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https://github.com/gerdguerrero/study-chatbot

AI Study Assistant. Upload PDFs, chat with your study materials, and generate practice exams with answer keys. Built with Streamlit, powered by OpenAI GPT-4, and enhanced with RAG (ChromaDB) for intelligent document retrieval.
https://github.com/gerdguerrero/study-chatbot

embeddings gpt-4o gpt4 gpt4o-mini langchain openai openai-api pdfplumber pymupdf pypdf2 python rag rag-chatbot streamlit streamlit-webapp

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AI Study Assistant. Upload PDFs, chat with your study materials, and generate practice exams with answer keys. Built with Streamlit, powered by OpenAI GPT-4, and enhanced with RAG (ChromaDB) for intelligent document retrieval.

Awesome Lists containing this project

README 

          
# 🤖 AI Study Chatbot with RAG & Exam Generation



[![Python](https://img.shields.io/badge/Python-3.8%2B-blue)](https://python.org)

[![OpenAI](https://img.shields.io/badge/OpenAI-GPT--3.5--turbo-green)](https://openai.com)

[![Streamlit](https://img.shields.io/badge/Streamlit-Web%20App-red)](https://streamlit.io)

[![ChromaDB](https://img.shields.io/badge/ChromaDB-Vector%20DB-orange)](https://chromadb.com)



A powerful AI-powered study assistant that processes your academic PDFs and enables natural conversation about the content using **OpenAI's API** and **RAG (Retrieval Augmented Generation)** technology.



![Study Chatbot Demo](https://img.shields.io/badge/Status-Production%20Ready-brightgreen)



## ✨ Key Features



- **🤖 Real AI Conversations**: Powered by OpenAI GPT-3.5-turbo for natural language understanding

- **📚 Smart PDF Processing**: Upload and process academic modules, textbooks, research papers

- **🔍 RAG Implementation**: Retrieval Augmented Generation for accurate, document-based responses

- **📝 Intelligent Exam Generation**: Create practice tests with 4 difficulty levels from your materials

- **🎯 Document-Specific Responses**: AI responses based strictly on your uploaded content

- **💬 Natural Language Interface**: Ask questions in plain English about your documents

- **🎓 Educational Focus**: Designed specifically for academic study and learning



## 🚀 Quick Demo



1. **Upload** your study materials (PDFs)

2. **Ask questions** like:

   - "What are the main concepts in this document?"

   - "Explain the key principles of [topic] from my uploaded files"

   - "What does Chapter 3 say about [concept]?"

3. **Generate exams** with customizable difficulty:

   - Easy: Basic recall and definitions

   - Medium: Application and understanding

   - Hard: Analysis and synthesis

   - Expert: Critical thinking and mastery



## 🛠️ Technology Stack



- **AI Engine**: OpenAI API (GPT-3.5-turbo + text-embedding-ada-002)

- **RAG System**: ChromaDB for vector storage with semantic search

- **PDF Processing**: PyMuPDF, pdfplumber, PyPDF2 with intelligent text extraction

- **Backend**: Python with modular architecture

- **Frontend**: Streamlit for intuitive web interface

- **Vector Search**: OpenAI embeddings with similarity search



## 📋 Prerequisites



- Python 3.8+

- OpenAI API key ([Get one here](https://platform.openai.com/api-keys))

- 2GB+ RAM recommended for vector processing



## ⚡ Quick Start



### 1. Clone & Install

```bash

git clone https://github.com/yourusername/Study-Chatbot.git

cd Study-Chatbot

pip install -r requirements.txt

```



### 2. Configure API Key

```bash

# Create .env file

echo "OPENAI_API_KEY=your_openai_api_key_here" > .env

```



### 3. Run the Application

```bash

streamlit run src/app.py

```



### 4. Start Learning! 🎓

- Open http://localhost:8501 in your browser

- Upload your study PDFs

- Start asking questions!



## 📁 Project Structure



```

Study-Chatbot/

├── src/

│   ├── app.py              # Streamlit web interface

│   ├── chatbot.py          # Main chatbot orchestration

│   ├── rag_system.py       # RAG implementation with ChromaDB

│   ├── pdf_processor.py    # Advanced PDF text extraction

│   ├── exam_generator.py   # AI-powered exam generation

│   └── config.py           # Configuration management

├── documents/              # Sample documents (optional)

├── requirements.txt        # Python dependencies

├── .env.example           # Environment template

└── README.md              # You are here!

```



## 💡 Usage Examples



### Chat with Your Documents

```

You: "What is this document about?"

AI: "This document is a comprehensive guide to Non-Destructive Testing (NDT) 

     methods, covering ultrasonic testing, radiographic inspection, and 

     magnetic particle testing techniques..."



You: "Generate 5 questions about NDT methods"

AI: Creates targeted multiple-choice, true/false, and essay questions

    based on your specific document content.

```



### Advanced Features

- **Document Overview**: "Summarize the key topics in my uploaded files"

- **Specific Queries**: "What does section 4.2 say about ultrasonic testing?"

- **Comparative Analysis**: "Compare the advantages of different NDT methods"

- **Exam Generation**: Create custom practice tests with answer keys



## 🔧 Configuration Options



### Environment Variables (.env)

```env

# Required

OPENAI_API_KEY=your_openai_api_key_here



# Optional Customizations

OPENAI_MODEL=gpt-3.5-turbo

OPENAI_EMBEDDING_MODEL=text-embedding-ada-002

CHROMA_PERSIST_DIRECTORY=./embeddings

MAX_FILE_SIZE_MB=50

```



### Exam Generation Settings

- **Question Types**: Multiple choice, True/False, Short answer, Essay

- **Difficulty Levels**: Easy, Medium, Hard, Expert

- **Customizable Counts**: Configure questions per type

- **Answer Keys**: Toggle show/hide functionality



## 🎯 Core AI Capabilities



### RAG (Retrieval Augmented Generation)

1. **Document Ingestion**: Processes PDFs with advanced text extraction

2. **Semantic Chunking**: Intelligent text segmentation for optimal retrieval

3. **Vector Embedding**: OpenAI embeddings for semantic similarity

4. **Contextual Retrieval**: Finds most relevant document sections

5. **Response Generation**: AI responses grounded in your content



### Intelligent Content Processing

- **Multi-format PDF Support**: Handles various PDF types and layouts

- **Content Quality Filtering**: Removes headers, footers, and noise

- **Subject-Specific Queries**: Optimizes retrieval for technical content

- **Overview Generation**: Synthesizes document summaries



## 🚨 Known Limitations



- **PDF-only Support**: Currently limited to PDF documents

- **English Language**: Optimized for English-language content

- **OpenAI Dependency**: Requires active OpenAI API subscription

- **Single Session**: No persistent user accounts (yet)



## 🤝 Contributing



We welcome contributions! Areas for improvement:



- [ ] Support for more document formats (DOCX, TXT)

- [ ] Multi-language support

- [ ] User authentication and session persistence

- [ ] Advanced analytics and usage tracking

- [ ] Collaborative study features



## 📄 License



This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



## 🙏 Acknowledgments



- **OpenAI** for providing the GPT and embedding APIs

- **ChromaDB** for the vector database solution

- **Streamlit** for the amazing web framework

- **LangChain** for RAG implementation patterns



## 📞 Support



- **Issues**: [GitHub Issues](https://github.com/yourusername/Study-Chatbot/issues)

- **Documentation**: Check the wiki for advanced usage

- **Discussions**: Share your use cases and get help



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