llm_provider_google_gemini: Create a new Google Gemini LLM provider

In tidyprompt: Prompt Large Language Models and Enhance Their Functionality

Create a new Google Gemini LLM provider

Description

This function creates a new llm_provider object that interacts with the Google Gemini API.

Usage

llm_provider_google_gemini(
  parameters = list(model = "gemini-1.5-flash"),
  verbose = getOption("tidyprompt.verbose", TRUE),
  url = "https://generativelanguage.googleapis.com/v1beta/models/",
  api_key = Sys.getenv("GOOGLE_AI_STUDIO_API_KEY")
)

Arguments

parameters	A named list of parameters. Currently the following parameters are required: model: The name of the model to use (see: https://ai.google.dev/gemini-api/docs/models/gemini) Additional parameters are appended to the request body; see the Google AI Studio API documentation for more information: https://ai.google.dev/gemini-api/docs/text-generation and https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/get-started/rest.ipynb
verbose	A logical indicating whether the interaction with the LLM provider should be printed to the console
url	The URL to the Google Gemini API endpoint for chat completion
api_key	The API key to use for authentication with the Google Gemini API (see: https://aistudio.google.com/app/apikey)

Details

Streaming is not yet supported in this implementation. Native functions like structured output and tool calling are also not supported in this implemetation. This may however be achieved through creating a llm_provider_ellmer() object with as input a ellmer::chat_google_gemini() object. Therefore, this function is now superseded by llm_provider_ellmer(ellmer::chat_google_gemini()).

Value

A new llm_provider object for use of the Google Gemini API

See Also

Other llm_provider: llm_provider-class, llm_provider_ellmer(), llm_provider_groq(), llm_provider_mistral(), llm_provider_ollama(), llm_provider_openai(), llm_provider_openrouter(), llm_provider_xai()

Examples

# Various providers:
ollama <- llm_provider_ollama()
openai <- llm_provider_openai()
openrouter <- llm_provider_openrouter()
mistral <- llm_provider_mistral()
groq <- llm_provider_groq()
xai <- llm_provider_xai()
gemini <- llm_provider_google_gemini()

# From an `ellmer::chat()` (e.g., `ellmer::chat_openai()`, ...):
## Not run: 
ellmer <- llm_provider_ellmer(ellmer::chat_openai())

## End(Not run)

# Initialize with settings:
ollama <- llm_provider_ollama(
  parameters = list(
    model = "llama3.2:3b",
    stream = TRUE
  ),
  verbose = TRUE,
  url = "http://localhost:11434/api/chat"
)

# Change settings:
ollama$verbose <- FALSE
ollama$parameters$stream <- FALSE
ollama$parameters$model <- "llama3.1:8b"

## Not run: 
# Try a simple chat message with '$complete_chat()':
response <- ollama$complete_chat("Hi!")
response
# $role
# [1] "assistant"
#
# $content
# [1] "How's it going? Is there something I can help you with or would you like
# to chat?"
#
# $http
# Response [http://localhost:11434/api/chat]
# Date: 2024-11-18 14:21
# Status: 200
# Content-Type: application/json; charset=utf-8
# Size: 375 B

# Use with send_prompt():
"Hi" |>
  send_prompt(ollama)
# [1] "How's your day going so far? Is there something I can help you with or
# would you like to chat?"

## End(Not run)

tidyprompt documentation built on Aug. 25, 2025, 5:11 p.m.