8839075805
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>
40 lines
743 B
JavaScript
40 lines
743 B
JavaScript
"use strict";
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Object.defineProperty(exports, "__esModule", {
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value: true
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});
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exports.findSuggestion = findSuggestion;
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const {
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min
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} = Math;
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function levenshtein(a, b) {
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let t = [],
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u = [],
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i,
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j;
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const m = a.length,
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n = b.length;
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if (!m) {
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return n;
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}
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if (!n) {
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return m;
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}
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for (j = 0; j <= n; j++) {
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t[j] = j;
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}
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for (i = 1; i <= m; i++) {
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for (u = [i], j = 1; j <= n; j++) {
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u[j] = a[i - 1] === b[j - 1] ? t[j - 1] : min(t[j - 1], t[j], u[j - 1]) + 1;
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}
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t = u;
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}
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return u[n];
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}
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function findSuggestion(str, arr) {
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const distances = arr.map(el => levenshtein(el, str));
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return arr[distances.indexOf(min(...distances))];
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}
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//# sourceMappingURL=find-suggestion.js.map
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