⚡ Bolt: Optimize O(N*M) lookups to O(N) using dictionaries

[glacy]

Replaced O(N*M) nested loops with O(1) dictionary lookups in material_extractor.py and rag_indexer.py.

• What: Pre-computed a dictionary mapping exercise_label to the corresponding solution object before iterating over exercises.
• Why: Reduces time complexity from O(N*M) to O(N) when mapping exercises to their solutions.
• Impact: Significantly improves performance when processing files with many exercises/solutions, avoiding repetitive full-list iterations.
• Measurement: Compare processing time on large markdown files with many exercises and solutions.

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PR created automatically by Jules for task 1638187351201234768 started by @glacy

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[Copilot]

Pull request overview

Replaces nested O(N*M) loops that match exercises to solutions with pre-computed dictionaries for O(1) lookups in two files, while applying Black formatting across both files. First-match semantics are preserved by only inserting into the dictionary when the key is missing.

Changes:

• In MaterialExtractor.get_all_exercises, build a solutions_dict per material before iterating exercises.
• In RAGIndexer.index_materials, same pattern: build a solutions_dict per material and look up by label.
• Add a learning note in .jules/bolt.md; apply Black-style reformatting to both Python files.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated no comments.

File	Description
evolutia/material_extractor.py	Replaces inner exercise→solution loop with dict lookup; Black reformatting.
evolutia/rag/rag_indexer.py	Same dict-lookup optimization in index_materials; Black reformatting.
.jules/bolt.md	Documents the O(N*M)→O(N) learning.

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