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AI/MLruvnet

vector-hyperbolic

Embed hierarchical data via npx ruvector@0.2.25 embed text and project into the Poincare ball in user code (no --model poincare flag in 0.2.25)

Stars
56,726
Source
ruvnet/claude-flow
Updated
2026-05-31
Slug
ruvnet--claude-flow--vector-hyperbolic
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/ruvnet/claude-flow/HEAD/plugins/ruflo-ruvector/skills/vector-hyperbolic/SKILL.md -o .claude/skills/vector-hyperbolic.md

Drops the SKILL.md into .claude/skills/vector-hyperbolic.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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__claude-flow__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

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