/**
 * Enhanced service for generating embeddings from text
 *
 * Features:
 * - Improved caching for frequent embeddings
 * - Enhanced error handling with automatic retries
 * - Support for text chunking and batching
 * - Fallback model support
 */
/**
 * Configuration for the enhanced embedding service
 */
export interface EmbeddingServiceConfig {
    model?: string;
    apiKey?: string;
    dimensions?: number;
    batchSize?: number;
    fallbackModel?: string;
    maxRetries?: number;
    retryDelayMs?: number;
    enableCache?: boolean;
    cacheSize?: number;
    chunkSize?: number;
    chunkOverlap?: number;
}
/**
 * Enhanced service that generates embeddings from text using OpenAI's API
 * with caching, batching, and retry logic
 */
export declare class EmbeddingService {
    private openai;
    private config;
    private logger;
    private cache;
    /**
     * Creates a new enhanced embedding service
     *
     * @param config - Configuration for the embedding service
     */
    constructor(config?: EmbeddingServiceConfig);
    /**
     * Sets up cache management to prevent memory leaks
     */
    private setupCacheManagement;
    /**
     * Executes a function with retry logic
     */
    private withRetry;
    /**
     * Create a hash key for the cache
     */
    private createCacheKey;
    /**
     * Chunks text into smaller pieces for better embedding
     */
    private chunkText;
    /**
     * Generates an embedding for a single text with caching and chunking
     *
     * @param text - The text to embed
     * @returns Promise resolving to the embedding vector
     */
    embedText(text: string): Promise<number[]>;
    /**
     * Embeds a long text by chunking and then combining chunks
     *
     * @param text - The long text to embed
     * @returns Promise resolving to the combined embedding vector
     */
    private embedLongText;
    /**
     * Combines multiple embeddings into one
     *
     * @param embeddings - Array of embeddings to combine
     * @param weights - Optional weights for each embedding (e.g. chunk lengths)
     * @returns Combined embedding vector
     */
    private combineEmbeddings;
    /**
     * Generates embeddings for multiple texts in batches
     *
     * @param texts - Array of texts to embed
     * @returns Promise resolving to an array of embedding vectors
     */
    embedBatch(texts: string[]): Promise<number[][]>;
    /**
     * Calculate cosine similarity between two vectors
     *
     * @param a - First vector
     * @param b - Second vector
     * @returns Cosine similarity (0-1)
     */
    calculateSimilarity(a: number[], b: number[]): number;
}