pinecone

Store, index, and search embeddings in a managed vector database.

Updated Apr 16, 2026

Search Tags:#serverless #rag #pinecone #semantic-search #vector-database #hybrid-search

Ready to run

npx skills add https://github.com/jacardl/New-Radar --skill pinecone-jacardl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pinecone
Source: https://github.com/jacardl/New-Radar/tree/main/backend/frameworks/hermes-agent/optional-skills/mlops/pinecone
Command: npx skills add https://github.com/jacardl/New-Radar --skill pinecone-jacardl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Pinecone provides a managed, scalable vector database for production AI applications, enabling teams to store, index, and search embeddings with low latency without managing infrastructure.

Core Features & Use Cases

Hybrid search with dense and sparse vectors, metadata filtering, and namespaces to support multi-tenant data.
Serverless options for auto-scaling and reduced operational overhead, suitable for production RAG, semantic search, and recommendations.
Use cases include building scalable semantic search engines, real-time recommendations, and knowledge-grounded assistants.

Quick Start

Install pinecone-client and initialize a serverless index to start storing and querying vectors.

Frequently Asked Questions about pinecone

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

FAQPage Schema

How do I build a scalable vector database for production RAG workflows?▼

Pinecone solves this by providing a serverless vector database for RAG workflows that stores and searches embeddings with low latency and auto-scaling. It eliminates infrastructure management while supporting metadata filtering and namespaces.

What is hybrid search and how does it work with dense and sparse vectors?▼

Hybrid search combines dense and sparse vectors to improve retrieval accuracy for semantic search applications. It supports both semantic similarity and exact keyword matching, along with metadata filtering and namespaces for multi-tenant data.

How do I set up a serverless vector index for semantic search?▼

You can set up a serverless vector index for semantic search by installing the client library and initializing an index. This provides a managed environment to store and query vectors with auto-scaling and reduced operational overhead.

Does this vector database support multi-tenant data isolation?▼

Yes, multi-tenant data isolation is supported through namespaces. Namespaces allow you to partition data within a serverless index, enabling secure and organized metadata filtering for different tenants.

When should I use a managed serverless vector database instead of self-hosting?▼

You should use a managed serverless vector database when low latency and auto-scaling are critical for production AI applications. It is ideal for large-scale semantic search and recommendations where you want zero-ops infrastructure management.