README

The goal of rollama is to wrap the Ollama API, which allows you to run different LLMs locally and create an experience similar to ChatGPT/OpenAI’s API. Ollama is very easy to deploy and handles a huge number of models. Checkout the project here: https://github.com/ollama/ollama.

Installation

install.packages("rollama")

Or you can install the development version of rollama from GitHub. This version is updated more frequently and may contain bug fixes (or new bugs):

# install.packages("remotes")
remotes::install_github("JBGruber/rollama")

However, rollama is just the client package. The models are run in Ollama, which you need to install on your system, on a remote system or through Docker. The easiest way is to simply download and install the Ollama application from their website. Once Ollama is running, you can see if you can access it with:

rollama::ping_ollama()
#> ▶ Ollama (v0.6.1) is running at <http://localhost:11434>!

Installation of Ollama through Docker

For beginners we recommend to download Ollama from their website. However, if you are familiar with Docker, you can also run Ollama through Docker. The advantage of running things through Docker is that the application is isolated from the rest of your system, behaves the same on different systems, and is easy to download and update. You can also get a nice web interface. After making sure Docker is installed, you can simply use the Docker Compose file from this gist.

If you don’t know how to use Docker Compose, you can follow this video to use the compose file and start Ollama and Open WebUI.

Example

The first thing you should do after installation is to pull one of the models from https://ollama.com/library. By calling pull_model() without arguments, you are pulling the (current) default model — “llama3.1 8b”:

There are two ways to communicate with the Ollama API. You can make single requests, which does not store any history and treats each query as the beginning of a new chat:

# ask a single question
query("Why is the sky blue? Answer with one sentence.")
#> 
#> ── Answer from llama3.1 ────────────────────────────────────────────────────────
#> The sky appears blue because of a phenomenon called Rayleigh scattering, in
#> which shorter (blue) wavelengths of light are scattered more than longer (red)
#> wavelengths by the tiny molecules of gases in the Earth's atmosphere.

With the output argument, we can specify the format of the response. Available options include “text”, “list”, “data.frame”, “response”, “httr2_response”, and “httr2_request”:

# ask a single question and specify the output format
query("Why is the sky blue? Answer with one sentence." , output = "text")
#> 
#> ── Answer from llama3.1 ────────────────────────────────────────────────────────
#> The sky appears blue because of a phenomenon called Rayleigh scattering, in
#> which shorter (blue) wavelengths of light are scattered more than longer (red)
#> wavelengths by the tiny molecules of gases in the Earth's atmosphere.

Or you can use the chat function, treats all messages sent during an R session as part of the same conversation:

# hold a conversation
chat("Why is the sky blue? Give a short answer.")
#> 
#> ── Answer from llama3.1 ────────────────────────────────────────────────────────
#> The sky appears blue because of a phenomenon called Rayleigh scattering, where
#> shorter (blue) wavelengths of light are scattered more than longer (red)
#> wavelengths by the tiny molecules of gases in the atmosphere. This scattering
#> effect gives our sky its distinctive blue color during the daytime.
chat("And how do you know that? Give a short answer.")
#> 
#> ── Answer from llama3.1 ────────────────────────────────────────────────────────
#> I was trained on a vast amount of scientific knowledge and data, including
#> information from various fields like physics, atmospheric science, and
#> astronomy. Additionally, I've been fine-tuned to recognize and recall reliable
#> sources, such as NASA, the Royal Society, and other reputable institutions that
#> explain the phenomenon of Rayleigh scattering and its effect on the sky's
#> color.

If you are done with a conversation and want to start a new one, you can do that like so:

Model parameters

You can set a number of model parameters, either by creating a new model, with a modelfile, or by including the parameters in the prompt:

query("Why is the sky blue? Answer with one sentence.", output = "text",
      model_params = list(
        seed = 42,
        num_gpu = 0)
      )
#> 
#> ── Answer from llama3.1 ────────────────────────────────────────────────────────
#> The sky appears blue because of a phenomenon called Rayleigh scattering, in
#> which shorter (blue) wavelengths of light are scattered more than longer (red)
#> wavelengths by the tiny molecules of gases in the Earth's atmosphere.

Configuration

You can configure the server address, the system prompt and the model used for a query or chat. If not configured otherwise, rollama assumes you are using the default port (11434) of a local instance (“localhost”). Let’s make this explicit by setting the option:

options(rollama_server = "http://localhost:11434")

You can change how a model answers by setting a configuration or system message in plain English (or another language supported by the model):

options(rollama_config = "You make short answers understandable to a 5 year old")
query("Why is the sky blue?")
#> 
#> ── Answer from llama3.1 ────────────────────────────────────────────────────────
#> The sky looks blue because of tiny particles in the air that bounce sunlight
#> around. Imagine throwing a ball off a cliff and watching it bounce on the
#> ground - the light from the sun does the same thing with these tiny particles,
#> making it look blue!

By default, the package uses the “llama3.1 8B” model. Supported models can be found at https://ollama.com/library. To download a specific model make use of the additional information available in “Tags” https://ollama.com/library/llama3.2/tags. Change this via rollama_model:

options(rollama_model = "llama3.2:3b-instruct-q4_1")
# if you don't have the model yet: pull_model("llama3.2:3b-instruct-q4_1")
query("Why is the sky blue? Answer with one sentence.")
#> 
#> ── Answer from llama3.2:3b-instruct-q4_1 ───────────────────────────────────────
#> The Earth's sky looks blue because of something called light, which bounces off
#> tiny things in the air and comes back to us as blue!

Easy query generation

The make_query function simplifies the creation of structured queries, which can, for example, be used in annotation tasks.

# Create a query using make_query
q_zs <- make_query(
  text = "the pizza tastes terrible",
  prompt = "Is this text: 'positive', 'neutral', or 'negative'?",
  system = "You assign texts into categories. Answer with just the correct category."
)
# Print the query
print(q_zs)
#> [[1]]
#> # A tibble: 2 × 2
#>   role   content                                                                
#>   <chr>  <glue>                                                                 
#> 1 system You assign texts into categories. Answer with just the correct categor…
#> 2 user   the pizza tastes terrible
#> Is this text: 'positive', 'neutral', or 'neg…
# Run the query
query(q_zs, output = "text")
#> 
#> ── Answer from llama3.2:3b-instruct-q4_1 ───────────────────────────────────────
#> Negative

`rollama`