skills.rest
simplysmartai
simplysmartaiCOMMUNITY
Explore all skills by this author →
GitHub

similarity-search-patterns

Implement similarity search patterns with Pinecone, Qdrant, pgvector, and Weaviate.

Updated Feb 13, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#retrieval#similarity search
One-click install
npx skills add https://github.com/simplysmartai/5cypressautomation --skill similarity-search-patterns-simplysmartai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: similarity-search-patterns
Source: https://github.com/simplysmartai/5cypressautomation/tree/main/agents/plugins/llm-application-dev/skills/similarity-search-patterns
Command: npx skills add https://github.com/simplysmartai/5cypressautomation --skill similarity-search-patterns-simplysmartai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pinecone-client, qdrant-client, asyncpg, weaviate-client, sentence-transformers, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently searching and retrieving information from large datasets of vector embeddings, crucial for modern AI applications.

Core Features & Use Cases

  • Vector Database Integration: Provides templates for popular vector databases like Pinecone, Qdrant, pgvector, and Weaviate.
  • Search Optimization: Covers core concepts like distance metrics and index types (Flat, HNSW, IVF+PQ) to optimize retrieval performance.
  • Use Case: Implement a RAG system by using this Skill to find the most relevant document chunks for a user's query, ensuring accurate and fast responses.

Quick Start

Use the similarity-search-patterns skill to implement a Pinecone vector store for semantic search.

Frequently Asked Questions about similarity-search-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement similarity search in a RAG system using vector databases?▼

To implement similarity search for RAG, you can use templates for vector databases like Pinecone, Qdrant, pgvector, and Weaviate to retrieve relevant document chunks for user queries, ensuring accurate and fast responses.

What is the best way to optimize vector search latency for large-scale datasets?▼

Optimizing vector search latency involves configuring distance metrics and index types like Flat, HNSW, or IVF+PQ. These patterns help tune retrieval performance when searching large datasets of vector embeddings.

Can I use sentence-transformers with pgvector and Weaviate for semantic search?▼

Yes, you can use sentence-transformers to generate embeddings and store them in supported databases like pgvector and Weaviate using the provided upserting and searching templates for semantic search.

How do I perform hybrid search combining semantic and keyword retrieval?▼

Hybrid search combines semantic vector searches with keyword filtering. This Skill offers templates to implement hybrid search functionalities across supported vector database integrations like Qdrant and Pinecone.

What distance metrics and index types are supported for vector retrieval?▼

Supported index types include Flat, HNSW, and IVF+PQ. These are combined with configurable distance metrics to optimize vector retrieval performance across various vector database platforms.

How do I upsert and search vector embeddings in Pinecone?▼

You can upsert and search vector embeddings in Pinecone using the provided integration templates. These templates facilitate building semantic search and recommendation engines efficiently.

Related Skills

Explore More Software Engineering Skills→
handsomelong922handsomelong922

sentence-transformers

Generate embeddings for semantic similarity, clustering, and retrieval with PyTorch.

Community
Advanced
h4vzzh4vzz
33

context-optimization

Optimize raw context chunks for AI models within a token budget.

Community
Intermediate
icyzhicyzh
1

pinecone

Manage Pinecone vector indexes, upserts, and similarity queries via the Python client.

Community
Intermediate
MLAI-AUS-IncMLAI-AUS-Inc
4

connect-users

Search community members by expertise using vector embeddings and cosine similarity.

Official
Intermediate