embeddings

Name: embeddings
Author: ruvnet

INSTALLATION

npx skills add https://github.com/ruvnet/ruflo --skill embeddings

Run in your project or agent environment. Adjust flags if your CLI version differs.

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