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Assistant checkpoint: Added architecture diagrams and removed Replit section
Assistant generated file changes: - README.md: Remove Replit deployment section and add architecture diagrams at the beginning, Remove the Deployment on Replit section --- User prompt: remove this : Deployment on Replit section. also on the top add two architectures in the beginning how mindmap and processor works ..just architecture so that people at glance understands. remaining everthing will be same . Replit-Commit-Author: Assistant Replit-Commit-Session-Id: e5c8aa2d-05f7-401c-b338-9e102c33d9ab
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README.md

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A revolutionary lightweight library that combines **interactive mind map visualization** with **intelligent data pipeline building**. Transform JSON data into beautiful mind maps AND create ML-powered data transformation pipelines through simple configuration.
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## 🏗️ System Architecture Overview
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### Mind Map Visualization Architecture
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```mermaid
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flowchart TB
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subgraph "📄 Data Input"
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A[JSON Data]
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B[Nested Objects]
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C[Arrays & Lists]
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end
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subgraph "🔄 Processing Engine"
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D[Schema Detection]
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E[Node Generation]
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F[Hierarchy Mapping]
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G[Layout Calculation]
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end
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subgraph "🎨 Visualization Layer"
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H[D3.js Rendering]
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I[Interactive Nodes]
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J[Zoom & Pan]
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K[Theme System]
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end
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subgraph "👥 User Interaction"
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L[Click Events]
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M[Hover Effects]
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N[Expand/Collapse]
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O[Export Options]
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end
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A --> D
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B --> D
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C --> D
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D --> E
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E --> F
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F --> G
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G --> H
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H --> I
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I --> J
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J --> K
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K --> L
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L --> M
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M --> N
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N --> O
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classDef input fill:#e3f2fd
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classDef processing fill:#f3e5f5
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classDef visual fill:#e8f5e8
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classDef interaction fill:#fff3e0
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class A,B,C input
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class D,E,F,G processing
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class H,I,J,K visual
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class L,M,N,O interaction
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```
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### Data Pipeline Processor Architecture
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```mermaid
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flowchart TB
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subgraph "📥 Input Sources"
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A[IoT Sensors]
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B[REST APIs]
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C[Kafka Streams]
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D[Databases]
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E[WebSockets]
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end
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subgraph "🧠 ML Analysis Engine"
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F[Pattern Recognition]
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G[Schema Mapping]
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H[Rule Generation]
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I[Transformation Logic]
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end
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subgraph "⚡ Pipeline Engine"
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J[Data Validation]
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K[Static Execution]
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L[Error Handling]
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M[Performance Monitoring]
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end
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subgraph "📤 Output Destinations"
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N[Analytics DB]
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O[Real-time Dashboard]
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P[Alert Systems]
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Q[ML Training Data]
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end
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A -.->|Stream| F
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B -.->|Poll| F
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C -.->|Subscribe| F
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D -.->|CDC| F
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E -.->|Live| F
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F --> G
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G --> H
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H --> I
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I --> J
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J --> K
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K --> L
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L --> M
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M --> N
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M --> O
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M --> P
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M --> Q
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classDef sources fill:#ffebee
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classDef ml fill:#e8eaf6
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classDef pipeline fill:#e0f2f1
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classDef outputs fill:#fafafa
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class A,B,C,D,E sources
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class F,G,H,I ml
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class J,K,L,M pipeline
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class N,O,P,Q outputs
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```
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## 🚀 What's New in v2.1.0
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**Enhanced Pipeline Engine** with ML-powered data transformation
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- Date format conversions
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- Calculated fields
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## 🚀 Deployment on Replit
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Deploy your pipeline-powered application on Replit:
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1. **Import Project**: Upload your NodeQ project to Replit
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2. **Install Dependencies**: Run `npm install`
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3. **Configure Port**: Use port 5000 for web applications
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4. **Deploy**: Use Replit's deployment feature to publish your app
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5. **Share**: Get a public URL for your pipeline builder
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### Replit Configuration
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```javascript
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// Use 0.0.0.0 for public access
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const server = app.listen(5000, '0.0.0.0', () => {
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console.log('Pipeline Builder running on port 5000');
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});
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```
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## 🔬 Advanced Pipeline Features
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