Does this skill help with RAG accuracy?

Yes, it provides patterns for hybrid search and cross-encoder reranking, which are industry standards for improving the relevance of retrieved documents in RAG pipelines.

Is this suitable for large-scale vector datasets?

Absolutely. It includes guidance on choosing index types like HNSW for graph-based speed or IVF+PQ for memory-efficient quantization when handling millions of vectors.

Which vector databases are supported by these patterns?

The skill includes specialized implementation templates for Pinecone, Qdrant, and pgvector (PostgreSQL), covering the most popular cloud and self-hosted options.

How do I choose the right distance metric?

The skill provides a comparative guide for Cosine, Euclidean (L2), Dot Product, and Manhattan metrics to help you match the metric to your specific embedding model.

Vector Search & Similarity Patterns

Name: Vector Search & Similarity Patterns
Author: wshobson

bywshobson

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23,194

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Data Science & ML

Implements efficient similarity search and vector database patterns for semantic retrieval and RAG systems.

This skill provides a comprehensive library of implementation patterns for modern vector databases, including Pinecone, Qdrant, and pgvector. It equips developers with production-ready templates for high-performance similarity search, covering essential concepts like distance metrics, HNSW indexing, and hybrid search. By using these standardized patterns, you can quickly build scalable RAG pipelines, recommendation engines, and semantic search interfaces while ensuring optimal latency and recall accuracy.

Key Features

01Production templates for Pinecone, Qdrant, and pgvector

02Optimized index configurations for HNSW and IVF+PQ

03Integrated reranking patterns using cross-encoders

04Advanced metadata filtering and batch upserting logic

05Support for hybrid search (dense + sparse vectors)

0623,194 GitHub stars

Use Cases

01Developing semantic recommendation engines for large datasets

02Building production-grade Retrieval-Augmented Generation (RAG) systems

03Optimizing vector search latency and memory usage in PostgreSQL

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add wshobson/agents similarity-search-patterns

For use in Claude.ai and ChatGPT

Download Skill