vector-embed

Original：🇺🇸 English

Translated

Generate embeddings via npx ruvector (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index

4installs

Sourceruvnet/ruflo

Added on2026-05-03

NPX Install

npx skill4agent add ruvnet/ruflo vector-embed

SKILL.md Content

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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

Ensure ruvector is available:

bash

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

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"

Normalization -- ruvector L2-normalizes by default (unit sphere, cosine similarity). Alternatives:
```
--norm l1
```
,
```
--norm minmax
```
,
```
--norm zscore
```
Confirm -- report vector ID, dimension (384), norm, and index stored in

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