Interface FeatureView.IndexConfigOrBuilder (3.43.0) 

public static interface FeatureView.IndexConfigOrBuilder extends MessageOrBuilder

Implements

MessageOrBuilder

Methods

getAlgorithmConfigCase()

public abstract FeatureView.IndexConfig.AlgorithmConfigCase getAlgorithmConfigCase()
Returns
Type Description
FeatureView.IndexConfig.AlgorithmConfigCase

getBruteForceConfig()

public abstract FeatureView.IndexConfig.BruteForceConfig getBruteForceConfig()

Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.BruteForceConfig brute_force_config = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
FeatureView.IndexConfig.BruteForceConfig

The bruteForceConfig.

getBruteForceConfigOrBuilder()

public abstract FeatureView.IndexConfig.BruteForceConfigOrBuilder getBruteForceConfigOrBuilder()

Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.BruteForceConfig brute_force_config = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
FeatureView.IndexConfig.BruteForceConfigOrBuilder

getCrowdingColumn()

public abstract String getCrowdingColumn()

Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.

string crowding_column = 3 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
String

The crowdingColumn.

getCrowdingColumnBytes()

public abstract ByteString getCrowdingColumnBytes()

Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.

string crowding_column = 3 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
ByteString

The bytes for crowdingColumn.

getDistanceMeasureType()

public abstract FeatureView.IndexConfig.DistanceMeasureType getDistanceMeasureType()

Optional. The distance measure used in nearest neighbor search.

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.DistanceMeasureType distance_measure_type = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
FeatureView.IndexConfig.DistanceMeasureType

The distanceMeasureType.

getDistanceMeasureTypeValue()

public abstract int getDistanceMeasureTypeValue()

Optional. The distance measure used in nearest neighbor search.

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.DistanceMeasureType distance_measure_type = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
int

The enum numeric value on the wire for distanceMeasureType.

getEmbeddingColumn()

public abstract String getEmbeddingColumn()

Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.

string embedding_column = 1 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
String

The embeddingColumn.

getEmbeddingColumnBytes()

public abstract ByteString getEmbeddingColumnBytes()

Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.

string embedding_column = 1 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
ByteString

The bytes for embeddingColumn.

getEmbeddingDimension()

public abstract int getEmbeddingDimension()

Optional. The number of dimensions of the input embedding.

optional int32 embedding_dimension = 4 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
int

The embeddingDimension.

getFilterColumns(int index)

public abstract String getFilterColumns(int index)

Optional. Columns of features that're used to filter vector search results.

repeated string filter_columns = 2 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name Description
index int

The index of the element to return.
Returns
Type Description
String

The filterColumns at the given index.

getFilterColumnsBytes(int index)

public abstract ByteString getFilterColumnsBytes(int index)

Optional. Columns of features that're used to filter vector search results.

repeated string filter_columns = 2 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name Description
index int

The index of the value to return.
Returns
Type Description
ByteString

The bytes of the filterColumns at the given index.

getFilterColumnsCount()

public abstract int getFilterColumnsCount()

Optional. Columns of features that're used to filter vector search results.

repeated string filter_columns = 2 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
int

The count of filterColumns.

getFilterColumnsList()

public abstract List<String> getFilterColumnsList()

Optional. Columns of features that're used to filter vector search results.

repeated string filter_columns = 2 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
List<String>

A list containing the filterColumns.

getTreeAhConfig()

public abstract FeatureView.IndexConfig.TreeAHConfig getTreeAhConfig()

Optional. Configuration options for the tree-AH algorithm (Shallow tree

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.TreeAHConfig tree_ah_config = 6 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
FeatureView.IndexConfig.TreeAHConfig

The treeAhConfig.

getTreeAhConfigOrBuilder()

public abstract FeatureView.IndexConfig.TreeAHConfigOrBuilder getTreeAhConfigOrBuilder()

Optional. Configuration options for the tree-AH algorithm (Shallow tree

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.TreeAHConfig tree_ah_config = 6 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
FeatureView.IndexConfig.TreeAHConfigOrBuilder

hasBruteForceConfig()

public abstract boolean hasBruteForceConfig()

Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.BruteForceConfig brute_force_config = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
boolean

Whether the bruteForceConfig field is set.

hasEmbeddingDimension()

public abstract boolean hasEmbeddingDimension()

Optional. The number of dimensions of the input embedding.

optional int32 embedding_dimension = 4 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
boolean

Whether the embeddingDimension field is set.

hasTreeAhConfig()

public abstract boolean hasTreeAhConfig()

Optional. Configuration options for the tree-AH algorithm (Shallow tree

.google.cloud.aiplatform.v1beta1.FeatureView.IndexConfig.TreeAHConfig tree_ah_config = 6 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type Description
boolean

Whether the treeAhConfig field is set.