---
title: "Text to Knowledge Graph on DGX Station"
publisher: "nvidia"
type: "playbook"
updated: "2026-03-05T18:17:27.876Z"
description: "Transform unstructured text into interactive knowledge graphs with LLM inference and graph visualization"
canonical: "https://build.nvidia.com/station/txt2kg.md"
---

# Basic idea

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 GB300 Ultra's massive GPU memory enables running the Llama 3.1 405B model, producing the highest-quality knowledge graphs and delivering 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

> **Future Enhancements**: Vector embeddings and GraphRAG capabilities are planned enhancements.

# What you'll accomplish

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

# What to know before starting

- Basic Docker container usage
- Familiarity with command line operations
- Understanding of knowledge graphs (helpful but not required)

# Prerequisites

- NVIDIA DGX Station with GB300 Ultra Blackwell GPU
- Docker installed and configured with NVIDIA Container Toolkit
- Docker Compose
- Network access for container image downloads

# Ancillary files

All required assets are in the playbook directory `nvidia/station-txt2kg/assets` (see Instructions, Step 1). Key files:

- `start.sh` - Launch script for all services
- `stop.sh` - Stop script to shut down services
- `deploy/compose/` - Docker Compose configurations

# Time & risk

- **Duration**:
- 2-3 minutes for initial setup and container deployment
- 5-10 minutes for Ollama model download (depending on model size)
- Immediate document processing and knowledge graph generation

- **Risks**:
- GPU memory requirements depend on chosen Ollama model size
- Document processing time scales with document size and complexity

- **Rollback**: Stop and remove Docker containers, delete downloaded models if needed
* **Last Updated:** 03/02/2026
* First Publication

## More

- [Instructions](/station/txt2kg/instructions.md)
- [Troubleshooting](/station/txt2kg/troubleshooting.md)