/**
 * cosine.ts — exact cosine similarity + full-corpus ranking.
 *
 * Stands in for Harper's HNSW approximate-nearest-neighbor index (see
 * README "Comparable to flair, with a caveat"): flair-bench has no HNSW
 * graph, so instead of an approximate top-K it scores EVERY corpus vector
 * against the query and sorts — the correct answer for a corpus this size
 * (251 records), and the reason the tool can claim exact recall numbers
 * rather than approximate ones.
 */
export declare function cosineSimilarity(a: readonly number[], b: readonly number[]): number;
/**
 * 0-based rank of `targetIndex` within `corpusVectors` when sorted by
 * descending cosine similarity to `query`. Ties break on corpus `.index`
 * ascending (the total-order tail — not `.id`; this ranking never has one).
 * Input order of the scored array must not leak: the same corpus in two
 * permutations of equal-score rows ranks identically (flair#1412).
 * See README's "exact-vs-HNSW caveat" for what this does and doesn't
 * guarantee to reproduce of Harper's HNSW.
 */
export declare function rankOf(query: readonly number[], corpusVectors: readonly (readonly number[])[], targetIndex: number): number;