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  1. docs/en/docs/tutorial/body-fields.md

        Actually, `Query`, `Path` and others you'll see next create objects of subclasses of a common `Param` class, which is itself a subclass of Pydantic's `FieldInfo` class.
    
        And Pydantic's `Field` returns an instance of `FieldInfo` as well.
    
        `Body` also returns objects of a subclass of `FieldInfo` directly. And there are others you will see later that are subclasses of the `Body` class.
    
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  2. docs/en/docs/advanced/testing-database.md

        The new docs will include Pydantic v2 and will use <a href="https://sqlmodel.tiangolo.com/" class="external-link" target="_blank">SQLModel</a> (which is also based on SQLAlchemy) once it is updated to use Pydantic v2 as well.
    
    You can use the same dependency overrides from [Testing Dependencies with Overrides](testing-dependencies.md){.internal-link target=_blank} to alter a database for testing.
    
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  3. fastapi/security/oauth2.py

        You could have custom internal logic to separate it by colon caracters (`:`) or
        similar, and get the two parts `items` and `read`. Many applications do that to
        group and organize permissions, you could do it as well in your application, just
        know that that it is application specific, it's not part of the specification.
        """
    
        def __init__(
            self,
            *,
            grant_type: Annotated[
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  4. docs/en/docs/deployment/manually.md

            When you install FastAPI with something like `pip install fastapi` you already get `uvicorn[standard]` as well.
    
    === "Hypercorn"
    
        * <a href="https://gitlab.com/pgjones/hypercorn" class="external-link" target="_blank">Hypercorn</a>, an ASGI server also compatible with HTTP/2.
    
        <div class="termy">
    
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  5. docs/en/docs/alternatives.md

    It can't handle nested models very well. So, if the JSON body in the request is a JSON object that has inner fields that in turn are nested JSON objects, it cannot be properly documented and validated.
    
    !!! check "Inspired **FastAPI** to"
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  6. docs/en/docs/how-to/conditional-openapi.md

    If you want to secure your API, there are several better things you can do, for example:
    
    * Make sure you have well defined Pydantic models for your request bodies and responses.
    * Configure any required permissions and roles using dependencies.
    * Never store plaintext passwords, only password hashes.
    * Implement and use well-known cryptographic tools, like Passlib and JWT tokens, etc.
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  7. docs/en/docs/how-to/async-sql-encode-databases.md

        The current version assumes Pydantic v1.
    
        The new docs will include Pydantic v2 and will use <a href="https://sqlmodel.tiangolo.com/" class="external-link" target="_blank">SQLModel</a> once it is updated to use Pydantic v2 as well.
    
    !!! warning "Deprecated"
        This tutorial is deprecated and will be removed in a future version.
    
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  8. docs/en/docs/fastapi-people.md

    ## Top Translation Reviewers
    
    These users are the **Top Translation Reviewers**. 🕵️
    
    I only speak a few languages (and not very well 😅). So, the reviewers are the ones that have the [**power to approve translations**](contributing.md#translations){.internal-link target=_blank} of the documentation. Without them, there wouldn't be documentation in several other languages.
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  9. docs/en/docs/tutorial/security/oauth2-jwt.md

    ---
    
    **FastAPI** doesn't make any compromise with any database, data model or tool.
    
    It gives you all the flexibility to choose the ones that fit your project the best.
    
    And you can use directly many well maintained and widely used packages like `passlib` and `python-jose`, because **FastAPI** doesn't require any complex mechanisms to integrate external packages.
    
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  10. docs/en/docs/deployment/concepts.md

    <img src="/img/deployment/concepts/process-ram.svg">
    
    And of course, the same machine would probably have **other processes** running as well, apart from your application.
    
    An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.
    
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