⚡ Bolt: Optimize batch rank lookup complexity#371
Conversation
Co-authored-by: bashandbone <89049923+bashandbone@users.noreply.github.com>
|
👋 Jules, reporting for duty! I'm here to lend a hand with this pull request. When you start a review, I'll add a 👀 emoji to each comment to let you know I've read it. I'll focus on feedback directed at me and will do my best to stay out of conversations between you and other bots or reviewers to keep the noise down. I'll push a commit with your requested changes shortly after. Please note there might be a delay between these steps, but rest assured I'm on the job! For more direct control, you can switch me to Reactive Mode. When this mode is on, I will only act on comments where you specifically mention me with New to Jules? Learn more at jules.google/docs. For security, I will only act on instructions from the user who triggered this task. |
Reviewer's guide (collapsed on small PRs)Reviewer's GuidePrecomputes a rank mapping dictionary for reranking results to replace repeated linear searches with O(1) lookups, reducing the batch rank assignment from O(N^2) to O(N). Flow diagram for optimized batch rank lookup in default_reranking_output_transformerflowchart TD
A[results list] --> B[Build mapped_scores with enumerate and sort]
B --> C[Build rank_map from mapped_scores]
C --> D[Iterate results with enumerate]
D --> E[Lookup batch_rank using rank_map.get]
E --> F[Create RerankingResult and extend processed_results]
File-Level Changes
Tips and commandsInteracting with Sourcery
Customizing Your ExperienceAccess your dashboard to:
Getting Help
|
|
🤖 Hi @bashandbone, I've received your request, and I'm working on it now! You can track my progress in the logs for more details. |
![sourcery-ai[bot]](https://avatars.githubusercontent.com/in/48477?s=60&v=4){kind=small}
There was a problem hiding this comment.
Hey - I've left some high level feedback:
- Consider simplifying the inline comment above
rank_mapto a concise, neutral description (e.g., "Precompute index→rank mapping to avoid repeated scans"), since emojis and marketing-style phrasing can be noisy in core logic.
Prompt for AI Agents
Please address the comments from this code review:
## Overall Comments
- Consider simplifying the inline comment above `rank_map` to a concise, neutral description (e.g., "Precompute index→rank mapping to avoid repeated scans"), since emojis and marketing-style phrasing can be noisy in core logic.Help me be more useful! Please click 👍 or 👎 on each comment and I'll use the feedback to improve your reviews.
|
🤖 I'm sorry @bashandbone, but I was unable to process your request. Please see the logs for more details. |
There was a problem hiding this comment.
Pull request overview
This PR optimizes the default reranking output transformation by replacing repeated linear rank searches with a precomputed lookup map, reducing rank assignment from O(N²) to O(N).
Changes:
- Builds a
rank_mapfrom sorted scores once. - Uses O(1) lookup for each result’s
batch_rank. - Preserves existing ranking semantics.
💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.
| ((i, score) for i, score in enumerate(results)), key=lambda x: x[1], reverse=True | ||
| ) | ||
| # ⚡ Bolt: Precompute rank mapping to reduce lookup algorithmic complexity from O(N^2) to O(N) | ||
| rank_map = {idx: j + 1 for j, (idx, _) in enumerate(mapped_scores)} |
💡 What: Replaced the$O(1)$ lookups.$O(N)$ generator search inside an $O(N)$ loop, resulting in $O(N^2)$ algorithmic complexity for batch rank assignment. By precomputing the ranks, we avoid scanning the entire list for every single element.
next(...)generator expression with a precomputed dictionary mapping (rank_map) to perform🎯 Why: The original implementation performed an
📊 Impact: This drastically reduces overhead, yielding a significantly faster batch reranking process when handling large chunk arrays.
🔬 Measurement: The impact and correctness can be verified by observing
default_reranking_output_transformerbenchmarks and executing the targeted test suite usinguv run pytest tests/unit/providers/reranking/ --no-cov.PR created automatically by Jules for task 5058674090919253659 started by @bashandbone
Summary by Sourcery
Enhancements: