import { type RecallRankingOptions, type RecallWeights } from "./score.js";
import type { MemoryRecord, VectorRankingOptions } from "./types.js";
/**
 * Rank keyword candidates by IDF relevance (+ recency/confidence per weights).
 * `candidates` are the rows the store retrieved as matching ≥1 query token;
 * `dfByToken`/`corpusSize` are the store's live stats. Returns the full sorted
 * list (caller pages + tag-filters). `now` absent → newest candidate's updatedAt.
 * `tokenize` defaults to the shared `tokenize()` — callers pass their own only if
 * they index tokens differently (both stores pass the imported `tokenize`).
 */
export declare function rankKeywordCandidates(candidates: readonly MemoryRecord[], dfByToken: ReadonlyMap<string, number>, corpusSize: number, queryTokens: readonly string[], now: string | undefined, options?: RecallRankingOptions, tk?: (s: string) => string[]): MemoryRecord[];
/**
 * Fuse a keyword-ranked list and a vector-ranked list (already cosine-sorted and
 * sliced to vectorK by the store) via co-equal RRF, then a bounded recency/
 * confidence second stage. Returns the fused sorted records (caller pages +
 * tag-filters). `options.recencyHalfLifeMs` should be pre-resolved by the caller
 * (the pure fn cannot see recall config).
 */
export declare function fuseHybrid(args: {
    readonly keywordRanked: readonly MemoryRecord[];
    readonly vectorRanked: readonly MemoryRecord[];
    readonly now?: string | undefined;
    readonly options?: VectorRankingOptions | undefined;
}): MemoryRecord[];
export type { RecallWeights };
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