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 Embed Text Batch Embed Semantic Search Memory Integration Quantization | Type | Memory Reduction | Speed…