Google Cloud Ai Platform V1 Client - Class IndexConfig (1.7.0)

Version 1.7.0

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Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class IndexConfig.

Configuration for vector indexing.

Generated from protobuf message google.cloud.aiplatform.v1.FeatureView.IndexConfig

Namespace

Google \ Cloud \ AIPlatform \ V1 \ FeatureView

Methods

__construct

Constructor.

Parameters
Name	Description
data	array Optional. Data for populating the Message object.
↳ tree_ah_config	Google\Cloud\AIPlatform\V1\FeatureView\IndexConfig\TreeAHConfig 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
↳ brute_force_config	Google\Cloud\AIPlatform\V1\FeatureView\IndexConfig\BruteForceConfig 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.
↳ embedding_column	string 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.
↳ filter_columns	array Optional. Columns of features that're used to filter vector search results.
↳ crowding_column	string 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.
↳ embedding_dimension	int Optional. The number of dimensions of the input embedding.
↳ distance_measure_type	int Optional. The distance measure used in nearest neighbor search.

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

Returns
Type	Description
Google\Cloud\AIPlatform\V1\FeatureView\IndexConfig\TreeAHConfig|null

hasTreeAhConfig

setTreeAhConfig

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

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\FeatureView\IndexConfig\TreeAHConfig

Returns
Type	Description
$this

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.

Returns
Type	Description
Google\Cloud\AIPlatform\V1\FeatureView\IndexConfig\BruteForceConfig|null

hasBruteForceConfig

setBruteForceConfig

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.

Parameter
Name	Description
var	Google\Cloud\AIPlatform\V1\FeatureView\IndexConfig\BruteForceConfig

Returns
Type	Description
$this

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.

Returns
Type	Description
string

setEmbeddingColumn

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.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getFilterColumns

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

Returns
Type	Description
Google\Protobuf\Internal\RepeatedField

setFilterColumns

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

Parameter
Name	Description
var	string[]

Returns
Type	Description
$this

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.

Returns
Type	Description
string

setCrowdingColumn

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.

Parameter
Name	Description
var	string

Returns
Type	Description
$this

getEmbeddingDimension

Optional. The number of dimensions of the input embedding.

Returns
Type	Description
int

hasEmbeddingDimension

clearEmbeddingDimension

setEmbeddingDimension

Optional. The number of dimensions of the input embedding.

Parameter
Name	Description
var	int

Returns
Type	Description
$this

getDistanceMeasureType

Optional. The distance measure used in nearest neighbor search.

Returns
Type	Description
int

setDistanceMeasureType

Optional. The distance measure used in nearest neighbor search.

Parameter
Name	Description
var	int

Returns
Type	Description
$this

getAlgorithmConfig

Returns
Type	Description
string