It used custom types in its declarations instead of standard Python types, but it was still a huge step forward.

It also was one of the first frameworks to generate a custom schema declaring the whole API in JSON.

It was not based on a standard like OpenAPI and JSON Schema. So it wouldn't be straightforward to integrate it with other tools, like Swagger UI. But again, it was a very innovative idea.

* **FastAPI**:
    * The same way that Starlette uses Uvicorn and cannot be faster than it, **FastAPI** uses Starlette, so it cannot be faster than it.

You can check it at <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>:

<img src="/img/tutorial/behind-a-proxy/image01.png">

But if we access the docs UI at the "official" URL using the proxy with port `9999`, at `/api/v1/docs`, it works correctly! 🎉

It is based on <a href="https://www.python-httpx.org" class="external-link" target="_blank">HTTPX</a>, which in turn is designed based on Requests, so it's very familiar and intuitive.

With it, you can use <a href="https://docs.pytest.org/" class="external-link" target="_blank">pytest</a> directly with **FastAPI**.

* Add a **JSON Schema** for the response, in the OpenAPI *path operation*.
    * This will be used by the **automatic docs**.

#### Generate Client Code

To generate the client code you can use the command line application `openapi-ts` that would now be installed.

Because it is installed in the local project, you probably wouldn't be able to call that command directly, but you would put it on your `package.json` file.

It could look like this:

```JSON  hl_lines="7"
{
  "name": "frontend-app",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",

Just keep in mind that when you read "server" in general, it could refer to one of those two things.

When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.

## Install the Server Program

```Python hl_lines="7"
{!../../../docs_src/first_steps/tutorial001.py!}
```

This is a Python function.

It will be called by **FastAPI** whenever it receives a request to the URL "`/`" using a `GET` operation.

In this case, it is an `async` function.

---

You could also define it as a normal function instead of `async def`:

```Python hl_lines="7"
{!../../../docs_src/first_steps/tutorial003.py!}
```

This is still supported thanks to **Pydantic**, as it has <a href="https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel" class="external-link" target="_blank">internal support for `dataclasses`</a>.

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation

# Background Tasks - `BackgroundTasks`

You can declare a parameter in a *path operation function* or dependency function with the type `BackgroundTasks`, and then you can use it to schedule the execution of background tasks after the response is sent.

You can import it directly from `fastapi`:

```python
from fastapi import BackgroundTasks
```