Is there a way to set a Token Limit for the OpenAI Embedding API Endpoint?

The examples in the embeddings guide use the tiktoken library to count tokens before a request.

But since I’m creating a serverless extension, it doesn’t seem convenient to bundle complex libraries into it (I could be wrong, I’m not familiar with Node.js libraries).

Is it possible to limit the tokens in the HTTP request itself?

If it’s not possible, considering that I’m still new to programming, how can I include a tokenizer in my extension?

This is the function I use to request the embeddings:

async function OPENAI_Embedding(texts) {
  const apiUrl = '';
  const requestOptions = {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${OPENAI_API_KEY}`,
      'Content-Type': 'application/json',
    body: JSON.stringify({
      'input': texts,
      'model': 'text-embedding-ada-002'

  try {
    const response = await fetch(apiUrl, requestOptions);
    const data = await response.json();

  } catch (error) {
    console.error('Error making request to OpenAI EmbedTexts:', error);
    return null;

Language models also don’t have a way of setting a limit for the inputs. What you send is what you pay for.

The output of embeddings is not tokens but a vector aka tensor.

To count, you can use the tiktoken library. What you do with the information about the true size is then up to you. text-embedding-ada-002 uses the same 100k token dictionary as chat models.

“serverless” is not consistent with the typical use of embeddings, which is similarity matching or database retrieval on a large sampling of existing data to compare.

1 Like