NoteBot - PDF Question Answering ChatBot

A Streamlit-based chatbot that allows you to upload PDF documents and ask questions about their content using Google's Gemini AI.

🚀 Live Demo

Try the live app here: NoteBot on Streamlit Cloud

Features

• 📄 Upload and process PDF documents
• 🤖 Ask questions about your PDF content
• 🔍 Semantic search using FAISS vector database
• ✨ Powered by Google Gemini AI
• 💬 Intelligent context-aware responses

Prerequisites

• Python 3.8+
• Google AI API Key (Gemini)

Installation

1. Clone the repository:

git clone https://github.com/ArnavM21git/CHATBOT.git
cd CHATBOT

2. Create and activate a virtual environment (recommended):

python -m venv venv
# On Windows PowerShell:
.\venv\Scripts\Activate.ps1
# On Windows CMD:
.\venv\Scripts\activate.bat
# On Linux/Mac:
source venv/bin/activate

3. Install required packages from requirements.txt:

pip install -r requirements.txt

4. Set up your environment variables:
   • Create a .env file in the project root
   • Add your Google AI API key:

     GEMINI_API_KEY=your_api_key_here
     

Usage

1. Run the Streamlit app:

streamlit run ChatBot.py

2. Upload a PDF file using the sidebar

3. Ask questions about the PDF content in the text input

4. Get AI-powered answers based on the document context

How It Works

1. PDF Processing: Extracts text from uploaded PDF files
2. Text Chunking: Splits text into manageable chunks for processing
3. Embedding: Converts text chunks into vector embeddings
4. Vector Storage: Stores embeddings in FAISS vector database
5. Similarity Search: Finds relevant chunks based on user questions
6. Answer Generation: Uses Gemini AI to generate contextual answers

Configuration

• Chunk Size: 250 characters (adjustable in code)
• Chunk Overlap: 50 characters
• Model: Gemini 2.5 Flash
• Max Output Tokens: 300
• Embedding Model: models/embedding-001
• Vector Database: FAISS

Deployment on Streamlit Cloud

This app is deployed on Streamlit Cloud. To deploy your own version:

1. Fork this repository
2. Go to share.streamlit.io
3. Connect your GitHub account
4. Select your forked repository
5. Add your GEMINI_API_KEY in the Secrets section (Settings → Secrets):

   GEMINI_API_KEY = "your-api-key-here"

6. Click Deploy!

Contributing

Feel free to submit issues and enhancement requests!

License

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

Copyright (c) 2025 ArnavM21git

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Made with ❤️ using Streamlit and Google Gemini AI