motedb 0.1.6

AI-native embedded multimodal database for embodied intelligence (robots, AR glasses, industrial arms).
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

//! Distance metrics for vector similarity computation
//!
//! Optimized distance functions for embedded environments.

pub mod euclidean;
pub mod cosine;

pub use euclidean::euclidean_distance;
pub use cosine::{cosine_distance, cosine_similarity};

/// Distance metric trait
pub trait DistanceMetric: Send + Sync {
    /// Compute distance between two vectors
    fn distance(&self, a: &[f32], b: &[f32]) -> f32;
}

/// Euclidean distance metric
#[derive(Debug, Clone, Copy)]
pub struct Euclidean;

impl DistanceMetric for Euclidean {
    #[inline]
    fn distance(&self, a: &[f32], b: &[f32]) -> f32 {
        euclidean_distance(a, b)
    }
}

/// Cosine distance metric (1 - cosine_similarity)
#[derive(Debug, Clone, Copy)]
pub struct Cosine;

impl DistanceMetric for Cosine {
    #[inline]
    fn distance(&self, a: &[f32], b: &[f32]) -> f32 {
        cosine_distance(a, b)
    }
}

/// Monomorphized distance metric enum (zero-cost alternative to `Arc<dyn DistanceMetric>`)
///
/// Eliminates virtual dispatch overhead for inner-loop distance computations
/// in DiskANN/Vamana graph search (saves ~2-5ns per distance call on ARM).
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum DistanceKind {
    Euclidean,
    Cosine,
}

impl DistanceKind {
    #[inline]
    pub fn distance(&self, a: &[f32], b: &[f32]) -> f32 {
        match self {
            DistanceKind::Euclidean => euclidean_distance(a, b),
            DistanceKind::Cosine => cosine_distance(a, b),
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_euclidean_metric() {
        let metric = Euclidean;
        let a = vec![1.0, 2.0, 3.0];
        let b = vec![4.0, 5.0, 6.0];
        let dist = metric.distance(&a, &b);
        assert!((dist - 5.196152).abs() < 0.001);
    }

    #[test]
    fn test_cosine_metric() {
        let metric = Cosine;
        let a = vec![1.0, 0.0, 0.0];
        let b = vec![1.0, 0.0, 0.0];
        let dist = metric.distance(&a, &b);
        assert!(dist < 0.01); // Same vector should have ~0 distance
    }
}