Automated Knowledge Graph Construction

Welcome to the Automated Knowledge Graph Construction project, developed during the Stylumia Hackathon. This project aims to streamline the creation of knowledge graphs by integrating a React-based frontend with a Flask backend, utilizing Apache Kafka for efficient data processing.

Table of Contents

• Sample Output
• Project Structure
• Setup Instructions
  • 1. Running the React Application
  • 2. Setting Up Apache Kafka with Docker
  • 3. Running the Flask Application
• Additional Notes
• License

Sample Output

An example ontology generated for two products.

Project Structure

Stylumia-Hackathon/
├── frontend/                     # React-based frontend
│   ├── src/                      # Source files for the React app
│   ├── .bolt/                    # Configuration files
│   ├── package.json              # Node.js project metadata
│   └── vite.config.ts            # Vite configuration
├── generate-description/         # Backend-related scripts
│   ├── flask_app/                # Flask-based backend API
│   └── myenv/                    # Python virtual environment
├── docker-compose.yml            # Docker Compose setup for Apache Kafka
├── unifashionLLM.ipynb           # Code for loading and fine-tuning the LLava model with LoRA
└── README.md                     # Project documentation

Setup Instructions

To set up and run the project locally, follow these steps:

1. Running the React Application

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

4. Access the application:

   Open your browser and navigate to http://localhost:5173.

2. Setting Up Apache Kafka with Docker

1. Ensure Docker is installed and running on your system.

2. Navigate to the root of the project directory:

   cd ..

3. Start the Kafka services using Docker Compose:

   docker-compose up -d

4. Verify that Kafka is running:

   docker ps

   Ensure the Kafka container is listed and running.

3. Running the Flask Application

1. Navigate to the kg-construct directory:

   cd kg-construct

2. Create a virtual environment (if not already done):

   python -m venv myenv

3. Activate the virtual environment:

   • On macOS/Linux:

     source myenv/bin/activate

   • On Windows:

     myenv\Scripts\activate

4. Navigate to the flask_app directory:

   cd flask_app

5. Install required Python dependencies:

   pip install -r requirements.txt

6. Start the Flask application:

   python app.py

   Ensure the Kafka container is running for proper Flask-Kafka integration.

Additional Notes

• Prerequisites:

  • Ensure Node.js and Python are installed on your machine.
  • Make sure Docker is properly configured and has access to the necessary resources (memory, CPU).

• Service Dependencies:

  • The Flask application communicates with Kafka; thus, Kafka must be running before starting the Flask app.