@kaibanjs/tools/dist/website-search/index.d.ts

A set of tools to work with LLMs and KaibanJS

/**
 * Website Search Tool
 *
 * This tool is specifically crafted for conducting semantic searches within the content of a particular website.
 * Leveraging a Retrieval-Augmented Generation (RAG) model, it navigates through the information provided on a given URL.
 * Users have the flexibility to either initiate a search across any website known or discovered during its usage or to
 * concentrate the search on a predefined, specific website.
 *
 * The tool uses the following components:
 * - A RAG Toolkit instance, which handles the RAG process
 * - A Chunker instance, which chunks and processes text for the RAG model
 * - An Embeddings instance, which handles embeddings for the RAG model
 * - A VectorStore instance, which stores vectors for the RAG model
 * - An LLM instance, which handles the language model for the RAG model
 * - A promptQuestionTemplate, which defines the template for asking questions
 * - An OpenAI API key, which is used for interacting with the OpenAI API
 */
import { StructuredTool } from '@langchain/core/tools';
import { z } from 'zod';
import { OpenAIEmbeddings } from '@langchain/openai';
import { MemoryVectorStore } from 'langchain/vectorstores/memory';
import { ChatOpenAI } from '@langchain/openai';
/**
 * Type for the parameters in WebsiteSearch
 * @typedef {string} WebsiteSearchParams
 * @example
 * {
 *   query: "What is the main idea of the website?"
 *   url: "https://example.com",
 */
type WebsiteSearchParams = {
    query: string;
    url?: string;
};
/**
 * Response type for the WebsiteSearch tool
 * @typedef {string} RagToolkitAnswerResponse
 * @example
 * "The answer to your question is: [answer]"
 */
type RagToolkitAnswerResponse = string;
/**
 * Error type for the WebsiteSearch tool
 * @typedef {string} WebsiteSearchError
 * @example
 * "ERROR_MISSING_URL: No url was provided for analysis. Agent should provide valid url in the 'url' field."
 */
type WebsiteSearchError = string;
/**
 * Type for the response from the WebsiteSearch tool
 * @typedef {RagToolkitAnswerResponse | WebsiteSearchError} WebsiteSearchResponse
 * @example
 * "The answer to your question is: [answer]"
 */
type WebsiteSearchResponse = RagToolkitAnswerResponse | WebsiteSearchError;
/**
 * Interface for the WebsiteSearch tool
 * @typedef {Object} WebsiteSearchFields
 * @property {string} OPENAI_API_KEY - The OpenAI API key
 * @property {string} [url - The URL to process
 */
interface WebsiteSearchFields {
    OPENAI_API_KEY: string;
    url?: string;
    chunkOptions?: {
        chunkSize: number;
        chunkOverlap: number;
    };
    embeddings?: OpenAIEmbeddings;
    vectorStore?: MemoryVectorStore;
    llmInstance?: ChatOpenAI;
    promptQuestionTemplate?: string;
}
/**
 * WebsiteSearch tool class
 * @extends StructuredTool
 */
export declare class WebsiteSearch extends StructuredTool {
    private OPENAI_API_KEY;
    private url?;
    private chunkOptions?;
    private embeddings?;
    private vectorStore?;
    private llmInstance?;
    private promptQuestionTemplate?;
    private ragToolkit;
    name: string;
    description: string;
    schema: z.ZodObject<{
        url: z.ZodString;
        query: z.ZodString;
    }, "strip", z.ZodTypeAny, {
        url: string;
        query: string;
    }, {
        url: string;
        query: string;
    }>;
    /**
     * @param {WebsiteSearchFields} fields - The fields for the WebsiteSearch tool
     */
    constructor(fields: WebsiteSearchFields);
    /**
     * @param {WebsiteSearchParams} input - The input for the WebsiteSearch tool
     * @returns {Promise<WebsiteSearchResponse>} The response from the WebsiteSearch tool
     */
    _call(input: WebsiteSearchParams): Promise<WebsiteSearchResponse>;
}
export {};