https://github.com/cypherfelix/pawait-llm-qa-app
Full-stack Q&A app: Next.js frontend, FastAPI backend, LLM AI answers
https://github.com/cypherfelix/pawait-llm-qa-app
ai chatbot fastapi fullstack llm nextjs openai python qna railway react taiwindcss typescript vercel webapp
Last synced: about 1 month ago
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Full-stack Q&A app: Next.js frontend, FastAPI backend, LLM AI answers
- Host: GitHub
- URL: https://github.com/cypherfelix/pawait-llm-qa-app
- Owner: Cypherfelix
- Created: 2025-05-17T15:53:40.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-17T17:42:27.000Z (5 months ago)
- Last Synced: 2025-06-16T09:50:02.828Z (4 months ago)
- Topics: ai, chatbot, fastapi, fullstack, llm, nextjs, openai, python, qna, railway, react, taiwindcss, typescript, vercel, webapp
- Language: TypeScript
- Homepage: https://pawait-llm-qa-app.vercel.app
- Size: 108 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# pawait-llm-qa-app
A modern, production-ready full-stack Q&A web application powered by FastAPI (backend) and Next.js + TailwindCSS + TypeScript (frontend), integrating an LLM (Language Learning Model) for AI-generated responses.
**GitHub Repository:** [https://github.com/Cypherfelix/pawait-llm-qa-app](https://github.com/Cypherfelix/pawait-llm-qa-app)
---
## Table of Contents
- [Project Overview](#project-overview)
- [Architecture](#architecture)
- [Backend Documentation](#backend-documentation)
- [Features](doc/backend/features.md)
- [Tasks](doc/backend/tasks.md)
- [Requirements](doc/backend/requirements.md)
- [Testing](doc/backend/testing.md)
- [Implementation](doc/backend/implementation.md)
- [Frontend Documentation](#frontend-documentation)
- [Features](doc/frontend/features.md)
- [Tasks](doc/frontend/tasks.md)
- [Requirements](doc/frontend/requirements.md)
- [Testing](doc/frontend/testing.md)
- [Implementation](doc/frontend/implementation.md)
- [Deployment Strategy](#deployment-strategy)
- [Contributing](#contributing)
- [License](#license)
---
## Project Overview
**pawait-llm-qa-app** is a full-stack application that allows users to input questions and receive AI-generated answers using an LLM. The project demonstrates modern web development practices, clean code, and effective API integration. It is designed for scalability, maintainability, and ease of deployment.
---
## Architecture
```mermaid
graph TD
A[User] -->|Types Question| B[Next.js Frontend]
B -->|API Request| C[FastAPI Backend]
C -->|LLM API Call| D[LLM Provider]
D -->|Response| C
C -->|API Response| B
B -->|Displays Answer| A
```
- **Frontend:** Next.js (TypeScript, TailwindCSS) for a modern, responsive UI.
- **Backend:** FastAPI (Python) for robust, high-performance API endpoints.
- **LLM Integration:** Easily switchable between providers (OpenAI, Gemini, etc.).
- **Deployment:** Ready for Vercel, Netlify, Render, Railway, or Docker.
---
## Backend Documentation
- [Features](doc/backend/features.md)
- [Tasks](doc/backend/tasks.md)
- [Requirements](doc/backend/requirements.md)
- [Testing](doc/backend/testing.md)
- [Implementation](doc/backend/implementation.md)
## Frontend Documentation
- [Features](doc/frontend/features.md)
- [Tasks](doc/frontend/tasks.md)
- [Requirements](doc/frontend/requirements.md)
- [Testing](doc/frontend/testing.md)
- [Implementation](doc/frontend/implementation.md)
## Deployment Strategy
### Overview
Our deployment strategy is designed for speed, reliability, and zero-cost hosting during development and demonstration phases. We leverage modern cloud platforms that integrate seamlessly with GitHub for CI/CD and environment management.
---
### Architecture Diagram
```mermaid
graph TD
A(User) --> B(Vercel - Next.js Frontend)
B --> C(Railway - FastAPI Backend)
C --> D(LLM Provider - OpenAI)
```
---
### Frontend (Next.js)
- **Platform:** Vercel
- **Why:** Vercel is optimized for Next.js, offers instant global deployment, automatic HTTPS, and a generous free tier. It supports environment variables and integrates directly with GitHub for continuous deployment.
- **Strategy:** Every push to the main branch triggers an automatic build and deployment. Environment variables (such as the backend API URL) are managed securely in the Vercel dashboard.
---
### Backend (FastAPI)
- **Platform:** Railway
- **Why:** Railway provides a fast, free, and developer-friendly platform for Python web apps. It supports automatic deployments from GitHub, easy environment variable management, and public URLs for API access.
- **Strategy:** The backend is deployed as a web service. On each push to the main branch, Railway builds and redeploys the FastAPI app. Environment variables (such as LLM API keys) are managed securely in the Railway dashboard.
---
### Key Points
- **CI/CD:** Both frontend and backend are continuously deployed from GitHub.
- **Environment Variables:** Managed securely on each platform, never committed to code.
- **Scalability:** Both platforms allow for easy scaling or migration to paid plans if needed.
- **Custom Domains:** Supported on both Vercel and Railway for production readiness.
---
## Contributing
Contributions are welcome! Please open issues or submit pull requests via [GitHub](https://github.com/Cypherfelix/pawait-llm-qa-app).
## License
This project is licensed under the MIT License.