Embeddings

Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.

client.embeddings.create(, ?): CreateEmbeddingResponse { data, model, object, usage }

POST/embeddings

CreateEmbeddingResponse { data, model, object, usage }

data: Array<Embedding { embedding, index, object } >

The list of embeddings generated by the model.

embedding: Array<number>

The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding guide.

index: number

The index of the embedding in the list of embeddings.

object: "embedding"

The object type, which is always "embedding".

model: string

The name of the model used to generate the embedding.

object: "list"

The object type, which is always "list".

usage: Usage { prompt_tokens, total_tokens }

The usage information for the request.

prompt_tokens: number

The number of tokens used by the prompt.

total_tokens: number

The total number of tokens used by the request.

Embedding { embedding, index, object }

Represents an embedding vector returned by embedding endpoint.

embedding: Array<number>

The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding guide.

index: number

The index of the embedding in the list of embeddings.

object: "embedding"

The object type, which is always "embedding".

EmbeddingModel = "text-embedding-ada-002" | "text-embedding-3-small" | "text-embedding-3-large"

One of the following:

"text-embedding-ada-002"

"text-embedding-3-small"

"text-embedding-3-large"