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