Understanding Embeddings
Explore how embeddings convert text into numerical vectors to enable machines to process language for applications like semantic search, clustering, recommendations, and anomaly detection. Understand retrieval-augmented generation (RAG) and how embeddings improve AI accuracy and relevance by linking queries to external knowledge.
Embeddings can be thought of as a mathematical mechanism that translates words into numbers. These numbers are representative of the words' meanings, thereby simplifying the process for machines to understand and process human language. In essence, embeddings serve as a connector between humans and machines, transforming text into a format digestible by machines.
Imagine an embedding as a numerical vector filled with floating-point numbers. This vector condenses text into a more manageable, lower-dimensional space, similar to downsizing to a compact apartment in a dense metropolis. The value of embeddings lies in their ability to encode vast amounts of information into a more compact and efficient format, facilitating operations like data clustering, similarity assessment, and text searches.
Embeddings and OpenAI
OpenAI has developed powerful tools that utilize embeddings to unlock a multitude of applications, ranging from enhanced search capabilities to sophisticated anomaly detection.
OpenAI recently introduced text-embedding-3-small and text-embedding-3-large, which are their latest and most effective embedding models. These models offer advantages such as lower costs, superior multilingual performance, and adjustable parameters that control the size of the embeddings, ...