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docs/en/docs/tutorial/response-model.md
``` FastAPI will use this return type to: * **Validate** the returned data. * If the data is invalid (e.g. you are missing a field), it means that *your* app code is broken, not returning what it should, and it will return a server error instead of returning incorrect data. This way you and your clients can be certain that they will receive the data and the data shape expected. * Add a **JSON Schema** for the response, in the OpenAPI *path operation*.
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docs/en/docs/advanced/dataclasses.md
And of course, it supports the same: * data validation * data serialization * data documentation, etc. This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic. !!! info
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docs/en/docs/alternatives.md
One of the main features needed by API systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.
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docs/en/docs/benchmarks.md
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docs/en/docs/tutorial/path-params.md
## Recap With **FastAPI**, by using short, intuitive and standard Python type declarations, you get: * Editor support: error checks, autocompletion, etc. * Data "<abbr title="converting the string that comes from an HTTP request into Python data">parsing</abbr>" * Data validation * API annotation and automatic documentation And you only have to declare them once.
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docs/en/docs/tutorial/first-steps.md
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docs/en/docs/tutorial/testing.md
* To pass a JSON body, pass a Python object (e.g. a `dict`) to the parameter `json`. * If you need to send *Form Data* instead of JSON, use the `data` parameter instead. * To pass *headers*, use a `dict` in the `headers` parameter. * For *cookies*, a `dict` in the `cookies` parameter.
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docs/vi/docs/features.md
```Python my_user: User = User(id=3, name="John Doe", joined="2018-07-19") second_user_data = { "id": 4, "name": "Mary", "joined": "2018-11-30", } my_second_user: User = User(**second_user_data) ``` !!! info `**second_user_data` nghĩa là: Truyền các khóa và giá trị của dict `second_user_data` trực tiếp như các tham số kiểu key-value, tương đương với: `User(id=4, name="Mary", joined="2018-11-30")`
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docs/en/docs/tutorial/security/get-current-user.md
## Other models You can now get the current user directly in the *path operation functions* and deal with the security mechanisms at the **Dependency Injection** level, using `Depends`. And you can use any model or data for the security requirements (in this case, a Pydantic model `User`). But you are not restricted to using some specific data model, class or type.
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docs/em/docs/tutorial/request-files.md
`UploadFile` ✔️ 📄 `async` 👩🔬. 👫 🌐 🤙 🔗 📁 👩🔬 🔘 (⚙️ 🔗 `SpooledTemporaryFile`). * `write(data)`: ✍ `data` (`str` ⚖️ `bytes`) 📁. * `read(size)`: ✍ `size` (`int`) 🔢/🦹 📁. * `seek(offset)`: 🚶 🔢 🧘 `offset` (`int`) 📁. * 🤶 Ⓜ., `await myfile.seek(0)` 🔜 🚶 ▶️ 📁.
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