OpenAI Embedding Models: From Text to Vector Representations OpenAI text embedding models convert text into dense numerical vectors (embeddings) that capture semantic meaning. Instead of matching keywords, applications compare these vectors to find text with similar meaning. What are embeddings? An embedding is a list of floating-point numbers representing the meaning of text. For example: Input: "The customer wants to cancel their subscription." Embedding: [0.024, -0.183, 0.761, ..., 0.092] Humans can't interpret these numbers directly, but machine learning algorithms can compare them. Available OpenAI Embedding Models The primary embedding models are: Model Dimensions Best For Cost text-embedding-3-small 1,536 (or fewer with dimension reduction) General-purpose search, RAG, classification Lowest text-embedding-3-large 3,072 (or fewer with dimension reduction) Highest-quality semantic search and retrieval Higher text-embedding-3-small Pros Fas...