graphdoc

Automating the generation of subgraph documentation through the utilization of Large Language Models.

Development

We utilize poetry for dependency management. Please run poetry install to install the dependencies from within the relevant directory (e.g. graphdoc or mlflow-manager). You can also run poetry shell to activate the virtual environment. Please see the poetry documentation for more information.

We utilize commitizen for commit messages and semantic versioning. Please run cz commit to commit your changes. Commitizen can be installed with pip install commitizen or brew install commitizen.

We utilize docker for managing the tracking of our service and associated expirements through mlflow. In our docker image, we spin up a mlflow, postgres, and minio instance. This is very similar to our production setup, and allows for a pretty smooth development flow between local and prod. Please ensure you have downloaded and are running docker in the background of your machine.

Here are some quick commands for getting started:

brew install poetry
brew install commitizen

cd graphdoc/graphdoc
poetry install 

cd ../mlflow-manager
poetry install 

.env

There are two .env files that we expect the user to set up. They are divided between mlflow-manager and graphdoc. First, let's setup the mlflow-manager .env file. You can leave these values as they are, or modify them as you see fit:

# navigate to the docker root
cd mlflow-manager
cd docker

# copy the .env.example for setup
cp .env.example .env # set values directly in your newly created .env file 

Next, let's set up the .env file to be used by our graphdoc program.

# navigate to the graphdoc root 
cd ../..

# copy the .env.example for setup
cp .env.example .env # set values directly in your newly created .env file 

run.sh

The run.sh script is a convenience script for development. It provides a few shortcuts for running useful commands.

# make sure you are in the root of the repository 
# ensure that the script is executable
chmod +x run.sh

# install both packages in development mode
./run.sh dev

To setup the mlflow-manager services, run the following command:

# default username: admin
# default password: password
./run.sh mlflow-setup

Below, we provide an overview of the commands available in the run.sh script.

Command	Description
./run.sh dev	Install both packages (graphdoc and mlflow-manager) in development mode
./run.sh install	Install both packages (graphdoc and mlflow-manager) in production mode
./run.sh mlflow-setup	Setup the mlflow-manager services
./run.sh mlflow-teardown	Teardown the mlflow-manager services
./run.sh doc-quality-train	Train a document quality model (using the default config in graphdoc/assets/configs/single_prompt_doc_quality_trainer.yaml)
./run.sh build-and-run-doc-quality-trainer	Build and run the document quality trainer (./run.sh mlflow-setup && ./run.sh doc-quality-train)

python

We utilize python==3.13.0 for this project. Please see the python documentation for more information. We recommend using pyenv to install python.

Below, we include some useful commands for installing python using pyenv.

# install pyenv
brew install pyenv

# install python 3.13.0
pyenv install 3.13.0

# set the python version
pyenv local 3.13.0

# check the python version
python --version

There is a chance that your system may have python allocated under the name python3. If this is the case, the following commands may help resolve the python namespace to your python3 installation.

# export the pyenv path
export PYENV_ROOT="$HOME/.pyenv"
[[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"

# reload the shell
source ~/.zshrc

# check the versions
python --version  # Should show 3.13.0
which python  # Should show ~/.pyenv/shims/python

License

This project is licensed under the Apache 2.0 License.