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⚡ Bolt: Optimize get_all_exercises and fix caching in material_extractor.py #91

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⚡ Bolt: Optimize get_all_exercises and fix caching in material_extractor.py #91

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301 changes: 151 additions & 150 deletions evolutia/material_extractor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,28 +6,28 @@
from typing import Dict, List, Optional, Union
import logging
import time
try:
from utils.markdown_parser import (
read_markdown_file,
extract_frontmatter,
extract_exercise_blocks,
extract_solution_blocks,
resolve_include_path
)
except ImportError:
from .utils.markdown_parser import (
read_markdown_file,
extract_frontmatter,
extract_exercise_blocks,
extract_solution_blocks,
resolve_include_path
)
logger = logging.getLogger(__name__)

try:
from utils.markdown_parser import (
read_markdown_file,
extract_frontmatter,
extract_exercise_blocks,
extract_solution_blocks,
resolve_include_path
)
except ImportError:
from .utils.markdown_parser import (
read_markdown_file,
extract_frontmatter,
extract_exercise_blocks,
extract_solution_blocks,
resolve_include_path
)


logger = logging.getLogger(__name__)


class MaterialExtractor:
"""Extrae ejercicios y soluciones de materiales didácticos."""

Expand All @@ -47,7 +47,7 @@ def __init__(self, base_path: Union[Path, str]):
self._last_scan_timestamp: float = 0
# TTL del caché en segundos (5 minutos)
self._cache_ttl = 300

def extract_from_file(self, file_path: Path, use_cache: bool = True) -> Dict:
"""
Extrae ejercicios y soluciones de un archivo Markdown.
Expand All @@ -65,19 +65,19 @@ def extract_from_file(self, file_path: Path, use_cache: bool = True) -> Dict:
return self._file_cache[file_path]['data']

try:
content = read_markdown_file(file_path)
frontmatter, content_body = extract_frontmatter(content)
exercises = extract_exercise_blocks(content_body)
solutions = extract_solution_blocks(content_body)
# Resolver includes de ejercicios
for exercise in exercises:
if exercise['include_path']:
include_path = resolve_include_path(
exercise['include_path'],
file_path.parent
)
content = read_markdown_file(file_path)
frontmatter, content_body = extract_frontmatter(content)

exercises = extract_exercise_blocks(content_body)
solutions = extract_solution_blocks(content_body)

# Resolver includes de ejercicios
for exercise in exercises:
if exercise['include_path']:
include_path = resolve_include_path(
exercise['include_path'],
file_path.parent
)
if include_path.exists():
exercise['resolved_content'] = read_markdown_file(include_path)
else:
Expand All @@ -103,8 +103,8 @@ def extract_from_file(self, file_path: Path, use_cache: bool = True) -> Dict:
solution['resolved_content'] = '\n\n---\n\n'.join(resolved_content_parts)
else:
solution['resolved_content'] = solution['content']
return {

result = {
'file_path': file_path,
'frontmatter': frontmatter,
'exercises': exercises,
Comment on lines +106 to 110
Expand Down Expand Up @@ -153,118 +153,119 @@ def extract_from_directory(self, directory: Path, pattern: str = "*.md") -> List
directory = Path(directory)
if not directory.exists():
logger.warning(f"[MaterialExtractor] Directorio no existe: {directory}")
return []

materials = []
for md_file in directory.rglob(pattern):
# Ignorar archivos en _build y otros directorios temporales
if '_build' in md_file.parts or 'node_modules' in md_file.parts:
continue

material = self.extract_from_file(md_file)
# Incluirlos si tienen ejercicios/soluciones O si parecen ser materiales de lectura/teoría
if material['exercises'] or material['solutions'] or 'lectura' in md_file.name.lower() or 'teoria' in md_file.name.lower():
materials.append(material)

return materials

def extract_by_topic(self, topic: str) -> List[Dict]:
"""
Extrae materiales de un tema específico.

Busca en:
- {topic}/semana*_practica.md
- {topic}/semana*_lectura.md
- tareas/tarea*/tarea*.md

Args:
topic: Nombre del tema (ej: "analisis_vectorial")

Returns:
Lista de materiales extraídos
"""
materials = []

# Buscar en directorio del tema
topic_dir = self.base_path / topic
if topic_dir.exists():
# Buscar prácticas
practice_files = list(topic_dir.glob("*practica*.md"))
for file in practice_files:
materials.append(self.extract_from_file(file))

# Buscar lecturas (pueden tener ejercicios)
reading_files = list(topic_dir.glob("*lectura*.md"))
for file in reading_files:
materials.append(self.extract_from_file(file))

# Buscar en tareas (pueden ser de múltiples temas)
tareas_dir = self.base_path / "tareas"
if tareas_dir.exists():
for tarea_dir in tareas_dir.iterdir():
if tarea_dir.is_dir():
tarea_file = tarea_dir / f"{tarea_dir.name}.md"
if tarea_file.exists():
material = self.extract_from_file(tarea_file)
# Filtrar por tema si es relevante (checking subject or tags)
subject_match = material['frontmatter'].get('subject', '').lower().find(topic.lower()) != -1
tags_match = any(topic.lower() in tag.lower() for tag in material['frontmatter'].get('tags', []))
if subject_match or tags_match:
materials.append(material)

# Buscar en examenes (pueden ser de múltiples temas)
examenes_dir = self.base_path / "examenes"
if examenes_dir.exists():
for examen_dir in examenes_dir.iterdir():
if examen_dir.is_dir():
examen_file = examen_dir / f"{examen_dir.name}.md"
if examen_file.exists():
material = self.extract_from_file(examen_file)
# Filtrar por tema si es relevante
subject_match = material['frontmatter'].get('subject', '').lower().find(topic.lower()) != -1
tags_match = any(topic.lower() in tag.lower() for tag in material['frontmatter'].get('tags', []))

# Si es examen, a veces no tiene subject especifico o tiene "Examen X".
# Si no hay match explícito, tal vez incluirlo si no se encontraron otros materiales?
# Para seguridad, requerimos algún match en subject, tags o keywords
keywords_match = any(topic.lower() in kw.lower() for kw in material['frontmatter'].get('keywords', []))

if subject_match or tags_match or keywords_match:
materials.append(material)

return materials

def get_all_exercises(self, materials: List[Dict]) -> List[Dict]:
"""
Obtiene todos los ejercicios de una lista de materiales.

Args:
materials: Lista de materiales extraídos

Returns:
Lista de ejercicios con sus metadatos
"""
all_exercises = []

for material in materials:
for exercise in material['exercises']:
# Buscar solución correspondiente
solution = None
for sol in material['solutions']:
if sol['exercise_label'] == exercise['label']:
solution = sol
break

exercise_data = {
'label': exercise['label'],
'content': exercise['resolved_content'],
'source_file': material['file_path'],
'frontmatter': material['frontmatter'],
'solution': solution['resolved_content'] if solution else None,
'solution_label': solution['label'] if solution else None
}
all_exercises.append(exercise_data)

return []

materials = []
for md_file in directory.rglob(pattern):
# Ignorar archivos en _build y otros directorios temporales
if '_build' in md_file.parts or 'node_modules' in md_file.parts:
continue

material = self.extract_from_file(md_file)
# Incluirlos si tienen ejercicios/soluciones O si parecen ser materiales de lectura/teoría
if material['exercises'] or material['solutions'] or 'lectura' in md_file.name.lower() or 'teoria' in md_file.name.lower():
materials.append(material)

return materials

def extract_by_topic(self, topic: str) -> List[Dict]:
"""
Extrae materiales de un tema específico.

Busca en:
- {topic}/semana*_practica.md
- {topic}/semana*_lectura.md
- tareas/tarea*/tarea*.md

Args:
topic: Nombre del tema (ej: "analisis_vectorial")

Returns:
Lista de materiales extraídos
"""
materials = []

# Buscar en directorio del tema
topic_dir = self.base_path / topic
if topic_dir.exists():
# Buscar prácticas
practice_files = list(topic_dir.glob("*practica*.md"))
for file in practice_files:
materials.append(self.extract_from_file(file))

# Buscar lecturas (pueden tener ejercicios)
reading_files = list(topic_dir.glob("*lectura*.md"))
for file in reading_files:
materials.append(self.extract_from_file(file))

# Buscar en tareas (pueden ser de múltiples temas)
tareas_dir = self.base_path / "tareas"
if tareas_dir.exists():
for tarea_dir in tareas_dir.iterdir():
if tarea_dir.is_dir():
tarea_file = tarea_dir / f"{tarea_dir.name}.md"
if tarea_file.exists():
material = self.extract_from_file(tarea_file)
# Filtrar por tema si es relevante (checking subject or tags)
subject_match = material['frontmatter'].get('subject', '').lower().find(topic.lower()) != -1
tags_match = any(topic.lower() in tag.lower() for tag in material['frontmatter'].get('tags', []))
if subject_match or tags_match:
materials.append(material)

# Buscar en examenes (pueden ser de múltiples temas)
examenes_dir = self.base_path / "examenes"
if examenes_dir.exists():
for examen_dir in examenes_dir.iterdir():
if examen_dir.is_dir():
examen_file = examen_dir / f"{examen_dir.name}.md"
if examen_file.exists():
material = self.extract_from_file(examen_file)
# Filtrar por tema si es relevante
subject_match = material['frontmatter'].get('subject', '').lower().find(topic.lower()) != -1
tags_match = any(topic.lower() in tag.lower() for tag in material['frontmatter'].get('tags', []))

# Si es examen, a veces no tiene subject especifico o tiene "Examen X".
# Si no hay match explícito, tal vez incluirlo si no se encontraron otros materiales?
# Para seguridad, requerimos algún match en subject, tags o keywords
keywords_match = any(topic.lower() in kw.lower() for kw in material['frontmatter'].get('keywords', []))

if subject_match or tags_match or keywords_match:
materials.append(material)

return materials

def get_all_exercises(self, materials: List[Dict]) -> List[Dict]:
"""
Obtiene todos los ejercicios de una lista de materiales.

Args:
materials: Lista de materiales extraídos

Returns:
Lista de ejercicios con sus metadatos
"""
all_exercises = []

for material in materials:
# Precompute solution lookup for O(1) matching, preserving first-match behavior
solutions_by_exercise = {}
for sol in material['solutions']:
if sol['exercise_label'] not in solutions_by_exercise:
solutions_by_exercise[sol['exercise_label']] = sol

for exercise in material['exercises']:
solution = solutions_by_exercise.get(exercise['label'])

exercise_data = {
'label': exercise['label'],
'content': exercise['resolved_content'],
'source_file': material['file_path'],
'frontmatter': material['frontmatter'],
'solution': solution['resolved_content'] if solution else None,
'solution_label': solution['label'] if solution else None
}
all_exercises.append(exercise_data)

return all_exercises

def clear_cache(self):
Expand Down