Kuberise Bot

[mojtabaimani]

Title: Automate Training and Updating of kuberiseBot with Latest Repository Changes

Description

This issue outlines the steps to automate the training and updating of a custom GPT-based bot, named kuberiseBot, using GitHub Actions. The bot will be trained with information from our open-source project and docs repositories (kuberise.io and kuberise.github.io) and the documentation website (https://kuberise.io). The goal is to ensure that kuberiseBot always has the latest information to assist with questions about installation, configuration, and architecture for new features.

Steps to Implement

1. Create a Training Script:

   • Develop a script that can pull the latest data from the kuberise.io and kuberise.github.io repositories.
   • Extract documentation from the Markdown files in the kuberise.github.io repository.
   • Format the extracted data for training the GPT model.

2. Set Up GitHub Actions Workflow:

   • Create a GitHub Actions workflow that triggers on every push to the main branch of both repositories.
   • The workflow should:
     • Clone the repositories.
     • Run the training script to prepare the data.
     • Trigger the training process for the GPT model with the prepared data.

3. Train the GPT Model:

   • Use a cloud-based service (such as OpenAI, AWS, or Google Cloud) to train the GPT model with the latest data.
   • Ensure that the trained model is updated with the new information from the repositories.

4. Deploy the Updated Model:

   • Once the training is complete, deploy the updated GPT model.
   • Ensure that kuberiseBot uses the latest model to answer queries.

5. Integration with GitHub Actions:

   • Add steps in the GitHub Actions workflow to automatically deploy the updated model after training.
   • Set up environment variables and secrets in GitHub Actions for secure access to the cloud service used for training and deployment.

Example GitHub Actions Workflow

name: Update kuberiseBot

on:
  push:
    branches:
      - main
    paths:
      - 'kuberise.io/**'
      - 'kuberise.github.io/**'

jobs:
  update-bot:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout kuberise.io repository
        uses: actions/checkout@v2
        with:
          repository: 'kuberise/kuberise.io'
          path: 'kuberise.io'

      - name: Checkout kuberise.github.io repository
        uses: actions/checkout@v2
        with:
          repository: 'kuberise/kuberise.github.io'
          path: 'kuberise.github.io'

      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'

      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r kuberise.io/requirements.txt

      - name: Run training script
        run: |
          python kuberise.io/scripts/train_bot.py

      - name: Deploy updated model
        run: |
          python kuberise.io/scripts/deploy_bot.py
        env:
          CLOUD_API_KEY: ${{ secrets.CLOUD_API_KEY }}
          CLOUD_API_SECRET: ${{ secrets.CLOUD_API_SECRET }}

Additional Notes

• Ensure that the training and deployment scripts are robust and handle errors gracefully.
• Consider setting up notifications for the team when the bot is updated.
• Test the entire workflow in a staging environment before deploying to production.

Resources

• GitHub Actions Documentation
• OpenAI API Documentation

By following the steps outlined above, we can automate the training and updating process for kuberiseBot, ensuring it always has the latest information from our repositories and documentation.