RAGSum - Text Summarization with Retrieval-Augmented Generation (RAG)

This is the codebase to my Master's thesis at Maastricht University.

Run code in the background

nohup python my_script.py > my_script.log 2>&1 &

Python Environment

Linux

On Linux, we need to make sure we have the proper Python environment, the following shell script gets us the required 3.12.3 Python version:

apt install software-properties-common
add-apt-repository ppa:deadsnakes/ppa 
apt update
apt install python3.10
python3.10 --version 
which python3.10 - /usr/bin/python3.10
pip install --upgrade virtualenv
cd persistent
virtualenv -p $(which python3.10) venv
source venv/bin/activate 

To install the jupyter kernel and make it visible in VS Code:

pip install ipykernel
python -m ipykernel install --user --name venv

This way, the virtual environment will be created, and sourced, we are ready to code!

Windows

First, download the python version you need.

Second, create a virtual environment for development:

python -m venv venv
venv\Scripts\Activate.bat
pip install -r requirements.txt

Ollama Environment

To shoot up locally running LLMs, you need to install Ollama

Linux installation

curl -fsSL https://ollama.com/install.sh | sh

This command will install ollama to run LLMs locally.

In a new terminal, run your desired model, I'll use deepseek-r1:8b for now

To start an ollama server use the following prompt:

ollama serve
ollama run deepseek-r1:8b

Run Pre-commit

pre-commit run nbqa-black
pre-commit run nbqa-pyupgrade
pre-commit run nbqa-isort