agsamantha/node_modules/@langchain/community/dist/chat_models/zhipuai.d.ts

import { BaseChatModel, type BaseChatModelParams } from "@langchain/core/language_models/chat_models";
import { type BaseMessage } from "@langchain/core/messages";
import { ChatGenerationChunk, type ChatResult } from "@langchain/core/outputs";
import { type CallbackManagerForLLMRun } from "@langchain/core/callbacks/manager";
export type ZhipuMessageRole = "system" | "assistant" | "user";
interface ZhipuMessage {
    role: ZhipuMessageRole;
    content: string;
}
/**
 * Interface representing a request for a chat completion.
 *
 * See https://open.bigmodel.cn/dev/howuse/model
 */
type ModelName = (string & NonNullable<unknown>) | "chatglm_pro" | "chatglm_std" | "chatglm_lite" | "glm-4" | "glm-4v" | "glm-3-turbo" | "chatglm_turbo";
interface ChatCompletionRequest {
    model: ModelName;
    messages?: ZhipuMessage[];
    do_sample?: boolean;
    stream?: boolean;
    request_id?: string;
    max_tokens?: number | null;
    top_p?: number | null;
    top_k?: number | null;
    temperature?: number | null;
    stop?: string[];
}
/**
 * Interface defining the input to the ZhipuAIChatInput class.
 */
export interface ChatZhipuAIParams {
    /**
     * @default "glm-3-turbo"
     * Alias for `model`
     */
    modelName: ModelName;
    /**
     * @default "glm-3-turbo"
     */
    model: ModelName;
    /** Whether to stream the results or not. Defaults to false. */
    streaming?: boolean;
    /** Messages to pass as a prefix to the prompt */
    messages?: ZhipuMessage[];
    /**
     * API key to use when making requests. Defaults to the value of
     * `ZHIPUAI_API_KEY` environment variable.
     * Alias for `apiKey`
     */
    zhipuAIApiKey?: string;
    /**
     * API key to use when making requests. Defaults to the value of
     * `ZHIPUAI_API_KEY` environment variable.
     */
    apiKey?: string;
    /** Amount of randomness injected into the response. Ranges
     * from 0 to 1 (0 is not included). Use temp closer to 0 for analytical /
     * multiple choice, and temp closer to 1 for creative
     * and generative tasks. Defaults to 0.95
     */
    temperature?: number;
    /** Total probability mass of tokens to consider at each step. Range
     * from 0 to 1 Defaults to 0.7
     */
    topP?: number;
    /**
     * Unique identifier for the request. Defaults to a random UUID.
     */
    requestId?: string;
    /**
     * turn on sampling strategy when do_sample is true,
     * do_sample is false, temperature、top_p will not take effect
     */
    doSample?: boolean;
    /**
     * max value is 8192，defaults to 1024
     */
    maxTokens?: number;
    stop?: string[];
}
export declare class ChatZhipuAI extends BaseChatModel implements ChatZhipuAIParams {
    static lc_name(): string;
    get callKeys(): string[];
    get lc_secrets(): {
        zhipuAIApiKey: string;
        apiKey: string;
    };
    get lc_aliases(): undefined;
    zhipuAIApiKey?: string;
    apiKey?: string;
    streaming: boolean;
    doSample?: boolean;
    messages?: ZhipuMessage[];
    requestId?: string;
    modelName: ChatCompletionRequest["model"];
    model: ChatCompletionRequest["model"];
    apiUrl: string;
    maxTokens?: number | undefined;
    temperature?: number | undefined;
    topP?: number | undefined;
    stop?: string[];
    constructor(fields?: Partial<ChatZhipuAIParams> & BaseChatModelParams);
    /**
     * Get the parameters used to invoke the model
     */
    invocationParams(): Omit<ChatCompletionRequest, "messages">;
    /**
     * Get the identifying parameters for the model
     */
    identifyingParams(): Omit<ChatCompletionRequest, "messages">;
    /** @ignore */
    _generate(messages: BaseMessage[], options?: this["ParsedCallOptions"], runManager?: CallbackManagerForLLMRun): Promise<ChatResult>;
    /** @ignore */
    completionWithRetry(request: ChatCompletionRequest, stream: boolean, signal?: AbortSignal, onmessage?: (event: MessageEvent) => void): Promise<any>;
    private createZhipuStream;
    private _deserialize;
    _streamResponseChunks(messages: BaseMessage[], options?: this["ParsedCallOptions"], runManager?: CallbackManagerForLLMRun): AsyncGenerator<ChatGenerationChunk>;
    _llmType(): string;
    /** @ignore */
    _combineLLMOutput(): never[];
}
export {};