vector-hyperbolic

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

1. Ensure ruvector is available:

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

2. Embed in Poincare ball:

   npx ruvector embed --model poincare "hierarchical concept"
   

   Coordinates near origin = generic/root; near boundary = specific/leaf.
3. Search in hyperbolic space:

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

4. 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.
5. 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