Embeddings

by ruvnet6051f6702b61No license74K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.

Instructions onlyAI & Agents
AI-generated overview

Provides vector embeddings with HNSW indexing, sql.js persistence, quantization and hyperbolic support for semantic search.

What it does
This skill documents a command-line workflow for creating and querying vector embeddings. It covers initialization with a sql.js SQLite backend, single and batch embedding of text, semantic search with a top-k parameter, and memory store/search integration. It also lists features such as HNSW indexing, hyperbolic Poincare ball support, normalization options, chunking, and Int8/Int4/binary quantization.
When to use it
Use it for semantic search, pattern matching, similarity queries and knowledge retrieval where meaning-based matching is needed. Skip it for exact text matching, simple lookups, or cases where no semantic understanding is required.
Requirements
Requires the claude-flow CLI invoked via npx, which implies Node.js and network access for package retrieval. No scripts ship with the skill; it is instructions only.

Embeddings Skill

Purpose

Vector embeddings for semantic search and pattern matching with HNSW indexing.

Features

FeatureDescription
sql.jsCross-platform SQLite persistent cache (WASM)
HNSW150x-12,500x faster search
HyperbolicPoincare ball model for hierarchical data
NormalizationL2, L1, min-max, z-score
ChunkingConfigurable overlap and size
75x fasterWith agentic-flow ONNX integration

Commands

Initialize Embeddings

bash
npx claude-flow embeddings init --backend sqlite

Embed Text

bash
npx claude-flow embeddings embed --text "authentication patterns"

Batch Embed

bash
npx claude-flow embeddings batch --file documents.json

Semantic Search

bash
npx claude-flow embeddings search --query "security best practices" --top-k 5

Memory Integration

bash
# Store with embeddingsnpx claude-flow memory store --key "pattern-1" --value "description" --embed
# Search with embeddingsnpx claude-flow memory search --query "related patterns" --semantic

Quantization

TypeMemory ReductionSpeed
Int83.92xFast
Int47.84xFaster
Binary32xFastest

Best Practices

  1. Use HNSW for large pattern databases
  2. Enable quantization for memory efficiency
  3. Use hyperbolic for hierarchical relationships
  4. Normalize embeddings for consistency

Source and attribution

Source:ruvnet/rufloin.agents/skills/embeddingsat commit6051f67

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Embeddings Agent Skill | SourceWeft