tftsr-devops_investigation/node_modules/jszip/lib/flate.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

86 lines
2.3 KiB
JavaScript

"use strict";
var USE_TYPEDARRAY = (typeof Uint8Array !== "undefined") && (typeof Uint16Array !== "undefined") && (typeof Uint32Array !== "undefined");
var pako = require("pako");
var utils = require("./utils");
var GenericWorker = require("./stream/GenericWorker");
var ARRAY_TYPE = USE_TYPEDARRAY ? "uint8array" : "array";
exports.magic = "\x08\x00";
/**
* Create a worker that uses pako to inflate/deflate.
* @constructor
* @param {String} action the name of the pako function to call : either "Deflate" or "Inflate".
* @param {Object} options the options to use when (de)compressing.
*/
function FlateWorker(action, options) {
GenericWorker.call(this, "FlateWorker/" + action);
this._pako = null;
this._pakoAction = action;
this._pakoOptions = options;
// the `meta` object from the last chunk received
// this allow this worker to pass around metadata
this.meta = {};
}
utils.inherits(FlateWorker, GenericWorker);
/**
* @see GenericWorker.processChunk
*/
FlateWorker.prototype.processChunk = function (chunk) {
this.meta = chunk.meta;
if (this._pako === null) {
this._createPako();
}
this._pako.push(utils.transformTo(ARRAY_TYPE, chunk.data), false);
};
/**
* @see GenericWorker.flush
*/
FlateWorker.prototype.flush = function () {
GenericWorker.prototype.flush.call(this);
if (this._pako === null) {
this._createPako();
}
this._pako.push([], true);
};
/**
* @see GenericWorker.cleanUp
*/
FlateWorker.prototype.cleanUp = function () {
GenericWorker.prototype.cleanUp.call(this);
this._pako = null;
};
/**
* Create the _pako object.
* TODO: lazy-loading this object isn't the best solution but it's the
* quickest. The best solution is to lazy-load the worker list. See also the
* issue #446.
*/
FlateWorker.prototype._createPako = function () {
this._pako = new pako[this._pakoAction]({
raw: true,
level: this._pakoOptions.level || -1 // default compression
});
var self = this;
this._pako.onData = function(data) {
self.push({
data : data,
meta : self.meta
});
};
};
exports.compressWorker = function (compressionOptions) {
return new FlateWorker("Deflate", compressionOptions);
};
exports.uncompressWorker = function () {
return new FlateWorker("Inflate", {});
};