Claude-code-flow embeddings

Name: embeddings
Author: ruvnet

install

source · Clone the upstream repo

git clone https://github.com/ruvnet/ruflo

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ruvnet/ruflo "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.agents/skills/embeddings" ~/.claude/skills/ruvnet-claude-code-flow-embeddings && rm -rf "$T"

manifest: .agents/skills/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