mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
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a680d0472b
Adds a Claude Code plugin structure that exposes each NVIDIA DGX Spark
playbook as a triggerable skill, with an index skill ('dgx-spark') that
routes users to the right leaf based on intent and encodes the
relationship graph between playbooks (prerequisites, alternatives,
composes-with, upgrade paths).
Structure:
- overrides/*.md hand-curated frontmatter + Related sections
- scripts/generate.mjs zero-dep Node generator: nvidia + overrides → skills
- scripts/install.sh symlinks skills into ~/.claude/skills (--plugin mode available)
- skills/ committed, browsable, installable without Node
- .github/workflows/ auto-regenerates skills/ when playbooks/overrides change
Initial curated leaves: ollama, open-webui, vllm, connect-to-your-spark.
Remaining 37 leaves use generator fallback (title + tagline + summary
extracted from README) and can be curated incrementally via overrides/.
31 lines
2.0 KiB
Markdown
31 lines
2.0 KiB
Markdown
---
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name: dgx-spark-txt2kg
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description: Transform unstructured text into interactive knowledge graphs with LLM inference and graph visualization — on NVIDIA DGX Spark. Use when setting up txt2kg on Spark hardware.
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---
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<!-- GENERATED:BEGIN from nvidia/txt2kg/README.md -->
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# Text to Knowledge Graph on DGX Spark
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> Transform unstructured text into interactive knowledge graphs with LLM inference and graph visualization
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This playbook demonstrates how to build and deploy a comprehensive knowledge graph generation and visualization solution that serves as a reference for knowledge graph extraction.
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The unified memory architecture enables running larger, more accurate models that produce higher-quality knowledge graphs and deliver superior downstream GraphRAG performance.
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This txt2kg playbook transforms unstructured text documents into structured knowledge graphs using:
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- **Knowledge Triple Extraction**: Using Ollama with GPU acceleration for local LLM inference to extract subject-predicate-object relationships
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- **Graph Database Storage**: ArangoDB for storing and querying knowledge triples with relationship traversal
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- **GPU-Accelerated Visualization**: Three.js WebGPU rendering for interactive 2D/3D graph exploration
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**Outcome**: You will have a fully functional system capable of processing documents, generating and editing knowledge graphs, and providing querying, accessible through an interactive web interface.
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The setup includes:
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- **Local LLM Inference**: Ollama for GPU-accelerated LLM inference with no API keys required
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- **Graph Database**: ArangoDB for storing and querying triples with relationship traversal
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- **Interactive Visualization**: GPU-accelerated graph rendering with Three.js WebGPU
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- **Modern Web Interface**: Next.js frontend with document management and query interface
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- **Fully Containerized**: Reproducible deployment with Docker Compose and GPU support
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Duration: - 2-3 minutes for initial setup and container deployment
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**Full playbook**: `/Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/txt2kg/README.md`
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<!-- GENERATED:END -->
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