⚡ Bolt: [performance improvement] Optimize solution lookups to O(N)#140
⚡ Bolt: [performance improvement] Optimize solution lookups to O(N)#140glacy wants to merge 1 commit into
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- Replaces O(N*M) search logic for solutions in `get_all_exercises` (`evolutia/material_extractor.py`) and `index_materials` (`evolutia/rag/rag_indexer.py`). - Precomputes a lookup dictionary in O(N) time for matching `exercise_label`. - Preserves the original `break`'s first-match logic by verifying `if key not in dict` before inserting. - Formatted modified files with black and ruff. Co-authored-by: glacy <1131951+glacy@users.noreply.github.com>
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Pull request overview
This PR optimizes exercise→solution matching by replacing per-exercise nested scans of solutions with a precomputed per-material dictionary lookup, reducing matching from (O(N \times M)) to (O(N)) during extraction and RAG indexing.
Changes:
- Precompute
solutions_by_labeldictionaries to do (O(1)) solution retrieval per exercise in both material extraction and RAG indexing. - Preserve original “first match wins” behavior by only setting a dict entry when the key is missing.
- Apply broad formatting/quoting cleanup in the touched modules.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| evolutia/rag/rag_indexer.py | Uses a per-material solutions_by_label dict for faster solution lookup during indexing; includes refactors around embedding/chunk handling and formatting. |
| evolutia/material_extractor.py | Uses a per-material solutions_by_label dict for faster solution lookup when aggregating exercises; includes formatting cleanup. |
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| elif self.embedding_provider == "sentence-transformers": | ||
| return self.embedding_model.encode( | ||
| texts, show_progress_bar=True, batch_size=32 | ||
| ).tolist() |
| documents = [] | ||
| metadatas = [] | ||
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| for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)): |
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| source = metadata.get("source_file", "reading") | ||
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| for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)): |
💡 What: Refactored the$O(N \times M)$ nested loop used to find solutions for exercises into an $O(N)$ dictionary lookup strategy.
🎯 Why: To improve performance when processing large numbers of materials. Nested loops iterating over exercises and solutions can degrade performance unnecessarily (quadratic scaling). A dictionary lookup reduces the time complexity of the matching phase to$O(1)$ per exercise.
📊 Impact: Reduces time complexity of matching exercises with their solutions from$O(N \times M)$ to $O(N)$ , saving processing time during material extraction and indexing.
🔬 Measurement: Extracting and indexing materials from a large directory structure will measure noticeably faster. Validated by ensuring the precomputed lookup logic accurately replicates the first-match functionality of the original nested loop's
breakstatement.PR created automatically by Jules for task 5598398590228265232 started by @glacy