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vector-embed

Convert text, code, or documents into 384-dimensional vectors, numerical representations used for meaning-based search, similarity comparison, and clustering. Save vectors to files, check their dimensions and magnitude, and optionally record metadata in AgentDB.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Generate embeddings via npx ruvector@0.3.3 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index

ruvnet/ruflo
설치 방법 보기

이럴 때 유용합니다

  • Preparing document vectors for meaning-based search
  • Converting text or code for similarity comparison
  • Preparing vectors for clustering related documents
  • Saving vectors and recording metadata

포함된 파일(1)

  • SKILL.md2.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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@0.3.3 is available: 
    npm ls ruvector 2>/dev/null | grep '0.3.3' || npm install ruvector@0.3.3
    ruvector 0.3.3 bundles the ONNX runtime; no add-on is needed. If embed text reports ONNX WASM files not bundled, an older ruvector (0.2.x) is being resolved — rerun with the pinned npx -y ruvector@0.3.3.
  2. Embed the input (use the text subcommand, with text as a positional arg):
    • Single string: npx -y ruvector@0.3.3 embed text "your text here"
    • With output file: npx -y ruvector@0.3.3 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@0.3.3 has no built-in --batch/--glob flags.
  3. Adaptive (LoRA) variant: npx -y ruvector@0.3.3 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:

claude mcp add ruvector -- npx -y ruvector@0.3.3 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 flags do not exist in ruvector@0.3.3. embed text accepts one text, --stdin, or --input-file <path> (keeps sensitive text off argv); call it per file.
  • If embedding fails, run npx -y ruvector@0.3.3 doctor to diagnose.

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