# Enterprise Features

Advanced features for enterprise deployments.

## Audit Logging

All AI actions are logged for compliance and debugging.

### Configuration

```yaml
enterprise:
  audit_log: true
  audit_path: "/var/log/ai-review/"
```

### Log Format

Logs are stored as JSONL (JSON Lines) with daily rotation:

```
/var/log/ai-review/audit-2024-01-15.jsonl
```

Each line is a JSON object:

```json
{
  "timestamp": "2024-01-15T10:30:45.123Z",
  "action": "review_pr",
  "agent": "PRAgent",
  "repository": "org/repo",
  "success": true,
  "details": {
    "pr_number": 123,
    "severity": "MEDIUM",
    "issues_found": 3
  }
}
```

### Actions Logged

| Action | Description |
|--------|-------------|
| `review_pr` | PR review completed |
| `triage_issue` | Issue triaged |
| `llm_call` | LLM API call made |
| `comment_posted` | Comment created/updated |
| `labels_applied` | Labels added |
| `security_scan` | Security scan completed |

### Querying Logs

```python
from enterprise import get_audit_logger

logger = get_audit_logger()

# Get all logs for a date range
logs = logger.get_logs(
    start_date="2024-01-01",
    end_date="2024-01-31",
    action="review_pr",
    repository="org/repo",
)

# Generate summary report
report = logger.generate_report(
    start_date="2024-01-01",
    end_date="2024-01-31",
)
print(f"Total events: {report['total_events']}")
print(f"Success rate: {report['success_rate']:.1%}")
```

---

## Metrics & Observability

Track performance and usage metrics.

### Configuration

```yaml
enterprise:
  metrics_enabled: true
```

### Available Metrics

**Counters:**
- `ai_review_requests_total` - Total requests processed
- `ai_review_requests_success` - Successful requests
- `ai_review_requests_failed` - Failed requests
- `ai_review_llm_calls_total` - Total LLM API calls
- `ai_review_llm_tokens_total` - Total tokens consumed
- `ai_review_comments_posted` - Comments posted
- `ai_review_security_findings` - Security issues found

**Gauges:**
- `ai_review_active_requests` - Currently processing

**Histograms:**
- `ai_review_request_duration_seconds` - Request latency
- `ai_review_llm_duration_seconds` - LLM call latency

### Getting Metrics

```python
from enterprise import get_metrics

metrics = get_metrics()

# Get summary
summary = metrics.get_summary()
print(f"Total requests: {summary['requests']['total']}")
print(f"Success rate: {summary['requests']['success_rate']:.1%}")
print(f"Avg latency: {summary['latency']['avg_ms']:.0f}ms")
print(f"P95 latency: {summary['latency']['p95_ms']:.0f}ms")
print(f"LLM tokens used: {summary['llm']['tokens']}")

# Export Prometheus format
prometheus_output = metrics.export_prometheus()
```

### Prometheus Integration

Expose metrics endpoint:

```python
from flask import Flask
from enterprise import get_metrics

app = Flask(__name__)

@app.route("/metrics")
def metrics():
    return get_metrics().export_prometheus()
```

---

## Rate Limiting

Prevent API overload and manage costs.

### Configuration

```yaml
enterprise:
  rate_limit:
    requests_per_minute: 30
    max_concurrent: 4
```

### Built-in Rate Limiting

The `BaseAgent` class includes automatic rate limiting:

```python
class BaseAgent:
    def __init__(self):
        self._min_request_interval = 1.0  # seconds

    def _rate_limit(self):
        elapsed = time.time() - self._last_request_time
        if elapsed < self._min_request_interval:
            time.sleep(self._min_request_interval - elapsed)
```

---

## Queue Management

The dispatcher handles concurrent execution:

```python
dispatcher = Dispatcher(max_workers=4)
```

For high-volume environments, use async dispatch:

```python
future = dispatcher.dispatch_async(event_type, event_data, owner, repo)
# Continue with other work
result = future.result()  # Block when needed
```

---

## Security Considerations

### Token Permissions

Minimum required permissions for `AI_REVIEW_TOKEN`:
- `repo:read` - Read repository contents
- `repo:write` - Create branches (if needed)
- `issue:read` - Read issues and PRs
- `issue:write` - Create comments, labels

### Network Isolation

For air-gapped environments, use Ollama:

```yaml
provider: ollama

# Internal network address
# Set via environment: OLLAMA_HOST=http://ollama.internal:11434
```

### Data Privacy

By default:
- Code is sent to LLM provider for analysis
- Review comments are stored in Gitea
- Audit logs are stored locally

For sensitive codebases:
1. Use self-hosted Ollama
2. Disable external LLM providers
3. Review audit log retention policies