# Templates

Sie können jede gewünschte Template-Engine mit **FastAPI** verwenden.

Eine häufige Wahl ist Jinja2, dasselbe, was auch von Flask und anderen Tools verwendet wird.

Es gibt Werkzeuge zur einfachen Konfiguration, die Sie direkt in Ihrer **FastAPI**-Anwendung verwenden können (bereitgestellt von Starlette).

## Abhängigkeiten installieren

Installieren Sie `jinja2`:

<div class="termy">

```console
$ pip install jinja2

# Templates

Você pode usar qualquer template engine com o **FastAPI**.

Uma escolha comum é o Jinja2, o mesmo usado pelo Flask e outras ferramentas.

Existem utilitários para configurá-lo facilmente que você pode usar diretamente em sua aplicação **FastAPI** (fornecidos pelo Starlette).

## Instalação de dependências

Para instalar o `jinja2`, siga o código abaixo:

<div class="termy">

```console
$ pip install jinja2
```

from fastapi import FastAPI
from pydantic import BaseModel


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


app = FastAPI()


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item, q: str | None = None):
    result = {"item_id": item_id, **item.dict()}
    if q:
        result.update({"q": q})

from typing import Annotated

from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Annotated[Item, Body(embed=True)]):
    results = {"item_id": item_id, "item": item}

from typing import List, Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: List[str] = []


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

from typing import Set, Union

from fastapi import FastAPI, status
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: Set[str] = set()


@app.post("/items/", response_model=Item, status_code=status.HTTP_201_CREATED)
async def create_item(item: Item):

from fastapi import FastAPI, Query
from pydantic import BaseModel, Field
from typing_extensions import Literal

app = FastAPI()


class FilterParams(BaseModel):
    limit: int = Field(100, gt=0, le=100)
    offset: int = Field(0, ge=0)
    order_by: Literal["created_at", "updated_at"] = "created_at"
    tags: list[str] = []


@app.get("/items/")
async def read_items(filter_query: FilterParams = Query()):

## 开发

当我启动 **FastAPI** 开发的时候，绝大多数部件都已经就位，设计已经定义，需求项和工具也已经准备就绪，相关标准与规范的知识储备也非常清晰而新鲜。

## 未来

至此，**FastAPI** 及其理念已经为很多人所用。

对于很多用例，它比以前很多备选方案都更适用。

很多开发者和开发团队已经依赖 **FastAPI** 开发他们的项目（包括我和我的团队）。

但，**FastAPI** 仍有很多改进的余地，也还需要添加更多的功能。

总之，**FastAPI** 前景光明。

from typing import List

from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class UserCreate(BaseModel):
    email: str
    password: str


class UserDB(BaseModel):
    email: str
    hashed_password: str


class User(BaseModel):
    email: str


class PetDB(BaseModel):
    name: str
    owner: UserDB

from typing import Annotated

from fastapi import FastAPI, Query

app = FastAPI()


@app.get("/items/")
async def read_items(
    q: Annotated[
        str | None, Query(min_length=3, max_length=50, pattern="^fixedquery$")
    ] = None,
):
    results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
    if q:
        results.update({"q": q})