GONEXT

GONEXT is a generative AI-driven analysis tool designed for live League of Legends games. It provides real-time insights and strategic recommendations through an intuitive chat interface. By analyzing ally and enemy champions, player statistics, and live match conditions, GONEXT offers detailed gameplay guidance, champion builds, and tactical advice generated by an AI agent.

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Table of Contents

• Features
• Screenshots
• Tech Stack
• Installation
• Usage
• Contributing
• Support
• License

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Features

• GenAI-powered Chatbot
  A knowledge-enriched chatbot that adapts to the current game state. It can suggest item builds, devise game plans, and answer any in-game questions in natural language.

• Win Chance Estimation
  Advanced AI calculates the probability of victory, factoring in the chosen champions and player performance data.

• Game Overview
  An LLM-generated concise summary that highlights the key strategic considerations for each match.

• Live Game Analysis
  Real-time insights powered by AI and integrated data from the Riot API.

• Match History & Rank Analysis
  Explore detailed match histories and rank information for all players with a single click.

• Multi-Language Support
  Obtain AI-driven insights in various supported languages (English, Korean, Chinese, Spanish, Bulgarian, etc.).

• Seamless LLM Switch
  Instantly switch between GPT-4o, GPT-4o-mini, and Gemini 1.5 Flash for optimal performance and cost efficiency.

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Screenshots

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Tech Stack

• UI: Built with React for a component-based structure, styled using Ant Design for pre-built UI components and Tailwind CSS for utility-first styling, ensuring a sleek and responsive interface.
• API: Node.js with the Express framework provides a robust backend, seamlessly interfacing with the Riot API for real-time game data.
• Machine Learning API: FastAPI offers a high-performance framework for the ML services, integrated with LangChain for building LLM applications, leveraging OpenAI API and Google AI Studio for advanced AI-driven insights. (ML side available at GoNext ML GitHub Repository)
• Databases: PostgreSQL is used for structured data analytics, MongoDB provides flexible storage for general-purpose data, and Redis ensures high-performance caching for frequently accessed information.

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Installation

1. Clone the repository:

   git clone https://github.com/kostadindev/gonext.git
   cd gonext
   

2. Install backend dependencies:

   # Navigate to the backend directory
   cd backend
   npm install
   

3. Install frontend dependencies:

   # Navigate to the frontend directory
   cd ../frontend
   npm install
   

4. Install ML dependencies: Refer to the GoNext ML GitHub Repository for instructions.
5. Set up environment variables:
   • Create .env files in the backend, frontend, and ML project root directories (or copy from .env.sample files).
   • Populate them with the necessary API keys (Riot Games, OpenAI, Google AI Studio) and database connection strings.
6. Set up databases:
   • Ensure PostgreSQL, MongoDB, and Redis instances are running.
   • Configure connection details in the backend .env file.
   • (Optional) Run any necessary database migrations or seeding scripts.

Backend Environment Variables

The backend requires a .env file in the backend directory. You can copy .env.sample as a starting point:

cp backend/.env.sample backend/.env

Variable Explanations

• PORT: The port on which the backend server will run. Default is 8080.
• LEAGUE_API_KEY: Your Riot Games API key. Required for accessing live game and player data.
• MONGO_URI: MongoDB connection string. Used for storing general-purpose data.
• JWT_SECRET: Secret key for signing JSON Web Tokens (JWT) for authentication.
• DATABASE_URL: (OPTIONAL) PostgreSQL connection string. Used for structured analytics data.
• REDIS_URL: (OPTIONAL) Redis connection string. Used for caching and session management.
• ML_SERVER_URL: URL of the Machine Learning server. Default is http://localhost:8000.

Make sure to fill in all required values, especially API keys and database URLs, before running the backend server.

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Usage

1. Start the backend server:

   # From the backend directory
   npm start
   

2. Start the frontend development server:

   # From the frontend directory
   npm run dev
   

3. Start the ML server: Refer to the GoNext ML GitHub Repository for instructions.
4. Access the application: Open your web browser and navigate to http://localhost:5173 (or the port specified by the frontend development server).
5. Enter a League of Legends summoner name and region to begin the analysis.

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Contributing

Contributions are welcome! Please follow these steps:

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

Please ensure your code adheres to the project's coding standards and includes tests where applicable.

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Support

If you need help or have questions:

• Open an issue on GitHub
• For commercial use inquiries, please contact the project maintainer

Acknowledgments

• Riot Games API for providing the game data
• OpenAI and Google AI for their AI models
• All our contributors and supporters

Roadmap

• [ ] Enhanced game analysis features
• [ ] Additional language support
• [ ] Mobile application
• [ ] Advanced statistics and analytics
• [ ] Custom AI model training

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License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license with an additional commercial use clause. This means you are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material

Under the following terms:

• Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
• NonCommercial — You may not use the material for commercial purposes without explicit written permission from the licensor.

For commercial use inquiries, please contact the project maintainer.

For more information, see the full license text or visit Creative Commons.