Send prediction requests to a deployment

After the deployment is added successfully, you can query the deployment for intent and entities predictions from your utterance based on the model you assigned to the deployment. You can query the deployment programmatically through the prediction API or through the client libraries (Azure SDK).

Test deployed model

You can use Language Studio to submit an utterance, get predictions and visualize the results.

To test your deployed models from within the Language Studio:

1. Select Testing deployments from the left side menu.

2. For multilingual projects, from the Select text language dropdown, select the language of the utterance you're testing.

3. From the Deployment name dropdown, select the deployment name corresponding to the model that you want to test. You can only test models that are assigned to deployments.

4. In the text box, enter an utterance to test. For example, if you created an application for email-related utterances you could enter Delete this email.

5. Towards the top of the page, select Run the test.

6. After you run the test, you should see the response of the model in the result. You can view the results in entities cards view or view it in JSON format.

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Send a conversational language understanding request

Language Studio

1. After the deployment job is completed successfully, select the deployment you want to use and from the top menu select Get prediction URL.

   

2. In the window that appears, copy the sample request URL and body into your command line.

3. Replace <YOUR_QUERY_HERE> with the actual text you want to send to extract intents and entities from.

4. Submit the POST cURL request in your terminal or command prompt. You'll receive a 202 response with the API results if the request was successful.

REST APIs

First you will need to get your resource key and endpoint:

Go to your resource overview page in the Azure portal. From the menu on the left side, select Keys and Endpoint. You will use the endpoint and key for the API requests

Query your model

Create a POST request using the following URL, headers, and JSON body to start testing a conversational language understanding model.

Request URL

{ENDPOINT}/language/:analyze-conversations?api-version={API-VERSION}

Placeholder	Value	Example
{ENDPOINT}	The endpoint for authenticating your API request.	https://<your-custom-subdomain>.cognitiveservices.azure.com
{API-VERSION}	The version of the API you are calling.	2023-04-01

Headers

Use the following header to authenticate your request.

Key	Value
Ocp-Apim-Subscription-Key	The key to your resource. Used for authenticating your API requests.

Request body

{
  "kind": "Conversation",
  "analysisInput": {
    "conversationItem": {
      "id": "1",
      "participantId": "1",
      "text": "Text 1"
    }
  },
  "parameters": {
    "projectName": "{PROJECT-NAME}",
    "deploymentName": "{DEPLOYMENT-NAME}",
    "stringIndexType": "TextElement_V8"
  }
}

Key	Placeholder	Value	Example
participantId	{JOB-NAME}		"MyJobName
id	{JOB-NAME}		"MyJobName
text	{TEST-UTTERANCE}	The utterance that you want to predict its intent and extract entities from.	"Read Matt's email
projectName	{PROJECT-NAME}	The name of your project. This value is case-sensitive.	myProject
deploymentName	{DEPLOYMENT-NAME}	The name of your deployment. This value is case-sensitive.	staging

Once you send the request, you will get the following response for the prediction

Response body

{
  "kind": "ConversationResult",
  "result": {
    "query": "Text1",
    "prediction": {
      "topIntent": "inten1",
      "projectKind": "Conversation",
      "intents": [
        {
          "category": "intent1",
          "confidenceScore": 1
        },
        {
          "category": "intent2",
          "confidenceScore": 0
        },
        {
          "category": "intent3",
          "confidenceScore": 0
        }
      ],
      "entities": [
        {
          "category": "entity1",
          "text": "text1",
          "offset": 29,
          "length": 12,
          "confidenceScore": 1
        }
      ]
    }
  }
}

Key	Sample Value	Description
query	"Read Matt's email"	the text you submitted for query.
topIntent	"Read"	The predicted intent with highest confidence score.
intents	[]	A list of all the intents that were predicted for the query text each of them with a confidence score.
entities	[]	array containing list of extracted entities from the query text.

API response for a conversations project

In a conversations project, you'll get predictions for both your intents and entities that are present within your project.

• The intents and entities include a confidence score between 0.0 to 1.0 associated with how confident the model is about predicting a certain element in your project.
• The top scoring intent is contained within its own parameter.
• Only predicted entities will show up in your response.
• Entities indicate:
  • The text of the entity that was extracted
  • Its start location denoted by an offset value
  • The length of the entity text denoted by a length value.

Client libraries (Azure SDK)

Use the client libraries (Azure SDK)

You can also use the client libraries provided by the Azure SDK to send requests to your model.

Note

The client library for conversational language understanding is only available for:

• .NET
• Python

1. Go to your resource overview page in the Azure portal

2. From the menu on the left side, select Keys and Endpoint. Use endpoint for the API requests and you will need the key for Ocp-Apim-Subscription-Key header.

   

3. Download and install the client library package for your language of choice:

   Language	Package version
   .NET	1.0.0
   Python	1.0.0

4. After you've installed the client library, use the following samples on GitHub to start calling the API.

   • C#
   • Python

5. See the following reference documentation for more information:

   • C#
   • Python

Next steps

• Conversational language understanding overview