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Tech Stack Document

Tech Stack Document: Xmind AI Desktop Client

1. Introduction

Project Name: Xmind AI Desktop Client
Objective: Develop a cross-platform desktop application for streamlined access to Xmind software downloads, featuring AI-enhanced mind mapping capabilities. Combines secure distribution infrastructure with local AI processing for real-time mind map generation and optimization.

2. Technology Stack

Category Technology & Version Rationale
Core Framework Electron 25.3.0 Cross-platform compatibility (Windows/macOS/Linux), native OS integration
UI Framework React 18.2 + TypeScript 5.1 Component-based architecture, type safety, and responsive design
AI Engine TensorFlow.js 4.5.0 Client-side ML for text-to-mind-map conversion, GPU acceleration support
State Management Recoil 0.7.7 Atomic state management for complex AI workflows
Database SQLite 3.42.0 (Embedded) Local storage for user preferences/download history
Packaging Electron Forge 7.0.0 Automated builds, code signing, and auto-update (Squirrel/S3 backend)
Security WebCrypto API + Keytar 7.9.0 Secure credential storage and encrypted communications
Testing Jest 29.6 + Playwright 1.38 Unit/integration tests + cross-browser E2E validation

3. AI Component Architecture

🔄 正在加载流程图...

graph LR A[User Input] --> B(TensorFlow.js Model) B --> C{AI Processing} C -->|Text| D[NLP Entity Extraction] C -->|Image| E[OpenCV.js Handwriting Recognition] D --> F[Auto-Mind-Map Generation] E --> F F --> G[Xmind File Export]

AI Models:

  • text-to-mindmap: Fine-tuned BERT transformer (Hugging Face) for keyword/relationship extraction.
  • sketch-to-digital: CNN model (TensorFlow.js) trained on hand-drawn diagram datasets.

4. Implementation Steps

Phase 1: Core Application Setup

  1. Scaffold Electron-React App: 
    npx create-electron-app xmind-ai --template=typescript-webpack
npm install @electron-forge/cli
  2. Integrate TensorFlow.js: 
    import * as tf from '@tensorflow/tfjs';
const model = await tf.loadGraphModel('https://cdn.xmind.ai/text-mapper/1.0/model.json');

Phase 2: AI Feature Implementation

  • Text Processing Pipeline: 
    const runInference = (text: string) => {
  const embeddings = tokenizer.encode(text);
  const predictions = model.predict(tf.tensor([embeddings]));
  return extractMindMapNodes(predictions); // Outputs Xmind-compatible JSON
};
  • Image Processing:
    Use OpenCV.js (compiled to WebAssembly) for sketch preprocessing before TensorFlow inference.

Phase 3: Security & Distribution

  1. Secure Download Protocol:
    • Validate download URLs via SHA-256 checksum comparison against Xmind’s signed manifest.
    • Implement certificate pinning using electron-signed.
  2. Auto-Update Workflow:
    • Configure Electron Forge to publish signed builds to S3 with incremental updates.

Phase 4: Performance Optimization

  • WebWorker Parallelization: Offload AI processing to dedicated threads.
  • Model Quantization: Convert TF.js models to INT8 format (40% size reduction).
  • Lazy Loading: Split TensorFlow.js and OpenCV.js into separate chunks.

5. Scalability & Extensibility

  • Modular AI Add-ons: 
    ```mermaid