import type { IDatabaseAdapter } from '../../database/core/interfaces.js';
import type { JournalEntry } from '../../types/index.js';
import type { ProgressContext } from '../../utils/progress-utils.js';
import { type SemanticSearchResult } from './types.js';
export declare class VectorSearchManager {
    private dbAdapter;
    private modelName;
    private dimensions;
    private pipeline;
    private transformers;
    private initializationPromise;
    constructor(dbAdapter: IDatabaseAdapter, options?: {
        modelName?: string;
    });
    /**
     * Get the active model name
     */
    getModelName(): string;
    /**
     * Get the detected dimensions for the current model
     */
    getDimensions(): number | null;
    /**
     * Check if the vector search manager is initialized
     */
    isInitialized(): boolean;
    /**
     * Initialize the vector search manager.
     * This loads the huggingface transformer model and creates the virtual table.
     */
    initialize(): Promise<void>;
    private _initialize;
    /**
     * Verify the vec_journal_entries table exists and has the correct dimensions.
     * If the model or dimensions changed, drops the old table and creates a new one.
     */
    private verifyAndCreateVecTable;
    /**
     * Generate an embedding for text
     */
    generateEmbedding(text: string): Promise<Float32Array>;
    addEntry(entry: JournalEntry): Promise<void>;
    /**
     * Remove an entry from the vector index
     */
    removeEntry(entryId: number): boolean;
    /**
     * Search for similar entries by text query
     */
    search(query: string, limit?: number, threshold?: number, workspaceId?: string): Promise<SemanticSearchResult[]>;
    /**
     * Search for similar entries using an existing entry as the query
     */
    searchByEntryId(entryId: number, limit?: number, threshold?: number, workspaceId?: string): Promise<SemanticSearchResult[]>;
    /**
     * Helper to get vector search statistics
     */
    getStats(): {
        itemCount: number;
        dimensions: number;
        modelName: string;
        isReady: boolean;
    };
    rebuildIndex(progress?: ProgressContext, options?: {
        force?: boolean;
        batchSize?: number;
        pageSize?: number;
    }): Promise<{
        success: boolean;
        indexed: number;
        failed: number;
        firstError: string | null;
    }>;
}
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