Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Quickly install embeddings AI skill to your development environment via command line
Source: ruvnet/ruflo.
Purpose Vector embeddings for semantic search and pattern matching with HNSW indexing.
| sql.js | Cross-platform SQLite persistent cache (WASM) | | HNSW | 150x-12,500x faster search | | Hyperbolic | Poincare ball model for hierarchical data | | Normalization | L2, L1, min-max, z-score | | Chunking | Configurable overlap and size | | 75x faster | With agentic-flow ONNX integration |
| Int8 | 3.92x | Fast | | Int4 | 7.84x | Faster | | Binary | 32x | Fastest |
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed. Source: ruvnet/ruflo.
Stable fields and commands for AI/search citations.
npx skills add https://github.com/ruvnet/ruflo --skill embeddingsVector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed. Source: ruvnet/ruflo.
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/ruvnet/ruflo --skill embeddings Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code, Cursor, or OpenClaw
https://github.com/ruvnet/ruflo