Ireneusz Bachanowicz
c5202ca4c5
- Updated .cursorrules with clearer project organization - Removed setup-project.sh script and consolidated project documentation - Simplified and restructured project rules, overview, and resources - Added comprehensive task list with detailed development roadmap - Cleaned up and standardized markdown files in .cursor/rules directory
1.6 KiB
1.6 KiB
| description | globs |
|---|---|
| Details the technology stack chosen for the CV Optimization Platform. |
Technology Stack
Frontend
- Framework: React.js (using Next.js for server-side rendering and routing)
- Styling: Tailwind CSS
- State Management: (Initially simple React state, consider Context API or Zustand if complexity grows)
- UI Library: (Consider a component library like Material UI or Ant Design if needed for more complex UI elements later)
Backend
- Language: Node.js (with Express.js framework) or Python (with Flask/FastAPI - Decision needed based on your preference and libraries for LLM interaction)
- API Framework: Express.js (Node.js) or Flask/FastAPI (Python)
- Document Processing Libraries:
- Python:
PyPDF2,python-docx(if using Python backend) - Node.js:
pdf-parse,docx-parser(if using Node.js backend)
- Python:
Database
- Initial Stage: File-based storage (for simplicity in MVP) - Consider moving to MongoDB or PostgreSQL for user accounts and scalable data storage later.
- Future Scalable Database: MongoDB (NoSQL) or PostgreSQL (Relational) - To be decided based on data structure and scalability needs.
LLM Integration
- Primary LLM Provider: OpenAI API (GPT models) - for initial development and ease of use.
- Potential Alternative: Hugging Face Transformers - for exploring open-source models and potential cost optimization in the future.
Deployment
- Frontend Hosting: Vercel (for Next.js applications)
- Backend Hosting: Render or Heroku (or AWS/Google Cloud/Azure for more control and scalability)