Embeddings
技能 已验证 活跃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.
To enable efficient and scalable semantic search, pattern matching, similarity queries, and knowledge retrieval using advanced vector embedding techniques.
功能
- HNSW indexing for fast search
- sql.js persistence for cross-platform SQLite
- Hyperbolic embeddings for hierarchical data
- Multiple normalization options (L2, L1, min-max, z-score)
- Configurable chunking for text processing
- ONNX integration for 75x faster agentic-flow performance
- Quantization for memory efficiency (Int8, Int4, Binary)
使用场景
- Performing semantic search on large datasets
- Implementing pattern matching and similarity queries
- Building knowledge retrieval systems
- Integrating vector embeddings with agentic workflows
非目标
- Exact text matching
- Simple key-value lookups
- Scenarios requiring no semantic understanding
安装
npx skills add ruvnet/ruflo通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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