gpu-document-processing

Extract tabular and textual data from uploaded PDFs and feeds it into LLM workflows.

Updated May 26, 2026

Search Tags:#embeddings #pdf extraction #gpu #chunking #table parsing #document collections

Ready to run

npx skills add https://github.com/anukkrit149/anukkrit-skills --skill gpu-document-processing-anukkrit149

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gpu-document-processing
Source: https://github.com/anukkrit149/anukkrit-skills/tree/main/cloud/skills/gpu-document-processing
Command: npx skills add https://github.com/anukkrit149/anukkrit-skills --skill gpu-document-processing-anukkrit149

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Large PDFs and document collections are slow and error-prone to extract, normalize, and analyze manually, especially when you need text, tables, and embeddings at scale.

Core Features & Use Cases

GPU-accelerated parsing for bulk workloads: Extract text with layout cues and convert tables into structured formats suitable for downstream analysis.
Semantic chunking and embedding generation: Split large documents into meaningful chunks and generate embeddings for large sets to enable search and retrieval.
Document-collection workflows: Process multiple files in parallel batches, produce per-document summaries, and return consolidated findings with citations and page references.

Quick Start

Use gpu-document-processing to extract text, tables, and chunked embeddings from the uploaded document set.

Frequently Asked Questions about gpu-document-processing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract structured tables from large PDFs in bulk?▼

Use a GPU-accelerated workflow to extract structured tables from large PDFs in bulk; it preserves layout cues and converts tables into structured formats suitable for downstream analysis.

What is semantic chunking and how does it work for document collections?▼

Semantic chunking splits large documents into meaningful chunks and generates embeddings for document collections, enabling semantic search and retrieval across the processed files.

Do I need a GPU to process large document collections for embeddings?▼

Yes, you need a GPU. Processing large document collections for layout-preserving text extraction, semantic chunking, and embedding generation requires GPU-accelerated execution in a sandbox-as-tool workflow.

Can I process multiple PDFs in parallel batches with citations?▼

Yes, you can process multiple PDFs in parallel batches. The workflow produces per-document summaries and returns consolidated findings with citations and page references.

What's the best way to normalize extracted PDF text for downstream analysis?▼

The best way to normalize extracted PDF text is to use layout-preserving extraction that maintains structural cues, then apply semantic chunking to split text into meaningful segments for downstream analysis.

Why does PDF table parsing fail on complex layouts without GPU acceleration?▼

PDF table parsing fails on complex layouts without GPU acceleration because processing large files sequentially is slow and error-prone, struggling to preserve the intricate layout cues needed for structured conversion.