Embeddings

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

resource openai_embedding

required Expand Collapse

input: String

Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for all embedding models), cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens. In addition to the per-input token limit, all embedding models enforce a maximum of 300,000 tokens summed across all inputs in a single request.

model: String

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

optional Expand Collapse

dimensions?: Int64

The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

user?: String

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

encoding_format?: String

The format to return the embeddings in. Can be either float or base64.

computed Expand Collapse

object: String

The object type, which is always “list”.

data: List[Attributes]

The list of embeddings generated by the model.

embedding: List[Float64]

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

index: Int64

The index of the embedding in the list of embeddings.

object: String

The object type, which is always “embedding”.

usage: Attributes

The usage information for the request.

prompt_tokens: Int64

The number of tokens used by the prompt.

total_tokens: Int64

The total number of tokens used by the request.

openai_embedding

resource "openai_embedding" "example_embedding" {
  input = "The quick brown fox jumped over the lazy dog"
  model = "text-embedding-3-small"
  dimensions = 1
  encoding_format = "float"
  user = "user-1234"
}