OpenAI Embeddings

OpenAI Embeddings convert unstructured text (words, sentences, documents) into high-dimensional vectors to quantify semantic relatedness for AI applications.

OpenAI Embeddings transform text into numerical vectors: This process is foundational for machines to measure semantic similarity via vector distance (e.g., cosine similarity). Models like `text-embedding-3-small` and `text-embedding-3-large` are the current standard, offering up to 3072 dimensions for rich representation. Developers leverage these vectors for high-precision semantic search, content clustering, building efficient Retrieval-Augmented Generation (RAG) systems, and powering recommendation engines.

https://platform.openai.com/docs/guides/embeddings/embedding-models