QueryPilot-SmartDocsAssistant

🤖 Next-Generation AI Documentation Assistant with Intelligent Conversational Forms

🌟 Live Demo • 📚 Documentation • 🚀 Deploy • 🤝 Contributing

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🎯 What is QueryPilot?

QueryPilot is a cutting-edge AI documentation assistant that revolutionizes how you interact with your documents. Powered by advanced RAG (Retrieval-Augmented Generation) technology and Google's Gemini AI, it transforms static documents into interactive knowledge bases.

✨ Key Highlights

• Smart Document Analysis - Upload PDFs, DOCX, or TXT files and get instant answers
• Precision RAG Technology - Advanced vector search with FAISS for accurate responses
• Conversational UI - Modern Next.js interface with real-time chat experience
• Intelligent Booking - AI-powered appointment scheduling with natural language processing
• Enterprise-Ready - Containerized architecture with professional-grade deployment
• Multi-Format Support - Handles various document types with smart text extraction

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🛠️ Tech Stack

Category	Technologies
Frontend	Next.js 15, React 19, TypeScript, Tailwind CSS v4
Backend	FastAPI, Python 3.10+, Uvicorn
AI/ML	Google Gemini 1.5-Flash, LangChain, FAISS
Database	SQLite, Vector Storage
DevOps	Docker, Docker Compose
Styling	Modern UI/UX with Dark/Light themes

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Features That Set Us Apart

📄 Document Intelligence Multi-format Support: PDF, DOCX, TXT processing Smart Chunking: Optimal text segmentation for better retrieval Vector Search: Advanced FAISS-powered semantic search Context Preservation: Maintains document relationships 💫 User Experience Modern Interface: Sleek Next.js with responsive design Real-time Chat: Instant responses with typing indicators Theme Support: Professional dark/light mode switching File Management: Drag-and-drop upload with progress tracking

🤖 AI-Powered Features Natural Conversations: Context-aware chat interactions Intelligent Routing: Smart request classification Error Recovery: Graceful API quota handling Multi-session: Concurrent user support 📅 Smart Booking System Conversational Forms: Natural language data collection Input Validation: Email, phone, date verification Date Parsing: "next Monday" → structured dates Database Integration: Persistent appointment storage

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📂 Project Structure

QueryPilot-Docs/
├── 🎨 frontend/                 # Next.js Application
│   ├── src/
│   │   ├── app/                 # App Router pages
│   │   ├── components/          # Reusable UI components
│   │   └── contexts/            # React contexts (Theme, etc.)
│   ├── tailwind.config.ts       # Tailwind configuration
│   └── package.json
│
├── ⚙️ backend/                  # FastAPI Server
│   ├── main.py                  # API endpoints & routing
│   ├── chatbot.py              # Core AI logic (LangChain + Gemini)
│   ├── form_agent.py           # Conversational form handler
│   ├── db.py                   # Database operations
│   ├── requirements.txt        # Python dependencies
│   └── data/                   # Document storage & vectors
│       ├── faiss_index/        # Vector database
│       └── sample_docs/        # Example documents
│
├── 🐳 Docker Files
│   ├── docker-compose.yml      # Multi-container orchestration
│   ├── Dockerfile              # Backend container
│   └── Dockerfile.frontend     # Frontend container
│
└── 📋 Documentation
    └── README.md               # This file

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⚙️ Quick Start Guide

📋 Prerequisites

• Docker & Docker Compose (Recommended)
• Python 3.10+ & Node.js 18+ (For local development)
• Google AI API Key (Get it here)

🐳 Docker Deployment (Recommended)

# 1️⃣ Clone the repository
git clone https://github.com/kar137/QueryPilot-Docs.git
cd QueryPilot-Docs

# 2️⃣ Set up environment variables
echo "GOOGLE_API_KEY=your_gemini_api_key_here" > backend/.env

# 3️⃣ Launch with Docker Compose
docker-compose up --build -d

# 🎉 Access the application
# Frontend: http://localhost:3000
# Backend API: http://localhost:8000
# API Docs: http://localhost:8000/docs

🛠️ Local Development

🔧 Expand for local setup instructions

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt
echo "GOOGLE_API_KEY=your_api_key" > .env
uvicorn main:app --reload --port 8000

Frontend Setup

cd frontend
npm install
npm run dev

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How to Use QueryPilot

📤 1. Upload Documents

• Drag and drop or click to upload PDF, DOCX, or TXT files
• Watch the real-time upload progress
• Get confirmation when documents are processed

💬 2. Start Conversations

👋 "Hello" → Get a friendly introduction
❓ "What is machine learning?" → Ask about document content
📄 "Summarize the key points" → Get document summaries
📅 "Book an appointment" → Start the booking process

📅 3. Smart Appointment Booking

The AI will conversationally collect:

• ✅ Name: Your full name
• ✅ Email: Validated email address
• ✅ Phone: Formatted phone number
• ✅ Date: Natural language ("next Friday", "tomorrow", "Dec 25th")

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📸 Demo Screenshots

🎨 Modern Interface

💬 Intelligent Chat

📅 Smart Booking

📱 View More Screenshots

🌙 Dark Mode

📊 Document Analysis

⚡ Real-time Processing

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🔧 API Reference

📚 Expand API Documentation

Core Endpoints

Method	Endpoint	Description
POST	/upload	Upload documents for processing
POST	/chat-json	Send chat messages and get AI responses
POST	/book-appointment	Direct appointment booking
POST	/clear-documents	Clear all uploaded documents
GET	/appointments	List all appointments

Example Usage

// Chat with the AI
const response = await fetch("/chat-json", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    message: "What are the key findings?",
    conversation_id: "user-123",
  }),
});

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🤝 Contributing

We welcome contributions! Here's how you can help:

🌟 Ways to Contribute

• 🐛 Bug Reports: Found an issue? Open an issue
• 💡 Feature Requests: Have ideas? Share them with us
• 🔧 Code Contributions: Submit pull requests
• 📖 Documentation: Help improve our docs
• ⭐ Star the Repo: Show your support!

📝 Development Guidelines

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

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Support & Contact

Need Help? We're Here for You!

Built with ❤️ by Karan Bista

⭐ If you found QueryPilot helpful, please star the repository! ⭐

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_{Ready to revolutionize your document workflow? Get started now!}