feat: initial implementation of TFTSR IT Triage & RCA application
Implements Phases 1-8 of the TFTSR implementation plan.
Rust backend (Tauri 2.x, src-tauri/):
- Multi-provider AI: OpenAI-compatible, Anthropic, Gemini, Mistral, Ollama
- PII detection engine: 11 regex patterns with overlap resolution
- SQLCipher AES-256 encrypted database with 10 versioned migrations
- 28 Tauri IPC commands for triage, analysis, document, and system ops
- Ollama: hardware probe, model recommendations, pull/delete with events
- RCA and blameless post-mortem Markdown document generators
- PDF export via printpdf
- Audit log: SHA-256 hash of every external data send
- Integration stubs for Confluence, ServiceNow, Azure DevOps (v0.2)
Frontend (React 18 + TypeScript + Vite, src/):
- 9 pages: full triage workflow NewIssue→LogUpload→Triage→Resolution→RCA→Postmortem→History+Settings
- 7 components: ChatWindow, TriageProgress, PiiDiffViewer, DocEditor, HardwareReport, ModelSelector, UI primitives
- 3 Zustand stores: session, settings (persisted), history
- Type-safe tauriCommands.ts matching Rust backend types exactly
- 8 IT domain system prompts (Linux, Windows, Network, K8s, DB, Virt, HW, Obs)
DevOps:
- .woodpecker/test.yml: rustfmt, clippy, cargo test, tsc, vitest on every push
- .woodpecker/release.yml: linux/amd64 + linux/arm64 builds, Gogs release upload
Verified:
- cargo check: zero errors
- tsc --noEmit: zero errors
- vitest run: 13/13 unit tests passing
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 03:36:25 +00:00
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import { invoke } from "@tauri-apps/api/core";
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// ─── Types matching Rust backend models ───────────────────────────────────────
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export interface ProviderConfig {
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provider_type?: string;
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max_tokens?: number;
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temperature?: number;
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name: string;
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api_url: string;
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api_key: string;
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model: string;
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}
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export interface Message {
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role: string;
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content: string;
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}
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export interface TokenUsage {
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prompt_tokens: number;
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completion_tokens: number;
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total_tokens: number;
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}
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export interface ChatResponse {
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content: string;
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model: string;
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usage?: TokenUsage;
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}
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export interface AnalysisResult {
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summary: string;
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key_findings: string[];
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suggested_why1: string;
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severity_assessment: string;
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}
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export interface ProviderInfo {
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name: string;
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supports_streaming: boolean;
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models: string[];
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}
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export interface Issue {
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id: string;
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title: string;
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description: string;
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severity: string;
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status: string;
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category: string;
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source: string;
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created_at: string;
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updated_at: string;
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resolved_at?: string;
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assigned_to: string;
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tags: string;
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}
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export interface FiveWhyEntry {
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id: string;
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why_number: number;
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question: string;
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answer?: string;
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created_at: number;
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}
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export interface TimelineEvent {
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id: string;
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event_type: string;
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description: string;
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created_at: number;
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}
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export interface AiConversation {
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id: string;
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issue_id: string;
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provider: string;
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model: string;
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created_at: string;
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title: string;
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}
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export interface ResolutionStep {
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id: string;
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issue_id: string;
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step_order: number;
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why_question: string;
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answer: string;
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evidence: string;
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created_at: string;
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}
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export interface IssueDetail {
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issue: Issue;
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log_files: LogFile[];
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resolution_steps: ResolutionStep[];
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conversations: AiConversation[];
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}
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export interface IssueSummary {
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id: string;
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title: string;
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severity: string;
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status: string;
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category: string;
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created_at: string;
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updated_at: string;
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domain?: string;
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log_count: number;
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step_count: number;
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}
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export interface IssueListQuery {
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status?: string;
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domain?: string;
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severity?: string;
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search?: string;
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limit?: number;
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offset?: number;
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}
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export interface NewIssue {
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title: string;
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domain: string;
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description?: string;
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severity?: string;
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}
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export interface LogFile {
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id: string;
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issue_id: string;
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file_name: string;
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file_path: string;
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file_size: number;
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mime_type: string;
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content_hash: string;
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uploaded_at: string;
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redacted: boolean;
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}
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export interface PiiSpan {
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id: string;
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pii_type: string;
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start: number;
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end: number;
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original: string;
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replacement: string;
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}
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export interface PiiDetectionResult {
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log_file_id: string;
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detections: PiiSpan[];
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total_pii_found: number;
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}
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export interface RedactedLogFile {
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id: string;
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original_file_id: string;
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file_name: string;
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file_hash: string;
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redaction_count: number;
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}
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export interface Document_ {
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id: string;
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issue_id: string;
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doc_type: string;
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title: string;
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content_md: string;
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created_at: number;
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updated_at: number;
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}
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export interface HardwareInfo {
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total_ram_gb: number;
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cpu_arch: string;
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gpu_vendor?: string;
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gpu_vram_gb?: number;
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}
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export interface ModelRecommendation {
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name: string;
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size: string;
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min_ram_gb: number;
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description: string;
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recommended: boolean;
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}
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export interface OllamaModel {
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name: string;
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size: number;
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modified: string;
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}
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export interface OllamaStatus {
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installed: boolean;
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version?: string;
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running: boolean;
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}
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export interface InstallGuide {
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platform: string;
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steps: string[];
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url: string;
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}
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export interface AuditEntry {
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id: string;
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timestamp: string;
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action: string;
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entity_type: string;
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entity_id: string;
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user_id: string;
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details: string;
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}
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export interface AuditFilter {
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action?: string;
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entity_type?: string;
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entity_id?: string;
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limit?: number;
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}
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export interface AppSettings {
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theme: string;
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ai_providers: ProviderConfig[];
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active_provider?: string;
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default_provider: string;
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default_model: string;
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ollama_url: string;
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}
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// ─── TriageMessage (for UI store, not a DB type) ──────────────────────────────
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export interface TriageMessage {
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id: string;
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issue_id: string;
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role: string;
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content: string;
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why_level?: number;
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created_at: number;
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}
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// ─── AI commands ──────────────────────────────────────────────────────────────
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export const analyzeLogsCmd = (issueId: string, logFileIds: string[], providerConfig: ProviderConfig) =>
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invoke<AnalysisResult>("analyze_logs", { issueId, logFileIds, providerConfig });
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export const chatMessageCmd = (issueId: string, message: string, providerConfig: ProviderConfig) =>
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invoke<ChatResponse>("chat_message", { issueId, message, providerConfig });
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export const listProvidersCmd = () => invoke<ProviderInfo[]>("list_providers");
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// ─── Analysis / PII commands ──────────────────────────────────────────────────
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export const uploadLogFileCmd = (issueId: string, filePath: string) =>
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invoke<LogFile>("upload_log_file", { issueId, filePath });
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export const detectPiiCmd = (logFileId: string) =>
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invoke<PiiDetectionResult>("detect_pii", { logFileId });
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export const applyRedactionsCmd = (logFileId: string, approvedSpanIds: string[]) =>
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invoke<RedactedLogFile>("apply_redactions", { logFileId, approvedSpanIds });
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// ─── Issue CRUD ───────────────────────────────────────────────────────────────
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2026-03-31 12:46:36 +00:00
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export const testProviderConnectionCmd = (providerConfig: ProviderConfig) =>
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invoke<ChatResponse>("test_provider_connection", { providerConfig });
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feat: initial implementation of TFTSR IT Triage & RCA application
Implements Phases 1-8 of the TFTSR implementation plan.
Rust backend (Tauri 2.x, src-tauri/):
- Multi-provider AI: OpenAI-compatible, Anthropic, Gemini, Mistral, Ollama
- PII detection engine: 11 regex patterns with overlap resolution
- SQLCipher AES-256 encrypted database with 10 versioned migrations
- 28 Tauri IPC commands for triage, analysis, document, and system ops
- Ollama: hardware probe, model recommendations, pull/delete with events
- RCA and blameless post-mortem Markdown document generators
- PDF export via printpdf
- Audit log: SHA-256 hash of every external data send
- Integration stubs for Confluence, ServiceNow, Azure DevOps (v0.2)
Frontend (React 18 + TypeScript + Vite, src/):
- 9 pages: full triage workflow NewIssue→LogUpload→Triage→Resolution→RCA→Postmortem→History+Settings
- 7 components: ChatWindow, TriageProgress, PiiDiffViewer, DocEditor, HardwareReport, ModelSelector, UI primitives
- 3 Zustand stores: session, settings (persisted), history
- Type-safe tauriCommands.ts matching Rust backend types exactly
- 8 IT domain system prompts (Linux, Windows, Network, K8s, DB, Virt, HW, Obs)
DevOps:
- .woodpecker/test.yml: rustfmt, clippy, cargo test, tsc, vitest on every push
- .woodpecker/release.yml: linux/amd64 + linux/arm64 builds, Gogs release upload
Verified:
- cargo check: zero errors
- tsc --noEmit: zero errors
- vitest run: 13/13 unit tests passing
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 03:36:25 +00:00
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export const createIssueCmd = (newIssue: NewIssue) =>
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2026-03-30 20:52:43 +00:00
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invoke<Issue>("create_issue", {
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title: newIssue.title,
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description: newIssue.description ?? "",
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severity: newIssue.severity ?? "P3",
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category: newIssue.domain,
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});
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feat: initial implementation of TFTSR IT Triage & RCA application
Implements Phases 1-8 of the TFTSR implementation plan.
Rust backend (Tauri 2.x, src-tauri/):
- Multi-provider AI: OpenAI-compatible, Anthropic, Gemini, Mistral, Ollama
- PII detection engine: 11 regex patterns with overlap resolution
- SQLCipher AES-256 encrypted database with 10 versioned migrations
- 28 Tauri IPC commands for triage, analysis, document, and system ops
- Ollama: hardware probe, model recommendations, pull/delete with events
- RCA and blameless post-mortem Markdown document generators
- PDF export via printpdf
- Audit log: SHA-256 hash of every external data send
- Integration stubs for Confluence, ServiceNow, Azure DevOps (v0.2)
Frontend (React 18 + TypeScript + Vite, src/):
- 9 pages: full triage workflow NewIssue→LogUpload→Triage→Resolution→RCA→Postmortem→History+Settings
- 7 components: ChatWindow, TriageProgress, PiiDiffViewer, DocEditor, HardwareReport, ModelSelector, UI primitives
- 3 Zustand stores: session, settings (persisted), history
- Type-safe tauriCommands.ts matching Rust backend types exactly
- 8 IT domain system prompts (Linux, Windows, Network, K8s, DB, Virt, HW, Obs)
DevOps:
- .woodpecker/test.yml: rustfmt, clippy, cargo test, tsc, vitest on every push
- .woodpecker/release.yml: linux/amd64 + linux/arm64 builds, Gogs release upload
Verified:
- cargo check: zero errors
- tsc --noEmit: zero errors
- vitest run: 13/13 unit tests passing
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 03:36:25 +00:00
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export const getIssueCmd = (issueId: string) =>
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invoke<IssueDetail>("get_issue", { issueId });
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export const listIssuesCmd = (query: IssueListQuery) =>
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2026-03-31 14:09:57 +00:00
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invoke<IssueSummary[]>("list_issues", { filter: query });
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feat: initial implementation of TFTSR IT Triage & RCA application
Implements Phases 1-8 of the TFTSR implementation plan.
Rust backend (Tauri 2.x, src-tauri/):
- Multi-provider AI: OpenAI-compatible, Anthropic, Gemini, Mistral, Ollama
- PII detection engine: 11 regex patterns with overlap resolution
- SQLCipher AES-256 encrypted database with 10 versioned migrations
- 28 Tauri IPC commands for triage, analysis, document, and system ops
- Ollama: hardware probe, model recommendations, pull/delete with events
- RCA and blameless post-mortem Markdown document generators
- PDF export via printpdf
- Audit log: SHA-256 hash of every external data send
- Integration stubs for Confluence, ServiceNow, Azure DevOps (v0.2)
Frontend (React 18 + TypeScript + Vite, src/):
- 9 pages: full triage workflow NewIssue→LogUpload→Triage→Resolution→RCA→Postmortem→History+Settings
- 7 components: ChatWindow, TriageProgress, PiiDiffViewer, DocEditor, HardwareReport, ModelSelector, UI primitives
- 3 Zustand stores: session, settings (persisted), history
- Type-safe tauriCommands.ts matching Rust backend types exactly
- 8 IT domain system prompts (Linux, Windows, Network, K8s, DB, Virt, HW, Obs)
DevOps:
- .woodpecker/test.yml: rustfmt, clippy, cargo test, tsc, vitest on every push
- .woodpecker/release.yml: linux/amd64 + linux/arm64 builds, Gogs release upload
Verified:
- cargo check: zero errors
- tsc --noEmit: zero errors
- vitest run: 13/13 unit tests passing
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 03:36:25 +00:00
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export const updateIssueCmd = (
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issueId: string,
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updates: { title?: string; status?: string; severity?: string; description?: string; domain?: string }
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2026-03-31 17:50:39 +00:00
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) => invoke<Issue>("update_issue", { issueId, updates });
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feat: initial implementation of TFTSR IT Triage & RCA application
Implements Phases 1-8 of the TFTSR implementation plan.
Rust backend (Tauri 2.x, src-tauri/):
- Multi-provider AI: OpenAI-compatible, Anthropic, Gemini, Mistral, Ollama
- PII detection engine: 11 regex patterns with overlap resolution
- SQLCipher AES-256 encrypted database with 10 versioned migrations
- 28 Tauri IPC commands for triage, analysis, document, and system ops
- Ollama: hardware probe, model recommendations, pull/delete with events
- RCA and blameless post-mortem Markdown document generators
- PDF export via printpdf
- Audit log: SHA-256 hash of every external data send
- Integration stubs for Confluence, ServiceNow, Azure DevOps (v0.2)
Frontend (React 18 + TypeScript + Vite, src/):
- 9 pages: full triage workflow NewIssue→LogUpload→Triage→Resolution→RCA→Postmortem→History+Settings
- 7 components: ChatWindow, TriageProgress, PiiDiffViewer, DocEditor, HardwareReport, ModelSelector, UI primitives
- 3 Zustand stores: session, settings (persisted), history
- Type-safe tauriCommands.ts matching Rust backend types exactly
- 8 IT domain system prompts (Linux, Windows, Network, K8s, DB, Virt, HW, Obs)
DevOps:
- .woodpecker/test.yml: rustfmt, clippy, cargo test, tsc, vitest on every push
- .woodpecker/release.yml: linux/amd64 + linux/arm64 builds, Gogs release upload
Verified:
- cargo check: zero errors
- tsc --noEmit: zero errors
- vitest run: 13/13 unit tests passing
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 03:36:25 +00:00
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export const deleteIssueCmd = (issueId: string) =>
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invoke<void>("delete_issue", { issueId });
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export const searchIssuesCmd = (query: string) =>
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invoke<IssueSummary[]>("search_issues", { query });
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2026-03-31 17:50:39 +00:00
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export interface IssueMessage {
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id: string;
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conversation_id: string;
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role: string;
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content: string;
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token_count: number;
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created_at: string;
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}
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export const getIssueMessagesCmd = (issueId: string) =>
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invoke<IssueMessage[]>("get_issue_messages", { issueId });
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export const addFiveWhyCmd = (
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issueId: string,
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stepOrder: number,
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whyQuestion: string,
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answer: string,
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evidence: string
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) => invoke<ResolutionStep>("add_five_why", { issueId, stepOrder, whyQuestion, answer, evidence });
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feat: initial implementation of TFTSR IT Triage & RCA application
Implements Phases 1-8 of the TFTSR implementation plan.
Rust backend (Tauri 2.x, src-tauri/):
- Multi-provider AI: OpenAI-compatible, Anthropic, Gemini, Mistral, Ollama
- PII detection engine: 11 regex patterns with overlap resolution
- SQLCipher AES-256 encrypted database with 10 versioned migrations
- 28 Tauri IPC commands for triage, analysis, document, and system ops
- Ollama: hardware probe, model recommendations, pull/delete with events
- RCA and blameless post-mortem Markdown document generators
- PDF export via printpdf
- Audit log: SHA-256 hash of every external data send
- Integration stubs for Confluence, ServiceNow, Azure DevOps (v0.2)
Frontend (React 18 + TypeScript + Vite, src/):
- 9 pages: full triage workflow NewIssue→LogUpload→Triage→Resolution→RCA→Postmortem→History+Settings
- 7 components: ChatWindow, TriageProgress, PiiDiffViewer, DocEditor, HardwareReport, ModelSelector, UI primitives
- 3 Zustand stores: session, settings (persisted), history
- Type-safe tauriCommands.ts matching Rust backend types exactly
- 8 IT domain system prompts (Linux, Windows, Network, K8s, DB, Virt, HW, Obs)
DevOps:
- .woodpecker/test.yml: rustfmt, clippy, cargo test, tsc, vitest on every push
- .woodpecker/release.yml: linux/amd64 + linux/arm64 builds, Gogs release upload
Verified:
- cargo check: zero errors
- tsc --noEmit: zero errors
- vitest run: 13/13 unit tests passing
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 03:36:25 +00:00
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export const updateFiveWhyCmd = (entryId: string, answer: string) =>
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invoke<void>("update_five_why", { entryId, answer });
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export const addTimelineEventCmd = (issueId: string, eventType: string, description: string) =>
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invoke<TimelineEvent>("add_timeline_event", { issueId, eventType, description });
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// ─── Document commands ────────────────────────────────────────────────────────
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export const generateRcaCmd = (issueId: string) => invoke<Document_>("generate_rca", { issueId });
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export const generatePostmortemCmd = (issueId: string) =>
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invoke<Document_>("generate_postmortem", { issueId });
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export const updateDocumentCmd = (docId: string, contentMd: string) =>
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invoke<Document_>("update_document", { docId, contentMd });
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export const exportDocumentCmd = (docId: string, format: string, outputDir: string) =>
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invoke<string>("export_document", { docId, format, outputDir });
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// ─── Ollama & System ──────────────────────────────────────────────────────────
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export const checkOllamaInstalledCmd = () => invoke<OllamaStatus>("check_ollama_installed");
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export const getOllamaInstallGuideCmd = (platform: string) =>
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invoke<InstallGuide>("get_ollama_install_guide", { platform });
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export const listOllamaModelsCmd = () => invoke<OllamaModel[]>("list_ollama_models");
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export const pullOllamaModelCmd = (modelName: string) =>
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invoke<void>("pull_ollama_model", { modelName });
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export const deleteOllamaModelCmd = (modelName: string) =>
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invoke<void>("delete_ollama_model", { modelName });
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export const detectHardwareCmd = () => invoke<HardwareInfo>("detect_hardware");
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export const recommendModelsCmd = () => invoke<ModelRecommendation[]>("recommend_models");
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// ─── Settings & Audit ─────────────────────────────────────────────────────────
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export const getSettingsCmd = () => invoke<AppSettings>("get_settings");
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export const updateSettingsCmd = (partialSettings: Partial<AppSettings>) =>
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invoke<AppSettings>("update_settings", { partialSettings });
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export const getAuditLogCmd = (filter: AuditFilter) =>
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invoke<AuditEntry[]>("get_audit_log", { filter });
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