A comprehensive data-driven AI system leveraging intelligent agent coordination, MCP server integration, and advanced semantic matching for real-time decision making and ecosystem discovery.
# Clone and setup
git clone <repository-url>
cd datadecisions
poetry install
# Start development environment
docker-compose up -d
# Run tests
poetry run pytest
# Start development server
poetry run uvicorn src.api.main:app --reload --port 8000- 🤖 Agent Coordination System - Multi-agent workflows with MCP server integration
- 🧠 Semantic Matching Engine - Advanced AI-powered relationship discovery
- 📊 Graph Database Layer - ArangoDB-based ecosystem modeling
- ⚡ Real-time Processing - Kafka-based streaming pipeline
- 🔐 Security Layer - JWT authentication with RBAC
- 📈 Monitoring & Observability - Prometheus + Grafana metrics
- Intelligent Agent Orchestration via SuperClaude framework
- Real-time Data Ingestion from multiple sources
- Semantic Relationship Discovery using transformer models
- Dynamic Ecosystem Visualization with graph traversal
- Performance-optimized Matching with Redis caching
- Microservices Architecture with Docker containerization
- Kubernetes-ready Deployment with Helm charts
datadecisions/
├── src/ # Main application code
│ ├── agents/ # AI agents and coordination logic
│ ├── ai/ # ML models and semantic processing
│ ├── api/ # FastAPI REST and GraphQL endpoints
│ ├── core/ # Core business logic
│ └── utils/ # Utility functions
├── libs/ # Reusable libraries
│ ├── dd-core/ # Core data models and utilities
│ ├── dd-semantic/ # Semantic matching algorithms
│ ├── dd-graph/ # Graph database operations
│ ├── dd-streaming/ # Kafka streaming utilities
│ └── dd-auth/ # Authentication and security
├── services/ # Microservices
│ ├── dd-relate/ # Main relationship discovery service
│ ├── dd-auth/ # Authentication service
│ ├── dd-stream/ # Stream processing service
│ └── dd-gateway/ # API gateway service
├── infrastructure/ # Infrastructure as code
│ ├── docker/ # Docker configurations
│ ├── kubernetes/ # K8s manifests
│ ├── terraform/ # Cloud infrastructure
│ └── helm/ # Helm charts
├── tests/ # Test suites
│ ├── unit/ # Unit tests
│ ├── integration/ # Integration tests
│ ├── e2e/ # End-to-end tests
│ └── performance/ # Performance tests
├── data/ # Data storage
│ ├── raw/ # Raw input data
│ ├── processed/ # Processed datasets
│ ├── models/ # ML model artifacts
│ └── embeddings/ # Precomputed embeddings
└── docs/ # Documentation
├── api/ # API documentation
├── architecture/ # System architecture docs
├── guides/ # Development guides
└── tutorials/ # User tutorials
- Python 3.11+
- Poetry
- Docker & Docker Compose
- ArangoDB
- Redis
- Kafka
# Install dependencies
poetry install
# Setup pre-commit hooks
poetry run pre-commit install
# Start development services
docker-compose up -d arangodb redis kafka
# Run database migrations
poetry run alembic upgrade head# Format code
poetry run black .
poetry run isort .
# Lint code
poetry run flake8
poetry run mypy .
# Security scan
poetry run bandit -r src/ libs/
poetry run safety check# Run all tests
poetry run pytest
# Run specific test types
poetry run pytest -m unit
poetry run pytest -m integration
poetry run pytest -m e2e
# Run with coverage
poetry run pytest --cov
# Performance testing
poetry run locust -f tests/performance/load_test.py# Build images
docker-compose build
# Production deployment
docker-compose -f docker-compose.prod.yml up -d# Deploy with Helm
helm install datadecisions infrastructure/helm/datadecisions
# Scale services
kubectl scale deployment dd-relate --replicas=5
# Monitor deployment
kubectl get pods -l app=datadecisions- Prometheus: Metrics collection at
:9090 - Grafana: Visualization dashboard at
:3000 - Health Checks: Service health at
/health - API Docs: OpenAPI documentation at
/docs
# Database
ARANGO_HOST=localhost
ARANGO_PORT=8529
ARANGO_DATABASE=datadecisions
# Redis
REDIS_HOST=localhost
REDIS_PORT=6379
# Kafka
KAFKA_BOOTSTRAP_SERVERS=localhost:9092
# Security
SECRET_KEY=your-secret-key
JWT_EXPIRE_MINUTES=30
# ML Models
SENTENCE_TRANSFORMER_MODEL=all-MiniLM-L6-v2
EMBEDDING_DIMENSION=384The project includes specialized AI agents for different domains:
- Semantic Matching Agent - Handles relationship discovery
- Graph Database Agent - Manages graph operations
- Data Pipeline Agent - Orchestrates data flow
- API Development Agent - Manages endpoint development
- Infrastructure Agent - Handles deployment and scaling
- Testing Agent - Manages test automation
- Security Agent - Handles security and compliance
- Monitoring Agent - Manages observability
# Coordinate agents for complex tasks
/sc:coordinate "Implement real-time flight data ingestion" --agents data-pipeline,semantic-matching,graph-database
# Use specific agent expertise
/sc:agent semantic-matching "Optimize relationship discovery algorithm for flight data"
# Master coordination for project-wide tasks
/sc:agent master-coordinator "Setup CI/CD pipeline with security scanning"- JWT-based authentication
- Role-based access control (RBAC)
- API rate limiting
- Input validation and sanitization
- Data encryption at rest and in transit
- Regular security scanning
- Target Latency: <200ms for API responses
- Semantic Matching: >95% accuracy
- Throughput: 1000+ requests/second
- Test Coverage: >85%
- Memory Usage: <500MB per service
- Fork the repository
- Create a feature branch
- Write tests for new functionality
- Ensure all tests pass
- Submit a pull request
MIT License - see LICENSE file for details.
- Documentation: docs/
- Issues: GitHub Issues
- Discussions: GitHub Discussions# dataDescisions-Demo