node-ejs-renderer/node_modules/openai/resources/embeddings.d.ts
2024-06-09 13:55:01 -04:00

103 lines
3.6 KiB
TypeScript

import * as Core from "../core.js";
import { APIResource } from "../resource.js";
import * as EmbeddingsAPI from "./embeddings.js";
export declare class Embeddings extends APIResource {
/**
* Creates an embedding vector representing the input text.
*/
create(body: EmbeddingCreateParams, options?: Core.RequestOptions): Core.APIPromise<CreateEmbeddingResponse>;
}
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 declare namespace CreateEmbeddingResponse {
/**
* The usage information for the request.
*/
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://platform.openai.com/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 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
* `text-embedding-ada-002`), 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.
*/
input: string | Array<string> | Array<number> | Array<Array<number>>;
/**
* ID of the model to use. You can use the
* [List models](https://platform.openai.com/docs/api-reference/models/list) API to
* see all of your available models, or see our
* [Model overview](https://platform.openai.com/docs/models/overview) for
* descriptions of them.
*/
model: (string & {}) | 'text-embedding-ada-002' | 'text-embedding-3-small' | 'text-embedding-3-large';
/**
* 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://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
*/
user?: string;
}
export declare namespace Embeddings {
export import CreateEmbeddingResponse = EmbeddingsAPI.CreateEmbeddingResponse;
export import Embedding = EmbeddingsAPI.Embedding;
export import EmbeddingCreateParams = EmbeddingsAPI.EmbeddingCreateParams;
}
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