⚡ Bolt: [performance improvement] Optimize nested loops to dictionary lookups#130
⚡ Bolt: [performance improvement] Optimize nested loops to dictionary lookups#130glacy wants to merge 1 commit into
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…tching - Replaced O(N*M) nested loop matching exercises to solutions with an O(N) pre-computed dictionary lookup in `evolutia/material_extractor.py` and `evolutia/rag/rag_indexer.py`. - Fixed unused variable warnings caught by ruff. Co-authored-by: glacy <1131951+glacy@users.noreply.github.com>
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Pull request overview
This PR aims to improve performance when matching exercises to their solutions by replacing O(N×M) nested-loop scans with an O(N) precomputed dictionary lookup, targeting large materials with many exercises/solutions.
Changes:
- Precompute
solutions_dictper material to mapexercise_label -> solutioninMaterialExtractor.get_all_exercises. - Precompute
solutions_dictper material to mapexercise_label -> solutioninRAGIndexer.index_materials. - Apply broad formatting/style normalization (quotes, wrapping) in both modules.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 5 comments.
| File | Description |
|---|---|
evolutia/rag/rag_indexer.py |
Uses a per-material solutions lookup dict in index_materials; also includes formatting changes and some minor cleanups. |
evolutia/material_extractor.py |
Uses a per-material solutions lookup dict in get_all_exercises; also includes formatting and minor cleanups. |
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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 [] |
| # 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 [] |
| self.client.delete_collection(name=collection_name) | ||
| _ = self.config.get("vector_store", {}) | ||
| self.collection = self.client.create_collection( | ||
| name=collection_name, metadata={"hnsw:space": "cosine"} | ||
| ) |
| try: | ||
| cache_entry = self._file_cache[file_path] | ||
| _ = self._file_cache[file_path] | ||
| file_mtime = file_path.stat().st_mtime |
| # Pre-compute solutions lookup | ||
| solutions_dict = {} | ||
| for sol in material.get("solutions", []): | ||
| if sol["exercise_label"] not in solutions_dict: | ||
| solutions_dict[sol["exercise_label"]] = sol | ||
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💡 What:
Replaced O(N*M) nested loop matching between exercises and solutions with an O(N) pre-computed dictionary lookup in both
MaterialExtractor.get_all_exercisesandRAGIndexer.index_materials.🎯 Why:
When processing materials that contain numerous exercises and solutions, using a nested loop (
for exercise... for solution...) leads to O(N*M) time complexity. By pre-computing a lookup dictionarysolutions_dict, we significantly reduce this to O(N) time complexity, eliminating a potential bottleneck when scaling to larger datasets or course structures.📊 Impact:
Eliminates an O(N^2) bottleneck, scaling linearly instead of quadratically for files containing many exercises. Performance gains will be highly measurable on massive markdown files.
🔬 Measurement:
Run full material extraction or indexing on a large dataset (e.g., >1000 exercises) and observe reduced CPU utilization and lower execution time. You can verify the behavior is identical since the dictionary strictly mimics the "first match" logic of the previous loop's
breakcondition. Tests ensure everything functions correctly.PR created automatically by Jules for task 17602088928189494892 started by @glacy