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

A set of tools to work with LLMs and KaibanJS

/**
 * Text File (txt) Search Tool
 *
 * This tool is used to perform a RAG (Retrieval-Augmented Generation) search within the content of a text file.
 * It allows for semantic searching of a query within a specified text file's content, making it an invaluable resource
 * for quickly extracting information or finding specific sections of text based on the query provided.
 *
 * The tool uses the following components:
 * - A Chunker options, 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 TextFileSearch
 * @typedef {string} TextFileSearchParams
 * @example
 * {
 *   query: "What is the main idea of the document?"
 *   file: "path/to/file.txt",
 * }
 */
type TextFileSearchParams = {
    query: string;
    file?: string;
};
/**
 * Response type for the PdfSearch tool
 * @typedef {string} RagToolkitAnswerResponse
 * @example
 * "The answer to your question is: [answer]"
 */
type RagToolkitAnswerResponse = string;
/**
 * Error type for the TextFileSearch tool
 * @typedef {string} TextFileSearchError
 * @example
 * "ERROR_MISSING_FILE: No file was provided for analysis. Agent should provide valid file in the 'file' field."
 */
type TextFileSearchError = string;
/**
 * Type for the response from the TextFileSearch tool
 * @typedef {RagToolkitAnswerResponse | TextFileSearchError} TextFileSearchResponse
 * @example
 * "The answer to your question is: [answer]"
 */
type TextFileSearchResponse = RagToolkitAnswerResponse | TextFileSearchError;
/**
 * Interface for the TextFileSearch tool
 * @typedef {Object} TextFileSearchFields
 * @property {string} OPENAI_API_KEY - The OpenAI API key
 * @property {string} [file] - The text file (txt) path to process
 * @property {Object} [chunkOptions] - The chunk options for the RAG model
 */
interface TextFileSearchFields {
    OPENAI_API_KEY: string;
    file?: string | File;
    chunkOptions?: {
        chunkSize: number;
        chunkOverlap: number;
    };
    embeddings?: OpenAIEmbeddings;
    vectorStore?: MemoryVectorStore;
    llmInstance?: ChatOpenAI;
    promptQuestionTemplate?: string;
}
/**
 * TextFileSearch tool class
 * @extends StructuredTool
 */
export declare class TextFileSearch extends StructuredTool {
    private OPENAI_API_KEY;
    private file?;
    private chunkOptions?;
    private embeddings?;
    private vectorStore?;
    private llmInstance?;
    private promptQuestionTemplate?;
    private ragToolkit;
    private httpClient;
    name: string;
    description: string;
    schema: z.ZodObject<{
        file: z.ZodString;
        query: z.ZodString;
    }, "strip", z.ZodTypeAny, {
        query: string;
        file: string;
    }, {
        query: string;
        file: string;
    }>;
    /**
     * @param {TextFileSearchFields} fields - The fields for the TextFileSearch tool
     */
    constructor(fields: TextFileSearchFields);
    /**
     * @param {TextFileSearchParams} input - The input for the TextFileSearch tool
     * @returns {Promise<TextFileSearchResponse>} The response from the TextFileSearch tool
     */
    _call(input: TextFileSearchParams): Promise<TextFileSearchResponse>;
}
export {};