rankops

Operations on ranked lists: fuse multiple retrievers, then rerank. Pairs with rankfns (scoring kernels).

rankops covers the post-retrieval pipeline:

• Fusion -- combine ranked lists from heterogeneous retrievers (BM25, dense, sparse)
• Reranking -- MaxSim/ColBERT late interaction, MMR/DPP diversity, Matryoshka two-stage
• Evaluation -- NDCG, MRR, recall@k, fusion parameter optimization

Quickstart

[dependencies]
rankops = "0.1.0"

Fusion

Fuse two ranked lists with Reciprocal Rank Fusion (score-agnostic, works across incompatible scales):

use rankops::rrf;

let bm25  = vec![("doc_a", 12.5), ("doc_b", 11.0), ("doc_c", 9.2)];
let dense = vec![("doc_b", 0.95), ("doc_c", 0.88), ("doc_d", 0.70)];

let fused = rrf(&bm25, &dense);
// doc_b ranks highest: appears in both lists
assert_eq!(fused[0].0, "doc_b");

Score-based fusion when scales are comparable:

use rankops::combmnz;

let fused = combmnz(&bm25, &dense);
// CombMNZ: sum of normalized scores * overlap count

Select the algorithm at runtime via FusionMethod:

use rankops::FusionMethod;

let method = FusionMethod::Rrf { k: 60 };
let result = method.fuse(&bm25, &dense);

Diversity reranking (requires rerank feature, on by default)

use rankops::rerank::diversity::{mmr, MmrConfig};

let candidates = vec![("d1", 0.95), ("d2", 0.90), ("d3", 0.85)];
let similarity = vec![
    1.0, 0.9, 0.2,
    0.9, 1.0, 0.3,
    0.2, 0.3, 1.0,
];

let config = MmrConfig::default().with_lambda(0.5).with_k(2);
let selected = mmr(&candidates, &similarity, config);
// Picks d1 (highest relevance), then d3 (diverse from d1)

Fusion algorithms

Function	Uses scores	Description
rrf	No	Reciprocal Rank Fusion -- rank-based, works across incompatible scales
isr	No	Inverse Square Root fusion -- gentler rank decay than RRF
borda	No	Borda count -- (N - rank) voting points
condorcet	No	Pairwise Condorcet voting -- outlier-robust
combsum	Yes	Sum of min-max normalized scores
combmnz	Yes	CombSUM * overlap count -- rewards multi-list presence
combmax	Yes	Max score across lists
combmin	Yes	Min score -- conservative, requires all retrievers to agree
combmed	Yes	Median score -- robust to outliers
weighted	Yes	Weighted combination with per-list weights
dbsf	Yes	Distribution-Based Score Fusion (z-score normalization)
standardized	Yes	ERANK-style z-score fusion with clipping

All two-list functions have *_multi variants for 3+ lists. Explainability variants (rrf_explain, combsum_explain, etc.) return full provenance.

Reranking (feature: rerank)

Module	Description
rerank::colbert	MaxSim late interaction scoring (ColBERT, ColPali, Jina-ColBERT)
rerank::diversity	MMR and DPP diversity selection
rerank::matryoshka	Two-stage reranking with nested (Matryoshka) embeddings
rerank::embedding	Normalized vectors, masked token MaxSim
rerank::quantization	int8 quantization/dequantization for token embeddings

Features

Feature	Default	Description
rerank	Yes	MaxSim, diversity, Matryoshka reranking (depends on innr for SIMD)
hierarchical	No	Hierarchical ColBERT clustering (depends on kodama)
serde	No	Serialization for configs and types

Examples

cargo run --example fusion                        # RRF, CombMNZ, Borda
cargo run --example rerank_maxsim --features rerank  # ColBERT MaxSim scoring
cargo run --example diversity --features rerank      # MMR and DPP diversity

See also

• rankfns -- scoring kernels (BM25, TF-IDF, cosine) that pair with rankops
• innr -- SIMD dot product and MaxSim primitives used by the rerank feature

License

MIT OR Apache-2.0