query_assistant ¶

def query_assistant(
    self,
    assistant_id: "capo_qconnect.types.uuid_or_arn.UuidOrArn",
    *,
    config_overrides: Optional[QConnectClientConfig] = None,
    query_text: Optional["capo_qconnect.types.query_text.QueryText"] = None,
    next_token: Optional["capo_qconnect.types.next_token.NextToken"] = None,
    max_results: Optional["capo_qconnect.types.max_results.MaxResults"] = None,
    session_id: Optional["capo_qconnect.types.uuid_or_arn.UuidOrArn"] = None,
    query_condition: Optional[
        "capo_qconnect.types.query_condition_expression.QueryConditionExpression"
    ] = None,
    query_input_data: Optional[
        "capo_qconnect.types.query_input_data.QueryInputData"
    ] = None,
    override_knowledge_base_search_type: Optional[
        "capo_qconnect.types.knowledge_base_search_type.KnowledgeBaseSearchType"
    ] = None,
) -> "capo_qconnect.types.query_assistant_response.QueryAssistantResponse":
    """<important> <p>This API will be discontinued starting June 1, 2024. To receive generative responses after March 1, 2024, you will need to create a new Assistant in the Connect Customer console and integrate the Amazon Q in Connect JavaScript library (amazon-q-connectjs) into your applications.</p> </important> <p>Performs a manual search against the specified assistant. To retrieve recommendations for an assistant, use <a href="https://docs.aws.amazon.com/amazon-q-connect/latest/APIReference/API_GetRecommendations.html">GetRecommendations</a>. </p>

    Args:
        assistant_id: <p>The identifier of the Amazon Q in Connect assistant. Can be either the ID or the ARN. URLs cannot contain the ARN.</p>
        query_text: <p>The text to search for.</p>
        next_token: <p>The token for the next set of results. Use the value returned in the previous response in the next request to retrieve the next set of results.</p>
        max_results: <p>The maximum number of results to return per page.</p>
        session_id: <p>The identifier of the Amazon Q in Connect session. Can be either the ID or the ARN. URLs cannot contain the ARN.</p>
        query_condition: <p>Information about how to query content.</p>
        query_input_data: <p>Information about the query.</p>
        override_knowledge_base_search_type: <p>The search type to be used against the Knowledge Base for this request. The values can be <code>SEMANTIC</code> which uses vector embeddings or <code>HYBRID</code> which use vector embeddings and raw text.</p>

    Raises:
        capo_qconnect.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_qconnect.errors.request_timeout_exception.RequestTimeoutException: <p>The request reached the service more than 15 minutes after the date stamp on the request or more than 15 minutes after the request expiration date (such as for pre-signed URLs), or the date stamp on the request is more than 15 minutes in the future.</p>
        capo_qconnect.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource does not exist.</p>
        capo_qconnect.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by a service.</p>
        capo_qconnect.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_qconnect.types.query_assistant_request.QueryAssistantRequest]",
    ) -> OperationResponse[
        "capo_qconnect.types.query_assistant_response.QueryAssistantResponse"
    ]:
        import capo_qconnect._operations.wisdom_service.query_assistant

        output, http_response = (
            capo_qconnect._operations.wisdom_service.query_assistant.query_assistant(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_qconnect.types.query_assistant_request.QueryAssistantRequest",
    ) -> dict[str, Any]:
        import capo_qconnect.types.query_assistant_request

        return capo_qconnect.types.query_assistant_request.redact_sensitive(input_)

    def _redact_output(
        output: "capo_qconnect.types.query_assistant_response.QueryAssistantResponse",
    ) -> dict[str, Any]:
        import capo_qconnect.types.query_assistant_response

        return capo_qconnect.types.query_assistant_response.redact_sensitive(output)

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "QueryAssistant",
            redact_input=_redact_input,
            redact_output=_redact_output,
        ),
    )
    input_: capo_qconnect.types.query_assistant_request.QueryAssistantRequest = {
        "assistant_id": assistant_id
    }
    if query_text is not None:
        input_["query_text"] = query_text
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if session_id is not None:
        input_["session_id"] = session_id
    if query_condition is not None:
        input_["query_condition"] = query_condition
    if query_input_data is not None:
        input_["query_input_data"] = query_input_data
    if override_knowledge_base_search_type is not None:
        input_["override_knowledge_base_search_type"] = (
            override_knowledge_base_search_type
        )

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