intermediateAnalysis & Research

Similarity Search Patterns

Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

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76OpxScoreProvisional
Community resultNot enough feedback0 votes
Model evidenceNo verified tests
ClaudeChatGPTGemini+5

Score breakdown

Estimated from the available content and source signals.

Provisional
Documentation82
Practical value74
Evidence63
Source trust80

Model compatibility

Inferred fit is not the same as a recorded hands-on test.

ClaudeinferredThe skill uses model-agnostic prompt or LLM terminology.
ChatGPTinferredThe skill uses model-agnostic prompt or LLM terminology.
GeminiinferredThe skill uses model-agnostic prompt or LLM terminology.
CopilotinferredThe skill uses model-agnostic prompt or LLM terminology.
LlamainferredThe skill uses model-agnostic prompt or LLM terminology.
PerplexityinferredThe skill uses model-agnostic prompt or LLM terminology.
MistralinferredThe skill uses model-agnostic prompt or LLM terminology.
GrokinferredThe skill uses model-agnostic prompt or LLM terminology.

Overview

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

Best for

  • Building semantic search systems
  • Implementing RAG retrieval
  • Creating recommendation engines
  • Optimizing search latency
  • Scaling to millions of vectors
  • Combining semantic and keyword search

Tips and best practices

  • Review the source instructions and adapt inputs before running the workflow.

What This Skill Can Do

AI-generated examples showing real capabilities

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