/similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
One skill from wshobson-agents.
shell
$ npx -y skills add wshobson/agents --skill similarity-search-patterns --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
How it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/similarity-search-patterns
Context preview
The summary Claude sees to decide when to auto-load this skill.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Stats
Stars38,174
Forks4,092
LanguagePython
LicenseMIT
Ships with wshobson-agents
SKILL.md
similarity-search-patterns.SKILL.md
--- 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 ``` ┌─────────────────────────────────────────────────┐
