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pinecone

Create and query serverless vector indexes with hybrid dense and sparse matching.

Updated May 26, 2026
Search Tags:#production#serverless#rag#vector database#hybrid search#namespaces#metadata filtering
One-click install
npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill pinecone-ruiyangruiyi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone
Source: https://github.com/ruiyangruiyi/hermes-agent/tree/main/optional-skills/mlops/pinecone
Command: npx skills add https://github.com/ruiyangruiyi/hermes-agent --skill pinecone-ruiyangruiyi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Pinecone eliminates the effort of operating and scaling a production-grade vector database for semantic search and RAG, so teams can ship fast, reliable retrieval over millions to billions of vectors.

Core Features & Use Cases

  • Fully managed vector database: auto-scaling infrastructure without managing servers.
  • Hybrid search (dense + sparse): combine semantic embeddings with keyword-style sparse signals for better recall.
  • Metadata filtering and namespaces: support scoped retrieval per tenant/project and efficient filtered queries.
  • Use case: Build a production RAG system for a customer support assistant that retrieves relevant answers using hybrid search, filtered by product line and served from isolated namespaces.

Quick Start

Use pinecone to create a serverless index, upsert embedded documents with metadata, and run similarity or hybrid queries with top-k results.

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 production RAG with low-latency retrieval?▼

Hybrid vector search combines dense semantic embeddings with sparse keyword signals to improve recall. You run similarity or hybrid queries by upserting embedded documents with metadata and retrieving top-k results.

Can I scope vector search results to specific tenants using metadata filtering and namespaces?▼

Yes, you can scope vector search results to specific tenants using namespaces and metadata filters. This enables efficient filtered queries and supports isolated retrieval per tenant or project across multi-tenant datasets.

What is hybrid vector search and how does it improve semantic retrieval?▼

Hybrid vector search combines dense semantic embeddings with sparse keyword signals to improve recall. You run similarity or hybrid queries by upserting embedded documents with metadata and retrieving top-k results.

Do I need to manage servers to scale a vector database for millions of vectors?▼

No, you do not need to manage servers to scale a vector database for millions of vectors. Pinecone provides a fully managed, auto-scaling infrastructure that eliminates the effort of operating production-grade vector search.

What are the limitations of using namespaces for multi-tenant vector search?▼

Namespaces support scoped retrieval per tenant or project for multi-tenant datasets, but require a compatible embedding dimension for index creation and upsert/query APIs to maintain low-latency retrieval targets.

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