@langchain/openai/dist/embeddings.d.cts

OpenAI integrations for LangChain.js

import { OpenAIApiKey } from "./types.cjs";
import { ClientOptions, OpenAI as OpenAI$1 } from "openai";
import { Embeddings, EmbeddingsParams } from "@langchain/core/embeddings";

//#region src/embeddings.d.ts

/**
 * @see https://platform.openai.com/docs/guides/embeddings#embedding-models
 */
type OpenAIEmbeddingModelId = OpenAI$1.EmbeddingModel | (string & NonNullable<unknown>);
/**
 * Interface for OpenAIEmbeddings parameters. Extends EmbeddingsParams and
 * defines additional parameters specific to the OpenAIEmbeddings class.
 */
interface OpenAIEmbeddingsParams extends EmbeddingsParams {
  /**
   * Model name to use
   * Alias for `model`
   * @deprecated Use "model" instead.
   */
  modelName: OpenAIEmbeddingModelId;
  /** Model name to use */
  model: OpenAIEmbeddingModelId;
  /**
   * The number of dimensions the resulting output embeddings should have.
   * Only supported in `text-embedding-3` and later models.
   */
  dimensions?: number;
  /**
   * Timeout to use when making requests to OpenAI.
   */
  timeout?: number;
  /**
   * The maximum number of documents to embed in a single request. This is
   * limited by the OpenAI API to a maximum of 2048.
   */
  batchSize?: number;
  /**
   * Whether to strip new lines from the input text. This is recommended by
   * OpenAI for older models, but may not be suitable for all use cases.
   * See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
   */
  stripNewLines?: boolean;
  /**
   * The format to return the embeddings in. Can be either 'float' or 'base64'.
   */
  encodingFormat?: "float" | "base64";
}
/**
 * Class for generating embeddings using the OpenAI API.
 *
 * To use with Azure, import the `AzureOpenAIEmbeddings` class.
 *
 * @example
 * ```typescript
 * // Embed a query using OpenAIEmbeddings to generate embeddings for a given text
 * const model = new OpenAIEmbeddings();
 * const res = await model.embedQuery(
 *   "What would be a good company name for a company that makes colorful socks?",
 * );
 * console.log({ res });
 *
 * ```
 */
declare class OpenAIEmbeddings<TOutput = number[]> extends Embeddings<TOutput> implements Partial<OpenAIEmbeddingsParams> {
  model: string;
  /** @deprecated Use "model" instead */
  modelName: string;
  batchSize: number;
  stripNewLines: boolean;
  /**
   * The number of dimensions the resulting output embeddings should have.
   * Only supported in `text-embedding-3` and later models.
   */
  dimensions?: number;
  timeout?: number;
  organization?: string;
  encodingFormat?: "float" | "base64";
  protected client: OpenAI$1;
  protected clientConfig: ClientOptions;
  protected apiKey?: OpenAIApiKey;
  constructor(fields?: Partial<OpenAIEmbeddingsParams> & {
    verbose?: boolean;
    /**
     * The OpenAI API key to use.
     * Alias for `apiKey`.
     */
    openAIApiKey?: OpenAIApiKey;
    /** The OpenAI API key to use. */
    apiKey?: OpenAIApiKey;
    configuration?: ClientOptions;
  });
  /**
   * Method to generate embeddings for an array of documents. Splits the
   * documents into batches and makes requests to the OpenAI API to generate
   * embeddings.
   * @param texts Array of documents to generate embeddings for.
   * @returns Promise that resolves to a 2D array of embeddings for each document.
   */
  embedDocuments(texts: string[]): Promise<TOutput[]>;
  /**
   * Method to generate an embedding for a single document. Calls the
   * embeddingWithRetry method with the document as the input.
   * @param text Document to generate an embedding for.
   * @returns Promise that resolves to an embedding for the document.
   */
  embedQuery(text: string): Promise<TOutput>;
  /**
   * Private method to make a request to the OpenAI API to generate
   * embeddings. Handles the retry logic and returns the response from the
   * API.
   * @param request Request to send to the OpenAI API.
   * @returns Promise that resolves to the response from the API.
   */
  protected embeddingWithRetry(request: OpenAI$1.EmbeddingCreateParams): Promise<OpenAI$1.CreateEmbeddingResponse & {
    _request_id?: string | null | undefined;
  }>;
}
//#endregion
export { OpenAIEmbeddingModelId, OpenAIEmbeddings, OpenAIEmbeddingsParams };
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