vector-hyperbolic

Original：🇺🇸 English

Translated

Embed hierarchical data in hyperbolic space via npx ruvector Poincare ball model, compute geodesic distances

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Sourceruvnet/ruflo

Added on2026-05-03

NPX Install

npx skill4agent add ruvnet/ruflo vector-hyperbolic

SKILL.md Content

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Vector Hyperbolic

Embed hierarchical data in the Poincare ball model using

ruvector

.

When to use

Use this skill when your data has inherent hierarchy — dependency trees, module structures, taxonomies, org charts, ontologies. Hyperbolic space captures hierarchical distances with far fewer dimensions than Euclidean embeddings.

Steps

Ensure ruvector is available:

bash

npm ls ruvector 2>/dev/null || npm install ruvector

Embed in Poincare ball:
bash
```
npx ruvector embed --model poincare "hierarchical concept"
```
Coordinates near origin = generic/root; near boundary = specific/leaf.

Search in hyperbolic space:

bash

npx ruvector search --model poincare "query" --limit 10

Geodesic distance:
```
d(u, v) = arcosh(1 + 2 * ||u-v||^2 / ((1-||u||^2)(1-||v||^2)))
```
Distance grows logarithmically with tree depth, preserving hierarchy.

Store results:

mcp__claude-flow__memory_store({ key: "hyperbolic-CONCEPT", value: "COORDINATES_AND_NEIGHBORS", namespace: "hyperbolic-embeddings" })

Poincare ball properties

Property	Meaning
Norm close to 0	Generic, root-level concept
Norm close to 1	Specific, leaf-level concept
Small geodesic distance	Closely related in hierarchy
Large geodesic distance	Distant or different subtrees

Use cases

Dependency analysis: embed module imports to find tightly coupled subtrees
Code architecture: map class hierarchies to discover structural patterns
Knowledge organization: embed concepts to reveal taxonomic relationships
Codebase navigation: find most specific/general modules relative to a query