import { ClientOptions } from "@langchain/openai";
export declare const VLLM = "vllm";
export declare const SvgIcon = "<svg height=\"1em\" style=\"flex:none;line-height:1\" viewBox=\"0 0 24 24\" width=\"1em\" xmlns=\"http://www.w3.org/2000/svg\"><title>vLLM</title><path d=\"M0 4.973h9.324V23L0 4.973z\" fill=\"#FDB515\"></path><path d=\"M13.986 4.351L22.378 0l-6.216 23H9.324l4.662-18.649z\" fill=\"#30A2FF\"></path></svg>";
export type VLLMModelCredentials = {
    api_key: string;
    endpoint_url?: string;
    endpoint_model_name?: string;
    mode?: 'completion' | 'chat';
    context_size?: number;
    max_tokens_to_sample?: number;
    agent_though_support?: 'supported' | 'unsupported';
    function_calling_type?: 'function_call' | 'tool_call' | 'no_call';
    stream_function_calling?: 'supported' | 'unsupported';
    vision_support?: 'supported' | 'unsupported';
    stream_mode_delimiter?: string;
    thinking?: boolean;
};
export declare function toCredentialKwargs(credentials: VLLMModelCredentials, model: string): {
    modelKwargs: {};
    configuration: ClientOptions;
    temperature: number;
    maxTokens?: number;
    maxCompletionTokens?: number;
    topP: number;
    frequencyPenalty: number;
    presencePenalty: number;
    n: number;
    logitBias?: Record<string, number>;
    user?: string;
    streaming: boolean;
    streamUsage?: boolean;
    modelName: string;
    model: import("@langchain/openai").OpenAIChatModelId;
    stop?: string[];
    stopSequences?: string[];
    timeout?: number;
    openAIApiKey?: string;
    apiKey?: string;
    verbosity?: import("@langchain/openai").OpenAIVerbosityParam;
};
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