Required. A fully-qualified entityType resource name or an entity_type ID. Example: "projects/123/locations/us-central1/featurestores/my_featurestore_id/entityTypes/my_entity_type_id" or "my_entity_type_id" when project and location are initialized or passed, with featurestore_id passed.

Optional. Featurestore ID of an existing featurestore to retrieve entityType from, when entity_type_name is passed as entity_type ID.

Optional. Project to retrieve entityType from. If not set, project set in aiplatform.init will be used.

Optional. Location to retrieve entityType from. If not set, location set in aiplatform.init will be used.

Optional. Custom credentials to use to retrieve this EntityType. Overrides credentials set in aiplatform.init.

Required. A user defined Dict containing configurations for feature creation. The feature_configs Dict[str, Dict] i.e. {feature_id: feature_config} contains configuration for each creating feature: .. rubric:: Example feature_configs = { "my_feature_id_1": feature_config_1, "my_feature_id_2": feature_config_2, "my_feature_id_3": feature_config_3, } Each feature_config requires "value_type", and optional "description", "labels": .. rubric:: Example feature_config_1 = { "value_type": "INT64", } feature_config_2 = { "value_type": "BOOL", "description": "my feature id 2 description" } feature_config_3 = { "value_type": "STRING", "labels": { "my key": "my value", } }

Optional. Strings which should be sent along with the request as metadata.

Optional. Whether to execute this creation synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. The ID to use for the EntityType, which will become the final component of the EntityType's resource name. This value may be up to 60 characters, and valid characters are[a-z0-9_]. The first character cannot be a number. The value must be unique within a featurestore.

Required. A fully-qualified featurestore resource name or a featurestore ID of an existing featurestore to create EntityType in. Example: "projects/123/locations/us-central1/featurestores/my_featurestore_id" or "my_featurestore_id" when project and location are initialized or passed.

Optional. Description of the EntityType.

Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Seehttps://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.

Optional. Project to create EntityType in iffeaturestore_name is passed an featurestore ID. If not set, project set in aiplatform.init will be used.

Optional. Location to create EntityType in iffeaturestore_name is passed an featurestore ID. If not set, location set in aiplatform.init will be used.

Optional. Custom credentials to use to create EntityTypes. Overrides credentials set in aiplatform.init.

Optional. Strings which should be sent along with the request as metadata.

Optional. Whether to execute this creation synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. The ID to use for the Feature, which will become the final component of the Feature's resource name, which is immutable. This value may be up to 60 characters, and valid characters are[a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType.

Required. Immutable. Type of Feature value. One of BOOL, BOOL_ARRAY, DOUBLE, DOUBLE_ARRAY, INT64, INT64_ARRAY, STRING, STRING_ARRAY, BYTES.

Optional. Description of the Feature.

Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Seehttps://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.

Optional. Strings which should be sent along with the request as metadata.

Optional. Whether to execute this creation synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.)

Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. The list of feature IDs to be deleted.

Optional. Whether to execute this deletion synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. The managed feature resource ID in this EntityType.

Required. IDs of the Feature to import values of. The Features must exist in the target EntityType, or the request will fail.

Required. The feature_time can be one of: - The source column that holds the Feature timestamp for all Feature values in each entity. - A single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision.

Required. BigQuery URI to the input table. .. rubric:: Example 'bq://project.dataset.table_name'

Optional. User defined dictionary to map ID of the Feature for importing values of to the source column for getting the Feature values from. Specify the features whose ID and source column are not the same. If not provided, the source column need to be the same as the Feature ID. .. rubric:: Example feature_ids = ['my_feature_id_1', 'my_feature_id_2', 'my_feature_id_3'] feature_source_fields = { 'my_feature_id_1': 'my_feature_id_1_source_field', } Note: The source column of 'my_feature_id_1' is 'my_feature_id_1_source_field', The source column of 'my_feature_id_2' is the ID of the feature, same for 'my_feature_id_3'.

Optional. Source column that holds entity IDs. If not provided, entity IDs are extracted from the column namedentity_id.

Optional. If set, data will not be imported for online serving. This is typically used for backfilling, where Feature generation timestamps are not in the timestamp range needed for online serving.

Optional. Specifies the number of workers that are used to write data to the Featurestore. Consider the online serving capacity that you require to achieve the desired import throughput without interfering with online serving. The value must be positive, and less than or equal to 100. If not set, defaults to using 1 worker. The low count ensures minimal impact on online serving performance.

Optional. Strings which should be sent along with the request as metadata.

Optional. Whether to execute this import synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. IDs of the Feature to import values of. The Features must exist in the target EntityType, or the request will fail.

Required. The feature_time can be one of: - The source column that holds the Feature timestamp for all Feature values in each entity. Note: The dtype of the source column should bedatetime64. - A single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision. Example: feature_time = datetime.datetime(year=2022, month=1, day=1, hour=11, minute=59, second=59) or feature_time_str = datetime.datetime.now().isoformat(sep=" ", timespec="milliseconds") feature_time = datetime.datetime.strptime(feature_time_str, "%Y-%m-%d %H:%M:%S.%f")

Optional. User defined dictionary to map ID of the Feature for importing values of to the source column for getting the Feature values from. Specify the features whose ID and source column are not the same. If not provided, the source column need to be the same as the Feature ID. .. rubric:: Example feature_ids = ['my_feature_id_1', 'my_feature_id_2', 'my_feature_id_3'] feature_source_fields = { 'my_feature_id_1': 'my_feature_id_1_source_field', } Note: The source column of 'my_feature_id_1' is 'my_feature_id_1_source_field', The source column of 'my_feature_id_2' is the ID of the feature, same for 'my_feature_id_3'.

Optional. Source column that holds entity IDs. If not provided, entity IDs are extracted from the column namedentity_id.

Optional. Strings which should be sent along with the request as metadata.

Required. Pandas DataFrame containing the source data for ingestion.

Required. IDs of the Feature to import values of. The Features must exist in the target EntityType, or the request will fail.

Required. The feature_time can be one of: - The source column that holds the Feature timestamp for all Feature values in each entity. - A single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision.

Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, seehttps://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. .. rubric:: Example ["gs://my_bucket/my_file_1.csv", "gs://my_bucket/my_file_2.csv"] or "gs://my_bucket/my_file.avro"

Required. The type of the input file(s) provided bygcs_source_uris, the value of gcs_source_type can only be eithercsv, oravro.

Optional. User defined dictionary to map ID of the Feature for importing values of to the source column for getting the Feature values from. Specify the features whose ID and source column are not the same. If not provided, the source column need to be the same as the Feature ID. .. rubric:: Example feature_ids = ['my_feature_id_1', 'my_feature_id_2', 'my_feature_id_3'] feature_source_fields = { 'my_feature_id_1': 'my_feature_id_1_source_field', } Note: The source column of 'my_feature_id_1' is 'my_feature_id_1_source_field', The source column of 'my_feature_id_2' is the ID of the feature, same for 'my_feature_id_3'.

Optional. Source column that holds entity IDs. If not provided, entity IDs are extracted from the column namedentity_id.

Optional. If set, data will not be imported for online serving. This is typically used for backfilling, where Feature generation timestamps are not in the timestamp range needed for online serving.

Optional. Specifies the number of workers that are used to write data to the Featurestore. Consider the online serving capacity that you require to achieve the desired import throughput without interfering with online serving. The value must be positive, and less than or equal to 100. If not set, defaults to using 1 worker. The low count ensures minimal impact on online serving performance.

Optional. Strings which should be sent along with the request as metadata.

Optional. Whether to execute this import synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Required. A fully-qualified featurestore resource name or a featurestore ID of an existing featurestore to list entityTypes in. Example: "projects/123/locations/us-central1/featurestores/my_featurestore_id" or "my_featurestore_id" when project and location are initialized or passed.

Optional. Lists the EntityTypes that match the filter expression. The following filters are supported: -create_time: Supports=,!=,<,>,>=, and<= comparisons. Values must be in RFC 3339 format. -update_time: Supports=,!=,<,>,>=, and<= comparisons. Values must be in RFC 3339 format. -labels: Supports key-value equality as well as key presence. Examples: -create_time > "2020-01-31T15:30:00.000000Z" OR update_time > "2020-01-31T15:30:00.000000Z" --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. -labels.active = yes AND labels.env = prod --> EntityTypes having both (active: yes) and (env: prod) labels. -labels.env: * --> Any EntityType which has a label with 'env' as the key.

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: -entity_type_id -create_time -update_time

Optional. Project to list entityTypes in. If not set, project set in aiplatform.init will be used.

Optional. Location to list entityTypes in. If not set, location set in aiplatform.init will be used.

Optional. Custom credentials to use to list entityTypes. Overrides credentials set in aiplatform.init.

Optional. Lists the Features that match the filter expression. The following filters are supported: -value_type: Supports = and != comparisons. -create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. -update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. -labels: Supports key-value equality as well as key presence. Examples: -value_type = DOUBLE --> Features whose type is DOUBLE. -create_time > "2020-01-31T15:30:00.000000Z" OR update_time > "2020-01-31T15:30:00.000000Z" --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. -labels.active = yes AND labels.env = prod --> Features having both (active: yes) and (env: prod) labels. -labels.env: * --> Any Feature which has a label with 'env' as the key.

Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: -feature_id -value_type -create_time -update_time

Required. ID for a specific entity, or a list of IDs of entities to read Feature values of. The maximum number of IDs is 100 if a list.

Required. ID for a specific feature, or a list of IDs of Features in the EntityType for reading feature values. Default to "*", where value of all features will be read.

Optional. Strings which should be sent along with the request as metadata.

Optional. Description of the EntityType.

Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. Seehttps://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.

Required. Strings which should be sent along with the request as metadata.