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Databases · Agent Development / AGENT SKILL
embeddings
0
Use vector embeddings, HNSW indexing, and persistent storage for semantic search and similarity queries.
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Understand the trade-offs.
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The full skill.
Original instructions from the publisher’s SKILL.md
# Embeddings Skill ## Purpose Vector embeddings for semantic search and pattern matching with HNSW indexing. ## Features | Feature | Description | |---------|-------------| | **sql.js** | Cross-platform SQLite persistent cache (WASM) | | **HNSW** | 150x-12,500x faster search | | **Hyperbolic** | Poincare ball model for hierarchical data | | **Normalization** | L2, L1, min-max, z-score | | **Chunking** | Configurable overlap and size | | **75x faster** | With agentic-flow ONNX integration | ## Commands ### Initialize Embeddings ```bash npx claude-flow embeddings init --backend sqlite ``` ### Embed Text ```bash npx claude-flow embeddings embed --text "authentication patterns" ``` ### Batch Embed ```bash npx claude-flow embeddings batch --file documents.json ``` ### Semantic Search ```bash npx claude-flow embeddings search --query "security best practices" --top-k 5 ``` ## Memory Integration ```bash # Store with embeddings npx claude-flow memory store --key "pattern-1" --value "description" --embed # Search with embeddings npx claude-flow memory search --query "related patterns" --semantic ``` ## Quantization | Type | Memory Reduction | Speed | |------|-----------------|-------| | Int8 | 3.92x | Fast | | Int4 | 7.84x | Faster | | Binary | 32x | Fastest | ## Best Practices 1. Use HNSW for large pattern databases 2. Enable quantization for memory efficiency 3. Use hyperbolic for hierarchical relationships 4. Normalize embeddings for consistency