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https://github.com/simonpierreboucher/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.
https://github.com/simonpierreboucher/llm_r_notebook
anthropic cohere large-language-models llm mistral openai r
Last synced: 16 days ago
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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.
- Host: GitHub
- URL: https://github.com/simonpierreboucher/llm_r_notebook
- Owner: simonpierreboucher
- Created: 2024-11-13T22:26:06.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-13T22:31:42.000Z (about 2 months ago)
- Last Synced: 2024-12-04T13:19:15.739Z (about 1 month ago)
- Topics: anthropic, cohere, large-language-models, llm, mistral, openai, r
- Language: Jupyter Notebook
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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](https://github.com/simonpierreboucher/llm_R_notebook/blob/main/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](https://github.com/simonpierreboucher/llm_R_notebook/blob/main/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](https://github.com/simonpierreboucher/llm_R_notebook/blob/main/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](https://github.com/simonpierreboucher/llm_R_notebook/blob/main/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:
```bash
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:
```bash
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:
```R
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:
```bash
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.