Queries and chats may also embody uploaded pictures with the pictures argument.
ollamar
The ollamar bundle begins up equally, with a test_connection() perform to examine that R can connect with a operating Ollama server, and pull("the_model_name") to obtain the mannequin reminiscent of pull("gemma3:4b") or pull("gemma3:12b").
The generate() perform generates one completion from an LLM and returns an httr2_response, which might then be processed by the resp_process() perform.
library(ollamar)
resp <- generate("gemma2", "What's ggplot2?")
resp_text <- resp_process(resp)
Or, you may request a textual content response straight with a syntax reminiscent of resp <- generate("gemma2", "What's ggplot2?", output = "textual content"). There may be an choice to stream the textual content with stream = TRUE:
resp <- generate("gemma2", "Inform me in regards to the knowledge.desk R bundle", output = "textual content", stream = TRUE)
ollamar has different performance, together with producing textual content embeddings, defining and calling instruments, and requesting formatted JSON output. See particulars on GitHub.
rollama was created by Johannes B. Gruber; ollamar by by Hause Lin.
Roll your personal
If all you need is a fundamental chatbot interface for Ollama, one straightforward choice is combining ellmer, shiny, and the shinychat bundle to make a easy Shiny app. As soon as these are put in, assuming you even have Ollama put in and operating, you may run a fundamental script like this one:
library(shiny)
library(shinychat)
ui <- bslib::page_fluid(
chat_ui("chat")
)
server <- perform(enter, output, session) {
chat <- ellmer::chat_ollama(system_prompt = "You're a useful assistant", mannequin = "phi4")
observeEvent(enter$chat_user_input, {
stream <- chat$stream_async(enter$chat_user_input)
chat_append("chat", stream)
})
}
shinyApp(ui, server)
That ought to open a particularly fundamental chat interface with a mannequin hardcoded. If you happen to don’t choose a mannequin, the app received’t run. You’ll get an error message with the instruction to specify a mannequin together with these you’ve already put in domestically.
I’ve constructed a barely extra strong model of this, together with dropdown mannequin choice and a button to obtain the chat. You’ll be able to see that code right here.
Conclusion
There are a rising variety of choices for utilizing giant language fashions with R, whether or not you wish to add performance to your scripts and apps, get assist along with your code, or run LLMs domestically with ollama. It’s value making an attempt a few choices on your use case to seek out one that most closely fits each your wants and preferences.
