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VersusQA

Introduction

This is a repository for the Cam 2.0 project. The goal of this project is to create a question answering system that can answer questions about the differences between two entities. For example, given the entities "Harry Potter" and "LotR", the system is able to answer a question such as "What is better: Harry Potter or LotR?". The arguments for "Harry Potter" and "LotR" are displayed along with a clear and coherent summary.

Sub-Reposities

This repository contains the following sub-repositories:

  • CQI: Contains the code for the Comparative Questions Identification (CQI) task.
  • OAI: Contains the code for the Objects and Aspects Identification (OAI) task.
  • SC: Contains the code for the Stance Classification (SC) task.
  • CQAS: Contains the code for the Comparative Question Answering Summarization (CQAS) task.
  • backend: Contains the code for the backend of the VersusQA system.
  • frontend: Contains the code for the frontend of the VersusQA system.

Local Setup

With Docker

  1. Install Docker and Docker Compose.
  2. Clone this repository.
  3. Run docker-compose up in the ./backend directory of this repository.
  4. Frontend is available at http://localhost:15557.
  5. Backend is available at http://localhost:15558.

Without Docker

  1. Install Python 3.8 and pip.
  2. Clone this repository.
  3. Create a virtual environment with python3 -m venv venv in all sub-repositories.
  4. Activate the virtual environment with source venv/bin/activate in all sub-repositories (separate terminals).
  5. Install the dependencies with pip install -r requirements.txt in all sub-repositories.
  6. In each sub-repository, run the FastAPI server with uvicorn main:app --host=0.0.0.0 --port=[See Below] --reload.
    • CQI: --port=8001
    • OAI: --port=8002
    • SC: --port=8003
    • CQAS: --port=8004
  7. Run a PostgresSQL database server using an installation on your local machine or a Docker container.
    • If you use a Docker container, run docker run --name postgres -p 5432:5432 -d postgres in a terminal.
    • If you use an installation on your local machine, run sudo service postgresql start in a terminal.
  8. Change the database connection string, the username and password in the ./backend/src/main/resources/application.properties file.
  9. Install Maven.
  10. Build the Spring Boot server with mvn clean install in the ./backend directory.
  11. Run the Spring Boot server with java -jar target/Comparative-Question-Answering---Backend-0.0.1-SNAPSHOT.jar in the ./backend directory.
  12. Navigate to the ./frontend directory.
  13. Install Node.js and npm.
  14. Install the Angular CLI with npm install -g @angular/cli.
  15. Install the dependencies with npm install.
  16. Run the Angular frontend with ng serve in the ./frontend directory.
  17. Frontend is available at http://localhost:4200.
  18. Backend is available at http://localhost:8080.

Deployment on a production server

With Docker

  1. Install Docker and Docker Compose.
  2. Clone this repository.
  3. Change the backend URL in the ./frontend/src/environments/environment.prod.ts file to your exposed backend URL.
  4. Run docker-compose -f docker-compose.prod.yml up in the ./backend directory of this repository.
  5. Map the ports 15557 and 15558 to your exposed ports, with backend on 15558 and frontend on 15557.

Contributors

This System was built during a Master Project at the University of Hamburg. The following people contributed to this project:

This project was under the supervision of the Language Technology Group at the University of Hamburg.

Contributing

To contribute you need to install pre-commit hooks in your git repository.

pip install pre-commit
pre-commit install

License

This project is licensed under the terms of the Apache 2.0 license. See LICENSE for more information.

Deployed System

The deployed system is available at https://cam-v2.ltdemos.informatik.uni-hamburg.de/.