Align with group members on Integration Method for Recommendation Engine

[JohnsL-U]

We are at the juncture where we need to decide on how to integrate our Python-based recommendation engine with our existing Spring Boot web app. There are multiple ways to achieve this, and the decision will affect the architecture, scalability, maintainability, and performance of our solution.

Integration Options:

1. API Endpoint (within Spring Boot)

   • Embed the Python engine within our Spring Boot application and expose the recommendations through an API endpoint.

2. Python Server (using Flask/Django/FastAPI)

   • Create a standalone Python server to handle recommendation requests. The main Spring Boot app communicates with this server for recommendations.

3. Database Integration

   • Compute recommendations in Python and store them in our PostgreSQL database. Fetch recommendations from the database when needed.

Action Items:

• [x] Discuss the pros and cons of each integration method in the team.
• [x] Consider the long-term goals, anticipated user base growth, and potential scalability challenges.
• [x] Check the expertise within the team. Are we more comfortable with a particular approach?
• [x] Decide on a method by [Meeting 3].

[JohnsL-U]

A discussion was held between Backend developers during Meeting 2. Pros and Cons were evaluated. Due to challenges database integration method was eliminated. We have collectively decided on going with option 1, API Endpoint.