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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

Sample

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.