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docs/en/docs/deployment/versions.md
# About FastAPI versions **FastAPI** is already being used in production in many applications and systems. And the test coverage is kept at 100%. But its development is still moving quickly. New features are added frequently, bugs are fixed regularly, and the code is still continuously improving.
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docs/en/docs/about/index.md
# About
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docs/en/docs/alternatives.md
## Intro **FastAPI** wouldn't exist if not for the previous work of others. There have been many tools created before that have helped inspire its creation. I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.
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docs/en/docs/advanced/generate-clients.md
Some of them also ✨ [**sponsor FastAPI**](../help-fastapi.md#sponsor-the-author){.internal-link target=_blank} ✨, this ensures the continued and healthy **development** of FastAPI and its **ecosystem**. And it shows their true commitment to FastAPI and its **community** (you), as they not only want to provide you a **good service** but also want to make sure you have a **good and healthy framework**, FastAPI. 🙇
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docs/en/docs/advanced/dataclasses.md
```Python hl_lines="1 7-13 19" {!../../../docs_src/dataclasses/tutorial002.py!} ``` The dataclass will be automatically converted to a Pydantic dataclass. This way, its schema will show up in the API docs user interface: <img src="/img/tutorial/dataclasses/image01.png"> ## Dataclasses in Nested Data Structures
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docs/en/docs/tutorial/testing.md
!!! info Note that the `TestClient` receives data that can be converted to JSON, not Pydantic models. If you have a Pydantic model in your test and you want to send its data to the application during testing, you can use the `jsonable_encoder` described in [JSON Compatible Encoder](encoder.md){.internal-link target=_blank}. ## Run it After that, you just need to install `pytest`:
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docs/en/docs/tutorial/response-model.md
FastAPI will use this `response_model` to do all the data documentation, validation, etc. and also to **convert and filter the output data** to its type declaration. !!! tip If you have strict type checks in your editor, mypy, etc, you can declare the function return type as `Any`.
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docs/en/docs/advanced/behind-a-proxy.md
[[http.services.app.loadBalancer.servers]] url = "http://127.0.0.1:8000" ``` This file configures Traefik to use the path prefix `/api/v1`. And then it will redirect its requests to your Uvicorn running on `http://127.0.0.1:8000`. Now start Traefik: <div class="termy"> ```console $ ./traefik --configFile=traefik.toml
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docs/en/docs/contributing.md
* Do not change the paths in links to images, code files, Markdown documents. * However, when a Markdown document is translated, the `#hash-parts` in links to its headings may change. Update these links if possible. * Search for such links in the translated document using the regex `#[^# ]`.
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docs/en/docs/advanced/sub-applications.md
### Top-level application First, create the main, top-level, **FastAPI** application, and its *path operations*: ```Python hl_lines="3 6-8" {!../../../docs_src/sub_applications/tutorial001.py!} ``` ### Sub-application Then, create your sub-application, and its *path operations*. This sub-application is just another standard FastAPI application, but this is the one that will be "mounted":
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