/**
 * Local Offline Embeddings Service
 *
 * Provides 100% offline text embeddings using Transformers.js (WASM-based).
 * No external API calls. Model is downloaded once and cached locally.
 *
 * @module @aitytech/agentkits-memory/embeddings
 */
import type { EmbeddingGenerator } from '../types.js';
/**
 * Embedding provider type
 */
export type EmbeddingProvider = 'transformers' | 'mock';
/**
 * Local embeddings configuration
 */
export interface LocalEmbeddingsConfig {
    /** Provider to use (default: 'transformers') */
    provider?: EmbeddingProvider;
    /**
     * Model ID for Transformers.js
     * Default: 'Xenova/multilingual-e5-small' (100+ languages, optimized for retrieval)
     * Alternative: 'Xenova/paraphrase-multilingual-MiniLM-L12-v2' (50+ languages)
     * Alternative: 'Xenova/all-MiniLM-L6-v2' (English only, faster)
     */
    modelId?: string;
    /** Vector dimensions (default: 384) */
    dimensions?: number;
    /** Enable in-memory cache for repeated texts */
    cacheEnabled?: boolean;
    /** Maximum cache size (default: 1000) */
    maxCacheSize?: number;
    /** Show progress during model download */
    showProgress?: boolean;
    /** Custom cache directory for models */
    cacheDir?: string;
}
/**
 * Embedding result with metadata
 */
export interface EmbeddingResult {
    /** The embedding vector */
    embedding: Float32Array;
    /** Time taken in milliseconds */
    timeMs: number;
    /** Whether result was from cache */
    cached: boolean;
    /** Token count (approximate) */
    tokenCount?: number;
}
/**
 * Local embeddings service statistics
 */
export interface EmbeddingsStats {
    /** Total embeddings generated */
    totalEmbeddings: number;
    /** Cache hits */
    cacheHits: number;
    /** Cache misses */
    cacheMisses: number;
    /** Average time per embedding (ms) */
    avgTimeMs: number;
    /** Total time spent (ms) */
    totalTimeMs: number;
    /** Model loaded */
    modelLoaded: boolean;
    /** Provider being used */
    provider: EmbeddingProvider;
}
/**
 * Local Embeddings Service
 *
 * Provides offline text embeddings using Transformers.js.
 * Models are downloaded once and cached locally in ~/.cache/huggingface.
 */
export declare class LocalEmbeddingsService {
    private config;
    private cache;
    private pipeline;
    private modelLoading;
    private stats;
    constructor(config?: LocalEmbeddingsConfig);
    /**
     * Initialize the embeddings service (loads model)
     */
    initialize(): Promise<void>;
    private loadModel;
    /**
     * Generate embedding for text
     */
    embed(text: string): Promise<EmbeddingResult>;
    /**
     * Generate embeddings for multiple texts (batch)
     */
    embedBatch(texts: string[]): Promise<EmbeddingResult[]>;
    /**
     * Get embedding generator function compatible with ProjectMemoryService
     */
    getGenerator(): EmbeddingGenerator;
    /**
     * Get service statistics
     */
    getStats(): EmbeddingsStats;
    /**
     * Clear the embedding cache
     */
    clearCache(): void;
    /**
     * Get vector dimensions
     */
    getDimensions(): number;
    /**
     * Shutdown and cleanup
     */
    shutdown(): Promise<void>;
}
/**
 * Create a local embeddings service with default configuration
 */
export declare function createLocalEmbeddings(config?: LocalEmbeddingsConfig): LocalEmbeddingsService;
/**
 * Create an embedding generator function for use with ProjectMemoryService
 *
 * @example
 * ```typescript
 * import { createEmbeddingGenerator } from '@aitytech/agentkits-memory/embeddings';
 * import { ProjectMemoryService } from '@aitytech/agentkits-memory';
 *
 * const embeddingGenerator = await createEmbeddingGenerator();
 *
 * const memory = new ProjectMemoryService({
 *   projectPath: '/path/to/project',
 *   enableVectorIndex: true,
 *   embeddingGenerator,
 * });
 * ```
 */
export declare function createEmbeddingGenerator(config?: LocalEmbeddingsConfig): Promise<EmbeddingGenerator>;
export default LocalEmbeddingsService;
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