Is there a free tier for testing Pinecone?

Yes, Pinecone provides a free tier that includes one serverless index and supports up to 100,000 vectors, making it ideal for prototyping and development.

Does this skill support hybrid search?

Yes, it provides implementation patterns for combining dense and sparse vectors (keyword + semantic) to achieve more accurate search results.

Can I use this with LangChain or LlamaIndex?

Absolutely, the skill includes specific instructions and code patterns for integrating Pinecone with popular orchestration frameworks like LangChain and LlamaIndex.

Pinecone is a fully managed, cloud-native vector database designed for high-performance AI applications like semantic search and Retrieval-Augmented Generation (RAG).

How does Pinecone handle scaling?

Pinecone offers a serverless architecture that automatically scales to handle billions of vectors while maintaining low latency, typically under 100ms.

Pinecone Vector Database Integration

Name: Pinecone Vector Database Integration
Author: Microck

byMicrock

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Database Management

Simplifies implementation and management of Pinecone, a serverless vector database designed for high-performance AI applications and RAG systems.

About

This skill equips Claude with specialized knowledge to architect, deploy, and manage Pinecone vector databases within your application stack. It provides comprehensive guidance on index configuration for both serverless and pod-based environments, optimized batch upserting strategies, and complex metadata filtering. Designed for developers building production-grade Retrieval-Augmented Generation (RAG) systems, it covers advanced implementation patterns including hybrid search (combining dense and sparse vectors), namespace isolation for multi-tenancy, and seamless integrations with popular frameworks like LangChain and LlamaIndex.

Key Features

Namespace management for multi-tenant data isolation and organization
Advanced query implementation including hybrid search and metadata filtering
Pre-configured integration patterns for LangChain and LlamaIndex
Automated index management for serverless and pod-based deployments
81 GitHub stars
Optimized vector upsertion patterns with batching and metadata support

Use Cases

Implementing large-scale semantic search and real-time recommendation systems
Managing multi-tenant AI applications using namespaces and metadata filters
Building production-grade Retrieval-Augmented Generation (RAG) pipelines

About

Key Features

Namespace management for multi-tenant data isolation and organization
Advanced query implementation including hybrid search and metadata filtering
Pre-configured integration patterns for LangChain and LlamaIndex
Automated index management for serverless and pod-based deployments
81 GitHub stars
Optimized vector upsertion patterns with batching and metadata support

Use Cases

Implementing large-scale semantic search and real-time recommendation systems
Managing multi-tenant AI applications using namespaces and metadata filters
Building production-grade Retrieval-Augmented Generation (RAG) pipelines