Struct IndexDatapoint 

#[non_exhaustive]
pub struct IndexDatapoint {
    pub datapoint_id: String,
    pub feature_vector: Vec<f32>,
    pub sparse_embedding: Option<SparseEmbedding>,
    pub restricts: Vec<Restriction>,
    pub numeric_restricts: Vec<NumericRestriction>,
    pub crowding_tag: Option<CrowdingTag>,
    pub embedding_metadata: Option<Struct>,
    /* private fields */
}

Available on crate features index-service or match-service only.

A datapoint of Index.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

datapoint_id: String

Required. Unique identifier of the datapoint.

feature_vector: Vec<f32>

Required. Feature embedding vector for dense index. An array of numbers with the length of [NearestNeighborSearchConfig.dimensions].

sparse_embedding: Option<SparseEmbedding>

Optional. Feature embedding vector for sparse index.

restricts: Vec<Restriction>

Optional. List of Restrict of the datapoint, used to perform “restricted searches” where boolean rule are used to filter the subset of the database eligible for matching. This uses categorical tokens. See: https://cloud.google.com/vertex-ai/docs/matching-engine/filtering

numeric_restricts: Vec<NumericRestriction>

Optional. List of Restrict of the datapoint, used to perform “restricted searches” where boolean rule are used to filter the subset of the database eligible for matching. This uses numeric comparisons.

crowding_tag: Option<CrowdingTag>

Optional. CrowdingTag of the datapoint, the number of neighbors to return in each crowding can be configured during query.

embedding_metadata: Option<Struct>

Optional. The key-value map of additional metadata for the datapoint.

Implementations

impl IndexDatapoint

pub fn new() -> Self

pub fn set_datapoint_id<T: Into<String>>(self, v: T) -> Self

Sets the value of datapoint_id.

Example

let x = IndexDatapoint::new().set_datapoint_id("example");

pub fn set_feature_vector<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<f32>,

Sets the value of feature_vector.

Example

let x = IndexDatapoint::new().set_feature_vector([1.0, 2.0, 3.0]);

pub fn set_sparse_embedding<T>(self, v: T) -> Self

where T: Into<SparseEmbedding>,

Sets the value of sparse_embedding.

Example

use google_cloud_aiplatform_v1::model::index_datapoint::SparseEmbedding;
let x = IndexDatapoint::new().set_sparse_embedding(SparseEmbedding::default()/* use setters */);

pub fn set_or_clear_sparse_embedding<T>(self, v: Option<T>) -> Self

where T: Into<SparseEmbedding>,

Sets or clears the value of sparse_embedding.

Example

use google_cloud_aiplatform_v1::model::index_datapoint::SparseEmbedding;
let x = IndexDatapoint::new().set_or_clear_sparse_embedding(Some(SparseEmbedding::default()/* use setters */));
let x = IndexDatapoint::new().set_or_clear_sparse_embedding(None::<SparseEmbedding>);

pub fn set_restricts<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<Restriction>,

Sets the value of restricts.

Example

use google_cloud_aiplatform_v1::model::index_datapoint::Restriction;
let x = IndexDatapoint::new()
    .set_restricts([
        Restriction::default()/* use setters */,
        Restriction::default()/* use (different) setters */,
    ]);

pub fn set_numeric_restricts<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<NumericRestriction>,

Sets the value of numeric_restricts.

Example

use google_cloud_aiplatform_v1::model::index_datapoint::NumericRestriction;
let x = IndexDatapoint::new()
    .set_numeric_restricts([
        NumericRestriction::default()/* use setters */,
        NumericRestriction::default()/* use (different) setters */,
    ]);

pub fn set_crowding_tag<T>(self, v: T) -> Self

where T: Into<CrowdingTag>,

Sets the value of crowding_tag.

Example

use google_cloud_aiplatform_v1::model::index_datapoint::CrowdingTag;
let x = IndexDatapoint::new().set_crowding_tag(CrowdingTag::default()/* use setters */);

pub fn set_or_clear_crowding_tag<T>(self, v: Option<T>) -> Self

where T: Into<CrowdingTag>,

Sets or clears the value of crowding_tag.

Example

use google_cloud_aiplatform_v1::model::index_datapoint::CrowdingTag;
let x = IndexDatapoint::new().set_or_clear_crowding_tag(Some(CrowdingTag::default()/* use setters */));
let x = IndexDatapoint::new().set_or_clear_crowding_tag(None::<CrowdingTag>);

pub fn set_embedding_metadata<T>(self, v: T) -> Self

where T: Into<Struct>,

Sets the value of embedding_metadata.

Example

use wkt::Struct;
let x = IndexDatapoint::new().set_embedding_metadata(Struct::default()/* use setters */);

pub fn set_or_clear_embedding_metadata<T>(self, v: Option<T>) -> Self

where T: Into<Struct>,

Sets or clears the value of embedding_metadata.

Example

use wkt::Struct;
let x = IndexDatapoint::new().set_or_clear_embedding_metadata(Some(Struct::default()/* use setters */));
let x = IndexDatapoint::new().set_or_clear_embedding_metadata(None::<Struct>);

Trait Implementations

impl Clone for IndexDatapoint

fn clone(&self) -> IndexDatapoint

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for IndexDatapoint

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

Formats the value using the given formatter.

impl Default for IndexDatapoint

fn default() -> IndexDatapoint

Returns the “default value” for a type.

impl Message for IndexDatapoint

fn typename() -> &'static str

The typename of this message.

impl PartialEq for IndexDatapoint

fn eq(&self, other: &IndexDatapoint) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for IndexDatapoint