Class FeatureView.VectorSearchConfig.Builder (3.46.0)

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public static final class FeatureView.VectorSearchConfig.Builder extends GeneratedMessageV3.Builder<FeatureView.VectorSearchConfig.Builder> implements FeatureView.VectorSearchConfigOrBuilder

Deprecated. Use IndexConfig instead.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > FeatureView.VectorSearchConfig.Builder

Implements

FeatureView.VectorSearchConfigOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
Descriptor

Methods

addAllFilterColumns(Iterable<String> values)

public FeatureView.VectorSearchConfig.Builder addAllFilterColumns(Iterable<String> values)

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

repeated string filter_columns = 4 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
values	Iterable<String> The filterColumns to add.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

addFilterColumns(String value)

public FeatureView.VectorSearchConfig.Builder addFilterColumns(String value)

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

repeated string filter_columns = 4 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	String The filterColumns to add.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

addFilterColumnsBytes(ByteString value)

public FeatureView.VectorSearchConfig.Builder addFilterColumnsBytes(ByteString value)

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

repeated string filter_columns = 4 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	ByteString The bytes of the filterColumns to add.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public FeatureView.VectorSearchConfig.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public FeatureView.VectorSearchConfig build()

Returns
Type	Description
FeatureView.VectorSearchConfig

buildPartial()

public FeatureView.VectorSearchConfig buildPartial()

Returns
Type	Description
FeatureView.VectorSearchConfig

clear()

public FeatureView.VectorSearchConfig.Builder clear()

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearAlgorithmConfig()

public FeatureView.VectorSearchConfig.Builder clearAlgorithmConfig()

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

clearBruteForceConfig()

public FeatureView.VectorSearchConfig.Builder clearBruteForceConfig()

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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

clearCrowdingColumn()

public FeatureView.VectorSearchConfig.Builder clearCrowdingColumn()

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 = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

clearDistanceMeasureType()

public FeatureView.VectorSearchConfig.Builder clearDistanceMeasureType()

Optional. The distance measure used in nearest neighbor search.

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.DistanceMeasureType distance_measure_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

clearEmbeddingColumn()

public FeatureView.VectorSearchConfig.Builder clearEmbeddingColumn()

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

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

clearEmbeddingDimension()

public FeatureView.VectorSearchConfig.Builder clearEmbeddingDimension()

Optional. The number of dimensions of the input embedding.

optional int32 embedding_dimension = 6 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public FeatureView.VectorSearchConfig.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
field	FieldDescriptor

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearFilterColumns()

public FeatureView.VectorSearchConfig.Builder clearFilterColumns()

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

repeated string filter_columns = 4 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public FeatureView.VectorSearchConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
oneof	OneofDescriptor

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearTreeAhConfig()

public FeatureView.VectorSearchConfig.Builder clearTreeAhConfig()

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

clone()

public FeatureView.VectorSearchConfig.Builder clone()

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getAlgorithmConfigCase()

public FeatureView.VectorSearchConfig.AlgorithmConfigCase getAlgorithmConfigCase()

Returns
Type	Description
FeatureView.VectorSearchConfig.AlgorithmConfigCase

getBruteForceConfig()

public FeatureView.VectorSearchConfig.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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.BruteForceConfig	The bruteForceConfig.

getBruteForceConfigBuilder()

public FeatureView.VectorSearchConfig.BruteForceConfig.Builder getBruteForceConfigBuilder()

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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.BruteForceConfig.Builder

getBruteForceConfigOrBuilder()

public FeatureView.VectorSearchConfig.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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.BruteForceConfigOrBuilder

getCrowdingColumn()

public 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 = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
String	The crowdingColumn.

getCrowdingColumnBytes()

public 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 = 5 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
ByteString	The bytes for crowdingColumn.

getDefaultInstanceForType()

public FeatureView.VectorSearchConfig getDefaultInstanceForType()

Returns
Type	Description
FeatureView.VectorSearchConfig

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getDistanceMeasureType()

public FeatureView.VectorSearchConfig.DistanceMeasureType getDistanceMeasureType()

Optional. The distance measure used in nearest neighbor search.

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.DistanceMeasureType distance_measure_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.DistanceMeasureType	The distanceMeasureType.

getDistanceMeasureTypeValue()

public int getDistanceMeasureTypeValue()

Optional. The distance measure used in nearest neighbor search.

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.DistanceMeasureType distance_measure_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
int	The enum numeric value on the wire for distanceMeasureType.

getEmbeddingColumn()

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

Returns
Type	Description
String	The embeddingColumn.

getEmbeddingColumnBytes()

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

Returns
Type	Description
ByteString	The bytes for embeddingColumn.

getEmbeddingDimension()

public int getEmbeddingDimension()

Optional. The number of dimensions of the input embedding.

optional int32 embedding_dimension = 6 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
int	The embeddingDimension.

getFilterColumns(int index)

public String getFilterColumns(int index)

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

repeated string filter_columns = 4 [(.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 ByteString getFilterColumnsBytes(int index)

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

repeated string filter_columns = 4 [(.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 int getFilterColumnsCount()

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

repeated string filter_columns = 4 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
int	The count of filterColumns.

getFilterColumnsList()

public ProtocolStringList getFilterColumnsList()

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

repeated string filter_columns = 4 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
ProtocolStringList	A list containing the filterColumns.

getTreeAhConfig()

public FeatureView.VectorSearchConfig.TreeAHConfig getTreeAhConfig()

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.TreeAHConfig	The treeAhConfig.

getTreeAhConfigBuilder()

public FeatureView.VectorSearchConfig.TreeAHConfig.Builder getTreeAhConfigBuilder()

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.TreeAHConfig.Builder

getTreeAhConfigOrBuilder()

public FeatureView.VectorSearchConfig.TreeAHConfigOrBuilder getTreeAhConfigOrBuilder()

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
FeatureView.VectorSearchConfig.TreeAHConfigOrBuilder

hasBruteForceConfig()

public 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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the bruteForceConfig field is set.

hasEmbeddingDimension()

public boolean hasEmbeddingDimension()

Optional. The number of dimensions of the input embedding.

optional int32 embedding_dimension = 6 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the embeddingDimension field is set.

hasTreeAhConfig()

public boolean hasTreeAhConfig()

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Returns
Type	Description
boolean	Whether the treeAhConfig field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeBruteForceConfig(FeatureView.VectorSearchConfig.BruteForceConfig value)

public FeatureView.VectorSearchConfig.Builder mergeBruteForceConfig(FeatureView.VectorSearchConfig.BruteForceConfig value)

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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	FeatureView.VectorSearchConfig.BruteForceConfig

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

mergeFrom(FeatureView.VectorSearchConfig other)

public FeatureView.VectorSearchConfig.Builder mergeFrom(FeatureView.VectorSearchConfig other)

Parameter
Name	Description
other	FeatureView.VectorSearchConfig

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public FeatureView.VectorSearchConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
IOException

mergeFrom(Message other)

public FeatureView.VectorSearchConfig.Builder mergeFrom(Message other)

Parameter
Name	Description
other	Message

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig value)

public FeatureView.VectorSearchConfig.Builder mergeTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig value)

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	FeatureView.VectorSearchConfig.TreeAHConfig

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final FeatureView.VectorSearchConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setBruteForceConfig(FeatureView.VectorSearchConfig.BruteForceConfig value)

public FeatureView.VectorSearchConfig.Builder setBruteForceConfig(FeatureView.VectorSearchConfig.BruteForceConfig value)

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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	FeatureView.VectorSearchConfig.BruteForceConfig

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

setBruteForceConfig(FeatureView.VectorSearchConfig.BruteForceConfig.Builder builderForValue)

public FeatureView.VectorSearchConfig.Builder setBruteForceConfig(FeatureView.VectorSearchConfig.BruteForceConfig.Builder builderForValue)

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.VectorSearchConfig.BruteForceConfig brute_force_config = 9 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
builderForValue	FeatureView.VectorSearchConfig.BruteForceConfig.Builder

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

setCrowdingColumn(String value)

public FeatureView.VectorSearchConfig.Builder setCrowdingColumn(String value)

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 = 5 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	String The crowdingColumn to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setCrowdingColumnBytes(ByteString value)

public FeatureView.VectorSearchConfig.Builder setCrowdingColumnBytes(ByteString value)

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 = 5 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	ByteString The bytes for crowdingColumn to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setDistanceMeasureType(FeatureView.VectorSearchConfig.DistanceMeasureType value)

public FeatureView.VectorSearchConfig.Builder setDistanceMeasureType(FeatureView.VectorSearchConfig.DistanceMeasureType value)

Optional. The distance measure used in nearest neighbor search.

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.DistanceMeasureType distance_measure_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	FeatureView.VectorSearchConfig.DistanceMeasureType The distanceMeasureType to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setDistanceMeasureTypeValue(int value)

public FeatureView.VectorSearchConfig.Builder setDistanceMeasureTypeValue(int value)

Optional. The distance measure used in nearest neighbor search.

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.DistanceMeasureType distance_measure_type = 7 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	int The enum numeric value on the wire for distanceMeasureType to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setEmbeddingColumn(String value)

public FeatureView.VectorSearchConfig.Builder setEmbeddingColumn(String value)

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

Parameter
Name	Description
value	String The embeddingColumn to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setEmbeddingColumnBytes(ByteString value)

public FeatureView.VectorSearchConfig.Builder setEmbeddingColumnBytes(ByteString value)

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

Parameter
Name	Description
value	ByteString The bytes for embeddingColumn to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setEmbeddingDimension(int value)

public FeatureView.VectorSearchConfig.Builder setEmbeddingDimension(int value)

Optional. The number of dimensions of the input embedding.

optional int32 embedding_dimension = 6 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	int The embeddingDimension to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

public FeatureView.VectorSearchConfig.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
field	FieldDescriptor
value	Object

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setFilterColumns(int index, String value)

public FeatureView.VectorSearchConfig.Builder setFilterColumns(int index, String value)

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

repeated string filter_columns = 4 [(.google.api.field_behavior) = OPTIONAL];

Parameters
Name	Description
index	int The index to set the value at.
value	String The filterColumns to set.

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public FeatureView.VectorSearchConfig.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
field	FieldDescriptor
index	int
value	Object

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig value)

public FeatureView.VectorSearchConfig.Builder setTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig value)

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
value	FeatureView.VectorSearchConfig.TreeAHConfig

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

setTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig.Builder builderForValue)

public FeatureView.VectorSearchConfig.Builder setTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig.Builder builderForValue)

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

• Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

.google.cloud.aiplatform.v1beta1.FeatureView.VectorSearchConfig.TreeAHConfig tree_ah_config = 8 [(.google.api.field_behavior) = OPTIONAL];

Parameter
Name	Description
builderForValue	FeatureView.VectorSearchConfig.TreeAHConfig.Builder

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

setUnknownFields(UnknownFieldSet unknownFields)

public final FeatureView.VectorSearchConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
unknownFields	UnknownFieldSet

Returns
Type	Description
FeatureView.VectorSearchConfig.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)