mirror of
https://github.com/The-Low-Code-Foundation/OpenNoodl.git
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1179 lines
49 KiB
Markdown
1179 lines
49 KiB
Markdown
# Phase 9+: AI-Powered Creation System
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## "I Described a Backend and It Built Itself"
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**Status:** BLUE SKY / PANDORA'S BOX
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**Priority:** WORLD DOMINATION
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**Risk Level:** 🔥🔥🔥🔥🔥 (Heads May Literally Explode)
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**Dependencies:** Phase 6 UBA complete, Sanity check optional
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---
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## The Vision
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ │
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│ User: "I need a backend that uses Claude to generate web components │
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│ for my clients. I want endpoints to create, save, and retrieve │
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│ components from a Postgres database." │
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│ │
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│ AI: "I'll create a Python/FastAPI backend with Anthropic SDK, │
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│ SQLAlchemy for Postgres, and expose these endpoints via UBA..." │
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│ │
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│ [Vibe coding in progress...] │
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│ [Generating UBA schema...] │
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│ [Building Docker image...] │
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│ [Spinning up container...] │
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│ │
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│ AI: "Done. Check your Backend Services panel. I've created: │
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│ - /generate endpoint for Claude component generation │
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│ - /save endpoint for persisting to Postgres │
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│ - /retrieve endpoint for loading saved components │
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│ - A config panel for your API keys and database settings │
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│ - Debug streaming so you can watch Claude think" │
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│ │
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│ User: [head explodes] │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Part 1: The Backend Creation AI
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### What It Does
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User describes backend needs in natural language → AI:
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1. Analyzes requirements
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2. Chooses optimal tech stack
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3. Writes the backend code
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4. Generates UBA schema YAML
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5. Creates Dockerfile
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6. Spins up container
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7. Registers with Nodegx
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8. Config panel appears
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### The Conversation Flow
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ Backend Creator AI [New Chat] │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ User: I need a backend that: │
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│ - Calls Claude to generate React components from descriptions │
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│ - Saves generated components to a database │
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│ - Has version history for each component │
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│ - Can retrieve and diff previous versions │
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│ - Sends me a Slack notification when generation completes │
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│ │
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│ AI: Great! Let me understand your requirements: │
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│ │
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│ Core Features: │
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│ ✓ AI generation (Claude API) │
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│ ✓ Persistent storage (needs database) │
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│ ✓ Version history (need versioning strategy) │
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│ ✓ Diff capability (text diff algorithm) │
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│ ✓ Notifications (Slack webhook) │
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│ │
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│ Recommended Stack: │
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│ • Python 3.11 + FastAPI (best Anthropic SDK support) │
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│ • PostgreSQL (robust, supports JSONB for components) │
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│ • SQLAlchemy + Alembic (ORM + migrations) │
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│ • Redis (optional: caching, rate limiting) │
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│ │
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│ Estimated Endpoints: │
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│ • POST /generate - Generate component from description │
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│ • GET /components - List all components │
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│ • GET /components/{id} - Get component with versions │
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│ • GET /components/{id}/versions - Version history │
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│ • GET /components/{id}/diff/{v1}/{v2} - Diff two versions │
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│ • POST /components/{id}/regenerate - Regenerate with feedback │
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│ │
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│ Should I proceed with this architecture? │
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│ │
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│ User: Yes, but also add the ability to export as a zip file │
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│ │
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│ AI: Added: │
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│ • GET /components/{id}/export - Download as zip │
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│ • GET /export/bulk - Export multiple components │
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│ │
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│ Creating your backend now... │
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│ │
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│ [████████████████████████████████████████] 100% │
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│ │
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│ ✓ Created project structure │
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│ ✓ Generated FastAPI application │
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│ ✓ Created database models │
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│ ✓ Generated UBA schema (12 config fields) │
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│ ✓ Created Dockerfile │
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│ ✓ Built image: nodegx-user-backend-abc123 │
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│ ✓ Container running on port 8042 │
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│ ✓ Registered with Nodegx │
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│ │
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│ Your backend is ready! Check the Backend Services panel. │
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│ You'll need to configure: │
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│ • Anthropic API key │
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│ • PostgreSQL connection (or I can spin up a container) │
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│ • Slack webhook URL │
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│ │
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│ User: Spin up Postgres too │
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│ │
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│ AI: Done. PostgreSQL container running. │
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│ Database auto-configured in your backend settings. │
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│ Connection string saved securely. │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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### Generated Project Structure
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```
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user-backend-abc123/
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├── docker-compose.yml # Backend + Postgres + Redis
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├── Dockerfile
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├── nodegx-schema.yaml # UBA schema for config panel
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├── requirements.txt
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├── alembic/ # Database migrations
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│ └── versions/
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├── app/
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│ ├── main.py # FastAPI app
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│ ├── config.py # Nodegx config integration
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│ ├── models/
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│ │ ├── component.py # SQLAlchemy models
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│ │ └── version.py
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│ ├── routers/
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│ │ ├── generate.py # /generate endpoints
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│ │ ├── components.py # CRUD endpoints
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│ │ └── export.py # Export endpoints
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│ ├── services/
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│ │ ├── claude.py # Anthropic integration
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│ │ ├── versioning.py # Version management
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│ │ ├── diff.py # Diff generation
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│ │ └── slack.py # Notifications
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│ └── nodegx/
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│ ├── config_handler.py # Receives Nodegx config
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│ └── debug_emitter.py # Debug streaming
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└── tests/
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└── ...
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```
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### Auto-Generated UBA Schema
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```yaml
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# nodegx-schema.yaml (AI-generated)
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schema_version: '1.0'
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backend:
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id: 'component-generator-abc123'
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name: 'Component Generator'
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description: 'AI-powered React component generation with versioning'
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version: '1.0.0'
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endpoints:
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config: '/nodegx/config'
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health: '/health'
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debug_stream: '/nodegx/debug'
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capabilities:
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hot_reload: true
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debug: true
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sections:
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- id: 'ai'
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name: 'AI Configuration'
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icon: 'cpu'
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fields:
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- id: 'anthropic_api_key'
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type: 'secret'
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name: 'Anthropic API Key'
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required: true
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- id: 'model'
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type: 'select'
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name: 'Claude Model'
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options:
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- value: 'claude-sonnet-4-20250514'
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label: 'Claude Sonnet 4'
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- value: 'claude-opus-4-20250514'
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label: 'Claude Opus 4'
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default: 'claude-sonnet-4-20250514'
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- id: 'max_tokens'
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type: 'number'
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name: 'Max Response Tokens'
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default: 4096
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- id: 'system_prompt'
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type: 'prompt'
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name: 'Component Generation Prompt'
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rows: 10
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default: |
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You are a React component generator. Create clean,
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modern React components using TypeScript and Tailwind CSS.
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- id: 'database'
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name: 'Database'
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icon: 'database'
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fields:
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- id: 'connection_mode'
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type: 'select'
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name: 'Database Mode'
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options:
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- value: 'managed'
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label: 'Nodegx Managed (Docker)'
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- value: 'external'
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label: 'External Database'
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default: 'managed'
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- id: 'external_url'
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type: 'secret'
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name: 'PostgreSQL Connection URL'
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visible_when:
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field: 'database.connection_mode'
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equals: 'external'
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- id: 'notifications'
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name: 'Notifications'
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icon: 'bell'
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fields:
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- id: 'slack_enabled'
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type: 'boolean'
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name: 'Enable Slack Notifications'
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default: false
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- id: 'slack_webhook'
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type: 'secret'
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name: 'Slack Webhook URL'
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visible_when:
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field: 'notifications.slack_enabled'
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equals: true
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- id: 'notify_on'
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type: 'multi_select'
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name: 'Notify On'
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visible_when:
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field: 'notifications.slack_enabled'
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equals: true
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options:
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- value: 'generation_complete'
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label: 'Generation Complete'
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- value: 'generation_error'
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label: 'Generation Error'
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- value: 'export'
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label: 'Component Exported'
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default: ['generation_complete', 'generation_error']
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- id: 'advanced'
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name: 'Advanced'
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icon: 'settings'
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collapsed: true
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fields:
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- id: 'rate_limit'
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type: 'number'
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name: 'Rate Limit (requests/minute)'
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default: 10
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- id: 'cache_ttl'
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type: 'number'
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name: 'Cache TTL (seconds)'
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default: 3600
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- id: 'version_retention'
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type: 'number'
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name: 'Version Retention Count'
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description: 'How many versions to keep per component'
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default: 50
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debug:
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enabled: true
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event_types:
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- id: 'generation_start'
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name: 'Generation Started'
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- id: 'claude_call'
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name: 'Claude API Call'
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- id: 'generation_complete'
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name: 'Generation Complete'
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- id: 'db_operation'
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name: 'Database Operation'
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```
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### The AI's Toolset
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The Backend Creator AI needs these tools:
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```typescript
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interface BackendCreatorTools {
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// Analysis
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analyzeRequirements(description: string): RequirementAnalysis;
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suggestArchitecture(requirements: RequirementAnalysis): Architecture;
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// Code Generation
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generateProjectStructure(architecture: Architecture): FileTree;
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generateFile(path: string, purpose: string): string;
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generateUBASchema(endpoints: Endpoint[], config: ConfigNeeds[]): string;
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// Docker
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generateDockerfile(stack: TechStack): string;
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generateDockerCompose(services: Service[]): string;
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buildImage(projectPath: string): ImageId;
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runContainer(imageId: string, ports: PortMapping[]): ContainerId;
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// Nodegx Integration
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registerBackend(url: string, schema: UBASchema): BackendId;
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// Iteration
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readFile(path: string): string;
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modifyFile(path: string, changes: string): void;
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runTests(projectPath: string): TestResult;
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getLogs(containerId: string): string[];
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}
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```
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### Follow-up Conversation Support
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The AI maintains conversation context to allow iterative refinement:
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```
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User: "Actually, can you add rate limiting per user, not global?"
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AI: "I'll modify the rate limiting to be per-user based on the
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X-User-ID header. Updating..."
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[Modified: app/middleware/rate_limit.py]
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[Modified: nodegx-schema.yaml - added user_id_header field]
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[Rebuilding container...]
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"Done. The config panel now has a 'User ID Header' field.
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Rate limits are tracked per unique user."
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```
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---
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## Part 2: The Frontend AI Assistant
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### The Problem: One Giant JSON
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Current Nodegx project structure:
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```
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project/
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└── project.json # 50,000+ lines, EVERYTHING in one file
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```
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This is **impossible** for AI assistance because:
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- Can't fit in context window
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- Can't target specific components
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- Every change risks corrupting unrelated things
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- No diff-friendly structure
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### The Solution: Component-Based File Structure
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Proposed new structure:
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```
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project/
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├── nodegx.config.json # Project metadata, settings
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├── routes.json # Route definitions
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├── components/
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│ ├── _index.json # Component registry
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│ ├── HomePage/
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│ │ ├── component.json # Component definition
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│ │ ├── nodes.json # Node graph
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│ │ ├── connections.json # Wiring
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│ │ └── styles.json # Component-specific styles
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│ ├── UserProfile/
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│ │ ├── component.json
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│ │ ├── nodes.json
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│ │ └── connections.json
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│ └── shared/
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│ ├── Header/
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│ ├── Footer/
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│ └── Button/
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├── models/
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│ ├── _index.json # Model registry
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│ ├── User.json
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│ └── Product.json
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├── styles/
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│ ├── global.json # Global styles
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│ └── themes/
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│ ├── light.json
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│ └── dark.json
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└── assets/
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└── ...
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```
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### Component File Format
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```json
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// components/UserProfile/component.json
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{
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"id": "user-profile-abc123",
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"name": "UserProfile",
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"type": "visual",
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"created": "2026-01-07T14:30:00Z",
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"modified": "2026-01-07T15:45:00Z",
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"description": "Displays user profile with avatar, name, and bio",
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"inputs": [
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{ "name": "userId", "type": "string", "required": true },
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{ "name": "showAvatar", "type": "boolean", "default": true }
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],
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"outputs": [
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{ "name": "onEdit", "type": "signal" },
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{ "name": "onLogout", "type": "signal" }
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],
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"dependencies": ["components/shared/Avatar", "components/shared/Button", "models/User"]
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}
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```
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```json
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// components/UserProfile/nodes.json
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{
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"nodes": [
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{
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"id": "node-1",
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"type": "Group",
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"label": "Profile Container",
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"properties": {
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"layout": "column",
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"padding": "16px",
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"gap": "12px"
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},
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"children": ["node-2", "node-3", "node-4"]
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},
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{
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"id": "node-2",
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"type": "component:shared/Avatar",
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"label": "User Avatar",
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"properties": {
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"size": "large"
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}
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},
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{
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"id": "node-3",
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"type": "Text",
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"label": "User Name",
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"properties": {
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"variant": "heading",
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"text": ""
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}
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}
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// ... more nodes
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]
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}
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```
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```json
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// components/UserProfile/connections.json
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{
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"connections": [
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{
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"from": { "node": "input:userId", "port": "value" },
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"to": { "node": "fetch-user", "port": "userId" }
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},
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{
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"from": { "node": "fetch-user", "port": "result.name" },
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"to": { "node": "node-3", "port": "text" }
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},
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{
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"from": { "node": "fetch-user", "port": "result.avatarUrl" },
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"to": { "node": "node-2", "port": "src" }
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}
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]
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}
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```
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### Migration Strategy
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```
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Phase 1: Add export capability
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- Export existing project to new format
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- Import back to old format
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- Validate round-trip
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Phase 2: Dual format support
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- Editor can open both formats
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- Auto-save in new format
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- Migration wizard
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Phase 3: New format default
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- New projects use new format
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- Old projects auto-migrate on open
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- Deprecation warnings
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```
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### The Frontend AI Assistant
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With component-based files, the AI can now:
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ Frontend AI Assistant │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ User: "Add a loading spinner to the UserProfile component while │
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│ fetching user data" │
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│ │
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│ AI: Let me check the UserProfile component... │
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│ │
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│ [Reading components/UserProfile/component.json] │
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│ [Reading components/UserProfile/nodes.json] │
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│ [Reading components/UserProfile/connections.json] │
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│ │
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│ I see you're fetching user data via a REST node. I'll: │
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│ 1. Add a loading state variable │
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│ 2. Add a Spinner component (from shared library) │
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│ 3. Show spinner while fetch is pending │
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│ 4. Hide spinner and show content when complete │
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│ │
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│ [Modified: components/UserProfile/nodes.json] │
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│ - Added: Condition node for loading state │
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│ - Added: Spinner component reference │
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│ │
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│ [Modified: components/UserProfile/connections.json] │
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│ - Connected: fetch.isPending → condition.condition │
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│ - Connected: condition.true → spinner.visible │
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│ - Connected: condition.false → content.visible │
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│ │
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│ Done! The component now shows a spinner during data fetch. │
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│ Would you like me to customize the spinner appearance? │
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│ │
|
|
│ User: "Yes, make it match our brand blue color" │
|
|
│ │
|
|
│ AI: [Reading styles/themes/light.json for brand colors] │
|
|
│ │
|
|
│ Found your brand blue: #3B82F6 │
|
|
│ │
|
|
│ [Modified: components/UserProfile/nodes.json] │
|
|
│ - Updated: Spinner color to use design token │
|
|
│ │
|
|
│ Done! The spinner now uses your brand blue. │
|
|
│ │
|
|
└─────────────────────────────────────────────────────────────────────────────┘
|
|
```
|
|
|
|
### AI Tools for Frontend Assistance
|
|
|
|
```typescript
|
|
interface FrontendAssistantTools {
|
|
// Project Navigation
|
|
listComponents(): ComponentSummary[];
|
|
listModels(): ModelSummary[];
|
|
getComponentDependencyGraph(): DependencyGraph;
|
|
|
|
// Component Operations
|
|
readComponent(componentPath: string): ComponentDefinition;
|
|
readComponentNodes(componentPath: string): NodeGraph;
|
|
readComponentConnections(componentPath: string): Connections;
|
|
|
|
// Modifications
|
|
addNode(componentPath: string, node: Node): void;
|
|
removeNode(componentPath: string, nodeId: string): void;
|
|
updateNodeProperty(componentPath: string, nodeId: string, property: string, value: any): void;
|
|
addConnection(componentPath: string, connection: Connection): void;
|
|
removeConnection(componentPath: string, connectionId: string): void;
|
|
|
|
// Creation
|
|
createComponent(name: string, template?: string): ComponentPath;
|
|
duplicateComponent(source: string, newName: string): ComponentPath;
|
|
|
|
// Understanding
|
|
explainComponent(componentPath: string): string;
|
|
findSimilarComponents(description: string): ComponentPath[];
|
|
suggestImprovements(componentPath: string): Suggestion[];
|
|
|
|
// Validation
|
|
validateComponent(componentPath: string): ValidationResult;
|
|
checkConnections(componentPath: string): ConnectionIssue[];
|
|
}
|
|
```
|
|
|
|
---
|
|
|
|
## Part 3: The Unified AI Architecture
|
|
|
|
### System Overview
|
|
|
|
```
|
|
┌─────────────────────────────────────────────────────────────────────────────┐
|
|
│ Nodegx AI System │
|
|
├─────────────────────────────────────────────────────────────────────────────┤
|
|
│ │
|
|
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
|
|
│ │ User Input │───▶│ AI Orchestrator │───▶│ Tool Execution │ │
|
|
│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
|
|
│ │ │ │
|
|
│ ▼ ▼ │
|
|
│ ┌─────────────────────────────────────────┐ │
|
|
│ │ Conversation Memory │ │
|
|
│ │ (prompt caching, history) │ │
|
|
│ └─────────────────────────────────────────┘ │
|
|
│ │
|
|
│ Tools Available: │
|
|
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
|
|
│ │ Frontend │ │ Backend │ │ Docker │ │ File │ │
|
|
│ │ Tools │ │ Tools │ │ Tools │ │ System │ │
|
|
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
|
|
│ │
|
|
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
|
|
│ │ UBA │ │ Database │ │ Git │ │ Testing │ │
|
|
│ │ Tools │ │ Tools │ │ Tools │ │ Tools │ │
|
|
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ │
|
|
│ │
|
|
└─────────────────────────────────────────────────────────────────────────────┘
|
|
```
|
|
|
|
### The Orchestrator
|
|
|
|
```python
|
|
# Conceptual architecture
|
|
|
|
class NodgexAIOrchestrator:
|
|
"""
|
|
Central AI system that routes requests to appropriate specialists
|
|
and maintains conversation context.
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.frontend_agent = FrontendAssistant()
|
|
self.backend_agent = BackendCreator()
|
|
self.conversation_memory = ConversationMemory()
|
|
self.tool_executor = ToolExecutor()
|
|
|
|
async def process(self, user_message: str, context: ProjectContext) -> Response:
|
|
# Add to conversation memory
|
|
self.conversation_memory.add_user_message(user_message)
|
|
|
|
# Classify intent
|
|
intent = await self.classify_intent(user_message)
|
|
|
|
# Route to appropriate agent
|
|
if intent.type == "frontend":
|
|
response = await self.frontend_agent.process(
|
|
user_message,
|
|
context,
|
|
self.conversation_memory
|
|
)
|
|
elif intent.type == "backend":
|
|
response = await self.backend_agent.process(
|
|
user_message,
|
|
context,
|
|
self.conversation_memory
|
|
)
|
|
elif intent.type == "mixed":
|
|
# Coordinate both agents
|
|
response = await self.coordinate_agents(
|
|
user_message,
|
|
context,
|
|
self.conversation_memory
|
|
)
|
|
|
|
# Add response to memory
|
|
self.conversation_memory.add_assistant_message(response)
|
|
|
|
return response
|
|
```
|
|
|
|
### Prompt Caching Strategy
|
|
|
|
For efficient token usage:
|
|
|
|
```python
|
|
class ConversationMemory:
|
|
"""
|
|
Manages conversation history with intelligent caching
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.messages: List[Message] = []
|
|
self.project_context_cache: Dict[str, CachedContext] = {}
|
|
self.max_context_tokens = 100000
|
|
|
|
def get_context_for_request(self, request_type: str) -> List[Message]:
|
|
"""
|
|
Build context for LLM request with caching
|
|
"""
|
|
|
|
# Static system prompt (cacheable)
|
|
system_prompt = self.get_system_prompt(request_type)
|
|
|
|
# Project structure summary (cache for 5 min)
|
|
project_summary = self.get_cached_project_summary()
|
|
|
|
# Relevant conversation history (last N turns)
|
|
recent_history = self.get_recent_history(turns=10)
|
|
|
|
# Component-specific context (cache per component)
|
|
if request_type == "frontend":
|
|
component_context = self.get_component_context()
|
|
|
|
return [
|
|
{"role": "system", "content": system_prompt, "cache_control": {"type": "ephemeral"}},
|
|
{"role": "user", "content": project_summary, "cache_control": {"type": "ephemeral"}},
|
|
*recent_history,
|
|
{"role": "user", "content": component_context} if component_context else None
|
|
]
|
|
|
|
def summarize_old_messages(self):
|
|
"""
|
|
When conversation gets long, summarize older messages
|
|
"""
|
|
if len(self.messages) > 20:
|
|
old_messages = self.messages[:-10]
|
|
summary = self.llm.summarize(old_messages)
|
|
|
|
# Replace old messages with summary
|
|
self.messages = [
|
|
{"role": "system", "content": f"Previous conversation summary: {summary}"},
|
|
*self.messages[-10:]
|
|
]
|
|
```
|
|
|
|
### Tool Execution Pattern
|
|
|
|
```python
|
|
class ToolExecutor:
|
|
"""
|
|
Executes tools requested by AI agents
|
|
"""
|
|
|
|
def __init__(self):
|
|
self.tools = {
|
|
# Frontend tools
|
|
"list_components": self.list_components,
|
|
"read_component": self.read_component,
|
|
"modify_node": self.modify_node,
|
|
"add_connection": self.add_connection,
|
|
|
|
# Backend tools
|
|
"generate_file": self.generate_file,
|
|
"build_docker": self.build_docker,
|
|
"run_container": self.run_container,
|
|
|
|
# Shared tools
|
|
"read_file": self.read_file,
|
|
"write_file": self.write_file,
|
|
"run_command": self.run_command,
|
|
}
|
|
|
|
async def execute(self, tool_name: str, params: Dict) -> ToolResult:
|
|
if tool_name not in self.tools:
|
|
return ToolResult(success=False, error=f"Unknown tool: {tool_name}")
|
|
|
|
try:
|
|
result = await self.tools[tool_name](**params)
|
|
return ToolResult(success=True, data=result)
|
|
except Exception as e:
|
|
return ToolResult(success=False, error=str(e))
|
|
```
|
|
|
|
---
|
|
|
|
## Part 4: OSS Options for Implementation
|
|
|
|
### Option A: LangGraph (Recommended)
|
|
|
|
We already have experience with LangGraph from Erleah. Benefits:
|
|
|
|
- Stateful graph-based agent architecture
|
|
- Built-in persistence for conversation memory
|
|
- Easy tool integration
|
|
- Streaming support
|
|
- Python ecosystem
|
|
|
|
```python
|
|
from langgraph.graph import StateGraph
|
|
from langgraph.checkpoint import MemorySaver
|
|
|
|
class NodegxAssistantGraph:
|
|
def __init__(self):
|
|
self.graph = StateGraph(AssistantState)
|
|
|
|
# Add nodes
|
|
self.graph.add_node("classify", self.classify_intent)
|
|
self.graph.add_node("frontend", self.frontend_agent)
|
|
self.graph.add_node("backend", self.backend_agent)
|
|
self.graph.add_node("execute_tools", self.execute_tools)
|
|
self.graph.add_node("respond", self.generate_response)
|
|
|
|
# Add edges
|
|
self.graph.add_conditional_edges(
|
|
"classify",
|
|
self.route_to_agent,
|
|
{
|
|
"frontend": "frontend",
|
|
"backend": "backend"
|
|
}
|
|
)
|
|
|
|
# Compile with memory
|
|
self.memory = MemorySaver()
|
|
self.app = self.graph.compile(checkpointer=self.memory)
|
|
```
|
|
|
|
### Option B: Custom with Anthropic Tools
|
|
|
|
Simpler, direct integration with Claude:
|
|
|
|
```python
|
|
from anthropic import Anthropic
|
|
|
|
class NodegxAssistant:
|
|
def __init__(self):
|
|
self.client = Anthropic()
|
|
self.tools = self.define_tools()
|
|
|
|
def define_tools(self):
|
|
return [
|
|
{
|
|
"name": "read_component",
|
|
"description": "Read a Nodegx component's definition",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {
|
|
"component_path": {"type": "string"}
|
|
},
|
|
"required": ["component_path"]
|
|
}
|
|
},
|
|
# ... more tools
|
|
]
|
|
|
|
async def process(self, message: str, history: List[Dict]):
|
|
response = await self.client.messages.create(
|
|
model="claude-sonnet-4-20250514",
|
|
max_tokens=4096,
|
|
system=SYSTEM_PROMPT,
|
|
tools=self.tools,
|
|
messages=[*history, {"role": "user", "content": message}]
|
|
)
|
|
|
|
# Handle tool calls
|
|
while response.stop_reason == "tool_use":
|
|
tool_results = await self.execute_tool_calls(response.content)
|
|
response = await self.client.messages.create(
|
|
model="claude-sonnet-4-20250514",
|
|
max_tokens=4096,
|
|
tools=self.tools,
|
|
messages=[
|
|
*history,
|
|
{"role": "user", "content": message},
|
|
{"role": "assistant", "content": response.content},
|
|
{"role": "user", "content": tool_results}
|
|
]
|
|
)
|
|
|
|
return response
|
|
```
|
|
|
|
### Option C: Cline/Aider Integration
|
|
|
|
Use existing AI coding assistants:
|
|
|
|
- Cline already works with VS Code
|
|
- Could wrap Cline's core for Nodegx
|
|
- Benefit from existing file management
|
|
|
|
But: Would need significant customization for Nodegx's visual paradigm.
|
|
|
|
### Recommendation
|
|
|
|
**Use LangGraph** with custom Nodegx-specific tools:
|
|
|
|
- Proven architecture from Erleah
|
|
- Good balance of flexibility and structure
|
|
- Python ecosystem for backend creation
|
|
- Can add specialized agents for different tasks
|
|
- Built-in conversation memory
|
|
|
|
---
|
|
|
|
## Part 5: Implementation Roadmap
|
|
|
|
### Phase 9A: Project Structure Modernization (4 weeks)
|
|
|
|
**Goal:** Enable AI assistance by restructuring project files
|
|
|
|
```
|
|
Week 1-2: Design & Export
|
|
- Design new file structure
|
|
- Build export from old format to new
|
|
- Validate structure works for AI access
|
|
|
|
Week 3-4: Editor Support
|
|
- Add support for new format in editor
|
|
- Migration wizard
|
|
- Backward compatibility layer
|
|
```
|
|
|
|
### Phase 9B: Frontend AI Assistant (6 weeks)
|
|
|
|
**Goal:** AI that can modify frontend components
|
|
|
|
```
|
|
Week 1-2: Tool Implementation
|
|
- Component reading tools
|
|
- Component modification tools
|
|
- Validation tools
|
|
|
|
Week 3-4: Agent Development
|
|
- LangGraph agent setup
|
|
- Conversation memory
|
|
- Prompt engineering
|
|
|
|
Week 5-6: Integration & Polish
|
|
- Editor UI for assistant
|
|
- Streaming responses
|
|
- Error handling
|
|
```
|
|
|
|
### Phase 9C: Backend Creation AI (8 weeks)
|
|
|
|
**Goal:** AI that can create backends from scratch
|
|
|
|
```
|
|
Week 1-2: Code Generation
|
|
- Project structure templates
|
|
- FastAPI/Express generators
|
|
- UBA schema generator
|
|
|
|
Week 3-4: Docker Integration
|
|
- Dockerfile generation
|
|
- docker-compose generation
|
|
- Container management
|
|
|
|
Week 5-6: Agent Development
|
|
- Requirements analysis
|
|
- Architecture decisions
|
|
- Iterative refinement
|
|
|
|
Week 7-8: Integration
|
|
- Nodegx registration
|
|
- End-to-end flow
|
|
- Testing
|
|
```
|
|
|
|
### Phase 9D: Unified Experience (4 weeks)
|
|
|
|
**Goal:** Seamless AI assistance across frontend and backend
|
|
|
|
```
|
|
Week 1-2: Orchestration
|
|
- Intent classification
|
|
- Agent coordination
|
|
- Context sharing
|
|
|
|
Week 3-4: Polish
|
|
- Unified UI
|
|
- Performance optimization
|
|
- Documentation
|
|
```
|
|
|
|
---
|
|
|
|
## Part 6: The User Experience
|
|
|
|
### The AI Panel
|
|
|
|
```
|
|
┌─────────────────────────────────────────────────────────────────────────────┐
|
|
│ Nodegx AI Assistant [New] [History] [⚙]│
|
|
├─────────────────────────────────────────────────────────────────────────────┤
|
|
│ │
|
|
│ ┌─────────────────────────────────────────────────────────────────────┐ │
|
|
│ │ Quick Actions: │ │
|
|
│ │ [+ Create Component] [+ Create Backend] [✨ Improve Selected] │ │
|
|
│ └─────────────────────────────────────────────────────────────────────┘ │
|
|
│ │
|
|
│ ┌───────────────────────────────────────────────────────────────┐ │
|
|
│ │ ┌──────┐ │ │
|
|
│ │ │ You │ I need a component that displays a list of products │ │
|
|
│ │ └──────┘ with filters for category and price range │ │
|
|
│ └───────────────────────────────────────────────────────────────┘ │
|
|
│ │
|
|
│ ┌───────────────────────────────────────────────────────────────┐ │
|
|
│ │ ┌──────┐ │ │
|
|
│ │ │ AI │ I'll create a ProductList component with: │ │
|
|
│ │ └──────┘ │ │
|
|
│ │ • Category dropdown filter │ │
|
|
│ │ • Price range slider │ │
|
|
│ │ • Grid layout for products │ │
|
|
│ │ • Loading and empty states │ │
|
|
│ │ │ │
|
|
│ │ Creating component... │ │
|
|
│ │ │ │
|
|
│ │ ✓ Created components/ProductList/ │ │
|
|
│ │ ✓ Added 12 nodes │ │
|
|
│ │ ✓ Connected to Products model │ │
|
|
│ │ │ │
|
|
│ │ [View Component] [Modify] [Undo] │ │
|
|
│ └───────────────────────────────────────────────────────────────┘ │
|
|
│ │
|
|
│ ┌───────────────────────────────────────────────────────────────┐ │
|
|
│ │ ┌──────┐ │ │
|
|
│ │ │ You │ Now I need a backend to fetch products from an │ │
|
|
│ │ └──────┘ external API and cache them in Redis │ │
|
|
│ └───────────────────────────────────────────────────────────────┘ │
|
|
│ │
|
|
│ ┌───────────────────────────────────────────────────────────────┐ │
|
|
│ │ ┌──────┐ │ │
|
|
│ │ │ AI │ I'll create a Product API backend with: │ │
|
|
│ │ └──────┘ │ │
|
|
│ │ • External API fetching │ │
|
|
│ │ • Redis caching layer │ │
|
|
│ │ • Configurable TTL │ │
|
|
│ │ │ │
|
|
│ │ Building backend... │ │
|
|
│ │ [████████░░░░░░░░░░░░] 40% │ │
|
|
│ └───────────────────────────────────────────────────────────────┘ │
|
|
│ │
|
|
├─────────────────────────────────────────────────────────────────────────────┤
|
|
│ [ ] [Send] [🎤] │
|
|
└─────────────────────────────────────────────────────────────────────────────┘
|
|
```
|
|
|
|
### Context Menu Integration
|
|
|
|
Right-click on any component in the canvas:
|
|
|
|
```
|
|
┌─────────────────────────────┐
|
|
│ Cut ⌘X │
|
|
│ Copy ⌘C │
|
|
│ Paste ⌘V │
|
|
├─────────────────────────────┤
|
|
│ 🤖 Ask AI about this... │
|
|
│ ✨ Improve with AI │
|
|
│ 📝 Document with AI │
|
|
│ 🔍 Explain this component │
|
|
├─────────────────────────────┤
|
|
│ Delete ⌫ │
|
|
└─────────────────────────────┘
|
|
```
|
|
|
|
---
|
|
|
|
## Part 7: Risk Analysis
|
|
|
|
### Technical Risks
|
|
|
|
| Risk | Severity | Mitigation |
|
|
| ------------------------------------- | -------- | -------------------------------------------- |
|
|
| AI generates broken code | HIGH | Validation layers, sandboxed execution, undo |
|
|
| Token costs explode | HIGH | Aggressive caching, smart context management |
|
|
| Hallucination creates wrong structure | MEDIUM | Schema validation, type checking |
|
|
| Docker security vulnerabilities | HIGH | Container isolation, resource limits |
|
|
| Conversation memory grows unbounded | MEDIUM | Summarization, sliding window |
|
|
|
|
### Product Risks
|
|
|
|
| Risk | Severity | Mitigation |
|
|
| ----------------------------------- | -------- | ------------------------------------------- |
|
|
| Users over-rely on AI | MEDIUM | Education, progressive disclosure |
|
|
| AI creates unmaintainable spaghetti | HIGH | Best practices enforcement, code review AI |
|
|
| Support burden increases | MEDIUM | Self-service docs, AI explains its own work |
|
|
| Competitive copying | LOW | Speed of execution, community building |
|
|
|
|
### The "Too Powerful" Risk
|
|
|
|
This is genuinely powerful. Mitigations:
|
|
|
|
1. **Guardrails** - AI can't access sensitive data, network restrictions
|
|
2. **Audit trail** - Log all AI actions for review
|
|
3. **Human approval** - Require confirmation for destructive actions
|
|
4. **Sandboxing** - Generated backends run in isolated containers
|
|
5. **Rate limiting** - Prevent runaway generation
|
|
|
|
---
|
|
|
|
## Part 8: Success Metrics
|
|
|
|
### Phase 9 Success Looks Like
|
|
|
|
**Quantitative:**
|
|
|
|
- 80% of new projects use AI assistant
|
|
- Average time to create backend: 5 minutes (from 2 hours)
|
|
- AI-created components work first try: 70%
|
|
- User retention increases 40%
|
|
|
|
**Qualitative:**
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- "I described what I needed and it just worked"
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- "I haven't written a backend in months"
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- "The AI understands my project better than I do"
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### The "Head Explodes" Moment
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When a user:
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1. Describes a complex backend in plain English
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2. Watches it build itself in real-time
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3. Sees the config panel appear automatically
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4. Connects their frontend to it immediately
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5. Has a working full-stack app in 15 minutes
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That's the moment.
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---
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## Summary
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This is Pandora's Box, but it's also the future. The combination of:
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- **UBA** (any backend can integrate)
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- **AI Backend Creator** (any backend can be generated)
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- **AI Frontend Assistant** (any component can be modified)
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- **Docker** (everything runs anywhere)
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...creates a system where the line between "describing what you want" and "having what you want" becomes nearly invisible.
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The technical challenges are significant but solvable:
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1. Restructure project files → enables AI access
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2. Build LangGraph agents → enables intelligent automation
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3. Integrate Docker → enables isolated execution
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4. Create unified UX → enables seamless experience
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Is it too powerful? Maybe. But the alternative is that someone else builds it first.
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**The question isn't whether to build it. The question is how fast can we move.**
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---
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## Next Steps
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1. **Immediate:** Complete UBA (Phase 6) - the foundation
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2. **Q2 2026:** Project restructuring (Phase 9A)
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3. **Q3 2026:** Frontend AI Assistant (Phase 9B)
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4. **Q4 2026:** Backend Creation AI (Phase 9C)
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5. **Q1 2027:** World domination (Phase 9D)
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_"The best way to predict the future is to build it."_
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---
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## Appendix: Competitive Analysis
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Nobody else has this exact combination:
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| Platform | Visual Dev | AI Assist | Backend Create | Config UI |
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|
| ----------- | ---------- | --------- | -------------- | --------- |
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| Retool | ✓ | Partial | ✗ | ✗ |
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| Webflow | ✓ | ✗ | ✗ | ✗ |
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| Bubble | ✓ | Partial | ✗ | ✗ |
|
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| FlutterFlow | ✓ | Partial | ✗ | ✗ |
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| v0 | ✗ | ✓ | ✗ | ✗ |
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| Bolt | ✗ | ✓ | Partial | ✗ |
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| **Nodegx** | ✓ | ✓ | ✓ | ✓ |
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The full stack, AI-powered, visual development platform with universal backend integration.
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That's the vision. Let's build it.
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