/**
 * MAMA (Memory-Augmented MCP Architecture) - Embedding Generation
 *
 * Story M1.4: Configurable embedding model selection
 * Generates embeddings using configurable model (default: multilingual-e5-large)
 * Supports: Korean-English cross-lingual similarity, enhanced metadata
 *
 * @module embeddings
 * @version 1.1
 * @date 2025-11-20
 */
import { embeddingCache } from './embedding-cache.js';
import { loadConfig, getModelName, getEmbeddingDim, getQuantized } from './config-loader.js';
export declare const EMBEDDING_MODEL_MAX_LENGTH = 512;
export declare const EMBEDDING_MAX_TOKENISH_SEGMENTS = 480;
export declare function prepareEmbeddingText(text: string): string;
/**
 * Decision object for enhanced embedding generation
 */
export interface DecisionForEmbedding {
    topic: string;
    decision: string;
    reasoning?: string;
    outcome?: string;
    confidence?: number;
    user_involvement?: string;
    evidence?: string | string[] | unknown;
    alternatives?: string | string[] | unknown;
    risks?: string;
}
/**
 * Generate embedding vector from text
 *
 * Story M1.4 AC #1: Uses configurable embeddingDim from config
 * Target: < 30ms latency
 *
 * @param text - Input text to embed
 * @returns Embedding vector (dimension from config)
 * @throws Error if text is empty or embedding fails
 */
export declare function generateEmbedding(text: string): Promise<Float32Array>;
/**
 * Generate enhanced embedding with content + metadata
 *
 * Task 3.4: Implement enhanced embedding format
 * Inspired by A-mem: Content + Metadata for richer semantic representation
 *
 * @param decision - Decision object
 * @returns 1024-dim enhanced embedding
 */
export declare function generateEnhancedEmbedding(decision: DecisionForEmbedding): Promise<Float32Array>;
/**
 * Batch generate embeddings (optimized)
 *
 * Task 1: Implement Batch Embedding Generation
 * AC #3: Target - 30ms for 10 embeddings (vs 300ms sequential)
 *
 * Strategy: Use native transformer batch processing for parallel inference
 *
 * @param texts - Array of texts to embed (max 10 per batch)
 * @returns Array of embeddings
 */
export declare function generateBatchEmbeddings(texts: string[]): Promise<Float32Array[]>;
/**
 * Calculate cosine similarity between two embeddings
 *
 * Utility for testing and validation
 *
 * @param embA - First embedding
 * @param embB - Second embedding
 * @returns Cosine similarity (0-1)
 */
export declare function cosineSimilarity(embA: Float32Array, embB: Float32Array): number;
export { embeddingCache };
export declare const EMBEDDING_DIM: number;
export declare const MODEL_NAME: string;
export { loadConfig, getModelName, getEmbeddingDim, getQuantized };
//# sourceMappingURL=embeddings.d.ts.map