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>,
    /* private fields */
}

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.

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.

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.

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

Sets the value of sparse_embedding.

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

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

Sets the value of restricts.

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.

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

Sets the value of crowding_tag.

Trait Implementations

impl Clone for IndexDatapoint

fn clone(&self) -> IndexDatapoint

Returns a copy 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<'de> Deserialize<'de> for IndexDatapoint

where IndexDatapoint: Default,

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

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 Serialize for IndexDatapoint

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for IndexDatapoint