Implement critical production readiness features for AI integration: per-request provider config, admin dashboard, workflow inspection, and plugin framework foundation.
Per-Request Provider Config:
- Add ProviderConfig struct to all AI request types
- Update queue to resolve provider/model/API key from cascade (repo > org > system)
- Pass resolved config to AI sidecar on every request
- Fixes multi-tenant issue where all orgs shared sidecar's hardcoded config
Admin AI Dashboard:
- Add /admin/ai page with sidecar health status
- Display global operation stats (total, 24h, success/fail/escalated counts)
- Show operations by tier, top 5 repos, token usage
- Recent operations table with repo, operation, status, duration
- Add GetGlobalOperationStats model method
Workflow Inspection:
- Add InspectWorkflow client method and types
- Implement workflow-inspect queue handler
- Add notifier trigger on workflow file push
- Analyzes YAML for syntax errors, security issues, best practices
- Returns structured issues with line numbers and suggested fixes
Plugin Framework (Phase 5 Foundation):
- Add external plugin config loading from app.ini
- Define ExternalPlugin interface and manager
- Add plugin.proto contract (Initialize, Shutdown, HealthCheck, OnEvent, HandleHTTP)
- Implement health monitoring with auto-restart for managed plugins
- Add event routing to subscribed plugins
- HTTP proxy support for plugin-served routes
This completes Tasks 1-4 from the production readiness plan and establishes the foundation for managed plugin lifecycle.
Refactor AI service layer to reduce code duplication and improve consistency.
Changes:
- Rename AIOperationRequest to OperationRequest for consistency
- Extract shared logic for issue-targeted operations (respond, triage) into triggerIssueAIOp helper
- Standardize field alignment in struct definitions
- Remove redundant error handling patterns
This reduces the API operations file by ~40 lines while maintaining identical functionality.
Add database models and infrastructure for AI operation tracking and organization-level AI configuration.
OperationLog model tracks all AI operations for auditing, including:
- Operation type, tier, and trigger event
- Token usage (input/output)
- Status tracking (pending, success, failed, escalated)
- Performance metrics (duration)
- Rate limiting support via CountRecentOperations
OrgAISettings model stores per-organization AI configuration:
- Provider and model selection
- Encrypted API key storage
- Rate limits (max operations per hour)
- Allowed operations whitelist
- Agent mode permissions
Also adds AI unit type to repository units for enabling/disabling AI features per repo.
Go's semantic import versioning requires v2+ modules to include the
major version in the module path. This enables using proper version
tags (v3.x.x) instead of pseudo-versions.
Updated module path: code.gitcaddy.com/server/v3
- Add workflow filter to clear-cancelled, clear-failed, clear-running, clear-old-success
- Add any option to AI Learning runner type filter
- Fix ServerStats int64 types for FileSize template function
- Add CPULoad field with platform-specific implementations
- Fix actions list template with conditional button visibility
- Use Gitea styled modal dialogs for all clear buttons
🤖 Generated with Claude Code
- Add options/seed/error_patterns.json with default upload error patterns
- Add models/ai/seed.go with SeedErrorPatternsIfEmpty function
- Call seed on startup after models.Init if error_pattern table is empty
- Seeds 10 common error patterns for uploads, .NET, etc.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fix lint errors in AI learning MCP tools (interface{} -> any, append pattern)
- Update OAuth realm names from Gitea to GitCaddy
- Update User-Agent strings to GitCaddy
- Update default git user.name, mailer X-Mailer header
- Update API docs descriptions
- Update template comments and swagger descriptions
- Keep Gitea attribution in footer and as OAuth provider option
🤖 Generated with Claude Code
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add ErrorPattern and WorkflowTelemetry models for storing solutions
- Add 5 new MCP tools: get_error_patterns, report_error_solution,
report_solution_success, get_compatibility_matrix, diagnose_job_failure
- Error pattern regex extraction for NETSDK, XA, MSB, npm, go, cargo errors
- AIs can now query known solutions and report new ones
🤖 Generated with Claude Code
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
