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  1. docs/en/docs/benchmarks.md

        * FastAPI provides more features on top of Starlette. Features that you almost always need when building APIs, like data validation and serialization. And by using it, you get automatic documentation for free (the automatic documentation doesn't even add overhead to running applications, it is generated on startup).
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  2. docs/en/docs/tutorial/path-params.md

        This is incredibly helpful while developing and debugging code that interacts with your API.
    
    ## Documentation
    
    And when you open your browser at <a href="http://127.0.0.1:8000/docs" class="external-link" target="_blank">http://127.0.0.1:8000/docs</a>, you will see an automatic, interactive, API documentation like:
    
    <img src="/img/tutorial/path-params/image01.png">
    
    !!! check
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  3. docs/fr/docs/deployment/docker.md

    ```JSON
    {"item_id": 5, "q": "somequery"}
    ```
    
    ## Documentation interactive de l'API
    
    Vous pouvez maintenant visiter <a href="http://192.168.99.100/docs" class="external-link" target="_blank">http://192.168.99.100/docs</a> ou <a href="http://127.0.0.1/docs" class="external-link" target="_blank">http://127.0.0.1/docs</a> (ou équivalent, en utilisant votre hôte Docker).
    
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  4. docs/fr/docs/project-generation.md

        * **Facile** : Fait pour être facile à utiliser et apprendre. Moins de temps passé à lire de la documentation.
        * **Concis** : Minimise la duplication de code. Plusieurs fonctionnalités à chaque déclaration de paramètre.
        * **Robuste** : Obtenez du code prêt pour être utilisé en production. Avec de la documentation automatique interactive.
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  5. docs_src/metadata/tutorial003.py

    from fastapi import FastAPI
    
    app = FastAPI(docs_url="/documentation", redoc_url=None)
    
    
    @app.get("/items/")
    async def read_items():
    Python
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  6. 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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  7. docs/en/docs/how-to/graphql.md

    ```Python hl_lines="3  22  25-26"
    {!../../../docs_src/graphql/tutorial001.py!}
    ```
    
    You can learn more about Strawberry in the <a href="https://strawberry.rocks/" class="external-link" target="_blank">Strawberry documentation</a>.
    
    And also the docs about <a href="https://strawberry.rocks/docs/integrations/fastapi" class="external-link" target="_blank">Strawberry with FastAPI</a>.
    
    ## Older `GraphQLApp` from Starlette
    
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  8. docs/en/docs/advanced/testing-websockets.md

    For this, you use the `TestClient` in a `with` statement, connecting to the WebSocket:
    
    ```Python hl_lines="27-31"
    {!../../../docs_src/app_testing/tutorial002.py!}
    ```
    
    !!! note
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  9. docs/fr/docs/advanced/additional-responses.md

    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.
    
    Mais pour ces réponses supplémentaires, vous devez vous assurer de renvoyer directement une `Response` comme `JSONResponse`, avec votre code HTTP et votre contenu.
    
    ## Réponse supplémentaire avec `model`
    
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  10. docs/fr/docs/deployment/versions.md

    ## Mise à jour des versions FastAPI
    
    Vous devriez tester votre application.
    
    Avec **FastAPI** c'est très facile (merci à Starlette), consultez la documentation : [Testing](../tutorial/testing.md){.internal-link target=_blank}
    
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