extracting-pdf-text

What problem does it solve?

This skill addresses the challenge of converting PDF documents, including text-based, table-heavy, and scanned images, into a format that Large Language Models (LLMs) can effectively process for tasks like Retrieval Augmented Generation (RAG) or in-depth document analysis.

Core Features & Use Cases

• Versatile Extraction: Supports various PDF types, from simple text to scanned documents requiring OCR.
• Multiple Tools: Integrates local libraries (PyMuPDF, pdfplumber, pytesseract) and API services (Mistral OCR) for flexible and accurate text extraction.
• RAG Optimization: Formats extracted text for optimal use in RAG pipelines.
• Use Case: Automatically extract all textual content from a research paper PDF to feed into an LLM for summarization and question-answering.

Quick Start

Use the extracting-pdf-text skill to extract all text from the attached file 'report.pdf' into 'output.md'.