# 查詢參數模型

如果你有一組具有相關性的**查詢參數**，你可以建立一個 **Pydantic 模型**來聲明它們。

這將允許你在**多個地方**去**重複使用模型**，並且一次性為所有參數聲明驗證和元資料 (metadata)。😎

/// note

FastAPI 從 `0.115.0` 版本開始支援這個特性。🤓

///

## 使用 Pydantic 模型的查詢參數

在一個 **Pydantic 模型**中聲明你需要的**查詢參數**，然後將參數聲明為 `Query`：

{* ../../docs_src/query_param_models/tutorial001_an_py310.py hl[9:13,17] *}

**FastAPI** 將會從請求的**查詢參數**中**提取**出**每個欄位**的資料，並將其提供給你定義的 Pydantic 模型。

## 查看文件

				false, r, cred, owner)
			if apiErr == ErrAccessDenied {
				return errAuthentication
			}
			return nil
		}
		return nil
	} // No pre-existing retention metadata present.

	apiErr := isPutRetentionAllowed(oi.Bucket, oi.Name,
		days, objRetention.RetainUntilDate.Time,
		objRetention.Mode, byPassSet, r, cred, owner)
	if apiErr == ErrAccessDenied {
		return errAuthentication

		st.Versioning = madmin.MetaStatus{IsSet: true, Err: errMsg}
	default:
		st.Err = errMsg
	}
	i.Buckets[bucket] = st
}

// ImportBucketMetadataHandler - imports all bucket metadata from a zipped file and overwrite bucket metadata config
// There are some caveats regarding the following:
// 1. object lock config - object lock should have been specified at time of bucket creation. Only default retention settings are imported here.

Check how deprecated and non-deprecated *path operations* look like:

<img src="/img/tutorial/path-operation-configuration/image05.png">

## Recap { #recap }

	objAPI, disks, err := prepareErasure(t.Context(), 8)
	if err != nil {
		t.Fatalf("Failed to initialize object layer: %v", err)
	}
	defer removeRoots(disks)
	setObjectLayer(objAPI)

	// Prepare bucket metadata
	globalBucketMetadataSys = NewBucketMetadataSys()
	globalBucketObjectLockSys = &BucketObjectLockSys{}
	globalBucketVersioningSys = &BucketVersioningSys{}

	lcXML := `
<LifecycleConfiguration>
       <Rule>

{* ../../docs_src/request_files/tutorial001_02_an_py310.py hl[9,17] *}

## `UploadFile` con Metadatos Adicionales { #uploadfile-with-additional-metadata }

También puedes usar `File()` con `UploadFile`, por ejemplo, para establecer metadatos adicionales:

{* ../../docs_src/request_files/tutorial001_03_an_py39.py hl[9,15] *}

If you have a group of related **header parameters**, you can create a **Pydantic model** to declare them.

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`. 🤓

///

## Header Parameters with a Pydantic Model { #header-parameters-with-a-pydantic-model }

## Add ILM rules
./mc ilm add sitea/bucket --transition-days 0 --transition-tier WARM-TIER
./mc ilm rule list sitea/bucket

./mc cp README.md sitea/bucket/README.md

until $(./mc stat sitea/bucket/README.md --json | jq -r '.metadata."X-Amz-Storage-Class"' | grep -q WARM-TIER); do
	echo "waiting until the object is tiered to run heal"
	sleep 1s
done
./mc stat sitea/bucket/README.md

To access MinIO from localhost, run the below commands:

  1. export POD_NAME=$(kubectl get pods --namespace {{ .Release.Namespace }} -l "release={{ .Release.Name }}" -o jsonpath="{.items[0].metadata.name}")

  2. kubectl port-forward $POD_NAME 9000 --namespace {{ .Release.Namespace }}

Read more about port forwarding here: http://kubernetes.io/docs/user-guide/kubectl/kubectl_port-forward/

     *
     * @param artifact         Artifact to be transformed.
     * @param remoteRepository the repository to deploy to
     * @param localRepository  the local repository the metadata is stored in
     */
    void transformForDeployment(
            Artifact artifact, ArtifactRepository remoteRepository, ArtifactRepository localRepository)
            throws ArtifactDeploymentException;