LLM R Notebook

This repository contains Jupyter notebooks that interact with various Large Language Model (LLM) APIs using the R programming language. It provides a structured approach to accessing Anthropic, Cohere, Mistral, and OpenAI APIs from R, enabling a range of NLP tasks such as text generation, retrieval-augmented generation, and conversation.

Repository Structure

• R_ANTHROPIC_API.ipynb: Demonstrates how to set up and use Anthropic's API within an R notebook, including parameter configuration and API call examples.
• R_COHERE_API.ipynb: A notebook for interacting with Cohere's API in R, featuring examples for text generation and retrieval tasks.
• R_MISTRAL_API.ipynb: Explains how to connect with the Mistral API using R, showing examples for configuring requests and handling responses.
• R_OPENAI_API.ipynb: Guides users through accessing the OpenAI API with R, covering setup, customization of parameters, and various example calls.

Getting Started

Prerequisites

To run these notebooks, you will need:

• Jupyter Notebook with R kernel (IRkernel)
• R 4.0 or newer
• API keys for each respective service (Anthropic, Cohere, Mistral, OpenAI)
• Required dependencies as listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/simonpierreboucher/llm_R_notebook.git
   cd llm_R_notebook

2. Install the dependencies for Jupyter Notebook and any Python API calls:

   pip install -r requirements.txt

3. Install R Packages: Open an R session or include these commands at the beginning of each notebook to install necessary R packages:

   install.packages("httr")  # for API requests
   install.packages("jsonlite")  # for JSON parsing
   install.packages("tidyverse")  # for data manipulation if needed
   install.packages("reticulate")  # for Python interoperability if required

Running the Notebooks

1. Start Jupyter Notebook: Open Jupyter with the R kernel by running:

   jupyter notebook

2. Select a Notebook: Open the notebook for the desired API provider (Anthropic, Cohere, Mistral, or OpenAI).
3. Follow Instructions: Each notebook includes specific instructions on authenticating, setting up, and making API requests in R.

Use Cases

• API Interactions: Each notebook provides examples of API calls for generating text, performing retrieval, and other LLM tasks.
• Parameter Configuration: Customize parameters such as temperature, prompt structure, and token limits to fine-tune model responses.
• Cross-Model Comparisons: Enables testing and comparing outputs from different LLM providers within the R environment.

Contributing

We welcome contributions! Feel free to submit issues or pull requests to enhance functionality, add features, or address bugs.

License

This repository is licensed under the MIT License.