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gpu-document-processing

Extract text and tables from large PDFs using GPU-accelerated workflows.

Updated Mar 17, 2026
Search Tags:#pdf#embeddings#gpu#chunking#document-processing#table-extraction
One-click install
npx skills add https://github.com/marlo9981/Movara --skill gpu-document-processing-marlo9981
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpu-document-processing
Source: https://github.com/marlo9981/Movara/tree/main/examples/nvidia_deep_agent/skills/gpu-document-processing
Command: npx skills add https://github.com/marlo9981/Movara --skill gpu-document-processing-marlo9981

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Process very large PDFs and document collections efficiently by offloading heavy extraction, table parsing, and embedding generation to GPU-enabled environments while keeping agent reasoning on CPU to preserve security and cost efficiency.

Core Features & Use Cases

  • PDF Text Extraction: Layout-preserving extraction that detects headers, paragraphs, lists, tables, page numbers, and multi-column layouts.
  • Tabular Data Extraction: Convert PDF tables into CSVs or DataFrames with column type detection and support for merged cells and multi-row headers.
  • Document Chunking & Embeddings: Semantic and fixed-size chunking with overlap and GPU-accelerated embedding generation compatible with vector stores such as Milvus and ChromaDB.
  • Sandbox-as-Tool Architecture: Sends heavy processing to a GPU sandbox, enabling parallel batch processing, protecting API keys, and keeping agent state separate.
  • Batch Workflows: Designed for bulk jobs (large PDFs, 10+ documents), metadata-first processing, per-document summaries, and consolidated cross-document analysis.

Quick Start

Process the 120-page PDF annual-report-2024.pdf on the GPU sandbox to extract layout-aware text, tables, and generate embeddings for all document chunks.

Frequently Asked Questions about gpu-document-processing

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

FAQPage Schema
How do I process large PDFs with GPU acceleration for batch extraction and embeddings?▼

Process large PDFs by sending heavy extraction, table parsing, and embedding generation to a GPU sandbox, executing parallel batch workflows while keeping agent reasoning on CPU to preserve security and cost efficiency.

Does GPU document processing work with vector stores like Milvus and ChromaDB?▼

Yes, GPU document processing generates embeddings compatible with vector stores like Milvus and ChromaDB, applying semantic chunking with overlap to document chunks before storing them for downstream analysis.

How do I extract tables from multi-page PDFs and convert them to DataFrames?▼

Extract tables from multi-page PDFs using layout-preserving parsing that detects merged cells and multi-row headers, converting tabular PDF data into CSVs or DataFrames with column type detection.

Can I run bulk document analysis on 10+ PDFs using GPU sandbox execution?▼

Yes, bulk document analysis supports batch workflows for 10+ documents, utilizing GPU sandbox execution to parallelize metadata-first processing, per-document summaries, and consolidated cross-document analysis.

What is semantic chunking and how does it handle multi-column PDF layouts?▼

Semantic chunking divides extracted text into overlapping segments for embedding generation, while layout-preserving parsing accurately detects and processes headers, paragraphs, lists, and multi-column layouts in PDF documents.

Why use a sandbox-as-tool architecture for GPU-accelerated document processing?▼

A sandbox-as-tool architecture isolates heavy GPU processing from agent reasoning, protecting API keys, enabling parallel batch processing, and maintaining separate agent state for secure production document extraction workflows.

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