/**
 * MediaPipe LLM Inference adapter for local Gemma models
 *
 * This adapter integrates with Google's MediaPipe LLM Inference Task API
 * to run Gemma models locally using WebGPU acceleration.
 *
 * Supported models:
 * - Gemma 2B / 7B
 * - Gemma 2 2B
 * - Gemma 3n (2B, 4B parameters)
 *
 * Requirements:
 * - WebGPU-capable browser or Node.js with WebGPU support
 * - @mediapipe/tasks-genai package
 * - Downloaded model weights (.bin file)
 *
 * @packageDocumentation
 */
import type { LLMProvider, MediaPipeConfig, CompletionRequest, CompletionResponse } from '../../types/llm.js';
import { BaseLLMAdapter } from './base.js';
/**
 * Gemma model variants for MediaPipe
 */
export declare const MEDIAPIPE_MODELS: {
    readonly 'gemma-2b': "gemma-2b-it-gpu-int4.bin";
    readonly 'gemma-7b': "gemma-7b-it-gpu-int8.bin";
    readonly 'gemma2-2b': "gemma2-2b-it-gpu-int4.bin";
    readonly 'gemma3n-e2b': "gemma-3n-E2B-it-int4.task";
    readonly 'gemma3n-e4b': "gemma-3n-E4B-it-int4.task";
};
/**
 * Adapter for MediaPipe LLM Inference API
 *
 * Runs Gemma models locally using WebGPU acceleration.
 * The adapter lazily loads the model on first use.
 *
 * @example
 * ```typescript
 * import { MediaPipeAdapter } from 'learngraph/llm';
 *
 * const adapter = new MediaPipeAdapter({
 *   provider: 'mediapipe',
 *   model: 'gemma3n-e2b',
 *   modelPath: '/models/gemma-3n-E2B-it-int4.task',
 * });
 *
 * // Use with orchestrator
 * const orchestrator = createOrchestrator(adapter);
 * ```
 */
export declare class MediaPipeAdapter extends BaseLLMAdapter {
    private inference;
    private readonly modelPath;
    private readonly randomSeed?;
    private readonly topK;
    private readonly loraRanks?;
    private initPromise;
    constructor(config: MediaPipeConfig);
    get provider(): LLMProvider;
    /**
     * Check if MediaPipe is configured (model path exists)
     */
    isConfigured(): boolean;
    /**
     * Initialize the MediaPipe LLM Inference engine
     * Called automatically on first request
     */
    initialize(): Promise<void>;
    private doInitialize;
    /**
     * Load MediaPipe library dynamically
     */
    private loadMediaPipe;
    complete(request: CompletionRequest): Promise<CompletionResponse>;
    /**
     * Build a prompt string from chat messages
     * Uses Gemma's instruction format
     */
    private buildPrompt;
    /**
     * Parse the generated response
     */
    private parseGeneratedResponse;
    /**
     * Close the inference engine and release resources
     */
    close(): void;
}
/**
 * Create a MediaPipe adapter for local Gemma models
 *
 * @param modelPath - Path to the model file (.bin or .task)
 * @param model - Model identifier (for reference)
 * @param overrides - Additional configuration options
 *
 * @example
 * ```typescript
 * // Using a Gemma 3n model
 * const adapter = createMediaPipeAdapter(
 *   '/models/gemma-3n-E2B-it-int4.task',
 *   'gemma3n-e2b'
 * );
 *
 * // With custom options
 * const adapter = createMediaPipeAdapter(
 *   '/models/gemma2-2b-it-gpu-int4.bin',
 *   'gemma2-2b',
 *   { maxTokens: 2048, temperature: 0.5 }
 * );
 * ```
 */
export declare function createMediaPipeAdapter(modelPath: string, model?: string, overrides?: Partial<Omit<MediaPipeConfig, 'provider' | 'modelPath'>>): MediaPipeAdapter;
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