gpu-document-processing
Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user provides large documents or needs bulk document analysis.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/langchain-ai/deepagents --skill gpu-document-processing- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads gpu-document-processing automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
GPU Document Processing Skill
Process large documents and document collections using GPU-accelerated tools. This skill uses the sandbox-as-tool pattern: the agent runs on CPU for reasoning, and sends document processing work to a GPU-equipped environment.
When to Use This Skill
Use this skill when:
- Processing large PDF files (50+ pages)
- Analyzing collections of documents (10+ files)
- Extracting structured data from unstructured documents
- Performing bulk text extraction and chunking
- Generating embeddings for large document sets
- The user uploads or references large documents for analysis
Architecture: Sandbox as Tool
This skill follows the sandbox-as-tool pattern for GPU execution:
- Agent reasons on CPU - planning, synthesis, report writing
- Processing sent to GPU sandbox - document parsing, embedding, extraction
- Results returned to agent - structured output for further analysis
This separation ensures:
- API keys stay outside the sandbox (security)
- Agent state persists independently of processing jobs
- Processing can be parallelized across documents
- Cost-efficient: GPU used only during processing, not during reasoning
Capabilities
PDF Text Extraction
Extract text content from PDF documents with layout preservation:
- Headers, paragraphs, lists, and tables detected separately
- Page numbers and section boundaries preserved
- Multi-column layout handling
Tabular Data Extraction
Extract tables from documents into structured formats:
- PDF tables to CSV/DataFrames using GPU-accelerated parsing
- Automatic column type detection
- Handles merged cells and multi-row headers
Document Chunking
Split large documents into meaningful chunks for analysis:
- Semantic chunking (by topic/section boundaries)
- Fixed-size chunking with overlap for embedding
- Configurable chunk sizes (default: 512 tokens)
Embedding Generation
Generate vector embeddings for document chunks:
- Uses NVIDIA NeMo Retriever NIM for GPU-accelerated embedding
- Supports batch processing for large document sets
- Compatible with standard vector stores (Milvus, ChromaDB)
Workflow
- Receive document reference from the orchestrator
- Determine processing type (extraction, analysis, embedding)
- Send to GPU sandbox for processing
- Collect structured results (text, tables, embeddings)
- Write findings to /shared/ for the orchestrator to synthesize
Processing Large Document Collections
For multiple documents:
- Process documents in parallel batches (3-5 concurrent)
- Extract key metadata first (title, date, author, page count)
- Generate per-document summaries
- Cross-reference findings across documents
- Write consolidated findings with per-document citations
Output Format
When reporting document processing results:
- Include document metadata (filename, pages, size)
- Structure extracted content by section/chapter
- Format tables as markdown tables
- Include page references for all extracted content
- Note any extraction quality issues (scanned images, corrupted pages)
Integration with NVIDIA NIM
For production deployments, GPU document processing can leverage:
- NVIDIA NeMo Retriever: GPU-accelerated embedding and retrieval
- NVIDIA RAPIDS cuDF: Tabular data processing from extracted tables
- NVIDIA Triton: Scalable inference for document classification models
See NVIDIA's NIM documentation for self-hosted deployment options.
FAQ
What does the gpu-document-processing skill do?
Use when processing large PDFs, document collections, or bulk text extraction tasks that benefit from GPU-accelerated processing. Triggers when the user provides large documents or needs bulk document analysis.
How do I install gpu-document-processing?
Run npx skills add https://github.com/langchain-ai/deepagents --skill gpu-document-processing in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/gpu-document-processing/ (personal) or .claude/skills/gpu-document-processing/ (project).
Which AI agents can use gpu-document-processing?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is gpu-document-processing safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains gpu-document-processing?
It is published by langchain-ai in langchain-ai/deepagents, and it is an official skill from the maker of the technology it covers. It has 223 recorded installs.
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