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docs/en/docs/tutorial/security/oauth2-jwt.md
And be compatible with all of them at the same time. Create a utility function to hash a password coming from the user. And another utility to verify if a received password matches the hash stored. And another one to authenticate and return a user. === "Python 3.10+"
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docs/en/docs/python-types.md
* **Define requirements**: from request path parameters, query parameters, headers, bodies, dependencies, etc. * **Convert data**: from the request to the required type. * **Validate data**: coming from each request: * Generating **automatic errors** returned to the client when the data is invalid. * **Document** the API using OpenAPI: * which is then used by the automatic interactive documentation user interfaces.
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docs/en/docs/features.md
You will get completion in code you might even consider impossible before. As for example, the `price` key inside a JSON body (that could have been nested) that comes from a request. No more typing the wrong key names, coming back and forth between docs, or scrolling up and down to find if you finally used `username` or `user_name`. ### Short
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docs/en/docs/async.md
return burgers ``` With `async def`, Python knows that, inside that function, it has to be aware of `await` expressions, and that it can "pause" ⏸ the execution of that function and go do something else 🔀 before coming back. When you want to call an `async def` function, you have to "await" it. So, this won't work: ```Python # This won't work, because get_burgers was defined with: async def burgers = get_burgers(2) ```
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README.md
* Automatic and clear errors when the data is invalid. * Validation even for deeply nested JSON objects. * <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from: * JSON. * Path parameters. * Query parameters. * Cookies. * Headers. * Forms. * Files.
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docs/en/docs/advanced/custom-response.md
3. This `yield from` tells the function to iterate over that thing named `file_like`. And then, for each part iterated, yield that part as coming from this generator function. So, it is a generator function that transfers the "generating" work to something else internally.
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docs/en/docs/advanced/settings.md
## Pydantic `Settings` Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>. ### Install `pydantic-settings`
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docs/en/docs/index.md
* Automatic and clear errors when the data is invalid. * Validation even for deeply nested JSON objects. * <abbr title="also known as: serialization, parsing, marshalling">Conversion</abbr> of input data: coming from the network to Python data and types. Reading from: * JSON. * Path parameters. * Query parameters. * Cookies. * Headers. * Forms. * Files.
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