embeddings.ts

// File generated from our OpenAPI spec by Castiron. See CONTRIBUTING.md for details.

import { APIResource } from '../core/resource';
import { APIPromise } from '../core/api-promise';
import { RequestOptions } from '../internal/request-options';
import { createEmbedding } from '../lib/embeddings';

type Base64EmbeddingResponse = Omit<CreateEmbeddingResponse, 'data'> & {
  data: Array<Omit<Embedding, 'embedding'> & { embedding: string }>;
};

/**
 * Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
 */
export class Embeddings extends APIResource {
  /**
   * Creates an embedding vector representing the input text.
   *
   * @example
   * ```ts
   * const createEmbeddingResponse =
   *   await client.embeddings.create({
   *     input: 'The quick brown fox jumped over the lazy dog',
   *     model: 'text-embedding-3-small',
   *   });
   * ```
   */
  create(
    body: EmbeddingCreateParams & { encoding_format: 'base64' },
    options?: RequestOptions,
  ): APIPromise<Base64EmbeddingResponse>;
  create(body: EmbeddingCreateParams, options?: RequestOptions): APIPromise<CreateEmbeddingResponse>;
  create(
    body: EmbeddingCreateParams,
    options?: RequestOptions,
  ): APIPromise<CreateEmbeddingResponse | Base64EmbeddingResponse> {
    return createEmbedding(this._client, body, options);
  }
}

export interface CreateEmbeddingResponse {
  /**
   * The list of embeddings generated by the model.
   */
  data: Array<Embedding>;

  /**
   * The name of the model used to generate the embedding.
   */
  model: string;

  /**
   * The object type, which is always "list".
   */
  object: 'list';

  /**
   * The usage information for the request.
   */
  usage: CreateEmbeddingResponse.Usage;
}

export namespace CreateEmbeddingResponse {
  /**
   * The usage information for the request.
   */
  export interface Usage {
    /**
     * The number of tokens used by the prompt.
     */
    prompt_tokens: number;

    /**
     * The total number of tokens used by the request.
     */
    total_tokens: number;
  }
}

/**
 * Represents an embedding vector returned by embedding endpoint.
 */
export interface Embedding {
  /**
   * The embedding vector, which is a list of floats. The length of vector depends on
   * the model as listed in the
   * [embedding guide](https://developers.openai.com/api/docs/guides/embeddings).
   */
  embedding: Array<number>;

  /**
   * The index of the embedding in the list of embeddings.
   */
  index: number;

  /**
   * The object type, which is always "embedding".
   */
  object: 'embedding';
}

export type EmbeddingModel = 'text-embedding-ada-002' | 'text-embedding-3-small' | 'text-embedding-3-large';

export interface EmbeddingCreateParams {
  /**
   * Input text to embed, encoded as a string or array of tokens. To embed multiple
   * inputs in a single request, pass an array of strings or array of token arrays.
   * The input must not exceed the max input tokens for the model (8192 tokens for
   * all embedding models), cannot be an empty string, and any array must be 2048
   * dimensions or less.
   * [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
   * for counting tokens. In addition to the per-input token limit, all embedding
   * models enforce a maximum of 300,000 tokens summed across all inputs in a single
   * request.
   */
  input: string | Array<string> | Array<number> | Array<Array<number>>;

  /**
   * ID of the model to use. You can use the
   * [List models](https://developers.openai.com/api/reference/resources/models/methods/list)
   * API to see all of your available models, or see our
   * [Model overview](https://developers.openai.com/api/docs/models) for descriptions
   * of them.
   */
  model: (string & {}) | EmbeddingModel;

  /**
   * The number of dimensions the resulting output embeddings should have. Only
   * supported in `text-embedding-3` and later models.
   */
  dimensions?: number;

  /**
   * The format to return the embeddings in. Can be either `float` or
   * [`base64`](https://pypi.org/project/pybase64/).
   */
  encoding_format?: 'float' | 'base64';

  /**
   * A unique identifier representing your end-user, which can help OpenAI to monitor
   * and detect abuse.
   * [Learn more](https://developers.openai.com/api/docs/guides/safety-best-practices#implement-safety-identifiers).
   */
  user?: string;
}

export declare namespace Embeddings {
  export {
    type CreateEmbeddingResponse as CreateEmbeddingResponse,
    type Embedding as Embedding,
    type EmbeddingModel as EmbeddingModel,
    type EmbeddingCreateParams as EmbeddingCreateParams,
  };
}