Merge pull request #445 from thecodacus/docs

docs: added documentation creation framework

Author: thecodacus

.github/workflows/docs.yaml

@@ -0,0 +1,34 @@
+name: Docs CI/CD
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+permissions:
+  contents: write
+jobs:
+  build_docs:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ./docs
+    steps:
+      - uses: actions/checkout@v4
+      - name: Configure Git Credentials
+        run: |
+          git config user.name github-actions[bot]
+          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
+      - uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - run: echo "cache_id=$(date --utc '+%V')" >> $GITHUB_ENV 
+      - uses: actions/cache@v4
+        with:
+          key: mkdocs-material-${{ env.cache_id }}
+          path: .cache
+          restore-keys: |
+            mkdocs-material-
+
+      - run: pip install mkdocs-material
+      - run: mkdocs gh-deploy --force 

.gitignore

@@ -33,3 +33,6 @@ _worker.bundle
 
 Modelfile
 modelfiles
+
+# docs ignore
+site

docs/.gitignore

@@ -0,0 +1,2 @@
+.venv
+site/

docs/README.md

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+# Contribution Guidelines
+
+## DEFAULT_NUM_CTX
+
+The `DEFAULT_NUM_CTX` environment variable can be used to limit the maximum number of context values used by the qwen2.5-coder model. For example, to limit the context to 24576 values (which uses 32GB of VRAM), set `DEFAULT_NUM_CTX=24576` in your `.env.local` file.
+
+First off, thank you for considering contributing to Bolt.new! This fork aims to expand the capabilities of the original project by integrating multiple LLM providers and enhancing functionality. Every contribution helps make Bolt.new a better tool for developers worldwide.
+
+## 📋 Table of Contents
+- [Code of Conduct](#code-of-conduct)
+- [How Can I Contribute?](#how-can-i-contribute)
+- [Pull Request Guidelines](#pull-request-guidelines)
+- [Coding Standards](#coding-standards)
+- [Development Setup](#development-setup)
+- [Deploymnt with Docker](#docker-deployment-documentation)
+
+## 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](https://forms.gle/TBSteXSDCtBDwr5m7).
+
+## 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
+
+## Development Setup
+
+### 🔄 Initial Setup
+1. Clone the repository:
+```bash
+git clone https://github.com/coleam00/bolt.new-any-llm.git
+```
+
+2. Install dependencies:
+```bash
+pnpm install
+```
+
+3. Set up environment variables:
+   - Rename `.env.example` to `.env.local`
+   - Add your LLM API keys (only set the ones you plan to use):
+```bash
+GROQ_API_KEY=XXX
+HuggingFace_API_KEY=XXX
+OPENAI_API_KEY=XXX
+ANTHROPIC_API_KEY=XXX
+...
+```
+   - Optionally set debug level:
+```bash
+VITE_LOG_LEVEL=debug
+```
+
+   - Optionally set context size:
+```bash
+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
+```bash
+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:
+
+```bash
+pnpm test
+```
+
+## Deployment
+
+To deploy the application to Cloudflare Pages:
+
+```bash
+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:
+
+```bash
+# 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:
+
+```bash
+# 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:
+
+```bash
+# 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:
+
+```bash
+# 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](https://github.com/coollabsio/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:
+   ```bash
+   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

docs/docs/CONTRIBUTING.md

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+# FAQ
+
+### How do I get the best results with oTToDev?
+
+- **Be specific about your stack**: If you want to use specific frameworks or libraries (like Astro, Tailwind, ShadCN, or any other popular JavaScript framework), mention them in your initial prompt to ensure Bolt scaffolds the project accordingly.
+
+- **Use the enhance prompt icon**: Before sending your prompt, try clicking the 'enhance' icon to have the AI model help you refine your prompt, then edit the results before submitting.
+
+- **Scaffold the basics first, then add features**: Make sure the basic structure of your application is in place before diving into more advanced functionality. This helps oTToDev understand the foundation of your project and ensure everything is wired up right before building out more advanced functionality.
+
+- **Batch simple instructions**: Save time by combining simple instructions into one message. For example, you can ask oTToDev to change the color scheme, add mobile responsiveness, and restart the dev server, all in one go saving you time and reducing API credit consumption significantly.
+
+### How do I contribute to oTToDev?
+
+[Please check out our dedicated page for contributing to oTToDev here!](CONTRIBUTING.md)
+
+### Do you plan on merging oTToDev back into the official Bolt.new repo?
+
+More news coming on this coming early next month - stay tuned!
+
+### What are the future plans for oTToDev?
+
+[Check out our Roadmap here!](https://roadmap.sh/r/ottodev-roadmap-2ovzo)
+
+Lot more updates to this roadmap coming soon!
+
+### Why are there so many open issues/pull requests?
+
+oTToDev was started simply to showcase how to edit an open source project and to do something cool with local LLMs on my (@ColeMedin) YouTube channel! However, it quickly
+grew into a massive community project that I am working hard to keep up with the demand of by forming a team of maintainers and getting as many people involved as I can.
+That effort is going well and all of our maintainers are ABSOLUTE rockstars, but it still takes time to organize everything so we can efficiently get through all
+the issues and PRs. But rest assured, we are working hard and even working on some partnerships behind the scenes to really help this project take off!
+
+### How do local LLMs fair compared to larger models like Claude 3.5 Sonnet for oTToDev/Bolt.new?
+
+As much as the gap is quickly closing between open source and massive close source models, you’re still going to get the best results with the very large models like GPT-4o, Claude 3.5 Sonnet, and DeepSeek Coder V2 236b. This is one of the big tasks we have at hand - figuring out how to prompt better, use agents, and improve the platform as a whole to make it work better for even the smaller local LLMs!
+
+### I'm getting the error: "There was an error processing this request"
+
+If you see this error within oTToDev, that is just the application telling you there is a problem at a high level, and this could mean a number of different things. To find the actual error, please check BOTH the terminal where you started the application (with Docker or pnpm) and the developer console in the browser. For most browsers, you can access the developer console by pressing F12 or right clicking anywhere in the browser and selecting “Inspect”. Then go to the “console” tab in the top right.
+
+### I'm getting the error: "x-api-key header missing"
+
+We have seen this error a couple times and for some reason just restarting the Docker container has fixed it. This seems to be Ollama specific. Another thing to try is try to run oTToDev with Docker or pnpm, whichever you didn’t run first. We are still on the hunt for why this happens once and a while!
+
+### I'm getting a blank preview when oTToDev runs my app!
+
+We promise you that we are constantly testing new PRs coming into oTToDev and the preview is core functionality, so the application is not broken! When you get a blank preview or don’t get a preview, this is generally because the LLM hallucinated bad code or incorrect commands. We are working on making this more transparent so it is obvious. Sometimes the error will appear in developer console too so check that as well.
+
+### Everything works but the results are bad
+
+This goes to the point above about how local LLMs are getting very powerful but you still are going to see better (sometimes much better) results with the largest LLMs like GPT-4o, Claude 3.5 Sonnet, and DeepSeek Coder V2 236b. If you are using smaller LLMs like Qwen-2.5-Coder, consider it more experimental and educational at this point. It can build smaller applications really well, which is super impressive for a local LLM, but for larger scale applications you want to use the larger LLMs still!