Using the embedding model for text comparisons and other use cases
OpenAI has a model and endpoint that enables users to create embeddings. It’s a lesser-known feature of the API but has vast applications in enabling plenty of use cases (searching through text, text classification, and much more).
What are embeddings? Text embedding is a sophisticated technique employed in NLP that transforms text into a numerical format that machines can understand. Essentially, embeddings are high-dimensional vectors that capture the essence of words, sentences, or even entire documents, encapsulating not just their individual meanings but also the nuances and relationships between them.
Mathematically, a vector is a point in an n-dimensional vector space, but for our purposes, you can think of a vector as just a list of numbers. However, the recipes discussed in this chapter do not require you to work with the process and science behind converting words to numbers. For more information...
