vector-embed

Vector Embed

Generate and store vector embeddings using the swarmvector 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. swarmvector 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 swarmvector 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 swarmvector-onnx-embeddings-wasm
   

2. 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.
3. Adaptive (LoRA) variant: npx -y [email protected] embed text "..." --adaptive --domain code
4. Confirm — report vector dimension (384), norm, and any output path written.
5. Store metadata in AgentDB if needed: mcp__swarmdo__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 swarmvector -- 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 swarmvector-onnx-embeddings-wasm or run npx -y [email protected] doctor to diagnose.