--- name: dgx-spark-txt2kg 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. --- # 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`