/opt/canhelp/node_modules/openai/resources
Edit: /opt/canhelp/node_modules/openai/resources/embeddings.d.mts (4248B)
import { APIResource } from "../core/resource.mjs";
import { APIPromise } from "../core/api-promise.mjs";
import { RequestOptions } from "../internal/request-options.mjs";
/**
* Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
*/
export declare 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, options?: RequestOptions): APIPromise
;
}
export interface CreateEmbeddingResponse {
/**
* The list of embeddings generated by the model.
*/
data: Array;
/**
* 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;
/**
* 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 | Array | Array>;
/**
* 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) 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://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
*/
user?: string;
}
export declare namespace Embeddings {
export { type CreateEmbeddingResponse as CreateEmbeddingResponse, type Embedding as Embedding, type EmbeddingModel as EmbeddingModel, type EmbeddingCreateParams as EmbeddingCreateParams, };
}
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