⚡ Bolt: [performance improvement] Optimize exercise-solution matching

[glacy]

💡 What:
Refactored nested for loops used to match exercises to their corresponding solutions into hash map dictionary lookups in both evolutia/material_extractor.py and evolutia/rag/rag_indexer.py.

🎯 Why:
The previous implementation iterated through all solutions for every exercise within a material to find a matching label. This resulted in an $O(N \times M)$ time complexity (where $N$ is exercises and $M$ is solutions), creating a significant performance bottleneck during bulk processing of large materials. By pre-computing a dictionary, the lookup is optimized to $O(N)$.

📊 Impact:
Reduces the time complexity of exercise-solution matching from $O(N \times M)$ to $O(N + M)$. In local benchmarks with large materials (1000 exercises and 1000 solutions), execution time dropped from ~3.97s to ~0.16s, a ~95% speed improvement.

🔬 Measurement:
Run python -m pytest tests/ -v to verify that existing extractions and indexations pass identically. The logic strictly preserves the original break (first-match) semantics by using if label not in solutions_dict:.

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PR created automatically by Jules for task 6193627798938315585 started by @glacy

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[Copilot]

Pull request overview

Optimizes exercise-to-solution matching in extraction and RAG indexing by replacing nested scans with precomputed lookup dictionaries to reduce runtime on large materials.

Changes:

• Refactor exercise→solution matching to use a solutions_dict in MaterialExtractor.get_all_exercises and RAGIndexer.index_materials.
• Add a local benchmark script (perf_test_loops.py) and a Jules “Bolt” learning note documenting the optimization pattern.
• Update packaging metadata to include an additional test file in evolutia.egg-info/SOURCES.txt.

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
perf_test_loops.py	Adds a quick benchmark script for measuring extraction performance.
evolutia/rag/rag_indexer.py	Uses precomputed solutions_dict for O(1) solution lookup during indexing (plus formatting cleanup).
evolutia/material_extractor.py	Uses precomputed solutions_dict for O(1) solution lookup when aggregating exercises (plus formatting cleanup).
evolutia.egg-info/SOURCES.txt	Updates source manifest to include tests/test_markdown_parser.py.
.jules/bolt.md	Documents the O(N·M) → O(N) lookup refactor pattern.

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[Copilot]

extract_from_file returns the result dict immediately, so the cache-write block below is unreachable and references result (which is never defined). This breaks the file cache and makes the use_cache flag ineffective. Assign the dict to a result variable, update the cache before returning, and remove the unreachable return/dead code path.

[Copilot]

This benchmark script executes work at import time (no if __name__ == "__main__": guard), which is risky if the file is ever imported by tooling. Also the output label says O(N*M) even though get_all_exercises is now optimized. Consider guarding the entrypoint and renaming/moving the script under a dedicated scripts/ or benchmarks/ location to avoid accidental execution.

Suggested change

	# Create large mock materials
	materials = []
	for i in range(10):
	exercises = [{'label': f'ex_{j}', 'content': 'test', 'resolved_content': 'test'} for j in range(1000)]
	solutions = [{'exercise_label': f'ex_{j}', 'label': f'sol_{j}', 'content': 'test', 'resolved_content': 'test'} for j in range(1000)]
	materials.append({
	'file_path': 'test.md',
	'frontmatter': {},
	'exercises': exercises,
	'solutions': solutions
	})
	extractor = MaterialExtractor('.')
	start = time.time()
	for _ in range(10):
	extractor.get_all_exercises(materials)
	print(f"O(N*M) time: {time.time() - start:.4f}s")
	def main():
	# Create large mock materials
	materials = []
	for i in range(10):
	exercises = [{'label': f'ex_{j}', 'content': 'test', 'resolved_content': 'test'} for j in range(1000)]
	solutions = [{'exercise_label': f'ex_{j}', 'label': f'sol_{j}', 'content': 'test', 'resolved_content': 'test'} for j in range(1000)]
	materials.append({
	'file_path': 'test.md',
	'frontmatter': {},
	'exercises': exercises,
	'solutions': solutions
	})
	extractor = MaterialExtractor('.')
	start = time.time()
	for _ in range(10):
	extractor.get_all_exercises(materials)
	print(f"Benchmark time: {time.time() - start:.4f}s")
	if __name__ == "__main__":
	main()