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  1. docs/en/docs/benchmarks.md

        * If you are comparing Uvicorn, compare it against Daphne, Hypercorn, uWSGI, etc. Application servers.
    * **Starlette**:
        * Will have the next best performance, after Uvicorn. In fact, Starlette uses Uvicorn to run. So, it probably can only get "slower" than Uvicorn by having to execute more code.
        * But it provides you the tools to build simple web applications, with routing based on paths, etc.
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  2. docs/en/docs/tutorial/response-model.md

    `response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`.
    
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  3. docs/fr/docs/history-design-future.md

    Voici un petit bout de cette histoire.
    
    ## Alternatives
    
    Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs.
    
    Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.
    
    L'histoire de **FastAPI** est en grande partie l'histoire de ses prédécesseurs.
    
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  4. 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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  5. 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
        Keep in mind that dataclasses can't do everything Pydantic models can do.
    
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  6. docs/en/docs/tutorial/first-steps.md

    This schema definition includes your API paths, the possible parameters they take, etc.
    
    #### Data "schema"
    
    The term "schema" might also refer to the shape of some data, like a JSON content.
    
    In that case, it would mean the JSON attributes, and data types they have, etc.
    
    #### OpenAPI and JSON Schema
    
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  7. docs/vi/docs/features.md

    * Tự động tài liệu hóa data model theo <a href="https://json-schema.org/" class="external-link" target="_blank"><strong>JSON Schema</strong></a> (OpenAPI bản thân nó được dựa trên JSON Schema).
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  8. docs/fr/docs/advanced/additional-responses.md

        Ceci concerne un sujet plutôt avancé.
    
        Si vous débutez avec **FastAPI**, vous n'en aurez peut-être pas besoin.
    
    Vous pouvez déclarer des réponses supplémentaires, avec des codes HTTP, des types de médias, des descriptions, etc.
    
    Ces réponses supplémentaires seront incluses dans le schéma OpenAPI, elles apparaîtront donc également dans la documentation de l'API.
    
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  9. docs/pt/docs/tutorial/index.md

    Usá-lo em seu editor é o que realmente te mostra os benefícios do FastAPI, ver quão pouco código você tem que escrever, todas as conferências de tipo, auto completações etc.
    
    ---
    
    ## Instale o FastAPI
    
    O primeiro passo é instalar o FastAPI.
    
    Para o tutorial, você deve querer instalá-lo com todas as dependências e recursos opicionais.
    
    <div class="termy">
    
    ```console
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  10. docs/en/docs/deployment/concepts.md

    Here are some possible ideas:
    
    * An "Init Container" in Kubernetes that runs before your app container
    * A bash script that runs the previous steps and then starts your application
        * You would still need a way to start/restart *that* bash script, detect errors, etc.
    
    !!! tip
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