Project Architecture Overview

This project is designed with a modern, scalable architecture leveraging multiple technologies for different components:

Backend APIs

1. .NET API

   • Serves as the main backend for core business logic and data management.
   • Handles authentication, authorization, and main application workflows.

2. FastAPI (Python)

   • Dedicated to utility endpoints and AI/ML services.
   • Provides fast, asynchronous APIs for machine learning inference and data processing.

Frontend

• Angular
  • Single Page Application (SPA) for user interface.
  • Communicates with backend APIs via RESTful endpoints.

Supporting Services

• CDN Server (C#/.net)

  • Delivers static assets (images, scripts, styles) efficiently.
  • Built with .NET for performance and integration.

• Redis Cache

  • Used for caching frequently accessed data.
  • Improves response times and reduces load on databases.

• Qdrant (Vector Database)

  • Stores and searches vector embeddings for AI/ML features.
  • Enables semantic search and recommendation functionalities.

• PostgreSQL

  • Main relational database for persistent storage.
  • Stores user data, application state, and transactional records.

Deployment & Hosting

• Docker

  • Services and components will be containerized if they do not natively support deployment on Linux environments. This ensures compatibility and consistent deployment where needed.

• Vercel

  • The Angular frontend will be deployed and hosted on Vercel for fast, global delivery and easy CI/CD integration.

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