Generate Text Embeddings with OpenAI

This notebook provides code snippets for embedding text using OpenAI's text-embedding-3-small model through the OpenAI API. It demonstrates best practices for managing API rate limits using exponential backoff strategies to ensure embeddings are generated as quickly as possible without triggering rate limit errors.

Use this notebook when you need to generate text embeddings via the OpenAI API and want to implement robust rate-limit handling from the start. It's particularly valuable when processing multiple texts in batch where hitting rate limits could interrupt your workflow.

Do not use this if you need a production-ready application framework - this is a learning resource with code snippets, not a complete application. Avoid using this if you're working with embedding models other than text-embedding-3-small, as the examples are specific to that model.

You provide text strings that you want to convert into vector embeddings. The notebook demonstrates how to send these to the OpenAI API using the text-embedding-3-small model. You receive numerical vector representations of your input text that can be used for semantic search, clustering, recommendations, and other machine learning tasks.

The notebook recommends using the tenacity package for implementing exponential backoff to manage API rate limits effectively. It integrates with the OpenAI API to access the text-embedding-3-small embedding model.

This resource is designed for developers and data scientists who need to generate text embeddings using OpenAI's API. It's particularly useful for those building semantic search systems, recommendation engines, or any application requiring vector representations of text. The notebook serves both beginners learning how to work with embeddings and experienced practitioners who want to implement proper rate-limit handling in their embedding pipelines.