⚡ Bolt: Optimize base reranking list comprehension

[bashandbone]

💡 What: Optimized the default_reranking_output_transformer to use a precomputed hash map for index lookups instead of a nested generator expression with next().
🎯 Why: The original implementation iterated over mapped_scores for every chunk, resulting in $O(N^2)$ algorithmic complexity which degraded performance as batch sizes grew.
📊 Impact: Reduces computational complexity from $O(N^2)$ to $O(N)$ by making the inner rank lookup an $O(1)$ dictionary operation.
🔬 Measurement: Run unit tests with larger batch sizes or reranking tests and observe CPU usage and execution time reductions.

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PR created automatically by Jules for task 4187152158761050231 started by @bashandbone

Summary by Sourcery

Enhancements:

• Use a precomputed index-to-rank map to achieve O(1) batch rank lookups during reranking result transformation.

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[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Optimizes the default_reranking_output_transformer reranking pipeline by replacing an O(N^2) per-chunk rank lookup using a nested generator with a precomputed O(1) hash map of index-to-rank, reducing overall complexity to O(N).

Flow diagram for optimized default_reranking_output_transformer rank lookup

flowchart TD
    A[results] --> B[enumerate results]
    B --> C[sorted to mapped_scores]
    C --> D[build rank_map idx -> rank]
    D --> E[iterate chunks and scores]
    E --> F[rank_map.get original_index]
    F --> G[create RerankingResult]
    G --> H[processed_results]

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File-Level Changes

Change	Details	Files
Optimize batch rank computation in the reranking output transformer to use a precomputed index-to-rank map.	Keep the sorted mapped_scores list of (index, score) pairs as before to define ranking order. Introduce a rank_map dictionary that maps each result index to its 1-based rank using a single pass over mapped_scores. Replace the nested generator with next() used to compute batch_rank for each chunk with a direct rank_map.get(i, -1) lookup, preserving the previous fallback value for missing indices. Update the surrounding comment to document the algorithmic complexity improvement from O(N^2) to O(N).	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]

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[sourcery-ai]

Hey - I've left some high level feedback:

• The inline comment with emojis and high-level algorithm explanation feels a bit noisy for production code; consider simplifying it to a concise, neutral comment or relying on the PR description for the performance rationale.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- The inline comment with emojis and high-level algorithm explanation feels a bit noisy for production code; consider simplifying it to a concise, neutral comment or relying on the PR description for the performance rationale.

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