Can I use this for large-scale production data?

Absolutely; the skill covers advanced indexing types like HNSW and IVF+PQ designed for scaling to millions of vectors with low latency.

How does this help with RAG performance?

It offers optimized search patterns like reranking and hybrid search, ensuring higher precision in retrieved context for LLM applications.

Is hybrid search supported?

Yes, the skill includes patterns for combining semantic vector search with keyword-based filtering and sparse vector search for more accurate results.

Does it cover distance metrics?

Yes, it includes detailed guidance on when to use Cosine, Euclidean, Dot Product, and Manhattan distance metrics based on your specific embedding model.

Which vector databases does this skill support?

This skill provides implementation patterns for major vector stores including Pinecone, Qdrant, and PostgreSQL with the pgvector extension.

Similarity Search Patterns

Name: Similarity Search Patterns
Author: EngineerWithAI

byEngineerWithAI

0•

Data Science & ML

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

This skill provides standardized implementation patterns for similarity search, enabling developers to build robust Retrieval-Augmented Generation (RAG) systems, recommendation engines, and semantic search applications. It offers expert guidance on selecting optimal distance metrics, choosing between graph-based or quantized index types, and provides production-ready integration templates for popular vector stores including Pinecone, Qdrant, and pgvector. Whether you are scaling to millions of vectors or optimizing search latency, this skill streamlines the process of integrating vector search into your AI applications.

Key Features

01Production-ready templates for Pinecone, Qdrant, and pgvector

02Index optimization patterns including HNSW and IVF+PQ

03Advanced search implementations with reranking and hybrid search

04Filtering and namespace management for multi-tenant vector stores

05Guidance on selecting distance metrics (Cosine, Euclidean, Dot Product)

060 GitHub stars

Use Cases

01Building semantic search for large-scale knowledge bases

02Creating personalized recommendation engines using vector embeddings

03Implementing retrieval-augmented generation (RAG) for LLM applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add engineerwithai/engineerwith-agents similarity-search-patterns

For use in Claude.ai and ChatGPT

Download Skill