PokeChat Universe

A full-stack web application built using the GoTTH stack (Golang, Templ, TailwindCSS, HTMX). The app allows users to explore a universe of Pokémon, collect their favorites, and engage in AI-driven conversations with them.

• Why? I mean why not?

Leveraging a local instance of the Llama3 model via Ollama, the app provides real-time chat features powered by SSE (Server-Sent Events) for a seamless experience.

The web app also includes OAuth2/OIDC-based login via Auth0, dynamic frontend features (search, sort, pagination), and a fully scalable backend with persistent chat histories stored in PostgreSQL. Designed with performance and scalability in mind, the app is capable of handling high volumes of concurrent users while maintaining a smooth, responsive interface.

Table of Contents

1. Product Features
2. Technical Scope
3. Future Improvements
4. Contributing
5. License

Product Features

• Explore Pokémon Universe: Browse and filter a comprehensive list of Pokémon.
• OAuth2/OIDC Authentication: Secure user login with Auth0.
• Collect Pokémon: Add favorite Pokémon to your personal collection.
• User-Specific Pokedex: View all collected Pokémon in a dedicated tab.
• Search, Sort, and Load More: Dynamic frontend with real-time search, sorting options, and pagination for large lists.
• Dynamic Frontend Updates: Seamless user interactions using HTMX without full-page reloads.
• Real-Time AI Chat: Engage in conversations with collected Pokémon powered by Llama3 via Ollama.
• Persistent Chat Histories: Conversations are stored in a database for later retrieval.

Technical Scope

• Frontend: TailwindCSS for styling, HTMX for dynamic interactions, and Templ for rendering.
• Backend: Golang-based API, handling user sessions, chat logic, and serving data via REST.
• Database: PostgreSQL for relational data (users, Pokémon, chat histories) with Redis for session storage.
• Authentication: OAuth2/OIDC-based login with Auth0.
• AI Integration: Local Llama3 instance running via Ollama for generating contextual responses in real-time.
• Real-Time Communication: Server-Sent Events for streaming AI responses to the frontend.

Future Improvements

• Microservices: Split the app into microservices to handle specific tasks like chat, authentication, and AI responses independently.
• Database Partitioning and Indexing: Implement sharding, table partitioning, and advanced indexing strategies as chat histories grow.
• Horizontal Scaling: Use container orchestration (like Kubernetes) to handle increased load across multiple instances.
• Message Queues for AI Processing: Offload AI requests to message queues (e.g., RabbitMQ) for more efficient handling of heavy processing tasks.
• Data Archival: Implement strategies for archiving older chat histories to keep the primary database lean.

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch:

   git checkout -b feature/AmazingFeature

3. Commit your Changes:

   git commit -m 'Add some AmazingFeature'

4. Push to the Branch:

   git push origin feature/AmazingFeature

5. Open a Pull Request

License

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