/**
 * @license
 * Copyright 2026 Alibaba Group Holding Limited
 * SPDX-License-Identifier: Apache-2.0
 */
import { type MemoryConfig, type MemoryConnection, type MemoryLogger } from './config.js';
import type { StoredVector } from './store.js';
export declare function normalizeVector(values: readonly number[]): Float32Array | null;
export declare class EmbeddingClient {
    readonly model: string;
    readonly minSim: number;
    readonly vecLimit: number;
    private readonly config;
    private readonly connection;
    private readonly log;
    private readonly fetcher;
    private readonly cache;
    private readonly pending;
    private closed;
    private warmup?;
    constructor(options: {
        config?: MemoryConfig['retrieve'];
        connection: MemoryConnection;
        log?: MemoryLogger;
        fetch?: typeof globalThis.fetch;
    });
    get available(): boolean;
    get unavailableReason(): string;
    embedQuery(text: string): Promise<Float32Array | null>;
    embedDocuments(texts: readonly string[]): Promise<Array<Float32Array | null>>;
    warmUp(): Promise<void>;
    private request;
    close(): void;
}
export declare function searchVectors(vectors: readonly StoredVector[], query: Float32Array, minSim: number, limit: number): Array<{
    refId: number;
    similarity: number;
}>;
export declare class EmbeddingBackfiller {
    private readonly client;
    private readonly write;
    private readonly log;
    private readonly maxQueue;
    private readonly queue;
    private readonly queued;
    private running?;
    private closed;
    constructor(client: EmbeddingClient, write: (id: number, vector: Float32Array, model: string) => void, log?: MemoryLogger, maxQueue?: number);
    enqueue(id: number, text: string): boolean;
    private run;
    drain(timeoutMs?: number): Promise<boolean>;
    close(): void;
}