In that case, it could be better to have only 2 servers and use a higher percentage of their resources (CPU, memory, disk, network bandwidth, etc).

The same models are shared among requests, so, it's not one model per request, or one per user or something similar.

Let's imagine that loading the model can **take quite some time**, because it has to read a lot of **data from disk**. So you don't want to do it for every request.

* the data from the client to be sent through the network
* the data sent by your program to be received by the client through the network
* the contents of a file in the disk to be read by the system and given to your program
* the contents your program gave to the system to be written to disk
* a remote API operation
* a database operation to finish
* a database query to return the results
* etc.

* You don't have to use `File()` in the default value of the parameter.
* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

framework'den geliyorsanız ve küçük bir performans kazancı (yaklaşık 100 nanosaniye) için  "def" ile *path fonksiyonu* tanımlamaya alışkınsanız, **FastAPI**'de tam tersi olacağını unutmayın. Bu durumlarda, *path fonksiyonu* <abbr title="Input/Output: disk okuma veya yazma, ağ iletişimleri.">G/Ç</abbr> engelleyen durum oluşturmadıkça "async def" kullanmak daha iyidir.

Yine de, her iki durumda da, **FastAPI**'nin önceki frameworkden [hala daha hızlı](index.md#performans){.internal-link target=_blank}...

      - name: Decompress source distribution
        run: |
          cd dist
          tar xvf fastapi*.tar.gz
      - name: Install test dependencies
        run: |
          cd dist/fastapi*/
          pip install -r requirements-tests.txt
      - name: Run source distribution tests
        run: |
          cd dist/fastapi*/
          bash scripts/test.sh
      - name: Build wheel distribution
        run: |

**Swagger UI** verwendet folgende Dateien:

* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@5.9.0/swagger-ui-bundle.js" class="external-link" target="_blank">`swagger-ui-bundle.js`</a>
* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@5.9.0/swagger-ui.css" class="external-link" target="_blank">`swagger-ui.css`</a>

Und **ReDoc** verwendet diese Datei:

下载文档所需的静态文件，把文件放到 `static/` 文件夹里。

右键点击链接，选择**另存为...**。

**Swagger UI** 使用如下文件：

* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@3/swagger-ui-bundle.js" class="external-link" target="_blank">`swagger-ui-bundle.js`</a>
* <a href="https://cdn.jsdelivr.net/npm/swagger-ui-dist@3/swagger-ui.css" class="external-link" target="_blank">`swagger-ui.css`</a>

**ReDoc** 使用如下文件：

def test_swagger_ui():
    response = client.get("/docs")
    assert response.status_code == 200, response.text
    assert response.headers["content-type"] == "text/html; charset=utf-8"
    assert "swagger-ui-dist" in response.text
    assert (
        "oauth2RedirectUrl: window.location.origin + '/docs/oauth2-redirect'"
        in response.text
    )


def test_swagger_ui_oauth2_redirect():

Here we define the config `env_file` inside of your Pydantic `Settings` class, and set the value to the filename with the dotenv file we want to use.

### Creating the `Settings` only once with `lru_cache`

Reading a file from disk is normally a costly (slow) operation, so you probably want to do it only once and then re-use the same settings object, instead of reading it for each request.

But every time we do:

```Python
Settings()
```