/**
 * Real Transformer-Based Embeddings
 * ADR-021: QE ReasoningBank for Pattern Learning
 *
 * Uses @xenova/transformers for actual ML embeddings instead of hash-based fallback.
 * Model: all-MiniLM-L6-v2 (384-dimensional sentence embeddings)
 */
/**
 * Embedding model configuration
 */
export interface EmbeddingConfig {
    /** Model name (default: Xenova/all-MiniLM-L6-v2) */
    modelName: string;
    /** Whether to use quantized model for faster inference */
    quantized: boolean;
    /** Cache embeddings in memory */
    enableCache: boolean;
    /** Maximum cache size */
    maxCacheSize: number;
}
export declare const DEFAULT_EMBEDDING_CONFIG: EmbeddingConfig;
/**
 * Compute real embedding using transformer model
 *
 * @param text - Text to embed
 * @param config - Optional embedding configuration
 * @returns 384-dimensional embedding vector
 */
export declare function computeRealEmbedding(text: string, config?: Partial<EmbeddingConfig>): Promise<number[]>;
/**
 * Compute embeddings for multiple texts in batch (more efficient)
 */
export declare function computeBatchEmbeddings(texts: string[], config?: Partial<EmbeddingConfig>): Promise<number[][]>;
/**
 * Compute cosine similarity between two embeddings
 */
export declare function cosineSimilarity(a: number[], b: number[]): number;
/**
 * Clear the embedding cache
 */
export declare function clearEmbeddingCache(): void;
/**
 * Get cache statistics
 */
export declare function getCacheStats(): {
    size: number;
    hitRate: number;
};
/**
 * Check if transformer model is available
 */
export declare function isTransformerAvailable(): boolean;
/**
 * Get the embedding dimension (384 for all-MiniLM-L6-v2)
 */
export declare function getEmbeddingDimension(): number;
/**
 * Reset initialization state (for testing)
 */
export declare function resetInitialization(): void;
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