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docs/en/docs/how-to/nosql-databases-couchbase.md
The analogy in **MongoDB** would be a "collection". In the code, a `Bucket` represents the main entrypoint of communication with the database. This utility function will: * Connect to a **Couchbase** cluster (that might be a single machine). * Set defaults for timeouts. * Authenticate in the cluster. * Get a `Bucket` instance. * Set defaults for timeouts.
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docs/en/docs/tutorial/security/oauth2-jwt.md
And be compatible with all of them at the same time. Create a utility function to hash a password coming from the user. And another utility to verify if a received password matches the hash stored. And another one to authenticate and return a user. === "Python 3.10+"
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docs/en/docs/tutorial/handling-errors.md
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docs/en/docs/how-to/async-sql-encode-databases.md
!!! tip You could adopt ideas from the section about SQLAlchemy ORM ([SQL (Relational) Databases](../tutorial/sql-databases.md){.internal-link target=_blank}), like using utility functions to perform operations in the database, independent of your **FastAPI** code.
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docs/en/docs/tutorial/security/get-current-user.md
Prefer to use the `Annotated` version if possible. ```Python hl_lines="25" {!> ../../../docs_src/security/tutorial002.py!} ``` ## Get the user `get_current_user` will use a (fake) utility function we created, that takes a token as a `str` and returns our Pydantic `User` model: === "Python 3.10+" ```Python hl_lines="19-22 26-27" {!> ../../../docs_src/security/tutorial002_an_py310.py!}
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docs/en/docs/tutorial/sql-databases.md
Import `models` (the SQLAlchemy models) and `schemas` (the Pydantic *models* / schemas). Create utility functions to: * Read a single user by ID and by email. * Read multiple users. * Read multiple items. ```Python hl_lines="1 3 6-7 10-11 14-15 27-28" {!../../../docs_src/sql_databases/sql_app/crud.py!}
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docs/en/docs/advanced/settings.md
That means that any value read in Python from an environment variable will be a `str`, and any conversion to a different type or validation has to be done in code. ## Pydantic `Settings` Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>.
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docs/fr/docs/benchmarks.md
La hiérarchie est la suivante : * **Uvicorn** : un serveur ASGI * **Starlette** : (utilise Uvicorn) un micro-framework web * **FastAPI**: (utilise Starlette) un micro-framework pour API disposant de fonctionnalités additionnelles pour la création d'API, avec la validation des données, etc. * **Uvicorn** :
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docs/fr/docs/async.md
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docs/pt/docs/contributing.md
* Utilize os mesmos exemplos Python e somente traduza o texto na documentação. Você não tem que alterar nada no código para que funcione. * Utilize as mesmas imagens, nomes de arquivo e links. Você não tem que alterar nada disso para que funcione.
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