🛡️ Sevak - AI Public Safety Ecosystem

A comprehensive platform for public safety reporting and intelligent assistance

Empowering communities with AI-driven chatbot support and seamless incident reporting.

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📖 Overview

Sevak is a full-scale public safety ecosystem designed to help communities report incidents easily while providing intelligent emergency and safety information through an AI Agent. The project is structured into three primary modules to ensure scalability, robust conversational capabilities, and a seamless user experience.

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✨ System Architecture

1️⃣ AI Public Safety Chatbot (/backend)

A powerful RAG-powered chatbot utilizing vector searches to provide contextually accurate responses.

• Features: Smart responses via Google Gemini / OpenAI fallback, vector search integration, and reliable relational data storage.
• Technologies: FastAPI, Python, Prisma ORM, PostgreSQL, ChromaDB / Qdrant.

2️⃣ ReportBox Core API (/ReportBox-Backend)

A reliable REST API built to handle incoming public reports and manage notifications.

• Features: Secure incident logging, database management, and Twilio integration for SMS/alert notifications.
• Technologies: Node.js, Express, Mongoose (MongoDB), and Twilio.

3️⃣ ReportBox Frontend (/ReportBox-front)

A modern, responsive user interface designed for public access and dashboard management.

• Features: Intuitive incident reporting, clean dashboards, and scalable component architecture.
• Technologies: React 19, Vite, Tailwind CSS, Lucide React, and React Router.

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🛠️ Tech Stack Directory

Component	Stack/Technology	Location	Purpose
Frontend UI	React, TailwindCSS, Vite	/ReportBox-front	Public-facing dashboards and reporting user interfaces.
Core API	Node.js, Express, MongoDB	/ReportBox-Backend	Incident handling, REST endpoints, SMS triggers.
AI Agent	FastAPI, Prisma, PostgreSQL	/backend	Vector-backed AI chatbot logic, semantic search, generative responses.

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🚀 Quick Start

Follow the instructions below to get the entire Sevak ecosystem running locally. Make sure you have Node.js, Python, MongoDB, and PostgreSQL installed.

1. Launch the AI RAG Backend

Navigate to the backend directory, set up your Python environment, and start the system:

cd backend
python -m venv venv
# Activate your environment
# Windows: venv\Scripts\activate
# macOS/Linux: source venv/bin/activate

pip install -r requirements.txt
python -m spacy download en_core_web_sm

Note: Make sure to follow the DB and .env setup instructions in backend/Readme.md.

uvicorn app.main:app --reload

2. Launch the Core Node.js API

Navigate to the ReportBox-Backend directory to set up the incident reporting API.

cd ReportBox-Backend
npm install

Note: Create your .env file containing your MongoDB URI and Twilio configurations.

npm start

3. Launch the User Interface

Finally, navigate to the frontend directory to run the UI application.

cd ReportBox-front
npm install
npm run dev

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🤝 Contributing

We welcome community contributions! Please feel free to submit a Pull Request.

1. Fork the Project
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

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📄 License

This project is licensed under the MIT License.

Built with 🛠️ for safer communities