/**
 * Embedding Model Management
 *
 * Generates vector embeddings for semantic search. Supports three backends:
 *
 *   1. harper-fabric-embeddings — Minimal native GGUF wrapper (~19 MB).
 *      Preferred on Fabric. Requires a pre-staged model file.
 *
 *   2. harper-fabric-onnx — ONNX Runtime backend.
 *      Alternative on Fabric; may be preferred depending on deployment.
 *
 *   3. node-llama-cpp — Full-featured wrapper (~250 MB+).
 *      Fallback for local dev. Downloads the model on first run.
 *
 * The backend is selected automatically by trying each in order,
 * or a specific backend can be requested via the `embeddingBackend` config option.
 */
type BackendId = 'gguf' | 'onnx' | 'llama-cpp';
/**
 * Initialize the embedding model.
 *
 * When `embeddingBackend` is set, that backend is used directly (no fallback).
 * Otherwise backends are tried in order: gguf → onnx → llama-cpp.
 */
export declare function initEmbeddingModel(config: {
    embeddingModel: string;
    componentDir: string;
    embeddingBackend?: BackendId;
    embeddingOnnxFile?: string;
    embeddingMaxTokens?: number;
}): Promise<void>;
/**
 * Generate an embedding vector for the given text.
 */
export declare function generateEmbedding(text: string): Promise<number[]>;
/**
 * Clean up embedding model resources.
 */
export declare function dispose(): Promise<void>;
export {};
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