/**
 * HNSW Embedding Store Helper Functions
 * @module @skillsmith/core/embeddings/hnsw-store.helpers
 */
import type { HierarchicalNSWConstructor, HNSWEmbeddingStoreOptions } from './hnsw-store.types.js';
import { HNSW_PRESETS } from './hnsw-store.types.js';
import { HNSWEmbeddingStore } from './hnsw-store.js';
/**
 * Create an HNSWEmbeddingStore with a preset configuration.
 *
 * @param preset - Preset name ('small', 'medium', 'large', 'xlarge')
 * @param options - Additional options (merged with preset); dbPath is opened async
 * @returns Promise resolving to a configured HNSWEmbeddingStore instance
 *
 * @example
 * ```typescript
 * const store = await createHNSWStore('large', {
 *   dbPath: './embeddings.db',
 *   indexPath: './embeddings.hnsw',
 * });
 * ```
 */
export declare function createHNSWStore(preset: keyof typeof HNSW_PRESETS, options?: Omit<HNSWEmbeddingStoreOptions, 'hnswConfig'>): Promise<HNSWEmbeddingStore>;
/**
 * Check if hnswlib-node is available.
 * Useful for conditional logic or graceful degradation.
 *
 * @returns true if hnswlib-node can be loaded
 */
export declare function isHNSWAvailable(): Promise<boolean>;
/**
 * Dynamically load hnswlib-node module.
 *
 * @returns The HierarchicalNSW constructor, or null if unavailable
 * @internal
 */
export declare function loadHNSWLib(): Promise<{
    HierarchicalNSW: HierarchicalNSWConstructor;
} | null>;
/**
 * Compute cosine similarity between two embeddings.
 * Standalone version for use outside of HNSWEmbeddingStore.
 *
 * @param a - First embedding
 * @param b - Second embedding
 * @returns Similarity score between -1 and 1
 */
export declare function computeCosineSimilarity(a: Float32Array, b: Float32Array): number;
/**
 * Convert HNSW distance to similarity score.
 * HNSW returns distances, we need similarities (higher = more similar).
 *
 * @param distance - Distance value from HNSW
 * @param metric - Distance metric used
 * @returns Similarity score
 */
export declare function distanceToSimilarity(distance: number, metric?: 'cosine' | 'l2' | 'ip'): number;
/**
 * Estimate memory usage for an HNSW index.
 *
 * @param vectorCount - Number of vectors
 * @param dimensions - Vector dimensionality
 * @param m - HNSW M parameter
 * @returns Estimated memory usage in bytes
 */
export declare function estimateMemoryUsage(vectorCount: number, dimensions: number, m: number): number;
/**
 * Validate embedding dimensions.
 *
 * @param embedding - Embedding to validate
 * @param expectedDimensions - Expected dimension count
 * @param context - Context for error message (default: 'Query')
 * @throws Error if dimensions don't match
 */
export declare function validateDimensions(embedding: Float32Array, expectedDimensions: number, context?: string): void;
/**
 * Determine whether to use HNSW based on explicit option or environment.
 *
 * @param explicit - Explicit useHNSW option (if provided)
 * @returns Whether to enable HNSW
 */
export declare function shouldUseHNSW(explicit?: boolean): boolean;
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