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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 \ FeatureViewMethods
__construct
Constructor.
data
array
Optional. Data for populating the Message object.
↳ tree_ah_config
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
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
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
$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.
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.
$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.
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.
var
string
$this
getFilterColumns
Optional. Columns of features that're used to filter vector search results.
setFilterColumns
Optional. Columns of features that're used to filter vector search results.
var
string[]
$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.
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.
var
string
$this
getEmbeddingDimension
Optional. The number of dimensions of the input embedding.
int
hasEmbeddingDimension
clearEmbeddingDimension
setEmbeddingDimension
Optional. The number of dimensions of the input embedding.
var
int
$this
getDistanceMeasureType
Optional. The distance measure used in nearest neighbor search.
int
setDistanceMeasureType
Optional. The distance measure used in nearest neighbor search.
var
int
$this
getAlgorithmConfig
string
