embeddings

SKILL.md

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