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 [email protected] is available:
   bashCopy

   npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install [email protected]
   

2. Generate a base ONNX embedding ([email protected] does not expose a --model poincare flag on embed text):
   bashCopy

   npx -y [email protected] embed text "hierarchical concept" -o concept.vec.json
   

3. Project into the Poincare ball in your own code (or via the experimental neural substrate):
   bashCopy

   npx -y [email protected] embed neural --help
   

   For an ad-hoc projection, normalize the 384-dim vector to live inside the unit ball (x_i / (||x|| * (1 + epsilon))) and persist the projected coordinates alongside the original embedding.
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" })

Caveats

• [email protected] has no first-class Poincare ball CLI flag. Treat hyperbolic projection as a post-processing step over a standard ONNX embedding.
• If you need a hyperbolic search index, store projected coordinates in AgentDB and compute geodesic distance in your own retrieval code.

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