/**
 * VertexEmbedProvider — Vertex AI text-embedding-004 via @google-cloud/aiplatform.
 *
 * Uses PredictionServiceClient (NOT @google-cloud/vertexai, which only supports
 * generative models). Returns 768-dimension vectors by default.
 */
import type { EmbedProvider } from '../core/embed.js';
export type VertexEmbedTaskType = 'RETRIEVAL_DOCUMENT' | 'RETRIEVAL_QUERY' | 'SEMANTIC_SIMILARITY' | 'CLASSIFICATION' | 'CLUSTERING';
export interface VertexEmbedOptions {
    /** GCP project ID. Falls back to GOOGLE_CLOUD_PROJECT env var. */
    projectId?: string;
    /** GCP region (default: us-central1). */
    location?: string;
    /** Embedding model name (default: text-embedding-004). */
    model?: string;
    /** Output dimensionality (default: 768). */
    dimensions?: number;
    /** Max concurrent requests per batch (default: 5). */
    batchConcurrency?: number;
    /** Default task type for embeddings (default: SEMANTIC_SIMILARITY). */
    taskType?: VertexEmbedTaskType;
}
export declare class VertexEmbedProvider implements EmbedProvider {
    readonly name = "vertex";
    readonly dimensions: number;
    private readonly projectId;
    private readonly location;
    private readonly model;
    private readonly batchConcurrency;
    private readonly taskType;
    private readonly client;
    private readonly helpers;
    constructor(options: VertexEmbedOptions, client: import('@google-cloud/aiplatform').PredictionServiceClient, helpersModule: typeof import('@google-cloud/aiplatform').helpers);
    private get endpoint();
    embed(text: string): Promise<number[]>;
    embedBatch(texts: string[]): Promise<number[][]>;
}
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