Similarity Search Patterns

When to use this skill

Use Similarity Search Patterns when an AI agent needs a reusable SKILL.md workflow for this job: Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.

When not to use it

Skip Similarity Search Patterns when the task is outside the analysis category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/similarity-search-patterns/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/similarity-search-patterns/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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

Intended uses

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