get_personalized_ranking async ¶

async def get_personalized_ranking(
    self,
    campaign_arn: "capo_personalize_runtime.types.arn.Arn",
    input_list: "capo_personalize_runtime.types.input_list.InputList",
    user_id: "capo_personalize_runtime.types.user_id.UserID",
    *,
    config_overrides: Optional[AsyncPersonalizeRuntimeClientConfig] = None,
    context: Optional["capo_personalize_runtime.types.context.Context"] = None,
    filter_arn: Optional["capo_personalize_runtime.types.arn.Arn"] = None,
    filter_values: Optional[
        "capo_personalize_runtime.types.filter_values.FilterValues"
    ] = None,
    metadata_columns: Optional[
        "capo_personalize_runtime.types.metadata_columns.MetadataColumns"
    ] = None,
) -> "capo_personalize_runtime.types.get_personalized_ranking_response.GetPersonalizedRankingResponse":
    """<p>Re-ranks a list of recommended items for the given user. The first item in the list is deemed the most likely item to be of interest to the user.</p> <note> <p>The solution backing the campaign must have been created using a recipe of type PERSONALIZED_RANKING.</p> </note>

    Args:
        campaign_arn: <p>The Amazon Resource Name (ARN) of the campaign to use for generating the personalized ranking.</p>
        input_list: <p>A list of items (by <code>itemId</code>) to rank. If an item was not included in the training dataset, the item is appended to the end of the reranked list. If you are including metadata in recommendations, the maximum is 50. Otherwise, the maximum is 500.</p>
        user_id: <p>The user for which you want the campaign to provide a personalized ranking.</p>
        context: <p>The contextual metadata to use when getting recommendations. Contextual metadata includes any interaction information that might be relevant when getting a user's recommendations, such as the user's current location or device type.</p>
        filter_arn: <p>The Amazon Resource Name (ARN) of a filter you created to include items or exclude items from recommendations for a given user. For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering Recommendations</a>.</p>
        filter_values: <p>The values to use when filtering recommendations. For each placeholder parameter in your filter expression, provide the parameter name (in matching case) as a key and the filter value(s) as the corresponding value. Separate multiple values for one parameter with a comma. </p> <p>For filter expressions that use an <code>INCLUDE</code> element to include items, you must provide values for all parameters that are defined in the expression. For filters with expressions that use an <code>EXCLUDE</code> element to exclude items, you can omit the <code>filter-values</code>.In this case, Amazon Personalize doesn't use that portion of the expression to filter recommendations.</p> <p>For more information, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/filter.html">Filtering Recommendations</a>.</p>
        metadata_columns: <p>If you enabled metadata in recommendations when you created or updated the campaign, specify metadata columns from your Items dataset to include in the personalized ranking. The map key is <code>ITEMS</code> and the value is a list of column names from your Items dataset. The maximum number of columns you can provide is 10.</p> <p> For information about enabling metadata for a campaign, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/campaigns.html#create-campaign-return-metadata">Enabling metadata in recommendations for a campaign</a>. </p>

    Raises:
        capo_personalize_runtime.errors.invalid_input_exception.InvalidInputException: <p>Provide a valid value for the field or parameter.</p>
        capo_personalize_runtime.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource does not exist.</p>
        capo_personalize_runtime.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_personalize_runtime.types.get_personalized_ranking_request.GetPersonalizedRankingRequest]",
    ) -> AsyncOperationResponse[
        "capo_personalize_runtime.types.get_personalized_ranking_response.GetPersonalizedRankingResponse"
    ]:
        import capo_personalize_runtime._operations.amazon_personalize_runtime.get_personalized_ranking

        (
            output,
            http_response,
        ) = await capo_personalize_runtime._operations.amazon_personalize_runtime.get_personalized_ranking.async_get_personalized_ranking(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_personalize_runtime.types.get_personalized_ranking_request.GetPersonalizedRankingRequest",
    ) -> dict[str, Any]:
        import capo_personalize_runtime.types.get_personalized_ranking_request

        return capo_personalize_runtime.types.get_personalized_ranking_request.redact_sensitive(
            input_
        )

    def _redact_output(
        output: "capo_personalize_runtime.types.get_personalized_ranking_response.GetPersonalizedRankingResponse",
    ) -> dict[str, Any]:
        import capo_personalize_runtime.types.get_personalized_ranking_response

        return capo_personalize_runtime.types.get_personalized_ranking_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "GetPersonalizedRanking",
            redact_input=_redact_input,
            redact_output=_redact_output,
        ),
    )
    input_: capo_personalize_runtime.types.get_personalized_ranking_request.GetPersonalizedRankingRequest = {
        "campaign_arn": campaign_arn,
        "input_list": input_list,
        "user_id": user_id,
    }
    if context is not None:
        input_["context"] = context
    if filter_arn is not None:
        input_["filter_arn"] = filter_arn
    if filter_values is not None:
        input_["filter_values"] = filter_values
    if metadata_columns is not None:
        input_["metadata_columns"] = metadata_columns

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output