🧠 SmartEvaluator-Omni

A Hybrid-AI Examination System powered by a Consensus Swarm of 4 Distinct AI Models

🌟 Overview

SmartEvaluator-Omni is a next-generation AI-powered examination grading system that leverages a Multi-Agent Swarm architecture to provide fair, unbiased, and comprehensive student answer evaluation.

Created by: Divya Mohan (Software Architect)

Core Features

• 🤖 Consensus Swarm: 4 specialized AI agents (Gemini + Llama + Mistral/Claude + BERT) work in parallel
• 👤 Digital Twin Engine: Mimics each teacher's unique grading personality using Vector RAG
• ⚡ Hybrid Infrastructure: Seamlessly routes between Cloud APIs and Local/Onboard LLM inference
• 📊 Weighted Consensus: Configurable scoring matrix with veto power for plagiarism detection

🏗️ Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                        SmartEvaluator-Omni                         │
├─────────────────────────────────────────────────────────────────────┤
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────────────┐  │
│  │   Swarm Engine  │  │  Digital Twin   │  │ Hybrid Infra Router │  │
│  │   (4 Agents)    │  │   (Persona RAG) │  │  (Cloud ↔ Local)    │  │
│  └────────┬────────┘  └────────┬────────┘  └──────────┬──────────┘  │
│           │                    │                      │             │
│  ┌────────▼────────────────────▼──────────────────────▼──────────┐  │
│  │                    FastAPI Async Backend                      │  │
│  └───────────────────────────────────────────────────────────────┘  │
├─────────────────────────────────────────────────────────────────────┤
│  ChromaDB (Teacher Vectors) │ Ollama (Local LLM) │ Cloud APIs      │
└─────────────────────────────────────────────────────────────────────┘

👥 Open for Contributors

This project is open source and welcomes contributions! Each module has its own README with detailed instructions.

Technical Modules (Programming Required)

Role	Module	Folder	Instructions
AI/ML Contributor	Swarm Engine	backend/swarm/	📖 Swarm README
Data Science Contributor	Digital Twin	backend/digital_twin/	📖 Digital Twin README
Cloud/DevOps Contributor	Infrastructure + Consensus	backend/infra/ + config/	📖 Infra README, 📖 Consensus README

Business Modules (No Programming Required)

Role	Focus Area	Instructions
Business/Marketing Contributor	Marketing & Finance	📖 Marketing Strategy

Getting Started (For Contributors)

1. Fork and clone the repo:

   git clone https://github.com/divyamohan1993/llm-evaluator.git
   cd llm-evaluator

2. Create a feature branch:

   git checkout -b feat/<your-feature>

3. Read your module's README - it contains:

   • Architecture diagrams
   • TODO list (organized by priority)
   • API references
   • Testing commands

4. Make changes, commit, and push:

   git add .
   git commit -m "Your descriptive message"
   git push origin feat/<your-feature>

5. Create a Pull Request for review

Getting Started (For Business Contributors)

1. Read your instructions: See Marketing Strategy Guide
2. Tools you'll use: Word, Excel, PowerPoint, Google Docs - no coding required!
3. Deliverables location: docs/ folder for all business documents

🔄 Auto-Push Monitor

Stop worrying about manual commits. We have included an automated tool that watches your changes and syncs them to Git automatically.

To start the monitor:

1. Open a terminal in the project root.
2. Run:

.\monitor.bat

3. Keep this window open. It will automatically detect changes, commit them with meaningful messages, and push to your branch.

🚀 Quick Start

The Easiest Way: Just run the all-in-one launcher. It handles Git updates, dependencies, and server startup.

.\run_everything.bat

Manual Way:

# Clone the repository
git clone https://github.com/divyamohan1993/llm-evaluator.git
cd llm-evaluator

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

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys

# Start the development server
uvicorn backend.main:app --reload --host 0.0.0.0 --port 8000

📦 Tech Stack

• Orchestration: LangChain / CrewAI (Python)
• Backend: FastAPI (Async/Await)
• Vector DB: ChromaDB (Local) / Pinecone (Cloud)
• Local Inference: Ollama (Llama 3)
• Cloud Inference: Google Gemini Pro, Anthropic Claude, OpenAI GPT-4

👨‍💻 Project Credits

• Created & Designed by: Divya Mohan
• Architecture: Divya Mohan
• Technical Specifications: Divya Mohan
• Workflow Orchestration: Divya Mohan

📄 License

MIT License - See LICENSE for details.