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@0.2.25 is available:

   npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install ruvector@0.2.25
   

2. Generate a base ONNX embedding (ruvector@0.2.25 does not expose a --model poincare flag on embed text):

   npx -y ruvector@0.2.25 embed text "hierarchical concept" -o concept.vec.json
   

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

   npx -y ruvector@0.2.25 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__plugin_ruflo-core_ruflo__memory_store({ key: "hyperbolic-CONCEPT", value: "COORDINATES_AND_NEIGHBORS", namespace: "hyperbolic-embeddings" })

Caveats

• ruvector@0.2.25 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

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