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Embeddings - OpenAI API

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An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness. Visit our pricing page to learn about Embeddings pricing. Requests are billed based on the number of tokens in the input. To get an embedding, send your text string to the embeddings API endpoint along with the embedding model name (e.g. text-embedding-3-small). The response will contain an embedding (list of floating point numbers), which you can extract, save in a vector database, and use for many different use cases: The response will contain the embedding vector along with some additional metadata. By default, the length of the embedding vector will be 1536 for text-embedding-3-small or 3072 for text-embedding-3-large. You can reduce the dimensions of the embedding by passing in the dimensions parameter without the embedding losing its concept-representing properties. We go into

New embedding models text-embedding-3-small and text-embedding-3-large , our newest and most performant embedding models, are now available. They feature lower costs, higher multilingual performance, and new parameters to control the overall size. What are embeddings? OpenAI’s text embeddings measure the relatedness of text strings. Embeddings are commonly used for: Search (where results are ranked by relevance to a query string) Clustering (where text strings are grouped by similarity) Recommendations (where items with related text strings are recommended) Anomaly detection (where outliers wi

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