Embedding Search
Vector-based semantic search using simulated embeddings. Provides hybrid search combining keyword matching with semantic similarity.
Capabilities
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Semantic document search
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Vector similarity matching
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Document chunking and indexing
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Hybrid keyword + vector search
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Relevance scoring and ranking
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Full-text search fallback
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Document categorization
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Configurable similarity thresholds
When to Use
Use the embedding-search skill when:
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Searching through large document collections
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Need semantic similarity matching
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Building a knowledge base
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Finding related documents
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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