Backend:
- Add smart provider fallback when selected model's provider isn't configured
- Automatically switch to a model from a configured provider instead of failing
- Log warning when fallback occurs for visibility
Frontend (AISettings.tsx):
- Add helper functions to check if model's provider is configured
- Group model dropdown: configured providers first, unconfigured marked with ⚠️
- Add inline warning when selecting model from unconfigured provider
- Validate on save that model's provider is configured (or being added)
- Warn before clearing last configured provider (would disable AI)
- Warn before clearing provider that current model uses
- Add patrol interval validation (must be 0 or >= 10 minutes)
- Show red border + inline error for invalid patrol intervals 1-9
- Update patrol interval hint: '(0=off, 10+ to enable)'
These changes prevent confusing '500 Internal Server Error' and
'AI is not enabled or configured' errors when model/provider mismatch.
- Add DOMPurify sanitization for AI chat markdown rendering (XSS fix)
- Configure DOMPurify to add target=_blank and rel=noopener to links
- Update system prompt to align with command approval policy
- Clarify safe vs destructive commands in prompt
- Improve patrol auto-fix mode guidance with safe operation list
- Add verification requirements for auto-fix actions
- Update observe-only mode to be clearer about read-only restrictions
Create internal/ai/correlation package:
1. Correlation Detector (detector.go):
- Tracks events across resources
- Detects when events on one resource follow events on another
- Calculates average delay between correlated events
- Confidence scoring based on occurrence count
- Persists to ai_correlations.json
2. Features:
- GetCorrelations() - All detected relationships
- GetCorrelationsForResource() - Relationships for one resource
- GetDependencies() - What resources depend on this one
- GetDependsOn() - What this resource depends on
- PredictCascade() - Predict what will be affected
- FormatForContext() - AI-consumable summary
3. Integration:
- Wire to alert history in router startup
- Map alert types to correlation event types
- Add correlation context to enriched AI context
Example AI context now includes:
'When local-zfs experiences high usage, database often follows within 5 minutes'
This enables the AI to understand infrastructure dependencies
and predict cascade failures.
All tests passing.
