⚡ Bolt: [performance improvement] Convert O(N²) rank lookup to O(N) dictionary in reranker#396
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…ictionary in reranker What: Replaced a generator comprehension used to look up element ranks during array extension with an explicitly pre-calculated hash map. Why: The nested generator lookup evaluated linearly within an outer loop, resulting in O(N²) complexity. The pre-calculated hash map provides O(1) constant-time access inside the same loop. Impact: Reduces the algorithmic time complexity of ranking evaluations from O(N²) down to O(N). Measurement: Confirmed by test executions within `tests/unit/providers/reranking/`. Co-authored-by: bashandbone <89049923+bashandbone@users.noreply.github.com>
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Reviewer's guide (collapsed on small PRs)Reviewer's GuideRefactors the reranking output transformer to precompute a rank lookup dictionary, replacing an O(N²) per-item generator-based search with an O(N) hash map lookup while preserving existing behavior and test coverage. File-Level Changes
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Hey - I've left some high level feedback:
- Given that
mapped_scoresis built fromenumerate(results), everyiwill exist inrank_map, so the-1default inrank_map.get(i, -1)is redundant; consider using direct indexing (or removing the default) to simplify. - The performance comment above
rank_mapis helpful but a bit verbose; consider tightening it to a brief note about avoiding repeated scans ofmapped_scoresfor clarity.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- Given that `mapped_scores` is built from `enumerate(results)`, every `i` will exist in `rank_map`, so the `-1` default in `rank_map.get(i, -1)` is redundant; consider using direct indexing (or removing the default) to simplify.
- The performance comment above `rank_map` is helpful but a bit verbose; consider tightening it to a brief note about avoiding repeated scans of `mapped_scores` for clarity.Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
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💡 What
Replaced a generator comprehension
next(...)used inside an array extension loop indefault_reranking_output_transformerwith a pre-calculated hash map (rank_map).🎯 Why
The nested generator comprehension repeatedly iterated over the elements of
mapped_scoresfor each index sequentially, causing an O(N²) time complexity algorithmic bottleneck when iterating over document chunks in a reranking process.📊 Impact
Reduces the algorithmic evaluation complexity of document reranking assignments strictly from O(N²) to O(N), offering significantly shorter evaluation periods during heavily populated return arrays by leveraging O(1) constant-time dictionary lookups.
🔬 Measurement
The functionality is fully verified by the successful execution of the core
tests/unit/providers/reranking/test suite and standard testing metrics, preserving exact match outputs functionally.PR created automatically by Jules for task 4362230007984950645 started by @bashandbone
Summary by Sourcery
Enhancements: