tftsr-devops_investigation/node_modules/sucrase/dist/parser/tokenizer/readWord.js
Shaun Arman 8839075805 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>
2026-03-14 22:36:25 -05:00

65 lines
2.2 KiB
JavaScript

"use strict";Object.defineProperty(exports, "__esModule", {value: true});var _base = require('../traverser/base');
var _charcodes = require('../util/charcodes');
var _identifier = require('../util/identifier');
var _index = require('./index');
var _readWordTree = require('./readWordTree');
var _types = require('./types');
/**
* Read an identifier, producing either a name token or matching on one of the existing keywords.
* For performance, we pre-generate big decision tree that we traverse. Each node represents a
* prefix and has 27 values, where the first value is the token or contextual token, if any (-1 if
* not), and the other 26 values are the transitions to other nodes, or -1 to stop.
*/
function readWord() {
let treePos = 0;
let code = 0;
let pos = _base.state.pos;
while (pos < _base.input.length) {
code = _base.input.charCodeAt(pos);
if (code < _charcodes.charCodes.lowercaseA || code > _charcodes.charCodes.lowercaseZ) {
break;
}
const next = _readWordTree.READ_WORD_TREE[treePos + (code - _charcodes.charCodes.lowercaseA) + 1];
if (next === -1) {
break;
} else {
treePos = next;
pos++;
}
}
const keywordValue = _readWordTree.READ_WORD_TREE[treePos];
if (keywordValue > -1 && !_identifier.IS_IDENTIFIER_CHAR[code]) {
_base.state.pos = pos;
if (keywordValue & 1) {
_index.finishToken.call(void 0, keywordValue >>> 1);
} else {
_index.finishToken.call(void 0, _types.TokenType.name, keywordValue >>> 1);
}
return;
}
while (pos < _base.input.length) {
const ch = _base.input.charCodeAt(pos);
if (_identifier.IS_IDENTIFIER_CHAR[ch]) {
pos++;
} else if (ch === _charcodes.charCodes.backslash) {
// \u
pos += 2;
if (_base.input.charCodeAt(pos) === _charcodes.charCodes.leftCurlyBrace) {
while (pos < _base.input.length && _base.input.charCodeAt(pos) !== _charcodes.charCodes.rightCurlyBrace) {
pos++;
}
pos++;
}
} else if (ch === _charcodes.charCodes.atSign && _base.input.charCodeAt(pos + 1) === _charcodes.charCodes.atSign) {
pos += 2;
} else {
break;
}
}
_base.state.pos = pos;
_index.finishToken.call(void 0, _types.TokenType.name);
} exports.default = readWord;