ProjectTech4DevAI / ProjectTech4DevAI/kaapi-backend
Inqui-Lab: Improving duplicate-detection
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- Dominant language
- Python
- Stars
- 18
- Forks
- 10
- Avg merge
- 2d 20h
- Merged PRs (30d)
- 14
Description
Is your feature request related to a problem?
The pilot testing of the input app revealed inadequate duplicate detection, the need for a global knowledge base, and the correlation between duplicate detection and topic relevance. This lack affects processing effectiveness and user experience.
Describe the solution you'd like
- Keep duplicate detection separate from L2 scoring, using weighted averages in post-processing.
- Improve communication about duplicate detection accuracy (40–50%) and maintain a human-in-loop.
- Utilize the model's trained knowledge for cost-effective duplicate assessment.
- Integrate duplicate detection results as input for the next LLM stage.
- Adjust prompts to guide LLM on handling duplicate results and novelty scoring.
- Collaborate with the Input app team on prompt modifications while limiting Google search use.
Original issue
Context
Input app pilot testing (~2,000 dataset) surfaced missing duplicate detection, a desire for a global knowledge base, and duplicate detection linked to topic relevance. These are mostly prompt + pipeline changes.
Approach
Option 1:
- Keep duplicate detection independent of L2 scoring; handle it in post-processing via a weighted average (a flagged idea may still be strong relative to the cohort — a decision input, not a score penalty).
- Duplicate detection is ~40–50% accurate — communicate plainly, keep human-in-loop.
- Cheaper alternative to global web search: lean on the model's trained knowledge via rubric wording ("if the idea is generally common, score lower on novelty").
Option 2: - Feed the duplicate-detection result as an input into the next LLM stage (not done today).
- Prompt then instructs the LLM to consider the duplicate result and assign appropriate novelty scores.
- Need the Input app team's help on how they want the prompt changes shaped.
- Keep some scope for Google search but treat cautiously (nondeterministic wide search).
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 by tracing the duplicate-detection and LLM scoring pipeline in the backend, then review how prompts are assembled and how the Input app supplies results. Confirm the desired separation, handoff, and human-review behavior with the Input app team. Done means the agreed duplicate signal reaches the next LLM stage and its effect on novelty scoring is documented and tested.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, backend
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100