AI Blog Content Assistant Lecture Notes

Introduction

• Speaker: Balagopal Reddy
• Purpose: Demonstrate the AI Blog Content Assistant project.

Project Overview

• Built using:
  • Langchain framework
  • Streamlit for user interface
• Background AI model:
  • Metalama 3 8 billion instruct model from Hugging Face via API calls

Application Features

1. Title Generation

   • AI suggests titles based on a given topic.
   • Example: Topic "Data Science" yields titles like:
     • The Magic of Data Science
     • Unlocking the Secrets of the Universe
     • The Art of Data Science
     • A Guide for the Creatively Inclined

2. Block Generation

   • Users can input a title or select from suggested titles.
   • Allows tuning the number of words in the block and including specific keywords.
   • Example: Title "Data Science for the Curious" with keywords like "machine learning," generates a 200-word blog post.

Development Steps

Setting Up

• Create a new folder for the project.
• Install required packages using terminal commands.
  • Important packages: Langchain, Streamlit
• Create a file for API keys (secret_api_keys.py).
• Set up environment variables for API access token from Hugging Face.

API Integration

• Reasons for using API over local model download:
  • Avoids storage and computation power issues.
  • Focus on application logic instead of infrastructure setup.
• Hugging Face models:
  • Over 7.8 million models available.
  • Selected Metalama 3 8 billion instruct model due to downloads and performance.

Model Characteristics

• Trained on 15 trillion tokens; cutoff in March 2023.
• Group query attention technique:
  • Faster than traditional multi-head attention models.
• Environmental impact:
  • 7.7 million GPU hours used; 2300 tons CO2 equivalent emitted, offset by Meta’s sustainability program.

Code Overview

Components

• Prompt Templates:
  • Title suggestion and block generation templates.
• Chains:
  • Title and blog generation chains defined using Langchain syntax.

User Interface

• Using Streamlit for interactive components.
• Features:
  • Input fields for topics and titles.
  • Slider for word count selection.
  • Dynamic updates with live coding.
• Output displays generated titles and blog content.

Conclusion

• Application built with less than 100 lines of code.
• Demonstrates the power and simplicity of AI in content generation.
• Encouragement for viewers to learn AI tools and techniques.

Closing

• Invitation to subscribe and share the video content.
• Speaker: Balagopal Reddy
• Thank you for attending.