🌌 NOVA - Intelligent Multi-Document AI Assistant

NOVA is an advanced AI assistant with built-in multi-document intelligence and real-time web search capabilities. Designed for professionals, researchers, and curious minds, NOVA combines state-of-the-art language models with powerful document analysis tools.

Access the Deployed version of NOVA Here: https://nova-ai-v1.streamlit.app

✨ Features

• Multi-Document Intelligence
  • 📄 Support for 15+ file types (PDF, DOCX, XLSX, PPT, CSV, JSON, etc.)
  • 🔍 Semantic search across uploaded documents
  • 📊 Structured data analysis (Excel, CSV, JSON)
• Smart Web Integration
  • 🌐 Real-time web search powered by Tavily
  • ⚡ Automatic search triggering based on query context
• Advanced NLP Capabilities
  • 💬 Natural conversation interface
  • 🤖 Groq-powered ultra-fast LLM responses
  • 🔧 Customizable system prompts and templates
• Enterprise-Ready Features
  • 🧠 In-memory vector store for document embeddings
  • ⚙️ Chunk-based document processing
  • 🎨 Streamlit-based professional UI

🚀 Quick Start

Prerequisites

• Python 3.9+
• API keys for:
  • Groq
  • Tavily
  • HuggingFace

Installation

# Clone repository
git clone https://github.com/yourusername/nova-ai-assistant.git
cd nova-ai-assistant

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Add your API keys to .env

# ⚙️ Configuration
Edit the .env file with your API credentials:
GROQ_API_KEY=your_groq_key
TAVILY_API_KEY=your_tavily_key
COHERE_API_KEY=your_cohere_key  
HUGGINGFACE_API_KEY=your_hf_key 

# 💻 Usage
streamlit run app.py

Basic Workflow:

1. Upload documents through the web interface

2. Ask questions about content or request analysis

3. NOVA automatically determines when to use:

• Document content

• Web search results

• Internal knowledge base

Example Queries:

• "Summarize the key points from my PDF"

• "Compare Q2 sales figures from the Excel sheet"

• "What's the latest news about AI advancements?"

• "Create a bullet list from Section 3 of the contract"

Project Structure

nova-ai-assistant/
├── app.py                 # Main Streamlit application
├── config.py              # Configuration settings
├── models/
│   └── llm.py             # LLM initialization and tools
├── prompts/
│   └── templates.py       # System prompt templates
├── utils/
│   ├── document_utils.py  # Document processing
│   ├── search_utils.py    # Web search functions
│   └── ui_utils.py        # Streamlit UI components
└── requirements.txt       # Dependencies

Key Components

• Document Processing

  • Automatic file type detection
  • Chunk-based text splitting
  • In-memory vector store with Cohere/HuggingFace embeddings

• AI Pipeline

  • Dynamic prompt engineering
  • Automatic tool selection (web/doc search)
  • Thought process visualization

• UI Features

  • Dark/light mode support
  • Document preview pane
  • Interactive chat history
  • File-type specific visual indicators

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository

2. Create your feature branch (git checkout -b feature/AmazingFeature)

3. Commit your changes (git commit -m 'Add some AmazingFeature')

4. Push to the branch (git push origin feature/AmazingFeature)

5. Open a Pull Request

📜 License

Distributed under the MIT License. See LICENSE for more information.

🌟 Acknowledgments

• Groq for ultra-fast LLM inference
• Tavily for real-time web search API
• LangChain team for document processing tools
• Streamlit for interactive UI framework
• Developed by Lalan Kumar