@trapar-waves/llm-template

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A production-ready template for LLM (Large Language Model) application development, integrating AI tools, TypeScript type safety, Zod validation, and modern dev utilities.

✨ Features

• Type Safety: Leverages TypeScript to improve code quality and reduce runtime errors.
• Fast Development Workflow: Utilizes Vite for quick server starts and hot module replacement.
• Optimized Builds: Employs Rslib for efficient library bundling and optimized production outputs.
• AI Integration: Pre-configured with @ai-sdk/openai and ai for seamless interaction with large language models.
• Robust Validation: Utilizes Zod for runtime schema validation, ensuring data integrity.
• Focus on Testing: Includes Vitest for fast and reliable unit testing.
• Code Consistency: Enforces code style and quality using Prettier and Antfu's ESLint configuration.
• Environment Management: Uses dotenv for secure configuration of API keys and environment-specific settings.
• Cross-Platform Paths: Employs pathe for consistent file path handling across different operating systems.

💻 Tech Stack

Key technologies used in this project include:

• Language: TypeScript
• LLM Framework: AI SDK (@ai-sdk/openai, ai)
• Validation: Zod
• Testing Framework: Vitest
• Build Tool: Rslib
• Development Server: Vite
• Code Quality: ESLint (Antfu's config), Prettier
• Utilities: Dotenv, Pathe

See the package.json for a full list of dependencies.

🚀 Getting Started

Follow these instructions to get the project running locally.

Prerequisites

Ensure you have the following installed:

• Node.js (>= 18.x recommended)
• Package manager (npm, yarn, or pnpm)

node -v
npm -v

Installation

Run script

pnpm create trapar-waves

Install dependencies

npm install
yarn install
pnpm install

Available Scripts

Common scripts available via npm run <script>, yarn <script>, or pnpm <script>:

• build: Creates a production-ready build using Rslib.
• build:watch: Creates a production-ready build using Rslib in watch mode.
• lint: Checks the code for style and errors using ESLint.

Example:

# Create production build
npm run build

🛠️ Usage

This template provides a foundational structure for building LLM applications. It includes:

• A basic project structure with src/ directory.
• Integration with OpenAI API through @ai-sdk/openai.
• Example usage of ai and zod for generating structured output.
• Configuration for building and testing with Rslib and Vitest.

Developers can extend this template by adding their own prompts, models, and application logic within the src/ directory.

🤝 Contributing

Contributions are welcome and greatly appreciated! Please follow these steps to contribute:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE file for more information.

👤 Author

• Rikka: ([email protected])
• GitHub Profile: Muromi-Rikka

🔗 Links

• Repository: https://github.com/Trapar-waves/llm-template
• Homepage: https://github.com/Trapar-waves/llm-template
• Issues: https://github.com/Trapar-waves/llm-template/issues