Can this help improve RAG retrieval accuracy?

Yes, it provides patterns for cross-encoder reranking and hybrid search to significantly improve the relevance of retrieved context.

How does it handle distance metrics?

It provides a clear framework for choosing between Cosine similarity, Euclidean (L2) distance, and Dot Product based on your embedding model.

What vector databases are supported by this skill?

The skill includes production-ready implementation patterns for Pinecone, Qdrant, and pgvector (PostgreSQL).

Does it provide guidance on indexing strategies?

Yes, it covers when to use Flat, HNSW, or IVF+PQ indexes based on your specific requirements for speed, memory, and recall.

Similarity Search Patterns

Name: Similarity Search Patterns
Author: pur3v4d3r

bypur3v4d3r

•

数据科学与机器学习

Implements high-performance similarity search and vector retrieval patterns for RAG and semantic search applications.

This skill provides standardized implementation patterns for vector similarity search across industry-leading databases like Pinecone, Qdrant, and pgvector. It equips developers with optimized templates for upserting embeddings, performing nearest-neighbor queries, and implementing advanced techniques such as hybrid search and cross-encoder reranking. Whether you are scaling a RAG system to millions of vectors or optimizing search latency, this skill offers the domain-specific guidance needed to choose the right distance metrics and indexing strategies for production-grade AI applications.

主要功能

011 GitHub stars

02Guidance on selecting distance metrics like Cosine and Euclidean

03Support for hybrid search combining semantic and keyword retrieval

04Implementation patterns for cross-encoder reranking

05Optimization strategies for HNSW, IVF+PQ, and flat indexing

06Multi-database templates for Pinecone, Qdrant, and pgvector

使用场景

01Developing high-performance recommendation engines based on vector embeddings

02Implementing semantic search for large-scale document repositories

03Building production-ready Retrieval-Augmented Generation (RAG) pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase similarity-search-patterns

For use in Claude.ai and ChatGPT

主要功能

011 GitHub stars

02Guidance on selecting distance metrics like Cosine and Euclidean

03Support for hybrid search combining semantic and keyword retrieval

04Implementation patterns for cross-encoder reranking

05Optimization strategies for HNSW, IVF+PQ, and flat indexing

06Multi-database templates for Pinecone, Qdrant, and pgvector

使用场景

01Developing high-performance recommendation engines based on vector embeddings

02Implementing semantic search for large-scale document repositories

03Building production-ready Retrieval-Augmented Generation (RAG) pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase similarity-search-patterns

For use in Claude.ai and ChatGPT