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.github/workflows/test.yml
run: bash scripts/test.sh env: COVERAGE_FILE: coverage/.coverage.${{ runner.os }}-py${{ matrix.python-version }} CONTEXT: ${{ runner.os }}-py${{ matrix.python-version }} - name: Store coverage files uses: actions/upload-artifact@v3 with: name: coverage path: coverage coverage-combine: needs: [test] runs-on: ubuntu-latest steps:
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docs/en/docs/tutorial/response-model.md
* `tax: float = 10.5` has a default of `10.5`. * `tags: List[str] = []` has a default of an empty list: `[]`. but you might want to omit them from the result if they were not actually stored. For example, if you have models with many optional attributes in a NoSQL database, but you don't want to send very long JSON responses full of default values. ### Use the `response_model_exclude_unset` parameter
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docs/zh/docs/tutorial/body-updates.md
然后再用它生成一个只含已设置(在请求中所发送)数据,且省略了默认值的 `dict`: ```Python hl_lines="34" {!../../../docs_src/body_updates/tutorial002.py!} ``` ### 使用 Pydantic 的 `update` 参数 接下来,用 `.copy()` 为已有模型创建调用 `update` 参数的副本,该参数为包含更新数据的 `dict`。 例如,`stored_item_model.copy(update=update_data)`: ```Python hl_lines="35" {!../../../docs_src/body_updates/tutorial002.py!} ``` ### 更新部分数据小结 简而言之,更新部分数据应: * 使用 `PATCH` 而不是 `PUT` (可选,也可以用 `PUT`); * 提取存储的数据;
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docs/en/docs/alternatives.md
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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+" ```Python hl_lines="7 48 55-56 59-60 69-75" {!> ../../../docs_src/security/tutorial004_an_py310.py!} ```
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docs/en/docs/tutorial/extra-models.md
* The **input model** needs to be able to have a password. * The **output model** should not have a password. * The **database model** would probably need to have a hashed password. !!! danger Never store user's plaintext passwords. Always store a "secure hash" that you can then verify. If you don't know, you will learn what a "password hash" is in the [security chapters](security/simple-oauth2.md#password-hashing){.internal-link target=_blank}.
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docs/en/docs/features.md
* Query parameters. * Cookies, etc. Plus all the security features from Starlette (including **session cookies**). All built as reusable tools and components that are easy to integrate with your systems, data stores, relational and NoSQL databases, etc. ### Dependency Injection
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docs/fr/docs/features.md
* Les cookies, etc. Plus toutes les fonctionnalités de sécurités venant de Starlette (incluant les **cookies de sessions**). Le tout conçu en composant réutilisable facilement intégrable à vos systèmes, data stores, base de données relationnelle ou NoSQL, etc. ### Injection de dépendances
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docs/en/docs/tutorial/dependencies/index.md
```Python commons: Annotated[dict, Depends(common_parameters)] ``` But because we are using `Annotated`, we can store that `Annotated` value in a variable and use it in multiple places: === "Python 3.10+" ```Python hl_lines="12 16 21" {!> ../../../docs_src/dependencies/tutorial001_02_an_py310.py!} ```
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docs/en/docs/async.md
The fast food store has 8 processors (cashiers/cooks). While the concurrent burgers store might have had only 2 (one cashier and one cook). But still, the final experience is not the best. 😞 --- This would be the parallel equivalent story for burgers. 🍔 For a more "real life" example of this, imagine a bank.
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