A guide to interacting with online Large Language Models (LLMs) using R and Python, with examples for Google Gemini, local Ollama, and ChatGPT.

This document provides a comprehensive guide to interacting with online Large Language Models (LLMs) using both R and Python. It demonstrates how to use the ellmer and chattr packages in R, and the chatlas library in Python, to connect to various LLM services, including Google Gemini, locally hosted Ollama models, and ChatGPT. The guide covers setting up API keys, defining models, sending prompts, and processing responses for tasks like text generation and translation.

run LLM model online with ellmer or chatter

1.ellmer for R

Code

#pak::pkg_install('ellmer')
library(ellmer)
library(keyring)

google gemini

gemini-2.0-flash

Code

chat_gemini_model=chat_gemini(
  system_prompt = NULL,
  turns = NULL,
  base_url = "https://generativelanguage.googleapis.com/v1beta/",
  api_key = key_get("google_ai_api_key"),
  model = "gemini-2.0-flash",
  api_args = list(),
  echo = NULL
)

chat_gemini_model

Code

chat_gemini_model$chat("Tell me three jokes about statisticians")

ollama on local

set up ollama local

Code

library(ollamar)
ollamar::pull("llama3.1")

Code

ollamar::list_models()

difine model

Code

chat=chat_ollama(
  system_prompt = NULL,
  turns = NULL,
  base_url = "http://localhost:11434",
  model="llama3.1",
  seed = NULL,
  api_args = list(),
  echo = NULL
)

chat$get_model()

run LLM

Code

chat$chat("Tell me three jokes about statisticians")

run on console

Code

live_console(chat)

check token usage

Code

token_usage()

2. chattr LLM pacakge for R

Step 1 Install package

Code

#remotes::install_github("mlverse/chattr")

Code

library(chattr)

Step 2 set key

Login at https://platform.openai.com/
Goto Settings (gear icon on top right)
Find API Keys from menu on left
Follow the process to Create new secret key
Copy your secret key (it will only show once so make sure you copy it)

Code

Sys.setenv(OpenAI_API_KEY="sk-xxxxxxxx")

Step 3 run ChatGPT as background job

select model

Code

#copilot do not need OpenAI_API_KEY
chattr_use("copilot")

add prompt

Code

chattr_defaults(prompt = "{readLines(system.file('prompt/base.txt', package = 'chattr'))}")

run ChatGPT as background jobs

Do not use Copilot (GitHub) model for chattr(). Github will block this behavior.

Code

# run 
chattr_app(as_job = TRUE)

Done!

Or setup auto open Chat GPT when Rstudio start

Step 1 find Rprofile file

Code

#install.packages("usethis")  # Install if not already installed
usethis::edit_r_profile()

Step 2 edit Rprofile file as below

Code

.RProfile

#|eval: false
# Load chattr app after RStudio is fully loaded
setHook("rstudio.sessionInit", function(newSession) {
  if (newSession) {
    Sys.sleep(2)  # Wait 2 seconds before starting chattr to ensure RStudio is ready
    tryCatch({
      library(chattr)
      chattr_use("copilot")
      #Sys.setenv("OPENAI_API_KEY" = "your-api-key-here")
      chattr_defaults(prompt = "{readLines(system.file('prompt/base.txt', package = 'chattr'))}")

      chattr_app(as_job = TRUE)
    }, error = function(e)
      message("Error starting chattr: ", e$message))
  }
}, action = "append")

3. google python API

Code

pip install -q -U google-genai

Code

from google import genai
import keyring

client = genai.Client(api_key=keyring.get_password("system", "google_ai_api_key"))

response = client.models.generate_content(
    model="gemini-2.0-flash", contents="Explain how AI works in a few words"
)
print(response.text)

chatlas for python

https://github.com/posit-dev/chatlas

Code

pip install -U chatlas

Code

from chatlas import ChatGoogle
from chatlas import ChatOllama
from chatlas import token_usage
import keyring

gemini model

Code

from chatlas import ChatGoogle

chat_google_model = ChatGoogle(
  model = "gemini-2.0-flash",
  api_key=keyring.get_password("system", "google_ai_api_key"),
  system_prompt = "You are a whisky expert",
)

chat_google_model

Code

chat_google_model.chat("translate following whisky tasting note to English:微酸，脏麦芽。菲特肯还是要找1988")

local Ollama model

Code

from chatlas import ChatOllama

chat_llama_model = ChatOllama(
  model="llama3.2",
  #api_key=keyring.get_password("system", "google_ai_api_key"),
  system_prompt = "You are a whisky expert",
)

chat_llama_model

Code

chat_llama_model.chat("translate following whisky tasting note to English:微酸，脏麦芽。菲特肯还是要找1988")

Code

token_usage()

--- title: "调用网络端AI模型" subtitle: "Run AI model online" author: "Tony D" date: "2025-03-18" categories: - AI - R - Python image: "images/S__6840322.jpg" execute: warning: false error: false eval: false --- A guide to interacting with online Large Language Models (LLMs) using R and Python, with examples for Google Gemini, local Ollama, and ChatGPT. This document provides a comprehensive guide to interacting with online Large Language Models (LLMs) using both R and Python. It demonstrates how to use the `ellmer` and `chattr` packages in R, and the `chatlas` library in Python, to connect to various LLM services, including Google Gemini, locally hosted Ollama models, and ChatGPT. The guide covers setting up API keys, defining models, sending prompts, and processing responses for tasks like text generation and translation. run LLM model online with ellmer or chatter # 1.ellmer for R ```{r} #pak::pkg_install('ellmer') library(ellmer) library(keyring) ``` ## google gemini ### gemini-2.0-flash ```{r} chat_gemini_model=chat_gemini( system_prompt = NULL, turns = NULL, base_url = "https://generativelanguage.googleapis.com/v1beta/", api_key = key_get("google_ai_api_key"), model = "gemini-2.0-flash", api_args = list(), echo = NULL ) chat_gemini_model ``` ```{r} chat_gemini_model$chat("Tell me three jokes about statisticians") ``` ## ollama on local ### set up ollama local ```{r} library(ollamar) ollamar::pull("llama3.1") ``` ```{r} ollamar::list_models() ``` ### difine model ```{r} chat=chat_ollama( system_prompt = NULL, turns = NULL, base_url = "http://localhost:11434", model="llama3.1", seed = NULL, api_args = list(), echo = NULL ) chat$get_model() ``` ### run LLM ```{r} chat$chat("Tell me three jokes about statisticians") ``` ### run on console ```{r} live_console(chat) ``` ![](images/clipboard-52512780.png){width="700"} ### check token usage ```{r} token_usage() ``` # 2. chattr LLM pacakge for R ## Step 1 Install package ```{r} #remotes::install_github("mlverse/chattr") ``` ```{r} library(chattr) ``` ## Step 2 set key - Login at https://platform.openai.com/ - Goto Settings (gear icon on top right) - Find API Keys from menu on left - Follow the process to Create new secret key - Copy your secret key (it will only show once so make sure you copy it) ```{r} Sys.setenv(OpenAI_API_KEY="sk-xxxxxxxx") ``` ## Step 3 run ChatGPT as background job ### select model ```{r} #copilot do not need OpenAI_API_KEY chattr_use("copilot") ``` ### add prompt ```{r} chattr_defaults(prompt = "{readLines(system.file('prompt/base.txt', package = 'chattr'))}") ``` ### run ChatGPT as background jobs Do not use Copilot (GitHub) model for chattr(). Github will block this behavior. ```{r} # run chattr_app(as_job = TRUE) ``` Done! ![](https://blogs.rstudio.com/ai/posts/2024-04-04-chat-with-llms-using-chattr/images/buttons.png) ## Or setup auto open Chat GPT when Rstudio start ### Step 1 find Rprofile file ```{r} #install.packages("usethis") # Install if not already installed usethis::edit_r_profile() ``` ### Step 2 edit Rprofile file as below ```{r filename='.RProfile'} #|eval: false # Load chattr app after RStudio is fully loaded setHook("rstudio.sessionInit", function(newSession) { if (newSession) { Sys.sleep(2) # Wait 2 seconds before starting chattr to ensure RStudio is ready tryCatch({ library(chattr) chattr_use("copilot") #Sys.setenv("OPENAI_API_KEY" = "your-api-key-here") chattr_defaults(prompt = "{readLines(system.file('prompt/base.txt', package = 'chattr'))}") chattr_app(as_job = TRUE) }, error = function(e) message("Error starting chattr: ", e$message)) } }, action = "append") ``` # 3. google python API ```{bash} pip install -q -U google-genai ``` ```{python} from google import genai import keyring client = genai.Client(api_key=keyring.get_password("system", "google_ai_api_key")) response = client.models.generate_content( model="gemini-2.0-flash", contents="Explain how AI works in a few words" ) print(response.text) ``` # chatlas for python https://github.com/posit-dev/chatlas ```{bash} pip install -U chatlas ``` ```{python} from chatlas import ChatGoogle from chatlas import ChatOllama from chatlas import token_usage import keyring ``` ## gemini model ```{python} from chatlas import ChatGoogle chat_google_model = ChatGoogle( model = "gemini-2.0-flash", api_key=keyring.get_password("system", "google_ai_api_key"), system_prompt = "You are a whisky expert", ) chat_google_model ``` ```{python} chat_google_model.chat("translate following whisky tasting note to English:微酸，脏麦芽。菲特肯还是要找1988") ``` ## local Ollama model ```{python} from chatlas import ChatOllama chat_llama_model = ChatOllama( model="llama3.2", #api_key=keyring.get_password("system", "google_ai_api_key"), system_prompt = "You are a whisky expert", ) chat_llama_model ``` ```{python} chat_llama_model.chat("translate following whisky tasting note to English:微酸，脏麦芽。菲特肯还是要找1988") ``` ```{python} token_usage() ```