Embedding Models

Embedding models are powerful machine learning techniques that simplify high-dimensional data into lower-dimensional space, while preserving essential features. Crucial in natural language processing (NLP), they transform sparse word representations into dense vectors, capturing semantic similarities between words. Embedding models also process images, audio, video, and structured data, enhancing applications in recommendation systems, anomaly detection, and clustering.

Here is an example of an embedding model in action. Suppose the full plot in a database of movies has been previously embedded using OpenAI’s text-embedding-ada-002 embedding model. Your goal is to find all movies and animations for Guardians of the Galaxy, but not by traditional phonetic or lexical matching (where you would type some of the words in the title). Instead, you will search by semantic means, say, the phrase Awkward team of space defenders. You will then use the same embedding...