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docs/de/docs/tutorial/path-params.md
{!../../../docs_src/path_params/tutorial005.py!} ``` ### Testen Sie es in der API-Dokumentation Weil die erlaubten Werte für den *Pfad-Parameter* nun vordefiniert sind, kann die interaktive Dokumentation sie als Auswahl-Drop-Down anzeigen: <img src="/img/tutorial/path-params/image03.png"> ### Mit Python-*<abbr title="Enumeration – Aufzählung">Enums</abbr>* arbeiten
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docs/en/docs/deployment/concepts.md
If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly). ### Examples of Replication Tools and Strategies
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tests/test_tutorial/test_security/test_tutorial005_an.py
"content": {"application/json": {"schema": {}}}, } }, "summary": "Read Own Items", "operationId": "read_own_items_users_me_items__get", "security": [{"OAuth2PasswordBearer": ["items", "me"]}], } }, "/status/": { "get": {
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docs/em/docs/advanced/security/oauth2-scopes.md
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docs/en/docs/tutorial/response-model.md
## See it in the docs When you see the automatic docs, you can check that the input model and output model will both have their own JSON Schema: <img src="/img/tutorial/response-model/image01.png"> And both models will be used for the interactive API documentation: <img src="/img/tutorial/response-model/image02.png">
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docs/en/docs/alternatives.md
* Based on Python type hints. * Validation and documentation from these types. * Dependency Injection system. It doesn't use a data validation, serialization and documentation third-party library like Pydantic, it has its own. So, these data type definitions would not be reusable as easily.
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docs/en/docs/tutorial/bigger-applications.md
* and from it, import the function `get_token_header`. That would refer to some package above `app/`, with its own file `__init__.py`, etc. But we don't have that. So, that would throw an error in our example. 🚨 But now you know how it works, so you can use relative imports in your own apps no matter how complex they are. 🤓 ### Add some custom `tags`, `responses`, and `dependencies`
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tests/test_tutorial/test_security/test_tutorial005_py39.py
"content": {"application/json": {"schema": {}}}, } }, "summary": "Read Own Items", "operationId": "read_own_items_users_me_items__get", "security": [{"OAuth2PasswordBearer": ["items", "me"]}], } }, "/status/": { "get": {
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docs/en/docs/python-types.md
{!../../../docs_src/python_types/tutorial005.py!} ``` ### Generic types with type parameters There are some data structures that can contain other values, like `dict`, `list`, `set` and `tuple`. And the internal values can have their own type too. These types that have internal types are called "**generic**" types. And it's possible to declare them, even with their internal types.
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docs/de/docs/advanced/security/oauth2-scopes.md
So sieht die Hierarchie der Abhängigkeiten und Scopes aus: * Die *Pfadoperation* `read_own_items` hat: * Erforderliche Scopes `["items"]` mit der Abhängigkeit: * `get_current_active_user`: * Die Abhängigkeitsfunktion `get_current_active_user` hat:
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