Glücklicherweise bietet Pydantic ein großartiges Werkzeug zur Verarbeitung dieser Einstellungen, die von Umgebungsvariablen stammen, mit [Pydantic: Settings Management](https://docs.pydantic.dev/latest/concepts/pydantic_settings/).

### `pydantic-settings` installieren { #install-pydantic-settings }

		Name:               oi.Name,
		UserTags:           oi.UserTags,
		VersionID:          oi.VersionID,
		ModTime:            oi.ModTime,
		Size:               oi.Size,
		IsLatest:           oi.IsLatest,
		NumVersions:        oi.NumVersions,
		DeleteMarker:       oi.DeleteMarker,
		SuccessorModTime:   oi.SuccessorModTime,
		RestoreOngoing:     oi.RestoreOngoing,
		RestoreExpires:     oi.RestoreExpires,

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

這之所以可行，要感謝 **Pydantic**，因為它 [內建支援 `dataclasses`](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel)。

所以，即使上面的程式碼沒有明確使用 Pydantic，FastAPI 仍會使用 Pydantic 將那些標準的 dataclass 轉換為 Pydantic 版本的 dataclass。

而且當然一樣支援：

- 資料驗證
- 資料序列化
- 資料文件化等

## Pydantic `Settings` { #pydantic-settings }

На щастя, Pydantic надає чудовий інструмент для обробки цих налаштувань із змінних оточення - [Pydantic: Settings management](https://docs.pydantic.dev/latest/concepts/pydantic_settings/).

### Встановіть `pydantic-settings` { #install-pydantic-settings }

   * lists are sublist views of the original list, produced on demand using {@link List#subList(int,
   * int)}, and are subject to all the usual caveats about modification as explained in that API.
   *
   * @param list the list to return consecutive sublists of
   * @param size the desired size of each sublist (the last may be smaller)

## Pydantic `Settings` { #pydantic-settings }

Heureusement, Pydantic fournit un excellent utilitaire pour gérer ces paramètres provenant des variables d'environnement avec [Pydantic : gestion des paramètres](https://docs.pydantic.dev/latest/concepts/pydantic_settings/).

### Installer `pydantic-settings` { #install-pydantic-settings }

		}
		return rinfo
	}
	defer gr.Close()

	objInfo := gr.ObjInfo

	// make sure we have the latest metadata for metrics calculation
	rinfo.PrevReplicationStatus = objInfo.TargetReplicationStatus(tgt.ARN)

	// use latest ObjectInfo to check if previous replication attempt succeeded

## Pydantic `Settings` { #pydantic-settings }

Neyse ki Pydantic, environment variable'lardan gelen bu ayarları yönetmek için [Pydantic: Settings yönetimi](https://docs.pydantic.dev/latest/concepts/pydantic_settings/) ile çok iyi bir yardımcı araç sunar.

### `pydantic-settings`'i kurun { #install-pydantic-settings }

			Err:            fmt.Sprintf("server is running the latest version: %s", Version),
			CurrentVersion: Version,
		})

		for _, client := range globalNotificationSys.peerClients {
			updateStatus.Results = append(updateStatus.Results, madmin.ServerPeerUpdateStatus{
				Host:           client.String(),
				Err:            fmt.Sprintf("server is running the latest version: %s", Version),
				CurrentVersion: Version,
			})

{* ../../docs_src/dataclasses_/tutorial001_py310.py hl[1,6:11,18:19] *}

これは **Pydantic** によって引き続きサポートされています。Pydantic には [`dataclasses` の内部サポート](https://docs.pydantic.dev/latest/concepts/dataclasses/#use-of-stdlib-dataclasses-with-basemodel) があるためです。

そのため、上記のように明示的に Pydantic を使っていないコードでも、FastAPI は標準の dataclass を Pydantic 独自の dataclass に変換するために Pydantic を使用しています。

そして当然ながら、次の点も同様にサポートされます: