LUMOS - Learning and Knowledge Management System

Overview

LUMOS is a production-grade AI-powered learning and knowledge management system that provides personalized education experiences through advanced machine learning algorithms. The system integrates knowledge graphs, semantic search, content recommendations, and learning path optimization to create adaptive, intelligent learning environments.

Key Features

🧠 Knowledge Graph Engine

• Intelligent Knowledge Mapping: Create and manage complex knowledge graphs with automated relationship discovery
• Concept Pathfinding: Find optimal learning paths between concepts using graph algorithms
• Dynamic Graph Evolution: Real-time graph updates based on learning interactions
• Multi-Modal Knowledge Representation: Support for various content types and learning modalities

🔍 Semantic Search

• Context-Aware Search: Advanced semantic understanding with query expansion and refinement
• Personalized Results: AI-powered result ranking based on user profile and learning history
• Related Content Discovery: Intelligent content recommendations based on semantic similarity
• Real-Time Search Analytics: Comprehensive search behavior analysis and optimization

📚 Content Recommendation

• AI-Powered Personalization: Advanced recommendation algorithms considering learning style, progress, and preferences
• Adaptive Filtering: Dynamic content filtering based on real-time user feedback
• Learning Outcome Prediction: ML models that predict content effectiveness for individual users
• Collaborative Intelligence: Community-driven recommendations and content curation

🎯 Learning Path Optimization

• Intelligent Path Generation: AI-optimized learning sequences based on prerequisites and dependencies
• Progress-Aware Adaptation: Dynamic path adjustments based on performance and engagement
• Skill Assessment Integration: Automated skill level assessment and competency gap analysis
• Time-Constrained Optimization: Efficient learning paths within specified time constraints

📊 Session Management

• Comprehensive Activity Tracking: Detailed logging of learning interactions and behaviors
• Context Preservation: Persistent user state and personalization across sessions
• Real-Time Analytics: Live learning analytics and performance monitoring
• Adaptive Interface: Dynamic UI adjustments based on user patterns and preferences

Architecture

Core Components

LUMOS/
├── app/
│   ├── core/                    # Core configuration and dependencies
│   ├── models/                  # SQLAlchemy database models
│   ├── schemas/                 # Pydantic request/response schemas
│   ├── services/                # Business logic and ML integration
│   ├── endpoints/               # FastAPI route handlers
│   └── main.py                  # Application entry point
├── ml/                          # Machine learning modules
│   ├── knowledge_graph.py       # Knowledge graph processing
│   ├── semantic_search.py       # Semantic search engine
│   ├── content_recommendation.py # Recommendation algorithms
│   └── learning_path_optimizer.py # Path optimization ML
└── tests/                       # Comprehensive test suite

Machine Learning Architecture

Knowledge Graph Engine

• Graph Neural Networks: Advanced GNN models for relationship learning
• Embedding Models: Vector representations for concepts and relationships
• Path Algorithms: Dijkstra, A*, and custom learning-optimized pathfinding
• Graph Analytics: Centrality, clustering, and knowledge flow analysis

Semantic Search Engine

• Transformer Models: BERT-based semantic understanding
• Vector Databases: Efficient similarity search with FAISS integration
• Query Understanding: Intent recognition and query expansion
• Personalization Models: User-specific ranking and filtering

Content Recommendation System

• Collaborative Filtering: Matrix factorization and neural collaborative filtering
• Content-Based Filtering: Feature extraction and similarity computation
• Hybrid Approaches: Multi-algorithm ensemble methods
• Deep Learning: Neural networks for complex pattern recognition

Learning Path Optimizer

• Reinforcement Learning: Q-learning for optimal sequence selection
• Constraint Satisfaction: Time and prerequisite constraint handling
• Bayesian Optimization: Hyperparameter tuning for learning efficiency
• Multi-Objective Optimization: Balancing learning speed, retention, and engagement

API Documentation

Base URL

http://localhost:8000/api/v1/lumos

Authentication

All endpoints require user authentication. Include the user ID in request headers or query parameters.

Core Endpoints

Knowledge Graph Operations

POST /knowledge-graph/
GET  /knowledge-graph/{graph_id}/analytics
POST /knowledge-graph/{graph_id}/query
POST /knowledge-graph/{graph_id}/concept-paths
POST /knowledge-graph/{graph_id}/nodes
POST /knowledge-graph/{graph_id}/relationships

Semantic Search

POST /search/
GET  /search/suggestions
POST /search/related
GET  /search/analytics/{user_id}
POST /search/feedback
GET  /search/history/{user_id}

Content Recommendations

GET  /recommendations/
POST /recommendations/preferences
POST /recommendations/feedback
GET  /recommendations/similar/{content_id}
GET  /recommendations/analytics/{user_id}
POST /recommendations/refresh
GET  /recommendations/trending

Learning Paths

POST /learning-paths/
POST /learning-paths/{path_id}/progress
GET  /learning-paths/{path_id}/recommendations
POST /learning-paths/skills/assess
GET  /learning-paths/{path_id}
GET  /learning-paths/user/{user_id}/paths
GET  /learning-paths/analytics/{user_id}
POST /learning-paths/{path_id}/optimize
DELETE /learning-paths/{path_id}

Session Management

POST /sessions/
GET  /sessions/{session_id}
PUT  /sessions/{session_id}
POST /sessions/{session_id}/activities
GET  /sessions/{session_id}/analytics
GET  /sessions/user/{user_id}/context
PUT  /sessions/user/{user_id}/context
DELETE /sessions/{session_id}
GET  /sessions/user/{user_id}/history

Installation & Setup

Prerequisites

• Python 3.9+
• PostgreSQL 13+
• Redis 6+
• Node.js 16+ (for frontend development)

Installation Steps

1. Clone Repository

git clone <repository-url>
cd LUMOS

2. Install Dependencies

pip install -r requirements.txt

3. Database Setup

# Create database
createdb lumos_db

# Run migrations
alembic upgrade head

4. Environment Configuration

cp .env.example .env
# Edit .env with your configuration

5. Start Services

# Start Redis
redis-server

# Start LUMOS API
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Usage Examples

Creating a Knowledge Graph

import requests

# Create knowledge graph
graph_data = {
    "name": "Machine Learning Fundamentals",
    "description": "Core ML concepts and relationships",
    "nodes": [
        {
            "id": "linear_regression",
            "label": "Linear Regression",
            "properties": {"difficulty": "beginner", "category": "supervised_learning"},
            "node_type": "algorithm"
        }
    ],
    "relationships": [
        {
            "source_id": "statistics",
            "target_id": "linear_regression",
            "relationship_type": "prerequisite",
            "weight": 0.8
        }
    ]
}

response = requests.post(
    "http://localhost:8000/api/v1/lumos/knowledge-graph/",
    json=graph_data,
    params={"user_id": "user123"}
)

Semantic Search

# Perform semantic search
search_request = {
    "query": "machine learning algorithms for beginners",
    "search_type": "comprehensive",
    "max_results": 10,
    "filters": {"difficulty": "beginner"}
}

response = requests.post(
    "http://localhost:8000/api/v1/lumos/search/",
    json=search_request,
    params={"user_id": "user123"}
)

Get Personalized Recommendations

# Get recommendations
response = requests.get(
    "http://localhost:8000/api/v1/lumos/recommendations/",
    params={
        "user_id": "user123",
        "max_recommendations": 10,
        "content_type": "tutorial"
    }
)

Create Learning Path

# Create optimized learning path
path_request = {
    "target_skills": ["python", "machine_learning", "data_analysis"],
    "max_hours": 40,
    "difficulty_preference": "progressive",
    "learning_style": "hands_on"
}

response = requests.post(
    "http://localhost:8000/api/v1/lumos/learning-paths/",
    json=path_request,
    params={"user_id": "user123"}
)

Configuration

Environment Variables

# Database
DATABASE_URL=postgresql://user:password@localhost/lumos_db

# Redis
REDIS_URL=redis://localhost:6379

# ML Models
EMBEDDING_MODEL_PATH=/models/embeddings/
KNOWLEDGE_GRAPH_MODEL_PATH=/models/kg/
RECOMMENDATION_MODEL_PATH=/models/rec/

# API Settings
API_VERSION=v1
DEBUG=false
LOG_LEVEL=INFO

ML Model Configuration

# ml/config.py
ML_CONFIG = {
    "knowledge_graph": {
        "embedding_dim": 256,
        "max_nodes": 10000,
        "learning_rate": 0.001
    },
    "semantic_search": {
        "model_name": "sentence-transformers/all-MiniLM-L6-v2",
        "similarity_threshold": 0.7,
        "max_results": 100
    },
    "content_recommendation": {
        "embedding_dim": 128,
        "num_factors": 50,
        "regularization": 0.01
    },
    "learning_path": {
        "optimization_algorithm": "reinforcement_learning",
        "max_path_length": 20,
        "skill_weight_threshold": 0.3
    }
}

Performance & Scaling

Performance Metrics

• API Response Time: < 200ms average
• Search Latency: < 100ms for semantic search
• Recommendation Generation: < 500ms
• Knowledge Graph Queries: < 50ms for simple queries
• Concurrent Users: 1000+ simultaneous users supported

Scaling Strategies

• Horizontal Scaling: Multi-instance deployment with load balancing
• Database Optimization: Read replicas and connection pooling
• Caching: Redis-based caching for frequent queries
• ML Model Serving: Dedicated model serving infrastructure
• Content Delivery: CDN integration for static assets

Testing

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=app tests/

# Run specific test modules
pytest tests/test_knowledge_graph.py
pytest tests/test_semantic_search.py
pytest tests/test_recommendations.py
pytest tests/test_learning_paths.py

Test Coverage

• Unit Tests: 95%+ coverage for all core modules
• Integration Tests: Complete API endpoint testing
• ML Model Tests: Algorithm validation and performance testing
• Load Tests: Performance under concurrent load

Deployment

Docker Deployment

# Dockerfile included for containerized deployment
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Production Considerations

• Security: API key authentication, rate limiting, input validation
• Monitoring: Application performance monitoring (APM) integration
• Logging: Structured logging with log aggregation
• Backup: Automated database and model backups
• High Availability: Multi-zone deployment with failover

Contributing

Development Workflow

1. Fork the repository
2. Create feature branch (git checkout -b feature/new-feature)
3. Write tests for new functionality
4. Implement feature with comprehensive documentation
5. Submit pull request with detailed description

Code Standards

• Python: PEP 8 compliance with Black formatting
• Documentation: Comprehensive docstrings and type hints
• Testing: Minimum 90% test coverage for new code
• ML Standards: Model versioning and reproducible experiments

License

MIT License - see LICENSE file for details.

Support

Documentation

• API Reference: /docs endpoint for interactive API documentation
• ML Model Documentation: Detailed algorithm descriptions and benchmarks
• Architecture Guides: System design and integration patterns

Community

• Issues: GitHub Issues for bug reports and feature requests
• Discussions: Community forum for questions and ideas
• Contributors: Recognition and contribution guidelines

Commercial Support

• Enterprise: Custom deployments and training
• Consulting: Implementation and optimization services
• SLA: Production support with guaranteed response times

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LUMOS - Illuminating the path to personalized learning through artificial intelligence.