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| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x 1x | import z from 'zod';
export const CreateEmbeddingsRequestBody = z.object({
/**
* 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.
*/
input: z.union([
z.string(),
z.array(z.string()),
z.array(z.number()),
z.array(z.array(z.number())),
]),
/**
* ID of the model to use. You can use the List models API to see all of your available models,
* or see our Model overview for descriptions of them.
* Examples: text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
*/
model: z.string(),
/**
* The format to return the embeddings in. Can be either float or base64.
* @default "float"
*/
encoding_format: z.enum(['float', 'base64']).optional(),
/**
* The number of dimensions the resulting output embeddings should have.
* Only supported in text-embedding-3 and later models.
*/
dimensions: z.number().optional(),
/**
* A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
*/
user: z.string().optional(),
/**
* Input type of embedding search to use.
* Only supported in certain models.
*/
input_type: z.string().optional(),
});
export type CreateEmbeddingsRequestBody = z.infer<
typeof CreateEmbeddingsRequestBody
>;
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