Testing a deployed model

How to test a deployed model

As soon as you deploy one of your models, you can test it manually.

To test a deployed model, proceed as follows:

You can only test running models. If a deployed model is stopped, the Test box will be greyed out.

The response is returned as JSON and displayed in the Response tab. Click the Copy icon to copy the response body.

6. To see the request body, select the Request tab. Click the Copy icon to copy the request body.

Below you can find the description of both request and response properties.

Property		Description
`flow_id`		The NLU workflow ID that indicates the engine running an annotation.
`input`		User input.
	`entries`	Utterances input by the user.
`dialog_context`		Dialog context information used to evaluate user input and decide on the next system actions. Part of this context can be referenced in the creation of context sensitive rules.
	`max_nbest`	Reserved.
	`nbest`	Reserved.
	`ambiguous_utterance`	User’s previous utterance(s) in cases the status of the previous step’s intent was ambiguous.
	`system_actions`	System actions.

Property		Description
`utterance`		Phrase under query
`intents`		List of intents and their parameters.
	`name`	intent the queried phrase is associated with
	`source`	The source the intent is originated from. Valid values: ml is Machine Learning, used to expand your model’s understanding using your custom data. rb is rule-based NLU, the out-of-the-box understanding based on the model’s domain.
	`constraint`	Details about the constraint that matches this intent. Null for ml intents.
`nbest_intents`		List of n best intents and their parameters.
	`name`	intent the queried phrase is associated with
	`source`	The source the intent is originated from. Valid values: ml is Machine Learning, used to expand your model’s understanding using your custom data. rb is rule-based NLU, the out-of-the-box understanding based on the model’s domain.
	`confidence`	The Machine Learning confidence for the intent.