PDF Chatbot with Appointment Booking System

A powerful Streamlit application that combines PDF document chat capabilities with an intelligent appointment booking system using LangChain and Google's Gemini Pro model.

🌟 Features

• PDF Document Processing

  • Upload and process multiple PDF documents
  • Extract and chunk text content
  • Create searchable vector embeddings
  • Intelligent question-answering based on document content

• Smart Appointment Booking

  • Natural language date parsing
  • Email and phone number validation
  • Automated email confirmation
  • Appointment storage in JSON format
  • LangChain Tools integration for enhanced functionality

• User-Friendly Interface

  • Clean and intuitive Streamlit UI
  • Real-time processing feedback
  • Error handling and user guidance
  • Seamless integration of both chatbot and booking features

https://github.com/user-attachments/assets/e228592e-6161-440d-8776-c787e2bd13d2

🚀 Installation

1. Clone the repository:

git clone https://github.com/sobit-nep/AI-Document-QA-Appointment-Booking.git
cd AI-Document-QA-Appointment-Booking

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install required packages:

pip install streamlit PyPDF2 langchain-community langchain-google-genai python-dotenv validators email-validator google-generativeai faiss-cpu

⚙️ Configuration

1. Create a .env file in the project root directory with the following variables:

GOOGLE_API_KEY=your_google_api_key
EMAIL_ADDRESS=your_email_address
EMAIL_PASSWORD=your_email_app_password

Note:

• Get your Google API key from Google AI Studio
• For Gmail, use an App Password instead of your regular password. Learn how to create one

🚀 Usage

1. Start the application:

streamlit run app.py

2. Upload PDF Documents:

   • Use the sidebar to upload one or more PDF files
   • Click "Process Documents" to extract and index the content

3. Interact with the Chatbot:

   • Ask questions about the content of your uploaded PDFs
   • The system will provide relevant answers based on the document content

4. Book Appointments:

   • Type keywords like "appointment", "schedule", or "book" to trigger the booking flow
   • Fill in your details (name, email, phone, preferred date)
   • Receive email confirmation upon successful booking

🏗️ Project Structure

AI-Document-QA-Appointment-Booking/
├── app.py                 # Main application file
├── .env                   # Environment variables
├── appointments.json      # Stored appointments
├── faiss_index/          # Vector store for PDF content
├── requirements.txt       # Project dependencies
└── README.md             # Project documentation

💡 Key Components

• DocumentProcessor: Handles PDF processing and text extraction
• ChatTools: Manages validation, date parsing, and email functions
• ChatbotUI: Combines PDF chat and appointment booking functionality
• LangChain Tools: Provides structured agent-based interactions

📝 Important Notes

• Ensure your Google API key has access to the Gemini model
• Configure your email settings properly for appointment confirmations
• The system stores appointments locally in a JSON file
• Vector store is created in the local directory

✨ Future Improvements

• Implement user authentication
• Add calendar integration for appointment scheduling
• Create a dashboard for appointment management
• Add support for multiple languages
• Implement conversation history