# زیر برنامه ها - اتصال

اگر نیاز دارید که دو برنامه مستقل FastAPI، با OpenAPI مستقل و رابط‌های کاربری اسناد خود داشته باشید، می‌توانید یک برنامه
اصلی داشته باشید و یک (یا چند) زیر برنامه را به آن متصل کنید.

## اتصال (mount) به یک برنامه **FastAPI**

کلمه "Mounting" به معنای افزودن یک برنامه کاملاً مستقل در یک مسیر خاص است، که پس از آن مدیریت همه چیز در آن مسیر، با path operations (عملیات های مسیر) اعلام شده در آن زیر برنامه می باشد.

### برنامه سطح بالا

from fastapi import Depends, FastAPI

app = FastAPI()


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


class CommonQueryParams:
    def __init__(self, q: 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(CommonQueryParams)):
    response = {}

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float = 10.5
    tags: list[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},

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: Item = Body(
        examples=[
            {
                "name": "Foo",
                "description": "A very nice Item",

from fastapi import Depends, FastAPI

app = FastAPI()


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


class CommonQueryParams:
    def __init__(self, q: 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 = {}

from typing import Any, 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.post("/items/", response_model=Item)
async def create_item(item: Item) -> Any:
    return item


@app.get("/items/", response_model=List[Item])

from typing import Union

from fastapi import FastAPI, Header
from pydantic import BaseModel

app = FastAPI()


class CommonHeaders(BaseModel):
    host: str
    save_data: bool
    if_modified_since: Union[str, None] = None
    traceparent: Union[str, None] = None
    x_tag: list[str] = []


@app.get("/items/")
async def read_items(headers: CommonHeaders = Header()):

from typing import Annotated, Union

from fastapi import Cookie, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Cookies(BaseModel):
    class Config:
        extra = "forbid"

    session_id: str
    fatebook_tracker: Union[str, None] = None
    googall_tracker: Union[str, None] = None


@app.get("/items/")
async def read_items(cookies: Annotated[Cookies, Cookie()]):

# Templates

You can use any template engine you want with **FastAPI**.

A common choice is Jinja2, the same one used by Flask and other tools.

There are utilities to configure it easily that you can use directly in your **FastAPI** application (provided by Starlette).

## Install dependencies

Make sure you create a [virtual environment](../virtual-environments.md){.internal-link target=_blank}, activate it, and install `jinja2`:

<div class="termy">

{!../../docs_src/templates/tutorial001.py!}
```

/// note | "笔记"

在FastAPI 0.108.0，Starlette 0.29.0之前，`name`是第一个参数。
并且，在此之前，`request`对象是作为context的一部分以键值对的形式传递的。

///

/// tip | "提示"

通过声明 `response_class=HTMLResponse`，API 文档就能识别响应的对象是 HTML。

///

/// note | "技术细节"

您还可以使用 `from starlette.templating import Jinja2Templates`。