AIVY - AI Powered Learning Assistant

Overview

Aivy: AI Learning Assistant Aivy is a next-generation learning assistant that integrates various educational tools into a unified platform. Built with Airflow, FastAPI, and Streamlit, it offers a personalized, engaging, and efficient learning experience by combining text, videos, flashcards, and quizzes.

Why Aivy?

• Unified Learning Tools: Access all your learning content (text, images, videos, quizzes) in one place.
• Personalized Assistance: AI tailors learning content and assessments to user needs.
• Advanced Search: Powered by RAG (Retrieval-Augmented Generation) for context-aware results.
• Engaging UI: Intuitive, interactive, and user-friendly.

Architecture diagram for the same:

Key Features

1. Dynamic Lesson Plans:
   • Lessons organized into modules with titles, descriptions, and detailed explanations.
2. Quiz and Flashcard Generation:
   • Auto-generated quizzes and flashcards based on lesson content.
3. RAG Functionality:
   • Retrieves relevant YouTube videos, images, and text for user queries.
4. Logging and Testing:
   • Comprehensive logging in Streamlit and Airflow for error tracking.
   • APIs tested extensively with Postman.
5. Scalable Design:
   • Separate environments for Streamlit and FastAPI to ensure modularity.

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Project Structure

The project is divided into three main components:

1. Airflow

• Handles data extraction pipelines for lesson content, images, and links.
• Key files:
  • extraction.py: Extracts lesson content.
  • image_extraction.py: Processes images.
  • links.py: Manages and fetches additional learning resources.
  • GFG_Data_Extraction_DAG.py: Manages and schedules DAG workflows.

2. FastAPI

• Backend API for:
  • Fetching lessons, images, and YouTube video URLs.
  • Generating quizzes and flashcards.
• Lightweight and scalable.

3. Streamlit

• User-friendly frontend for:
  • Navigating and viewing lesson plans.
  • Interacting with quizzes and results.
• Files include:
  • lesson.py: Displays detailed lesson content.
  • quiz.py: Manages quiz interface and results.
  • planner.py: Allows navigation across modules and plans.

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Folder Structure

├── Airflow
│   ├── dags
│   │   ├── extraction_files
│   │   │   ├── extraction.py
│   │   │   ├── image_extraction.py
│   │   │   ├── links.py
│   │   │   ├── testfiles/
│   │   ├── GFG_Data_Extraction_DAG.py
│   ├── Dockerfile
│   ├── docker-compose.yml
│   ├── entrypoint.sh
│   ├── requirements.txt
├── fastapi
│   ├── config.py
│   ├── lessons.py
│   ├── main.py
│   ├── Dockerfile
│   ├── poetry.lock
├── streamlit
│   ├── ui
│   │   ├── lesson.py
│   │   ├── planner.py
│   │   ├── plans.py
│   │   ├── quiz.py
│   ├── app.py
│   ├── Dockerfile
│   ├── docker-compose.yml
│   ├── README.md
├── diagrams
│   ├── images
│   │   ├── folder_structure.png
│   │   ├── architecture_diagram.png

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Setup Instructions

1. Clone the Repository

git clone <repository-link>
cd <repository-folder>

2. Setup Environments

For Streamlit:

cd streamlit
python -m venv venv_streamlit  # Create virtual environment
source venv_streamlit/bin/activate  # Activate the environment
poetry install  # Install dependencies

For FastAPI:

cd fastapi
python -m venv venv_fastapi  # Create virtual environment
source venv_fastapi/bin/activate  # Activate the environment
poetry install  # Install dependencies

3. Run the Components

Airflow:

cd Airflow
docker-compose up --build

FastAPI:

cd fastapi
source venv_fastapi/bin/activate
poetry install
uvicorn main:app --reload

Streamlit:

cd streamlit
source venv_streamlit/bin/activate
poetry install
streamlit run app.py

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You can access the related files to this project here:

• Documentation: Google Docs - Documentation
• Deployed FastAPI: FastAPI Docs
• Deployed Streamlit: Streamlit App

Conclusion

This project integrates Airflow, FastAPI, and Streamlit to provide a dynamic learning assistant. With separate environments and comprehensive testing, it is scalable, efficient, and user-friendly.