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 [email protected] is available:

   npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install [email protected]

If embed text later reports ONNX WASM files not bundled, also run:

   npm install ruvector-onnx-embeddings-wasm

1. Embed the input (use the text subcommand, with text as a positional arg):

• Single string: npx -y [email protected] embed text "your text here"
• With output file: npx -y [email protected] embed text "your text here" -o vec.json
• For a file: read its content via the Read tool, then pass it as the positional argument.
• For batch: loop over files in shell — [email protected] has no built-in --batch/--glob flags.

1. Adaptive (LoRA) variant: npx -y [email protected] embed text "..." --adaptive --domain code
2. Confirm — report vector dimension (384), norm, and any output path written.
3. Store metadata in AgentDB if needed:

mcp__claude-flow__memory_store({ key: "embed-SOURCE", value: "VECTOR_METADATA", namespace: "vector-patterns" })

MCP alternative

Register the MCP server once with the pinned version:

claude mcp add ruvector -- npx -y [email protected] mcp start

Then call MCP tools directly: hooks_rag_context (semantic context), brain_search (collective brain), hooks_ast_analyze, hooks_route.

Caveats

• The embed --batch --glob and embed --file flags do not exist in [email protected]; only embed text <text> is supported. Read files yourself and call embed text per file.
• ONNX runtime is not bundled by default. If embedding fails, install ruvector-onnx-embeddings-wasm or run npx -y [email protected] doctor to diagnose.