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