> ## Documentation Index
> Fetch the complete documentation index at: https://docs.prepst.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Deployment Guide

> Deploy the Cognitive Learning Engine to production

## Deployment Options

### Option 1: Using the API Service

Best for: Most use cases, hosted solution

<Steps>
  <Step title="Get API Key">
    Contact support to obtain your API key for the hosted service.
  </Step>

  <Step title="Configure Environment">
    ```bash theme={null}
    export COGNITION_API_KEY="your-api-key"
    export COGNITION_API_URL="https://api.prepst.com"
    ```
  </Step>

  <Step title="Start Using">
    ```python theme={null}
    from cognition_engine import CognitionEngine

    engine = CognitionEngine(api_key="your-api-key")
    ```
  </Step>
</Steps>

### Option 2: Self-Hosted SDK

Best for: Full control, on-premise deployments

### Option 3: Docker Deployment

Best for: Consistent environments, container orchestration

## Docker Deployment

### Basic Dockerfile

```dockerfile theme={null}
FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Expose API port
EXPOSE 8000

# Run the application
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
```

### Docker Compose

```yaml theme={null}
version: '3.8'

services:
  cognition-engine:
    build: .
    ports:
      - "8000:8000"
    environment:
      - SUPABASE_URL=${SUPABASE_URL}
      - SUPABASE_KEY=${SUPABASE_KEY}
    volumes:
      - ./data:/app/data
    restart: unless-stopped
```

### Build and Run

```bash theme={null}
# Build image
docker build -t cognition-engine .

# Run container
docker run -p 8000:8000 \
  -e SUPABASE_URL="your-url" \
  -e SUPABASE_KEY="your-key" \
  cognition-engine
```

## Kubernetes Deployment

### Deployment Configuration

```yaml theme={null}
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cognition-engine
spec:
  replicas: 3
  selector:
    matchLabels:
      app: cognition-engine
  template:
    metadata:
      labels:
        app: cognition-engine
    spec:
      containers:
      - name: cognition-engine
        image: cognition-engine:latest
        ports:
        - containerPort: 8000
        env:
        - name: SUPABASE_URL
          valueFrom:
            secretKeyRef:
              name: supabase-secrets
              key: url
        - name: SUPABASE_KEY
          valueFrom:
            secretKeyRef:
              name: supabase-secrets
              key: key
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
```

### Service Configuration

```yaml theme={null}
apiVersion: v1
kind: Service
metadata:
  name: cognition-engine-service
spec:
  selector:
    app: cognition-engine
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: LoadBalancer
```

## Environment Configuration

### Required Variables

<ParamField name="SUPABASE_URL" required>
  Your Supabase project URL
</ParamField>

<ParamField name="SUPABASE_KEY" required>
  Your Supabase anon key
</ParamField>

<ParamField name="API_KEY" required>
  API key for authentication (if using API service)
</ParamField>

### Optional Variables

<ParamField name="LOG_LEVEL" default="INFO">
  Logging level (DEBUG, INFO, WARNING, ERROR)
</ParamField>

<ParamField name="MAX_WORKERS" default="4">
  Number of worker processes
</ParamField>

<ParamField name="REDIS_URL">
  Redis URL for caching
</ParamField>

## Health Checks

### Health Check Endpoint

```python theme={null}
@app.get("/health")
async def health_check():
    return {
        "status": "healthy",
        "timestamp": datetime.now(),
        "version": "1.0.0"
    }
```

### Kubernetes Health Checks

```yaml theme={null}
livenessProbe:
  httpGet:
    path: /health
    port: 8000
  initialDelaySeconds: 30
  periodSeconds: 10

readinessProbe:
  httpGet:
    path: /health
    port: 8000
  initialDelaySeconds: 5
  periodSeconds: 5
```

## Monitoring

### Metrics Collection

```python theme={null}
from prometheus_client import Counter, Histogram

# Track metrics
api_requests = Counter('api_requests_total', 'Total API requests')
response_time = Histogram('api_response_time', 'API response time')

@app.middleware("http")
async def track_metrics(request, call_next):
    start_time = time.time()
    response = await call_next(request)
    
    api_requests.inc()
    response_time.observe(time.time() - start_time)
    
    return response
```

### Logging Setup

```python theme={null}
import logging
import sys

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout),
        logging.FileHandler('cognition_engine.log')
    ]
)
```

## Scaling Considerations

### Horizontal Scaling

* Run multiple instances behind a load balancer
* Use Redis for shared session state
* Ensure database connection pooling

### Vertical Scaling

* Increase worker processes for CPU-bound tasks
* Add more memory for large data processing
* Use async processing for I/O-bound operations

### Database Connection Pooling

```python theme={null}
from sqlalchemy.pool import QueuePool

engine = create_engine(
    database_url,
    poolclass=QueuePool,
    pool_size=20,
    max_overflow=40
)
```

## Security

### API Key Management

<Warning>
  Never commit API keys to version control. Use environment variables or secret management services.
</Warning>

### Rate Limiting

```python theme={null}
from slowapi import Limiter
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)

@app.post("/track-answer")
@limiter.limit("100/minute")
async def track_answer(request: Request, ...):
    ...
```

### Data Encryption

* Use HTTPS for all API communication
* Encrypt sensitive data at rest
* Implement authentication and authorization

## Backup & Recovery

### Database Backups

```bash theme={null}
# Automated backup script
pg_dump $DATABASE_URL > backup_$(date +%Y%m%d).sql
```

### Recovery Procedures

1. Restore database from latest backup
2. Verify data integrity
3. Restart services
4. Monitor logs for errors

## Next Steps

<CardGroup cols={2}>
  <Card title="Integration Guide" icon="plug" href="/guides/integration">
    Learn how to integrate with your platform
  </Card>

  <Card title="API Reference" icon="code" href="/api/introduction">
    Explore the complete API documentation
  </Card>
</CardGroup>