📚 StudyBuddy – Open Source RAG-Based AI Notebook and Google NotebookLM Alternative

StudyBuddy is a free, open source alternative to Google NotebookLM that lets you chat with your documents, generate smart summaries, and organize knowledge with AI—all securely on your terms.

🎯 Project Description

StudyBuddy is a comprehensive Retrieval-Augmented Generation (RAG) system that transforms static PDF documents into interactive learning experiences. Built with FastAPI and Streamlit, it leverages advanced AI technologies including HuggingFace embeddings, FAISS vector databases, and Large Language Models to create an intelligent document analysis platform.

Technical Architecture

• RAG Pipeline: Implements state-of-the-art retrieval-augmented generation for accurate, context-aware responses
• Vector Database: Uses FAISS for efficient similarity search and document retrieval
• Document Processing: Converts PDFs to structured markdown using Docling for optimal text extraction
• Embeddings: Utilizes BGE-small-en-v1.5 embeddings with CUDA acceleration for fast processing
• LLM Integration: Powered by Gemma3:12b-it-q4_K_M via Ollama for high-quality text generation
• Topic Modeling: Implements LDA (Latent Dirichlet Allocation) for intelligent topic discovery

Key Features

• Document Ingestion: Upload and process multiple PDFs into searchable knowledge bases
• Interactive Q&A: Chat with your documents using natural language queries
• Smart Summaries: Generate student-friendly summaries with key concepts and formulas
• Visual Diagrams: Create ASCII flowcharts and concept maps
• Quiz Generation: Automatically generate multiple-choice questions for self-assessment
• FAQ Creation: Extract frequently asked questions from document content
• Topic Extraction: Discover and organize important themes using AI-powered analysis
• Analytics Dashboard: Monitor usage patterns and system performance

📁 Project Structure

StudyBuddy/
├── configuration.py          # System configuration and model setup
├── FastAPI.py                # Main API server with all endpoints
├── streamlit_ui_fixed.py     # Enhanced web interface
├── ingestion.py              # PDF processing and vector store creation
├── QA_Rag.py                 # Question-answering RAG implementation
├── create_summary.py         # Document summarization module
├── create_diagram.py         # ASCII diagram generation
├── create_quiz.py            # Quiz generation system
├── create_faq.py             # FAQ extraction module
├── create_topics.py          # Topic modeling and extraction
├── Sample_outputs/           # Example outputs and demonstrations
├── Data/                     # PDF document storage directory
└── vector_store/             # FAISS vector database storage

🚀 Use Cases

📚 Education & Learning

• Students: Convert textbooks into interactive study materials with summaries, quizzes, and Q&A
• Teachers: Create educational content and assessments from curriculum documents
• Researchers: Analyze academic papers and extract key insights quickly

💼 Professional & Business

• Knowledge Management: Transform company documents into searchable knowledge bases
• Training Materials: Create interactive training modules from policy documents
• Research & Development: Analyze technical documentation and generate insights

🏥 Specialized Domains

• Medical Education: Process medical textbooks for case studies and exam preparation
• Legal Research: Analyze legal documents and extract relevant precedents
• Technical Documentation: Create interactive guides from complex technical manuals

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🎬 Demo Video

See StudyBuddy in action! Watch our comprehensive demo showcasing all features:

📹 What's Covered in the Demo:

• PDF Upload & Vector Store Creation: See how easy it is to upload documents
• Interactive Q&A: Real-time question answering with source references
• Content Generation: Summaries, quizzes, diagrams, and FAQs in action
• Analytics Dashboard: Comprehensive usage tracking and metrics
• User Interface: Complete walkthrough of the enhanced Streamlit UI

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🛠️ Installation Instructions

Prerequisites

• Python 3.9+
• CUDA-compatible GPU (optional, for faster processing)
• 8GB+ RAM recommended

Step 1: Clone Repository

git clone https://github.com/Ginga1402/StudyBuddy–OpenSource-NotebookLM-RAG-Based-AI.git
cd studybuddy

Step 2: Install Dependencies

pip install -r requirements.txt

Step 3: Set Up Ollama (LLM Backend)

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the required model
ollama pull gemma3:12b-it-q4_K_M

Step 4: Configure Paths

Update the paths in configuration.py to match your system:

DIRECTORY_PATH = "your/path/to/Data"
VECTORSORE_PATH = "your/path/to/vector_store"

📖 Usage

Starting the Application

1. Start the FastAPI Server:

python FastAPI.py

The API will be available at http://localhost:9000

2. Launch the Streamlit Interface:

streamlit run streamlit_app.py

The web interface will open at http://localhost:8501

Basic Workflow

1. Create Vector Store: Upload PDF files and create a searchable knowledge base
2. Generate Content: Use various features to create summaries, quizzes, diagrams, and FAQs
3. Interactive Q&A: Ask questions about your documents and get contextual answers
4. Download Results: Save generated content as text files for offline use
5. Monitor Analytics: Track usage patterns and system performance

API Endpoints

• POST /create-vectorstore/ - Create vector store from PDFs
• POST /QA-Guide/ - Question-answering with RAG
• POST /generate-summary/ - Generate document summaries
• POST /generate-diagram/ - Create ASCII diagrams
• POST /generate-quiz/ - Generate multiple-choice quizzes
• POST /generate-FAQ/ - Create frequently asked questions
• POST /generate-important-topics/ - Extract key topics
• GET /heartbeat - Health check endpoint
• GET /metrics - System usage metrics

🔧 Technologies Used

Technology	Description	Link
FastAPI	High-performance web framework for building APIs	fastapi.tiangolo.com
Streamlit	Framework for creating interactive web applications	streamlit.io
LangChain	Framework for developing LLM-powered applications	langchain.com
FAISS	Library for efficient similarity search and clustering	github.com/facebookresearch/faiss
HuggingFace Transformers	State-of-the-art ML models and embeddings	huggingface.co
Ollama	Local LLM runtime for running language models	ollama.ai
Docling	Advanced document processing and conversion	github.com/DS4SD/docling
NLTK	Natural language processing toolkit	nltk.org
Gensim	Topic modeling and document similarity analysis	radimrehurek.com/gensim
PyTorch	Deep learning framework with CUDA support	pytorch.org
Pandas	Data manipulation and analysis library	pandas.pydata.org
Pydantic	Data validation using Python type annotations	pydantic.dev

🤝 Contributing

Contributions to this project are welcome! If you have ideas for improvements, bug fixes, or new features, feel free to open an issue or submit a pull request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🌟 Star History

If you find StudyBuddy useful, please consider giving it a star ⭐ on GitHub!