/**
 * Minimal vector math for concept-search mode. Stdlib only.
 *
 * `cosine(a, b)` returns a similarity in [-1, 1] (higher = more similar).
 * `rank` returns descending by score and stable by input order on ties.
 *
 * Length mismatch is a HARD ERROR, not a silent 0: two vectors with
 * different dimensions can only come from mixed embed models (e.g. silent
 * `:latest` drift, or a custom-named local model that changed dimensions
 * under the same alias). A 0 return would silently return zero hits
 * instead of surfacing the drift.
 */
export declare function cosine(a: number[], b: number[]): number;
export interface RankedCandidate<T> {
    item: T;
    score: number;
    /** Input-order index for stable sorting. */
    originalIndex: number;
}
/**
 * Rank candidates by cosine similarity to queryVec, descending.
 * Ties break on original input index so the output is deterministic.
 */
export declare function rankByCosine<T>(queryVec: number[], candidates: Array<{
    item: T;
    vec: number[];
}>): RankedCandidate<T>[];
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