/**
 * embeddingService.ts
 * Generate embeddings using transformers.js for semantic search
 */
/**
 * Embedding service using all-MiniLM-L6-v2 model
 * This is a lightweight model (23MB) optimized for semantic similarity
 */
export declare class EmbeddingService {
    private pipeline;
    private initialized;
    private modelName;
    /**
     * Initialize the embedding pipeline
     * Downloads model on first run (~23MB)
     */
    initialize(): Promise<void>;
    /**
     * Generate embedding for a text
     * @param text - Text to embed
     * @returns Vector embedding (384 dimensions for all-MiniLM-L6-v2)
     */
    generateEmbedding(text: string): Promise<number[]>;
    /**
     * Generate embeddings for multiple texts (batch processing)
     * @param texts - Array of texts to embed
     * @returns Array of vector embeddings
     */
    generateEmbeddings(texts: string[]): Promise<number[][]>;
    /**
     * Calculate cosine similarity between two vectors
     * @param a - First vector
     * @param b - Second vector
     * @returns Similarity score (0-1, higher is more similar)
     */
    cosineSimilarity(a: number[], b: number[]): number;
    /**
     * Find most similar vectors to a query vector
     * @param queryEmbedding - Query vector
     * @param docEmbeddings - Array of document vectors with metadata
     * @param topK - Number of results to return
     * @returns Array of {index, similarity} sorted by similarity desc
     */
    findMostSimilar(queryEmbedding: number[], docEmbeddings: Array<{
        embedding: number[];
        index: number;
    }>, topK: number): Array<{
        index: number;
        similarity: number;
    }>;
}
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