import { CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
import { type BaseLLMCallOptions, type BaseLLMParams, LLM } from "@langchain/core/language_models/llms";
import { GenerationChunk } from "@langchain/core/outputs";
/**
 * The FriendliParams interface defines the input parameters for
 * the Friendli class.
 */
export interface FriendliParams extends BaseLLMParams {
    /**
     * Model name to use.
     */
    model?: string;
    /**
     * Base endpoint url.
     */
    baseUrl?: string;
    /**
     * Friendli personal access token to run as.
     */
    friendliToken?: string;
    /**
     * Friendli team ID to run as.
     */
    friendliTeam?: string;
    /**
     * Number between -2.0 and 2.0. Positive values penalizes tokens that have been
     * sampled, taking into account their frequency in the preceding text. This
     * penalization diminishes the model's tendency to reproduce identical lines
     * verbatim.
     */
    frequencyPenalty?: number;
    /**
     * Number between -2.0 and 2.0. Positive values penalizes tokens that have been
     * sampled at least once in the existing text.
     * presence_penalty: Optional[float] = None
     * The maximum number of tokens to generate. The length of your input tokens plus
     * `max_tokens` should not exceed the model's maximum length (e.g., 2048 for OpenAI
     * GPT-3)
     */
    maxTokens?: number;
    /**
     * When one of the stop phrases appears in the generation result, the API will stop
     * generation. The phrase is included in the generated result. If you are using
     * beam search, all of the active beams should contain the stop phrase to terminate
     * generation. Before checking whether a stop phrase is included in the result, the
     * phrase is converted into tokens.
     */
    stop?: string[];
    /**
     * Sampling temperature. Smaller temperature makes the generation result closer to
     * greedy, argmax (i.e., `top_k = 1`) sampling. If it is `None`, then 1.0 is used.
     */
    temperature?: number;
    /**
     * Tokens comprising the top `top_p` probability mass are kept for sampling. Numbers
     * between 0.0 (exclusive) and 1.0 (inclusive) are allowed. If it is `None`, then 1.0
     * is used by default.
     */
    topP?: number;
    /**
     * Additional kwargs to pass to the model.
     */
    modelKwargs?: Record<string, unknown>;
}
/**
 * The Friendli class is used to interact with Friendli inference Endpoint models.
 * This requires your Friendli Token and Friendli Team which is autoloaded if not specified.
 */
export declare class Friendli extends LLM<BaseLLMCallOptions> {
    lc_serializable: boolean;
    static lc_name(): string;
    get lc_secrets(): {
        [key: string]: string;
    } | undefined;
    model: string;
    baseUrl: string;
    friendliToken?: string;
    friendliTeam?: string;
    frequencyPenalty?: number;
    maxTokens?: number;
    stop?: string[];
    temperature?: number;
    topP?: number;
    modelKwargs?: Record<string, unknown>;
    constructor(fields: FriendliParams);
    _llmType(): string;
    private constructHeaders;
    private constructBody;
    /**
     * Calls the Friendli endpoint and retrieves the result.
     * @param {string} prompt The input prompt.
     * @returns {Promise<string>} A promise that resolves to the generated string.
     */
    /** @ignore */
    _call(prompt: string, _options: this["ParsedCallOptions"]): Promise<string>;
    _streamResponseChunks(prompt: string, _options: this["ParsedCallOptions"], runManager?: CallbackManagerForLLMRun): AsyncGenerator<GenerationChunk>;
}
