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docs/en/docs/tutorial/middleware.md
# 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.
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docs/en/docs/contributing.md
<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.
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.github/workflows/test.yml
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
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docs/en/docs/alternatives.md
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
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docs/en/docs/tutorial/response-model.md
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
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docs/en/docs/deployment/concepts.md
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
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docs/en/docs/advanced/wsgi.md
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**.
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tests/test_tutorial/test_query_params/test_tutorial006_py310.py
c = TestClient(app) return c @needs_py310 def test_foo_needy_very(client: TestClient): response = client.get("/items/foo?needy=very") assert response.status_code == 200 assert response.json() == { "item_id": "foo", "needy": "very", "skip": 0, "limit": None, } @needs_py310 def test_foo_no_needy(client: TestClient):
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docs/en/docs/advanced/generate-clients.md
There are many tools to generate clients from **OpenAPI**. A common tool is <a href="https://openapi-generator.tech/" class="external-link" target="_blank">OpenAPI Generator</a>. If you are building a **frontend**, a very interesting alternative is <a href="https://github.com/hey-api/openapi-ts" class="external-link" target="_blank">openapi-ts</a>. ## Client and SDK Generators - Sponsor
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docs/en/docs/tutorial/testing.md
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**. ## Using `TestClient` !!! info
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