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Enhanced the support for the excel file and added endpoint to have optimized vector store and Rag for the Excel.
b953016 | from typing import Dict, Any | |
| from pathlib import Path | |
| class ExcelIntegration: | |
| def __init__(self, enhanced_processor): | |
| """ | |
| Initialize Excel integration | |
| Args: | |
| enhanced_processor: Instance of EnhancedExcelProcessor | |
| """ | |
| self.processor = enhanced_processor | |
| def process_for_rag(self, file_path: Path) -> Dict[str, Any]: | |
| """ | |
| Process Excel file for RAG system | |
| Args: | |
| file_path (Path): Path to Excel file | |
| Returns: | |
| Dict[str, Any]: Processed content and metadata | |
| """ | |
| # Process Excel file | |
| content = self.processor.process_excel(file_path) | |
| # Get all metadata | |
| metadata = { | |
| 'sheet_summaries': self.processor.sheet_summaries, | |
| 'relationships': self.processor.relationships, | |
| 'sheet_metadata': self.processor.sheet_metadata | |
| } | |
| # Create chunks based on logical divisions | |
| chunks = self._create_semantic_chunks(content) | |
| return { | |
| 'content': content, | |
| 'chunks': chunks, | |
| 'metadata': metadata | |
| } | |
| def _create_semantic_chunks(self, content: str) -> list: | |
| """ | |
| Create meaningful chunks from Excel content | |
| Args: | |
| content (str): Processed Excel content | |
| Returns: | |
| list: List of content chunks | |
| """ | |
| chunks = [] | |
| current_chunk = [] | |
| current_sheet = None | |
| for line in content.split('\n'): | |
| # Start new chunk for each sheet | |
| if line.startswith('Sheet: '): | |
| if current_chunk: | |
| chunks.append('\n'.join(current_chunk)) | |
| current_chunk = [] | |
| current_sheet = line | |
| current_chunk.append(line) | |
| # Start new chunk for major sections within sheet | |
| elif any(line.startswith(section) for section in | |
| ['Numeric Columns Summary:', 'Categorical Columns Summary:', | |
| 'Sample Data:', 'Sheet Relationships:']): | |
| if current_chunk: | |
| chunks.append('\n'.join(current_chunk)) | |
| current_chunk = [] | |
| if current_sheet: | |
| current_chunk.append(current_sheet) | |
| current_chunk.append(line) | |
| else: | |
| current_chunk.append(line) | |
| # Add final chunk | |
| if current_chunk: | |
| chunks.append('\n'.join(current_chunk)) | |
| return chunks | |
| def get_sheet_context(self, sheet_name: str) -> str: | |
| """ | |
| Get specific context for a sheet | |
| Args: | |
| sheet_name (str): Name of the sheet | |
| Returns: | |
| str: Contextual information about the sheet | |
| """ | |
| if sheet_name not in self.processor.sheet_metadata: | |
| return "" | |
| metadata = self.processor.sheet_metadata[sheet_name] | |
| summary = self.processor.sheet_summaries[sheet_name] | |
| context_parts = [ | |
| f"Sheet: {sheet_name}", | |
| f"Total Rows: {metadata['rows']}", | |
| f"Columns: {', '.join(metadata['columns'])}", | |
| ] | |
| # Add numeric column summaries | |
| if metadata['numeric_columns']: | |
| context_parts.append("\nNumeric Columns:") | |
| for col in metadata['numeric_columns']: | |
| stats = summary['numeric_summaries'][col] | |
| context_parts.append(f"- {col}: Range {stats['min']} to {stats['max']}, " | |
| f"Average {stats['mean']:.2f}") | |
| # Add categorical column summaries | |
| if metadata['categorical_columns']: | |
| context_parts.append("\nCategorical Columns:") | |
| for col in metadata['categorical_columns']: | |
| cats = summary['categorical_summaries'][col] | |
| context_parts.append(f"- {col}: {cats['unique_values']} unique values") | |
| return "\n".join(context_parts) | |
| def get_relationship_context(self) -> str: | |
| """ | |
| Get context about relationships between sheets | |
| Returns: | |
| str: Information about sheet relationships | |
| """ | |
| if not self.processor.relationships: | |
| return "No relationships detected between sheets." | |
| context_parts = ["Sheet Relationships:"] | |
| for rel_key, rel_info in self.processor.relationships.items(): | |
| if rel_info['type'] == 'potential_join': | |
| sheets = rel_ |