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

•

23,194

データサイエンスとML

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

概要

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.

主な機能

Production templates for Pinecone, Qdrant, and pgvector
Optimized index configurations for HNSW and IVF+PQ
Integrated reranking patterns using cross-encoders
Advanced metadata filtering and batch upserting logic
Support for hybrid search (dense + sparse vectors)
23,194 GitHub stars

ユースケース

Developing semantic recommendation engines for large datasets
Building production-grade Retrieval-Augmented Generation (RAG) systems
Optimizing vector search latency and memory usage in PostgreSQL

概要

主な機能

Production templates for Pinecone, Qdrant, and pgvector
Optimized index configurations for HNSW and IVF+PQ
Integrated reranking patterns using cross-encoders
Advanced metadata filtering and batch upserting logic
Support for hybrid search (dense + sparse vectors)
23,194 GitHub stars

ユースケース

Developing semantic recommendation engines for large datasets
Building production-grade Retrieval-Augmented Generation (RAG) systems
Optimizing vector search latency and memory usage in PostgreSQL