⚡ Bolt: Optimize linear lookups in file extension checking

[bashandbone]

💡 What: Replaced the next() with generator comprehension pattern used in the is_doc and is_data property methods of ChunkKind (src/codeweaver/core/metadata.py) with standard for loops utilizing early returns. Added # noqa: SIM110 to preserve the optimization from Ruff's simplify rules.

🎯 Why: In Python, creating a generator comprehension inside next() (or any()) incurs overhead because it has to allocate a generator frame. For simple linear lookups over a collection (like iterating through DOC_FILES_EXTENSIONS), a standard for loop with an early return is measurably faster as it bypasses this generator allocation overhead entirely. Since these properties are likely evaluated frequently during file parsing and indexing, this micro-optimization provides a small but consistent speed boost.

📊 Impact: Reduces the execution time of is_doc and is_data extension checks by eliminating generator frame allocation overhead per call, leading to a small improvement in file parsing and metadata resolution speed.

🔬 Measurement: Verify by running the test suite (uv run pytest tests/unit/core/ --no-cov). The tests pass, ensuring no change in functionality. Performance can be benchmarked using timeit comparing the next(...) approach vs the for ... return approach over a sample array.

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PR created automatically by Jules for task 3287533106860394289 started by @bashandbone

Summary by Sourcery

Enhancements:

• Replace generator-based next() usage in ChunkKind.is_doc and ChunkKind.is_data with early-return for loops for faster linear extension lookups.

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[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Optimizes the ChunkKind.is_doc and ChunkKind.is_data property methods by replacing generator-based next() lookups with explicit for-loops that short-circuit on match, and annotates them to avoid Ruff simplification so the micro-optimization is preserved.

Flow diagram for optimized ChunkKind.is_doc and ChunkKind.is_data lookups

flowchart TD
    subgraph is_doc
        A[Call ChunkKind.is_doc] --> B[Iterate DOC_FILES_EXTENSIONS]
        B --> C{doc_ext.ext == self.ext?}
        C -->|yes| D[return True]
        C -->|no| B
        B -->|no more items| E[return False]
    end

    subgraph is_data
        F[Call ChunkKind.is_data] --> G[Iterate DATA_FILES_EXTENSIONS]
        G --> H{data_ext.ext == self.ext?}
        H -->|yes| I[return True]
        H -->|no| G
        G -->|no more items| J[return False]
    end

Loading

File-Level Changes

Change	Details	Files
Optimize extension membership checks for documentation files.	Replace next() with generator comprehension pattern with an explicit for-loop that returns True on first matching doc_ext.ext and False otherwise Add a brief code comment explaining the performance rationale for preferring the for-loop over the generator-based next() Add a Ruff noqa directive (SIM110) on the for-loop to prevent automatic simplification that would negate the optimization	src/codeweaver/core/metadata.py
Optimize extension membership checks for data files.	Replace next() with generator comprehension pattern with an explicit for-loop that returns True on first matching data_ext.ext and False otherwise Add a brief code comment explaining the performance rationale for preferring the for-loop over the generator-based next() Add a Ruff noqa directive (SIM110) on the for-loop to prevent automatic simplification that would negate the optimization	src/codeweaver/core/metadata.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.

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[sourcery-ai]

Hey - I've left some high level feedback:

• Given that this is a micro-optimization on relatively small collections, consider whether the added complexity (inline performance comments and noqa override) is justified here versus keeping the simpler next(...) or any(...) form for readability and maintainability.
• If these lookups are truly hot paths, you might get a clearer and more substantial speedup by normalizing the extensions into a set (or using a precomputed lookup structure) rather than repeatedly doing linear scans over the *_FILES_EXTENSIONS sequences.
• The optimization commentary in the method bodies is quite verbose; consider shortening it or relying on the commit/PR description so the code itself remains focused on intent rather than micro-benchmark details.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Given that this is a micro-optimization on relatively small collections, consider whether the added complexity (inline performance comments and noqa override) is justified here versus keeping the simpler `next(...)` or `any(...)` form for readability and maintainability.
- If these lookups are truly hot paths, you might get a clearer and more substantial speedup by normalizing the extensions into a `set` (or using a precomputed lookup structure) rather than repeatedly doing linear scans over the `*_FILES_EXTENSIONS` sequences.
- The optimization commentary in the method bodies is quite verbose; consider shortening it or relying on the commit/PR description so the code itself remains focused on intent rather than micro-benchmark details.

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[Copilot]

Pull request overview

This PR optimizes file-extension category checks in ExtLangPair by replacing generator-based next(...) lookups with explicit for loops that early-return, aiming to reduce per-call overhead during frequent metadata resolution.

Changes:

• Replaced next((True for ...), False) patterns in ExtLangPair.is_doc and ExtLangPair.is_data with early-return for loops.
• Added inline # noqa: SIM110 to prevent Ruff simplify from rewriting the optimized loops back into generator-based forms.
• Added explanatory comments documenting the rationale for the loop-based implementation.

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