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Gpu Document Processing

OrganizationPopular
langchain-ai
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.

Overview

Publisherlangchain-ai
Repositorydeepagents
Skill namegpu-document-processing
Stars
29.9K
Forks
4.2K
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by langchain-ai on GitHub. Read the source before you install it.

Installation

Install the Gpu Document Processing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/langchain-ai/deepagents/tree/main/examples/nvidia_deep_agent/skills/gpu-document-processing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/langchain-ai/deepagents.git /tmp/deepagents
mkdir -p .claude/skills
cp -r /tmp/deepagents/examples/nvidia_deep_agent/skills/gpu-document-processing .claude/skills/gpu-document-processing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gpu Document Processing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gpu Document Processing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gpu Document Processing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

  1. Agent reasons on CPU - planning, synthesis, report writing
  2. Processing sent to GPU sandbox - document parsing, embedding, extraction
  3. 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

  1. Receive document reference from the orchestrator
  2. Determine processing type (extraction, analysis, embedding)
  3. Send to GPU sandbox for processing
  4. Collect structured results (text, tables, embeddings)
  5. Write findings to /shared/ for the orchestrator to synthesize

Processing Large Document Collections

For multiple documents:

  1. Process documents in parallel batches (3-5 concurrent)
  2. Extract key metadata first (title, date, author, page count)
  3. Generate per-document summaries
  4. Cross-reference findings across documents
  5. 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.

Frequently asked questions

What does the Gpu Document Processing AI 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.

Why use Gpu Document Processing on TypingMind?

Because you install it once and use it with any model. Gpu Document Processing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gpu Document Processing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/langchain-ai/deepagents/tree/main/examples/nvidia_deep_agent/skills/gpu-document-processing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gpu Document Processing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gpu Document Processing?

As many as you like. As long as a model supports skills, you can use Gpu Document Processing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gpu Document Processing AI skill free?

Yes. It is published on GitHub by langchain-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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