CHATGPT: AI SYSTEM ARCHITECTURE

🖥️ AI System Architecture

Complete Mermaid JS Implementation for Blogger/BlogSpot

1. Complete AI Architecture Overview

This diagram shows the complete AI system architecture with all major components, including client interfaces, edge computing, cloud services, data layers, and MLOps pipelines.

graph TB subgraph "Client Layer" A[🌐 Web Application] B[📱 Mobile App] C[🔌 IoT Devices] D[💬 Chat Interface] end subgraph "Edge Computing Layer" E[⚡ Edge AI Processor] F[🔒 Local Inference] G[📊 Real-time Analytics] E --> H[Edge Cache] end subgraph "Cloud Gateway" I[🔗 API Gateway] J[🔐 Authentication] K[⚖️ Load Balancer] L[📈 Rate Limiter] end subgraph "AI Microservices Cluster" M[🧠 LLM Service
GPT/Llama] N[👁️ Computer Vision
YOLO/CNN] O[🗣️ Speech AI
Whisper/Wav2Vec] P[🎯 Recommendation
Engine] Q[🔍 Search &
Retrieval] R[📝 Document
Processor] end subgraph "Model Management" S[📦 Model Registry] T[🔄 Version Control] U[🧪 A/B Testing] V[🎯 Canary Deployment] end subgraph "Data Storage Layer" W[📊 Vector Database
Pinecone/Weaviate] X[🗃️ Relational DB
PostgreSQL] Y[📁 NoSQL DB
MongoDB] Z[🗄️ Object Storage
S3/MinIO] AA[🔗 Graph Database
Neo4j] end subgraph "MLOps Pipeline" BB[📥 Data Ingestion] CC[🧹 Data Cleaning] DD[🏷️ Data Labeling] EE[⚙️ Feature Engineering] FF[🏋️ Model Training] GG[📊 Model Evaluation] HH[🚀 Model Deployment] end subgraph "Monitoring & Observability" II[📈 Performance Metrics] JJ[🚨 Alert System] KK[📝 Logging] LL[🔍 Tracing] end %% Connections A --> I B --> I C --> E E --> I D --> I I --> K K --> M K --> N K --> O K --> P K --> Q K --> R M --> W N --> Z O --> Y P --> AA Q --> W R --> Y HH --> S S --> T T --> U U --> V BB --> CC --> DD --> EE --> FF --> GG --> HH M --> II N --> II O --> II II --> JJ JJ --> KK KK --> LL classDef client fill:#e3f2fd,stroke:#2196f3 classDef edge fill:#e8f5e8,stroke:#4caf50 classDef gateway fill:#fff3e0,stroke:#ff9800 classDef microservice fill:#f3e5f5,stroke:#9c27b0 classDef model fill:#e8eaf6,stroke:#3f51b5 classDef storage fill:#e0f2f1,stroke:#009688 classDef mlops fill:#fff8e1,stroke:#ffb300 classDef monitoring fill:#ffebee,stroke:#f44336 class A,B,C,D client class E,F,G,H edge class I,J,K,L gateway class M,N,O,P,Q,R microservice class S,T,U,V model class W,X,Y,Z,AA storage class BB,CC,DD,EE,FF,GG,HH mlops class II,JJ,KK,LL monitoring

💡 Note: This architecture supports multiple AI services running as independent microservices, allowing for scalability and independent deployment of each AI capability.

2. AI Request Processing Flow

Detailed workflow showing how user requests are processed through the AI system, from input to response generation.

sequenceDiagram participant U as User participant C as Client App participant G as API Gateway participant A as Auth Service participant R as Request Router participant LLM as LLM Service participant CV as CV Service participant KB as Knowledge Base participant DB as Vector DB participant Cache as Cache Layer participant Log as Logger U->>C: Submit Request (Text/Image/Audio) C->>G: HTTP/WebSocket Request G->>A: Validate Token A-->>G: Authentication Result alt Authentication Failed G-->>C: 401 Unauthorized C-->>U: Show Error else Authentication Success G->>R: Route Request R->>Log: Log Request alt Text Request R->>LLM: Process NLP LLM->>KB: Retrieve Context KB->>DB: Query Embeddings DB-->>KB: Similar Documents KB-->>LLM: Enhanced Context LLM->>Cache: Check Response Cache alt Cache Hit Cache-->>LLM: Cached Response else Cache Miss LLM->>LLM: Generate Response LLM->>Cache: Store Response end LLM-->>R: AI Response else Image Request R->>CV: Process Image CV->>CV: Run Inference CV->>Log: Log Results CV-->>R: Image Analysis else Audio Request R->>LLM: Transcribe Audio LLM-->>R: Text Transcription R->>LLM: Process Text LLM-->>R: Audio Response end R->>G: Formatted Response G->>C: HTTP Response C->>U: Display Result R->>Log: Log Processing Time Log->>Log: Update Metrics end

3. MLOps Pipeline Architecture

The complete machine learning operations pipeline showing the journey from data collection to model deployment and monitoring.

flowchart TD subgraph "Phase 1: Data Management" A[📥 Data Collection] --> B[🧪 Data Validation] B --> C[🔒 Data Versioning
DVC/Delta Lake] C --> D[🏷️ Data Labeling
Label Studio/SageMaker] D --> E[📊 Dataset Creation] end subgraph "Phase 2: Experimentation" E --> F[⚙️ Feature Engineering] F --> G[🧮 Feature Store
Feast/Tecton] G --> H[🔬 Experiment Tracking
MLflow/Weights & Biases] H --> I[🤖 Model Training
PyTorch/TensorFlow] end subgraph "Phase 3: Model Management" I --> J[📈 Model Evaluation] J --> K{Performance Check} K -->|Pass| L[📦 Model Registry
MLflow Registry] K -->|Fail| M[🔄 Retrain Model] M --> F L --> N[🧪 Model Testing
Unit/Integration] N --> O[✅ Model Approval] end subgraph "Phase 4: Deployment" O --> P[🚀 Model Packaging
Docker/ONNX] P --> Q[🌐 Model Serving
KServe/Triton] Q --> R[⚖️ Load Balancing] R --> S[🔄 Canary Deployment] S --> T[📊 A/B Testing] end subgraph "Phase 5: Monitoring" T --> U[👀 Model Monitoring
Evidently/WhyLabs] U --> V[📈 Performance Metrics] V --> W[🚨 Alert System] W --> X{Drift Detected?} X -->|Yes| M X -->|No| Y[✅ Model in Production] end %% Styling classDef phase1 fill:#e3f2fd,stroke:#2196f3 classDef phase2 fill:#e8f5e8,stroke:#4caf50 classDef phase3 fill:#fff3e0,stroke:#ff9800 classDef phase4 fill:#f3e5f5,stroke:#9c27b0 classDef phase5 fill:#ffebee,stroke:#f44336 class A,B,C,D,E phase1 class F,G,H,I phase2 class J,K,L,M,N,O phase3 class P,Q,R,S,T phase4 class U,V,W,X,Y phase5

4. How to Implement in Blogger

Copy and paste this complete code into your Blogger HTML editor. The code includes all necessary Mermaid JS configuration and sample diagrams.

📝 Instructions:

1. Go to your Blogger Dashboard
2. Click on "Theme" → "Edit HTML"
3. Paste the entire code below
4. Save and view your blog

<!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>AI Architecture Diagrams</title> <!-- Mermaid JS --> <script src="https://cdn.jsdelivr.net/npm/mermaid@10.6.1/dist/mermaid.min.js"></script> <script> mermaid.initialize({ startOnLoad: true, theme: 'default', securityLevel: 'loose', flowchart: { curve: 'basis' } }); </script> <style> body { font-family: Arial, sans-serif; max-width: 1200px; margin: 0 auto; padding: 20px; background: #f5f5f5; } .mermaid { background: white; padding: 20px; border-radius: 8px; margin: 20px 0; overflow: auto; } .diagram-title { color: #333; margin: 30px 0 10px 0; } </style> </head> <body> <h1>AI Architecture Diagrams</h1> <h2 class="diagram-title">Complete AI System</h2> <div class="mermaid"> graph TB A[User Input] --> B[API Gateway] B --> C[Load Balancer] C --> D[LLM Service] C --> E[Vision Service] C --> F[Speech Service] D --> G[Vector Database] E --> H[Image Storage] F --> I[Audio Processor] G --> J[Response Generator] H --> J I --> J J --> K[User Output] </div> <h2 class="diagram-title">MLOps Pipeline</h2> <div class="mermaid"> flowchart LR A[Data Collection] --> B[Data Processing] B --> C[Model Training] C --> D[Model Evaluation] D --> E[Model Deployment] E --> F[Monitoring] F --> A </div> </body> </html>

✨ Pro Tips:

• Use the Blogger "HTML" editor mode when pasting this code
• Customize colors and styling by modifying the CSS section
• Add more diagrams by copying the mermaid div structure
• Test on mobile devices to ensure responsive display

AI Architecture Diagrams using Mermaid JS | Compatible with Blogger/BlogSpot

Last Updated: 2024 | Mermaid Version: 10.6.1