Build Knowledge Graphs with txt2kg

Clone the repository

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

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

Start the txt2kg services

Use the provided start script. By default, ./start.sh starts the ArangoDB + Ollama stack. Use one stack flag when you want a different graph database or LLM backend.

./start.sh

Common options:

# Stack flags
./start.sh --neo4j     # Neo4j + Ollama
./start.sh --vllm      # Neo4j + vLLM

# Optional flags
./start.sh --vector-search   # Add Qdrant + Sentence Transformers
./start.sh --help            # Full option list

DGX Spark is not affected by the 64 KB page-size issue. On affected DGX Station systems, start with ./start.sh --neo4j to use Neo4j + Ollama.

The script will:

• Check for GPU availability
• Start Docker Compose services for the selected stack
• For the vLLM stack, start the backend container; model load can take 30+ minutes, and progress is available with docker logs vllm-service -f
• Print the web UI URL when ready

Choose and load a model

Triple extraction quality depends on the LLM behind Ollama or vLLM. Start with the stack default below, then explore other models to trade quality vs. speed and memory.

Defaults by stack

Ollama stacks

Pull a language model for knowledge extraction (skip if the default was already pulled on start):

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

Examples:

docker exec ollama-compose ollama pull llama3.1:8b

Then select the model in the web UI.

Explore more models: browse the Ollama model library. Prefer models that fit the memory available on your hardware platform.

vLLM stack

The model is loaded automatically by the vLLM container from VLLM_MODEL in deploy/compose/docker-compose.vllm.yml.

To try a different Hugging Face checkpoint:

1. Set VLLM_MODEL to another handle (for example from Hugging Face Models or vLLM Recipes).
2. Restart: ./stop.sh then start again with your stack flags (for example ./start.sh --vllm).
3. Confirm readiness: docker logs vllm-service -f

Explore more models: vLLM Recipes — DGX Spark · vLLM Recipes — DGX Station · Hugging Face

Access the web interface

Open your browser and navigate to:

http://localhost:3001

You can also access stack-specific services:

Upload documents and build knowledge graphs

If the LLM backend is still loading, the UI may show an initializing banner until the backend is ready.

5.1. Document upload

• Upload text documents (markdown, text, and CSV are supported)
• Documents are chunked and processed for triple extraction

5.2. Knowledge graph generation

• The system extracts subject–predicate–object triples using the selected LLM (Ollama or vLLM)
• Triples are stored in the selected graph database: ArangoDB for the default stack, or Neo4j when started with --neo4j or --vllm

5.3. Interactive visualization

• View the 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 in the query interface
• Graph traversal enriches context with entity relationships
• The LLM generates responses using the enriched graph context

Cleanup and rollback

Stop services with the same stack flags you used to start:

# Stop services (match the stack you started)
./stop.sh
# If you started with Neo4j + Ollama: ./stop.sh --neo4j
# If you started with Neo4j + vLLM: ./stop.sh --vllm

# Remove containers and volumes (optional)
# Ollama stack:
# docker compose -f deploy/compose/docker-compose.yml down -v
# Neo4j + Ollama stack:
# docker compose -f deploy/compose/docker-compose.neo4j.yml down -v
# Neo4j + vLLM stack:
# docker compose -f deploy/compose/docker-compose.vllm.yml down -v

# Remove downloaded Ollama models (Ollama stacks only, optional)
# docker exec ollama-compose ollama rm <model-name>

Next steps

• Experiment with different models (Step 3 links) for extraction quality vs. speed
• Customize triple extraction prompts for domain-specific knowledge
• Explore advanced graph querying and visualization features