📦 Embeddings Package v3

The embeddings package (v3.0.0-alpha.12) provides high-performance vector embeddings with multiple backends.

Key Features:

Feature	Description	Performance
sql.js backend	Cross-platform SQLite (WASM)	No native compilation needed
Document chunking	Configurable overlap and size	Handles large documents
Normalization	L2, L1, min-max, z-score	4 normalization methods
Hyperbolic embeddings	Poincaré ball model	Better hierarchical representation
agentic-flow ONNX	Integrated ONNX runtime	faster with ONNX runtime than API calls
Neural substrate	RuVector integration	Full learning pipeline

Models Available:

Model	Dimensions	Speed	Quality
all-MiniLM-L6-v2	384	Fast	Good
all-mpnet-base-v2	768	Medium	Better

Usage:

bash
# Initialize embeddings system
npx ruflo@latest embeddings init

# Generate embedding for text
npx ruflo@latest embeddings embed "authentication patterns"

# Batch embed multiple texts
npx ruflo@latest embeddings batch --file texts.txt

# Search with semantic similarity
npx ruflo@latest embeddings search "login flow" --top-k 5

Programmatic:

typescript
import { createEmbeddingServiceAsync } from '@claude-flow/embeddings';

const service = await createEmbeddingServiceAsync({
  model: 'all-MiniLM-L6-v2',
  hyperbolic: true,  // Enable Poincaré ball embeddings
  cacheSize: 256
});

// Generate embedding
const embedding = await service.embed("authentication flow");

// Search similar patterns
const results = await service.search("login", { topK: 5 });