pdf-smart-extractor

PDF extraction can be done locally using OCR and text-parsing libraries like pypdf and pdfplumber. This Skill extracts all content—text, tables, images, and metadata—from PDFs up to 50MB+ while preserving 100% of the original content and requiring zero LLM calls during extraction.

Yes. After extracting PDF content, semantic chunking breaks it into ~2000-token segments that can be queried with natural language questions. The Skill caches chunks locally so only relevant sections load, reducing token usage on follow-up queries.

Yes. The Skill handles OCR for scanned documents and processes password-protected PDFs. It uses pdf2image and torch-based models to extract text from image-heavy or restricted files that standard text extraction tools cannot read.

For large technical PDFs (50+ pages, 1MB–50MB+), local extraction with semantic chunking preserves structure and tables while minimizing token costs. This Skill caches content persistently, so querying compliance guides or specifications becomes token-efficient after the first extraction.

Yes. After extraction, you can ask natural language questions to retrieve summaries, specific clauses, or key figures. Semantic chunking ensures answers come from relevant sections only, making it fast for multi-document workflows like contract review or financial report analysis.

Local extraction trades cloud scalability for privacy and cost—all processing happens on your machine with no external API calls. The trade-off is that complex layouts or heavily corrupted scans may require preprocessing, and extraction time scales with PDF size and your hardware.