Stellen Sie sich vor, Sie entwickeln eine Anwendung, mit der Sie Rechnungen erstellen können.

Diese Rechnungen haben eine `id`, einen optionalen `title`, einen `customer` (Kunde) und ein `total` (Gesamtsumme).

Der Benutzer Ihrer API (ein externer Entwickler) erstellt mit einem POST-Request eine Rechnung in Ihrer API.

Dann wird Ihre API (beispielsweise):

👉 💼, 👆 💪 💚 📄 ❔ 👈 🔢 🛠️ *🔜* 👀 💖. ⚫️❔ *➡ 🛠️* ⚫️ 🔜 ✔️, ⚫️❔ 💪 ⚫️ 🔜 ⌛, ⚫️❔ 📨 ⚫️ 🔜 📨, ♒️.

## 📱 ⏮️ ⏲

➡️ 👀 🌐 👉 ⏮️ 🖼.

🌈 👆 🛠️ 📱 👈 ✔ 🏗 🧾.

👉 🧾 🔜 ✔️ `id`, `title` (📦), `customer`, & `total`.

👩‍💻 👆 🛠️ (🔢 👩‍💻) 🔜 ✍ 🧾 👆 🛠️ ⏮️ 🏤 📨.

⤴️ 👆 🛠️ 🔜 (➡️ 🌈):

* 📨 🧾 🕴 🔢 👩‍💻.
* 📈 💸.
* 📨 📨 🔙 🛠️ 👩‍💻 (🔢 👩‍💻).

from fastapi import APIRouter, FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


class Invoice(BaseModel):
    id: str
    title: Union[str, None] = None
    customer: str
    total: float


class InvoiceEvent(BaseModel):
    description: str
    paid: bool


class InvoiceEventReceived(BaseModel):
    ok: bool


invoices_callback_router = APIRouter()

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

### Multiple Processes - An Example

                            {"title": "Title", "type": "string"}
                        ),
                        "customer": {"title": "Customer", "type": "string"},
                        "total": {"title": "Total", "type": "number"},
                    },
                },
                "InvoiceEvent": {
                    "title": "InvoiceEvent",
                    "required": ["description", "paid"],

## An app with callbacks

Let's see all this with an example.

Imagine you develop an app that allows creating invoices.

These invoices will have an `id`, `title` (optional), `customer`, and `total`.

The user of your API (an external developer) will create an invoice in your API with a POST request.

Then your API will (let's imagine):

* Send the invoice to some customer of the external developer.

API 应用调用外部 API 时的流程叫做**回调**。因为外部开发者编写的软件发送请求至您的 API，然后您的 API 要进行回调，并把请求发送至外部 API。

此时，我们需要存档外部 API 的*信息*，比如应该有哪些*路径操作*，返回什么样的请求体，应该返回哪种响应等。

## 使用回调的应用

示例如下。

假设要开发一个创建发票的应用。

发票包括 `id`、`title`（可选）、`customer`、`total` 等属性。

API 的用户 （外部开发者）要在您的 API 内使用 POST 请求创建一条发票记录。

（假设）您的 API 将：

* 把发票发送至外部开发者的消费者
* 归集现金
* 把通知发送至 API 的用户（外部开发者）
    * 通过（从您的 API）发送 POST 请求至外部 API （即**回调**）来完成

## 常规 **FastAPI** 应用

    return data
}

async function getData() {
    let page = 1
    let data = []
    let dataBatch = await getDataBatch(page)
    data = data.concat(dataBatch.items)
    const totalCount = dataBatch.total_count
    while (data.length < totalCount) {
        page += 1
        dataBatch = await getDataBatch(page)
        data = data.concat(dataBatch.items)
    }
    return data
}

function setupTermynal() {

                    "Event": {
                        "title": "Event",
                        "required": ["name", "total"],
                        "type": "object",
                        "properties": {
                            "name": {"title": "Name", "type": "string"},
                            "total": {"title": "Total", "type": "number"},
                        },
                    },
                    "HTTPValidationError": {

    * In requests and responses will be represented as a `str` in ISO 8601 format, like: `14:23:55.003`.
* `datetime.timedelta`:
    * A Python `datetime.timedelta`.
    * In requests and responses will be represented as a `float` of total seconds.
    * Pydantic also allows representing it as a "ISO 8601 time diff encoding", <a href="https://docs.pydantic.dev/latest/concepts/serialization/#json_encoders" class="external-link" target="_blank">see the docs for more info</a>.