How does hybrid search work in this skill?

The skill provides code patterns to combine dense vector similarity with sparse vector values (like TF-IDF or BM25) to improve retrieval accuracy.

What is Pinecone used for in AI development?

Pinecone is a managed vector database used to store and query high-dimensional embeddings, which is essential for tasks like RAG, semantic search, and recommendation engines.

Can I use this with frameworks like LangChain or LlamaIndex?

Absolutely; the skill provides specific integration examples for using Pinecone as a vector store within popular AI orchestration frameworks.

Does this skill support Pinecone's serverless architecture?

Yes, it includes implementation patterns for both Serverless and Pod-based index types across major clouds like AWS, GCP, and Azure.

Pinecone Vector Database

Name: Pinecone Vector Database
Author: Activer007

byActiver007

データサイエンスとML

Integrates Pinecone's serverless vector database to power production-grade RAG, semantic search, and recommendation systems.

概要

This Claude Code skill provides comprehensive guidance and implementation patterns for Pinecone, a fully managed vector database designed for high-performance AI applications. It enables Claude to assist with index management, efficient batch vector upserting, complex metadata filtering, and hybrid search (combining dense and sparse vectors). Whether you are building Retrieval-Augmented Generation (RAG) pipelines or large-scale recommendation engines, this skill streamlines the deployment of serverless vector storage with sub-100ms latency and auto-scaling capabilities.

主な機能

Serverless and Pod-based index configuration management
Native integration with LangChain and LlamaIndex
Advanced metadata filtering and namespace partitioning
0 GitHub stars
High-efficiency batch upserting with metadata support
Hybrid search implementation (dense + sparse vectors)

ユースケース

Developing scalable multi-tenant AI applications using namespaces
Building production-ready Retrieval-Augmented Generation (RAG) pipelines
Implementing high-speed semantic search across millions of documents

概要

主な機能

Serverless and Pod-based index configuration management
Native integration with LangChain and LlamaIndex
Advanced metadata filtering and namespace partitioning
0 GitHub stars
High-efficiency batch upserting with metadata support
Hybrid search implementation (dense + sparse vectors)

ユースケース

Developing scalable multi-tenant AI applications using namespaces
Building production-ready Retrieval-Augmented Generation (RAG) pipelines
Implementing high-speed semantic search across millions of documents