Class v2alpha.PredictionServiceClient (2.3.1)

const client = longrunning.operationsClient();
await client.cancelOperation({name: ''});

const client = longrunning.operationsClient();
await client.deleteOperation({name: ''});

const [response] = await client.getLocation(request);

const client = longrunning.operationsClient();
const name = '';
const [response] = await client.getOperation({name});
// doThingsWith(response)

const iterable = client.listLocationsAsync(request);
for await (const response of iterable) {
  // process response
}

const client = longrunning.operationsClient();
for await (const response of client.listOperationsAsync(request));
// doThingsWith(response)

  /**
   * This snippet has been automatically generated and should be regarded as a code template only.
   * It will require modifications to work.
   * It may require correct/in-range values for request initialization.
   * TODO(developer): Uncomment these variables before running the sample.
   */
  /**
   *  Required. Full resource name of the format:
   *  `{placement=projects/* /locations/global/catalogs/default_catalog/servingConfigs/*}`
   *  or
   *  `{placement=projects/* /locations/global/catalogs/default_catalog/placements/*}`.
   *  We recommend using the `servingConfigs` resource. `placements` is a legacy
   *  resource.
   *  The ID of the Recommendations AI serving config or placement.
   *  Before you can request predictions from your model, you must create at
   *  least one serving config or placement for it. For more information, see
   *  Manage serving configs 
   *  (https://cloud.google.com/retail/docs/manage-configs).
   *  The full list of available serving configs can be seen at
   *  https://console.cloud.google.com/ai/retail/catalogs/default_catalog/configs
   */
  // const placement = 'abc123'
  /**
   *  Required. Context about the user, what they are looking at and what action
   *  they took to trigger the predict request. Note that this user event detail
   *  won't be ingested to userEvent logs. Thus, a separate userEvent write
   *  request is required for event logging.
   *  Don't set
   *  UserEvent.visitor_id google.cloud.retail.v2alpha.UserEvent.visitor_id  or
   *  UserInfo.user_id google.cloud.retail.v2alpha.UserInfo.user_id  to the
   *  same fixed ID for different users. If you are trying to receive
   *  non-personalized recommendations (not recommended; this can negatively
   *  impact model performance), instead set
   *  UserEvent.visitor_id google.cloud.retail.v2alpha.UserEvent.visitor_id  to
   *  a random unique ID and leave
   *  UserInfo.user_id google.cloud.retail.v2alpha.UserInfo.user_id  unset.
   */
  // const userEvent = {}
  /**
   *  Maximum number of results to return. Set this property to the number of
   *  prediction results needed. If zero, the service will choose a reasonable
   *  default. The maximum allowed value is 100. Values above 100 will be coerced
   *  to 100.
   */
  // const pageSize = 1234
  /**
   *  This field is not used; leave it unset.
   */
  // const pageToken = 'abc123'
  /**
   *  Filter for restricting prediction results with a length limit of 5,000
   *  characters. Accepts values for tags and the `filterOutOfStockItems` flag.
   *   * Tag expressions. Restricts predictions to products that match all of the
   *     specified tags. Boolean operators `OR` and `NOT` are supported if the
   *     expression is enclosed in parentheses, and must be separated from the
   *     tag values by a space. `-"tagA"` is also supported and is equivalent to
   *     `NOT "tagA"`. Tag values must be double quoted UTF-8 encoded strings
   *     with a size limit of 1,000 characters.
   *     Note: "Recently viewed" models don't support tag filtering at the
   *     moment.
   *   * filterOutOfStockItems. Restricts predictions to products that do not
   *   have a
   *     stockState value of OUT_OF_STOCK.
   *  Examples:
   *   * tag=("Red" OR "Blue") tag="New-Arrival" tag=(NOT "promotional")
   *   * filterOutOfStockItems  tag=(-"promotional")
   *   * filterOutOfStockItems
   *  If your filter blocks all prediction results, the API will return *no*
   *  results. If instead you want empty result sets to return generic
   *  (unfiltered) popular products, set `strictFiltering` to False in
   *  `PredictRequest.params`. Note that the API will never return items with
   *  storageStatus of "EXPIRED" or "DELETED" regardless of filter choices.
   *  If `filterSyntaxV2` is set to true under the `params` field, then
   *  attribute-based expressions are expected instead of the above described
   *  tag-based syntax. Examples:
   *   * (colors: ANY("Red", "Blue")) AND NOT (categories: ANY("Phones"))
   *   * (availability: ANY("IN_STOCK")) AND
   *     (colors: ANY("Red") OR categories: ANY("Phones"))
   */
  // const filter = 'abc123'
  /**
   *  Use validate only mode for this prediction query. If set to true, a
   *  dummy model will be used that returns arbitrary products.
   *  Note that the validate only mode should only be used for testing the API,
   *  or if the model is not ready.
   */
  // const validateOnly = true
  /**
   *  Additional domain specific parameters for the predictions.
   *  Allowed values:
   *  * `returnProduct`: Boolean. If set to true, the associated product
   *     object will be returned in the `results.metadata` field in the
   *     prediction response.
   *  * `returnScore`: Boolean. If set to true, the prediction 'score'
   *     corresponding to each returned product will be set in the
   *     `results.metadata` field in the prediction response. The given
   *     'score' indicates the probability of a product being clicked/purchased
   *     given the user's context and history.
   *  * `strictFiltering`: Boolean. True by default. If set to false, the service
   *     will return generic (unfiltered) popular products instead of empty if
   *     your filter blocks all prediction results.
   *  * `priceRerankLevel`: String. Default empty. If set to be non-empty, then
   *     it needs to be one of {'no-price-reranking', 'low-price-reranking',
   *     'medium-price-reranking', 'high-price-reranking'}. This gives
   *     request-level control and adjusts prediction results based on product
   *     price.
   *  * `diversityLevel`: String. Default empty. If set to be non-empty, then
   *     it needs to be one of {'no-diversity', 'low-diversity',
   *     'medium-diversity', 'high-diversity', 'auto-diversity'}. This gives
   *     request-level control and adjusts prediction results based on product
   *     category.
   *  * `filterSyntaxV2`: Boolean. False by default. If set to true, the `filter`
   *    field is interpreteted according to the new, attribute-based syntax.
   */
  // const params = 1234
  /**
   *  The labels applied to a resource must meet the following requirements:
   *  * Each resource can have multiple labels, up to a maximum of 64.
   *  * Each label must be a key-value pair.
   *  * Keys have a minimum length of 1 character and a maximum length of 63
   *    characters and cannot be empty. Values can be empty and have a maximum
   *    length of 63 characters.
   *  * Keys and values can contain only lowercase letters, numeric characters,
   *    underscores, and dashes. All characters must use UTF-8 encoding, and
   *    international characters are allowed.
   *  * The key portion of a label must be unique. However, you can use the same
   *    key with multiple resources.
   *  * Keys must start with a lowercase letter or international character.
   *  See Google Cloud
   *  Document (https://cloud.google.com/resource-manager/docs/creating-managing-labels#requirements)
   *  for more details.
   */
  // const labels = 1234

  // Imports the Retail library
  const {PredictionServiceClient} = require('@google-cloud/retail').v2alpha;

  // Instantiates a client
  const retailClient = new PredictionServiceClient();

  async function callPredict() {
    // Construct request
    const request = {
      placement,
      userEvent,
    };

    // Run request
    const response = await retailClient.predict(request);
    console.log(response);
  }

  callPredict();