No specific use case defined.
I want to build a highly efficient Python-based document analysis tool specifically designed to pre-process web pages, PDFs, and Office documents into clean Markdown. The primary goal is to significantly reduce LLM token consumption by intelligently extracting only the essential content.
The core of this project should be a "smart extraction" engine that employs an adaptive strategy. It needs to dynamically assess the complexity and content density of an input document (whether a URL or a local file) and choose the most token-efficient parsing path. For this, I envision leveraging and integrating libraries like `Trafilatura` for robust web content extraction and `Microsoft MarkItDown` for its capabilities in handling various document types and generating clean Markdown. The system should be able to intelligently switch between these tools or combine their strengths to achieve optimal token savings.
In terms of functionality, it should support both single-document analysis, where a user provides a file path or URL and receives a cleaned Markdown output, and a "batch processing" mode. The batch mode should allow users to point to an input directory containing multiple documents and have the tool process all of them, saving their optimized Markdown versions into a specified output directory.
The project should be structured as a command-line interface (CLI) application in Python. Users should be able to invoke it simply, for example, `python eco_engine.py <file_path_or_url>` for single processing, or `python eco_engine.py --batch <input_folder> <output_folder>` for batch operations. How would you design the overall architecture, focusing on the adaptive extraction logic and the modularity to easily integrate or swap parsing strategies for maximum token efficiency?