| name | embeddings |
| description | 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.
|
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
npx claude-flow memory store --key "pattern-1" --value "description" --embed
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
