/**
 * Reciprocal Rank Fusion — the transparent, rank-only recall layer of hybrid
 * search (docs/hybrid-retrieval-design.md, Decision 2).
 *
 * Rank-only on purpose: BM25 and cosine scores are not comparable across
 * retrievers, so raw scores are carried through as diagnostics but never
 * enter the fused score.
 */
import type { FusedHit, RankedHit } from "./types.js";
/**
 * Default RRF constant, re-swept on the 62-query harness.
 *
 * History matters here, because the answer changed twice. The original
 * 12-query gold set picked k = 2 over k = 60; that measurement ran on an
 * unpinned corpus with a file-level metric and was never reproducible. The
 * 62-query re-sweep then found the two *indistinguishable* — a 3pp R@10 gap
 * carried by two queries (p = 0.50), with MRR worse on more queries than it
 * was better.
 *
 * What made k decidable was fixing the reranker. Once it could tell a
 * declaration from a call site, the deeper, flatter candidate mix that k = 60
 * produces became worth having: MRR 0.403 -> 0.464, 20 queries better against
 * 7 worse (p <= 0.05), with R@10 unchanged. Small k keeps fusion top-heavy
 * toward each retriever's best hits, which only pays when the reranker cannot
 * exploit the tail — and it now can.
 *
 * Re-sweep again (`bun run search-eval:compare`) after any reranker change,
 * since that is what this value trades against.
 */
export declare const DEFAULT_RRF_K = 60;
/**
 * Fuse ranked lists into one deterministic ordering by summed `1/(k + rank)`.
 *
 * Ties break by number of agreeing retrievers, then lexicographic id, so the
 * same inputs always produce the same context.
 *
 * Duplicate `source:id` pairs within one list are counted once (best rank
 * wins). The adapter dedupes upstream, so this guard should never fire in
 * practice — it exists so a misbehaving retriever cannot inflate its vote.
 */
export declare function rrfFuse(lists: readonly (readonly RankedHit[])[], k?: number): FusedHit[];
//# sourceMappingURL=rrf.d.ts.map