update_query_suggestions_config ¶

def update_query_suggestions_config(
    self,
    index_id: "capo_kendra.types.index_id.IndexId",
    *,
    config_overrides: Optional[kendraClientConfig] = None,
    mode: Optional["capo_kendra.types.mode.Mode"] = None,
    query_log_look_back_window_in_days: Optional[
        "capo_kendra.types.integer.Integer"
    ] = None,
    include_queries_without_user_information: Optional[
        "capo_kendra.types.object_boolean.ObjectBoolean"
    ] = None,
    minimum_number_of_querying_users: Optional[
        "capo_kendra.types.minimum_number_of_querying_users.MinimumNumberOfQueryingUsers"
    ] = None,
    minimum_query_count: Optional[
        "capo_kendra.types.minimum_query_count.MinimumQueryCount"
    ] = None,
    attribute_suggestions_config: Optional[
        "capo_kendra.types.attribute_suggestions_update_config.AttributeSuggestionsUpdateConfig"
    ] = None,
) -> None:
    """<p>Updates the settings of query suggestions for an index.</p> <p>Amazon Kendra supports partial updates, so you only need to provide the fields you want to update.</p> <p>If an update is currently processing, you need to wait for the update to finish before making another update.</p> <p>Updates to query suggestions settings might not take effect right away. The time for your updated settings to take effect depends on the updates made and the number of search queries in your index.</p> <p>You can still enable/disable query suggestions at any time.</p> <p> <code>UpdateQuerySuggestionsConfig</code> is currently not supported in the Amazon Web Services GovCloud (US-West) region.</p>

    Args:
        index_id: <p> The identifier of the index with query suggestions you want to update.</p>
        mode: <p>Set the mode to <code>ENABLED</code> or <code>LEARN_ONLY</code>.</p> <p>By default, Amazon Kendra enables query suggestions. <code>LEARN_ONLY</code> mode allows you to turn off query suggestions. You can to update this at any time.</p> <p>In <code>LEARN_ONLY</code> mode, Amazon Kendra continues to learn from new queries to keep suggestions up to date for when you are ready to switch to ENABLED mode again.</p>
        query_log_look_back_window_in_days: <p>How recent your queries are in your query log time window.</p> <p>The time window is the number of days from current day to past days.</p> <p>By default, Amazon Kendra sets this to 180.</p>
        include_queries_without_user_information: <p> <code>TRUE</code> to include queries without user information (i.e. all queries, irrespective of the user), otherwise <code>FALSE</code> to only include queries with user information.</p> <p>If you pass user information to Amazon Kendra along with the queries, you can set this flag to <code>FALSE</code> and instruct Amazon Kendra to only consider queries with user information.</p> <p>If you set to <code>FALSE</code>, Amazon Kendra only considers queries searched at least <code>MinimumQueryCount</code> times across <code>MinimumNumberOfQueryingUsers</code> unique users for suggestions.</p> <p>If you set to <code>TRUE</code>, Amazon Kendra ignores all user information and learns from all queries.</p>
        minimum_number_of_querying_users: <p>The minimum number of unique users who must search a query in order for the query to be eligible to suggest to your users.</p> <p>Increasing this number might decrease the number of suggestions. However, this ensures a query is searched by many users and is truly popular to suggest to users.</p> <p>How you tune this setting depends on your specific needs.</p>
        minimum_query_count: <p>The the minimum number of times a query must be searched in order to be eligible to suggest to your users.</p> <p>Decreasing this number increases the number of suggestions. However, this affects the quality of suggestions as it sets a low bar for a query to be considered popular to suggest to users.</p> <p>How you tune this setting depends on your specific needs.</p>
        attribute_suggestions_config: <p>Configuration information for the document fields/attributes that you want to base query suggestions on.</p>

    Raises:
        capo_kendra.errors.access_denied_exception.AccessDeniedException: <p>You don't have sufficient access to perform this action. Please ensure you have the required permission policies and user accounts and try again.</p>
        capo_kendra.errors.conflict_exception.ConflictException: <p>A conflict occurred with the request. Please fix any inconsistences with your resources and try again.</p>
        capo_kendra.errors.internal_server_exception.InternalServerException: <p>An issue occurred with the internal server used for your Amazon Kendra service. Please wait a few minutes and try again, or contact <a href="http://aws.amazon.com/contact-us/">Support</a> for help.</p>
        capo_kendra.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource you want to use doesn’t exist. Please check you have provided the correct resource and try again.</p>
        capo_kendra.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling. Please reduce the number of requests and try again.</p>
        capo_kendra.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints set by the Amazon Kendra service. Please provide the correct input and try again.</p>
        capo_kendra.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_kendra.types.update_query_suggestions_config_request.UpdateQuerySuggestionsConfigRequest]",
    ) -> OperationResponse[None]:
        import capo_kendra._operations.aws_kendra_frontend_service.update_query_suggestions_config

        output, http_response = (
            capo_kendra._operations.aws_kendra_frontend_service.update_query_suggestions_config.update_query_suggestions_config(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("UpdateQuerySuggestionsConfig")
    )
    input_: capo_kendra.types.update_query_suggestions_config_request.UpdateQuerySuggestionsConfigRequest = {
        "index_id": index_id
    }
    if mode is not None:
        input_["mode"] = mode
    if query_log_look_back_window_in_days is not None:
        input_["query_log_look_back_window_in_days"] = (
            query_log_look_back_window_in_days
        )
    if include_queries_without_user_information is not None:
        input_["include_queries_without_user_information"] = (
            include_queries_without_user_information
        )
    if minimum_number_of_querying_users is not None:
        input_["minimum_number_of_querying_users"] = (
            minimum_number_of_querying_users
        )
    if minimum_query_count is not None:
        input_["minimum_query_count"] = minimum_query_count
    if attribute_suggestions_config is not None:
        input_["attribute_suggestions_config"] = attribute_suggestions_config

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