/**
 * Embedding Provider for LLM Browser (V-002)
 *
 * Provides text embedding generation using @xenova/transformers.
 * Uses all-MiniLM-L6-v2 model (384 dimensions) by default.
 * Falls back gracefully when transformers library is not available.
 */
/**
 * Configuration options for EmbeddingProvider
 */
export interface EmbeddingProviderOptions {
    /** Model to use for embeddings (default: 'Xenova/all-MiniLM-L6-v2') */
    model?: string;
    /** Whether to quantize the model for faster inference (default: true) */
    quantized?: boolean;
    /** Cache directory for downloaded models */
    cacheDir?: string;
}
/**
 * Embedding generation result
 */
export interface EmbeddingResult {
    /** The embedding vector */
    vector: Float32Array;
    /** The model used to generate the embedding */
    model: string;
    /** Number of tokens in the input */
    tokenCount?: number;
}
/**
 * Batch embedding generation result
 */
export interface BatchEmbeddingResult {
    /** Array of embedding vectors */
    vectors: Float32Array[];
    /** The model used to generate the embeddings */
    model: string;
    /** Processing time in milliseconds */
    processingTimeMs: number;
}
/**
 * EmbeddingProvider - Wraps @xenova/transformers for text embedding generation
 *
 * Provides singleton access to embedding generation with lazy model loading.
 * Falls back gracefully when the library is unavailable.
 */
export declare class EmbeddingProvider {
    private static instance;
    private static loadError;
    private pipeline;
    private transformers;
    private initialized;
    private initializing;
    private initPromise;
    private readonly modelName;
    private readonly quantized;
    private readonly cacheDir?;
    /**
     * Model dimensions for known models
     */
    private static readonly MODEL_DIMENSIONS;
    /**
     * Default model for embeddings
     */
    static readonly DEFAULT_MODEL = "Xenova/all-MiniLM-L6-v2";
    private constructor();
    /**
     * Check if the transformers library is available
     */
    static isAvailable(): Promise<boolean>;
    /**
     * Create or get the singleton EmbeddingProvider instance
     *
     * @param options Configuration options (only used for first creation)
     * @returns EmbeddingProvider instance or null if unavailable
     */
    static create(options?: EmbeddingProviderOptions): Promise<EmbeddingProvider | null>;
    /**
     * Reset the singleton (for testing)
     */
    static reset(): void;
    /**
     * Initialize the embedding model
     */
    initialize(): Promise<void>;
    private doInitialize;
    /**
     * Generate an embedding for a single text
     *
     * @param text Text to embed
     * @returns Embedding result with vector
     */
    generateEmbedding(text: string): Promise<EmbeddingResult>;
    /**
     * Generate embeddings for multiple texts in batch
     *
     * @param texts Array of texts to embed
     * @returns Batch embedding result with vectors
     */
    generateBatch(texts: string[]): Promise<BatchEmbeddingResult>;
    /**
     * Get the number of dimensions for the current model
     */
    getDimensions(): number;
    /**
     * Get the current model name
     */
    getModelName(): string;
    /**
     * Check if the provider is initialized
     */
    isInitialized(): boolean;
    /**
     * Ensure the provider is initialized before use
     */
    private ensureInitialized;
}
/**
 * Create a new EmbeddingProvider instance
 * Convenience function for non-singleton usage
 */
export declare function createEmbeddingProvider(options?: EmbeddingProviderOptions): Promise<EmbeddingProvider | null>;
/**
 * Check if embedding generation is available
 */
export declare function isEmbeddingAvailable(): Promise<boolean>;
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