import { SimpleChatModel, type BaseChatModelParams } from "@langchain/core/language_models/chat_models"; import type { BaseLanguageModelCallOptions } from "@langchain/core/language_models/base"; import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager"; import { BaseMessage } from "@langchain/core/messages"; import { ChatGenerationChunk } from "@langchain/core/outputs"; import * as webllm from "@mlc-ai/web-llm"; export interface WebLLMInputs extends BaseChatModelParams { appConfig?: webllm.AppConfig; chatOptions?: webllm.ChatOptions; temperature?: number; model: string; } export interface WebLLMCallOptions extends BaseLanguageModelCallOptions { } /** * To use this model you need to have the `@mlc-ai/web-llm` module installed. * This can be installed using `npm install -S @mlc-ai/web-llm`. * * You can see a list of available model records here: * https://github.com/mlc-ai/web-llm/blob/main/src/config.ts * @example * ```typescript * // Initialize the ChatWebLLM model with the model record. * const model = new ChatWebLLM({ * model: "Phi-3-mini-4k-instruct-q4f16_1-MLC", * chatOptions: { * temperature: 0.5, * }, * }); * * // Call the model with a message and await the response. * const response = await model.invoke([ * new HumanMessage({ content: "My name is John." }), * ]); * ``` */ export declare class ChatWebLLM extends SimpleChatModel { static inputs: WebLLMInputs; protected engine: webllm.MLCEngine; appConfig?: webllm.AppConfig; chatOptions?: webllm.ChatOptions; temperature?: number; model: string; static lc_name(): string; constructor(inputs: WebLLMInputs); _llmType(): string; initialize(progressCallback?: webllm.InitProgressCallback): Promise; reload(modelId: string, newChatOpts?: webllm.ChatOptions): Promise; _streamResponseChunks(messages: BaseMessage[], options: this["ParsedCallOptions"], runManager?: CallbackManagerForLLMRun): AsyncGenerator; _call(messages: BaseMessage[], options: this["ParsedCallOptions"], runManager?: CallbackManagerForLLMRun): Promise; }