You have three options for running the software for this workshop. They are listed from easiest to hardest:
You can open the exercises in Google's Colab with the button below:
This assumes you have Docker and git installed. The image takes about 1.7GB of disk space.
$ git clone https://github.com/datakami/pydata-llama-index-tutorial.git
$ cd ./pydata-llama-index-tutorial
$ docker run -it --rm -v $PWD:/repo -p 127.0.0.1:8888:8888 ghcr.io/datakami/llamaindex-workshop:latest
In the output, find the line saying "Jupyter Server 2.7.3 is running at:", and click the link starting with http://127.0.0.1:8888
. Jupyterlab should launch in your browser. Open the Exercises-1.ipynb
file to get started.
This assumes you have git and Python 3.8 - 3.11 installed.
$ git clone https://github.com/datakami/pydata-llama-index-tutorial.git
$ cd ./pydata-llama-index-tutorial
$ python -m venv ./venv
$ source ./venv/bin/activate
$ pip install -r requirements.txt
$ python check_installation.py
$ jupyter-notebook
Jupyterlab should launch in your browser. Open the Exercises-1.ipynb
file to get started.
There are a few different files:
Exercises-*.ipynb
: These files contain exercises for workshop participants. Participants are meant to use these.Colab-*.ipynb
: merged notebooks that can be used to run this workshop in colab.Solutions-*.ipynb
: also contain answers. Used to generate the exercises notebooks.check_installation.py
: can be used to check that all dependencies are set up correctly and download the necessary models.data/
,indices/
: workshop data and vector indices, used in the exercises.Build-*.ipynb
: notebooks to prepare the workshop data and vector indices.build/
: code to generate the exercises and colab notebooks.
$ nb-clean clean -o [yournotebook.ipynb]
Solution filenames should start with "Solutions-". Code cells that start with a "#" are converted to questions with empty answers.
$ python -m build