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docs/en/docs/tutorial/body.md
{* ../../docs_src/body/tutorial002_py310.py *} /// info In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`. The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2. /// ## Request body + path parameters { #request-body-path-parameters }
Registered: Sun Sep 07 07:19:17 UTC 2025 - Last Modified: Sun Aug 31 10:58:56 UTC 2025 - 7.1K bytes - Viewed (0) -
docs/de/docs/advanced/dataclasses.md
Das funktioniert genauso wie mit Pydantic-Modellen. Und tatsächlich wird es unter der Haube mittels Pydantic auf die gleiche Weise bewerkstelligt. /// info Bedenken Sie, dass Datenklassen nicht alles können, was Pydantic-Modelle können. Daher müssen Sie möglicherweise weiterhin Pydantic-Modelle verwenden.
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docs/en/docs/tutorial/extra-models.md
/// info In Pydantic v1 the method was called `.dict()`, it was deprecated (but still supported) in Pydantic v2, and renamed to `.model_dump()`. The examples here use `.dict()` for compatibility with Pydantic v1, but you should use `.model_dump()` instead if you can use Pydantic v2. /// ### About `**user_in.dict()` { #about-user-in-dict } #### Pydantic's `.dict()` { #pydantics-dict }
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docs/en/docs/advanced/dataclasses.md
So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses. 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
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docs/de/docs/tutorial/body-updates.md
/// ### Pydantics `exclude_unset`-Parameter verwenden Wenn Sie Teil-Aktualisierungen entgegennehmen, ist der `exclude_unset`-Parameter in der `.model_dump()`-Methode von Pydantic-Modellen sehr nützlich. Wie in `item.model_dump(exclude_unset=True)`. /// info
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docs/de/docs/tutorial/body-nested-models.md
Um alle Optionen kennenzulernen, die Sie haben, schauen Sie sich <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantics Typübersicht</a> an. Sie werden im nächsten Kapitel ein paar Beispiele kennenlernen. Da wir zum Beispiel im `Image`-Modell ein Feld `url` haben, können wir deklarieren, dass das eine Instanz von Pydantics `HttpUrl` sein soll, anstelle eines `str`:
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docs/en/docs/tutorial/query-param-models.md
This would allow you to **re-use the model** in **multiple places** and also to declare validations and metadata for all the parameters at once. 😎 /// note This is supported since FastAPI version `0.115.0`. 🤓 /// ## Query Parameters with a Pydantic Model { #query-parameters-with-a-pydantic-model }
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docs/en/docs/tutorial/schema-extra-example.md
Here are several ways to do it. ## Extra JSON Schema data in Pydantic models { #extra-json-schema-data-in-pydantic-models } You can declare `examples` for a Pydantic model that will be added to the generated JSON Schema. //// tab | Pydantic v2 {* ../../docs_src/schema_extra_example/tutorial001_py310.py hl[13:24] *} //// //// tab | Pydantic v1
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docs/ko/docs/features.md
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docs/ko/docs/tutorial/response-model.md
/// note | 참고 `response_model`은 "데코레이터" 메소드(`get`, `post`, 등)의 매개변수입니다. 모든 매개변수들과 본문(body)처럼 *경로 작동 함수*가 아닙니다. /// Pydantic 모델 어트리뷰트를 선언한 것과 동일한 타입을 수신하므로 Pydantic 모델이 될 수 있지만, `List[Item]`과 같이 Pydantic 모델들의 `list`일 수도 있습니다. FastAPI는 이 `response_model`를 사용하여: * 출력 데이터를 타입 선언으로 변환. * 데이터 검증. * OpenAPI *경로 작동*의 응답에 JSON 스키마 추가. * 자동 생성 문서 시스템에 사용. 하지만 가장 중요한 것은:
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