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Vinicius Fagundes
Vinicius Fagundes

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My Favorite Constant in Retrieval Is 60. Nobody Tunes It. That's the Point.

#python #datascience #database #vectordatabase

Reciprocal rank fusion merges two ranked lists — say, BM25 results and vector-search results over your chargeback cases — into one. It has exactly one parameter, k. And the answer is just... 60. Not tuned per dataset. Not learned. Sixty, from the original paper, works essentially everywhere.

The entire algorithm:

def rrf(rankings: list[list[str]], k: int = 60) -> list[tuple[str, float]]:
    """Each ranking is a list of doc IDs, best first. Score = sum of 1/(k + rank)."""
    scores = {}
    for ranking in rankings:
        for rank, doc in enumerate(ranking, start=1):
            scores[doc] = scores.get(doc, 0) + 1 / (k + rank)
    return sorted(scores.items(), key=lambda x: -x[1])

# Fraud case search, two retrievers disagreeing:
bm25_top   = ["CB-1041", "CB-1250", "CB-1102"]   # keyword hits: rule ID matched
dense_top  = ["CB-1203", "CB-1041", "CB-1288"]   # semantic hits: similar pattern

for doc, score in rrf([bm25_top, dense_top]):
    print(f"{doc}: {score:.4f}")
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CB-1041: 0.0325   <- ranked #1: the case BOTH retrievers liked
CB-1203: 0.0164
CB-1250: 0.0161
CB-1102: 0.0156
CB-1288: 0.0156
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Notice what it just did without being told: the case both retrievers agreed on floated to the top, and the two scoring scales — BM25's unbounded scores, cosine's 0-to-1 — never had to be reconciled, because ranks don't care about scales.

Why 60 works everywhere: at k=60, the difference between rank 1 and rank 3 matters, but the difference between rank 40 and rank 50 is noise — which is exactly the shape of trust you should have in any retriever. The constant encodes an epistemics, not a dataset.

I think about this every time I watch a team burn a sprint tuning fusion weights per query type. Twenty years of information retrieval research, and one of its most robust results is a knob you never touch.

Some of the best engineering is knowing which knobs don't need turning.

I'm Vinicius Fagundes — principal data engineer and MBA lecturer in São Paulo. I build fraud and risk analytics pipelines for e-commerce through vf-insights.com.

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