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

Data Science & ML

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

About

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

Production-ready templates for Pinecone, Qdrant, and pgvector
Index optimization patterns including HNSW and IVF+PQ
Advanced search implementations with reranking and hybrid search
Filtering and namespace management for multi-tenant vector stores
Guidance on selecting distance metrics (Cosine, Euclidean, Dot Product)
0 GitHub stars

Use Cases

Building semantic search for large-scale knowledge bases
Creating personalized recommendation engines using vector embeddings
Implementing retrieval-augmented generation (RAG) for LLM applications

About

Key Features

Production-ready templates for Pinecone, Qdrant, and pgvector
Index optimization patterns including HNSW and IVF+PQ
Advanced search implementations with reranking and hybrid search
Filtering and namespace management for multi-tenant vector stores
Guidance on selecting distance metrics (Cosine, Euclidean, Dot Product)
0 GitHub stars

Use Cases

Building semantic search for large-scale knowledge bases
Creating personalized recommendation engines using vector embeddings
Implementing retrieval-augmented generation (RAG) for LLM applications