TextEmbeddingAda002
text-embedding-ada-002 is more performant version of the initial ada embedding model. But it's an older model compared to TextEmbeddingAda3Small and TextEmbeddingAda3Large.
Fast, cheap, good for many general tasks
Outputs: 1536-dimensional vectors Max input tokens: 8191 Released: Dec 2022
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.