⚡ Bolt: [performance improvement] Optimize nested loops in material extraction and RAG indexing

[glacy]

💡 What:

Converted O(N*M) nested loop searches in evolutia/material_extractor.py (get_all_exercises) and evolutia/rag/rag_indexer.py (index_materials) into O(N) hash map lookups by pre-computing solution_map dictionaries.

🎯 Why:

When iterating over exercises and repeatedly searching through a list of solutions for a matching label, the time complexity scales quadratically (O(N*M)). For repositories with large amounts of exercises and solutions, this becomes a noticeable bottleneck during extraction and RAG indexing.

📊 Impact:

Reduces the time complexity of exercise-to-solution matching from quadratic O(N*M) to linear O(N), significantly speeding up extraction and index generation pipelines.

🔬 Measurement:

To verify the improvement, generate materials or index using a large subset of exercises. Performance profiles before and after will show a stark decrease in cumulative execution time within get_all_exercises and index_materials functions. Testing verified the exact same output generation correctly preserving first-match semantics.

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PR created automatically by Jules for task 14618264829651679475 started by @glacy

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[Copilot]

Pull request overview

Este PR busca mejorar el rendimiento al eliminar búsquedas O(N*M) al asociar ejercicios con sus soluciones durante la extracción de materiales y el indexado RAG, reemplazándolas por lookups O(1) mediante diccionarios precomputados, preservando la semántica de “primera coincidencia”.

Changes:

• Optimiza el matching ejercicio→solución en MaterialExtractor.get_all_exercises usando un solution_map por material.
• Optimiza el matching ejercicio→solución en RAGIndexer.index_materials usando un solution_map por material.
• Reformat/normaliza estilo (comillas, wrapping, etc.) en los módulos afectados.

Reviewed changes

Copilot reviewed 2 out of 3 changed files in this pull request and generated 2 comments.

File	Description
tests/test_exercise_analyzer.py	Sin cambios funcionales relevantes; principalmente normalización de diff/format.
evolutia/rag/rag_indexer.py	Introduce solution_map para evitar nested loops en indexación; incluye refactors/formatting.
evolutia/material_extractor.py	Introduce solution_map para evitar nested loops al consolidar ejercicios; incluye refactors/formatting.

Comments suppressed due to low confidence (1)

evolutia/material_extractor.py:336

• La validación de caché usa _last_scan_timestamp (global) en lugar del timestamp del entry para este archivo, por lo que puede devolver datos obsoletos si un archivo cambia con mtime menor al de otro archivo escaneado. Además, el acceso _= self._file_cache[file_path] no aporta nada. Conviene validar por mtime del propio archivo y, si el stat() falla, usar el TTL para mantener el caché de errores y evitar reintentos constantes.

        if file_path not in self._file_cache:
            return False

        # Verificar si el archivo fue modificado
        try:
            _ = self._file_cache[file_path]
            file_mtime = file_path.stat().st_mtime

            # Usar el timestamp de escaneo más reciente para verificar
            if file_mtime > self._last_scan_timestamp:
                return False

            return True

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