/**
 * transformers.js (ONNX, dual-environment) Embeddings adapter battery.
 *
 * @module @nhtio/adk/batteries/embeddings/transformers_js/adapter
 *
 * @remarks
 * Embeddings battery backed by transformers.js's `feature-extraction` pipeline. **Environment-neutral**
 * — runs in Node (via `onnxruntime-node`) and the browser (via `onnxruntime-web` / WebGPU), auto-
 * selected by the package; there is no WebGPU requirement, so this battery is surfaced from the
 * environment-neutral `@nhtio/adk/batteries/embeddings` barrel alongside the OpenAI one.
 *
 * Same user-facing surface as the OpenAI / WebLLM embeddings batteries (`isAvailable` / `dimensions` /
 * `preload` / `reset` / `embed` / `embedMany`), same `number[]` return shape, same query/document
 * prefix handling (the shared `applyEmbeddingPrefix`).
 *
 * `@huggingface/transformers` is an optional peer dependency, imported lazily.
 *
 * **Cross-runtime vector caveat:** embeddings produced here are not guaranteed bit-identical to those
 * from a different runtime (WebLLM/MLC, or even node-ONNX vs web-ONNX for the same model). A vector
 * corpus must be embedded AND queried by one backend.
 */
import type { EmbedOptions } from "../openai/types";
/**
 * Embeddings adapter for transformers.js's feature-extraction pipeline.
 *
 * @remarks
 * Reusable: construct once, call {@link TransformersJsEmbeddingsAdapter.embed} / {@link embedMany} as
 * many times as needed. The pipeline is resolved lazily on first use (or via {@link preload}) and
 * cached with single-flight semantics so concurrent calls share one load.
 */
export declare class TransformersJsEmbeddingsAdapter {
    #private;
    /**
     * Whether this battery is available. transformers.js is environment-neutral (Node + browser), so
     * this is `true` whenever the runtime can import the peer — there is no WebGPU requirement.
     */
    static isAvailable(): boolean;
    /**
     * @param options - Constructor options. Validated eagerly.
     * @throws {@link @nhtio/adk/batteries!E_INVALID_TRANSFORMERS_JS_EMBEDDINGS_OPTIONS} when invalid.
     */
    constructor(options: unknown);
    /** Declared output dimensionality (from options), or `undefined` if not configured. */
    get dimensions(): number | undefined;
    /** Instance availability probe (honours an injected `isAvailable`). */
    isAvailable(): boolean;
    /** Eagerly loads (and caches) the pipeline so the first `embed` call is fast. Idempotent. */
    preload(): Promise<void>;
    /** Drops the cached pipeline and in-flight load so the next call reloads. */
    reset(): void;
    /**
     * Release the loaded model's ONNX sessions + GPU/wasm buffers, then drop the cached pipeline.
     *
     * @remarks
     * `reset()` only nulls the JS reference; the native ONNX Runtime sessions and WebGPU/wasm device memory
     * stay alive until GC. Loading many embedding models back-to-back in one browser session (e.g. a full
     * matrix run) accumulates those sessions until the heap is exhausted. `FeatureExtractionPipeline`
     * extends `Pipeline`, which exposes `dispose()` — this awaits it so the memory is reclaimed between
     * loads, swallows a disposal error (teardown must not throw), and finishes with `reset()`. Idempotent.
     */
    dispose(): Promise<void>;
    /**
     * Embeds a single string.
     *
     * @param text - The input text.
     * @param opts - Per-call options (`kind`).
     * @returns The embedding vector as a plain `number[]`.
     */
    embed(text: string, opts?: EmbedOptions): Promise<number[]>;
    /**
     * Embeds a batch of strings in a single pipeline call.
     *
     * @param texts - The input texts.
     * @param opts - Per-call options (`kind`). Defaults to `kind: 'document'`.
     * @returns One embedding vector per input, in input order, each a plain `number[]`.
     * @throws {@link @nhtio/adk/batteries!E_TRANSFORMERS_JS_EMBEDDINGS_ENGINE_ERROR} when the call fails
     *   or returns a malformed result.
     */
    embedMany(texts: string[], opts?: EmbedOptions): Promise<number[][]>;
}