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#### retail:v2alpha
The following keys were added:
- resources.projects.resources.locations.resources.catalogs.resources.models.methods.get (Total Keys: 11)
#### retail:v2beta
The following keys were added:
- resources.projects.resources.locations.resources.catalogs.resources.models.methods.get (Total Keys: 11)
Copy file name to clipboardExpand all lines: docs/dyn/retail_v2.projects.locations.catalogs.placements.html
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The object takes the form of:
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{ # Request message for Predict method.
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"filter": "A String", # 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"))
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"filter": "A String", # 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")) * (brands: ANY("Pixel")) AND (colors: ANY("Red") OR categories: ANY("Phones")) For more information, see [Filter recommendations](https://cloud.google.com/retail/docs/filter-recs).
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"labels": { # 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.
Copy file name to clipboardExpand all lines: docs/dyn/retail_v2.projects.locations.catalogs.servingConfigs.html
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The object takes the form of:
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{ # Request message for Predict method.
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"filter": "A String", # 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"))
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"filter": "A String", # 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")) * (brands: ANY("Pixel")) AND (colors: ANY("Red") OR categories: ANY("Phones")) For more information, see [Filter recommendations](https://cloud.google.com/retail/docs/filter-recs).
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"labels": { # 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.
name: string, Required. The resource name of the Model to get. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog}/models/{model_id}` (required)
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x__xgafv: string, V1 error format.
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Allowed values
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1 - v1 error format
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2 - v2 error format
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Returns:
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An object of the form:
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{ # Metadata that describes the training and serving parameters of a Model. A Model can be associated with a ServingConfig and then queried through the Predict API.
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"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
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"dataState": "A String", # Output only. The state of data requirements for this model: `DATA_OK` and `DATA_ERROR`. Recommendation model cannot be trained if the data is in `DATA_ERROR` state. Recommendation model can have `DATA_ERROR` state even if serving state is `ACTIVE`: models were trained successfully before, but cannot be refreshed because model no longer has sufficient data for training.
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"displayName": "A String", # Required. The display name of the model. Should be human readable, used to display Recommendation Models in the Retail Cloud Console Dashboard. UTF-8 encoded string with limit of 1024 characters.
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"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
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"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
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"name": "A String", # Required. The fully qualified resource name of the model. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` catalog_id has char limit of 50. recommendation_model_id has char limit of 40.
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"optimizationObjective": "A String", # Optional. The optimization objective e.g. `cvr`. Currently supported values: `ctr`, `cvr`, `revenue-per-order`. If not specified, we choose default based on model type. Default depends on type of recommendation: `recommended-for-you` => `ctr` `others-you-may-like` => `ctr` `frequently-bought-together` => `revenue_per_order` This field together with optimization_objective describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = `frequently-bought-together` and optimization_objective = `ctr`), you receive an error 400 if you try to create/update a recommendation with this set of knobs.
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"pageOptimizationConfig": { # The PageOptimizationConfig for model training. This determines how many panels to optimize for, and which serving configs to consider for each panel. The purpose of this model is to optimize which ServingConfig to show on which panels in way that optimizes the visitors shopping journey. # Optional. The page optimization config.
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"pageOptimizationEventType": "A String", # Required. The type of UserEvent this page optimization is shown for. Each page has an associated event type - this will be the corresponding event type for the page that the page optimization model is used on. Supported types: * `add-to-cart`: Products being added to cart. * `detail-page-view`: Products detail page viewed. * `home-page-view`: Homepage viewed * `category-page-view`: Homepage viewed * `shopping-cart-page-view`: User viewing a shopping cart. `home-page-view` only allows models with type `recommended-for-you`. All other page_optimization_event_type allow all Model.types.
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"panels": [ # Required. A list of panel configurations. Limit = 5.
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{ # An individual panel with a list of ServingConfigs to consider for it.
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"candidates": [ # Required. The candidates to consider on the panel.
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{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
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"servingConfigId": "A String", # This has to be a valid ServingConfig identifier. For example, for a ServingConfig with full name: `projects/*/locations/global/catalogs/default_catalog/servingConfigs/my_candidate_config`, this would be `my_candidate_config`.
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},
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],
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"defaultCandidate": { # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates. # Required. The default candidate. If the model fails at serving time, we fall back to the default.
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"servingConfigId": "A String", # This has to be a valid ServingConfig identifier. For example, for a ServingConfig with full name: `projects/*/locations/global/catalogs/default_catalog/servingConfigs/my_candidate_config`, this would be `my_candidate_config`.
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},
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"displayName": "A String", # Optional. The name to display for the panel.
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},
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],
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"restriction": "A String", # Optional. How to restrict results across panels e.g. can the same ServingConfig be shown on multiple panels at once. If unspecified, default to `UNIQUE_MODEL_RESTRICTION`.
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},
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"periodicTuningState": "A String", # Optional. The state of periodic tuning. The period we use is 3 months - to do a one-off tune earlier use the `TuneModel` method. Default value is `PERIODIC_TUNING_ENABLED`.
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"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
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{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
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"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
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"A String",
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],
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},
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],
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"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
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"trainingState": "A String", # Optional. The training state that the model is in (e.g. `TRAINING` or `PAUSED`). Since part of the cost of running the service is frequency of training - this can be used to determine when to train model in order to control cost. If not specified: the default value for `CreateModel` method is `TRAINING`. The default value for `UpdateModel` method is to keep the state the same as before.
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"tuningOperation": "A String", # Output only. The tune operation associated with the model. Can be used to determine if there is an ongoing tune for this recommendation. Empty field implies no tune is goig on.
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"type": "A String", # Required. The type of model e.g. `home-page`. Currently supported values: `recommended-for-you`, `others-you-may-like`, `frequently-bought-together`, `page-optimization`, `similar-items`, `buy-it-again`, `on-sale-items`, and `recently-viewed`(readonly value). This field together with optimization_objective describe model metadata to use to control model training and serving. See https://cloud.google.com/retail/docs/models for more details on what the model metadata control and which combination of parameters are valid. For invalid combinations of parameters (e.g. type = `frequently-bought-together` and optimization_objective = `ctr`), you receive an error 400 if you try to create/update a recommendation with this set of knobs.
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"updateTime": "A String", # Output only. Timestamp the Recommendation Model was last updated. E.g. if a Recommendation Model was paused - this would be the time the pause was initiated.
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