TextEmbeddingAda3Small
text-embedding-3-small is an improved, more performant version of the ada embedding model.
Smaller, faster, and more cost-effective than ada-002, with better quality.
Outputs: 1536-dimensional vectors (or 512-dimensional vectors, optionally) Max input tokens: 8191 Released: Jan 2024
Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.