Vector Embed
Skill Verifiziert AktivGenerate embeddings via npx ruvector@0.2.25 embed text (ONNX all-MiniLM-L6-v2, 384-dim), normalize, and store in HNSW index
To efficiently generate and prepare text data for semantic search, similarity comparison, and clustering by creating normalized vector embeddings.
Funktionen
- Generate text embeddings using ruvector
- Utilize ONNX all-MiniLM-L6-v2 model
- Output 384-dimensional vectors
- Handle potential ONNX WASM issues
- Support adaptive (LoRA) embedding variants
Anwendungsfälle
- Enriching text data for semantic search systems
- Enabling similarity comparisons between documents or text snippets
- Preparing data for clustering algorithms
- Integrating vector embeddings into RAG pipelines
Nicht-Ziele
- Managing the full lifecycle of a vector database
- Performing complex natural language processing beyond embedding generation
- Providing a direct interface to LLM providers
Workflow
- Ensure ruvector@0.2.25 is available and install ONNX WASM if needed.
- Embed input text or file content using `npx ruvector@0.2.25 embed text`.
- Optionally use adaptive embedding variants.
- Confirm vector dimension and metadata.
- Optionally store metadata using `mcp__claude-flow__memory_store`.
Voraussetzungen
- Node.js and npm installed
- Access to the internet for `npm install`
Installation
Zuerst Marketplace hinzufügen
/plugin marketplace add ruvnet/ruflo/plugin install ruflo-ruvector@rufloQualitätspunktzahl
VerifiziertVertrauenssignale
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