CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

Name: CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs
Author: Yarmoluk

@Yarmoluk

代码仓库

关于 CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

NVIDIA AI developer stack as a Compressed Knowledge Graph (CKG) — 20 domains, 998 nodes, every prerequisite chain declared as typed edges. Agents traverse REQUIRES/ENABLES relationships instead of scanning docs. 4× F1 vs RAG, 11× fewer tokens. No API key required

#nvidia #knowledge-graph #mcp #ai-agents #cuda #nim #tensorrt

基本信息

运行时

python

传输方式

stdio

发布者

Yarmoluk

提交者

Daniel Yarmoluk

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "nvidia-ai": {
      "command": "uvx",
      "args": [
        "ckg-nvidia-ai"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is CKG-NVIDIA-AI?

CKG-NVIDIA-AI is a read-only MCP server that provides a Compressed Knowledge Graph (CKG) covering the full NVIDIA AI developer stack across 20 domains and 998 nodes. It enables AI agents to traverse typed dependency graphs instead of using retrieval, achieving higher accuracy with drastically fewer tokens.

How to use CKG-NVIDIA-AI?

Install with pip install ckg-nvidia-ai and run the MCP server via uvx ckg-nvidia-ai. Configure it in Claude Desktop, Claude Code, Cursor, or other MCP clients using the provided JSON snippets. Tools include list_domains(), search_concepts(), query_ckg(), get_prerequisites(), and ask_nvidia() (the last requires Ollama with qwen2.5:14b).

Key features of CKG-NVIDIA-AI

4× F1 and 11× fewer tokens vs RAG
998 nodes across 20 NVIDIA AI domains
Deterministic graph traversal – no hallucination
Read-only: never writes, mutates, or executes
Typed edges (REQUIRES, ENABLES, RELATES_TO, IMPLEMENTS)
Three-state confidence (high, null, low) for every edge

Use cases of CKG-NVIDIA-AI

Deploying a real-time speech AI pipeline on NVIDIA Jetson
Mapping the full prerequisite chain for TensorRT-LLM on Hopper GPUs
Understanding Clara Parabricks dependencies for whole-genome sequencing
Building layered architecture maps with shared prerequisites across domains
Auditing deployment requirements before cold-start provisioning

FAQ from CKG-NVIDIA-AI

What is a Compressed Knowledge Graph (CKG) and how does it differ from retrieval?

A CKG is a layer that converts domain documentation into structured, agent-traversable knowledge. Instead of retrieval, the agent traverses declared relationships, which costs fewer tokens and does not guess.

Is CKG-NVIDIA-AI read-only?

Yes. All tools are read-only. The server never writes, mutates, or executes anything. Every response is a declared graph traversal.

What are the dependencies and runtime requirements?

The core server only requires the mcp[cli] package. For the ask_nvidia() tool,

CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

关于 CKG-NVIDIA- AI - Nvidia Developer Stack As A Compressed Knowledge Graph - 20 Domains, 998 Nodes, Agents Traverse Type Dependency Graphs

基本信息

配置

工具

概览

What is CKG-NVIDIA-AI?

How to use CKG-NVIDIA-AI?

Key features of CKG-NVIDIA-AI

Use cases of CKG-NVIDIA-AI

FAQ from CKG-NVIDIA-AI

What is a Compressed Knowledge Graph (CKG) and how does it differ from retrieval?

Is CKG-NVIDIA-AI read-only?

What are the dependencies and runtime requirements?

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