Expand description
Distance metrics for vector similarity search.
Modules§
- dispatch
- Dtype-aware distance dispatch shim.
- scalar
- Scalar (non-SIMD) distance metric implementations.
- simd
- Runtime SIMD dispatch for vector distance and bitmap operations.
Enums§
- Distance
Metric - Distance metric selection.
Functions§
- batch_
distances - Batch distance: compute distances from
queryto each candidate. - chebyshev
- Chebyshev (L-infinity) distance: max absolute difference.
- cosine_
distance - Cosine distance: 1.0 - cosine_similarity(a, b).
- distance
- Compute distance between two vectors using the specified metric.
- hamming_
f32 - Hamming distance for f32 vectors (values > 0.5 treated as 1).
- jaccard
- Jaccard distance for f32 vectors (values > 0.5 treated as set membership).
- l2_
squared - Euclidean (L2) squared distance.
- manhattan
- Manhattan (L1) distance: sum of absolute differences.
- neg_
inner_ product - Negative inner product (for max-inner-product search via min-heap).
- pearson
- Pearson distance: 1 - Pearson correlation coefficient.
- scalar_
distance - Compute distance between two vectors using scalar implementations.