Module distance 

Distance computation for multi-vector representations.

Provides asymmetric distance primitives for multi-vector search:

• MaxSim: Per-query-vector maximum similarities.
• Chamfer: Sum of MaxSim scores (asymmetric Chamfer distance).

Both are currently implemented using a simple double-loop kernel over InnerProduct.

Example

use diskann_quantization::multi_vector::{
    distance::{Chamfer, MaxSim, QueryMatRef},
    MatRef, Standard,
};
use diskann_vector::{DistanceFunctionMut, PureDistanceFunction};

// Query: 2 vectors of dim 3 (wrapped as QueryMatRef)
let query_data = [1.0f32, 0.0, 0.0, 0.0, 1.0, 0.0];
let query: QueryMatRef<_> = MatRef::new(
    Standard::new(2, 3).unwrap(),
    &query_data,
).unwrap().into();

// Doc: 2 vectors of dim 3
let doc_data = [1.0f32, 0.0, 0.0, 0.0, 0.0, 1.0];
let doc = MatRef::new(
    Standard::new(2, 3).unwrap(),
    &doc_data,
).unwrap();

// Chamfer distance (sum of max similarities)
let chamfer_dist = Chamfer::evaluate(query, doc);

// MaxSim (per-query-vector scores)
let mut scores = vec![0.0f32; 2];
let mut max_sim = MaxSim::new(&mut scores).unwrap();
max_sim.evaluate(query, doc);
// scores[0] = -1.0 (query[0] matches doc[0])
// scores[1] =  0.0 (query[1] has no good match)

Structs

Chamfer

Asymmetric Chamfer distance for multi-vector similarity.

MaxSim

Computes per-query-vector maximum similarities to document vectors.

QueryMatRef

A query matrix view for asymmetric distance functions.

Enums

MaxSimError

Error type for MaxSim operations.