⚡ Bolt: [performance improvement] optimize material_extractor loops

[glacy]

💡 What:

1. Replaced the $O(N \times M)$ nested loop used for matching solutions to exercises in get_all_exercises with an $O(N + M)$ dictionary lookup (solutions_by_label).
2. Hoisted topic.lower() into a local variable topic_lower outside of the nested directory iteration loops in extract_by_topic.
3. Fixed a minor unused variable assignment (cache_entry) that was failing the linter.

🎯 Why:

1. The previous get_all_exercises implementation used an inner loop with a break to find matching solutions. As the number of exercises and solutions grows, this nested loop becomes an unnecessary performance bottleneck. Building an O(1) access lookup table completely eliminates the inner loop overhead.
2. In extract_by_topic, calling topic.lower() repeatedly across every material file checked for every iteration of tareas_dir and examenes_dir causes unnecessary Python object allocation and string manipulations during large dataset traversals.

📊 Impact:

• Reduces the time complexity of exercise-to-solution matching in get_all_exercises from $O(N^2)$ to $O(N)$.
• Reduces redundant string lowercasing operations in extract_by_topic by a factor directly proportional to the number of materials scanned.

🔬 Measurement:

The optimization was verified by running the tests/ suite using pytest, which confirms there were no logic regressions (the dictionary logic correctly preserves the original first-match behavior of the break statement). Formatted with black and ruff.

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PR created automatically by Jules for task 17224167210372567553 started by @glacy

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[Copilot]

Pull request overview

This PR focuses on performance-oriented refactors in MaterialExtractor, primarily reducing nested-loop overhead during exercise→solution matching and avoiding repeated string lowercasing during topic-based extraction, plus a small linter-driven cleanup.

Changes:

• Replaced nested-loop solution matching in get_all_exercises() with an O(1) lookup map (solutions_by_label) while aiming to preserve first-match semantics.
• Hoisted topic.lower() to topic_lower in extract_by_topic() to avoid repeated lowercasing inside directory iteration loops.
• Adjusted formatting/quoting and removed an unused local assignment (changed to _) related to cache validation.

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 4 comments.

File	Description
evolutia/material_extractor.py	Performance refactors for matching and topic filtering; minor cache/lint-related edits.
.jules/bolt.md	Adds a “Bolt” note documenting the O(N*M)→O(N) lookup refactor guidance.

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[Copilot]

file_path.stat().st_mtime is computed twice when caching a successful extraction. Consider storing the mtime in a local variable and reusing it for both the cache entry and _last_scan_timestamp update to avoid duplicate syscalls and keep the value consistent.

[Copilot]

This lookup result is assigned to _ but never used, and file_path not in self._file_cache is already checked above, so the assignment is redundant. Removing it would simplify the control flow without changing behavior.

Suggested change

_ = self._file_cache[file_path]

[Copilot]

The error-path cache entry stores timestamp: time.time(), but _is_cache_valid() never consults the per-file cached timestamp (and _last_scan_timestamp is only updated on successful parses). If the first call for a file fails, _last_scan_timestamp can remain 0, making file_mtime > _last_scan_timestamp true and causing the cached error result to never be reused—contradicting the intent to avoid repeated failed retries/log spam. Consider validating the cache against the per-file cached timestamp (and/or TTL), and updating _last_scan_timestamp appropriately on error when the file exists.

[Copilot]

This guidance suggests using a dict comprehension for the lookup table, but dict comprehensions are "last value wins" for duplicate keys. In cases where multiple solutions share the same exercise_label, that would change semantics compared to the original nested-loop+break (first match wins). Consider updating the note to recommend manual population / setdefault when first-match behavior must be preserved, and only suggest a comprehension when duplicates are impossible or last-match is desired.

Suggested change

**Action:** Use dictionary comprehensions or manual population (like `solutions_by_label = {sol['exercise_label']: sol for sol in material['solutions']}`) to eliminate inner loops when mapping keys to values.

**Action:** Preserve the original duplicate-key semantics when building the lookup: if the nested-loop version used `break` on the first match, populate manually with `setdefault` (for example, `solutions_by_label = {}; for sol in material['solutions']: solutions_by_label.setdefault(sol['exercise_label'], sol)`) so the first match wins. Only use a dict comprehension like `solutions_by_label = {sol['exercise_label']: sol for sol in material['solutions']}` when duplicate labels are impossible or when last-match-wins behavior is desired.