Intento de crear un RagEx
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6
app.py
6
app.py
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@ -221,5 +221,11 @@ def handle_group_description(group):
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return jsonify({"status": "error", "message": str(e)}), 500
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@app.route("/api/directory-history/<group>")
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def get_directory_history(group):
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history = config_manager.get_directory_history(group)
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return jsonify(history)
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if __name__ == "__main__":
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app.run(debug=True)
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@ -0,0 +1,4 @@
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{
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"type": "object",
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"properties": {}
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}
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@ -0,0 +1,4 @@
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{
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"type": "object",
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"properties": {}
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}
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@ -0,0 +1,6 @@
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{
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"path": "C:\\Users\\migue\\OneDrive\\Miguel\\Obsidean\\Trabajo\\VM\\30 - 9.3941- Kosme - Portogallo (Modifica + Linea)\\Emails",
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"history": [
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"C:\\Users\\migue\\OneDrive\\Miguel\\Obsidean\\Trabajo\\VM\\30 - 9.3941- Kosme - Portogallo (Modifica + Linea)\\Emails"
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]
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}
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@ -0,0 +1,303 @@
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"""
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Script para hacer una union de los cambios generados por un LLM en un archivo de código C#.
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"""
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import os
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import sys
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import json
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import re
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from pathlib import Path
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from dataclasses import dataclass
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from typing import List, Dict, Optional, Tuple
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import difflib
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# Forzar UTF-8 en la salida estándar
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sys.stdout.reconfigure(encoding="utf-8")
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@dataclass
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class CodeSection:
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type: str
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name: str
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content: str
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start_line: int
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end_line: int
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parent: Optional['CodeSection'] = None
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children: List['CodeSection'] = None
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attributes: List[str] = None
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original_indent: str = ""
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def __post_init__(self):
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if self.children is None:
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self.children = []
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if self.attributes is None:
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self.attributes = []
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class CSharpParser:
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def __init__(self):
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# Definimos el orden específico de las secciones
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self.section_order = [
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'using',
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'comment',
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'attribute',
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'namespace',
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'class',
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'interface',
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'region',
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'field',
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'property',
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'method'
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]
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self.patterns = {
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'using': r'^\s*using\s+([^;]+);',
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'namespace': r'^\s*namespace\s+([^\s{]+)',
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'class': r'^\s*(?:public|private|internal|protected)?\s*(?:partial\s+)?(?:abstract\s+)?class\s+(\w+)',
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'interface': r'^\s*(?:public|private|internal|protected)?\s*interface\s+(\w+)',
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'method': r'^\s*(?:public|private|internal|protected)?\s*(?:virtual|override|static|async)?\s*[\w<>]+\s+(\w+)\s*\(',
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'property': r'^\s*(?:\[.+\]\s*)*(?:public|private|internal|protected)?\s*[\w<>]+\s+(\w+)\s*(?:{\s*get;|=>)',
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'field': r'^\s*(?:public|private|internal|protected)?\s*(?:readonly|static|const)?\s*[\w<>]+\s+(\w+)\s*(?:=|;)',
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'attribute': r'^\s*\[([^\]]+)\]',
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'comment': r'^\s*(?://.*|/\*.*?\*/)',
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'region': r'^\s*#region\s+(.+)$'
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}
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self.placeholder_pattern = r'//\s*\.\.\.\s*resto del código\s*\.\.\.'
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def get_section_order_index(self, section_type: str) -> int:
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try:
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return self.section_order.index(section_type)
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except ValueError:
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return len(self.section_order)
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def get_indent(self, line: str) -> str:
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match = re.match(r'^(\s*)', line)
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return match.group(1) if match else ""
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def parse_file(self, content: str) -> CodeSection:
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lines = content.split('\n')
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root = CodeSection('root', '', '', 0, len(lines))
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current_context = [root]
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current_attributes = []
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i = 0
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while i < len(lines):
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line = lines[i]
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stripped = line.strip()
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original_indent = self.get_indent(line)
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# Skip empty lines
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if not stripped:
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i += 1
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continue
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# Procesar cada tipo de sección según su orden definido
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matched = False
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for section_type in self.section_order:
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if section_type not in self.patterns:
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continue
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pattern = self.patterns[section_type]
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match = re.match(pattern, line)
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if match:
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name = match.group(1)
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if section_type in ['namespace', 'class', 'interface', 'region']:
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# Procesar secciones con bloques
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section_lines, j = self._process_block(lines, i)
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section = CodeSection(
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section_type,
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name,
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'\n'.join(section_lines),
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i,
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j,
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parent=current_context[-1],
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attributes=current_attributes.copy(),
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original_indent=original_indent
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)
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current_context[-1].children.append(section)
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if section_type in ['namespace', 'class', 'interface']:
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current_context.append(section)
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i = j + 1
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else:
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# Procesar secciones simples
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section = CodeSection(
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section_type,
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name,
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line,
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i,
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i,
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parent=current_context[-1],
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attributes=current_attributes.copy(),
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original_indent=original_indent
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)
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current_context[-1].children.append(section)
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i += 1
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current_attributes = []
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matched = True
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break
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if not matched:
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i += 1
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if stripped == '}' and len(current_context) > 1:
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current_context.pop()
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return root
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def _process_block(self, lines: List[str], start_index: int) -> Tuple[List[str], int]:
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brace_count = 0
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section_lines = []
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j = start_index
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while j < len(lines):
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current_line = lines[j]
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section_lines.append(current_line)
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brace_count += current_line.count('{') - current_line.count('}')
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if brace_count == 0 and len(section_lines) > 1:
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break
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j += 1
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return section_lines, j
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class CSharpCodeMerger:
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def __init__(self, original_code: str, llm_code: str):
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self.parser = CSharpParser()
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self.original_tree = self.parser.parse_file(original_code)
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self.llm_tree = self.parser.parse_file(llm_code)
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self.original_code = original_code
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self.llm_code = llm_code
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def _sort_sections(self, sections: List[CodeSection]) -> List[CodeSection]:
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return sorted(sections, key=lambda x: (
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self.parser.get_section_order_index(x.type),
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x.start_line
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))
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def _merge_sections(self, original: CodeSection, llm: CodeSection) -> CodeSection:
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merged = CodeSection(
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original.type,
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original.name,
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original.content,
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original.start_line,
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original.end_line,
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original.parent,
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original_indent=original.original_indent
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)
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# Crear mapas de hijos por tipo y nombre
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original_children = {(c.type, c.name): c for c in original.children}
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llm_children = {(c.type, c.name): c for c in llm.children}
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merged_children = []
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# Procesar hijos originales
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for key, orig_child in original_children.items():
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if key in llm_children:
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llm_child = llm_children[key]
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if orig_child.type in ['namespace', 'class', 'interface', 'region']:
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merged_children.append(self._merge_sections(orig_child, llm_child))
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else:
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merged_children.append(llm_child if orig_child.content != llm_child.content else orig_child)
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else:
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merged_children.append(orig_child)
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# Añadir nuevos hijos del LLM
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for key, llm_child in llm_children.items():
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if key not in original_children:
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merged_children.append(llm_child)
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# Ordenar los hijos según el orden definido
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merged.children = self._sort_sections(merged_children)
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return merged
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def _generate_code(self, section: CodeSection, indent_level: int = 0) -> str:
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lines = []
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base_indent = section.original_indent or " " * indent_level
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# Añadir atributos
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for attr in section.attributes:
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lines.append(base_indent + attr.lstrip())
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if section.type != 'root':
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content_lines = section.content.split('\n')
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lines.append(base_indent + content_lines[0].lstrip())
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if len(content_lines) > 1:
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for line in content_lines[1:]:
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if line.strip():
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current_indent = re.match(r'^(\s*)', line).group(1)
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content = line.lstrip()
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lines.append(base_indent + current_indent + content)
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if section.children:
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sorted_children = self._sort_sections(section.children)
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for child in sorted_children:
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child_code = self._generate_code(child, indent_level + 1 if section.type != 'root' else 0)
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if child_code:
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lines.append(child_code)
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return '\n'.join(lines)
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def merge_code(self) -> str:
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merged_tree = self._merge_sections(self.original_tree, self.llm_tree)
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return self._generate_code(merged_tree)
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def generate_diff(self) -> str:
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merged = self.merge_code()
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diff = difflib.unified_diff(
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self.original_code.splitlines(keepends=True),
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merged.splitlines(keepends=True),
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fromfile='original',
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tofile='merged'
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)
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return ''.join(diff)
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def main():
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configs = json.loads(os.environ.get("SCRIPT_CONFIGS", "{}"))
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working_directory = configs.get("working_directory", ".")
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work_config = configs.get("level3", {})
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input_file = work_config.get("input_file", "original.cs")
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llm_file = work_config.get("llm_file", "llm_generated.cs")
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output_directory = work_config.get("output_directory", ".")
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input_path = os.path.join(working_directory, input_file)
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llm_path = os.path.join(working_directory, llm_file)
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output_merged = os.path.join(output_directory, "merged.cs")
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output_diff = os.path.join(output_directory, "changes.diff")
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for path in [input_path, llm_path]:
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if not os.path.exists(path):
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print(f"Error: File {path} does not exist")
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return
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os.makedirs(output_directory, exist_ok=True)
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try:
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with open(input_path, "r", encoding="utf-8") as f:
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original_code = f.read()
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with open(llm_path, "r", encoding="utf-8") as f:
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llm_code = f.read()
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merger = CSharpCodeMerger(original_code, llm_code)
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merged_code = merger.merge_code()
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with open(output_merged, "w", encoding="utf-8") as f:
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f.write(merged_code)
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with open(output_diff, "w", encoding="utf-8") as f:
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f.write(merger.generate_diff())
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print(f"Successfully processed files:")
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print(f"- Merged code saved to: {output_merged}")
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print(f"- Diff file saved to: {output_diff}")
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except Exception as e:
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print(f"Error processing files: {str(e)}")
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return
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if __name__ == "__main__":
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main()
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@ -2,5 +2,5 @@
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"name": "Desempaquetado de Emails EML",
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"description": "This script processes email files (.eml) into a chronological narrative in Markdown format, optimized for processing with Large Language Models (LLMs). It extracts essential information from emails while removing unnecessary metadata, creating a clean, temporal narrative that can be easily analyzed. ",
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"version": "1.0",
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"author": "Unknown"
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"author": "Miguel"
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}
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Binary file not shown.
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@ -82,35 +82,101 @@ def _html_a_markdown(html):
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if not rows:
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continue
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markdown_table = []
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max_widths = []
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# Matriz para almacenar la tabla procesada
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table_matrix = []
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max_cols = 0
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# Calcular anchos máximos
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for row in rows:
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# Primera pasada: crear matriz y procesar rowspans/colspans
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row_idx = 0
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while row_idx < len(rows):
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row = rows[row_idx]
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cells = row.find_all(['th', 'td'])
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while len(max_widths) < len(cells):
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max_widths.append(0)
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for i, cell in enumerate(cells):
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if not cells:
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row_idx += 1
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continue
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# Expandir matriz si es necesario
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while len(table_matrix) <= row_idx:
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table_matrix.append([])
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col_idx = 0
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for cell in cells:
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# Encontrar la siguiente columna disponible
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while col_idx < len(table_matrix[row_idx]) and table_matrix[row_idx][col_idx] is not None:
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col_idx += 1
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# Obtener rowspan y colspan
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rowspan = int(cell.get('rowspan', 1))
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colspan = int(cell.get('colspan', 1))
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# Procesar el texto de la celda reemplazando saltos de línea por <br>
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cell_text = cell.get_text().strip()
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max_widths[i] = max(max_widths[i], len(cell_text))
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cell_text = cell_text.replace('\n', '<br>')
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cell_text = re.sub(r'\s*<br>\s*<br>\s*', '<br>', cell_text) # Eliminar <br> múltiples
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cell_text = cell_text.strip()
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# Rellenar la matriz con el texto y None para las celdas combinadas
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for r in range(rowspan):
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current_row = row_idx + r
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# Expandir matriz si es necesario
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while len(table_matrix) <= current_row:
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table_matrix.append([])
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# Expandir fila si es necesario
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while len(table_matrix[current_row]) <= col_idx + colspan - 1:
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table_matrix[current_row].append(None)
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for c in range(colspan):
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if r == 0 and c == 0:
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table_matrix[current_row][col_idx + c] = cell_text
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else:
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table_matrix[current_row][col_idx + c] = ''
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col_idx += colspan
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max_cols = max(max_cols, col_idx)
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row_idx += 1
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# Construir tabla markdown
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if max_widths: # Solo si tenemos celdas válidas
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header_row = rows[0].find_all(['th', 'td'])
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header = '| ' + ' | '.join(cell.get_text().strip().ljust(max_widths[i])
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for i, cell in enumerate(header_row)) + ' |'
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separator = '|' + '|'.join('-' * (width + 2) for width in max_widths) + '|'
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# Asegurar que todas las filas tengan el mismo número de columnas
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for row in table_matrix:
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while len(row) < max_cols:
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row.append('')
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# Calcular anchos máximos por columna
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col_widths = [0] * max_cols
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for row in table_matrix:
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for col_idx, cell in enumerate(row):
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if cell is not None:
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col_widths[col_idx] = max(col_widths[col_idx], len(str(cell)))
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# Generar tabla Markdown
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markdown_table = []
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# Cabecera
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if table_matrix:
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header = '|'
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for col_idx, width in enumerate(col_widths):
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cell = str(table_matrix[0][col_idx] or '')
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header += f' {cell.ljust(width)} |'
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markdown_table.append(header)
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# Separador
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separator = '|'
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for width in col_widths:
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separator += '-' * (width + 2) + '|'
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markdown_table.append(separator)
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for row in rows[1:]:
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cells = row.find_all(['td', 'th'])
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row_text = '| ' + ' | '.join(cell.get_text().strip().ljust(max_widths[i])
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for i, cell in enumerate(cells)) + ' |'
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# Contenido
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for row_idx in range(1, len(table_matrix)):
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row_text = '|'
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for col_idx, width in enumerate(col_widths):
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cell = str(table_matrix[row_idx][col_idx] or '')
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row_text += f' {cell.ljust(width)} |'
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markdown_table.append(row_text)
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table.replace_with(soup.new_string('\n' + '\n'.join(markdown_table)))
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# Reemplazar la tabla HTML con la versión Markdown
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if markdown_table:
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table.replace_with(soup.new_string('\n' + '\n'.join(markdown_table) + '\n'))
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except Exception as e:
|
||||
print(f"Error procesando tabla: {str(e)}")
|
||||
continue
|
||||
|
|
|
|||
|
|
@ -1,3 +1,11 @@
|
|||
{
|
||||
"path": "C:/Trabajo/VM/40 - 93040 - HENKEL - NEXT2 Problem/Reporte/Emails"
|
||||
"path": "C:\\Trabajo\\VM\\40 - 93040 - HENKEL - NEXT2 Problem\\Reporte\\EmailTody",
|
||||
"history": [
|
||||
"C:\\Trabajo\\VM\\40 - 93040 - HENKEL - NEXT2 Problem\\Reporte\\EmailTody",
|
||||
"C:\\Trabajo\\VM\\30 - 9.3941- Kosme - Portogallo (Modifica + Linea)\\Reporte\\Emails",
|
||||
"C:\\Users\\migue\\OneDrive\\Miguel\\Obsidean\\Trabajo\\VM\\30 - 9.3941- Kosme - Portogallo (Modifica + Linea)\\Emails",
|
||||
"C:\\Trabajo\\VM\\40 - 93040 - HENKEL - NEXT2 Problem\\Reporte\\Emails\\Trial",
|
||||
"C:\\Trabajo\\VM\\40 - 93040 - HENKEL - NEXT2 Problem\\Reporte\\Emails",
|
||||
"C:\\Trabajo\\VM\\40 - 93040 - HENKEL - NEXT2 Problem\\Reporte\\Emails\\Error de tablas"
|
||||
]
|
||||
}
|
||||
|
|
@ -1,3 +1,6 @@
|
|||
{
|
||||
"path": "C:/Estudio"
|
||||
"path": "C:\\Estudio",
|
||||
"history": [
|
||||
"C:\\Estudio"
|
||||
]
|
||||
}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
{
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"name": "RAGEX",
|
||||
"description": "This script processes text files into a chronological narrative in Markdown format, optimized for processing with Large Language Models (LLMs). It extracts essential information from text files while removing unnecessary metadata, creating a clean, temporal narrative that can be easily analyzed.",
|
||||
"version": "1.0",
|
||||
"author": "Miguel"
|
||||
}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
{
|
||||
|
||||
}
|
||||
|
|
@ -0,0 +1,21 @@
|
|||
{
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"in_dir": {
|
||||
"type": "string",
|
||||
"format": "directory",
|
||||
"title": "Subdirectorio desde donde hacer la ingesta de los datos",
|
||||
"description": "Subdirectorio desde donde hacer la ingesta de los datos"
|
||||
},
|
||||
"model": {
|
||||
"type": "string",
|
||||
"title": "Model",
|
||||
"description": "OpenAI Model"
|
||||
},
|
||||
"query": {
|
||||
"type": "string",
|
||||
"title": "Consulta",
|
||||
"description": "Consulta"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,3 @@
|
|||
# Configura tu clave API de OpenAI
|
||||
def openai_api_key():
|
||||
return 'sk-HIY5DSK03Lr'
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"path": "D:\\Proyectos\\Scripts\\RAG\\TEST",
|
||||
"history": [
|
||||
"D:\\Proyectos\\Scripts\\RAG\\TEST"
|
||||
]
|
||||
}
|
||||
|
|
@ -0,0 +1,112 @@
|
|||
"""
|
||||
Este script realiza la ingesta de los datos alammacenados en el subdirectorio de ingesta.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import json
|
||||
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_openai import OpenAIEmbeddings # Cambiado a OpenAI Embeddings
|
||||
from langchain_community.embeddings import HuggingFaceEmbeddings
|
||||
from langchain_core.documents import Document
|
||||
import os
|
||||
import glob
|
||||
|
||||
|
||||
def load_documents(directory):
|
||||
documents = []
|
||||
|
||||
# Cargar archivos markdown
|
||||
for md_file in glob.glob(os.path.join(directory, "**/*.md"), recursive=True):
|
||||
with open(md_file, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
documents.append(
|
||||
{
|
||||
"content": content,
|
||||
"metadata": {"source": md_file, "type": "markdown"},
|
||||
}
|
||||
)
|
||||
|
||||
# Cargar archivos de texto
|
||||
for txt_file in glob.glob(os.path.join(directory, "**/*.txt"), recursive=True):
|
||||
with open(txt_file, "r", encoding="utf-8") as f:
|
||||
content = f.read()
|
||||
documents.append(
|
||||
{"content": content, "metadata": {"source": txt_file, "type": "text"}}
|
||||
)
|
||||
|
||||
return documents
|
||||
|
||||
|
||||
def process_documents(documents, db_directory):
|
||||
# Usar OpenAI Embeddings en lugar de HuggingFace
|
||||
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
|
||||
|
||||
# Dividir documentos en chunks
|
||||
text_splitter = RecursiveCharacterTextSplitter(
|
||||
chunk_size=1000,
|
||||
chunk_overlap=200,
|
||||
separators=["\n## ", "\n### ", "\n#### ", "\n", " ", ""],
|
||||
keep_separator=True,
|
||||
)
|
||||
|
||||
docs = []
|
||||
for doc in documents:
|
||||
chunks = text_splitter.split_text(doc["content"])
|
||||
for i, chunk in enumerate(chunks):
|
||||
docs.append(
|
||||
Document(
|
||||
page_content=chunk,
|
||||
metadata={
|
||||
**doc["metadata"],
|
||||
"chunk_id": i,
|
||||
"chunk": chunk[:100] + "...", # Extracto para referencia
|
||||
},
|
||||
)
|
||||
)
|
||||
|
||||
# Configurar Chroma para evitar dependencia de ONNX
|
||||
from chromadb.config import Settings
|
||||
|
||||
# Crear o actualizar la base de datos vectorial con configuración específica
|
||||
db = Chroma.from_documents(
|
||||
docs,
|
||||
embeddings,
|
||||
persist_directory=db_directory,
|
||||
client_settings=Settings(anonymized_telemetry=False, is_persistent=True),
|
||||
)
|
||||
db.persist()
|
||||
|
||||
print(f"Procesados {len(docs)} fragmentos de {len(documents)} documentos")
|
||||
return db
|
||||
|
||||
|
||||
def main():
|
||||
# Cargar configuraciones del entorno
|
||||
configs = json.loads(os.environ.get("SCRIPT_CONFIGS", "{}"))
|
||||
|
||||
# Obtener working directory
|
||||
working_directory = configs.get("working_directory", ".")
|
||||
|
||||
# Obtener configuraciones de nivel 2 (grupo)
|
||||
group_config = configs.get("level2", {})
|
||||
|
||||
work_config = configs.get("level3", {})
|
||||
in_dir = work_config.get("in_dir", ".")
|
||||
|
||||
docs_directory = os.path.join(working_directory, in_dir)
|
||||
db_directory = os.path.join(working_directory, "chroma_db")
|
||||
|
||||
print("Cargando documentos...")
|
||||
documents = load_documents(docs_directory)
|
||||
print(f"Se encontraron {len(documents)} documentos.")
|
||||
|
||||
print("Procesando e indexando documentos...")
|
||||
db = process_documents(documents, db_directory)
|
||||
print("¡Ingesta completada con éxito!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,126 @@
|
|||
"""
|
||||
Este script realiza la consulta usando RAGEX a la base de datos de documentos.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import json
|
||||
from langchain_community.vectorstores import Chroma
|
||||
from langchain_openai import (
|
||||
OpenAIEmbeddings,
|
||||
) # Cambiado de HuggingFaceEmbeddings a OpenAIEmbeddings
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langchain_core.output_parsers import StrOutputParser
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
from rich.console import Console
|
||||
from rich.markdown import Markdown
|
||||
import os
|
||||
import argparse
|
||||
from openai_api_key import openai_api_key
|
||||
|
||||
|
||||
console = Console()
|
||||
|
||||
|
||||
class CitationTracker:
|
||||
def __init__(self):
|
||||
self.citations = []
|
||||
|
||||
def add_citation(self, text, metadata):
|
||||
self.citations.append({"text": text, "metadata": metadata})
|
||||
|
||||
def get_formatted_citations(self):
|
||||
result = "\n## Fuentes\n\n"
|
||||
for i, citation in enumerate(self.citations, 1):
|
||||
source = citation["metadata"]["source"]
|
||||
result += f"{i}. [{os.path.basename(source)}]({source}) - Fragmento {citation['metadata']['chunk_id']}\n"
|
||||
return result
|
||||
|
||||
|
||||
def search_with_citation(query, db_directory, model="gpt-3.5-turbo"):
|
||||
# Cargar embeddings y base de datos
|
||||
embeddings = OpenAIEmbeddings(
|
||||
model="text-embedding-3-small"
|
||||
) # Usar OpenAI Embeddings igual que en x1.py
|
||||
|
||||
db = Chroma(persist_directory=db_directory, embedding_function=embeddings)
|
||||
|
||||
api_key = openai_api_key()
|
||||
os.environ["OPENAI_API_KEY"] = api_key
|
||||
|
||||
# Configurar el LLM de OpenAI
|
||||
llm = ChatOpenAI(model_name=model)
|
||||
|
||||
# Rastreador de citas
|
||||
citation_tracker = CitationTracker()
|
||||
|
||||
# Recuperar documentos relevantes
|
||||
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 5})
|
||||
|
||||
# Plantilla para el prompt
|
||||
template = """
|
||||
Responde a la siguiente pregunta basándote exclusivamente en la información proporcionada.
|
||||
Incluye referencias a las fuentes originales para cada afirmación importante usando [Fuente N].
|
||||
Si la información no es suficiente, indícalo claramente.
|
||||
|
||||
Contexto:
|
||||
{context}
|
||||
|
||||
Pregunta: {question}
|
||||
|
||||
Respuesta (incluye [Fuente N] para citar):
|
||||
"""
|
||||
|
||||
prompt = ChatPromptTemplate.from_template(template)
|
||||
|
||||
# Función para formatear el contexto
|
||||
def format_docs(docs):
|
||||
formatted_context = ""
|
||||
for i, doc in enumerate(docs, 1):
|
||||
citation_tracker.add_citation(doc.page_content, doc.metadata)
|
||||
formatted_context += f"[Fuente {i}]: {doc.page_content}\n\n"
|
||||
return formatted_context
|
||||
|
||||
# Cadena RAG
|
||||
rag_chain = (
|
||||
{"context": retriever | format_docs, "question": RunnablePassthrough()}
|
||||
| prompt
|
||||
| llm
|
||||
| StrOutputParser()
|
||||
)
|
||||
|
||||
# Ejecutar búsqueda
|
||||
response = rag_chain.invoke(query)
|
||||
|
||||
# Agregar citas al final
|
||||
full_response = response + "\n\n" + citation_tracker.get_formatted_citations()
|
||||
|
||||
return full_response
|
||||
|
||||
|
||||
def main():
|
||||
# Cargar configuraciones del entorno
|
||||
configs = json.loads(os.environ.get("SCRIPT_CONFIGS", "{}"))
|
||||
|
||||
# Obtener working directory
|
||||
working_directory = configs.get("working_directory", ".")
|
||||
|
||||
# Obtener configuraciones de nivel 2 (grupo)
|
||||
group_config = configs.get("level2", {})
|
||||
|
||||
work_config = configs.get("level3", {})
|
||||
in_dir = work_config.get("in_dir", ".")
|
||||
|
||||
docs_directory = os.path.join(working_directory, in_dir)
|
||||
model = work_config.get("model", "gpt-3.5-turbo")
|
||||
query = work_config.get("query", "")
|
||||
db_directory = os.path.join(working_directory, "chroma_db")
|
||||
|
||||
result = search_with_citation(query, db_directory, model)
|
||||
console.print(Markdown(result))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -454,6 +454,9 @@ class ConfigurationManager:
|
|||
with open(work_dir_path, "r") as f:
|
||||
data = json.load(f)
|
||||
path = data.get("path", "")
|
||||
# Normalizar separadores de ruta
|
||||
if path:
|
||||
path = os.path.normpath(path)
|
||||
# Actualizar la variable de instancia si hay una ruta válida
|
||||
if path and os.path.exists(path):
|
||||
self.working_directory = path
|
||||
|
|
@ -462,16 +465,45 @@ class ConfigurationManager:
|
|||
return ""
|
||||
|
||||
def set_work_dir(self, group: str, path: str) -> Dict[str, str]:
|
||||
"""Set working directory path for a script group."""
|
||||
"""Set working directory path for a script group and update history."""
|
||||
# Normalizar el path recibido
|
||||
path = os.path.normpath(path)
|
||||
|
||||
if not os.path.exists(path):
|
||||
return {"status": "error", "message": "Directory does not exist"}
|
||||
|
||||
work_dir_path = os.path.join(self.script_groups_path, group, "work_dir.json")
|
||||
|
||||
try:
|
||||
# Guardar la ruta en work_dir.json
|
||||
# Cargar datos existentes o crear nuevos
|
||||
try:
|
||||
with open(work_dir_path, "r") as f:
|
||||
data = json.load(f)
|
||||
# Normalizar paths existentes en el historial
|
||||
if "history" in data:
|
||||
data["history"] = [os.path.normpath(p) for p in data["history"]]
|
||||
except (FileNotFoundError, json.JSONDecodeError):
|
||||
data = {"path": "", "history": []}
|
||||
|
||||
# Actualizar path actual
|
||||
data["path"] = path
|
||||
|
||||
# Actualizar historial
|
||||
if "history" not in data:
|
||||
data["history"] = []
|
||||
|
||||
# Eliminar la ruta del historial si ya existe (usando path normalizado)
|
||||
data["history"] = [p for p in data["history"] if os.path.normpath(p) != path]
|
||||
|
||||
# Agregar la ruta al principio del historial
|
||||
data["history"].insert(0, path)
|
||||
|
||||
# Mantener solo los últimos 10 directorios
|
||||
data["history"] = data["history"][:10]
|
||||
|
||||
# Guardar datos actualizados
|
||||
with open(work_dir_path, "w") as f:
|
||||
json.dump({"path": path}, f, indent=2)
|
||||
json.dump(data, f, indent=2)
|
||||
|
||||
# Actualizar la variable de instancia
|
||||
self.working_directory = path
|
||||
|
|
@ -485,3 +517,16 @@ class ConfigurationManager:
|
|||
return {"status": "success", "path": path}
|
||||
except Exception as e:
|
||||
return {"status": "error", "message": str(e)}
|
||||
|
||||
def get_directory_history(self, group: str) -> List[str]:
|
||||
"""Get the directory history for a script group."""
|
||||
work_dir_path = os.path.join(self.script_groups_path, group, "work_dir.json")
|
||||
try:
|
||||
with open(work_dir_path, "r") as f:
|
||||
data = json.load(f)
|
||||
# Normalizar todos los paths en el historial
|
||||
history = [os.path.normpath(p) for p in data.get("history", [])]
|
||||
# Filtrar solo directorios que existen
|
||||
return [p for p in history if os.path.exists(p)]
|
||||
except (FileNotFoundError, json.JSONDecodeError):
|
||||
return []
|
||||
|
|
|
|||
124
data/log.txt
124
data/log.txt
|
|
@ -1,116 +1,8 @@
|
|||
[09:40:16] Iniciando ejecución de x1.py
|
||||
[09:40:18] Working directory: C:/Trabajo/VM/40 - 93040 - HENKEL - NEXT2 Problem/Reporte/Emails
|
||||
[09:40:18] Input directory: C:/Trabajo/VM/40 - 93040 - HENKEL - NEXT2 Problem/Reporte/Emails
|
||||
[09:40:18] Output directory: C:/Users/migue/OneDrive/Miguel/Obsidean/Trabajo/VM/04-InLavoro/HENKEL/93040 - HENKEL - BowlingGreen/Description/HENKEL - ALPLA - AUTEFA - Batch Data
|
||||
[09:40:18] Cronologia file: C:/Users/migue/OneDrive/Miguel/Obsidean/Trabajo/VM/04-InLavoro/HENKEL/93040 - HENKEL - BowlingGreen/Description/HENKEL - ALPLA - AUTEFA - Batch Data\cronologia.md
|
||||
[09:40:18] Attachments directory: C:/Trabajo/VM/40 - 93040 - HENKEL - NEXT2 Problem/Reporte/Emails\adjuntos
|
||||
[09:40:18] Beautify rules file: D:\Proyectos\Scripts\ParamManagerScripts\backend\script_groups\EmailCrono\config\beautify_rules.json
|
||||
[09:40:18] Found 13 .eml files
|
||||
[09:40:18] Loaded 0 existing messages
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\I_ 9.3040-3074 ALPLA BG open points.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\I_ 9.3061-TLO26-L42 automatic change over test.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\I_ Alpla BOW2 - Line emptying button 6168.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\I_ IDH_BTL.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\I_ OPC-UA interface Vetro - Bowling Green 2 9.3040-3074.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\I_ R_ I_ [EXT] R_ Vetro Conveyor 9.3674.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\NEXT2 - Data - ALPLA information verification.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\RE_ Automatic changeover trial.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\RE_ OPC-UA interface Vetro - Bowling Green 2 9.3040-3074.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\RE_ [EXT] RE_ Vetro_ALPLA information verification.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\Re_ _EXT_ Next + 1 - HENKEL - ALPLA - AUTEFA.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\RV_ RE_ [EXT] RE_ Vetro_ALPLA information verification.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] Processing C:\Trabajo\VM\40 - 93040 - HENKEL - NEXT2 Problem\Reporte\Emails\R_ [EXT] RE_ Vetro_ALPLA information verification 9.3060-3067.eml
|
||||
[09:40:18] Aplicando reglas de prioridad 1
|
||||
[09:40:18] Aplicando reglas de prioridad 2
|
||||
[09:40:18] Aplicando reglas de prioridad 3
|
||||
[09:40:18] Aplicando reglas de prioridad 4
|
||||
[09:40:18] EstadÃsticas de procesamiento:
|
||||
[09:40:18] - Total mensajes encontrados: 38
|
||||
[09:40:18] - Mensajes únicos añadidos: 22
|
||||
[09:40:18] - Mensajes duplicados ignorados: 16
|
||||
[09:40:18] Writing 22 messages to C:/Users/migue/OneDrive/Miguel/Obsidean/Trabajo/VM/04-InLavoro/HENKEL/93040 - HENKEL - BowlingGreen/Description/HENKEL - ALPLA - AUTEFA - Batch Data\cronologia.md
|
||||
[09:40:18] Ejecución completada
|
||||
[20:41:53] Iniciando ejecución de x1.py
|
||||
[20:41:57] Cargando documentos...
|
||||
[20:41:57] Se encontraron 1 documentos.
|
||||
[20:41:57] Procesando e indexando documentos...
|
||||
[20:44:57] Iniciando ejecución de x1.py
|
||||
[20:45:01] Cargando documentos...
|
||||
[20:45:01] Se encontraron 1 documentos.
|
||||
[20:45:01] Procesando e indexando documentos...
|
||||
|
|
|
|||
|
|
@ -0,0 +1,3 @@
|
|||
# Configura tu clave API de OpenAI
|
||||
def openai_api_key():
|
||||
return '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'
|
||||
|
|
@ -6,11 +6,11 @@ from openai import OpenAI
|
|||
from typing import Dict, List
|
||||
import json
|
||||
from .base import LLMService
|
||||
from config.api_keys import APIKeyManager
|
||||
from openai_api_key import openai_api_key
|
||||
|
||||
class OpenAIService(LLMService):
|
||||
def __init__(self, model: str = "gpt-4o-mini", temperature: float = 0.3):
|
||||
api_key = APIKeyManager.get_openai_key()
|
||||
api_key = openai_api_key()
|
||||
if not api_key:
|
||||
raise ValueError("OpenAI API key not found. Please set up your API keys.")
|
||||
|
||||
|
|
|
|||
|
|
@ -535,6 +535,7 @@ async function setWorkingDirectory() {
|
|||
await updateWorkingDirectory(path);
|
||||
}
|
||||
|
||||
// Modificar initWorkingDirectory para cargar también el historial
|
||||
async function initWorkingDirectory() {
|
||||
if (!currentGroup) return;
|
||||
|
||||
|
|
@ -543,6 +544,7 @@ async function initWorkingDirectory() {
|
|||
if (result.status === 'success' && result.path) {
|
||||
await updateWorkingDirectory(result.path);
|
||||
}
|
||||
await loadDirectoryHistory();
|
||||
}
|
||||
|
||||
async function browseDirectory() {
|
||||
|
|
@ -565,28 +567,64 @@ async function browseDirectory() {
|
|||
async function updateWorkingDirectory(path) {
|
||||
console.log('Updating working directory:', { path, group: currentGroup }); // Debug line
|
||||
|
||||
const response = await fetch('/api/working-directory', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
path: path,
|
||||
group: currentGroup
|
||||
})
|
||||
});
|
||||
|
||||
const result = await response.json();
|
||||
console.log('Update result:', result); // Debug line
|
||||
|
||||
if (result.status === 'success') {
|
||||
// Actualizar input
|
||||
document.getElementById('working-directory').value = path;
|
||||
try {
|
||||
const response = await fetch('/api/working-directory', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
path: path,
|
||||
group: currentGroup
|
||||
})
|
||||
});
|
||||
|
||||
// Recargar configuración de nivel 3
|
||||
const configResponse = await fetch(`/api/config/3?group=${currentGroup}`);
|
||||
const data = await configResponse.json();
|
||||
await renderForm('level3-form', data);
|
||||
} else {
|
||||
alert('Error: ' + (result.message || 'No se pudo actualizar el directorio de trabajo'));
|
||||
const result = await response.json();
|
||||
console.log('Update result:', result); // Debug line
|
||||
|
||||
if (result.status === 'success') {
|
||||
// Actualizar input y lista de directorios
|
||||
document.getElementById('working-directory').value = path;
|
||||
await loadDirectoryHistory();
|
||||
|
||||
// Recargar configuración de nivel 3
|
||||
const configResponse = await fetch(`/api/config/3?group=${currentGroup}`);
|
||||
const data = await configResponse.json();
|
||||
await renderForm('level3-form', data);
|
||||
} else {
|
||||
alert('Error: ' + (result.message || 'No se pudo actualizar el directorio de trabajo'));
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('Error updating working directory:', error);
|
||||
alert('Error actualizando el directorio de trabajo: ' + error.message);
|
||||
}
|
||||
}
|
||||
|
||||
async function loadDirectoryHistory() {
|
||||
try {
|
||||
const response = await fetch(`/api/directory-history/${currentGroup}`);
|
||||
const history = await response.json();
|
||||
|
||||
const select = document.getElementById('directory-history');
|
||||
select.innerHTML = '<option value="">-- Directorios recientes --</option>';
|
||||
|
||||
history.forEach(dir => {
|
||||
const option = document.createElement('option');
|
||||
option.value = dir;
|
||||
option.textContent = dir;
|
||||
// Marcar como seleccionado si es el directorio actual
|
||||
if (dir === document.getElementById('working-directory').value) {
|
||||
option.selected = true;
|
||||
}
|
||||
select.appendChild(option);
|
||||
});
|
||||
} catch (error) {
|
||||
console.error('Error loading directory history:', error);
|
||||
}
|
||||
}
|
||||
|
||||
function loadHistoryDirectory(path) {
|
||||
if (path) {
|
||||
document.getElementById('working-directory').value = path;
|
||||
updateWorkingDirectory(path); // Cambiado de setWorkingDirectory a updateWorkingDirectory
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -657,6 +695,16 @@ async function initializeApp() {
|
|||
await initWorkingDirectory();
|
||||
await loadConfigs();
|
||||
|
||||
// Mostrar level3-content por defecto
|
||||
const level3Content = document.getElementById('level3-content');
|
||||
if (level3Content) {
|
||||
level3Content.classList.remove('hidden');
|
||||
const button = document.querySelector(`[onclick="toggleConfig('level3-content')"]`);
|
||||
if (button) {
|
||||
button.innerText = 'Ocultar Configuración';
|
||||
}
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
console.error('Error during initialization:', error);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -109,6 +109,12 @@
|
|||
Confirmar
|
||||
</button>
|
||||
</div>
|
||||
<!-- Add directory history dropdown -->
|
||||
<div class="mt-2">
|
||||
<select id="directory-history" class="w-full p-2 border rounded text-gray-600" onchange="loadHistoryDirectory(this.value)">
|
||||
<option value="">-- Directorios recientes --</option>
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Level 3 Configuration -->
|
||||
|
|
@ -117,10 +123,10 @@
|
|||
<h2 class="text-xl font-bold">Configuración del Directorio</h2>
|
||||
<button class="bg-blue-500 text-white px-4 py-2 rounded"
|
||||
onclick="toggleConfig('level3-content')">
|
||||
Mostrar Configuración
|
||||
Ocultar Configuración
|
||||
</button>
|
||||
</div>
|
||||
<div id="level3-content" class="hidden">
|
||||
<div id="level3-content">
|
||||
<div id="level3-form"></div>
|
||||
<div class="flex justify-end mt-4">
|
||||
<button class="bg-blue-500 text-white px-4 py-2 rounded" onclick="modifySchema(3)">
|
||||
|
|
|
|||
Loading…
Reference in New Issue