⚡ Bolt: Use O(1) hash map lookup for batch_rank assignment in default_reranking_output_transformer

[bashandbone]

💡 What: Replaced an $O(N^2)$ next() generator expression with an $O(N)$ pre-computed dictionary hash map for looking up batch_rank assignments inside default_reranking_output_transformer.
🎯 Why: The previous implementation used a nested generator inside a loop over the results length ($N$), which had an asymptotic time complexity of $O(N^2)$.
📊 Impact: Speeds up standard response processing on rerank batches. Lookups fall from $O(N^2)$ to $O(N)$ making parsing large reranking documents drastically faster.
🔬 Measurement: Run benchmark tests covering default_reranking_output_transformer to measure the reduction in CPU computation time.

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PR created automatically by Jules for task 15360662012186636373 started by @bashandbone

Summary by Sourcery

Enhancements:

• Improve performance of batch_rank assignment in default_reranking_output_transformer by using a precomputed index-to-rank map instead of repeated generator-based searches.

[google-labs-jules]

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[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Optimizes batch_rank computation in default_reranking_output_transformer by precomputing a rank lookup map in O(N) time instead of performing an O(N^2) search for each result.

File-Level Changes

Change	Details	Files
Optimize batch_rank lookup from O(N^2) to O(N) by precomputing a rank map for reranking results.	Create a rank_map dictionary from mapped_scores mapping original indices to 1-based ranks. Replace the per-item generator-based search for the rank with a direct dictionary lookup using rank_map.get(i, -1).	src/codeweaver/providers/reranking/providers/base.py

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[github-actions]

🤖 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.

[github-actions]

🤖 I'm sorry @bashandbone, but I was unable to process your request. Please see the logs for more details.

[sourcery-ai]

Hey - I've left some high level feedback:

• Since idx is an integer index in range(len(results)), you could use a preallocated list (e.g. rank_map = [0] * len(results)) instead of a dict to avoid hash overhead while keeping O(1) lookups.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Since `idx` is an integer index in `range(len(results))`, you could use a preallocated list (e.g. `rank_map = [0] * len(results)`) instead of a dict to avoid hash overhead while keeping O(1) lookups.

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[Copilot]

Pull request overview

Optimizes default_reranking_output_transformer by replacing repeated linear searches for batch_rank with a precomputed index→rank dictionary, reducing the rank-assignment step from O(N²) to O(N) while preserving existing behavior.

Changes:

• Build a rank_map from the score-sorted (index, score) list once.
• Replace the per-result next(... enumerate(mapped_scores) ...) lookup with rank_map.get(i, -1).

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