16. Capstone System

🏗️ System Architecture

The VOIDX Intelligence Platform consists of five interconnected subsystems:

• Collector: Multi-source data collection using requests (static), Playwright (dynamic), and API clients. Redis-backed distributed URL queue. Celery worker pool with auto-scaling. Proxy rotation for resilience.
• Processor: AI-powered data extraction using Claude API. Pydantic data validation and normalization. ETL pipeline with delta detection. SQLite/PostgreSQL persistent storage.
• Intelligence Engine: LLM-based trend analysis. Price movement pattern detection. Anomaly detection with alerting. Automated report generation.
• Interface: Telegram bot for queries and alerts. REST API (FastAPI) for dashboard consumption. Scheduled email reports. Discord integration for team notifications.
• Operator Tools: Loguru structured logging. Real-time health dashboard. Error rate monitoring. Self-healing workers.

📐 The Capstone Implementation Spec

# capstone/main.py — Entry point
from capstone.collector import CollectorService
from capstone.processor import ProcessorService
from capstone.intelligence import IntelligenceEngine
from capstone.interface import TelegramInterface, APIServer
from capstone.config import PlatformConfig
import asyncio

async def run_platform(config: PlatformConfig):
    collector = CollectorService(config)
    processor = ProcessorService(config)
    intelligence = IntelligenceEngine(config)
    telegram = TelegramInterface(config)
    api = APIServer(config)
    
    # Wire up the event pipeline
    collector.on_data_collected(processor.process)
    processor.on_data_processed(intelligence.analyze)
    intelligence.on_alert(telegram.send_alert)
    intelligence.on_report_ready(telegram.send_report)
    
    # Run all services concurrently
    await asyncio.gather(
        collector.run(),
        processor.run(),
        intelligence.run_scheduled_analysis(),
        telegram.run(),
        api.run(),
    )

if __name__ == '__main__':
    config = PlatformConfig.from_env()
    asyncio.run(run_platform(config))

📋 Capstone Evaluation Rubric

Your capstone implementation is evaluated across five dimensions. Each is worth 20 points (100 total):

• Reliability (20pts): Does the system handle failures gracefully? Are retries implemented? Does it self-heal after worker crashes? Are errors logged with sufficient detail for debugging?
• Scalability (20pts): Is the architecture genuinely distributed? Can you scale workers without changing application code? Does it handle 10x load increase without architectural changes?
• Data Quality (20pts): Is extracted data validated? Are errors tracked and reported? Is deduplication working? Is delta detection preventing redundant processing?
• Operational Tooling (20pts): Is there a health endpoint? Are metrics tracked? Is logging structured and queryable? Can you diagnose production issues from logs alone?
• Interface Quality (20pts): Is the Telegram bot responsive and informative? Are alerts actionable? Are reports readable and well-formatted? Does the API return consistent response shapes?

🚀 Deployment: From Local to Production

# Docker Compose for the full stack
cat > docker-compose.yml << EOF
version: '3.8'
services:
  redis:
    image: redis:7-alpine
    volumes: [redis_data:/data]
  
  worker:
    build: .
    command: celery -A capstone.tasks worker --loglevel=info --concurrency=4
    environment:
      - REDIS_URL=redis://redis:6379/0
    depends_on: [redis]
    volumes: [./data:/app/data]
  
  scheduler:
    build: .
    command: celery -A capstone.tasks beat --loglevel=info
    depends_on: [redis]
  
  api:
    build: .
    command: uvicorn capstone.api:app --host 0.0.0.0 --port 8000
    ports: ["8000:8000"]
    depends_on: [redis]
  
  flower:  # Celery monitoring UI
    image: mher/flower
    ports: ["5555:5555"]
    command: celery flower --broker=redis://redis:6379/0
    depends_on: [redis]

volumes:
  redis_data:
EOF

docker-compose up -d
docker-compose ps
docker-compose logs -f worker