Which vector databases are supported by this skill?

The skill provides implementation patterns and code templates for Pinecone, Qdrant, and PostgreSQL using the pgvector extension.

Does it provide guidance on choosing distance metrics?

Absolutely. It covers the best use cases for Cosine similarity, Euclidean distance (L2), Dot Product, and Manhattan distance (L1) based on your embedding model.

Can this skill help improve RAG retrieval accuracy?

Yes, it includes advanced patterns for hybrid search (combining keyword and semantic results) and cross-encoder reranking to maximize precision.

Is this skill suitable for scaling to millions of vectors?

Yes, it includes configurations for HNSW and Quantized (IVF+PQ) index types designed for sub-linear search time and memory efficiency at scale.

Vector Similarity Search Patterns

Name: Vector Similarity Search Patterns
Author: EricGrill

byEricGrill

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データベース管理

Implements high-performance similarity search and vector retrieval patterns for AI-driven applications.

概要

This skill provides production-ready implementation patterns for vector similarity search, enabling developers to build sophisticated semantic search systems, RAG (Retrieval-Augmented Generation) pipelines, and recommendation engines. It includes optimized templates for popular vector databases like Pinecone, Qdrant, and pgvector, covering critical concepts such as distance metrics, HNSW indexing, hybrid search, and cross-encoder reranking to ensure low-latency and high-accuracy retrieval for large-scale embedding datasets.

主な機能

Production-grade templates for batch upserts and complex metadata filtering
Comprehensive guidance on distance metrics like Cosine, Euclidean, and Dot Product
2 GitHub stars
Advanced search strategies including hybrid search and result reranking
Optimization patterns for HNSW and IVF+PQ indexing at scale
Multi-database support for Pinecone, Qdrant, and pgvector (PostgreSQL)

ユースケース

Developing high-performance recommendation engines using vector embeddings
Building robust Retrieval-Augmented Generation (RAG) systems
Implementing semantic search for high-volume document repositories

概要

主な機能

Production-grade templates for batch upserts and complex metadata filtering
Comprehensive guidance on distance metrics like Cosine, Euclidean, and Dot Product
2 GitHub stars
Advanced search strategies including hybrid search and result reranking
Optimization patterns for HNSW and IVF+PQ indexing at scale
Multi-database support for Pinecone, Qdrant, and pgvector (PostgreSQL)

ユースケース

Developing high-performance recommendation engines using vector embeddings
Building robust Retrieval-Augmented Generation (RAG) systems
Implementing semantic search for high-volume document repositories