Query deployment for intent predictions

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 Prediction API or through the Client libraries (Azure SDK).

Test deployed model

First you 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 API requests.

Query your model

Create a POST request using the following URL, headers, and JSON body to start testing an orchestration workflow 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're 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": {
      "text": "Text1",
      "participantId": "1",
      "id": "1"
    }
  },
  "parameters": {
    "projectName": "{PROJECT-NAME}",
    "deploymentName": "{DEPLOYMENT-NAME}",
    "directTarget": "qnaProject",
    "targetProjectParameters": {
      "qnaProject": {
        "targetProjectKind": "QuestionAnswering",
        "callingOptions": {
          "context": {
            "previousUserQuery": "Meet Surface Pro 4",
            "previousQnaId": 4
          },
          "top": 1,
          "question": "App Service overview"
        }
      }
    }
  }
}

Response Body

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

{
  "kind": "ConversationResult",
  "result": {
    "query": "App Service overview",
    "prediction": {
      "projectKind": "Orchestration",
      "topIntent": "qnaTargetApp",
      "intents": {
        "qnaTargetApp": {
          "targetProjectKind": "QuestionAnswering",
          "confidenceScore": 1,
          "result": {
            "answers": [
              {
                "questions": [
                  "App Service overview"
                ],
                "answer": "The compute resources you use are determined by the *App Service plan* that you run your apps on.",
                "confidenceScore": 0.7384000000000001,
                "id": 1,
                "source": "https://learn.microsoft.com/azure/app-service/overview",
                "metadata": {},
                "dialog": {
                  "isContextOnly": false,
                  "prompts": []
                }
              }
            ]
          }
        }
      }
    }
  }
}

Client libraries (Azure SDK)

First you 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 API requests.

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

• Orchestration workflow overview