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 React from "react";
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import type { HardwareInfo, ModelRecommendation } from "@/lib/tauriCommands";
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import { Badge, Progress } from "@/components/ui";
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import { Cpu, HardDrive, Monitor } from "lucide-react";
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interface HardwareReportProps {
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hardware: HardwareInfo | null;
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recommendations: ModelRecommendation[];
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}
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export function HardwareReport({ hardware, recommendations }: HardwareReportProps) {
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if (!hardware) {
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return (
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<div className="text-sm text-muted-foreground p-4">
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Loading hardware information...
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</div>
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);
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}
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const maxRamDisplay = 64;
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const ramPercentage = Math.min(100, (hardware.total_ram_gb / maxRamDisplay) * 100);
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return (
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<div className="space-y-6">
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{/* Hardware Info */}
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<div className="grid grid-cols-1 md:grid-cols-3 gap-4">
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{/* CPU */}
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<div className="flex items-start gap-3 rounded-lg border p-4">
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<Cpu className="w-5 h-5 text-muted-foreground mt-0.5" />
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<div>
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<p className="text-sm font-medium">CPU</p>
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<Badge variant="secondary" className="mt-1">
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{hardware.cpu_arch}
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</Badge>
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</div>
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</div>
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{/* RAM */}
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<div className="flex items-start gap-3 rounded-lg border p-4">
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<HardDrive className="w-5 h-5 text-muted-foreground mt-0.5" />
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<div className="flex-1">
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<p className="text-sm font-medium">RAM</p>
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<p className="text-xs text-muted-foreground mt-1 mb-2">
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{hardware.total_ram_gb.toFixed(1)} GB / {maxRamDisplay} GB
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</p>
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<Progress value={ramPercentage} />
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</div>
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</div>
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{/* GPU */}
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<div className="flex items-start gap-3 rounded-lg border p-4">
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<Monitor className="w-5 h-5 text-muted-foreground mt-0.5" />
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<div>
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<p className="text-sm font-medium">GPU</p>
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{hardware.gpu_vendor ? (
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<>
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<p className="text-xs mt-1">{hardware.gpu_vendor}</p>
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{hardware.gpu_vram_gb && (
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<p className="text-xs text-muted-foreground">
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{hardware.gpu_vram_gb} GB VRAM
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</p>
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)}
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</>
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) : (
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<p className="text-xs text-muted-foreground mt-1">No GPU detected</p>
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)}
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</div>
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</div>
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</div>
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{/* Model Recommendations */}
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{recommendations.length > 0 && (
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<div>
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<h4 className="text-sm font-medium mb-3">Model Recommendations</h4>
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<div className="space-y-2">
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{recommendations.map((rec) => (
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<div
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key={rec.name}
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className={`flex items-center justify-between rounded-lg border p-3 ${
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rec.recommended ? "border-green-600 bg-green-50 dark:bg-green-950" : ""
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}`}
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>
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<div className="flex items-center gap-3">
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{rec.recommended && (
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2026-03-31 12:46:36 +00:00
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<Badge variant="success">RECOMMENDED</Badge>
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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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)}
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<div>
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<p className="text-sm font-medium">{rec.name}</p>
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<p className="text-xs text-muted-foreground">
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{rec.size} | Min RAM: {rec.min_ram_gb} GB
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</p>
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</div>
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</div>
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<p className="text-xs text-muted-foreground max-w-[200px] text-right">
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{rec.description}
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</p>
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</div>
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))}
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</div>
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</div>
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)}
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</div>
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);
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}
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