import * as sqlite3 from 'sqlite3';
import { VectorDatabase } from './vector-database-interface';
/**
 * ChromaDB-based vector database implementation for Windows.
 * Uses ChromaDB as an external vector database via HTTP API.
 *
 * Note: ChromaDB must be running as a local server or embedded instance.
 * For embedded mode, ChromaDB requires Python, but we can use the HTTP client.
 */
export declare class ChromaDbVectorDatabase implements VectorDatabase {
    private db;
    private workspaceRoot;
    private chromaClient;
    private collection;
    private available;
    private readonly embeddingDimensions;
    private readonly collectionName;
    constructor(db: sqlite3.Database, workspaceRoot: string);
    /**
     * Checks if ChromaDB is available.
     */
    isAvailable(): boolean;
    /**
     * Initializes ChromaDB connection and collection.
     */
    initialize(): Promise<void>;
    /**
     * Upserts an embedding into ChromaDB.
     */
    upsertEmbedding(embeddingId: string, embeddingVector: Float32Array | number[]): Promise<void>;
    /**
     * Searches for similar embeddings using ChromaDB.
     */
    search(queryEmbedding: Float32Array | number[], limit: number, dimension?: 'X' | 'Y' | 'Z' | 'W' | 'T', pluginId?: string): Promise<Array<{
        rowid: number;
        distance: number;
        similarity?: number;
    }>>;
    /**
     * Gets the embedding ID from a rowid.
     */
    getEmbeddingIdFromRowid(rowid: number): Promise<string | null>;
    /**
     * Gets the rowid for an embedding ID.
     */
    private getRowidFromEmbeddingId;
    /**
     * Gets an embedding by ID (helper method).
     */
    private getEmbeddingById;
}
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