/**
 * First-boot preset auto-recommendation.
 *
 * When neither EMBEDDING_MODEL nor EMBEDDING_PRESET is set AND the DB has no
 * stored `embedding_model` metadata, this module:
 *   1. Walks the vault via collectMarkdownFiles.
 *   2. Samples the first ~2 KB of each note.
 *   3. Unicode-block classifies: if >5% of sampled chars fall in any non-Latin
 *      block (CJK, Cyrillic, Arabic, Devanagari, Hebrew, Thai) → multilingual.
 *   4. Picks 'english' (Latin-only) or 'multilingual' (non-Latin/mixed).
 *   5. Logs the rationale to stderr and returns the chosen preset name.
 *
 * The caller (bootstrap or server init) persists the result so subsequent
 * boots see a stored embedding_model and skip the auto-recommend path.
 */
import { type EmbeddingPresetName } from './presets.js';
export interface AutoRecommendResult {
    preset: EmbeddingPresetName;
    reason: string;
    skipped: boolean;
}
/**
 * Run the first-boot preset auto-recommendation.
 *
 * Returns immediately (skipped=true) if:
 *   - EMBEDDING_MODEL or EMBEDDING_PRESET is set in env, OR
 *   - storedEmbeddingModel is truthy (DB already has a model recorded).
 *
 * Otherwise walks the vault, classifies, picks a preset, logs to stderr,
 * and returns the recommendation.
 */
export declare function autoRecommendPreset(env: NodeJS.ProcessEnv, vaultPath: string, storedEmbeddingModel: string | undefined): Promise<AutoRecommendResult>;