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Generate Embeddings Using Voyage AI API

Source: R/api_voyage.R
voyage_embedding.Rd

This function creates embedding vectors from text or multimodal inputs (text and images) using the Voyage AI API. It supports three types of input:

Usage

voyage_embedding(
  .input,
  .model = "voyage-3",
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3,
  .verbose = FALSE
)

Arguments

.input

Input to embed. Can be:

  • A character vector of texts

  • An LLMMessage object (all textual components will be embedded)

  • A list containing a mix of character strings and tidyllm_image objects created with img()

.model

The embedding model identifier. For text-only: "voyage-3" (default). For multimodal inputs: "voyage-multimodal-3" is used automatically.

.timeout

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

.dry_run

If TRUE, perform a dry run and return the request object without sending.

.max_tries

Maximum retry attempts for requests (default: 3).

.verbose

Should information about current rate limits be printed? (default: FALSE).

Value

A tibble with two columns: input and embeddings.

  • The input column contains the input texts or image labels

  • The embeddings column is a list column where each row contains an embedding vector

Details

  1. Character vector: Embeds each text string separately

  2. LLMMessage object: Extracts and embeds text content from messages

  3. List of mixed content: Processes a combination of text strings and image objects created with img()

For multimodal inputs, the function automatically switches to Voyage's multimodal API and formats the response with appropriate labels (e.g., "[IMG] image.png") for images.

Examples

if (FALSE) { # \dontrun{
# Text embeddings
voyage_embedding("How does photosynthesis work?")

# Multimodal embeddings
list("A banana", img("banana.jpg"), "Yellow fruit") |>
  voyage_embedding()
} # }