Does this skill support hybrid search?

Yes, it includes implementation patterns for hybrid search, allowing you to combine dense vectors and sparse vectors for improved search accuracy.

Can I use this with LangChain or LlamaIndex?

Absolutely. The skill provides specific code patterns and configurations for integrating Pinecone with popular AI orchestration frameworks like LangChain and LlamaIndex.

How does metadata filtering work in this skill?

The skill provides guidance on using logical operators ($and, $or) and comparison operators ($eq, $gt, $in) to filter search results based on your vector metadata.

What is the Pinecone Claude Code skill used for?

It is used to integrate Pinecone's managed vector database capabilities, such as indexing and querying embeddings, into AI development workflows for building RAG and search applications.

Is Pinecone suitable for production environments?

Yes, Pinecone is built for production, offering p95 latency under 100ms, auto-scaling capabilities, and 99.9% uptime SLAs for enterprise-grade applications.

Pinecone Vector Database Integration

Name: Pinecone Vector Database Integration
Author: Activer007

byActiver007

データサイエンスとML

Integrates Pinecone's managed vector database into your AI workflows to enable high-performance RAG, semantic search, and recommendation systems.

概要

This skill provides a comprehensive toolkit for managing Pinecone vector databases directly within your Claude Code environment, facilitating the development of production-grade AI applications. It streamlines the lifecycle of vector data, from index creation and serverless scaling to advanced querying with hybrid search (dense + sparse) and metadata filtering. By offering domain-specific guidance on performance optimization and framework integrations like LangChain and LlamaIndex, this skill helps developers implement low-latency retrieval systems capable of scaling to billions of vectors without managing underlying infrastructure.

主な機能

Granular metadata filtering using logical and comparison operators
Advanced hybrid search capabilities combining semantic and keyword relevance
Comprehensive index management for serverless and pod-based architectures
0 GitHub stars
Optimized batch upserting with rich metadata and namespace support
Native integration patterns for LangChain and LlamaIndex frameworks

ユースケース

Building production-ready Retrieval-Augmented Generation (RAG) systems
Implementing high-scale semantic search for document and content discovery
Developing real-time recommendation engines based on vector similarity

概要

主な機能

Granular metadata filtering using logical and comparison operators
Advanced hybrid search capabilities combining semantic and keyword relevance
Comprehensive index management for serverless and pod-based architectures
0 GitHub stars
Optimized batch upserting with rich metadata and namespace support
Native integration patterns for LangChain and LlamaIndex frameworks

ユースケース

Building production-ready Retrieval-Augmented Generation (RAG) systems
Implementing high-scale semantic search for document and content discovery
Developing real-time recommendation engines based on vector similarity