/**
 * Embedding-based similarity search for memory recall.
 * Uses all-MiniLM-L6-v2 via @huggingface/transformers (worker thread).
 * Falls back gracefully if model loading fails.
 *
 * This module wraps the existing embeddings infrastructure (src/embeddings/)
 * and provides a higher-level API for recall-time similarity search.
 */
/**
 * Lazy-load the embedding model. Cache the pipeline.
 * Returns false on failure (doesn't crash).
 */
export declare function initEmbeddings(): Promise<boolean>;
/**
 * Generate embedding for text. Truncate to first ~256 tokens (~2000 chars).
 * Returns null on failure.
 */
export declare function embedText(text: string): Promise<Float32Array | null>;
/**
 * Standard cosine similarity between two embedding vectors.
 */
export declare function cosineSimilarity(a: Float32Array, b: Float32Array): number;
/**
 * Cache repeated query embeddings in-process to avoid re-embedding the same
 * recall queries during one MCP/API session.
 */
export declare function getCachedQueryEmbedding(query: string): Promise<Float32Array | null>;
export declare function clearQueryEmbeddingCache(): void;
/**
 * Embed the query, compute similarity against all memory embeddings,
 * return top K (default 10) sorted by score desc.
 *
 * Non-throwing: returns empty array on failure.
 */
export declare function findSimilarMemories(query: string, memories: Array<{
    id: number;
    title: string;
    content: string;
}>, topK?: number, queryEmbedding?: Float32Array | null): Promise<Array<{
    id: number;
    score: number;
}>>;
