egutierrez
c47b9474f4
- Added `text_manager.py` to handle the creation of text libraries via FastAPI. - Introduced database connection management in `conexion.py` using PostgreSQL credentials from environment variables. - Created abstract base class `EmbedderABC` in `Base_Embedder.py` for embedding models. - Developed `OpenAIEmbedder` class to generate embeddings using OpenAI's API. - Implemented `OpenAIEmbedderModel` and repository pattern for managing OpenAI embedders in `Openai_embedder_mmr.py`. - Established `Biblioteca` class for managing text libraries and their associated notes in `biblioteca.py`. - Created SQLAlchemy models and mappers for `Biblioteca` and `Nota` in `biblioteca_mmr.py` and `notas_biblioteca_mmr.py`. - Added functionality for dynamic table generation for notes associated with libraries. - Included comprehensive methods for adding, retrieving, and managing notes and libraries in their respective repositories.
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import os
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def generate_tree(start_path='.', max_depth=2):
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lines = []
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def _tree(dir_path, depth, prefix):
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if depth > max_depth:
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return
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entries = sorted(os.listdir(dir_path))
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for i, entry in enumerate(entries):
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path = os.path.join(dir_path, entry)
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connector = "└── " if i == len(entries) - 1 else "├── "
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lines.append(prefix + connector + entry)
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if os.path.isdir(path):
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extension = " " if i == len(entries) - 1 else "│ "
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_tree(path, depth + 1, prefix + extension)
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lines.append(start_path)
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_tree(start_path, 1, '')
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return '\n'.join(lines)
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def save_tree_to_file(start_path='.', max_depth=2, output_file='tree.txt'):
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tree_output = generate_tree(start_path, max_depth)
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write(tree_output)
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print(f"Árbol guardado en: {output_file}")
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# Ejemplo de uso:
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# Puedes cambiar estos valores según lo necesites
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save_tree_to_file(start_path=r'E:\Fitz_Studio', max_depth=3, output_file=r'E:\Fitz_Studio\data\files\txt\tree.txt')
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