113 lines
4.0 KiB
TypeScript
113 lines
4.0 KiB
TypeScript
import { APIResource } from "../core/resource.mjs";
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import { APIPromise } from "../core/api-promise.mjs";
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import { RequestOptions } from "../internal/request-options.mjs";
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export declare class Embeddings extends APIResource {
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/**
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* Creates an embedding vector representing the input text.
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*
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* @example
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* ```ts
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* const createEmbeddingResponse =
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* await client.embeddings.create({
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* input: 'The quick brown fox jumped over the lazy dog',
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* model: 'text-embedding-3-small',
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* });
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* ```
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*/
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create(body: EmbeddingCreateParams, options?: RequestOptions): APIPromise<CreateEmbeddingResponse>;
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}
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export interface CreateEmbeddingResponse {
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/**
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* The list of embeddings generated by the model.
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*/
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data: Array<Embedding>;
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/**
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* The name of the model used to generate the embedding.
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*/
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model: string;
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/**
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* The object type, which is always "list".
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*/
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object: 'list';
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/**
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* The usage information for the request.
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*/
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usage: CreateEmbeddingResponse.Usage;
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}
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export declare namespace CreateEmbeddingResponse {
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/**
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* The usage information for the request.
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*/
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interface Usage {
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/**
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* The number of tokens used by the prompt.
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*/
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prompt_tokens: number;
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/**
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* The total number of tokens used by the request.
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*/
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total_tokens: number;
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}
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}
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/**
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* Represents an embedding vector returned by embedding endpoint.
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*/
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export interface Embedding {
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/**
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* The embedding vector, which is a list of floats. The length of vector depends on
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* the model as listed in the
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* [embedding guide](https://platform.openai.com/docs/guides/embeddings).
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*/
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embedding: Array<number>;
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/**
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* The index of the embedding in the list of embeddings.
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*/
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index: number;
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/**
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* The object type, which is always "embedding".
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*/
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object: 'embedding';
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}
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export type EmbeddingModel = 'text-embedding-ada-002' | 'text-embedding-3-small' | 'text-embedding-3-large';
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export interface EmbeddingCreateParams {
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/**
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* Input text to embed, encoded as a string or array of tokens. To embed multiple
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* inputs in a single request, pass an array of strings or array of token arrays.
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* The input must not exceed the max input tokens for the model (8192 tokens for
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* all embedding models), cannot be an empty string, and any array must be 2048
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* dimensions or less.
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* [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
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* for counting tokens. In addition to the per-input token limit, all embedding
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* models enforce a maximum of 300,000 tokens summed across all inputs in a single
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* request.
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*/
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input: string | Array<string> | Array<number> | Array<Array<number>>;
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/**
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* ID of the model to use. You can use the
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* [List models](https://platform.openai.com/docs/api-reference/models/list) API to
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* see all of your available models, or see our
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* [Model overview](https://platform.openai.com/docs/models) for descriptions of
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* them.
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*/
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model: (string & {}) | EmbeddingModel;
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/**
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* The number of dimensions the resulting output embeddings should have. Only
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* supported in `text-embedding-3` and later models.
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*/
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dimensions?: number;
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/**
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* The format to return the embeddings in. Can be either `float` or
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* [`base64`](https://pypi.org/project/pybase64/).
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*/
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encoding_format?: 'float' | 'base64';
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/**
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* A unique identifier representing your end-user, which can help OpenAI to monitor
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* and detect abuse.
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* [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
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*/
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user?: string;
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}
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export declare namespace Embeddings {
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export { type CreateEmbeddingResponse as CreateEmbeddingResponse, type Embedding as Embedding, type EmbeddingModel as EmbeddingModel, type EmbeddingCreateParams as EmbeddingCreateParams, };
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}
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//# sourceMappingURL=embeddings.d.mts.map
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