gemini_embedding: Generate Embeddings Using the Google Gemini API

In tidyllm: Tidy Integration of Large Language Models

Generate Embeddings Using the Google Gemini API

Description

Generate Embeddings Using the Google Gemini API

Usage

gemini_embedding(
  .input,
  .model = "text-embedding-004",
  .truncate = TRUE,
  .timeout = 120,
  .dry_run = FALSE,
  .max_tries = 3
)

Arguments

.input	A character vector of texts to embed or an LLMMessage object
.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).

Value

A matrix where each column corresponds to the embedding of a message in the message history.

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