🤖 Intrabot-AI

Intrabot-AI is an offline-first organizational chatbot designed to answer employee queries by retrieving information from secure internal databases. It ensures data privacy, security, and reliability by running completely offline. When offline data is insufficient, it can fallback to Google Gemini API for extended answers.

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🚀 Features

• Offline-First → Retrieves answers from local knowledge base (ChromaDB + embeddings)
• Role-Based Access → Employees, HR, IT, and Admins see only what they are authorized to view
• Gemini Fallback → When the local database lacks answers, Gemini API provides extended support
• Secure & Private → No sensitive organizational data is sent outside unless explicitly needed
• Lightweight & Fast → Works without internet dependency
• Custom Chat UI → User-friendly interface for employees

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🛠️ Tech Stack

• Frontend: React / Streamlit (Chatbox UI)
• Backend: FastAPI (Python)
• Vector DB: ChromaDB
• Embeddings: all-MiniLM-L6-v2 (Sentence Transformers)
• LLM (Offline): GPT4All / LLaMA (optional)
• Fallback Model: Google Gemini (gemini-pro)

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📂 Project Structure

Intrabot-AI/
├── frontend/                    # React/Streamlit UI
├── offline-org-chatbot/         # Core chatbot logic
├── src/                        # Source code modules
├── venv/                       # Virtual environment
├── api.py                      # FastAPI backend
├── gemini_client.py            # Google Gemini integration
├── rbac.py                     # Role-based access control
├── retrieval.py                # Document retrieval logic
├── requirements.txt            # Dependencies
├── .gitignore                  # Git ignore rules
└── README.md                   # Documentation

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⚡ Setup & Installation

1. Clone the repository

git clone https://github.com/Adesh2204/Intrabot-AI.git
cd Intrabot-AI

2. Create virtual environment

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

3. Install dependencies

pip install -r requirements.txt

4. Set environment variables

Create a .env file and add:

GEMINI_API_KEY=your_api_key_here

5. Start the application

Backend Server

python api.py
# or
uvicorn api:app --reload

Frontend (if using Streamlit)

streamlit run frontend/app.py

Frontend (if using React)

cd frontend
npm install
npm start

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🎯 Usage

1. Open the chat UI
2. Ask questions like:
   • "How do I apply for leave?" → (Offline HR docs answer)
   • "Write a leave application draft" → (Gemini fallback answer)
   • "How do I reset my office email password?" → (Offline IT docs answer)
3. Role-based responses based on your access level

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📸 Screenshots

🔹 Chatbot Interface

Showcasing the clean, user-friendly chat interface

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🌟 Future Enhancements

• Multilingual support (Hindi, Spanish, etc.)
• Voice-enabled chatbot with speech recognition
• Analytics dashboard for admins to track usage
• Integration with Slack, MS Teams, and ERP systems
• Mobile app for on-the-go access
• Advanced RBAC with department-wise permissions

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🤝 Contributing

1. Fork the project
2. Create a 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

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📜 License

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

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👨‍💻 Authors

• Adesh2204 - Lead Developer - @Adesh2204
• Team Intrabot-AI - Hackathon 2025

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🏆 Acknowledgments

• Developed during Hackathon 2025
• Inspired by the need for secure, offline-first enterprise chatbots
• Thanks to the open-source community for amazing tools and libraries

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🚨 Security & Privacy

• Data Privacy: All organizational data stays on-premises
• Secure by Design: No external data transmission unless explicitly needed
• Role-Based Access: Users only see information they're authorized to access
• Audit Trail: All interactions are logged for compliance

Built with ❤️ for secure enterprise communication