winsorllc Embedding Search
Vector-based semantic search using simulated embeddings. Provides hybrid search combining keyword matching with semantic similarity.
Capabilities
- Semantic document search
- Vector similarity matching
- Document chunking and indexing
- Hybrid keyword + vector search
- Relevance scoring and ranking
- Full-text search fallback
- Document categorization
- Configurable similarity thresholds
When to Use
Use the embedding-search skill when:
- Searching through large document collections
- Need semantic similarity matching
- Building a knowledge base
- Finding related documents
- Implementing RAG (Retrieval Augmented Generation)
Usage Examples
Index documents
node /job/.pi/skills/embedding-search/embed.js index /path/to/documents --output index.json
Search documents
node /job/.pi/skills/embedding-search/embed.js search "machine learning concepts" --index index.json
Interactive mode
node /job/.pi/skills/embedding-search/embed.js --interactive --index index.json
Add documents to existing index
node /job/.pi/skills/embedding-search/embed.js add new-doc.md --index index.json
Hybrid search with weights
node /job/.pi/skills/embedding-search/embed.js search "cloud deployment" --hybrid --keyword-weight 0.3 --semantic-weight 0.7