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Text to Knowledge Graph

30 MIN

Transform unstructured text into interactive knowledge graphs with LLM inference and graph visualization

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Clone the repository

In a terminal, clone the txt2kg repository and navigate to the project directory.

git clone https://github.com/NVIDIA/dgx-spark-playbooks
cd dgx-spark-playbook/nvidia/txt2kg/assets

Start the txt2kg services

Use the provided start script to launch all required services. This will set up Ollama, ArangoDB, and the Next.js frontend:

./start.sh

The script will automatically:

  • Check for GPU availability
  • Start Docker Compose services
  • Set up ArangoDB database
  • Launch the web interface

Pull an Ollama model (optional)

Download a language model for knowledge extraction. The default model loaded is Llama 3.1 8B:

docker exec ollama-compose ollama pull <model-name>

Browse available models at https://ollama.com/search

NOTE

The unified memory architecture enables running larger models like 70B parameters, which produce significantly more accurate knowledge triples.

Access the web interface

Open your browser and navigate to:

http://localhost:3001

You can also access individual services:

Upload documents and build knowledge graphs

5.1. Document Upload

  • Use the web interface to upload text documents (markdown, text, CSV supported)
  • Documents are automatically chunked and processed for triple extraction

5.2. Knowledge Graph Generation

  • The system extracts subject-predicate-object triples using Ollama
  • Triples are stored in ArangoDB for relationship querying

5.3. Interactive Visualization

  • View your knowledge graph in 2D or 3D with GPU-accelerated rendering
  • Explore nodes and relationships interactively

5.4. Graph-based Queries

  • Ask questions about your documents using the query interface
  • Graph traversal enhances context with entity relationships from ArangoDB
  • LLM generates responses using the enriched graph context

Future Enhancement: GraphRAG capabilities with vector-based KNN search for entity retrieval are planned.

Cleanup and rollback

Stop all services and optionally remove containers:

# Stop services
docker compose down

# Remove containers and volumes (optional)
docker compose down -v

# Remove downloaded models (optional)
docker exec ollama-compose ollama rm llama3.1:8b

Next steps

  • Experiment with different Ollama models for varied extraction quality
  • Customize triple extraction prompts for domain-specific knowledge
  • Explore advanced graph querying and visualization features

Resources