tftsr-devops_investigation/node_modules/commander/lib/suggestSimilar.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
const maxDistance = 3;
function editDistance(a, b) {
// https://en.wikipedia.org/wiki/DamerauLevenshtein_distance
// Calculating optimal string alignment distance, no substring is edited more than once.
// (Simple implementation.)
// Quick early exit, return worst case.
if (Math.abs(a.length - b.length) > maxDistance)
return Math.max(a.length, b.length);
// distance between prefix substrings of a and b
const d = [];
// pure deletions turn a into empty string
for (let i = 0; i <= a.length; i++) {
d[i] = [i];
}
// pure insertions turn empty string into b
for (let j = 0; j <= b.length; j++) {
d[0][j] = j;
}
// fill matrix
for (let j = 1; j <= b.length; j++) {
for (let i = 1; i <= a.length; i++) {
let cost = 1;
if (a[i - 1] === b[j - 1]) {
cost = 0;
} else {
cost = 1;
}
d[i][j] = Math.min(
d[i - 1][j] + 1, // deletion
d[i][j - 1] + 1, // insertion
d[i - 1][j - 1] + cost, // substitution
);
// transposition
if (i > 1 && j > 1 && a[i - 1] === b[j - 2] && a[i - 2] === b[j - 1]) {
d[i][j] = Math.min(d[i][j], d[i - 2][j - 2] + 1);
}
}
}
return d[a.length][b.length];
}
/**
* Find close matches, restricted to same number of edits.
*
* @param {string} word
* @param {string[]} candidates
* @returns {string}
*/
function suggestSimilar(word, candidates) {
if (!candidates || candidates.length === 0) return '';
// remove possible duplicates
candidates = Array.from(new Set(candidates));
const searchingOptions = word.startsWith('--');
if (searchingOptions) {
word = word.slice(2);
candidates = candidates.map((candidate) => candidate.slice(2));
}
let similar = [];
let bestDistance = maxDistance;
const minSimilarity = 0.4;
candidates.forEach((candidate) => {
if (candidate.length <= 1) return; // no one character guesses
const distance = editDistance(word, candidate);
const length = Math.max(word.length, candidate.length);
const similarity = (length - distance) / length;
if (similarity > minSimilarity) {
if (distance < bestDistance) {
// better edit distance, throw away previous worse matches
bestDistance = distance;
similar = [candidate];
} else if (distance === bestDistance) {
similar.push(candidate);
}
}
});
similar.sort((a, b) => a.localeCompare(b));
if (searchingOptions) {
similar = similar.map((candidate) => `--${candidate}`);
}
if (similar.length > 1) {
return `\n(Did you mean one of ${similar.join(', ')}?)`;
}
if (similar.length === 1) {
return `\n(Did you mean ${similar[0]}?)`;
}
return '';
}
exports.suggestSimilar = suggestSimilar;