similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
Profile →npx skills add majiayu000/claude-skill-registry --skill similarity-search-patterns-tringo0108-z-command --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ### Template 1: Pinecone Implementation ```python from pinecone import Pinecone, ServerlessSpec from typing impor
- When to Use This Skill
- Core Concepts
- 1. Distance Metrics
- 2. Index Types
- Templates
- Template 1: Pinecone Implementation
- Template 2: Qdrant Implementation
- Template 3: pgvector with PostgreSQL
- Template 4: Weaviate Implementation
- Best Practices
- Do's
- Don'ts
- Resources
- Parent Hub
What does the similarity-search-patterns skill do?
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill similarity-search-patterns-tringo0108-z-command --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.