list_medical_vocabularies ¶

def list_medical_vocabularies(
    self,
    *,
    config_overrides: Optional[TranscribeClientConfig] = None,
    next_token: Optional["capo_transcribe.types.next_token.NextToken"] = None,
    max_results: Optional["capo_transcribe.types.max_results.MaxResults"] = None,
    state_equals: Optional[
        "capo_transcribe.types.vocabulary_state.VocabularyState"
    ] = None,
    name_contains: Optional[
        "capo_transcribe.types.vocabulary_name.VocabularyName"
    ] = None,
) -> "capo_transcribe.types.list_medical_vocabularies_response.ListMedicalVocabulariesResponse":
    """<p>Provides a list of custom medical vocabularies that match the specified criteria. If no criteria are specified, all custom medical vocabularies are returned.</p> <p>To get detailed information about a specific custom medical vocabulary, use the operation.</p>

    Args:
        next_token: <p>If your <code>ListMedicalVocabularies</code> request returns more results than can be displayed, <code>NextToken</code> is displayed in the response with an associated string. To get the next page of results, copy this string and repeat your request, including <code>NextToken</code> with the value of the copied string. Repeat as needed to view all your results.</p>
        max_results: <p>The maximum number of custom medical vocabularies to return in each page of results. If there are fewer results than the value that you specify, only the actual results are returned. If you do not specify a value, a default of 5 is used.</p>
        state_equals: <p>Returns only custom medical vocabularies with the specified state. Custom vocabularies are ordered by creation date, with the newest vocabulary first. If you do not include <code>StateEquals</code>, all custom medical vocabularies are returned.</p>
        name_contains: <p>Returns only the custom medical vocabularies that contain the specified string. The search is not case sensitive.</p>

    Raises:
        capo_transcribe.errors.bad_request_exception.BadRequestException: <p>Your request didn't pass one or more validation tests. This can occur when the entity you're trying to delete doesn't exist or if it's in a non-terminal state (such as <code>IN PROGRESS</code>). See the exception message field for more information.</p>
        capo_transcribe.errors.internal_failure_exception.InternalFailureException: <p>There was an internal error. Check the error message, correct the issue, and try your request again.</p>
        capo_transcribe.errors.limit_exceeded_exception.LimitExceededException: <p>You've either sent too many requests or your input file is too long. Wait before retrying your request, or use a smaller file and try your request again.</p>
        capo_transcribe.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_transcribe.types.list_medical_vocabularies_request.ListMedicalVocabulariesRequest]",
    ) -> OperationResponse[
        "capo_transcribe.types.list_medical_vocabularies_response.ListMedicalVocabulariesResponse"
    ]:
        import capo_transcribe._operations.transcribe.list_medical_vocabularies

        output, http_response = (
            capo_transcribe._operations.transcribe.list_medical_vocabularies.list_medical_vocabularies(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListMedicalVocabularies")
    )
    input_: capo_transcribe.types.list_medical_vocabularies_request.ListMedicalVocabulariesRequest = {}
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if state_equals is not None:
        input_["state_equals"] = state_equals
    if name_contains is not None:
        input_["name_contains"] = name_contains

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output