# Invoke the endpoint
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After the endpoint is running, use the SageMaker AI Runtime [InvokeEndpoint ](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_runtime_InvokeEndpoint.html) API in the SageMaker AI Runtime service to send requests to, or invoke the endpoint. In response, the requests are handled as explainability requests by the SageMaker Clarify explainer.

**Note**  
To invoke an endpoint, choose one of the following options:  
For instructions to use Boto3 or the Amazon CLI to invoke an endpoint, see [Invoke models for real-time inference](realtime-endpoints-test-endpoints.md).
To use the SageMaker SDK for Python to invoke an endpoint, see the [Predictor](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html) API.

## Request
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The `InvokeEndpoint` API has an optional parameter `EnableExplanations`, which is mapped to the HTTP header `X-Amzn-SageMaker-Enable-Explanations`. If this parameter is provided, it overrides the `EnableExplanations` parameter of the `ClarifyExplainerConfig`.

**Note**  
The `ContentType` and `Accept` parameters of the `InvokeEndpoint` API are required. Supported formats include MIME type `text/csv` and `application/jsonlines`.

Use the `sagemaker_runtime_client` to send a request to the endpoint, as follows:

```
response = sagemaker_runtime_client.invoke_endpoint(
    EndpointName='name-of-your-endpoint',
    EnableExplanations='`true`',
    ContentType='text/csv',
    Accept='text/csv',
    Body='1,2,3,4',  # single record (of four numerical features)
)
```

For multi-model endpoints, pass an additional `TargetModel` parameter in the previous example request to specifies which model to target at the endpoint. The multi-model endpoint dynamically loads target models as needed. For more information about multi-model endpoints, see [Multi-model endpoints](multi-model-endpoints.md). See the [SageMaker Clarify Online Explainability on Multi-Model Endpoint Sample Notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-clarify/online_explainability/tabular_multi_model_endpoint/multi_model_xgboost_with_online_explainability.ipynb) for an example of how to set up and invoke multiple target models from a single endpoint.

## Response
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If the endpoint is created with `ExplainerConfig`, then a new response schema is used, This new schema is different from, and is not compatible with, an endpoint that lacks the `ExplainerConfig` parameter provided.

The MIME type of the response is `application/json`, and the response payload can be decoded from UTF-8 bytes to a JSON object. The following shows the members of this JSON object are as follows:
+ `version`: The version of the response schema in string format. For example, `1.0`.
+ `predictions`: The predictions that the request makes have the following:
  + `content_type`: The MIME type of the predictions, referring to the `ContentType` of the model container response.
  + `data`: The predictions data string delivered as the payload of the model container response for the request.
+ `label_headers`: The label headers from the `LabelHeaders` parameter. This is provided in either the explainer configuration or the model container output.
+ `explanations`: The explanations provided in the request payload. If no records are explained, then this member returns the empty object `{}`.
+ 
  + `kernel_shap`: A key that refers to an array of Kernel SHAP explanations for each record in the request. If a record is not explained, the corresponding explanation is `null`.

The `kernel_shap` element has the following members:
+ `feature_header`: The header name of the features provided by the `FeatureHeaders` parameter in the explainer configuration `ExplainerConfig`.
+ `feature_type`: The feature type inferred by explainer or provided in the `FeatureTypes` parameter in the `ExplainerConfig`. This element is only available for NLP explainability problems.
+ `attributions`: An array of attribution objects. Text features can have multiple attribution objects, each for a unit. The attribution object has the following members:
  + `attribution`: A list of probability values, given for each class.
  + `description`: The description of the text units, available only for NLP explainability problems.
    + `partial_text`: The portion of the text explained by the explainer.
    + `start_idx`: A zero-based index to identify the array location of the beginning of the partial text fragment.