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https://github.com/hauselin/ollama-r
R library to run Ollama language models
https://github.com/hauselin/ollama-r
ai api llm llms ollama ollama-api r
Last synced: 28 days ago
JSON representation
R library to run Ollama language models
- Host: GitHub
- URL: https://github.com/hauselin/ollama-r
- Owner: hauselin
- License: other
- Created: 2024-04-28T19:42:25.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-09-11T14:22:26.000Z (about 2 months ago)
- Last Synced: 2024-09-30T10:40:59.325Z (about 1 month ago)
- Topics: ai, api, llm, llms, ollama, ollama-api, r
- Language: R
- Homepage: https://hauselin.github.io/ollama-r/
- Size: 3.27 MB
- Stars: 37
- Watchers: 1
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
Awesome Lists containing this project
- jimsghstars - hauselin/ollama-r - R library to run Ollama language models (R)
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# ollamar 
[](https://CRAN.R-project.org/package=ollamar)
[](https://github.com/hauselin/ollama-r/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=ollamar)
The [Ollama R library](https://hauselin.github.io/ollama-r/) is the easiest way to integrate R with [Ollama](https://ollama.com/), which lets you run language models locally on your own machine.
The library also makes it easy to work with data structures (e.g., conversational/chat histories) that are standard for different LLMs (such as those provided by OpenAI and Anthropic). It also lets you specify different output formats (e.g., dataframes, text/vector, lists) that best suit your need, allowing easy integration with other libraries/tools and parallelization via the `httr2` library.
To use this R library, ensure the [Ollama](https://ollama.com) app is installed. Ollama can use GPUs for accelerating LLM inference. See [Ollama GPU documentation](https://github.com/ollama/ollama/blob/main/docs/gpu.md) for more information.
See [Ollama's Github page](https://github.com/ollama/ollama) for more information. This library uses the [Ollama REST API (see documentation for details)](https://github.com/ollama/ollama/blob/main/docs/api.md) and has been tested on Ollama v0.1.30 and above. It was last tested on Ollama v0.3.10.
> Note: You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
## Ollama R vs Ollama Python/JS
This library has been inspired by the official [Ollama Python](https://github.com/ollama/ollama-python) and [Ollama JavaScript](https://github.com/ollama/ollama-js) libraries. If you're coming from Python or JavaScript, you should feel right at home. Alternatively, if you plan to use Ollama with Python or JavaScript, using this R library will help you understand the Python/JavaScript libraries as well.
## Installation
1. Download and install the [Ollama](https://ollama.com) app.
- [macOS](https://ollama.com/download/Ollama-darwin.zip)
- [Windows preview](https://ollama.com/download/OllamaSetup.exe)
- Linux: `curl -fsSL https://ollama.com/install.sh | sh`
- [Docker image](https://hub.docker.com/r/ollama/ollama)
2. Open/launch the Ollama app to start the local server.
3. Install either the stable or latest/development version of `ollamar`.
Stable version:
```{r eval=FALSE}
install.packages("ollamar")
```
For the latest/development version with more features/bug fixes (see latest changes [here](https://hauselin.github.io/ollama-r/news/index.html)), you can install it from GitHub using the `install_github` function from the `remotes` library. If it doesn't work or you don't have `remotes` library, please run `install.packages("remotes")` in R or RStudio before running the code below.
```{r eval=FALSE}
# install.packages("remotes") # run this line if you don't have the remotes library
remotes::install_github("hauselin/ollamar")
```
## Example usage
Below is a basic demonstration of how to use the library. For details, see the [getting started vignette](https://hauselin.github.io/ollama-r/articles/ollamar.html) on our [main page](https://hauselin.github.io/ollama-r/).
`ollamar` uses the [`httr2` library](https://httr2.r-lib.org/index.html) to make HTTP requests to the Ollama server, so many functions in this library returns an `httr2_response` object by default. If the response object says `Status: 200 OK`, then the request was successful.
```{r eval=FALSE}
library(ollamar)
test_connection() # test connection to Ollama server
# if you see "Ollama local server not running or wrong server," Ollama app/server isn't running
# download a model
pull("llama3.1") # download a model (equivalent bash code: ollama run llama3.1)
# generate a response/text based on a prompt; returns an httr2 response by default
resp <- generate("llama3.1", "tell me a 5-word story")
resp
#' interpret httr2 response object
#'
#' POST http://127.0.0.1:11434/api/generate # endpoint
#' Status: 200 OK # if successful, status code should be 200 OK
#' Content-Type: application/json
#' Body: In memory (414 bytes)
# get just the text from the response object
resp_process(resp, "text")
# get the text as a tibble dataframe
resp_process(resp, "df")
# alternatively, specify the output type when calling the function initially
txt <- generate("llama3.1", "tell me a 5-word story", output = "text")
# list available models (models you've pulled/downloaded)
list_models()
name size parameter_size quantization_level modified
1 codegemma:7b 5 GB 9B Q4_0 2024-07-27T23:44:10
2 llama3.1:latest 4.7 GB 8.0B Q4_0 2024-07-31T07:44:33
```
## Citing `ollamar`
If you use this library, please cite [this paper](https://doi.org/10.31234/osf.io/zsrg5) using the following BibTeX entry:
```bibtex
@article{Lin2024Aug,
author = {Lin, Hause and Safi, Tawab},
title = {{ollamar: An R package for running large language models}},
journal = {PsyArXiv},
year = {2024},
month = aug,
publisher = {OSF},
doi = {10.31234/osf.io/zsrg5},
url = {https://doi.org/10.31234/osf.io/zsrg5}
}
```