close()

Close httplib2 connections.

create(parent, body=None, dryRun=None, x__xgafv=None)

Creates a new model.

Args:
parent: string, Required. The parent resource under which to create the model. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}` (required)
body: object, The request body.
The object takes the form of:

{ # 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.
"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
"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.
"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.
"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
"modelFeaturesConfig": { # Additional model features config. # Optional. Additional model features config.
"frequentlyBoughtTogetherConfig": { # Additional configs for the frequently-bought-together model type. # Additional configs for frequently-bought-together models.
"contextProductsType": "A String", # Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the `frequently-bought-together` type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS.
},
},
"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.
"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.
"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.
"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.
"panels": [ # Required. A list of panel configurations. Limit = 5.
{ # An individual panel with a list of ServingConfigs to consider for it.
"candidates": [ # Required. The candidates to consider on the panel.
{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
"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`.
},
],
"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.
"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`.
},
"displayName": "A String", # Optional. The name to display for the panel.
},
],
"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`.
},
"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`.
"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
"A String",
],
},
],
"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
"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.
"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.
"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.
"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.
}

dryRun: boolean, Optional. Whether to run a dry run to validate the request (without actually creating the model).
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

delete(name, x__xgafv=None)

Deletes an existing model.

Args:
  name: string, Required. The resource name of the Model to delete. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); }
}

get(name, x__xgafv=None)

Gets a model.

Args:
name: string, Required. The resource name of the Model to get. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog}/models/{model_id}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # 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.
"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
"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.
"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.
"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
"modelFeaturesConfig": { # Additional model features config. # Optional. Additional model features config.
"frequentlyBoughtTogetherConfig": { # Additional configs for the frequently-bought-together model type. # Additional configs for frequently-bought-together models.
"contextProductsType": "A String", # Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the `frequently-bought-together` type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS.
},
},
"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.
"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.
"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.
"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.
"panels": [ # Required. A list of panel configurations. Limit = 5.
{ # An individual panel with a list of ServingConfigs to consider for it.
"candidates": [ # Required. The candidates to consider on the panel.
{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
"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`.
},
],
"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.
"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`.
},
"displayName": "A String", # Optional. The name to display for the panel.
},
],
"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`.
},
"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`.
"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
"A String",
],
},
],
"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
"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.
"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.
"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.
"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.
}

list(parent, pageSize=None, pageToken=None, x__xgafv=None)

Lists all the models linked to this event store.

Args:
parent: string, Required. The parent for which to list models. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}` (required)
pageSize: integer, Optional. Maximum number of results to return. If unspecified, defaults to 50. Max allowed value is 1000.
pageToken: string, Optional. A page token, received from a previous `ListModels` call. Provide this to retrieve the subsequent page.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # Response to a ListModelRequest.
"models": [ # List of Models.
{ # 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.
"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
"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.
"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.
"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
"modelFeaturesConfig": { # Additional model features config. # Optional. Additional model features config.
"frequentlyBoughtTogetherConfig": { # Additional configs for the frequently-bought-together model type. # Additional configs for frequently-bought-together models.
"contextProductsType": "A String", # Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the `frequently-bought-together` type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS.
},
},
"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.
"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.
"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.
"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.
"panels": [ # Required. A list of panel configurations. Limit = 5.
{ # An individual panel with a list of ServingConfigs to consider for it.
"candidates": [ # Required. The candidates to consider on the panel.
{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
"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`.
},
],
"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.
"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`.
},
"displayName": "A String", # Optional. The name to display for the panel.
},
],
"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`.
},
"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`.
"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
"A String",
],
},
],
"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
"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.
"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.
"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.
"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.
},
],
"nextPageToken": "A String", # Pagination token, if not returned indicates the last page.
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.

patch(name, body=None, updateMask=None, x__xgafv=None)

Update of model metadata. Only fields that currently can be updated are: `filtering_option` and `periodic_tuning_state`. If other values are provided, this API method ignores them.

Args:
name: 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. (required)
body: object, The request body.
The object takes the form of:

{ # 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.
"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
"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.
"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.
"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
"modelFeaturesConfig": { # Additional model features config. # Optional. Additional model features config.
"frequentlyBoughtTogetherConfig": { # Additional configs for the frequently-bought-together model type. # Additional configs for frequently-bought-together models.
"contextProductsType": "A String", # Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the `frequently-bought-together` type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS.
},
},
"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.
"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.
"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.
"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.
"panels": [ # Required. A list of panel configurations. Limit = 5.
{ # An individual panel with a list of ServingConfigs to consider for it.
"candidates": [ # Required. The candidates to consider on the panel.
{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
"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`.
},
],
"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.
"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`.
},
"displayName": "A String", # Optional. The name to display for the panel.
},
],
"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`.
},
"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`.
"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
"A String",
],
},
],
"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
"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.
"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.
"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.
"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.
}

updateMask: string, Optional. Indicates which fields in the provided 'model' to update. If not set, by default updates all fields.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # 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.
"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
"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.
"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.
"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
"modelFeaturesConfig": { # Additional model features config. # Optional. Additional model features config.
"frequentlyBoughtTogetherConfig": { # Additional configs for the frequently-bought-together model type. # Additional configs for frequently-bought-together models.
"contextProductsType": "A String", # Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the `frequently-bought-together` type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS.
},
},
"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.
"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.
"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.
"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.
"panels": [ # Required. A list of panel configurations. Limit = 5.
{ # An individual panel with a list of ServingConfigs to consider for it.
"candidates": [ # Required. The candidates to consider on the panel.
{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
"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`.
},
],
"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.
"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`.
},
"displayName": "A String", # Optional. The name to display for the panel.
},
],
"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`.
},
"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`.
"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
"A String",
],
},
],
"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
"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.
"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.
"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.
"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.
}

pause(name, body=None, x__xgafv=None)

Pauses the training of an existing model.

Args:
name: string, Required. The name of the model to pause. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` (required)
body: object, The request body.
The object takes the form of:

{ # Request for pausing training of a model.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # 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.
"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
"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.
"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.
"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
"modelFeaturesConfig": { # Additional model features config. # Optional. Additional model features config.
"frequentlyBoughtTogetherConfig": { # Additional configs for the frequently-bought-together model type. # Additional configs for frequently-bought-together models.
"contextProductsType": "A String", # Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the `frequently-bought-together` type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS.
},
},
"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.
"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.
"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.
"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.
"panels": [ # Required. A list of panel configurations. Limit = 5.
{ # An individual panel with a list of ServingConfigs to consider for it.
"candidates": [ # Required. The candidates to consider on the panel.
{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
"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`.
},
],
"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.
"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`.
},
"displayName": "A String", # Optional. The name to display for the panel.
},
],
"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`.
},
"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`.
"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
"A String",
],
},
],
"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
"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.
"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.
"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.
"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.
}

resume(name, body=None, x__xgafv=None)

Resumes the training of an existing model.

Args:
name: string, Required. The name of the model to resume. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` (required)
body: object, The request body.
The object takes the form of:

{ # Request for resuming training of a model.
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # 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.
"createTime": "A String", # Output only. Timestamp the Recommendation Model was created at.
"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.
"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.
"filteringOption": "A String", # Optional. If `RECOMMENDATIONS_FILTERING_ENABLED`, recommendation filtering by attributes is enabled for the model.
"lastTuneTime": "A String", # Output only. The timestamp when the latest successful tune finished.
"modelFeaturesConfig": { # Additional model features config. # Optional. Additional model features config.
"frequentlyBoughtTogetherConfig": { # Additional configs for the frequently-bought-together model type. # Additional configs for frequently-bought-together models.
"contextProductsType": "A String", # Optional. Specifies the context of the model when it is used in predict requests. Can only be set for the `frequently-bought-together` type. If it isn't specified, it defaults to MULTIPLE_CONTEXT_PRODUCTS.
},
},
"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.
"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.
"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.
"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.
"panels": [ # Required. A list of panel configurations. Limit = 5.
{ # An individual panel with a list of ServingConfigs to consider for it.
"candidates": [ # Required. The candidates to consider on the panel.
{ # A candidate to consider for a given panel. Currently only ServingConfig are valid candidates.
"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`.
},
],
"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.
"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`.
},
"displayName": "A String", # Optional. The name to display for the panel.
},
],
"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`.
},
"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`.
"servingConfigLists": [ # Output only. The list of valid serving configs associated with the PageOptimizationConfig.
{ # Represents an ordered combination of valid serving configs, which can be used for `PAGE_OPTIMIZATION` recommendations.
"servingConfigIds": [ # Optional. A set of valid serving configs that may be used for `PAGE_OPTIMIZATION`.
"A String",
],
},
],
"servingState": "A String", # Output only. The serving state of the model: `ACTIVE`, `NOT_ACTIVE`.
"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.
"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.
"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.
"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.
}

tune(name, body=None, x__xgafv=None)

Tunes an existing model.

Args:
name: string, Required. The resource name of the model to tune. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` (required)
body: object, The request body.
The object takes the form of:

{ # Request to manually start a tuning process now (instead of waiting for the periodically scheduled tuning to happen).
}

x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format

Returns:
An object of the form:

{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}

Vertex AI Search for Retail API . projects . locations . catalogs . models

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Method Details