ContextQMD
Back to Ruflo

Embeddings Skill

.agents/skills/embeddings/SKILL.md

3.6.301.4 KB
Original Source

Embeddings Skill

Purpose

Vector embeddings for semantic search and pattern matching with HNSW indexing.

Features

FeatureDescription
sql.jsCross-platform SQLite persistent cache (WASM)
HNSW150x-12,500x faster search
HyperbolicPoincare ball model for hierarchical data
NormalizationL2, L1, min-max, z-score
ChunkingConfigurable overlap and size
75x fasterWith agentic-flow ONNX integration

Commands

Initialize Embeddings

bash
npx claude-flow embeddings init --backend sqlite

Embed Text

bash
npx claude-flow embeddings embed --text "authentication patterns"

Batch Embed

bash
npx claude-flow embeddings batch --file documents.json
bash
npx claude-flow embeddings search --query "security best practices" --top-k 5

Memory Integration

bash
# 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

TypeMemory ReductionSpeed
Int83.92xFast
Int47.84xFaster
Binary32xFastest

Best Practices

  1. Use HNSW for large pattern databases
  2. Enable quantization for memory efficiency
  3. Use hyperbolic for hierarchical relationships
  4. Normalize embeddings for consistency