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Databases · Backend / AGENT SKILL
similarity-search-patterns
0
Implement vector similarity search and nearest neighbor queries for efficient semantic retrieval.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Understand the trade-offs.
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Original instructions from the publisher’s SKILL.md
# 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