AI System Architecture Design

AI System Architecture Design for Xmind AI Desktop Client

1. Introduction

The Xmind AI Desktop Client integrates generative AI capabilities into Xmind’s core mind-mapping platform, enabling features like automated brainstorming, semantic node organization, and intelligent template suggestions. This architecture supports Windows/macOS clients, backend AI services, and secure distribution via a dedicated download portal.

2. Architectural Goals

• Scalability: Handle 10K+ concurrent users with <500ms AI response latency.
• Security: GDPR/CCPA compliance, end-to-end encryption.
• Performance: Optimize for large mind maps (10K+ nodes).
• Extensibility: Modular design for future AI model upgrades.

3. High-Level Architecture

![Architecture Diagram: Client-Server-Microservices]

┌──────────────────┐       ┌──────────────────────────┐       ┌─────────────────────┐  
│ Desktop Client   │───────│ API Gateway              │───────│ AI Microservices    │  
│ (Electron 26.0.0)│       │ (NGINX 1.25 + Kong 3.4) │       │ (Python 3.11)       │  
└──────────────────┘       └──────────────────────────┘       ├─────────────────────┤  
                              │                              │ - LLM Inference     │  
                              │                              │   (Llama 3 70B)      │  
┌──────────────────┐       ┌──┴───┐                          ├─────────────────────┤  
│ Download Portal  │───────│ Redis│                          │ - NLP Engine        │  
│ (React 18 + AWS  │       │ 7.2  │                          │   (spaCy 3.7)       │  
│ S3)              │       └──────┘                          └─────────────────────┘  
└──────────────────┘               │  
                                   │  
                            ┌──────┴──────┐  
                            │ PostgreSQL  │  
                            │ 15 + pgvector│  
                            └─────────────┘  

4. Technology Stack

5. AI Component Design

• AI Microservices:
  • Idea Generation Service: Uses Llama 3 for context-aware brainstorming. Input: user prompts → Output: structured node hierarchies.
  • Semantic Clustering Engine: spaCy-based NLP to auto-group nodes by topic similarity (cosine distance <0.2).
  • Template Recommender: Collaborative filtering (k-NN) on pgvector-embedded user maps.
• Client Integration:
  • Electron IPC channels for async AI requests.
  • Local caching of frequent AI outputs (e.g., template suggestions).

6. Implementation Steps

1. Phase 1: Core Client (Weeks 1-4)
   • Build Electron shell with React UI.
   • Integrate Xmind SDK for map rendering.
   • Implement secure auto-update via AWS S3 presigned URLs.
2. Phase 2: AI Backend (Weeks 5-8)
   • Deploy Llama 3 on GPU-optimized EC2 instances (g5.4xlarge).
   • Develop FastAPI endpoints: /generate-ideas, /cluster-nodes.
   • Configure pgvector for storing map embeddings.
3. Phase 3: Security & Scaling (Weeks 9-12)
   • Add TLS 1.3 encryption for client-server comms.
   • Implement rate limiting (Kong: 100 reqs/user/min).
   • Kubernetes HPA for AI pods (scale at 70% CPU).

7. Security Measures

• Data Privacy: User data anonymization via SHA-3 hashing before AI processing.
• Client Hardening: Electron sandboxing + Context Isolation.
• Compliance: Audit trails in PostgreSQL; automatic PII redaction.

8. Scalability & Performance

• Caching: Redis stores AI responses (TTL=1hr) to reduce Llama 3 calls by 40%.
• Load Handling:
  • API Gateway queues requests >500ms; falls back to local ML model (TensorFlow.js).
  • pgvector indexing for sub-100ms template searches.
• Cost Control: Spot instances for non-critical AI tasks.

9. Conclusion

This architecture delivers a secure, low-latency AI-enhanced Xmind experience while enabling seamless future upgrades (e.g., multimodal input support). The decoupled microservices allow independent scaling of AI components, with quantized models ensuring resource efficiency.

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