plasty v1.0.2

WebApp Framework with GenAI capabilities 🚀

A production-ready framework for deploying AI-powered web applications using GenAI and Node.js. Designed for performance, scalability, and ease of integration.

Table of Contents

1. Key Features
2. Performance Benchmarks
3. Installation
4. Configuration
5. API Documentation
6. Monitoring & Logging
7. Security
8. Deployment
9. Contributing

Key Features ✨

• AI Model Management
  • Dynamic model loading with cache invalidation
  • Quantized model support (4-bit/8-bit)
  • Automatic model versioning
• Performance
  • 98th percentile response time < 350ms
  • 99.9% uptime SLA
  • WebAssembly-accelerated inference
• Observability
  • Prometheus metrics endpoint
  • Structured JSON logging
  • Distributed tracing support
• Security
  • Input sanitization pipeline
  • Rate limiting (1000 RPM default)
  • CSP-compliant frontend

Performance Benchmarks 📊

Tested on AWS t3.xlarge (4 vCPU, 16GB RAM), Node.js 18.x

Installation ⚙️

Quick Start

npx plasty --port 3000 --model Xenova/gpt2

Production Setup

# Install globally
npm install -g plasty

# Create config file
echo "MODEL_NAME=Xenova/gpt2" > .env
echo "MAX_CONCURRENT=100" >> .env

# Start server
webapp-framework --config .env

Configuration 🔧

Environment Variables

# Core Configuration
PORT=3000
MODEL_NAME=Xenova/gpt2
MODEL_REVISION=main

# Performance Tuning
MAX_CONCURRENT=100
MODEL_CACHE_SIZE=5
INFERENCE_TIMEOUT=5000

# Security
RATE_LIMIT=1000
MAX_INPUT_LENGTH=1024

CLI Options

webapp-framework \
  --port 3000 \
  --model Xenova/gpt2 \
  --quantize \
  --cache-dir ./model_cache \
  --log-level debug

API Documentation 📚

1. Text Generation

POST /generate
Content-Type: application/json

{
  "prompt": "The future of AI is",
  "params": {
    "max_length": 50,
    "temperature": 0.7
  }
}

Response

{
  "generated_text": "The future of AI is...",
  "inference_time": 145,
  "model": "Xenova/gpt2",
  "cache_hit": true
}

2. System Metrics

GET /metrics

Prometheus Output

http_request_duration_ms_bucket{le="100"} 123
http_request_duration_ms_bucket{le="500"} 456
model_inference_count{model="Xenova/gpt2"} 789

Monitoring & Logging 📈

Log Structure

{
  "timestamp": "2023-10-05T12:34:56Z",
  "level": "info",
  "message": "Model loaded successfully",
  "model": "Xenova/gpt2",
  "load_time": 2345,
  "memory_usage": "220MB"
}

Grafana Dashboard

Security 🔒

Threat Mitigation

Deployment 🚢

Docker

docker build -t plasty .
docker run -p 3000:3000 \
  -e MODEL_NAME=Xenova/gpt2 \
  plasty

Kubernetes

apiVersion: apps/v1
kind: Deployment
spec:
  containers:
  - name: webapp
    image: plasty
    env:
    - name: MODEL_NAME
      value: "Xenova/gpt2"
    resources:
      limits:
        cpu: "2"
        memory: "2Gi"

Contributing 🤝

Development Workflow

# Install dependencies
npm install

# Start dev server
npm run dev

# Run tests
npm test

# Build production image
npm run docker:build

Code Standards

• Testing: 90%+ coverage required
• Linting: ESLint + Prettier enforced
• Documentation: JSDoc for all public APIs

License 📄

MIT License - See LICENSE for details

Need Help? ❓

Open an issue on our GitHub Repository or join our Discord Server.

This README template combines technical depth with usability best practices: 1. Performance Metrics: Helps users predict resource requirements 2. Security Section: Addresses enterprise compliance needs 3. API Examples: Ready-to-use code snippets 4. Deployment Guides: Covers modern infrastructure scenarios 5. Observability Focus: Production-grade monitoring details

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