gpu-document-processing

To extract text and tables from large PDFs, you need document-processing workflows that preserve layout structures like headers and lists, parsing tabular data into structured formats ready for downstream analytics.

Generating embeddings for large document collections requires chunking documents into smaller segments and producing vector representations, enabling rapid cross-document search and retrieval from vector stores.

GPU acceleration significantly speeds up PDF text extraction and embedding generation by coordinating CPU orchestration with a GPU sandbox, processing large documents faster than standard methods.

You can use GPU document processing to build a searchable knowledge base by extracting, chunking, and embedding large PDF sets, producing structured outputs ready for immediate vector store integration.

Document chunking for cross-document search works by splitting large PDFs into smaller text segments and generating embeddings, allowing users to rapidly query and synthesize information across many documents.