mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
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7742a9f0de
- 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> |
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| .. | ||
| comfy-ui | ||
| connect-to-your-spark | ||
| connect-two-sparks | ||
| cuda-x-data-science | ||
| dgx-dashboard | ||
| flux-finetuning | ||
| jax | ||
| llama-factory | ||
| multi-agent-chatbot | ||
| multi-modal-inference | ||
| nccl | ||
| nemo-fine-tune | ||
| nim-llm | ||
| nvfp4-quantization | ||
| ollama | ||
| open-webui | ||
| pytorch-fine-tune | ||
| rag-ai-workbench | ||
| speculative-decoding | ||
| tailscale | ||
| trt-llm | ||
| txt2kg | ||
| unsloth | ||
| vibe-coding | ||
| vllm | ||
| vlm-finetuning | ||
| vscode | ||
| vss | ||