import { Cluster, KeywordClustererOptions } from './types';
/**
 * KeywordClusterer class for clustering keywords using OpenAI embeddings
 */
export declare class KeywordClusterer {
    private openai;
    private embeddingModel;
    private completionModel;
    private minClusterSize;
    private distanceThreshold;
    private algorithm;
    private k?;
    private maxIterations?;
    private linkage?;
    private context?;
    /**
     * Creates a new KeywordClusterer instance
     * @param options Configuration options
     */
    constructor(options: KeywordClustererOptions);
    /**
     * Clusters the provided keywords
     * @param keywords Array of keywords to cluster
     * @returns Array of clusters with names and descriptions
     */
    clusterKeywords(keywords: string[]): Promise<Cluster[]>;
    /**
     * Gets embeddings for the provided texts
     * @param texts Array of texts to get embeddings for
     * @returns Array of embeddings
     */
    getEmbeddings(texts: string[]): Promise<number[][]>;
    /**
     * Calculates cosine distances between all embeddings
     * @param embeddings Array of embeddings
     * @returns Matrix of distances
     */
    calculateDistances(embeddings: number[][]): number[][];
    /**
     * Calculates cosine similarity between two vectors
     * @param a First vector
     * @param b Second vector
     * @returns Cosine similarity
     */
    private cosineSimilarity;
    /**
     * Generates clusters based on the selected algorithm
     * @param keywords Array of keywords
     * @param distances Matrix of distances
     * @returns Array of clusters
     */
    generateClusters(keywords: string[], distances: number[][], embeddings?: number[][]): Cluster[];
    /**
     * Generates names and descriptions for clusters
     * @param clusters Array of clusters
     * @returns Array of clusters with names and descriptions
     */
    nameAndDescribeClusters(clusters: Cluster[]): Promise<Cluster[]>;
}