pinecone

Manage serverless vector databases with hybrid search and metadata filtering.

Updated Mar 8, 2026

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

Ready to run

npx skills add https://github.com/helix4u/hermes-agent-private --skill pinecone-helix4u

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pinecone
Source: https://github.com/helix4u/hermes-agent-private/tree/main/skills/mlops/pinecone
Command: npx skills add https://github.com/helix4u/hermes-agent-private --skill pinecone-helix4u

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 optimized for production AI applications, enabling efficient storage and retrieval of high-dimensional data.

Core Features & Use Cases

Managed Vector Database: Serverless, auto-scaling infrastructure for vector data.
Hybrid Search: Combines dense and sparse vector search for improved accuracy.
Low Latency: Achieves p95 latency under 100ms for critical applications.
Use Case: Deploy a production-ready RAG system by storing and querying document embeddings with Pinecone, ensuring fast and relevant search results.

Quick Start

Use the pinecone skill to create a new index named 'my-embeddings' with a dimension of 1536.

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 managed vector database for a production RAG application?▼

To set up a managed vector database for RAG, create a new index with your embedding dimensions, upsert document vectors, and query using metadata filtering. This Skill handles serverless deployment on AWS, GCP, or Azure with auto-scaling infrastructure.

Does Pinecone support hybrid search for combined dense and sparse vectors?▼

Yes, Pinecone supports hybrid search by combining dense and sparse vector search capabilities. This mechanism improves retrieval accuracy for production AI applications while maintaining low p95 latency under 100ms.

Can I use this vector database with LangChain and LlamaIndex frameworks?▼

Yes, you can use this vector database with LangChain and LlamaIndex. It integrates seamlessly with both frameworks to support application development, enabling efficient storage and retrieval of high-dimensional data for AI workflows.

How do I query document embeddings and filter by metadata in a vector database?▼

You query document embeddings and filter by metadata by creating an index, upserting vectors with metadata, and querying within specific namespaces. This Skill provides scripts to manage these operations with low latency.

What is the best way to deploy a serverless vector database on AWS for AI workloads?▼

The best way to deploy a serverless vector database on AWS for AI workloads is using this Skill's managed auto-scaling infrastructure. It creates, upserts, and queries high-dimensional data with p95 latency under 100ms without infrastructure management.