pinecone

Manage and query vector embeddings with the Pinecone vector database.

3|Updated Feb 21, 2026

Search Tags:#semantic search #vector database #RAG #managed service #AI applications #pinecone

Ready to run

npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill pinecone-ihatesea69

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pinecone
Source: https://github.com/ihatesea69/HieuNghi-AI-Skills/tree/main/airesearch_skills/15-rag/pinecone
Command: npx skills add https://github.com/ihatesea69/HieuNghi-AI-Skills --skill pinecone-ihatesea69

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pinecone-client, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a fully managed, auto-scaling vector database solution designed for production AI applications, simplifying the deployment and management of large-scale similarity search and RAG systems.

Core Features & Use Cases

Managed Vector Database: Offers a serverless, auto-scaling infrastructure for storing and querying vector embeddings.
Hybrid Search: Supports both dense and sparse vector search for improved retrieval accuracy.
Metadata Filtering: Allows precise filtering of search results based on associated metadata.
Use Case: Integrate Pinecone into your RAG pipeline to efficiently store and retrieve document embeddings, enabling context-aware responses for your AI assistant.

Quick Start

Use the pinecone skill to create a new index named 'my-rag-index' with a dimension of 1536 and cosine similarity.

Frequently Asked Questions about pinecone

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

FAQPage Schema

How do I set up a vector database for semantic search in a RAG pipeline?▼

To set up a vector database for semantic search in a RAG pipeline, create a Pinecone index with a specified dimension and cosine similarity. This managed service handles auto-scaling and stores vector embeddings for retrieval.

What is hybrid search and does Pinecone support it for AI applications?▼

Hybrid search combines dense and sparse vectors to improve retrieval accuracy. Pinecone supports hybrid search natively, allowing production AI applications to perform more precise similarity searches.

Can I filter vector search results by metadata in a managed vector database?▼

Yes, you can filter vector search results by metadata. Pinecone supports metadata filtering, allowing you to precisely narrow down similarity search results based on associated attributes.

What is the best way to scale vector embeddings for production AI applications?▼

The best way to scale vector embeddings for production AI applications is using a serverless, auto-scaling managed vector database. Pinecone provides this infrastructure, ensuring low latency for large-scale RAG systems.

Do I need pinecone-client to query vector embeddings?▼

Yes, you need the pinecone-client dependency to manage and query vector embeddings. It facilitates interaction with the Pinecone managed vector database service for your RAG and recommendation systems.