/**
 * Profile text embeddings → researcher similarity + collaboration prediction (#1501).
 *
 * The #1501 "node2vec embeddings" slot, implemented (per operator decision) by
 * reusing the text-embedding backend (#1493) instead of a heavy graph-embedding
 * stack: each person profile is embedded from its name + the titles of its
 * corpus-refs, then cosine similarity gives nearest researchers and
 * collaboration link-prediction (highly-similar people who have NOT co-authored —
 * corpus-refs overlap is the co-authorship proxy).
 *
 * Opt-in: requires `@xenova/transformers` (and `hnswlib-node` is not needed here —
 * similarity is an in-memory cosine over the ~hundreds of person profiles).
 * Degrades gracefully when the optional dep is absent.
 *
 * @source historical: profiles/graph_embeddings.py (node2vec → text-embedding here)
 * @tests @test/unit/artifacts/profile-embed.test.ts
 */
export interface ProfileEmbeddings {
    profIds: string[];
    names: (string | null)[];
    vectors: number[][];
    /** profId index → set of its corpus-refs (co-authorship proxy). */
    refs: Set<string>[];
}
export interface SimilarHit {
    profId: string;
    name: string | null;
    score: number;
}
export interface CollabPrediction {
    a: string;
    b: string;
    score: number;
}
/**
 * Build text embeddings for person profiles. Text = profile name + the titles of
 * its corpus-refs. Requires `@xenova/transformers`; throws a clear error when absent.
 */
export declare function buildProfileEmbeddings(corpusRoot: string, model?: string): Promise<ProfileEmbeddings>;
/** Nearest researchers to a profile by embedding cosine (excludes self). */
export declare function profileSimilar(emb: ProfileEmbeddings, profId: string, topK?: number): SimilarHit[];
/**
 * Collaboration link-prediction: pairs of people with embedding similarity ≥
 * threshold who share NO corpus-refs (i.e. have not co-authored). Ranked desc.
 */
export declare function collaborationPredictions(emb: ProfileEmbeddings, threshold?: number, limit?: number): CollabPrediction[];
export declare function renderSimilar(profId: string, hits: SimilarHit[]): string;
export declare function renderCollabPredictions(preds: CollabPrediction[], threshold: number): string;
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