Struct GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig 

pub struct GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig {
    pub embedding_dimension: Option<i32>,
    pub crowding_column: Option<String>,
    pub distance_measure_type: Option<String>,
    pub tree_ah_config: Option<GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig>,
    pub filter_columns: Option<Vec<String>>,
    pub brute_force_config: Option<GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig>,
    pub embedding_column: Option<String>,
}

Deprecated. Use IndexConfig instead.

This type is not used in any activity, and only used as part of another schema.

Fields

embedding_dimension: Option<i32>

Optional. The number of dimensions of the input embedding.

crowding_column: Option<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.

distance_measure_type: Option<String>

Optional. The distance measure used in nearest neighbor search.

tree_ah_config: Option<GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigTreeAHConfig>

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

filter_columns: Option<Vec<String>>

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

brute_force_config: Option<GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfigBruteForceConfig>

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: Option<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.

Trait Implementations

impl Clone for GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

fn clone(&self) -> GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

fn default() -> GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Part for GoogleCloudAiplatformV1beta1FeatureViewVectorSearchConfig