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/.
2.0 KiB
| name | description |
|---|---|
| dgx-spark-txt2kg | 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. |
Text to Knowledge Graph on DGX Spark
Transform unstructured text into interactive knowledge graphs with LLM inference and graph visualization
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. The unified memory architecture enables running larger, more accurate models that produce higher-quality knowledge graphs and deliver superior downstream GraphRAG performance.
This txt2kg playbook transforms unstructured text documents into structured knowledge graphs using:
- Knowledge Triple Extraction: Using Ollama with GPU acceleration for local LLM inference to extract subject-predicate-object relationships
- Graph Database Storage: ArangoDB for storing and querying knowledge triples with relationship traversal
- GPU-Accelerated Visualization: Three.js WebGPU rendering for interactive 2D/3D graph exploration
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. The setup includes:
- Local LLM Inference: Ollama for GPU-accelerated LLM inference with no API keys required
- Graph Database: ArangoDB for storing and querying triples with relationship traversal
- Interactive Visualization: GPU-accelerated graph rendering with Three.js WebGPU
- Modern Web Interface: Next.js frontend with document management and query interface
- Fully Containerized: Reproducible deployment with Docker Compose and GPU support
Duration: - 2-3 minutes for initial setup and container deployment
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/txt2kg/README.md