/**
 * ONNX Embedding Model
 *
 * Uses @huggingface/transformers to run Harrier-OSS-v1-270M for
 * text embeddings. Produces 640-dimensional normalized vectors suitable
 * for semantic search.
 *
 * Model details:
 *   - onnx-community/harrier-oss-v1-270m-ONNX (q4 quantized)
 *   - ~344MB download, cached in ~/.cache/huggingface/
 *   - 640-dimensional output vectors
 *   - Pre-pooled sentence_embedding output (no manual pooling needed)
 *   - Already L2-normalized
 *   - Lazy initialization: first call ~5-10s, subsequent ~100ms
 */
/**
 * Embedding model using @huggingface/transformers + ONNX Runtime
 */
export declare class EmbeddingModel {
    private tokenizer;
    private model;
    private isLoaded;
    /**
     * Load the ONNX model.
     *
     * Downloads the model on first use (~344MB, q4). Subsequent calls
     * use the cached model from ~/.cache/huggingface/.
     *
     * @param _modelPath - Ignored (kept for backward compatibility). The model
     *   is always downloaded from HuggingFace Hub.
     */
    load(_modelPath?: string): Promise<void>;
    /**
     * Check if the model is loaded
     */
    isReady(): boolean;
    /**
     * Get the embedding dimension
     */
    getDimension(): number;
    /**
     * Embed a single text
     *
     * @param text - Text to embed
     * @param options - Options. isQuery is accepted for forward compatibility but
     *                  does not change behavior (Harrier needs no instruction prefix).
     * @returns 640-dimensional normalized embedding vector
     */
    embed(text: string, options?: {
        isQuery?: boolean;
    }): Promise<number[]>;
    /**
     * Embed multiple texts in batch
     *
     * @param texts - Texts to embed
     * @returns Array of 640-dimensional embedding vectors
     */
    embedBatch(texts: string[]): Promise<number[][]>;
    /**
     * Dispose of the model resources
     */
    dispose(): Promise<void>;
}
/**
 * Create a dummy embedding for testing
 *
 * Useful when ONNX model is not available.
 *
 * @param seed - Seed for reproducible random embeddings
 * @returns Random embedding with current model dimensions
 */
export declare function createDummyEmbedding(seed?: number): number[];
/**
 * Create deterministic embedding from text hash
 *
 * This creates a consistent (but not semantically meaningful) embedding
 * based on the text content. Useful for testing without loading the model.
 *
 * @param text - Text to create embedding for
 * @returns Deterministic embedding with current model dimensions
 */
export declare function createHashBasedEmbedding(text: string): number[];
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