Cloud AI Platform v1beta1 API - Class FeatureView.Types.IndexConfig (1.0.0-beta05) 

public sealed class FeatureView.Types.IndexConfig : IMessage<FeatureView.Types.IndexConfig>, IEquatable<FeatureView.Types.IndexConfig>, IDeepCloneable<FeatureView.Types.IndexConfig>, IBufferMessage, IMessage

Reference documentation and code samples for the Cloud AI Platform v1beta1 API class FeatureView.Types.IndexConfig.

Configuration for vector indexing.

Inheritance

object > FeatureView.Types.IndexConfig

Namespace

Google.Cloud.AIPlatform.V1Beta1

Assembly

Google.Cloud.AIPlatform.V1Beta1.dll

Constructors

IndexConfig()

public IndexConfig()

IndexConfig(IndexConfig)

public IndexConfig(FeatureView.Types.IndexConfig other)
Parameter
Name Description
other FeatureViewTypesIndexConfig

Properties

AlgorithmConfigCase

public FeatureView.Types.IndexConfig.AlgorithmConfigOneofCase AlgorithmConfigCase { get; }
Property Value
Type Description
FeatureViewTypesIndexConfigAlgorithmConfigOneofCase

BruteForceConfig

public FeatureView.Types.IndexConfig.Types.BruteForceConfig BruteForceConfig { get; set; }

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.

Property Value
Type Description
FeatureViewTypesIndexConfigTypesBruteForceConfig

CrowdingColumn

public string CrowdingColumn { get; set; }

Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by [FeatureOnlineStoreService.SearchNearestEntities][google.cloud.aiplatform.v1beta1.FeatureOnlineStoreService.SearchNearestEntities] to diversify search results. If [NearestNeighborQuery.per_crowding_attribute_neighbor_count][google.cloud.aiplatform.v1beta1.NearestNeighborQuery.per_crowding_attribute_neighbor_count] is set to K in [SearchNearestEntitiesRequest][google.cloud.aiplatform.v1beta1.SearchNearestEntitiesRequest], it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.

Property Value
Type Description
string

DistanceMeasureType

public FeatureView.Types.IndexConfig.Types.DistanceMeasureType DistanceMeasureType { get; set; }

Optional. The distance measure used in nearest neighbor search.

Property Value
Type Description
FeatureViewTypesIndexConfigTypesDistanceMeasureType

EmbeddingColumn

public string EmbeddingColumn { get; set; }

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.

Property Value
Type Description
string

EmbeddingDimension

public int EmbeddingDimension { get; set; }

Optional. The number of dimensions of the input embedding.

Property Value
Type Description
int

FilterColumns

public RepeatedField<string> FilterColumns { get; }

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

Property Value
Type Description
RepeatedFieldstring

HasEmbeddingDimension

public bool HasEmbeddingDimension { get; }

Gets whether the "embedding_dimension" field is set

Property Value
Type Description
bool

TreeAhConfig

public FeatureView.Types.IndexConfig.Types.TreeAHConfig TreeAhConfig { get; set; }

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

Property Value
Type Description
FeatureViewTypesIndexConfigTypesTreeAHConfig