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docs/en/docs/advanced/dataclasses.md
!!! info Keep in mind that dataclasses can't do everything Pydantic models can do. So, you might still need to use Pydantic models. But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓 ## Dataclasses in `response_model` You can also use `dataclasses` in the `response_model` parameter:
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docs/en/docs/tutorial/response-model.md
# Response Model - Return Type You can declare the type used for the response by annotating the *path operation function* **return type**. You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc. === "Python 3.10+" ```Python hl_lines="16 21" {!> ../../../docs_src/response_model/tutorial001_01_py310.py!} ```
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docs/en/docs/reference/request.md
# `Request` class You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc. You can import it directly from `fastapi`: ```python from fastapi import Request ``` !!! tip
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docs/en/docs/tutorial/path-params.md
You can use the same type declarations with `str`, `float`, `bool` and many other complex data types. Several of these are explored in the next chapters of the tutorial. ## Order matters When creating *path operations*, you can find situations where you have a fixed path.
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docs/en/docs/advanced/generate-clients.md
#### Install `openapi-ts` You can install `openapi-ts` in your frontend code with: <div class="termy"> ```console $ npm install @hey-api/openapi-ts --save-dev ---> 100% ``` </div> #### Generate Client Code To generate the client code you can use the command line application `openapi-ts` that would now be installed.
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docs/en/docs/reference/background.md
# 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 ```
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docs/en/docs/advanced/behind-a-proxy.md
## Testing locally with Traefik You can easily run the experiment locally with a stripped path prefix using <a href="https://docs.traefik.io/" class="external-link" target="_blank">Traefik</a>. <a href="https://github.com/containous/traefik/releases" class="external-link" target="_blank">Download Traefik</a>, it's a single binary, you can extract the compressed file and run it directly from the terminal.
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fastapi/security/oauth2.py
class SecurityScopes: """ This is a special class that you can define in a parameter in a dependency to obtain the OAuth2 scopes required by all the dependencies in the same chain. This way, multiple dependencies can have different scopes, even when used in the same *path operation*. And with this, you can access all the scopes required in all those dependencies in a single place.
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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
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docs/en/docs/deployment/manually.md
## Install the Server Program When you install FastAPI, it comes with a production server, Uvicorn, and you can start it with the `fastapi run` command. But you can also install an ASGI server manually: === "Uvicorn"
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