@langchain/community/dist/embeddings/googlevertexai.d.ts

Third-party integrations for LangChain.js

import { GoogleAuthOptions } from "google-auth-library";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";
import { GoogleVertexAIBaseLLMInput } from "../types/googlevertexai-types.js";
/**
 * Defines the parameters required to initialize a
 * GoogleVertexAIEmbeddings instance. It extends EmbeddingsParams and
 * GoogleVertexAIConnectionParams.
 */
export interface GoogleVertexAIEmbeddingsParams extends EmbeddingsParams, GoogleVertexAIBaseLLMInput<GoogleAuthOptions> {
}
/**
 * Enables calls to the Google Cloud's Vertex AI API to access
 * the embeddings generated by Large Language Models.
 *
 * To use, you will need to have one of the following authentication
 * methods in place:
 * - You are logged into an account permitted to the Google Cloud project
 *   using Vertex AI.
 * - You are running this on a machine using a service account permitted to
 *   the Google Cloud project using Vertex AI.
 * - The `GOOGLE_APPLICATION_CREDENTIALS` environment variable is set to the
 *   path of a credentials file for a service account permitted to the
 *   Google Cloud project using Vertex AI.
 * @example
 * ```typescript
 * const model = new GoogleVertexAIEmbeddings();
 * const res = await model.embedQuery(
 *   "What would be a good company name for a company that makes colorful socks?"
 * );
 * console.log({ res });
 * ```
 */
export declare class GoogleVertexAIEmbeddings extends Embeddings implements GoogleVertexAIEmbeddingsParams {
    model: string;
    private connection;
    constructor(fields?: GoogleVertexAIEmbeddingsParams);
    /**
     * Takes an array of documents as input and returns a promise that
     * resolves to a 2D array of embeddings for each document. It splits the
     * documents into chunks and makes requests to the Google Vertex AI API to
     * generate embeddings.
     * @param documents An array of documents to be embedded.
     * @returns A promise that resolves to a 2D array of embeddings for each document.
     */
    embedDocuments(documents: string[]): Promise<number[][]>;
    /**
     * Takes a document as input and returns a promise that resolves to an
     * embedding for the document. It calls the embedDocuments method with the
     * document as the input.
     * @param document A document to be embedded.
     * @returns A promise that resolves to an embedding for the document.
     */
    embedQuery(document: string): Promise<number[]>;
}