Class FeatureView.VectorSearchConfig.Builder (3.36.0)

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

Configuration for vector search.

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

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
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
NameDescription
valuesIterable<String>

The filterColumns to add.

Returns
TypeDescription
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
NameDescription
valueString

The filterColumns to add.

Returns
TypeDescription
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
NameDescription
valueByteString

The bytes of the filterColumns to add.

Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder

This builder for chaining.

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public FeatureView.VectorSearchConfig.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

build()

public FeatureView.VectorSearchConfig build()
Returns
TypeDescription
FeatureView.VectorSearchConfig

buildPartial()

public FeatureView.VectorSearchConfig buildPartial()
Returns
TypeDescription
FeatureView.VectorSearchConfig

clear()

public FeatureView.VectorSearchConfig.Builder clear()
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

clearAlgorithmConfig()

public FeatureView.VectorSearchConfig.Builder clearAlgorithmConfig()
Returns
TypeDescription
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
TypeDescription
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 nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

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

Returns
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
FeatureView.VectorSearchConfig.Builder

This builder for chaining.

clearField(Descriptors.FieldDescriptor field)

public FeatureView.VectorSearchConfig.Builder clearField(Descriptors.FieldDescriptor field)
Parameter
NameDescription
fieldFieldDescriptor
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

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
TypeDescription
FeatureView.VectorSearchConfig.Builder

This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public FeatureView.VectorSearchConfig.Builder clearOneof(Descriptors.OneofDescriptor oneof)
Parameter
NameDescription
oneofOneofDescriptor
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

clearTreeAhConfig()

public FeatureView.VectorSearchConfig.Builder clearTreeAhConfig()

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

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

Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder

clone()

public FeatureView.VectorSearchConfig.Builder clone()
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

getAlgorithmConfigCase()

public FeatureView.VectorSearchConfig.AlgorithmConfigCase getAlgorithmConfigCase()
Returns
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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 nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

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

Returns
TypeDescription
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 nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

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

Returns
TypeDescription
ByteString

The bytes for crowdingColumn.

getDefaultInstanceForType()

public FeatureView.VectorSearchConfig getDefaultInstanceForType()
Returns
TypeDescription
FeatureView.VectorSearchConfig

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()
Returns
TypeDescription
Descriptor
Overrides

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
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
TypeDescription
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
NameDescription
indexint

The index of the element to return.

Returns
TypeDescription
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
NameDescription
indexint

The index of the value to return.

Returns
TypeDescription
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
TypeDescription
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
TypeDescription
ProtocolStringList

A list containing the filterColumns.

getTreeAhConfig()

public FeatureView.VectorSearchConfig.TreeAHConfig getTreeAhConfig()

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

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

Returns
TypeDescription
FeatureView.VectorSearchConfig.TreeAHConfig

The treeAhConfig.

getTreeAhConfigBuilder()

public FeatureView.VectorSearchConfig.TreeAHConfig.Builder getTreeAhConfigBuilder()

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

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

Returns
TypeDescription
FeatureView.VectorSearchConfig.TreeAHConfig.Builder

getTreeAhConfigOrBuilder()

public FeatureView.VectorSearchConfig.TreeAHConfigOrBuilder getTreeAhConfigOrBuilder()

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

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

Returns
TypeDescription
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
TypeDescription
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
TypeDescription
boolean

Whether the embeddingDimension field is set.

hasTreeAhConfig()

public boolean hasTreeAhConfig()

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

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

Returns
TypeDescription
boolean

Whether the treeAhConfig field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

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
NameDescription
valueFeatureView.VectorSearchConfig.BruteForceConfig
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder

mergeFrom(FeatureView.VectorSearchConfig other)

public FeatureView.VectorSearchConfig.Builder mergeFrom(FeatureView.VectorSearchConfig other)
Parameter
NameDescription
otherFeatureView.VectorSearchConfig
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public FeatureView.VectorSearchConfig.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides
Exceptions
TypeDescription
IOException

mergeFrom(Message other)

public FeatureView.VectorSearchConfig.Builder mergeFrom(Message other)
Parameter
NameDescription
otherMessage
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

mergeTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig value)

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

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

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

Parameter
NameDescription
valueFeatureView.VectorSearchConfig.TreeAHConfig
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final FeatureView.VectorSearchConfig.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

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
NameDescription
valueFeatureView.VectorSearchConfig.BruteForceConfig
Returns
TypeDescription
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
NameDescription
builderForValueFeatureView.VectorSearchConfig.BruteForceConfig.Builder
Returns
TypeDescription
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 nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

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

Parameter
NameDescription
valueString

The crowdingColumn to set.

Returns
TypeDescription
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 nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

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

Parameter
NameDescription
valueByteString

The bytes for crowdingColumn to set.

Returns
TypeDescription
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
NameDescription
valueFeatureView.VectorSearchConfig.DistanceMeasureType

The distanceMeasureType to set.

Returns
TypeDescription
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
NameDescription
valueint

The enum numeric value on the wire for distanceMeasureType to set.

Returns
TypeDescription
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
NameDescription
valueString

The embeddingColumn to set.

Returns
TypeDescription
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
NameDescription
valueByteString

The bytes for embeddingColumn to set.

Returns
TypeDescription
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
NameDescription
valueint

The embeddingDimension to set.

Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder

This builder for chaining.

setField(Descriptors.FieldDescriptor field, Object value)

public FeatureView.VectorSearchConfig.Builder setField(Descriptors.FieldDescriptor field, Object value)
Parameters
NameDescription
fieldFieldDescriptor
valueObject
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

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
NameDescription
indexint

The index to set the value at.

valueString

The filterColumns to set.

Returns
TypeDescription
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
NameDescription
fieldFieldDescriptor
indexint
valueObject
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides

setTreeAhConfig(FeatureView.VectorSearchConfig.TreeAHConfig value)

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

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

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

Parameter
NameDescription
valueFeatureView.VectorSearchConfig.TreeAHConfig
Returns
TypeDescription
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

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

Parameter
NameDescription
builderForValueFeatureView.VectorSearchConfig.TreeAHConfig.Builder
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder

setUnknownFields(UnknownFieldSet unknownFields)

public final FeatureView.VectorSearchConfig.Builder setUnknownFields(UnknownFieldSet unknownFields)
Parameter
NameDescription
unknownFieldsUnknownFieldSet
Returns
TypeDescription
FeatureView.VectorSearchConfig.Builder
Overrides