Text to Knowledge Graph

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 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

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

Prerequisites

• DGX Spark with latest NVIDIA drivers
• Docker installed and configured with NVIDIA Container Toolkit
• Docker Compose

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: 01/08/2025

  • Migrated from Pinecone to Qdrant for ARM64 compatibility
  • Added vLLM support with Neo4j
  • Added Palette UI components with accessibility improvements
  • Added CPU-only mode for development (./start.sh --cpu)
  • Optimized ArangoDB with deterministic keys and BM25 search
  • Added GNN preprocessing scripts for knowledge graph training