# Middleware

You can add middleware to **FastAPI** applications.

A "middleware" is a function that works with every **request** before it is processed by any specific *path operation*. And also with every **response** before returning it.

* It takes each **request** that comes to your application.
* It can then do something to that **request** or run any needed code.

<div class="termy">

```console
$ python -m pip install --upgrade pip

---> 100%
```

</div>

!!! tip
    Every time you install a new package with `pip` under that environment, activate the environment again.

name: Test

on:
  push:
    branches:
      - master
  pull_request:
    types:
      - opened
      - synchronize
  schedule:
    # cron every week on monday
    - cron: "0 0 * * 1"

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"
      - uses: actions/checkout@v4

They are, more or less, at opposite ends, complementing each other.

Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.

That's why, as said in the official website:

> Requests is one of the most downloaded Python packages of all time

The way you use it is very simple. For example, to do a `GET` request, you would write:

```Python

In this case, it might not be a problem, because it's the same user sending the password.

But if we use the same model for another *path operation*, we could be sending our user's passwords to every client.

!!! danger
    Never store the plain password of a user or send it in a response like this, unless you know all the caveats and you know what you are doing.

## Add an output model

labels: [question]
body:
  - type: markdown
    attributes:
      value: |
        Thanks for your interest in FastAPI! 🚀

        Please follow these instructions, fill every question, and do every step. 🙏

        I'm asking this because answering questions and solving problems in GitHub is what consumes most of the time.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

### Examples of Replication Tools and Strategies

Then wrap the WSGI (e.g. Flask) app with the middleware.

And then mount that under a path.

```Python hl_lines="2-3  23"
{!../../../docs_src/wsgi/tutorial001.py!}
```

## Check it

Now, every request under the path `/v1/` will be handled by the Flask application.

And the rest will be handled by **FastAPI**.

In an advanced scenario where you know you need the dependency to be called at every step (possibly multiple times) in the same request instead of using the "cached" value, you can set the parameter `use_cache=False` when using `Depends`:

=== "Python 3.8+"

    ```Python hl_lines="1"

```

### Cache the OpenAPI schema

You can use the property `.openapi_schema` as a "cache", to store your generated schema.

That way, your application won't have to generate the schema every time a user opens your API docs.

It will be generated only once, and then the same cached schema will be used for the next requests.

```Python hl_lines="13-14  25-26"
{!../../../docs_src/extending_openapi/tutorial001.py!}