Complete Phase 2 baseline integration:
- Add baseline_exports.go for clean type aliasing
- Wire baseline store initialization into StartPatrol
- Implement startBaselineLearning background loop
- Runs initial learning after 5 min delay
- Updates baselines every hour from metrics history
- Learns from 7 days of data for nodes, VMs, containers
- Add SetBaselineStore methods throughout the chain
(Router -> AIHandler -> Service -> PatrolService)
- Persists baselines to data directory as JSON
The baseline learning loop:
1. Starts automatically when AI patrol starts
2. Queries metrics history for all resources
3. Computes mean, stddev, percentiles for cpu/memory/disk
4. Saves baselines to disk for durability
5. Anomaly detection uses these baselines in context builder
All tests passing.
Phase 1 of Pulse AI differentiation:
- Create internal/ai/context package with types, trends, builder, formatter
- Implement linear regression for trend computation (growing/declining/stable/volatile)
- Add storage capacity predictions (predicts days until 90% and 100%)
- Wire MetricsHistory from monitor to patrol service
- Update patrol to use buildEnrichedContext instead of basic summary
- Update patrol prompt to reference trend indicators and predictions
This gives the AI awareness of historical patterns, enabling it to:
- Identify resources with concerning growth rates
- Predict capacity exhaustion before it happens
- Distinguish between stable high usage vs growing problems
- Provide more actionable, time-aware insights
All tests passing. Falls back to basic summary if metrics history unavailable.
- Changed patrol schedule from preset dropdown to freeform number input
- Users can now set any interval (min 10 minutes, max 7 days, or 0 to disable)
- Added patrol_interval_minutes to API request/response (preset is now deprecated)
- Backend validates: min 10 minutes when enabled, max 10080 (7 days)
- Frontend shows human-readable duration next to input (e.g., '6h', '2h 30m')
Also improved Auto-Fix Mode safety:
- Removed '(recommended)' from preset options (was subjective)
- Added 'I understand the risks' acknowledgement checkbox
- Toggle is disabled until user explicitly acknowledges the risks
- Shows prominent warning when Auto-Fix is enabled
- Acknowledgement is session-based (must re-acknowledge on page reload)
- Add clear_anthropic_key, clear_openai_key, clear_deepseek_key, clear_ollama_url flags to API
- Backend handles clearing with confirmation prompt
- Each provider accordion shows Test and Clear buttons when configured
- Clear button requires confirmation before removing credentials
- Frontend automatically refreshes settings after clearing
- Add /api/ai/test/{provider} endpoint for testing individual providers
- Add 'Test' button to each provider accordion (visible when configured)
- Shows test result inline (success/error message)
- Update help links with direct URLs to API key pages:
- Anthropic: console.anthropic.com/settings/keys
- OpenAI: platform.openai.com/api-keys
- DeepSeek: platform.deepseek.com/api_keys
- Ollama: ollama.ai
Backend:
- Add per-provider API key fields to AIConfig (AnthropicAPIKey, OpenAIAPIKey, DeepSeekAPIKey, OllamaBaseURL, OpenAIBaseURL)
- Add NewForProvider() and NewForModel() factory functions for multi-provider instantiation
- Update ListModels() to aggregate models from all configured providers with provider:model format
- Update Execute/ExecuteStream to dynamically create provider based on selected model
- Update TestConnection to use multi-provider aware provider creation
- Add helper functions: HasProvider(), GetConfiguredProviders(), GetAPIKeyForProvider(), GetBaseURLForProvider(), ParseModelString(), FormatModelString()
Frontend:
- Remove legacy single-provider UI (provider grid, single API key input, single base URL)
- Add accordion-style UI for configuring all providers independently
- Add model grouping by provider in selectors using optgroup
- Update AIChat model dropdown with grouped provider sections
- Add helper functions for parsing provider from model ID and grouping models
API:
- Add multi-provider fields to AISettingsResponse and AISettingsUpdateRequest
- Add /api/ai/models endpoint for dynamic model listing
- Update settings handlers for per-provider credential management
Users can now:
1. View all suppression rules (both from dismissed findings and manually created)
2. Create manual rules like 'ignore performance issues on debian-go'
3. Delete rules when they want alerts to come back
Backend:
- Added SuppressionRule type for user-defined rules
- Added suppressionRules storage to FindingsStore
- Added AddSuppressionRule/GetSuppressionRules/DeleteSuppressionRule methods
- Added isSuppressedInternal check for manual rules
- Added API handlers and routes for /api/ai/patrol/suppressions
Frontend:
- Added SuppressionRule interface
- Added getSuppressionRules/addSuppressionRule/deleteSuppressionRule API functions
- Added getDismissedFindings for viewing dismissed findings
Example usage:
POST /api/ai/patrol/suppressions
{
'resource_id': 'debian-go',
'category': 'performance',
'description': 'Dev container runs hot - expected'
}
Implements a comprehensive feedback system that allows the LLM to 'remember'
user decisions about findings, preventing repetitive/annoying alerts.
Backend changes:
- Extended Finding struct with dismissed_reason, user_note, times_raised, suppressed
- Added Dismiss(), Suppress(), SetUserNote(), IsSuppressed() methods to FindingsStore
- Added GetDismissedForContext() to format dismissed findings for LLM context
- Enhanced buildPatrolPrompt() to inject user feedback context
- Added POST /api/ai/patrol/dismiss and /api/ai/patrol/suppress endpoints
- Updated IsActive() to exclude suppressed findings
Frontend changes:
- Added Dismiss dropdown with options: Not an Issue, Expected Behavior, Will Fix Later
- Added Never Alert Again option for permanent suppression
- Expected Behavior prompts for optional note to help LLM understand context
- Added visual badges: recurrence count (×N), dismissed status, suppressed indicator
- Display user notes in expanded finding view
Also fixes:
- Fixed 403 error on Run Patrol (compilation errors from partial refactoring)
- Removed non-LLM patrol checks - patrol now uses LLM analysis only
- Fixed function signature mismatches in alert_triggered.go
The LLM now receives context about previously dismissed findings and is
instructed not to re-raise them unless severity has significantly worsened.
- Add alert-triggered AI analysis for real-time incident response
- Implement patrol history persistence across restarts
- Add patrol schedule configuration UI in AI Settings
- Enhance AIChat with patrol status and manual trigger controls
- Add resource store improvements for AI context building
- Expand Alerts page with AI-powered analysis integration
- Add Vite proxy config for AI API endpoints
- Support both Anthropic and OpenAI providers with streaming
Keep only the simple AI-powered approach:
- set_resource_url tool lets AI save discovered URLs
- Users ask AI directly: 'Find URLs for my containers'
- AI uses its intelligence to discover and set URLs
Removed:
- URLDiscoveryService (rigid port scanning)
- Bulk discovery API endpoints
- Frontend discovery button
The AI itself is smart enough to iterate through resources
and discover URLs when asked.
- Add URLDiscoveryService for scanning all resources at once
- Scans common web ports (80, 443, 8080, 8096, 3000, etc.)
- Automatically saves discovered URLs to resource metadata
- Add API endpoints for start/status/cancel discovery
- Progress tracking with results reporting
Endpoints:
- POST /api/ai/discover-urls/start - Start bulk discovery
- GET /api/ai/discover-urls/status - Check progress
- POST /api/ai/discover-urls/cancel - Cancel discovery
- Add MetadataProvider interface for AI to update resource URLs
- Add set_resource_url tool to AI service
- Wire up metadata stores to AI service via router
- Add URL discovery guidance to AI system prompt
- AI can now inspect guests/containers/hosts for web services
and automatically save discovered URLs to Pulse metadata
Usage: Ask the AI 'Find the web URL for this container' and it will:
1. Check for listening ports and web servers
2. Get the IP address
3. Verify the URL works
4. Save it to Pulse for quick dashboard access
- Add host metadata API for custom URL editing on hosts page
- Enhance AI routing with unified resource provider lookup
- Add encryption key watcher script for debugging key issues
- Improve AI service with better command timeout handling
- Update dev environment workflow with key monitoring docs
- Fix resource store deduplication logic
- Add Claude OAuth authentication support with hybrid API key/OAuth flow
- Implement Docker container historical metrics in backend and charts API
- Add CEPH cluster data collection and new Ceph page
- Enhance RAID status display with detailed tooltips and visual indicators
- Fix host deduplication logic with Docker bridge IP filtering
- Fix NVMe temperature collection in host agent
- Add comprehensive test coverage for new features
- Improve frontend sparklines and metrics history handling
- Fix navigation issues and frontend reload loops
This commit implements the Unified Resource Architecture for AI-first
infrastructure management. Key features:
Phase 1 - Backend Unification:
- New unified Resource type with 9 resource types, 7 platforms, 7 statuses
- Resource store with identity-based deduplication (hostname, machineID, IP)
- 8 converter functions (FromNode, FromVM, FromContainer, etc.)
- REST API endpoints: /api/resources, /api/resources/stats, /api/resources/{id}
- 28 comprehensive unit tests
Phase 2 - AI Context Enhancement:
- Unified context builder for AI system prompts
- Cross-platform query methods: GetTopByCPU, GetTopByMemory, GetTopByDisk
- Resource correlation: GetRelated (parent, children, siblings, cluster)
- Infrastructure summary: GetResourceSummary with health status counts
- AI context now includes top consumers and infrastructure overview
Phase 3 - Agent Preference & Hybrid Mode:
- Polling optimization methods in resource store
- ResourceStoreInterface added to Monitor
- SetResourceStore() and shouldSkipNodeMetrics() helper methods
- Store automatically wired into Monitor via Router.SetMonitor()
- Foundation ready for reduced API polling when agents are active
Files added:
- internal/resources/resource.go - Core Resource type
- internal/resources/store.go - Store with deduplication
- internal/resources/converters.go - Type converters
- internal/resources/platform_data.go - Platform-specific data
- internal/resources/store_test.go - 28 tests
- internal/resources/converters_test.go - Converter tests
- internal/api/resource_handlers.go - REST API handlers
- internal/ai/resource_context.go - AI context builder
- .gemini/docs/unified-resource-architecture.md - Architecture docs
All tests pass.
- Implement 'Show Problems Only' toggle combining degraded status, high CPU/memory alerts, and needs backup filters
- Add 'Investigate with AI' button to filter bar for problematic guests
- Fix dashboard column sizing inconsistencies between bars and sparklines view modes
- Fix PBS backups display and polling
- Refine AI prompt for general-purpose usage
- Fix frontend flickering and reload loops during initial load
- Integrate persistent SQLite metrics store with Monitor
- Fortify AI command routing with improved validation and logging
- Fix CSRF token handling for note deletion
- Debug and fix AI command execution issues
- Various AI reliability improvements and command safety enhancements
- Add AI service with Anthropic, OpenAI, and Ollama providers
- Add AI chat UI component with streaming responses
- Add AI settings page for configuration
- Add agent exec framework for command execution
- Add API endpoints for AI chat and configuration
