Text to Knowledge Graph

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-playbooks/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

Access the web interface

Open your browser and navigate to:

http://localhost:3001

You can also access individual services:

• ArangoDB Web Interface: http://localhost:8529
• Ollama API: http://localhost:11434

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