# 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:
   ```bash
   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.