Collection of step-by-step playbooks for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture.
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Santosh Bhavani 7742a9f0de Implement multi-hop graph traversal with depth tracking
- Extract ALL edges from graph traversal paths, not just endpoints
- Add depth field (edge position in path: 0, 1, 2...)
- Add pathLength field (total edges in path)
- Use numeric index iteration for AQL compatibility
- Apply depth penalty to edge scoring (earlier edges weighted higher)
- Enable visualization of knowledge chains in graph queries
- Increase topK default to 40 for richer multi-hop context

This allows Traditional Graph to show how information is connected
across multiple hops in the knowledge graph, similar to GraphRAG.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-25 13:48:52 -07:00
nvidia Implement multi-hop graph traversal with depth tracking 2025-10-25 13:48:52 -07:00
src/images chore: Regenerate all playbooks 2025-10-03 20:46:11 +00:00
LICENSE chore: Regenerate all playbooks 2025-10-03 20:46:11 +00:00
README.md chore: Regenerate all playbooks 2025-10-18 21:28:42 +00:00

NVIDIA DGX Spark

DGX Spark Playbooks

Collection of step-by-step playbooks for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture.

About

These playbooks provide detailed instructions for:

  • Installing and configuring popular AI frameworks
  • Running inference with optimized models
  • Setting up development environments
  • Connecting and managing your DGX Spark device

Each playbook includes prerequisites, step-by-step instructions, troubleshooting guidance, and example code.

Available Playbooks

NVIDIA

Resources

License

See: