🎓 GraphRAG Edu-Suite with Quality Control

Intelligent Educational Content Generation with Comprehensive Quality Assurance

Generate high-quality, context-grounded quiz questions and essay prompts using Graph-based RAG, dual-model AI validation, and comprehensive quality metrics.

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🌟 Key Features

🎯 Quality-Controlled Question Generation

• Multiple Choice Questions (MCQs): 6-10 questions with Bloom's Taxonomy coverage
• Essay Questions: 2-5 deep-reasoning prompts with expected concepts
• Per-Question Metrics: Individual quality scores for each question
• Automatic Filtering: Low-quality questions rejected automatically (>50% coverage threshold)
• Answer Verification: Validates correct answers for MCQs
• Concept Verification: Checks expected concepts for essays

📊 Comprehensive RAG Metrics

• Groundedness: Measures how well content is supported by source material (target: >90%)
• Hallucination Rate: Tracks content not found in retrieved context (target: <25%)
• Overall Quality: Combined score (target: >80% for "Excellent")
• Real-time Tracking: Monitor quality across all generated content

🔍 Advanced Validation System

• Dual-Model Architecture: Gemma3:4b for generation, Llama 3.2 for validation
• Coverage Checks: Questions must have >50% term overlap with context
• Critic Validation: 5-point validation checklist for every question
• Fuzzy Matching: Reduces false positives for technical terms

📈 Proven Quality Improvements

• 50% reduction in hallucination rate (50.6% → 20-25%)
• 100% fix for wrong answers (2/6 → 0/6)
• 11% increase in overall quality (74.7% → 83-87%)
• 100% question generation (3/6 → 6/6)

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🚀 Quick Start

Prerequisites

1. Python 3.8+
2. Neo4j Database (running locally or remotely)
3. Ollama with required models

Installation

# Clone the repository
git clone https://github.com/yourusername/GraphRAG-Edu-Suite.git
cd GraphRAG-Edu-Suite

# Install dependencies
pip install -r requirements.txt

# Pull required AI models
ollama pull gemma3:4b
ollama pull llama3.2:latest

# Configure environment
cp .env.example .env
# Edit .env with your Neo4j credentials:
# NEO4J_URI=bolt://localhost:7687
# NEO4J_USERNAME=neo4j
# NEO4J_PASSWORD=your_password

Run the Application

streamlit run main.py

Visit http://localhost:8501 in your browser!

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📖 How It Works

1️⃣ Upload Content

Upload PDF lecture materials to build your knowledge graph. The system extracts entities, relationships, and concepts using Gemma3.

2️⃣ Generate Questions

Choose between MCQs or Essays, select your lesson, and specify the number of questions.

3️⃣ Automatic Quality Validation

The system automatically:

• Retrieves 1500+ fys, select lesson and question count

3. Quality Validation

System automatically:

• Retrieves 1500+ facts from knowledge graph
• Generates questions with Gemma3
• Validates with Llama 3.2 critic
• Filters by coverage thresholds
• Calculates RAG metrics

4. Review & Use

See per-question quality metrics and use high-quality content!

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📊 Example Output

MCQ with Quality Metrics:

Q1: Understand
Which of the following best describes Data-Driven AI?

Groundedness: 100% ✅ Good
Hallucination: 20% ✅ Low

A. An AI approach that learns from examples and data. ✓
B. A system that relies solely on human-defined rules.
C. A system that mimics human expert decision-making.
D. A system using symbolic reasoning.

Overall Quality:

📊 Overall Quiz Quality
Average Groundedness: 100%
Average Hallucination: 🟢 24%
Overall Quality: 88% ⭐ Excellent

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🏗️ Architecture

User Interface (Streamlit)
    ↓
Generation Pipeline
    ├─ Context Retrieval (Neo4j)
    ├─ Generation (Gemma3:4b)
    ├─ Validation (Llama 3.2)
    ├─ Coverage Filtering
    └─ RAG Metrics
    ↓
Knowledge Graph (Neo4j)

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📁 Project Structure

GenertaiveQestionsModel/
├── main.py                 # Streamlit UI
├── engine/
│   ├── processor.py        # PDF processing
│   ├── generator.py        # Question generation
│   ├── graph_store.py      # Neo4j operations
│   ├── rag_metrics.py      # Metrics calculation
│   └── vram_util.py        # Model management
├── docs/                   # Documentation
├── .env                    # Configuration
└── requirements.txt        # Dependencies

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🎯 Quality Metrics Explained

Groundedness

Measures how well questions are supported by source material

• Formula: (Supported Sentences) / (Total Sentences)
• Target: >90%

Hallucination Rate

Tracks content not found in retrieved context

• Formula: (Words NOT in Context) / (Total Unique Words)
• Target: <25%

Overall Quality

Combined quality score

• Formula: (Groundedness × 50%) + ((1 - Hallucination) × 50%)
• Target: >80% for "Excellent"

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📚 Documentation

• Project Overview - Complete system documentation
• Metrics Guide - How metrics are calculated
• Visual Guide - Visual explanations
• Testing Guide - How to test improvements
• Before/After - Quality improvements comparison

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🔧 Configuration

Environment Variables (.env)

NEO4J_URI=bolt://localhost:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=your_password

Model Configuration

• Gemma3:4b: Generation model (questions, essays)
• Llama 3.2: Validation model (critic, grading)

Quality Thresholds

• Question coverage: >50% (configurable in engine/generator.py)
• Answer coverage: >40% (configurable in engine/generator.py)
• Concept coverage: >40% (configurable in engine/generator.py)

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📈 Performance

Before Quality Control:

• Hallucination: 50.6%
• Wrong answers: 2/6 questions
• Question count: 3/6 generated

After Quality Control:

• Hallucination: 20-25% ✅
• Wrong answers: 0/6 questions ✅
• Question count: 6/6 generated ✅

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🤝 Contributing

Contributions welcome! Areas for improvement:

• Additional LLM support
• Enhanced metrics algorithms
• UI/UX improvements
• Documentation
• Bug fixes

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🙏 Acknowledgments

• Ollama for local LLM inference
• Neo4j for graph database
• Streamlit for web framework
• LangChain for LLM orchestration

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🎓 Use Cases

For Educators:

• Generate quiz questions from lectures
• Create essay prompts with quality assurance
• Track content quality
• Ensure questions are answerable

For Students:

• Practice with high-quality questions
• Get clear essay expectations
• Receive AI-powered feedback
• Study with Bloom's Taxonomy alignment

For Researchers:

• Experiment with RAG metrics
• Study hallucination reduction
• Analyze knowledge graphs
• Benchmark LLM performance

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Version: 2.0 (Quality Control Update)
Status: Production Ready ✅
Last Updated: February 2026

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Made with ❤️ using Gemma3, Llama 3.2, Neo4j, and Streamlit