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@0.2.25 is available**: ```bash npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install ruvector@0.2.25 ``` If `embed text` later reports `ONNX WASM files not bundled`, also run: ```bash npm install ruvector-onnx-embeddings-wasm ``` 2. **Embed the input** (use the `text` subcommand, with text as a positional arg): - Single string: `npx -y ruvector@0.2.25 embed text "your text here"` - With output file: `npx -y ruvector@0.2.25 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 — ruvector@0.2.25 has no built-in `--batch`/`--glob` flags. 3. **Adaptive (LoRA) variant**: `npx -y ruvector@0.2.25 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__plugin_ruflo-core_ruflo__memory_store({ key: "embed-SOURCE", value: "VECTOR_METADATA", namespace: "vector-patterns" })` ## MCP alternative Register the MCP server once with the pinned version: ```bash claude mcp add ruvector -- npx -y ruvector@0.2.25 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 ruvector@0.2.25; 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 ruvector@0.2.25 doctor` to diagnose.