learningequality / learningequality/studio
Cleanup the flattening / unflattening logic
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- Dominant language
- Python
- Stars
- 191
- Forks
- 307
- Avg merge
- 5d 6h
- Merged PRs (30d)
- 10
Description
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Overview
This is a follow-up task to clean up the flattening/unflattening logic currently implemented on the search recommendation UI in Studio.
Description and outcomes
- We are currently using these functions to flatten and unflatten responses received from the recommender API.
- We should instead refactor this to use the models/querysets instead.
Acceptance Criteria
- The refactor doesn't introduce any regressions
Assumptions and Dependencies
- NA
Accessibility Requirements
- NA
Resources
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the flattening and unflattening functions in contentcuration/contentcuration/utils/recommendations.py, lines 265–369, and review the context in issue 4044. Trace how recommender API responses are represented by the existing models and querysets. Done means the logic uses those models/querysets and introduces no regressions.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- backend, database, search
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 20/100