Interactive LLM Processing Visualizer

Modern React application for visualizing Large Language Model text processing pipelines. Built with composable frontend architecture using React, Vite, and shadcn/ui components for real-time interactive demonstrations of LLM internals.

llm-learn-demo.mp4

Architecture

flowchart TB
    subgraph internet["Internet"]
        client(("Client Browser"))
    end

    subgraph app["React Application"]
        subgraph ui["UI Layer"]
            input["Text Input Component React + shadcn/ui"]
            viz["Visualization Component WebGL + Framer Motion"]
            controls["Control Panel shadcn/ui"]
        end
        
        subgraph core["Core Processing"]
            subgraph state["State Management"]
                store["Zustand Store"]
            end
            
            subgraph pipeline["LLM Processing Pipeline"]
                token["Tokenization Processor"]
                embed["Embedding Processor"]
                attn["Attention Visualizer"]
                ffn["Feed Forward Network"]
                gen["Output Generator"]
            end
        end

        subgraph services["Service Layer"]
            compute["Computation Service"]
            animate["Animation Service"]
            layout["Layout Engine"]
        end
    end

    client --> input
    input --> store
    store --> pipeline
    pipeline --> viz
    controls --> store
    services --> viz
    viz --> client

    classDef default fill:#f9f9f9,stroke:#333,stroke-width:2px
    classDef internet fill:#e9e9e9,stroke:#666
    classDef ui fill:#FFE4B5,stroke:#d49d4f
    classDef core fill:#B0E0E6,stroke:#4682B4
    classDef services fill:#98FB98,stroke:#3CB371
    
    class internet internet
    class input,viz,controls ui
    class store,pipeline core
    class services services

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Key Components

• User Interface Layer: React components using shadcn/ui
• State Management: Zustand store with middleware
• Processing Pipeline: Modular visualization processors
• Service Layer: Animation and computation services

Technical Stack

• Frontend Framework: React 18 with Vite
• UI Components: shadcn/ui with Tailwind CSS
• State Management: Zustand
• Animation: Framer Motion
• Icons: Lucide React
• Testing: Vitest and Testing Library
• Performance: React Suspense and lazy loading

Prerequisites

• Node.js 18.0+
• npm 8.0+
• Modern browser with WebGL support

Development Setup

1. Clone Repository

git clone https://github.com/organization/llm-visualizer
cd llm-visualizer

2. Install Dependencies

# Install project dependencies
npm install

# Install required shadcn/ui components
npm run shadcn:add button card input slider toast alert dialog

# Setup development environment
cp .env.example .env.local

3. Configure Environment

VITE_APP_VERSION=1.0.0
VITE_ENABLE_DEBUG=false
VITE_ANIMATION_SPEED=1
VITE_MAX_INPUT_LENGTH=1000

4. Start Development Server

npm run dev

Project Structure

├── src/
│   ├── components/
│   │   ├── ui/             # shadcn/ui components
│   │   ├── visualization/  # LLM visualization components
│   │   └── layout/        # Layout components
│   ├── hooks/
│   │   ├── useProcessor.ts
│   │   └── useAnimation.ts
│   ├── services/
│   │   ├── tokenizer.ts
│   │   ├── embedding.ts
│   │   └── attention.ts
│   ├── store/
│   │   └── index.ts
│   └── utils/
│       ├── constants.ts
│       └── helpers.ts
├── tests/
│   ├── unit/
│   └── integration/
└── public/

Testing

# Run unit tests
npm run test:unit

# Run integration tests
npm run test:integration

# Run all tests with coverage
npm run test:coverage

Performance Optimization

• Dynamic imports for visualization modules
• Memoization of expensive computations
• WebGL acceleration for matrix operations
• Debounced user input handling
• Virtual scrolling for large datasets

Browser Support

• Chrome/Edge 90+
• Firefox 90+
• Safari 14+

Error Handling

The application implements comprehensive error boundaries and fallbacks:

• Input validation errors
• Processing pipeline failures
• Animation engine errors
• Resource loading issues

Monitoring and Analytics

• Performance metrics collection
• User interaction tracking
• Error reporting
• Usage analytics

Contributing

1. Fork the repository
2. Create a feature branch
3. Implement changes with tests
4. Submit pull request

Development Guidelines

• Follow conventional commits
• Add tests for new features
• Update documentation
• Maintain type safety
• Follow ESLint configuration

Build and Deployment

# Production build
npm run build

# Preview production build
npm run preview

# Type check
npm run type-check

# Lint
npm run lint

Security

• Input sanitization
• XSS prevention
• CSP configuration
• Regular dependency audits

License

MIT License

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For questions and support, please open an issue in the repository.