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| 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: /home/runner/work/dgx-spark-playbooks/dgx-spark-playbooks/nvidia/txt2kg/README.md