Add /api/ai/intelligence/anomalies endpoint that compares live metrics
against learned baselines to surface deviations - all deterministic
(no LLM required).
Backend:
- Add AnomalyReport struct with severity classification
- Add CheckResourceAnomalies method to baseline store
- Add HandleGetAnomalies API handler
- Add GetStateProvider getter to AI service
Frontend:
- Add AnomalyReport and AnomaliesResponse types
- Add getAnomalies API function
- Add AnomalySeverity type
This is the first step toward surfacing deterministic intelligence
directly in the UI without requiring LLM interaction.
Backend:
- Enhanced buildEnrichedResourceContext to ALWAYS show learned baselines with
status indicators (normal/elevated/anomaly) instead of only when anomalous
- This makes Pulse Pro's 'moat' visible - users can see the AI understands
their infrastructure's normal behavior patterns
- Added baseline import to service.go
Frontend (user changes):
- Added incident event type filtering with toggle buttons
- Added resource incident panel to view all incidents for a resource
- Added timeline expand/collapse functionality in alert history
- Added incident note saving with proper incidentId tracking
- Added startedAt parameter for proper incident timeline loading
Multiple frontend components were using - as a fallback
when guest.id was falsy. This format drops the node component, which is
critical for clustered setups where the same VMID can exist on different
nodes.
Changes:
- GuestDrawer.tsx: Updated guestId() and handleAskAI() to use canonical format
- GuestRow.tsx: Updated buildGuestId() to use canonical format
- Dashboard.tsx: Updated handleGuestRowClick() and guest rendering loop,
also fixed legacy metadata fallback to use consistent keying
- ThresholdsTable.tsx: Updated guestsGroupedByNode() to use canonical format
Backend changes:
- Removed temporary debug logging added during investigation
- Added alert history section to AI buildEnrichedResourceContext() function
The backend generates VM/Container IDs in instance:node:vmid format (e.g.,
delly:delly:101) via makeGuestID(). This format is now consistently used
across all frontend fallbacks to prevent AI context, metadata, overrides,
and metrics from colliding or desyncing in clustered environments.
- Fixed normalizeStorageDefaults to allow Trigger=0
- Fixed normalizeNodeDefaults (Temperature) to allow Trigger=0
- Added comprehensive tests for all threshold normalization patterns
- Updated existing test that expected old behavior
Related to #864
- Add HandleLicenseFeatures handler that was missing from license_handlers.go
- Add /api/license/features route to router
- Update AI service and metadata provider
- Update frontend license API and components
- Fix CI build failure caused by tests referencing unimplemented method
Fixes#858
The patrol interval setting was not being properly applied due to:
1. ReconfigurePatrol() was setting the deprecated QuickCheckInterval field
instead of the preferred Interval field
2. SetConfig() was comparing raw field values instead of using GetInterval()
to compare effective intervals, causing change detection to fail
3. The API response was missing interval_ms, preventing the frontend from
displaying the correct interval
Changes:
- Update StartPatrol() and ReconfigurePatrol() to use the Interval field
- Fix SetConfig() to use GetInterval() for interval comparison
- Add IntervalMs to PatrolStatusResponse and include it in the API response
The issue was a SolidJS reactivity problem in the Dashboard component.
When guestMetadata signal was accessed inside a For loop callback and
assigned to a plain variable, SolidJS lost reactive tracking.
Changed from:
const metadata = guestMetadata()[guestId] || ...
customUrl={metadata?.customUrl}
To:
const getMetadata = () => guestMetadata()[guestId] || ...
customUrl={getMetadata()?.customUrl}
This ensures SolidJS properly tracks the signal dependency when the
getter function is called directly in JSX props.
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.
1. resources/store.go: Implement sorting in Query.Execute()
- Added sortResources function with support for common fields
- Supports: name, type, status, cpu, memory, disk, last_seen
- Both ascending and descending order supported
2. ai/service.go: Implement hasAgentForTarget properly
- Now maps target to specific agent based on hostname/node
- Uses ResourceProvider lookup for container→host mapping
- Supports cluster peer routing for Proxmox clusters
- Properly handles single-agent vs multi-agent scenarios
The set_resource_url tool had an incorrect example ID format ('pve1-delly-101')
which caused the AI to save URLs with wrong IDs that didn't match the actual
guest IDs used by Pulse ('instance-VMID' format like 'delly-150').
This fix updates the tool description to clearly document the correct format,
so URLs saved by the AI will now properly appear in the dashboard.
- 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.
Create internal/ai/patterns package:
1. Pattern Detector (detector.go):
- Records historical events (high memory, OOM, restarts, etc.)
- Detects recurring failure patterns
- Calculates average interval between occurrences
- Computes confidence based on pattern consistency
- Predicts when failures will occur again
- Persists to ai_patterns.json
2. Event types tracked:
- high_memory, high_cpu, disk_full
- oom, restart, unresponsive
- backup_failed
3. Integration:
- Wire PatternDetector into router startup
- Add to AI context in buildEnrichedContext
- FormatForContext generates failure predictions
Example AI context now includes:
'OOM events typically occurs every ~10 days (next expected in ~3 days)'
This enables proactive alerts before problems recur.
All tests passing.
Phase 4 - Remediation logging integration:
1. logRemediation hook after tool execution:
- Only logs run_command tools (main remediation action)
- Records resourceID, resourceType, findingID
- Extracts problem summary from user prompt
- Truncates output for storage (max 1000 chars)
- Distinguishes automatic (patrol) vs manual (chat) actions
2. buildRemediationContext for system prompts:
- Shows 'Past Successful Fixes for Similar Issues' section
- Uses keyword matching to find relevant past fixes
- Shows 'Remediation History for This Resource' section
- Includes timestamps and outcomes
This enables the AI to say things like:
- 'This worked before: apt clean to free 6GB (resolved)'
- 'Last time on this resource: restarted nginx (resolved)'
All tests passing.
Complete Phase 3 integration:
- Initialize ChangeDetector and RemediationLog in StartPatrol
- Add SetChangeDetector/SetRemediationLog to handler chain:
Router -> AISettingsHandler -> Service -> PatrolService
- Persist change history to ai_changes.json
- Persist remediation log to ai_remediations.json
- Both use the Pulse config directory for storage
Operational memory is now fully integrated:
- Change detector tracks infrastructure changes on each patrol
- Recent changes (24h) are appended to AI context
- Remediation log ready for command execution logging
All tests passing.
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.
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
Knowledge store notes are now included in the patrol LLM prompt. When users
save notes about resources (e.g., 'This VM intentionally runs hot'), the patrol
AI will see these notes and avoid flagging documented behavior as issues.
Changes:
- Added knowledge store reference to PatrolService
- Added SetKnowledgeStore() method to configure the store
- Enhanced buildPatrolPrompt() to include knowledge context
- Connected knowledge store to patrol in service.go SetStateProvider()
This complements the dismissed findings context to give the LLM a complete
picture of what the user considers normal/expected behavior.
- 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
The AI service now uses only buildUnifiedResourceContext() for
infrastructure context, since the resourceProvider is always set
during router initialization.
Removed:
- buildInfrastructureContext() function (~288 lines of dead code)
- Legacy fallback path in buildSystemPrompt()
The unified resource context provides a cleaner, deduplicated view
of infrastructure that includes:
- All resources grouped by platform and type
- Top CPU/Memory/Disk consumers
- Active alerts on resources
- Infrastructure summary statistics
This completes the AI service migration to unified resources.
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.
- Extended AI context selection to host rows in HostsOverview
- Added resourceId prop to StackedMemoryBar for sparkline support
- Relocated guest URL editing from GuestRow name click
- Added GuestNotes component with URL field in AI sidebar
- Refined host routing in AI service backend
- Minor animation and styling improvements
- 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
