Fusion algorithms for combining retrieval results.

This module implements rank fusion strategies for hybrid search, combining results from multiple retrieval sources (e.g., vector search, keyword search).

Supported Strategies

• RRF (Reciprocal Rank Fusion): Default and recommended. Uses only ranks, making it robust to score distribution differences.
• Weighted: Linear combination of scores with configurable weights.
• Union: Takes the maximum score per ID across sources.

Algorithm

RRF formula:

score(d) = Σ 1/(k + rank_i(d))

where:

• k = 60 (standard, dampens high-rank dominance)
• rank_i(d) = position of d in retriever i's results (1-indexed)
• If d not in retriever i, contribution = 0

Example

use khive_fusion::{fuse, FusionStrategy, reciprocal_rank_fusion};
use khive_score::DeterministicScore;

// Two retrieval sources with different rankings
let vector_results = vec![
    ("doc_a", DeterministicScore::from_f64(0.95)),
    ("doc_b", DeterministicScore::from_f64(0.90)),
    ("doc_c", DeterministicScore::from_f64(0.85)),
];

let keyword_results = vec![
    ("doc_b", DeterministicScore::from_f64(0.88)),
    ("doc_c", DeterministicScore::from_f64(0.75)),
    ("doc_d", DeterministicScore::from_f64(0.70)),
];

// Fuse using RRF with k=60
let fused = fuse(
    vec![vector_results, keyword_results],
    &FusionStrategy::Rrf { k: 60 },
    5,
);

// doc_b appears in both sources, so it gets highest RRF score
assert_eq!(fused[0].0, "doc_b");