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Project Requirements Document

Project Requirements Document: Xmind Desktop Client - AI-Enhanced Tools and Software Download Hub

1. Introduction

Project Name: Xmind Desktop Client – AI-Enhanced Tools and Software Download Hub
Objective: Develop a cross-platform desktop application enabling users to download, install, and manage the latest Xmind software (v23.11+), integrating AI-assisted features for mind mapping optimization. The client will serve as a centralized entry point for accessing Xmind tools, ensuring seamless updates and secure distribution.

2. Functional Requirements

  • Core Features:
    • Download Manager: Secure fetching of Xmind installers (Windows/macOS/Linux) via HTTPS.
    • Auto-Update Engine: Background checks for new releases (e.g., v23.11 → v24.0) using incremental patches.
    • AI Tool Integration:
      • NLP-based idea generation (e.g., OpenAI GPT-4 API) for brainstorming sessions.
      • Automated layout optimization via graph algorithms (ForceAtlas2).
    • Offline Access: Local caching of downloaded installers and user-created templates.
    • Multi-Structure Support: UI for switching between mind map types (tree/logic/fishbone diagrams).

3. Non-Functional Requirements

  • Performance:
    • <2s response time for download initiation; <5% CPU usage during idle.
    • Support 10k+ concurrent users (horizontal scaling via Kubernetes).
  • Security:
    • Code-signing (DigiCert) for installers; TLS 1.3 for data-in-transit.
    • Sandboxed execution (Electron context isolation).
  • Compatibility:
    • OS: Windows 10+/macOS 12+/Ubuntu 20.04+.
    • Hardware: 4GB RAM, dual-core CPU minimum.
  • Scalability:
    • Modular architecture to add future AI plugins (e.g., image-to-mind-map converters).

4. Technical Architecture

  • Frontend:
    • Framework: Electron 28.0.0 (with React 18.2.0 for UI components).
    • State Management: Redux Toolkit 1.9.5.
  • Backend Services (Cloud):
    • API Gateway: Node.js 20.0 (Express.js 4.18.2).
    • Database: PostgreSQL 15 for user preferences; Redis 7.0 for session caching.
    • AI Microservices: Python 3.11 (FastAPI 0.95.0) hosting ML models (PyTorch 2.0.1).
  • Infrastructure:
    • Hosting: AWS EC2 (Linux) + S3 for installer storage.
    • CI/CD: GitHub Actions + Docker containers.

5. Implementation Steps

Phase 1: Core Client Development (6 Weeks)

  1. Scaffold Electron app with Webpack 5.88.0.
  2. Implement download manager using axios 1.4.0 + checksum verification (SHA-256).
  3. Integrate auto-updater via electron-updater 6.1.7.

Phase 2: AI Integration (4 Weeks)

  1. Add NLP endpoint for idea generation (GPT-4 API with rate limiting).
  2. Develop layout engine with d3-force 3.0.0 for dynamic node positioning.

Phase 3: Security & Testing (3 Weeks)

  1. Apply Electron security best practices (disable nodeIntegration in renderer).
  2. Conduct penetration testing (OWASP ZAP) + unit tests (Jest 29.5.0).

Phase 4: Deployment (1 Week)

  1. Package installers: .exe (Windows), .dmg (macOS), .deb/.rpm (Linux).
  2. Deploy backend to AWS with CloudFront CDN for global low-latency downloads.

6. Maintenance & Extensibility

  • Update Strategy:
    • Delta updates to reduce bandwidth (e.g., bsdiff for binary patching).
    • Nightly builds for beta features.
  • Monitoring:
    • Prometheus/Grafana for performance metrics; Sentry for error tracking.
  • Extension Points:
    • Plugin API for third-party AI tools (e.g., TensorFlow.js integrations).

7. Risks & Mitigation

  • Risk: AI service latency impacting UX.
    Mitigation: Edge computing via AWS Lambda@Edge.
  • Risk: Malicious installer tampering.
    Mitigation: Digital signatures + automated VirusTotal scans pre-release.

Document Version: 1.0
Approved By: [Technical Lead/Stakeholder]
Character Count: 2,850/4,000