tftsr-devops_investigation/node_modules/@babel/helper-validator-option/lib/find-suggestion.js

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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
"use strict";
Object.defineProperty(exports, "__esModule", {
value: true
});
exports.findSuggestion = findSuggestion;
const {
min
} = Math;
function levenshtein(a, b) {
let t = [],
u = [],
i,
j;
const m = a.length,
n = b.length;
if (!m) {
return n;
}
if (!n) {
return m;
}
for (j = 0; j <= n; j++) {
t[j] = j;
}
for (i = 1; i <= m; i++) {
for (u = [i], j = 1; j <= n; j++) {
u[j] = a[i - 1] === b[j - 1] ? t[j - 1] : min(t[j - 1], t[j], u[j - 1]) + 1;
}
t = u;
}
return u[n];
}
function findSuggestion(str, arr) {
const distances = arr.map(el => levenshtein(el, str));
return arr[distances.indexOf(min(...distances))];
}
//# sourceMappingURL=find-suggestion.js.map