⚡ Bolt: [performance improvement] Optimize nested loops for exercise-solution lookups#98
⚡ Bolt: [performance improvement] Optimize nested loops for exercise-solution lookups#98glacy wants to merge 1 commit into
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- Pre-computes solution dictionaries to replace O(N*M) lookups with O(N) lookup. - Impacts `get_all_exercises` in MaterialExtractor and `index_materials` in RagIndexer. - Converts modified files to LF. - Applies auto-formatting with black and ruff. Co-authored-by: glacy <1131951+glacy@users.noreply.github.com>
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Pull request overview
Optimizes exercise→solution matching during material extraction and RAG indexing by replacing nested solution scans with precomputed dictionaries, reducing lookup time from O(n²) to O(n) while preserving “first match wins” behavior.
Changes:
- Precomputes
solutions_by_labeldictionaries to avoid per-exercise linear scans inMaterialExtractor.get_all_exercises()andRAGIndexer.index_materials(). - Refactors/reformats
rag_indexer.pyandmaterial_extractor.pyfor consistency (quotes, wrapping, spacing). - Adds a standalone
benchmark.pyscript to compare original vs optimized lookup performance.
Reviewed changes
Copilot reviewed 3 out of 3 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 O(1) solution lookup during indexing; also includes significant formatting/refactor churn. |
| evolutia/material_extractor.py | Uses a per-material solutions_by_label dict for O(1) solution lookup when aggregating exercises; formatting cleanup. |
| benchmark.py | Adds a quick benchmark script comparing nested-loop vs dict-based lookups. |
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| # Generar embeddings | ||
| embeddings = self._generate_embeddings_batch(chunks) | ||
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| # Sincronizar chunks con embeddings (por si se filtraron vacíos en _generate_embeddings_batch) | ||
| # Aunque aquí preferimos filtrar antes para mantener consistencia | ||
| valid_indices = [i for i, chunk in enumerate(chunks) if chunk and chunk.strip()] | ||
| chunks = [chunks[i] for i in valid_indices] | ||
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| if not chunks: | ||
| logger.warning( | ||
| f"Ejercicio {exercise.get('label', 'unknown')} no tiene contenido válido para indexar" | ||
| ) | ||
| return [] | ||
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| # Crear IDs y documentos | ||
| chunk_ids = [] | ||
| documents = [] | ||
| metadatas = [] | ||
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| for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)): |
| # Generar embeddings | ||
| embeddings = self._generate_embeddings_batch(chunks) | ||
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| # Sincronizar chunks con embeddings | ||
| valid_indices = [i for i, chunk in enumerate(chunks) if chunk and chunk.strip()] | ||
| chunks = [chunks[i] for i in valid_indices] | ||
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| if not chunks: | ||
| logger.warning( | ||
| f"Lectura {metadata.get('title', 'unknown')} no tiene contenido válido para indexar" | ||
| ) | ||
| return [] | ||
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| # Generate dummy data | ||
| materials = [] | ||
| for i in range(10): | ||
| exercises = [{'label': f'ex_{j}'} for j in range(1000)] | ||
| solutions = [{'exercise_label': f'ex_{j}', 'label': f'sol_{j}'} for j in range(1000)] | ||
| materials.append({'exercises': exercises, 'solutions': solutions}) | ||
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| start = time.time() | ||
| get_all_exercises_original(materials) | ||
| print(f"Original: {time.time() - start:.4f}s") | ||
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| start = time.time() | ||
| get_all_exercises_optimized(materials) | ||
| print(f"Optimized: {time.time() - start:.4f}s") |
💡 What: Replaced nested$O(n^2)$ loops searching for solutions by exercise label with pre-computed dictionaries in $O(n)$ search time.
MaterialExtractorandRagIndexer, resulting in an🎯 Why: As the number of exercises and solutions grows, searching linearly through all solutions for each exercise becomes a bottleneck during material extraction and indexing.
📊 Impact: Significantly reduces lookup time for matching exercises to solutions. Benchmark showed a drop from ~0.37s to ~0.01s (a ~96% improvement) for 10,000 exercises across materials.
🔬 Measurement: The change was verified to pass existing tests by mimicking the exact first-match behavior via dictionary manual insertion (
if key not in dict:).PR created automatically by Jules for task 13445378598198440381 started by @glacy