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Generate Embeddings Using OpenAI API

Source: R/api_chat_completions.R
openai_embedding.Rd

Generate Embeddings Using OpenAI API

Usage

openai_embedding(
  .input,
  .model = "text-embedding-3-small",
  .truncate = TRUE,
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3,
  .verbose = FALSE,
  .dimensions = NULL
)

Arguments

.input

An existing LLMMessage object (or a character vector of texts to embed)

.model

The embedding model identifier (default: "text-embedding-3-small").

.truncate

Whether to truncate inputs to fit the model's context length (default: TRUE).

.timeout

Timeout for the API request in seconds (default: 120).

.dry_run

If TRUE, perform a dry run and return the request object.

.max_tries

Maximum retry attempts for requests (default: 3).

.verbose

Should information about current ratelimits be printed? (default: FALSE)

.dimensions

Number of dimensions to return (default: NULL, the model's full size). The text-embedding-3 models can shorten their vectors; text-embedding-ada-002 cannot and returns an error.

Value

A tibble with two columns: input and embeddings. The input column contains the texts sent to embed, and the embeddings column is a list column where each row contains an embedding vector of the sent input.