Developing an Interactive RAG-Powered Learning Tool

In this project, I set out to create an engaging and personalized learning experience using Retrieval-Augmented Generation (RAG) technology and AI-driven tools. Here’s a detailed look at the journey and outcome.

The Problem Statement

The aim was to address two key challenges:

1. Implementing a RAG system and developing tools to leverage its capabilities.
2. Adding text-to-speech functionality to make learning more accessible and interactive.

To determine the tools to develop, I started with some fundamental questions:

• How can we make studying more interactive and engaging?
• Can we provide instant, accurate answers to students' questions?
• Is it possible to generate custom study materials tailored to individual needs?

With these objectives in mind, I ventured into the world of RAG and AI agents.

Developing the RAG System

The core of the project is the Retrieval-Augmented Generation (RAG) system, which I built through the following steps:

1. Document Ingestion:
2. Created an ingest.py script to process the NCERT Sound chapter PDF. Used PyPDFLoader and PDFPlumberLoader for robust PDF parsing. Split the text into manageable chunks using RecursiveCharacterTextSplitter.
3. Vector Database:
4. Implemented vector_db.py to create a Chroma vector store for efficient similarity searches. Used the sentence-transformers/all-MiniLM-L6-v2 model for generating embeddings, balancing performance and accuracy.
5. RAG System Implementation:
6. Developed the RAG system in rag_system.py using Google’s Gemini 1.5 Flash model for generating responses. Integrated context retrieval from the vector store to inform responses.
7. API Development:
8. Built a FastAPI backend (app.py) to expose endpoints for generating responses, creating quizzes, and more.
9. Frontend Design:
10. Designed a Streamlit frontend (frontend.py) to provide an intuitive interface for students to interact with the system.

Specialized Agents and Tools

To enhance the learning experience, I developed several specialized agents and tools:

1. Quiz Agent (quiz_agent.py):
2. Generates dynamic quizzes based on chapter content. Supports multiple question types (e.g., multiple-choice, true/false). Provides instant feedback on user responses.
3. Diagram Agent (diagram_agent.py):
4. Creates ASCII flowcharts to visualize complex concepts. Summarizes key chapter topics in an engaging format.
5. Exam Guide Agent (exam_guide_agent.py):
6. Generates custom exam guides with important questions and detailed solutions. Tailored for effective test preparation.
7. Summary Tool (summary_tool.py):
8. Produces concise summaries for quick revision. Lists important topics in a structured format.

Text-to-Speech Integration

To make learning accessible and interactive, I integrated the Sarvam.ai API for text-to-speech functionality. This feature allows students to:

• Listen to answers, summaries, and other content.
• Cater to auditory learners and enhance multi-modal learning experiences.

Integration Process:

1. Set up authentication with the Sarvam.ai API key.
2. Added a dedicated FastAPI endpoint for text-to-speech requests.
3. Implemented frontend controls for text-to-speech conversion and playback.

Key Features

1. Question & Answer System: Ask detailed questions about the Sound chapter and get accurate answers.
2. Text-to-Speech: Convert text responses to speech for auditory learning.
3. Chapter Summary: Generate concise summaries of chapter content.
4. Interactive Quiz: Take quizzes with dynamically generated questions.
5. Summary Flowchart: Visualize key concepts as flowcharts.
6. Exam Guide: Create tailored guides with practice questions.

Technology Stack

• Backend: FastAPI
• Frontend: Streamlit
• AI Model: Google’s Gemini 1.5 Flash
• Vector Database: Chroma
• Embeddings: Hugging Face (sentence-transformers/all-MiniLM-L6-v2)
• PDF Processing: PyPDFLoader, PDFPlumberLoader
• Text-to-Speech: Sarvam AI API

Setup and Installation

Clone the repository:

git clone <repository_url>
cd <repository_directory>

Install dependencies:

pip install fastapi streamlit langchain google-generativeai requests chromadb sentence_transformers langchain_community pydantic uvicorn

Set up environment variables:

• GOOGLE_API_KEY: Your Google API key for Gemini.
• SARVAM_API_KEY: Your Sarvam AI API key for text-to-speech.

Run the application:

Start the FastAPI backend:

uvicorn api:app --reload

Run the Streamlit frontend:

streamlit run frontend.py

Results and Applications

This interactive learning tool demonstrates the potential of AI in education by offering:

• Engaging Study Sessions: Interactive Q&A, quizzes, and flowcharts.
• Accessibility: Multi-modal learning with text-to-speech capabilities.
• Customization: Personalized study materials and exam guides.

Potential applications include:

• Student Learning: Enhancing comprehension of complex topics.
• Test Preparation: Efficiently revising and practicing key concepts.
• Educational Platforms: Integrating advanced tools for interactive learning.

Conclusion

Developing this RAG-powered interactive learning tool has been an incredible journey. By combining RAG technology, specialized AI agents, and text-to-speech capabilities, I’ve created a platform that redefines how students engage with study materials.

This open-source project is available on GitHub. Feel free to explore, contribute, or adapt it to your needs. Let’s make learning more accessible, engaging, and effective for everyone!

Happy learning!