import type { SemanticSearchOptions, SearchResult } from '../types/index.js';
import { FileRegistry } from '../indexing/file-registry.js';
import { FileCache } from '../cache/file-cache.js';
/**
 * Semantic search using embeddings
 */
export declare class SemanticSearch {
    private registry;
    private fileCache;
    private embeddingCache;
    private embeddingData;
    private embeddingModel;
    private provider;
    private modelName;
    constructor(registry: FileRegistry, fileCache: FileCache);
    /**
     * Gets the model name for the configured provider
     */
    private getModelName;
    /**
     * Initializes the semantic search (loads or builds embeddings)
     */
    initialize(): Promise<void>;
    /**
     * Builds embeddings for all documents
     */
    private buildEmbeddings;
    /**
     * Generates an embedding for text
     */
    private embedText;
    /**
     * Generates embedding using local model (@xenova/transformers)
     */
    private embedTextLocal;
    /**
     * Generates embedding using Voyage AI (stub - requires voyageai package)
     */
    private embedTextVoyage;
    /**
     * Generates embedding using OpenAI (stub - requires openai package)
     */
    private embedTextOpenAI;
    /**
     * Performs semantic search
     */
    search(options: SemanticSearchOptions): Promise<SearchResult[]>;
    /**
     * Gets embedding for a specific document (for related articles)
     */
    getDocumentEmbedding(filePath: string): Promise<number[] | null>;
}
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