ruvnet/ruflo
embeddings
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.
All-time #8796 First seen Feb 8, 2026
8-week activity · all time api
Installation
$
npx skills add ruvnet/ruflo --skill embeddings
SKILL.md
Embeddings Skill
Purpose
Vector embeddings for semantic search and pattern matching with HNSW indexing.
Features
| Feature |
Description |
| 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 |
Commands
Initialize Embeddings
npx claude-flow embeddings init --backend sqlite
Embed Text
npx claude-flow embeddings embed --text "authentication patterns"
Batch Embed
npx claude-flow embeddings batch --file documents.json
Semantic Search
npx claude-flow embeddings search --query "security best practices" --top-k 5
Memory Integration
# Store with embeddings
npx claude-flow memory store --key "pattern-1" --value "description" --embed
# Search with embeddings
npx claude-flow memory search --query "related patterns" --semantic
Quantization
| Type |
Memory Reduction |
Speed |
| Int8 |
3.92x |
Fast |
| Int4 |
7.84x |
Faster |
| Binary |
32x |
Fastest |
Best Practices
- Use HNSW for large pattern databases
- Enable quantization for memory efficiency
- Use hyperbolic for hierarchical relationships
- Normalize embeddings for consistency
