voyage_embedding: Generate Embeddings Using Voyage AI API

In tidyllm: Tidy Integration of Large Language Models

Generate Embeddings Using Voyage AI API

Description

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).

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.

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

Examples

## Not run: 
# Text embeddings
voyage_embedding("How does photosynthesis work?")

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

## End(Not run)

tidyllm documentation built on Aug. 22, 2025, 5:21 p.m.