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Added Docker Deployment documentation to CONTRIBUTING.md
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@ -8,6 +8,7 @@ First off, thank you for considering contributing to Bolt.new! This fork aims to
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- [Pull Request Guidelines](#pull-request-guidelines)
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- [Coding Standards](#coding-standards)
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- [Development Setup](#development-setup)
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- [Deploymnt with Docker](#docker-deployment-documentation)
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- [Project Structure](#project-structure)
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## Code of Conduct
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@ -106,16 +107,95 @@ pnpm run deploy
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Make sure you have the necessary permissions and Wrangler is correctly configured for your Cloudflare account.
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## Docker Dev
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# Docker Deployment Documentation
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To build docker image
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This guide outlines various methods for building and deploying the application using Docker.
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```
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docker build -t bolt-ai .
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## Build Methods
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### 1. Using Helper Scripts
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NPM scripts are provided for convenient building:
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```bash
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# Development build
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npm run dockerbuild
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# Production build
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npm run dockerbuild:prod
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```
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To run bolt dev in docker(Add ANTHROPIC_API_KEY=XXX before running)
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### 2. Direct Docker Build Commands
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You can use Docker's target feature to specify the build environment:
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```bash
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# Development build
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docker build . --target bolt-ai-development
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# Production build
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docker build . --target bolt-ai-production
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```
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docker run -p 5173:5173 --env-file .env.local bolt-ai
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```
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### 3. Docker Compose with Profiles
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Use Docker Compose profiles to manage different environments:
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```bash
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# Development environment
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docker-compose --profile development up
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# Production environment
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docker-compose --profile production up
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```
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## Running the Application
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After building using any of the methods above, run the container with:
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```bash
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# Development
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docker run -p 5173:5173 --env-file .env.local bolt-ai:development
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# Production
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docker run -p 5173:5173 --env-file .env.local bolt-ai:production
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```
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## Deployment with Coolify
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[Coolify](https://github.com/coollabsio/coolify) provides a straightforward deployment process:
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1. Import your Git repository as a new project
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2. Select your target environment (development/production)
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3. Choose "Docker Compose" as the Build Pack
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4. Configure deployment domains
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5. Set the custom start command:
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```bash
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docker compose --profile production up
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```
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6. Configure environment variables
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- Add necessary AI API keys
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- Adjust other environment variables as needed
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7. Deploy the application
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## VS Code Integration
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The `docker-compose.yaml` configuration is compatible with VS Code dev containers:
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1. Open the command palette in VS Code
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2. Select the dev container configuration
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3. Choose the "development" profile from the context menu
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## Environment Files
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Ensure you have the appropriate `.env.local` file configured before running the containers. This file should contain:
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- API keys
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- Environment-specific configurations
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- Other required environment variables
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## Notes
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- Port 5173 is exposed and mapped for both development and production environments
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- Environment variables are loaded from `.env.local`
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- Different profiles (development/production) can be used for different deployment scenarios
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- The configuration supports both local development and production deployment
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