lume-ai/dist/interfaces/LLM.d.ts

A powerful yet simple library to build your own AI applications.

import { Message } from './History';
import { Tool } from './Tool';
/**
 * Abstract class representing a Large Language Model (LLM) interface.
 * Implementations should provide a way to get responses from the LLM
 */
export declare abstract class LLM {
    /**
     * The underlying LLM instance or client.
     */
    protected llm: any;
    /**
     * Gets a response from the LLM based on the provided text and options.
     * @param text - The input text to send to the LLM.
     * @param options - Optional parameters including message history and tags for context.
     * @returns A promise that resolves to the LLM's response as a string.
     */
    abstract getResponse(text: string, options: {
        history?: Message[];
        tags?: string[];
        vectorMatches?: string[];
        tools?: Tool[];
        llmOptions: {
            systemPrompt: string;
            model?: string;
            temperature?: number;
            maxTokens?: number;
            topP?: number;
        };
        toolCallId?: string;
        toolCall?: any;
        toolCallDepth?: number;
    }): Promise<string>;
    /**
     * Streams a response from the LLM based on the provided text and options.
     * @param text - The input text to send to the LLM.
     * @param options - Optional parameters including message history and tags for context.
     * @returns A promise that resolves to the LLM's response as a string.
     */
    streamResponse?(text: string, options: {
        history?: Message[];
        tags?: string[];
        vectorMatches?: string[];
        tools?: Tool[];
        llmOptions: {
            systemPrompt: string;
            model?: string;
            temperature?: number;
            maxTokens?: number;
            topP?: number;
        };
        toolCallId?: string;
        toolCall?: any;
        toolCallDepth?: number;
        toolResult?: string;
    }): AsyncGenerator<string>;
    /**
     * Gets an embedding from the LLM based on the provided text.
     * @param text - The input text to get an embedding for.
     * @returns A promise that resolves to the LLM's embedding as an array of numbers.
     */
    abstract getEmbedding(text: string): Promise<number[]>;
    /**
     * Parses a tool into an object.
     * @param tool - The tool to parse.
     * @returns An object representing the tool compatible with the LLM.
     */
    parseTool?(tool: Tool): Object;
}