similarity-search-patterns
$
npx mdskill add wshobson/agents/similarity-search-patternsExecute precise similarity searches across massive vector datasets.
- Enables semantic retrieval for recommendation engines and RAG systems.
- Depends on vector databases supporting HNSW, IVF, or flat indexes.
- Selects optimal distance metrics like cosine or Euclidean for accuracy.
- Returns ranked nearest neighbors with configurable top-k results.
SKILL.md
.github/skills/similarity-search-patternsView on GitHub ↗
--- name: similarity-search-patterns description: Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance. --- # Similarity Search Patterns Patterns for implementing efficient similarity search in production systems. ## When to Use This Skill - Building semantic search systems - Implementing RAG retrieval - Creating recommendation engines - Optimizing search latency - Scaling to millions of vectors - Combining semantic and keyword search ## Core Concepts ### 1. Distance Metrics | Metric | Formula | Best For | | ------------------ | ------------------ | --------------------- | --- | -------------- | | **Cosine** | 1 - (A·B)/(‖A‖‖B‖) | Normalized embeddings | | **Euclidean (L2)** | √Σ(a-b)² | Raw embeddings | | **Dot Product** | A·B | Magnitude matters | | **Manhattan (L1)** | Σ | a-b | | Sparse vectors | ### 2. Index Types ``` ┌─────────────────────────────────────────────────┐ │ Index Types │ ├─────────────┬───────────────┬───────────────────┤ │ Flat │ HNSW │ IVF+PQ │ │ (Exact) │ (Graph-based) │ (Quantized) │ ├─────────────┼───────────────┼───────────────────┤ │ O(n) search │ O(log n) │ O(√n) │ │ 100% recall │ ~95-99% │ ~90-95% │ │ Small data │ Medium-Large │ Very Large │ └─────────────┴───────────────┴───────────────────┘ ``` ## Templates and detailed worked examples Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates. ## Best Practices ### Do's - **Use appropriate index** - HNSW for most cases - **Tune parameters** - ef_search, nprobe for recall/speed - **Implement hybrid search** - Combine with keyword search - **Monitor recall** - Measure search quality - **Pre-filter when possible** - Reduce search space ### Don'ts - **Don't skip evaluation** - Measure before optimizing - **Don't over-index** - Start with flat, scale up - **Don't ignore latency** - P99 matters for UX - **Don't forget costs** - Vector storage adds up
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