What embedding model does this notebook use?

This notebook uses OpenAI's `text-embedding-3-small` model accessed through the OpenAI API.

How does this notebook handle API rate limits?

It includes a helper function built around exponential backoff (using the `tenacity` package or equivalent) that automatically retries embedding calls when rate-limited instead of failing immediately.

What does this notebook NOT cover?

This notebook stops at generating embedding vectors and does not cover indexing, storage, or downstream search functionality — it is not a full RAG or retrieval pipeline.

What input does this notebook expect?

The notebook expects text strings as input and returns embedding vectors from the `text-embedding-3-small` model.

Prompt Chain

Generate Text Embeddings with OpenAI

Name: Using Embeddings
Availability: OnlineOnly
Author: OpenAI Cookbook

Notebook with code snippets to embed text using OpenAI's text-embedding-3-small model, with recommended rate-limit handling using exponential backoff.

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Works with openai

OpenAI Cookbook

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Spark score

out of 100

Updated 10 days ago

Source checked Sep 10, 2026

Version 1.0.0

Models

gpt 4o

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Why it matters

Leverage OpenAI's text-embedding-3-small model to generate high-quality text embeddings for your data. This asset provides code snippets to efficiently integrate embeddings into your applications for tasks like semantic search and classification.

Outcomes

What it gets done

Generate embeddings for text data using the OpenAI API.

Implement robust API calls with exponential backoff to avoid rate limits.

Utilize embeddings for downstream tasks such as information retrieval and content analysis.

Integrate embedding generation into existing Python workflows.

Install

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Run in your project directory:

curl -fsSL https://spark.entire.vc/get/oai-usingembeddings | bash

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Overview

Using Embeddings

This notebook contains code snippets for embedding text using OpenAI's text-embedding-3-small model via the OpenAI API. It recommends using the tenacity package or another exponential backoff implementation to manage API rate limits, helping ensure you get your embeddings as fast as possible. Use this when you need code snippets for embedding text with the text-embedding-3-small model and want to implement rate-limit handling to avoid hitting the API too much too fast.

What it does

This notebook contains code snippets for embedding text using OpenAI's text-embedding-3-small model via the OpenAI API. It recommends using the tenacity package or another exponential backoff implementation to manage API rate limits, helping you get your embeddings as fast as possible.

When to use - and when NOT to

Use this notebook when you need code snippets for embedding text with the text-embedding-3-small model. It is particularly relevant when you want to implement rate-limit handling to avoid triggering API limits.

Do not use this if you need embeddings from models other than text-embedding-3-small, as the snippets are specifically designed for this model.

Inputs and outputs

The notebook contains snippets for embedding text with the text-embedding-3-small model via the OpenAI API.

Integrations

The notebook uses the OpenAI API and recommends the tenacity package or another exponential backoff implementation for managing API rate limits.

Who it's for

This notebook contains helpful snippets for those working with the OpenAI API's text-embedding-3-small model who want to implement rate-limit handling.

Source README

Using embeddings

This notebook contains some helpful snippets you can use to embed text with the text-embedding-3-small model via the OpenAI API.

It's recommended to use the 'tenacity' package or another exponential backoff implementation to better manage API rate limits, as hitting the API too much too fast can trigger rate limits. Using the following function ensures you get your embeddings as fast as possible.

FAQ

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