Represent dependency trees, taxonomies, and other hierarchies inside a Poincare ball, a geometric model for hierarchical distances. Use tree depth for radius and text embeddings for direction, then measure distances to examine the encoded structure.
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Place hierarchical data in the Poincare ball using depth for the radius and npx ruvector@0.3.3 embed text for direction (a unit-norm text embedding alone has no depth)
安装前可以查看交给 AI 代理的指令内容。
Embed hierarchical data in the Poincare ball model using ruvector.
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.
npm ls ruvector 2>/dev/null | grep '0.3.3' || npm install ruvector@0.3.3
embeddings_hyperbolic convert gives tanh(0.5) = 0.4621 for all of them), so the radius carries no depth and parent/child distances come out nearly the same as cross-branch distances. Projecting a text embedding does not create hierarchy.npx -y ruvector@0.3.3 embed text "hierarchical concept" -o concept.vec.json # direction only
d(u, v) = arcosh(1 + 2 * ||u-v||^2 / ((1-||u||^2)(1-||v||^2))). The MCP embeddings_hyperbolic distance computes it (curvature -1) and returns success: false for a point on or outside the ball rather than a null distance.mcp__plugin_ruflo-core_ruflo__memory_store({ key: "hyperbolic-CONCEPT", value: "COORDINATES_AND_NEIGHBORS", namespace: "hyperbolic-embeddings" })--model poincare flag; attention hyperbolic is the Poincare-ball attention op, not a text-embedding projector.| Property | Meaning |
|---|---|
| Norm close to 0 | Root-level concept (only if you assigned radius by depth) |
| Norm close to 1 | Leaf-level concept (same condition) |
| Small geodesic distance | Close in the hierarchy you encoded |
| Large geodesic distance | Distant subtrees in the hierarchy you encoded |
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
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Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
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Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
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Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
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Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
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Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.
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