0_preprocessing/CONTRIBUTING.md

Contributing and Usage Guide

This project implements a SQL-based feedback analysis system using LLM-generated queries.

Goals:

• Make the API easy to run locally and deploy to Runpod or any container platform.
• Keep sensitive keys out of the repo; use environment variables.

Quick workflow:

1. Create branch: git checkout -b feat/improve-intents
2. Make changes and run tests locally.
3. Commit and push: git add . && git commit -m "feat: ..." && git push --set-upstream origin feat/improve-intents
4. Open a Pull Request and request review.

Building the image:

1. Update Dockerfile if you need to pre-bake models.
2. Build and tag:

   docker build -t youruser/feedback-analysis:v1 .
   docker push youruser/feedback-analysis:v1
   

Run on Runpod:

• See README.md section "Run on Runpod - Full guide" for step-by-step.

Tests:

• No unit tests included yet. Prefer adding pytest tests for app/analysis.py and the API layer.

Contact:

• For major changes, create an issue first describing the design and performance considerations.