vector-embed

Vector Embed

Generate and store vector embeddings using the ruvector npm package.

When to use

Use this skill to embed text, code, or documents into 384-dimensional vectors for semantic search, similarity comparison, or clustering. ruvector uses ONNX all-MiniLM-L6-v2 with HNSW indexing (52,000+ inserts/sec, ~0.045ms search).

Steps

1. Ensure ruvector is available:

   npm ls ruvector 2>/dev/null || npm install ruvector
   

2. Embed the input:
   • For text: npx ruvector embed "your text here"
   • For a file: npx ruvector embed --file src/module.ts
   • For batch: npx ruvector embed --batch --glob "src/**/*.ts"
3. Normalization -- ruvector L2-normalizes by default (unit sphere, cosine similarity). Alternatives: --norm l1, --norm minmax, --norm zscore
4. Confirm -- report vector ID, dimension (384), norm, and index stored in
5. Store metadata in AgentDB if needed: mcp__claude-flow__memory_store({ key: "embed-SOURCE", value: "VECTOR_METADATA", namespace: "vector-patterns" })

MCP alternative

If ruvector MCP server is connected (claude mcp add ruvector -- npx ruvector mcp start):

• hooks_rag_context — semantic context retrieval
• brain_search — shared brain knowledge search

Batch embedding

npx ruvector embed --batch --glob "src/**/*.ts"

Reports total vectors inserted and index growth.