azure.ai.language.conversations.aio package

class azure.ai.language.conversations.aio.ConversationAnalysisClient(endpoint: str, credential: Union[azure.core.credentials.AzureKeyCredential, azure.core.credentials_async.AsyncTokenCredential], **kwargs: Any)[source]

The language service conversations API is a suite of natural language processing (NLP) skills that can be used to analyze structured conversations (textual or spoken). Further documentation can be found in https://docs.microsoft.com/azure/cognitive-services/language-service/overview.

Parameters

  • endpoint (str) – Supported Cognitive Services endpoint (e.g., https://<resource-name>.cognitiveservices.azure.com). Required.

  • credential (AzureKeyCredential or AsyncTokenCredential) – Credential needed for the client to connect to Azure. This can be the an instance of AzureKeyCredential if using a Language API key or a token credential from azure.identity.

Keyword Arguments

  • api_version (str) – Api Version. Available values are “2023-04-01” and “2022-05-01”. Default value is “2023-04-01”. Note that overriding this default value may result in unsupported behavior.

  • polling_interval (int) – Default waiting time between two polls for LRO operations if no Retry-After header is present.

async analyze_conversation(task: Union[collections.abc.MutableMapping[str, Any], IO], **kwargs: Any) → collections.abc.MutableMapping[str, Any]

Analyzes the input conversation utterance.

See https://learn.microsoft.com/rest/api/language/2023-04-01/conversation-analysis-runtime/analyze-conversation for more information.

Parameters

task (JSON or IO) – A single conversational task to execute. Is either a JSON type or a IO type. Required.

Keyword Arguments

content_type (str) – Body Parameter content-type. Known values are: ‘application/json’. Default value is None.

Returns

JSON object

Return type

JSON

Raises

HttpResponseError –

Example

# The input is polymorphic. The following are possible polymorphic inputs based off
  discriminator "kind":

# JSON input template for discriminator value "Conversation":
analyze_conversation_task = {
    "analysisInput": {
        "conversationItem": {
            "id": "str",  # The ID of a conversation item. Required.
            "participantId": "str",  # The participant ID of a
              conversation item. Required.
            "language": "str",  # Optional. The override language of a
              conversation item in BCP 47 language representation.
            "modality": "str",  # Optional. Enumeration of supported
              conversational modalities. Known values are: "transcript" and "text".
            "role": "str"  # Optional. Role of the participant. Known
              values are: "agent", "customer", and "generic".
        }
    },
    "kind": "Conversation",
    "parameters": {
        "deploymentName": "str",  # The name of the deployment to use.
          Required.
        "projectName": "str",  # The name of the project to use. Required.
        "directTarget": "str",  # Optional. The name of a target project to
          forward the request to.
        "isLoggingEnabled": bool,  # Optional. If true, the service will keep
          the query for further review.
        "stringIndexType": "TextElements_v8",  # Optional. Default value is
          "TextElements_v8". Specifies the method used to interpret string offsets. Set
          to "UnicodeCodePoint" for Python strings. Known values are:
          "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit".
        "targetProjectParameters": {
            "str": analysis_parameters
        },
        "verbose": bool  # Optional. If true, the service will return more
          detailed information in the response.
    }
}

# JSON input template you can fill out and use as your body input.
task = analyze_conversation_task
# The response is polymorphic. The following are possible polymorphic responses based
  off discriminator "kind":

# JSON input template for discriminator value "ConversationResult":
analyze_conversation_task_result = {
    "kind": "ConversationResult",
    "result": {
        "prediction": base_prediction,
        "query": "str",  # The conversation utterance given by the caller.
          Required.
        "detectedLanguage": "str"  # Optional. The system detected language
          for the query in BCP 47 language representation..
    }
}

# JSON input template for discriminator value "Conversation":
base_prediction = {
    "entities": [
        {
            "category": "str",  # The entity category. Required.
            "confidenceScore": 0.0,  # The entity confidence score.
              Required.
            "length": 0,  # The length of the text. Required.
            "offset": 0,  # The starting index of this entity in the
              query. Required.
            "text": "str",  # The predicted entity text. Required.
            "extraInformation": [
                base_extra_information
            ],
            "resolutions": [
                base_resolution
            ]
        }
    ],
    "intents": [
        {
            "category": "str",  # A predicted class. Required.
            "confidenceScore": 0.0  # The confidence score of the class
              from 0.0 to 1.0. Required.
        }
    ],
    "projectKind": "Conversation",
    "topIntent": "str"  # Optional. The intent with the highest score.
}

# JSON input template for discriminator value "Orchestration":
base_prediction = {
    "intents": {
        "str": target_intent_result
    },
    "projectKind": "Orchestration",
    "topIntent": "str"  # Optional. The intent with the highest score.
}

# response body for status code(s): 200
response == analyze_conversation_task_result
async begin_conversation_analysis(task: Union[collections.abc.MutableMapping[str, Any], IO], **kwargs: Any) → azure.core.polling._async_poller.AsyncLROPoller[collections.abc.MutableMapping[str, Any]]

Submit analysis job for conversations.

Submit a collection of conversations for analysis. Specify one or more unique tasks to be executed.

See https://learn.microsoft.com/rest/api/language/2023-04-01/analyze-conversation/submit-job for more information.

Parameters

task (JSON or IO) – Collection of conversations to analyze and one or more tasks to execute. Is either a JSON type or a IO type. Required.

Keyword Arguments

  • content_type (str) – Body Parameter content-type. Known values are: ‘application/json’. Default value is None.

  • continuation_token (str) – A continuation token to restart a poller from a saved state.

  • polling (bool or AsyncPollingMethod) – By default, your polling method will be AsyncLROBasePolling. Pass in False for this operation to not poll, or pass in your own initialized polling object for a personal polling strategy.

  • polling_interval (int) – Default waiting time between two polls for LRO operations if no Retry-After header is present.

Returns

An instance of AsyncLROPoller that returns JSON object

Return type

AsyncLROPoller[JSON]

Raises

HttpResponseError –

Example

# JSON input template you can fill out and use as your body input.
task = {
    "analysisInput": {
        "conversations": [
            conversation
        ]
    },
    "tasks": [
        analyze_conversation_lro_task
    ],
    "displayName": "str"  # Optional. Display name for the analysis job.
}

# response body for status code(s): 200
response == {
    "createdDateTime": "2020-02-20 00:00:00",  # Required.
    "jobId": "str",  # Required.
    "lastUpdatedDateTime": "2020-02-20 00:00:00",  # Required.
    "status": "str",  # The status of the task at the mentioned last update time.
      Required. Known values are: "notStarted", "running", "succeeded", "failed",
      "cancelled", "cancelling", and "partiallyCompleted".
    "tasks": {
        "completed": 0,  # Count of tasks that finished successfully.
          Required.
        "failed": 0,  # Count of tasks that failed. Required.
        "inProgress": 0,  # Count of tasks that are currently in progress.
          Required.
        "total": 0,  # Total count of tasks submitted as part of the job.
          Required.
        "items": [
            analyze_conversation_job_result
        ]
    },
    "displayName": "str",  # Optional.
    "errors": [
        {
            "code": "str",  # One of a server-defined set of error codes.
              Required. Known values are: "InvalidRequest", "InvalidArgument",
              "Unauthorized", "Forbidden", "NotFound", "ProjectNotFound",
              "OperationNotFound", "AzureCognitiveSearchNotFound",
              "AzureCognitiveSearchIndexNotFound", "TooManyRequests",
              "AzureCognitiveSearchThrottling",
              "AzureCognitiveSearchIndexLimitReached", "InternalServerError",
              "ServiceUnavailable", "Timeout", "QuotaExceeded", "Conflict", and
              "Warning".
            "message": "str",  # A human-readable representation of the
              error. Required.
            "details": [
                ...
            ],
            "innererror": {
                "code": "str",  # One of a server-defined set of
                  error codes. Required. Known values are: "InvalidRequest",
                  "InvalidParameterValue", "KnowledgeBaseNotFound",
                  "AzureCognitiveSearchNotFound", "AzureCognitiveSearchThrottling",
                  "ExtractionFailure", "InvalidRequestBodyFormat", "EmptyRequest",
                  "MissingInputDocuments", "InvalidDocument", "ModelVersionIncorrect",
                  "InvalidDocumentBatch", "UnsupportedLanguageCode", and
                  "InvalidCountryHint".
                "message": "str",  # Error message. Required.
                "details": {
                    "str": "str"  # Optional. Error details.
                },
                "innererror": ...,
                "target": "str"  # Optional. Error target.
            },
            "target": "str"  # Optional. The target of the error.
        }
    ],
    "expirationDateTime": "2020-02-20 00:00:00",  # Optional.
    "nextLink": "str",  # Optional.
    "statistics": {
        "conversationsCount": 0,  # Number of conversations submitted in the
          request. Required.
        "documentsCount": 0,  # Number of documents submitted in the request.
          Required.
        "erroneousConversationsCount": 0,  # Number of invalid documents.
          This includes documents that are empty, over the size limit, or in
          unsupported languages. Required.
        "erroneousDocumentsCount": 0,  # Number of invalid documents. This
          includes empty, over-size limit or non-supported languages documents.
          Required.
        "transactionsCount": 0,  # Number of transactions for the request.
          Required.
        "validConversationsCount": 0,  # Number of conversation documents.
          This excludes documents that are empty, over the size limit, or in
          unsupported languages. Required.
        "validDocumentsCount": 0  # Number of valid documents. This excludes
          empty, over-size limit or non-supported languages documents. Required.
    }
}
async close() → None[source]
send_request(request: azure.core.rest._rest_py3.HttpRequest, **kwargs: Any) → Awaitable[azure.core.rest._rest_py3.AsyncHttpResponse][source]

Runs the network request through the client’s chained policies.

>>> from azure.core.rest import HttpRequest
>>> request = HttpRequest("GET", "https://www.example.org/")
<HttpRequest [GET], url: 'https://www.example.org/'>
>>> response = await client.send_request(request)
<AsyncHttpResponse: 200 OK>

For more information on this code flow, see https://aka.ms/azsdk/dpcodegen/python/send_request

Parameters

request (HttpRequest) – The network request you want to make. Required.

Keyword Arguments

stream (bool) – Whether the response payload will be streamed. Defaults to False.

Returns

The response of your network call. Does not do error handling on your response.

Return type

AsyncHttpResponse