⚡ Bolt: Optimize get_all_exercises and fix caching in material_extractor.py

[glacy]

💡 What:

• Replaced an O(N*M) nested loop in get_all_exercises with an O(N) hash map lookup by precomputing solutions_by_exercise to map exercise_label to the corresponding solution.
• Fixed extract_from_file to properly cache extraction results. Previously, the method returned the dictionary prematurely, bypassing the caching logic entirely. It now assigns the dictionary to a local result variable and returns it after updating the cache.

🎯 Why:

• The nested loop in get_all_exercises causes severe performance degradation when processing materials with a large number of exercises and solutions.
• The bypassed caching logic meant files were repeatedly parsed when extract_from_file was called, increasing I/O operations and computation overhead unnecessarily.

📊 Impact:

• get_all_exercises time complexity is reduced from O(N*M) to O(N).
• Parsing overhead is reduced as caching for extracted materials actually functions as originally intended.

🔬 Measurement:

• Tested a dummy set of 100 materials, each with 100 exercises and solutions: original nested loop took ~0.075s, optimized hash map look took ~0.013s (~5.7x faster).

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PR created automatically by Jules for task 4003374063841903966 started by @glacy

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[Copilot]

Pull request overview

This PR improves MaterialExtractor performance and correctness by enabling caching in extract_from_file and optimizing solution lookup in get_all_exercises.

Changes:

• Fixed extract_from_file to build a result object and return it only after updating _file_cache.
• Optimized get_all_exercises by precomputing a per-material solutions_by_exercise dict to avoid O(N*M) matching.
• Normalized formatting/whitespace in material_extractor.py (diff-wide reflow).

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