GenScAI

'GenScAI' is a suite of generative AI tools and recipes for science and research. The project includes Python modules for research and science tasks, such as downloading and processing research articles. It also includes notebooks demonstrating how to use LLMs for science and research tasks, and an example chatbot [notebooks/genscai_chatbot.py] for augmenting scientific workflows.

Requirements

Python

Python 3.10+

Ollama

For locally using LLMs via Ollama, you'll need to download and install Ollama from https://ollama.com/.

Setup

With uv [recommended]

Set up a virtual Python environment. The '--seed' argument has uv add pip and setuptools to the virtual environment. This is optional, and is only useful if using an IDE that accesses pip directly (e.g. Visual Studio Code).

uv venv --seed

Activate (enter) the virtual environment:

source .venv/bin/activate

Install all dependencies, including development and notebook dependencies:

uv sync --all-extras

Alternatively, dependencies can be installed per use case.

Install main project dependencies:

uv pip install -e .

To use developer tools such as ruff and pytest, install the 'dev' dependencies:

uv pip install -e '.[dev]'

For editing and running the example notebooks, you'll want to install the following supporting modules:

uv pip install -e '.[notebooks]'

And finally, for running LLMs locally on systems with nvidia GPUs:

uv pip install -e '.[cuda]'

Without uv

Set up a virtual Python environment:

python3 -m venv .venv

Activate (enter) the virtual environment:

source .venv/bin/activate

Build the genscai module and install dependencies:

pip install -e .

To use developer tools such as ruff and pytest, install the 'dev' dependencies:

pip install -e '.[dev]'

For editing and running the example notebooks, you'll want to install the following supporting modules:

pip install -e '.[notebooks]'

And finally, for running LLMs locally on systems with nvidia GPUs:

pip install -e '.[cuda]'

References

• MIDAS Papaer Abstracts: link