Optional,
    Union,
)
from weakref import WeakKeyDictionary

import fastapi
from fastapi._compat import (
    BaseConfig,
    ModelField,
    PydanticSchemaGenerationError,
    Undefined,
    UndefinedType,
    Validator,
    annotation_is_pydantic_v1,
)
from fastapi.datastructures import DefaultPlaceholder, DefaultType
from fastapi.exceptions import FastAPIDeprecationWarning, PydanticV1NotSupportedError

## Den `fastapi run` Befehl verwenden { #use-the-fastapi-run-command }

Kurz gesagt, nutzen Sie `fastapi run`, um Ihre FastAPI-Anwendung bereitzustellen:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run <u style="text-decoration-style:solid">main.py</u>

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀

**FastAPI** bietet dieselben `starlette.responses` auch via `fastapi.responses` an, nur als Annehmlichkeit für Sie, den Entwickler. Aber die meisten verfügbaren Responses kommen direkt von Starlette. Dasselbe gilt für `Request`.

///

## Die Default-Exceptionhandler überschreiben { #override-the-default-exception-handlers }

**FastAPI** hat einige Default-Exceptionhandler.

Using it in your editor is what really shows you the benefits of FastAPI, seeing how little code you have to write, all the type checks, autocompletion, etc.

---

## Install FastAPI { #install-fastapi }

The first step is to install FastAPI.

Make sure you create a [virtual environment](../virtual-environments.md){.internal-link target=_blank}, activate it, and then **install FastAPI**:

<div class="termy">

```console

* `Header()`
* `Form()`
* `File()`

You can import them all directly from `fastapi`:

```python
from fastapi import Body, Cookie, File, Form, Header, Path, Query
```

::: fastapi.Query

::: fastapi.Path

::: fastapi.Body

::: fastapi.Cookie

::: fastapi.Header

::: fastapi.Form

from fastapi import FastAPI
from fastapi.responses import JSONResponse
from pydantic import BaseModel


class Item(BaseModel):
    id: str
    value: str


class Message(BaseModel):
    message: str


app = FastAPI()


@app.get("/items/{item_id}", response_model=Item, responses={404: {"model": Message}})
async def read_item(item_id: str):
    if item_id == "foo":

from fastapi import FastAPI


def fake_answer_to_everything_ml_model(x: float):
    return x * 42


ml_models = {}


@asynccontextmanager
async def lifespan(app: FastAPI):
    # Load the ML model
    ml_models["answer_to_everything"] = fake_answer_to_everything_ml_model
    yield
    # Clean up the ML models and release the resources
    ml_models.clear()


app = FastAPI(lifespan=lifespan)

### Обычный **FastAPI** { #normal-fastapi }

Сначала напишите приложение **FastAPI** как обычно:

{* ../../docs_src/extending_openapi/tutorial001_py39.py hl[1,4,7:9] *}

### Сгенерируйте схему OpenAPI { #generate-the-openapi-schema }

    * Si estás comparando Starlette, compáralo con Sanic, Flask, Django, etc. Frameworks web (o microframeworks).
* **FastAPI**:
    * De la misma forma en que Starlette usa Uvicorn y no puede ser más rápido que él, **FastAPI** usa Starlette, por lo que no puede ser más rápido que él.

    * Если Вы сравниваете Starlette, сравнивайте его с Sanic, Flask, Django и т. д. — веб‑фреймворками (или микрофреймворками).
* **FastAPI**:
    * Точно так же, как Starlette использует Uvicorn и не может быть быстрее него, **FastAPI** использует Starlette, поэтому не может быть быстрее его.