Cognitive Engine Wiki

Modular cognitive architecture for advanced AI agents and research

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Deployment

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Deployment Guide

This guide covers deploying the Cognitive Engine in various environments.

Deployment Overview
System Requirements
Local Deployment
Docker Deployment
Cloud Deployment
Production Checklist
Monitoring and Maintenance
Scaling Strategies
Backup and Recovery

Deployment Overview

The Cognitive Engine can be deployed in various configurations:

Local/Development: Single instance for development and testing
Docker: Containerized deployment for consistency
Cloud: Cloud-native deployment on AWS, GCP, or Azure
Distributed: Multi-instance deployment for high availability

System Requirements

Minimum Requirements

CPU: 2 cores
RAM: 4 GB
Storage: 10 GB
Python: 3.9+
OS: Linux, macOS, or Windows

Recommended Requirements

CPU: 4+ cores
RAM: 8+ GB
Storage: 20+ GB SSD
Python: 3.10+
OS: Linux (Ubuntu 20.04+ recommended)

Network Requirements

Outbound: HTTPS access to LLM APIs (OpenAI, Anthropic)
Inbound: Port 8000 (if dashboard enabled)
Bandwidth: 1+ Mbps for API calls

Local Deployment

Development Setup

# Clone repository
git clone <repository-url>
cd cognitive_engine

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your settings

# Run tests
python run.py test

# Start engine
python run.py

Production-Ready Local Setup

# Install system dependencies
sudo apt-get update
sudo apt-get install python3.10 python3.10-venv sqlite3

# Create dedicated user
sudo useradd -r -s /bin/false cognitive_engine

# Set up directory structure
sudo mkdir -p /opt/cognitive_engine
sudo chown cognitive_engine:cognitive_engine /opt/cognitive_engine

# Install application
cd /opt/cognitive_engine
sudo -u cognitive_engine python3.10 -m venv venv
sudo -u cognitive_engine venv/bin/pip install -r requirements.txt

# Configure systemd service
sudo tee /etc/systemd/system/cognitive-engine.service > /dev/null <<EOF
[Unit]
Description=Cognitive Engine Service
After=network.target

[Service]
Type=simple
User=cognitive_engine
WorkingDirectory=/opt/cognitive_engine
Environment="PATH=/opt/cognitive_engine/venv/bin"
ExecStart=/opt/cognitive_engine/venv/bin/python run.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

# Enable and start service
sudo systemctl enable cognitive-engine
sudo systemctl start cognitive-engine

Docker Deployment

Dockerfile

Create a Dockerfile in the project root:

FROM python:3.10-slim

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements first for caching
COPY requirements.txt .

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

# Copy application code
COPY . .

# Create non-root user
RUN useradd -m -u 1000 cognitive_engine && \
    chown -R cognitive_engine:cognitive_engine /app
USER cognitive_engine

# Expose dashboard port
EXPOSE 8000

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD python -c "import requests; requests.get('http://localhost:8000/health')" || exit 1

# Run application
CMD ["python", "run.py"]

docker-compose.yml

Create a docker-compose.yml for easy deployment:

version: '3.8'

services:
  cognitive-engine:
    build: .
    container_name: cognitive-engine
    restart: unless-stopped
    ports:

      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
      - DEFAULT_LLM_PROVIDER=${DEFAULT_LLM_PROVIDER:-openai}
      - MIN_ITERATIONS=${MIN_ITERATIONS:-3}
      - MAX_ITERATIONS=${MAX_ITERATIONS:-50}
      - ENABLE_DASHBOARD=${ENABLE_DASHBOARD:-true}
      - DASHBOARD_PORT=${DASHBOARD_PORT:-8000}
      - LOG_LEVEL=${LOG_LEVEL:-INFO}
    volumes:
      - ./data:/app/data
      - ./cognitive_engine.db:/app/cognitive_engine.db
      - ./cognitive_engine.log:/app/cognitive_engine.log
    networks:
      - cognitive-network
    healthcheck:
      test: ["CMD", "python", "-c", "import requests; requests.get('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

networks:
  cognitive-network:
    driver: bridge

Building and Running

# Build image
docker build -t cognitive-engine:latest .

# Run with docker-compose
docker-compose up -d

# Run standalone
docker run -d \
  --name cognitive-engine \
  -p 8000:8000 \
  -e OPENAI_API_KEY=your_key \
  -e ANTHROPIC_API_KEY=your_key \
  -v $(pwd)/data:/app/data \
  cognitive-engine:latest

# View logs
docker logs -f cognitive-engine

# Stop container
docker stop cognitive-engine
docker rm cognitive-engine

Cloud Deployment

AWS Deployment

Using EC2

Launch EC2 Instance
AMI: Ubuntu 20.04 LTS
Instance Type: t3.medium (2 vCPU, 4 GB RAM)
Storage: 20 GB GP2
Security Groups
Inbound: SSH (22), HTTP (80), HTTPS (443), Custom (8000)
Outbound: All traffic
Deploy Application

:::bash

SSH into instance

ssh -i your-key.pem ubuntu@your-instance-ip

Update system

sudo apt-get update

Install Docker

sudo apt-get install docker.io docker-compose

Clone repository

git clone <repository-url>
cd cognitive_engine</repository-url>

Configure environment

cp .env.example .env
nano .env # Add your API keys

Deploy

docker-compose up -d

Using ECS

Create ECR Repository

:::bash
aws ecr create-repository --repository-name cognitive-engine
Build and Push Image

:::bash

Login to ECR

aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <account-id>.dkr.ecr.us-east-1.amazonaws.com</account-id>

Build and tag

docker build -t cognitive-engine .
docker tag cognitive-engine:latest <account-id>.dkr.ecr.us-east-1.amazonaws.com/cognitive-engine:latest</account-id>

Push

docker push <account-id>.dkr.ecr.us-east-1.amazonaws.com/cognitive-engine:latest</account-id>
Create Task Definition

:::json
{
"family": "cognitive-engine",
"networkMode": "awsvpc",
"requiresCompatibilities": ["FARGATE"],
"cpu": "512",
"memory": "1024",
"containerDefinitions": [
{
"name": "cognitive-engine",
"image": "<account-id>.dkr.ecr.us-east-1.amazonaws.com/cognitive-engine:latest",
"portMappings": [
{
"containerPort": 8000,
"protocol": "tcp"
}
],
"environment": [
{
"name": "OPENAI_API_KEY",
"value": "your-key"
},
{
"name": "ANTHROPIC_API_KEY",
"value": "your-key"
}
],
"logConfiguration": {
"logDriver": "awslogs",
"options": {
"awslogs-group": "/ecs/cognitive-engine",
"awslogs-region": "us-east-1",
"awslogs-stream-prefix": "ecs"
}
}
}
]
}</account-id>

Google Cloud Platform Deployment

Using Cloud Run

# Build and deploy
gcloud builds submit --tag gcr.io/PROJECT_ID/cognitive-engine

# Deploy to Cloud Run
gcloud run deploy cognitive-engine \
  --image gcr.io/PROJECT_ID/cognitive-engine \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated \
  --set-env-vars OPENAI_API_KEY=your_key,ANTHROPIC_API_KEY=your_key

Using GKE

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cognitive-engine
spec:
  replicas: 2
  selector:
    matchLabels:
      app: cognitive-engine
  template:
    metadata:
      labels:
        app: cognitive-engine
    spec:
      containers:

      - name: cognitive-engine
        image: gcr.io/PROJECT_ID/cognitive-engine:latest
        ports:
        - containerPort: 8000
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: openai-key
        - name: ANTHROPIC_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: anthropic-key
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"

:::bash
# Apply deployment
kubectl apply -f k8s/deployment.yaml

# Create service
kubectl expose deployment cognitive-engine --type=LoadBalancer --port 8000

Azure Deployment

Using Azure Container Instances

# Create resource group
az group create --name cognitive-engine-rg --location eastus

# Create container instance
az container create \
  --resource-group cognitive-engine-rg \
  --name cognitive-engine \
  --image cognitive-engine:latest \
  --cpu 2 \
  --memory 4 \
  --ports 8000 \
  --environment-variables \
    OPENAI_API_KEY=your_key \
    ANTHROPIC_API_KEY=your_key

Production Checklist

Pre-Deployment

[ ] All tests passing
[ ] Code reviewed and approved
[ ] Documentation updated
[ ] Security audit completed
[ ] Performance benchmarks met
[ ] Backup strategy in place
[ ] Monitoring configured
[ ] Alerting configured
[ ] Rollback plan documented

Configuration

[ ] Environment variables set correctly
[ ] API keys secured (use secrets manager)
[ ] Log level set to INFO or WARNING
[ ] Debug features disabled
[ ] Dashboard disabled or secured
[ ] Memory limits configured
[ ] Iteration limits appropriate
[ ] Database backup configured

Security

[ ] HTTPS enabled
[ ] Firewall rules configured
[ ] API keys not in code
[ ] Secrets manager configured
[ ] Access controls in place
[ ] Authentication configured (if needed)
[ ] Rate limiting configured
[ ] Input validation enabled

Performance

[ ] Load testing completed
[ ] Caching configured
[ ] Database indexed
[ ] Connection pooling configured
[ ] CDN configured (if applicable)
[ ] Autoscaling configured (if applicable)
[ ] Monitoring dashboards set up
[ ] Performance baselines established

Reliability

[ ] Health checks configured
[ ] Auto-restart configured
[ ] Load balancing configured
[ ] High availability setup
[ ] Disaster recovery plan
[ ] Backup schedule configured
[ ] Monitoring alerts configured
[ ] On-call rotation established

Monitoring and Maintenance

Health Checks

# Add to dashboard/server.py
@app.get("/health")
async def health_check():
    """Health check endpoint."""
    return {
        "status": "healthy",
        "timestamp": datetime.now().isoformat(),
        "version": "1.0.0"
    }

Metrics to Monitor

System Metrics
CPU usage
Memory usage
Disk usage
Network I/O
Application Metrics
Request rate
Response time
Error rate
Active connections
Cognitive Metrics
Average iterations per query
Average confidence scores
Memory growth rate
Learning effectiveness

Logging

Configure centralized logging:

# Use structured logging
import json
import logging

class JsonFormatter(logging.Formatter):
    def format(self, record):
        log_obj = {
            "timestamp": self.formatTime(record),
            "level": record.levelname,
            "message": record.getMessage(),
            "module": record.name,
            "line": record.lineno
        }
        if hasattr(record, 'extra'):
            log_obj.update(record.extra)
        return json.dumps(log_obj)

Alerting

Set up alerts for:

High error rate (> 5%)
High response time (> 10s)
High memory usage (> 80%)
High CPU usage (> 90%)
Disk space low (< 10%)
API key quota exceeded
Database connection failures

Scaling Strategies

Horizontal Scaling

Run multiple instances behind a load balancer:

# docker-compose.yml for scaling
version: '3.8'

services:
  cognitive-engine:
    image: cognitive-engine:latest
    deploy:
      replicas: 3
    environment:

      - ENABLE_DASHBOARD=false  # Disable dashboard on workers

Vertical Scaling

Increase resources for single instance:

services:
  cognitive-engine:
    deploy:
      resources:
        limits:
          cpus: '4'
          memory: 8G
        reservations:
          cpus: '2'
          memory: 4G

Database Scaling

For high-volume deployments:

Use PostgreSQL instead of SQLite
Implement read replicas
Use connection pooling
Cache frequently accessed data

Caching Strategy

Implement caching for:

LLM API responses
Thought graph queries
Memory lookups
Pattern extraction results

:::python

Example Redis caching

import redis

redis_client = redis.Redis(host='localhost', port=6379, db=0)

def cached_llm_call(prompt):
cache_key = f"llm:{hash(prompt)}"
cached = redis_client.get(cache_key)
if cached:
return cached.decode('utf-8')
```
result = llm_client.generate(prompt)
redis_client.setex(cache_key, 3600, result)
return result
```

Backup and Recovery

Database Backup

# Automated backup script
#!/bin/bash
BACKUP_DIR="/backups/cognitive-engine"
DATE=$(date +%Y%m%d_%H%M%S)
DB_PATH="/app/cognitive_engine.db"

mkdir -p $BACKUP_DIR

# Backup database
cp $DB_PATH $BACKUP_DIR/cognitive_engine_$DATE.db

# Compress
gzip $BACKUP_DIR/cognitive_engine_$DATE.db

# Keep last 7 days
find $BACKUP_DIR -name "*.db.gz" -mtime +7 -delete

Configuration Backup

# Backup environment configuration
cp .env $BACKUP_DIR/.env_$DATE.backup

Recovery Procedure

Stop the service
Restore database from backup
Restore configuration
Verify integrity
Start the service
Run health checks

Disaster Recovery

Store backups in multiple locations
Test recovery procedures regularly
Document recovery steps
Maintain recovery time objectives (RTO)
Maintain recovery point objectives (RPO)

Security Considerations

API Key Management

Never commit API keys to version control
Use environment variables or secrets manager
Rotate keys regularly
Monitor key usage
Revoke compromised keys immediately

Network Security

Use HTTPS for all communications
Implement rate limiting
Use firewall rules
Configure VPC/network segmentation
Use private networks where possible

Application Security

Validate all inputs
Sanitize outputs
Implement authentication (if needed)
Use parameterized queries
Keep dependencies updated
Run security scans

Data Security

Encrypt sensitive data at rest
Encrypt data in transit
Implement access controls
Audit data access
Comply with data regulations

Troubleshooting Deployment Issues

Container Won't Start

Symptoms: Container exits immediately or won't start

Solutions:

Check logs: docker logs <container-name>
Verify environment variables
Check resource limits
Verify API keys are valid
Check network connectivity

High Memory Usage

Symptoms: Memory usage grows continuously

Solutions:

Check memory limits in configuration
Implement memory cleanup
Check for memory leaks
Reduce MAX_MEMORY_SIZE
Restart service periodically

Slow Performance

Symptoms: Slow response times

Solutions:

Check system resources
Implement caching
Optimize database queries
Reduce iteration limits
Use faster LLM provider

API Rate Limits

Symptoms: API calls failing with rate limit errors

Solutions:

Implement exponential backoff
Cache responses
Use multiple API keys
Reduce request frequency
Upgrade API plan

Support

For deployment issues:

Email: autobotsolution@gmail.com
Address: Flushing MI
Check logs: cognitive_engine.log
Review this documentation
Check GitHub issues

Cognitive Engine Wiki

Modular cognitive architecture for advanced AI agents and research

Deployment

Deployment Guide

Table of Contents

Deployment Overview

System Requirements

Minimum Requirements

Recommended Requirements

Network Requirements

Local Deployment

Development Setup

Production-Ready Local Setup

Docker Deployment

Dockerfile

docker-compose.yml

Building and Running

Cloud Deployment

AWS Deployment

Using EC2

SSH into instance

Update system

Install Docker

Clone repository

Configure environment

Deploy

Using ECS

Login to ECR

Build and tag

Push

Google Cloud Platform Deployment

Using Cloud Run

Using GKE

Azure Deployment

Using Azure Container Instances

Production Checklist

Pre-Deployment

Configuration

Security

Performance

Reliability

Monitoring and Maintenance

Health Checks

Metrics to Monitor

Logging

Alerting

Scaling Strategies

Horizontal Scaling

Vertical Scaling

Database Scaling

Caching Strategy

Example Redis caching

Backup and Recovery

Database Backup

Configuration Backup

Recovery Procedure

Disaster Recovery

Security Considerations

API Key Management

Network Security

Application Security

Data Security

Troubleshooting Deployment Issues

Container Won't Start

High Memory Usage

Slow Performance

API Rate Limits

Support