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Contributing to oTToDev

First off, thank you for considering contributing to Bolt.diy! This fork aims to expand the capabilities of the original project by integrating multiple LLM providers and enhancing functionality. Every contribution helps make Bolt.diy a better tool for developers worldwide.

📋 Table of Contents

Code of Conduct

This project and everyone participating in it is governed by our Code of Conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to the project maintainers.

How Can I Contribute?

🐞 Reporting Bugs and Feature Requests

  • Check the issue tracker to avoid duplicates
  • Use the issue templates when available
  • Include as much relevant information as possible
  • For bugs, add steps to reproduce the issue

🔧 Code Contributions

  1. Fork the repository
  2. Create a new branch for your feature/fix
  3. Write your code
  4. Submit a pull request

✨ Becoming a Core Contributor

We're looking for dedicated contributors to help maintain and grow this project. If you're interested in becoming a core contributor, please fill out our Contributor Application Form.

Pull Request Guidelines

📝 PR Checklist

  • Branch from the main branch
  • Update documentation if needed
  • Manually verify all new functionality works as expected
  • Keep PRs focused and atomic

👀 Review Process

  1. Manually test the changes
  2. At least one maintainer review required
  3. Address all review comments
  4. Maintain clean commit history

Coding Standards

💻 General Guidelines

  • Follow existing code style
  • Comment complex logic
  • Keep functions focused and small
  • Use meaningful variable names
  • Lint your code. This repo contains a pre-commit-hook that will verify your code is linted properly, so set up your IDE to do that for you!

Development Setup

🔄 Initial Setup

  1. Clone the repository:
git clone https://github.com/coleam00/bolt.new-any-llm.git
  1. Install dependencies:
pnpm install
  1. Set up environment variables:
    • Rename .env.example to .env.local
    • Add your LLM API keys (only set the ones you plan to use):
GROQ_API_KEY=XXX
HuggingFace_API_KEY=XXX
OPENAI_API_KEY=XXX
ANTHROPIC_API_KEY=XXX
...
  • Optionally set debug level:
VITE_LOG_LEVEL=debug
  • Optionally set context size:
DEFAULT_NUM_CTX=32768

Some Example Context Values for the qwen2.5-coder:32b models are.

  • DEFAULT_NUM_CTX=32768 - Consumes 36GB of VRAM
  • DEFAULT_NUM_CTX=24576 - Consumes 32GB of VRAM
  • DEFAULT_NUM_CTX=12288 - Consumes 26GB of VRAM
  • DEFAULT_NUM_CTX=6144 - Consumes 24GB of VRAM

Important: Never commit your .env.local file to version control. It's already included in .gitignore.

🚀 Running the Development Server

pnpm run dev

Note: You will need Google Chrome Canary to run this locally if you use Chrome! It's an easy install and a good browser for web development anyway.

Testing

Run the test suite with:

pnpm test

Deployment

To deploy the application to Cloudflare Pages:

pnpm run deploy

Make sure you have the necessary permissions and Wrangler is correctly configured for your Cloudflare account.

Docker Deployment Documentation

This guide outlines various methods for building and deploying the application using Docker.

Build Methods

1. Using Helper Scripts

NPM scripts are provided for convenient building:

# Development build
npm run dockerbuild

# Production build
npm run dockerbuild:prod

2. Direct Docker Build Commands

You can use Docker's target feature to specify the build environment:

# Development build
docker build . --target bolt-ai-development

# Production build
docker build . --target bolt-ai-production

3. Docker Compose with Profiles

Use Docker Compose profiles to manage different environments:

# Development environment
docker-compose --profile development up

# Production environment
docker-compose --profile production up

Running the Application

After building using any of the methods above, run the container with:

# Development
docker run -p 5173:5173 --env-file .env.local bolt-ai:development

# Production
docker run -p 5173:5173 --env-file .env.local bolt-ai:production

Deployment with Coolify

Coolify provides a straightforward deployment process:

  1. Import your Git repository as a new project
  2. Select your target environment (development/production)
  3. Choose "Docker Compose" as the Build Pack
  4. Configure deployment domains
  5. Set the custom start command: 
    docker compose --profile production up
  6. Configure environment variables
    • Add necessary AI API keys
    • Adjust other environment variables as needed
  7. Deploy the application

VS Code Integration

The docker-compose.yaml configuration is compatible with VS Code dev containers:

  1. Open the command palette in VS Code
  2. Select the dev container configuration
  3. Choose the "development" profile from the context menu

Environment Files

Ensure you have the appropriate .env.local file configured before running the containers. This file should contain:

  • API keys
  • Environment-specific configurations
  • Other required environment variables

Notes

  • Port 5173 is exposed and mapped for both development and production environments
  • Environment variables are loaded from .env.local
  • Different profiles (development/production) can be used for different deployment scenarios
  • The configuration supports both local development and production deployment