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  1. docs/es/docs/benchmarks.md

             * **FastAPI**: (usa Starlette) un microframework API con varias características adicionales para construir APIs, con validación de datos, etc.
    * **Uvicorn**:
        * Tendrá el mejor rendimiento, ya que no tiene mucho código extra aparte del propio servidor.
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  2. docs/es/docs/async.md

    Muchos, muchos usuarios, pero el servidor está esperando 🕙 el envío de las peticiones ya que su conexión no es buena.
    
    Y luego esperando 🕙 nuevamente a que las respuestas retornen.
    
    Esta "espera" 🕙 se mide en microsegundos, pero aun así, sumando todo, al final es mucha espera.
    
    Es por eso que tiene mucho sentido usar código asíncrono ⏸🔀⏯ para las API web.
    
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  3. docs/es/docs/python-types.md

    !!! info "Información"
        Para aprender más sobre <a href="https://docs.pydantic.dev/" class="external-link" target="_blank">Pydantic mira su documentación</a>.
    
    **FastAPI** está todo basado en Pydantic.
    
    Vas a ver mucho más de esto en práctica en el [Tutorial - User Guide](tutorial/index.md){.internal-link target=_blank}.
    
    ## Type hints en **FastAPI**
    
    **FastAPI** aprovecha estos type hints para hacer varias cosas.
    
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  4. docs/es/docs/index.md

    * Muchas características extra (gracias a Starlette) como:
        * **WebSockets**
        * **GraphQL**
        * pruebas extremadamente fáciles con HTTPX y `pytest`
        * **CORS**
        * **Cookie Sessions**
        * ...y mucho más.
    
    ## Rendimiento
    
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  5. docs/es/docs/advanced/response-directly.md

    Y cuando devuelves una `Response`, **FastAPI** la pasará directamente.
    
    No hará ninguna conversión de datos con modelos Pydantic, no convertirá el contenido a ningún tipo, etc.
    
    Esto te da mucha flexibilidad. Puedes devolver cualquier tipo de dato, sobrescribir cualquier declaración de datos o validación, etc.
    
    ## Usando el `jsonable_encoder` en una `Response`
    
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  6. docs/en/docs/benchmarks.md

            * **FastAPI**: (uses Starlette) an API microframework with several additional features for building APIs, with data validation, etc.
    
    * **Uvicorn**:
        * Will have the best performance, as it doesn't have much extra code apart from the server itself.
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  7. docs/en/docs/contributing.md

    ```
    
    </div>
    
    as Uvicorn by default will use the port `8000`, the documentation on port `8008` won't clash.
    
    ### Translations
    
    Help with translations is VERY MUCH appreciated! And it can't be done without the help from the community. 🌎 🚀
    
    Here are the steps to help with translations.
    
    #### Tips and guidelines
    
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  8. docs/en/docs/deployment/manually.md

        ```Python
        from main import app
        ```
    
    !!! warning
        Uvicorn and others support a `--reload` option that is useful during development.
    
        The `--reload` option consumes much more resources, is more unstable, etc.
    
        It helps a lot during **development**, but you **shouldn't** use it in **production**.
    
    ## Hypercorn with Trio
    
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  9. docs/en/docs/tutorial/response-model.md

    FastAPI does several things internally with Pydantic to make sure that those same rules of class inheritance are not used for the returned data filtering, otherwise you could end up returning much more data than what you expected.
    
    This way, you can get the best of both worlds: type annotations with **tooling support** and **data filtering**.
    
    ## See it in the docs
    
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  10. docs/en/docs/alternatives.md

    And it generates OpenAPI schemas.
    
    That's how it works in Flask, Starlette, Responder, etc.
    
    But then, we have again the problem of having a micro-syntax, inside of a Python string (a big YAML).
    
    The editor can't help much with that. And if we modify parameters or Marshmallow schemas and forget to also modify that YAML docstring, the generated schema would be obsolete.
    
    !!! info
        APISpec was created by the same Marshmallow developers.
    
    
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