Struct Embedding 

pub struct Embedding {
    pub document: String,
    pub vec: Vec<f64>,
}

A document identifier and its vector. Equality compares only the document, not vector values.

Fields

document: String

The text that was embedded, or a non-sensitive input identifier for non-text embeddings. Used for debugging and equality.

vec: Vec<f64>

The embedding vector

Trait Implementations

impl Clone for Embedding

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Embedding

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

Formats the value using the given formatter.

impl Default for Embedding

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for Embedding

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Eq for Embedding

impl PartialEq for Embedding

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

Equality operator ==.

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

Inequality operator !=.

impl Serialize for Embedding

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

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl VectorDistance for Embedding

fn dot_product(&self, other: &Self) -> f64

The dot product.

fn cosine_similarity(&self, other: &Self, normalized: bool) -> f64

Cosine similarity, from -1 to 1. If normalized is true, the vectors are taken as unit length and the dot product is returned.

fn euclidean_distance(&self, other: &Self) -> f64

The straight-line (L2) distance.

fn manhattan_distance(&self, other: &Self) -> f64

The sum of the coordinates’ absolute differences (L1).

fn chebyshev_distance(&self, other: &Self) -> f64

The largest absolute difference of a coordinate (L-infinity).

fn angular_distance(&self, other: &Self, normalized: bool) -> f64

The angle between the vectors as a fraction of pi, from 0 (same direction) to 1 (opposite). normalized is as for Self::cosine_similarity.