mirror of
https://github.com/K-Dense-AI/claude-scientific-skills.git
synced 2026-03-28 07:33:45 +08:00
Update all the latest writing skills
This commit is contained in:
Vinayak Agarwal
@@ -1,317 +1,228 @@
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#!/usr/bin/env python3
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"""
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Batch conversion utility for MarkItDown.
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Batch convert multiple files to Markdown using MarkItDown.
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Converts all supported files in a directory to Markdown format.
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This script demonstrates how to efficiently convert multiple files
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in a directory to Markdown format.
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"""
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import os
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import sys
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from pathlib import Path
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from markitdown import MarkItDown
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from typing import Optional, List
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import argparse
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from pathlib import Path
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from typing import List, Optional
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from markitdown import MarkItDown
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import sys
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# Supported file extensions
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SUPPORTED_EXTENSIONS = {
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'.pdf', '.docx', '.pptx', '.xlsx', '.xls',
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'.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff',
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'.wav', '.mp3', '.flac', '.ogg', '.aiff',
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'.html', '.htm', '.epub',
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'.csv', '.json', '.xml',
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'.zip'
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}
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def setup_markitdown(
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use_llm: bool = False,
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llm_model: str = "gpt-4o",
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use_azure_di: bool = False,
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azure_endpoint: Optional[str] = None,
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azure_key: Optional[str] = None
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) -> MarkItDown:
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"""
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Setup MarkItDown instance with optional advanced features.
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Args:
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use_llm: Enable LLM-powered image descriptions
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llm_model: LLM model to use (default: gpt-4o)
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use_azure_di: Enable Azure Document Intelligence
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azure_endpoint: Azure Document Intelligence endpoint
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azure_key: Azure Document Intelligence API key
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Returns:
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Configured MarkItDown instance
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"""
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kwargs = {}
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if use_llm:
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try:
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from openai import OpenAI
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client = OpenAI()
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kwargs['llm_client'] = client
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kwargs['llm_model'] = llm_model
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print(f"✓ LLM integration enabled ({llm_model})")
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except ImportError:
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print("✗ Warning: OpenAI not installed, LLM features disabled")
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print(" Install with: pip install openai")
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if use_azure_di:
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if azure_endpoint and azure_key:
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kwargs['docintel_endpoint'] = azure_endpoint
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kwargs['docintel_key'] = azure_key
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print("✓ Azure Document Intelligence enabled")
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else:
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print("✗ Warning: Azure credentials not provided, Azure DI disabled")
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return MarkItDown(**kwargs)
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def convert_file(
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md: MarkItDown,
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input_path: Path,
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output_dir: Path,
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verbose: bool = False
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) -> bool:
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def convert_file(md: MarkItDown, file_path: Path, output_dir: Path, verbose: bool = False) -> tuple[bool, str, str]:
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"""
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Convert a single file to Markdown.
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Args:
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md: MarkItDown instance
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input_path: Path to input file
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file_path: Path to input file
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output_dir: Directory for output files
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verbose: Print detailed progress
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verbose: Print detailed messages
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Returns:
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True if successful, False otherwise
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Tuple of (success, input_path, message)
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"""
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try:
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if verbose:
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print(f" Processing: {input_path.name}")
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# Convert file
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result = md.convert(str(input_path))
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# Create output filename
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output_filename = input_path.stem + '.md'
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output_path = output_dir / output_filename
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# Write output
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with open(output_path, 'w', encoding='utf-8') as f:
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f.write(result.text_content)
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if verbose:
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print(f" ✓ Converted: {input_path.name} → {output_filename}")
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return True
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print(f"Converting: {file_path}")
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result = md.convert(str(file_path))
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# Create output path
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output_file = output_dir / f"{file_path.stem}.md"
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# Write content with metadata header
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content = f"# {result.title or file_path.stem}\n\n"
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content += f"**Source**: {file_path.name}\n"
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content += f"**Format**: {file_path.suffix}\n\n"
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content += "---\n\n"
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content += result.text_content
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output_file.write_text(content, encoding='utf-8')
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return True, str(file_path), f"✓ Converted to {output_file.name}"
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except Exception as e:
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print(f" ✗ Error converting {input_path.name}: {e}")
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return False
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def find_files(input_dir: Path, recursive: bool = False) -> List[Path]:
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"""
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Find all supported files in directory.
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Args:
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input_dir: Directory to search
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recursive: Search subdirectories
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Returns:
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List of file paths
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"""
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files = []
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if recursive:
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for ext in SUPPORTED_EXTENSIONS:
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files.extend(input_dir.rglob(f"*{ext}"))
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else:
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for ext in SUPPORTED_EXTENSIONS:
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files.extend(input_dir.glob(f"*{ext}"))
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return sorted(files)
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return False, str(file_path), f"✗ Error: {str(e)}"
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def batch_convert(
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input_dir: str,
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output_dir: str,
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input_dir: Path,
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output_dir: Path,
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extensions: Optional[List[str]] = None,
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recursive: bool = False,
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use_llm: bool = False,
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llm_model: str = "gpt-4o",
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use_azure_di: bool = False,
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azure_endpoint: Optional[str] = None,
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azure_key: Optional[str] = None,
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verbose: bool = False
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) -> None:
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workers: int = 4,
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verbose: bool = False,
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enable_plugins: bool = False
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) -> dict:
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"""
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Batch convert all supported files in a directory.
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Batch convert files in a directory.
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Args:
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input_dir: Input directory containing files
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output_dir: Output directory for Markdown files
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input_dir: Input directory
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output_dir: Output directory
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extensions: List of file extensions to convert (e.g., ['.pdf', '.docx'])
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recursive: Search subdirectories
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use_llm: Enable LLM-powered descriptions
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llm_model: LLM model to use
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use_azure_di: Enable Azure Document Intelligence
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azure_endpoint: Azure DI endpoint
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azure_key: Azure DI API key
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verbose: Print detailed progress
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workers: Number of parallel workers
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verbose: Print detailed messages
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enable_plugins: Enable MarkItDown plugins
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Returns:
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Dictionary with conversion statistics
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"""
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input_path = Path(input_dir)
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output_path = Path(output_dir)
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# Validate input directory
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if not input_path.exists():
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print(f"✗ Error: Input directory '{input_dir}' does not exist")
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sys.exit(1)
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if not input_path.is_dir():
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print(f"✗ Error: '{input_dir}' is not a directory")
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sys.exit(1)
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# Create output directory
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output_path.mkdir(parents=True, exist_ok=True)
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# Setup MarkItDown
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print("Setting up MarkItDown...")
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md = setup_markitdown(
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use_llm=use_llm,
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llm_model=llm_model,
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use_azure_di=use_azure_di,
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azure_endpoint=azure_endpoint,
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azure_key=azure_key
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)
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output_dir.mkdir(parents=True, exist_ok=True)
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# Default extensions if not specified
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if extensions is None:
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extensions = ['.pdf', '.docx', '.pptx', '.xlsx', '.html', '.jpg', '.png']
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# Find files
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print(f"\nScanning directory: {input_dir}")
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files = []
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if recursive:
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print(" (including subdirectories)")
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files = find_files(input_path, recursive)
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for ext in extensions:
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files.extend(input_dir.rglob(f"*{ext}"))
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else:
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for ext in extensions:
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files.extend(input_dir.glob(f"*{ext}"))
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if not files:
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print("✗ No supported files found")
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print(f" Supported extensions: {', '.join(sorted(SUPPORTED_EXTENSIONS))}")
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sys.exit(0)
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print(f"✓ Found {len(files)} file(s) to convert\n")
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# Convert files
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successful = 0
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failed = 0
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for file_path in files:
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if convert_file(md, file_path, output_path, verbose):
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successful += 1
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else:
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failed += 1
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# Summary
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print(f"\n{'='*60}")
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print(f"Conversion complete!")
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print(f" Successful: {successful}")
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print(f" Failed: {failed}")
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print(f" Output: {output_dir}")
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print(f"{'='*60}")
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print(f"No files found with extensions: {', '.join(extensions)}")
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return {'total': 0, 'success': 0, 'failed': 0}
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print(f"Found {len(files)} file(s) to convert")
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# Create MarkItDown instance
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md = MarkItDown(enable_plugins=enable_plugins)
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# Convert files in parallel
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results = {
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'total': len(files),
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'success': 0,
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'failed': 0,
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'details': []
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}
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with ThreadPoolExecutor(max_workers=workers) as executor:
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futures = {
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executor.submit(convert_file, md, file_path, output_dir, verbose): file_path
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for file_path in files
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}
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for future in as_completed(futures):
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success, path, message = future.result()
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if success:
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results['success'] += 1
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else:
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results['failed'] += 1
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results['details'].append({
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'file': path,
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'success': success,
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'message': message
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})
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print(message)
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return results
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def main():
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"""Main entry point."""
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parser = argparse.ArgumentParser(
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description="Batch convert files to Markdown using MarkItDown",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Basic usage
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python batch_convert.py documents/ output/
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# Recursive conversion
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python batch_convert.py documents/ output/ --recursive
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# With LLM-powered image descriptions
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python batch_convert.py documents/ output/ --llm
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# With Azure Document Intelligence
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python batch_convert.py documents/ output/ --azure \\
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--azure-endpoint https://example.cognitiveservices.azure.com/ \\
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--azure-key YOUR-KEY
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# All features enabled
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python batch_convert.py documents/ output/ --llm --azure \\
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--azure-endpoint $AZURE_ENDPOINT --azure-key $AZURE_KEY
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Supported file types:
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Documents: PDF, DOCX, PPTX, XLSX, XLS
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Images: JPG, PNG, GIF, BMP, TIFF
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Audio: WAV, MP3, FLAC, OGG, AIFF
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Web: HTML, EPUB
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Data: CSV, JSON, XML
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Archives: ZIP
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# Convert all PDFs in a directory
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python batch_convert.py papers/ output/ --extensions .pdf
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# Convert multiple formats recursively
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python batch_convert.py documents/ markdown/ --extensions .pdf .docx .pptx -r
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# Use 8 parallel workers
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python batch_convert.py input/ output/ --workers 8
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# Enable plugins
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python batch_convert.py input/ output/ --plugins
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"""
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)
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parser.add_argument('input_dir', type=Path, help='Input directory')
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parser.add_argument('output_dir', type=Path, help='Output directory')
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parser.add_argument(
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'input_dir',
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help='Input directory containing files to convert'
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'--extensions', '-e',
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nargs='+',
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help='File extensions to convert (e.g., .pdf .docx)'
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)
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parser.add_argument(
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'output_dir',
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help='Output directory for Markdown files'
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)
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parser.add_argument(
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'-r', '--recursive',
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'--recursive', '-r',
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action='store_true',
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help='Recursively search subdirectories'
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help='Search subdirectories recursively'
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)
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parser.add_argument(
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'--llm',
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'--workers', '-w',
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type=int,
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default=4,
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help='Number of parallel workers (default: 4)'
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)
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parser.add_argument(
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'--verbose', '-v',
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action='store_true',
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help='Enable LLM-powered image descriptions (requires OpenAI API key)'
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help='Verbose output'
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)
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parser.add_argument(
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'--llm-model',
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default='gpt-4o',
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help='LLM model to use (default: gpt-4o)'
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)
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parser.add_argument(
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'--azure',
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'--plugins', '-p',
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action='store_true',
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help='Enable Azure Document Intelligence for PDFs'
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help='Enable MarkItDown plugins'
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)
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parser.add_argument(
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'--azure-endpoint',
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help='Azure Document Intelligence endpoint URL'
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)
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parser.add_argument(
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'--azure-key',
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help='Azure Document Intelligence API key'
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)
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parser.add_argument(
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'-v', '--verbose',
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action='store_true',
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help='Print detailed progress'
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)
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args = parser.parse_args()
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# Environment variable fallbacks for Azure
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azure_endpoint = args.azure_endpoint or os.getenv('AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT')
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azure_key = args.azure_key or os.getenv('AZURE_DOCUMENT_INTELLIGENCE_KEY')
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batch_convert(
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# Validate input directory
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if not args.input_dir.exists():
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print(f"Error: Input directory '{args.input_dir}' does not exist")
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sys.exit(1)
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if not args.input_dir.is_dir():
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print(f"Error: '{args.input_dir}' is not a directory")
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sys.exit(1)
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# Run batch conversion
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results = batch_convert(
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input_dir=args.input_dir,
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output_dir=args.output_dir,
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extensions=args.extensions,
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recursive=args.recursive,
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use_llm=args.llm,
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llm_model=args.llm_model,
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use_azure_di=args.azure,
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azure_endpoint=azure_endpoint,
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azure_key=azure_key,
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verbose=args.verbose
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workers=args.workers,
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verbose=args.verbose,
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enable_plugins=args.plugins
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)
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# Print summary
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print("\n" + "="*50)
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print("CONVERSION SUMMARY")
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print("="*50)
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print(f"Total files: {results['total']}")
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print(f"Successful: {results['success']}")
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print(f"Failed: {results['failed']}")
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print(f"Success rate: {results['success']/results['total']*100:.1f}%" if results['total'] > 0 else "N/A")
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# Show failed files if any
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if results['failed'] > 0:
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print("\nFailed conversions:")
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for detail in results['details']:
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if not detail['success']:
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print(f" - {detail['file']}: {detail['message']}")
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sys.exit(0 if results['failed'] == 0 else 1)
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if __name__ == '__main__':
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main()
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