# Use Cases and Tutorials

Real-world implementation examples and complete code samples for Cortex Linux.

---

## Table of Contents

1. [Automated Debugging](#automated-debugging)
2. [DevOps Optimization](#devops-optimization)
3. [Architecture Planning](#architecture-planning)
4. [Code Review Automation](#code-review-automation)
5. [System Monitoring and Analysis](#system-monitoring-and-analysis)
6. [Configuration Optimization](#configuration-optimization)
7. [Documentation Generation](#documentation-generation)

---

## Automated Debugging

### Scenario: Application Error Analysis

Automatically analyze error logs and provide actionable debugging suggestions.

#### Implementation

```python
#!/usr/bin/env python3
"""
Automated error log analysis using Cortex AI
"""

from cortex import AI
import sys
import json
from pathlib import Path
from datetime import datetime

class ErrorAnalyzer:
    def __init__(self):
        self.ai = AI()
    
    def analyze_error_log(self, log_path: str) -> dict:
        """Analyze error log file and return structured analysis."""
        log_content = Path(log_path).read_text()
        
        query = f"""
        Analyze this error log and provide:
        1. Error classification (type, severity)
        2. Root cause analysis
        3. Suggested fixes with code examples
        4. Prevention strategies
        
        Error log:
        {log_content}
        """
        
        result = self.ai.reason(query, context={
            "application": "web_server",
            "language": "Python",
            "environment": "production"
        })
        
        return {
            "timestamp": datetime.now().isoformat(),
            "log_file": log_path,
            "analysis": result.result,
            "confidence": result.confidence
        }
    
    def analyze_stack_trace(self, stack_trace: str) -> dict:
        """Analyze Python stack trace."""
        query = f"""
        Analyze this Python stack trace:
        {stack_trace}
        
        Provide:
        - Error location and type
        - Call chain analysis
        - Suggested fixes
        - Related documentation links
        """
        
        result = self.ai.debug(
            error_log=stack_trace,
            context={"language": "Python"}
        )
        
        return {
            "error_type": result.analysis.get("error_type"),
            "location": result.analysis.get("location"),
            "suggestions": result.suggestions,
            "code_fixes": result.code_fixes
        }

def main():
    analyzer = ErrorAnalyzer()
    
    if len(sys.argv) < 2:
        print("Usage: error_analyzer.py <log_file>")
        sys.exit(1)
    
    log_file = sys.argv[1]
    
    print(f"Analyzing {log_file}...")
    analysis = analyzer.analyze_error_log(log_file)
    
    print("\n" + "="*80)
    print("ERROR ANALYSIS")
    print("="*80)
    print(f"File: {analysis['log_file']}")
    print(f"Timestamp: {analysis['timestamp']}")
    print(f"Confidence: {analysis['confidence']:.2%}")
    print("\nAnalysis:")
    print(analysis['analysis'])
    
    # Save to file
    output_file = f"{log_file}.analysis.json"
    with open(output_file, 'w') as f:
        json.dump(analysis, f, indent=2)
    print(f"\nAnalysis saved to: {output_file}")

if __name__ == "__main__":
    main()
```

#### Bash Implementation

```bash
#!/bin/bash
# Automated error log analyzer

LOG_FILE="${1:-/var/log/app/error.log}"
OUTPUT_DIR="/var/log/app/analysis"

mkdir -p "$OUTPUT_DIR"

# Analyze error log
ANALYSIS=$(cortex-ai debug "$LOG_FILE" --format json)

# Extract key information
ERROR_TYPE=$(echo "$ANALYSIS" | jq -r '.analysis.error_type')
SEVERITY=$(echo "$ANALYSIS" | jq -r '.analysis.severity')
SUGGESTIONS=$(echo "$ANALYSIS" | jq -r '.suggestions[]')

# Generate report
REPORT_FILE="$OUTPUT_DIR/error_analysis_$(date +%Y%m%d_%H%M%S).md"

cat > "$REPORT_FILE" << EOF
# Error Analysis Report

**Timestamp**: $(date)
**Log File**: $LOG_FILE
**Error Type**: $ERROR_TYPE
**Severity**: $SEVERITY

## Suggested Fixes

$SUGGESTIONS

## Full Analysis

\`\`\`
$(echo "$ANALYSIS" | jq -r '.analysis')
\`\`\`
EOF

echo "Analysis complete: $REPORT_FILE"
```

#### Integration with Monitoring

```python
# Integration with monitoring system
import subprocess
from cortex import AI

def monitor_and_debug():
    """Monitor application and auto-debug on errors."""
    ai = AI()
    
    # Watch log file
    process = subprocess.Popen(
        ['tail', '-f', '/var/log/app/error.log'],
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )
    
    error_buffer = []
    
    for line in process.stdout:
        line = line.decode('utf-8')
        
        # Detect error lines
        if 'ERROR' in line or 'Exception' in line:
            error_buffer.append(line)
            
            # Analyze when buffer reaches threshold
            if len(error_buffer) >= 10:
                error_text = '\n'.join(error_buffer)
                
                result = ai.debug(
                    error_log=error_text,
                    context={"application": "monitored_app"}
                )
                
                # Send alert
                send_alert(result)
                
                error_buffer = []
```

---

## DevOps Optimization

### Scenario: Infrastructure Configuration Optimization

Automatically analyze and optimize infrastructure configurations (Ansible, Terraform, Kubernetes).

#### Ansible Playbook Optimization

```python
#!/usr/bin/env python3
"""
Optimize Ansible playbooks using Cortex AI
"""

from cortex import AI
from pathlib import Path
import yaml

class AnsibleOptimizer:
    def __init__(self):
        self.ai = AI()
    
    def optimize_playbook(self, playbook_path: str) -> dict:
        """Analyze and optimize Ansible playbook."""
        playbook_content = Path(playbook_path).read_text()
        
        result = self.ai.optimize(
            target="ansible_playbook",
            content=playbook_content,
            goals=["performance", "maintainability", "best_practices"]
        )
        
        return {
            "original": playbook_content,
            "optimizations": result.optimizations,
            "improved_playbook": self._apply_optimizations(
                playbook_content,
                result.optimizations
            )
        }
    
    def _apply_optimizations(self, original: str, optimizations: list) -> str:
        """Apply suggested optimizations to playbook."""
        # Implementation would apply code changes
        # This is a simplified version
        optimized = original
        for opt in optimizations:
            if 'optimized_code' in opt:
                # Apply optimization
                pass
        return optimized

# Example usage
optimizer = AnsibleOptimizer()
result = optimizer.optimize_playbook("deploy.yml")

print("Optimizations suggested:")
for opt in result['optimizations']:
    print(f"- {opt['description']}")
    print(f"  Improvement: {opt['improvement_estimate']}")
```

#### Terraform Configuration Analysis

```bash
#!/bin/bash
# Terraform configuration optimizer

TERRAFORM_DIR="${1:-.}"

# Analyze Terraform files
for tf_file in $(find "$TERRAFORM_DIR" -name "*.tf"); do
    echo "Analyzing $tf_file..."
    
    ANALYSIS=$(cortex-ai optimize "$tf_file" \
        --target terraform \
        --goals "cost_optimization,security,best_practices" \
        --format json)
    
    # Extract recommendations
    RECS=$(echo "$ANALYSIS" | jq -r '.optimizations[] | "\(.type): \(.description)"')
    
    echo "Recommendations for $tf_file:"
    echo "$RECS"
    echo ""
done

# Generate optimization report
cortex-ai plan "Optimize Terraform infrastructure" \
    --requirements "Reduce costs, improve security, follow best practices" \
    --config "$TERRAFORM_DIR" \
    --output terraform_optimization_plan.md
```

#### Kubernetes Resource Optimization

```python
#!/usr/bin/env python3
"""
Kubernetes resource optimization
"""

from cortex import AI
import yaml
import json

class K8sOptimizer:
    def __init__(self):
        self.ai = AI()
    
    def optimize_deployment(self, deployment_yaml: str) -> dict:
        """Optimize Kubernetes deployment."""
        deployment = yaml.safe_load(deployment_yaml)
        
        # Extract resource requests/limits
        resources = self._extract_resources(deployment)
        
        query = f"""
        Analyze this Kubernetes deployment and optimize:
        1. Resource requests and limits
        2. Replica count
        3. Health checks
        4. Security contexts
        
        Current configuration:
        {json.dumps(resources, indent=2)}
        """
        
        result = self.ai.optimize(
            target="kubernetes_deployment",
            content=deployment_yaml,
            goals=["resource_efficiency", "high_availability", "security"]
        )
        
        return {
            "current": deployment,
            "recommendations": result.optimizations,
            "optimized": yaml.safe_load(result.optimizations[0]['optimized_code'])
        }
    
    def _extract_resources(self, deployment: dict) -> dict:
        """Extract resource configuration."""
        containers = deployment.get('spec', {}).get('template', {}).get('spec', {}).get('containers', [])
        resources = {}
        for container in containers:
            resources[container['name']] = container.get('resources', {})
        return resources

# Example
optimizer = K8sOptimizer()
with open('deployment.yaml') as f:
    deployment_yaml = f.read()

result = optimizer.optimize_deployment(deployment_yaml)
print("Optimization recommendations:")
for rec in result['recommendations']:
    print(f"- {rec['description']}")
```

---

## Architecture Planning

### Scenario: System Design Assistance

Use Cortex AI to assist in system architecture design and technology selection.

#### Architecture Planning Tool

```python
#!/usr/bin/env python3
"""
Architecture planning assistant
"""

from cortex import AI
import json
from typing import Dict, List

class ArchitecturePlanner:
    def __init__(self):
        self.ai = AI()
    
    def design_system(self, requirements: Dict) -> Dict:
        """Design system architecture based on requirements."""
        query = f"""
        Design a system architecture based on these requirements:
        
        Functional Requirements:
        {requirements.get('functional', [])}
        
        Non-Functional Requirements:
        {requirements.get('non_functional', {})}
        
        Constraints:
        {requirements.get('constraints', [])}
        
        Provide:
        1. High-level architecture diagram (textual)
        2. Technology stack recommendations
        3. Component breakdown
        4. Data flow
        5. Security considerations
        6. Scalability strategy
        """
        
        result = self.ai.plan(
            task="Design system architecture",
            requirements=json.dumps(requirements),
            format="markdown"
        )
        
        return {
            "architecture": result.plan,
            "estimated_complexity": result.risk_level,
            "confidence": result.confidence
        }
    
    def compare_technologies(self, technologies: List[str], use_case: str) -> Dict:
        """Compare technology options for a use case."""
        query = f"""
        Compare these technologies for the use case: {use_case}
        
        Technologies: {', '.join(technologies)}
        
        Provide comparison on:
        1. Performance
        2. Scalability
        3. Ease of use
        4. Community support
        5. Cost
        6. Best fit recommendation
        """
        
        result = self.ai.reason(query)
        
        return {
            "comparison": result.result,
            "recommendation": self._extract_recommendation(result.result)
        }
    
    def _extract_recommendation(self, comparison_text: str) -> str:
        """Extract recommendation from comparison text."""
        # Simple extraction - could be improved with NLP
        if "recommend" in comparison_text.lower():
            # Extract recommendation
            pass
        return "See comparison above"

# Example usage
planner = ArchitecturePlanner()

requirements = {
    "functional": [
        "User authentication and authorization",
        "Real-time data processing",
        "RESTful API",
        "Web dashboard"
    ],
    "non_functional": {
        "scalability": "Handle 1M+ users",
        "availability": "99.9% uptime",
        "latency": "< 100ms API response"
    },
    "constraints": [
        "Must use open-source technologies",
        "Budget: $10k/month infrastructure",
        "Team size: 5 developers"
    ]
}

architecture = planner.design_system(requirements)
print(architecture['architecture'])

# Technology comparison
tech_comparison = planner.compare_technologies(
    ["PostgreSQL", "MongoDB", "Cassandra"],
    "Storing user profiles and session data"
)
print(tech_comparison['comparison'])
```

#### Complete Example: Microservices Architecture

```bash
#!/bin/bash
# Generate microservices architecture plan

REQUIREMENTS_FILE="requirements.json"

# Create requirements
cat > "$REQUIREMENTS_FILE" << EOF
{
  "application_type": "e-commerce platform",
  "expected_users": "100000",
  "features": [
    "User management",
    "Product catalog",
    "Shopping cart",
    "Payment processing",
    "Order management"
  ],
  "constraints": {
    "budget": "$5000/month",
    "team_size": 8,
    "timeline": "6 months"
  }
}
EOF

# Generate architecture plan
cortex-ai plan "Design microservices architecture for e-commerce platform" \
    --requirements "$REQUIREMENTS_FILE" \
    --format markdown \
    --output architecture_plan.md

# Generate technology recommendations
cortex-ai reason "Recommend technology stack for microservices e-commerce platform with 100k users, $5k/month budget" \
    --output tech_stack.md

echo "Architecture plan generated: architecture_plan.md"
echo "Technology recommendations: tech_stack.md"
```

---

## Code Review Automation

### Scenario: Automated Code Review

Automatically review code changes and provide feedback.

#### Git Hook Integration

```python
#!/usr/bin/env python3
"""
Pre-commit hook for automated code review
"""

from cortex import AI
import subprocess
import sys
from pathlib import Path

class CodeReviewer:
    def __init__(self):
        self.ai = AI()
    
    def review_diff(self, diff: str) -> dict:
        """Review git diff and provide feedback."""
        query = f"""
        Review this code diff and provide feedback on:
        1. Code quality and style
        2. Potential bugs
        3. Security issues
        4. Performance concerns
        5. Best practices
        
        Diff:
        {diff}
        """
        
        result = self.ai.reason(query, context={
            "review_type": "code_review",
            "severity": "critical"
        })
        
        return {
            "review": result.result,
            "confidence": result.confidence,
            "should_block": self._should_block_commit(result.result)
        }
    
    def _should_block_commit(self, review_text: str) -> bool:
        """Determine if commit should be blocked."""
        blocking_keywords = [
            "security vulnerability",
            "critical bug",
            "data leak",
            "SQL injection",
            "XSS vulnerability"
        ]
        
        review_lower = review_text.lower()
        return any(keyword in review_lower for keyword in blocking_keywords)

def main():
    reviewer = CodeReviewer()
    
    # Get staged changes
    result = subprocess.run(
        ['git', 'diff', '--cached'],
        capture_output=True,
        text=True
    )
    
    if not result.stdout:
        print("No changes to review")
        sys.exit(0)
    
    # Review changes
    review = reviewer.review_diff(result.stdout)
    
    print("="*80)
    print("CODE REVIEW")
    print("="*80)
    print(review['review'])
    print("="*80)
    
    if review['should_block']:
        print("\n⚠️  BLOCKING ISSUES DETECTED")
        print("Please address the issues above before committing.")
        sys.exit(1)
    else:
        print("\n✓ Review complete. No blocking issues.")
        sys.exit(0)

if __name__ == "__main__":
    main()
```

#### Setup Git Hook

```bash
#!/bin/bash
# Setup pre-commit hook

HOOK_FILE=".git/hooks/pre-commit"

cat > "$HOOK_FILE" << 'EOF'
#!/bin/bash
python3 /path/to/code_reviewer.py
EOF

chmod +x "$HOOK_FILE"
echo "Pre-commit hook installed"
```

#### Pull Request Review

```python
#!/usr/bin/env python3
"""
Automated PR review using Cortex AI
"""

from cortex import AI
import requests
import os

class PRReviewer:
    def __init__(self):
        self.ai = AI()
        self.github_token = os.getenv('GITHUB_TOKEN')
    
    def review_pr(self, repo: str, pr_number: int):
        """Review GitHub pull request."""
        # Fetch PR diff
        diff = self._fetch_pr_diff(repo, pr_number)
        
        # Review code
        review = self.ai.reason(
            f"Review this pull request diff:\n\n{diff}\n\n"
            "Provide feedback on code quality, bugs, security, and best practices."
        )
        
        # Post review comment
        self._post_review_comment(repo, pr_number, review.result)
    
    def _fetch_pr_diff(self, repo: str, pr_number: int) -> str:
        """Fetch PR diff from GitHub."""
        url = f"https://api.github.com/repos/{repo}/pulls/{pr_number}"
        headers = {"Authorization": f"token {self.github_token}"}
        
        response = requests.get(f"{url}.diff", headers=headers)
        return response.text
    
    def _post_review_comment(self, repo: str, pr_number: int, comment: str):
        """Post review comment to PR."""
        url = f"https://api.github.com/repos/{repo}/pulls/{pr_number}/comments"
        headers = {
            "Authorization": f"token {self.github_token}",
            "Accept": "application/vnd.github.v3+json"
        }
        
        # Post as review comment
        # Implementation details...
```

---

## System Monitoring and Analysis

### Scenario: Automated System Health Analysis

Continuously monitor system metrics and provide intelligent analysis.

#### System Metrics Analyzer

```python
#!/usr/bin/env python3
"""
System metrics monitoring and analysis
"""

from cortex import AI
import psutil
import json
from datetime import datetime
from typing import Dict

class SystemAnalyzer:
    def __init__(self):
        self.ai = AI()
    
    def collect_metrics(self) -> Dict:
        """Collect system metrics."""
        return {
            "timestamp": datetime.now().isoformat(),
            "cpu": {
                "percent": psutil.cpu_percent(interval=1),
                "count": psutil.cpu_count(),
                "per_cpu": psutil.cpu_percent(percpu=True, interval=1)
            },
            "memory": {
                "total": psutil.virtual_memory().total,
                "available": psutil.virtual_memory().available,
                "percent": psutil.virtual_memory().percent,
                "used": psutil.virtual_memory().used
            },
            "disk": {
                "total": psutil.disk_usage('/').total,
                "used": psutil.disk_usage('/').used,
                "percent": psutil.disk_usage('/').percent
            },
            "network": {
                "bytes_sent": psutil.net_io_counters().bytes_sent,
                "bytes_recv": psutil.net_io_counters().bytes_recv
            }
        }
    
    def analyze_metrics(self, metrics: Dict) -> Dict:
        """Analyze system metrics and provide recommendations."""
        metrics_json = json.dumps(metrics, indent=2)
        
        query = f"""
        Analyze these system metrics and provide:
        1. Health assessment
        2. Performance issues
        3. Resource bottlenecks
        4. Optimization recommendations
        5. Alerts for critical issues
        
        Metrics:
        {metrics_json}
        """
        
        result = self.ai.reason(query)
        
        return {
            "metrics": metrics,
            "analysis": result.result,
            "confidence": result.confidence,
            "alerts": self._extract_alerts(result.result)
        }
    
    def _extract_alerts(self, analysis: str) -> list:
        """Extract alert-worthy issues from analysis."""
        alerts = []
        # Simple keyword-based extraction
        if "critical" in analysis.lower() or "urgent" in analysis.lower():
            alerts.append("Critical issue detected")
        return alerts

# Continuous monitoring
analyzer = SystemAnalyzer()

while True:
    metrics = analyzer.collect_metrics()
    analysis = analyzer.analyze_metrics(metrics)
    
    print(f"[{analysis['metrics']['timestamp']}] System Analysis:")
    print(analysis['analysis'])
    
    if analysis['alerts']:
        print("⚠️  ALERTS:", analysis['alerts'])
    
    time.sleep(300)  # Analyze every 5 minutes
```

---

## Configuration Optimization

### Scenario: Nginx Configuration Optimization

```bash
#!/bin/bash
# Nginx configuration optimizer

NGINX_CONFIG="/etc/nginx/nginx.conf"
BACKUP_DIR="/etc/nginx/backups"

# Create backup
mkdir -p "$BACKUP_DIR"
cp "$NGINX_CONFIG" "$BACKUP_DIR/nginx.conf.$(date +%Y%m%d_%H%M%S)"

# Analyze and optimize
OPTIMIZATION=$(cortex-ai optimize "$NGINX_CONFIG" \
    --target nginx \
    --goals "performance,security" \
    --format json)

# Extract optimized configuration
OPTIMIZED_CONFIG=$(echo "$OPTIMIZATION" | jq -r '.optimizations[0].optimized_code')

# Save optimized config
echo "$OPTIMIZED_CONFIG" > "$NGINX_CONFIG.optimized"

# Test configuration
if nginx -t -c "$NGINX_CONFIG.optimized"; then
    echo "Optimized configuration is valid"
    echo "Review changes: diff $NGINX_CONFIG $NGINX_CONFIG.optimized"
    echo "Apply with: cp $NGINX_CONFIG.optimized $NGINX_CONFIG && systemctl reload nginx"
else
    echo "Optimized configuration has errors"
    echo "Keeping original configuration"
fi
```

---

## Documentation Generation

### Scenario: Auto-generate API Documentation

```python
#!/usr/bin/env python3
"""
Generate API documentation from code
"""

from cortex import AI
import ast
import inspect

class DocGenerator:
    def __init__(self):
        self.ai = AI()
    
    def generate_docstring(self, function_code: str) -> str:
        """Generate docstring for a function."""
        query = f"""
        Generate a comprehensive docstring for this Python function following Google style:
        
        {function_code}
        
        Include:
        - Description
        - Args with types
        - Returns with type
        - Raises
        - Examples
        """
        
        result = self.ai.reason(query)
        return result.result
    
    def generate_api_docs(self, module_path: str) -> str:
        """Generate complete API documentation for a module."""
        # Parse module and extract functions/classes
        # Generate documentation for each
        # Format as Markdown
        
        # Implementation...
        pass

# Example
generator = DocGenerator()

function_code = """
def process_user_data(user_id: int, data: dict) -> bool:
    # Process user data
    return True
"""

docstring = generator.generate_docstring(function_code)
print(docstring)
```

---

## Next Steps

- **Explore More Examples**: Check GitHub repository for additional examples
- **API Reference**: [AI Integration Guide](AI-Integration.md)
- **Contributing**: [Developer Documentation](Developer-Documentation.md)

---

*Last updated: 2024*