c2bdc01eac
Wire hybrid search (FTS5 + vector + RRF) to CLI search command. Enhance robot-docs with quick_start guide and example_output. Fill data gaps in issue detail (references, note counts, closed_at). Add lore drift command for discussion divergence detection. Rewrite agent skills to mandate lore for reads, glab for writes. Closes: bd-1ksf, bd-91j1, bd-2g50, bd-1cjx, bd-kvij
49 lines
1.4 KiB
Rust
49 lines
1.4 KiB
Rust
/// Cosine similarity between two embedding vectors.
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/// Returns value in [-1, 1] range; higher = more similar.
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pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
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debug_assert_eq!(a.len(), b.len(), "embedding dimensions must match");
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let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum();
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let norm_a: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
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let norm_b: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
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if norm_a == 0.0 || norm_b == 0.0 {
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return 0.0;
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}
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dot / (norm_a * norm_b)
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_cosine_similarity_identical() {
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let v = [1.0, 2.0, 3.0];
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let sim = cosine_similarity(&v, &v);
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assert!((sim - 1.0).abs() < 1e-6);
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}
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#[test]
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fn test_cosine_similarity_orthogonal() {
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let a = [1.0, 0.0, 0.0];
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let b = [0.0, 1.0, 0.0];
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let sim = cosine_similarity(&a, &b);
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assert!(sim.abs() < 1e-6);
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}
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#[test]
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fn test_cosine_similarity_zero_vector() {
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let a = [1.0, 2.0, 3.0];
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let b = [0.0, 0.0, 0.0];
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let sim = cosine_similarity(&a, &b);
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assert!((sim - 0.0).abs() < 1e-6);
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}
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#[test]
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fn test_cosine_similarity_opposite() {
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let a = [1.0, 2.0, 3.0];
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let b = [-1.0, -2.0, -3.0];
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let sim = cosine_similarity(&a, &b);
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assert!((sim - (-1.0)).abs() < 1e-6);
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}
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}
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