pulumi-gcp-kotlin/com.pulumi.gcp.vertex.kotlin.outputs/AiIndexMetadataConfig

AiIndexMetadataConfig

data class AiIndexMetadataConfig(val algorithmConfig: AiIndexMetadataConfigAlgorithmConfig? = null, val approximateNeighborsCount: Int? = null, val dimensions: Int, val distanceMeasureType: String? = null, val featureNormType: String? = null, val shardSize: String? = null)

Constructors

fun AiIndexMetadataConfig(algorithmConfig: AiIndexMetadataConfigAlgorithmConfig? = null, approximateNeighborsCount: Int? = null, dimensions: Int, distanceMeasureType: String? = null, featureNormType: String? = null, shardSize: String? = null)

Types

Companion

object Companion

Properties

algorithmConfig

val algorithmConfig: AiIndexMetadataConfigAlgorithmConfig? = null

The configuration with regard to the algorithms used for efficient search. Structure is documented below.

approximateNeighborsCount

val approximateNeighborsCount: Int? = null

The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. Required if tree-AH algorithm is used.

dimensions

val dimensions: Int

The number of dimensions of the input vectors.

distanceMeasureType

val distanceMeasureType: String? = null

The distance measure used in nearest neighbor search. The value must be one of the followings:

featureNormType

val featureNormType: String? = null

Type of normalization to be carried out on each vector. The value must be one of the followings:

shardSize

val shardSize: String? = null

Index data is split into equal parts to be processed. These are called "shards". The shard size must be specified when creating an index. The value must be one of the followings: