惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Apple Machine Learning Research
Apple Machine Learning Research
Last Week in AI
Last Week in AI
Blog — PlanetScale
Blog — PlanetScale
V
Visual Studio Blog
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
博客园 - Franky
IT之家
IT之家
博客园 - 叶小钗
Engineering at Meta
Engineering at Meta
The GitHub Blog
The GitHub Blog
雷峰网
雷峰网
腾讯CDC
博客园 - 聂微东
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
V2EX
人人都是产品经理
人人都是产品经理
MongoDB | Blog
MongoDB | Blog
大猫的无限游戏
大猫的无限游戏
Martin Fowler
Martin Fowler
宝玉的分享
宝玉的分享
博客园_首页
G
Google Developers Blog

Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone Build more knowledgeable AI applications with new LLMs and greater control in Pinecone Assistant
Use the Pinecone Plugin for Claude Code to develop AI App...
Arjun Patel · 2026-02-12 · via Pinecone

Building apps with Pinecone and Claude Code just got way easier. We've launched the official Pinecone Plugin for Claude Code—now available in the Anthropic Claude Code Plugin Marketplace.

This plugin brings Pinecone's vector database and managed RAG service directly into your development workflow, alongside preset skills, slash commands, MCP, and other handy shortcuts to get started building with Claude Code, faster.

Search and manage indexes, query Assistants, and build intelligent applications—all without leaving Claude Code.

  • Natural language commands: Just tell Claude what you want, like "search my index for machine learning docs" or "create an assistant from my research-docs folder"
  • Explicit commands: Need precision? Use commands like /pinecone:query or /pinecone:assistant-chat for explicit access to integrated tooling
  • Complete vector database toolkit: Create indexes, upsert vectors, search with metadata filters, get statistics—everything you need to manage your vector data.
  • Generate code for Pinecone: use /pinecone:quickstart to learn how to build with Pinecone, and setup your development environment
  • Managed RAG with Pinecone Assistant: Upload documents, sync changes, and get cited answers with page numbers. No custom chunking or embedding pipeline required.

Getting Started in 60 Seconds

Install the Pinecone Plugin easily from Anthropic's Claude Code marketplace

1. Set your API key

Add your Pinecone API key as an environment variable:

export PINECONE_API_KEY=your-api-key-here

2. Install the plugin in Claude Code

claude plugin install pinecone

3. Start building

Restart Claude Code, then ask Claude to use Pinecone:

  • Claude, list my Pinecone indexes
  • Build a Pinecone Assistant from the pdfs in my local folder, and then suggest some great queries to retrieve them

Or use a slash command for semantic search:

/pinecone:query query "your query here" index your-index-name

That's it. You're ready to build!

Work with Pinecone Vector Database and Pinecone Assistant all through Claude Code

Vector Search and Index Management

Work with your vector data using natural language or explicit commands:

  • Search your indexes: Run semantic searches with /pinecone:query or just ask Claude to "search my index for X"
  • Manage indexes: List, create, and describe indexes using natural language or MCP tools
  • Insert and update vectors: Use upsert-records to add data to your indexes
  • Advanced filtering: Search with metadata filters and rerank results for better relevance
  • Get insights: Check index statistics including record counts and namespace details
Note: The /pinecone:query command works only with integrated indexes using Pinecone's hosted embedding models. For third-party embeddings (OpenAI, HuggingFace, etc.), you'll need to generate scripts instead

Managed RAG with Pinecone Assistant

Pinecone Assistant handles the entire RAG pipeline—chunking, embedding, retrieval, and citation—so you can focus on building your application. Remember, that you can invoke any of these commands just by asking Claude Code too!

  • Create an assistant
    • /pinecone:assistant-create --name product-docs-assistant
  • Upload your documents
    • Upload files or entire directories (PDF, Markdown, TXT, DOCX, JSON):
    • /pinecone:assistant-upload --assistant product-docs-assistant --source ./documentation
  • Keep docs in sync
    • Only upload new or changed files:
    • /pinecone:assistant-sync --assistant product-docs-assistant --source ./documentation
  • Get cited answers
    • /pinecone:assistant-chat --assistant product-docs-assistant --message "How do I configure authentication?"
  • Retrieve context for custom workflows
    • Get relevant snippets without a full chat response:
    • /pinecone:assistant-context --assistant product-docs-assistant --query "rate limiting"

The plugin remembers your last assistant, so you can use natural language for follow-ups: "Ask my assistant about API endpoints."

Important: Assistant commands require uv to be installed. Run uv --version to check, or see our troubleshooting guide below.

And, you can infinitely compose any of these capabilities to build better search and RAG experiences with Pinecone. Such as:

  • Upload my research-papers to an assistant, then generate five queries that retrieve those documents well
  • Search my support-tickets index for urgent customer issues, and rerank the top results
  • Create an assistant from my legal-contracts folder, then ask it to find clauses related to termination rights and explain the key provisions

Get Started Now

Install the Pinecone Plugin and start building context-aware applications:

claude plugin install pinecone

Happy building!