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131 lines
3.4 KiB
Markdown
131 lines
3.4 KiB
Markdown
# Getting Started
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This guide will help you set up and run the AI automation platform for development.
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## Prerequisites
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- Node.js 18+
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- Docker (for Kubernetes deployment)
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- Target application running with UI reflection system
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## Quick Start
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### 1. Start the AI API Service
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```bash
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cd tools/ai-automation
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npm install
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npm run dev # Starts on port 4000
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```
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This starts the automation server with:
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- Browser session management
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- WebSocket UI state broadcasting
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- Automation tool APIs
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- Real-time screenshot streaming
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### 2. Start the AI Web Service
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```bash
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cd tools/ai-automation/web
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npm install
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npm run dev # Starts on port 3000
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```
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This starts the control panel with:
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- Live browser feed
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- AI chat interface with tool integration
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- Browser session controls (pop-out/pop-in)
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- Real-time activity monitoring
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### 3. Access the Control Panel
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Open [http://localhost:3000](http://localhost:3000) to access the control panel.
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## Environment Configuration
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For LLM integration, you'll need to configure environment variables. The platform supports OpenRouter for accessing various LLM models.
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Create a `.env` file in your home directory:
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```bash
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# OpenRouter configuration for LLM access
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CUSTOM_OPENAI_API_KEY=sk-or-v1-your-openrouter-key
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CUSTOM_OPENAI_BASE_URL=https://openrouter.ai/api/v1
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CUSTOM_OPENAI_MODEL=google/gemini-flash-1.5
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```
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## Basic Usage
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### AI Automation
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1. Navigate to the control panel at `http://localhost:3000`
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2. Use the chat interface to interact with the AI
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3. The AI has access to automation tools for browser control and codebase analysis
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Example prompt:
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```
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Navigate to the companies page and help me understand the UI structure
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```
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### Browser Session Control
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- **Pop Out**: Switch to headed mode for manual intervention
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- **Pop In**: Return to headless mode for continued automation
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- **Status**: View current browser session information
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### Available Tools
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The AI can use these tools:
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- `get_ui_state` - Inspect current UI state
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- `observe_browser` - Get page content
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- `execute_automation_script` - Run Puppeteer scripts
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- `read_file` - Read files from codebase
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- `search_automation_ids` - Find automation IDs
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- `find_files` - Locate files by pattern
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## WebSocket Events
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The platform broadcasts real-time updates via WebSocket:
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- `UI_STATE_UPDATE` - UI state changes
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- `screenshot` - Browser screenshots
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- `browser_session_update` - Session mode changes
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## Development Workflow
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1. Start both services (API and Web)
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2. Navigate to the control panel
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3. Use AI chat for intelligent automation
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4. Switch browser modes as needed for manual intervention
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5. Monitor real-time feedback and logs
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## Docker Development
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```bash
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# Build AI API service
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cd tools/ai-automation
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docker build -t ai-automation-api .
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# Build AI Web service
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cd tools/ai-automation/web
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docker build -t ai-automation-web .
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```
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## Troubleshooting
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### Common Issues
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1. **Port conflicts**: Ensure ports 3000 and 4000 are available
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2. **WebSocket connection failed**: Check that the AI API service is running
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3. **Browser session errors**: Restart the AI API service to reset browser state
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4. **LLM authentication**: Verify your API keys are correctly configured
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### Logs
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- AI API Service logs: Check console output for automation server
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- AI Web Service logs: Check Next.js console for frontend issues
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- Browser session logs: Available in the control panel activity log
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For more detailed troubleshooting, check the main README.md file. |