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HAI-Learning

The microservice for executing First-Order-Logic commands generated by LLMs

📚 Papers

In case you use any of the components for your research, please refer to (and cite) the papers:

Creating Local World Models using LLMs. M. D. Longar, E. Novak, M. Grobelnik. Slovenian KDD Conference, Ljubljana, 2024.

☑️ Requirements

Before starting the project make sure these requirements are available:

  • python. For running the app. Alternatively, one can use conda (see next point).
  • prolog. For executing the logic commands.
  • conda. For setting up your research environment and python dependencies.
  • git. For versioning your code.

This app was developed and tested using Python 3.10 and Prolog 9.2.5.

🛠️ Setup

Create a python environment

First create the virtual environment where the service will store all the modules.

Using venv

Using the venv command, run the following commands:

# create a new virtual environment
python -m venv venv

# activate the environment (UNIX)
. ./venv/bin/activate

# activate the environment (WINDOWS)
./venv/Scripts/activate

# deactivate the environment (UNIX & WINDOWS)
deactivate

Using conda

Install conda, a program for creating python virtual environments. Then run the following commands:

# create a new virtual environment
conda create --name hai-learning python=3.10 pip

# activate the environment
conda activate hai-learning

# deactivate the environment
deactivate

Install

To install the project run

pip install -e .

🏗️ Development

To start the app in development mode, run the following command in the terminal:

uvicorn app.main:app --port 4000 --reload

This will start the app and listen it on port 4000.

API Docs

To see the API documentation, visit either:

URL Description
http://127.0.0.1:4000/docs Automatic iteractive API documentation (Swagger UI)
http://127.0.0.1:4000/redoc Alternative automatic documentation (ReDoc)

🐳 Dockerize

To dockerize the REST API, run the following command:

# build the docker image
docker build -t hai-learning .

# run the docker container
docker run -d --name hai-learning -p 4000:4000 hai-learning

To change the port number, modify the last line of the Dockerfile file.