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