export declare class Embeddings {
    private cache;
    private ollamaModel;
    private ollamaBaseUrl;
    private initialized;
    init(): Promise<void>;
    /** Embed a string — returns a unit-norm vector. Uses cache when available. */
    embed(text: string): Promise<number[]>;
    /** Cosine similarity between two same-length vectors (L2-normalized assumed). */
    static cosine(a: number[], b: number[]): number;
    /** Rank candidates by cosine similarity to the query embedding. */
    semanticSearch(query: string, candidates: Array<{
        id: string;
        text: string;
    }>, topK?: number): Promise<Array<{
        id: string;
        score: number;
    }>>;
    flush(): Promise<void>;
    private embedOllama;
    /** Zero-dep local embedding: char tri-grams → hashed bag → dim 128 → TF weighting → L2 normalize. */
    private embedLocal;
    private persist;
}
export declare function getEmbeddings(): Embeddings;
export declare function resetEmbeddingsForTests(): void;
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