@spec create_embedding(
  [[integer()]] | [integer()] | [String.t()] | String.t(),
  :"text-embedding-ada-002" | String.t(),
  openai_organization_key: String.t(),
  openai_api_key: String.t(),
  user: String.t(),
  encoding_format: :base64 | :float,
  stream_to: (... -> any()) | pid()
) :: {:ok, ExOpenAI.Components.CreateEmbeddingResponse.t()} | {:error, any()}

Creates an embedding vector representing the input text.

Endpoint: https://api.openai.com/v1/embeddings

Method: POST

Docs: https://platform.openai.com/docs/api-reference/embeddings

Required Arguments:

• input: 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) and cannot be an empty string. Example Python code for counting tokens.

• model: 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.

Optional Arguments:

• stream_to: PID or function of where to stream content to

• encoding_format: The format to return the embeddings in. Can be either float or base64.

Example: "float"

• user: A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example: "user-1234"

• openai_api_key: OpenAI API key to pass directly. If this is specified, it will override the api_key config value.

• openai_organization_key: OpenAI API key to pass directly. If this is specified, it will override the organization_key config value.