gpu-document-processing

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.

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

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.

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

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.

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.