import Depends, FastAPI
from typing_extensions import Annotated

app = FastAPI()


class FixedContentQueryChecker:
    def __init__(self, fixed_content: str):
        self.fixed_content = fixed_content

    def __call__(self, q: str = ""):
        if q:
            return self.fixed_content in q
        return False


checker = FixedContentQueryChecker("bar")


@app.get("/query-checker/")

app = FastAPI()


fake_items_db = [{"item_name": "Foo"}, {"item_name": "Bar"}, {"item_name": "Baz"}]


class CommonQueryParams:
    def __init__(self, q: Union[str, None] = None, skip: int = 0, limit: int = 100):
        self.q = q
        self.skip = skip
        self.limit = limit


@app.get("/items/")
async def read_items(commons: CommonQueryParams = Depends()):
    response = {}
    if commons.q:

app = FastAPI()


fake_items_db = [{"item_name": "Foo"}, {"item_name": "Bar"}, {"item_name": "Baz"}]


class CommonQueryParams:
    def __init__(self, q: Union[str, None] = None, skip: int = 0, limit: int = 100):
        self.q = q
        self.skip = skip
        self.limit = limit


@app.get("/items/")
async def read_items(commons: Annotated[CommonQueryParams, Depends()]):
    response = {}
    if commons.q: