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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 #NYTECHWEEK 2025 Retrieval-Augmented Generation (RAG) Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database | Pinecone Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale | Pinecone Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone | Pinecone Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness Vector databases aren't just for large-scale enterprise AI Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval | Pinecone Fast and Effective Early Termination for Simple Ranking Functions | Pinecone Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone | Pinecone Using Pinecone asynchronously with FastAPI A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings | Pinecone Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone Tool up: Pinecone’s first MCP servers are here Add context to your agent with Pinecone Assistant MCP remote server E2Rank: Efficient and Effective Layer-wise Reranking | Pinecone ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring | Pinecone Efficient Constant-Space Multi-Vector Retrieval | Pinecone How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses | Pinecone Pinecone Named to Fast Company's Annual List of the World's Most Innovative Companies of 2025 Launch Week: Pinecone for agents, search, recommendations, and more Optimizing Pinecone for agents (and more) Retrieval Inference for scale and performance How 1up Turns Sales Reps Into Product Experts with Pinecone | Pinecone Don’t be dense: Launching sparse indexes in Pinecone Unlock High-Precision Keyword Search with pinecone-sparse-english-v0 Evolving Pinecone's architecture to meet the demands of Knowledgeable AI Pinpoint references faster with citation highlights in Pinecone Assistant Bringing the leading vector database to your cloud Getting started with llama-text-embed-v2 Natural Language Counterfactual Explanations for Graphs Using Large Language Models | Pinecone Easily build knowledgeable chat and agent-based applications in minutes with Pinecone Assistant, now generally available How to build an agentic, chat or RAG knowledge system using Pinecone Assistant Real-time RAG with Pinecone and Estuary Flow BigQuery to Pinecone in Real-Time with Estuary Flow Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference | Pinecone Accelerate prototyping and development with Pinecone Local First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Strengthening security and increasing control with CMEK and API key roles Introducing Pinecone Rerank V0 Introducing cascading retrieval: Unifying dense and sparse with reranking From Idea to Action: How Pinecone Assistant Meaningfully Accelerates AI Business Building AI apps on Azure with Pinecone just got a lot easier Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone Four features of the Assistant API you aren't using - but should Deploying Pinecone with Infrastructure as Code (IaC) Streamlining CI/CD with Pinecone Local September 2024 Product Update Results of the Big ANN: NeurIPS'23 competition | Pinecone Introducing import from object storage for more efficient data transfer to Pinecone serverless Simplify, enhance, and evaluate RAG development with Pinecone Assistant, now in public preview Vectors and Graphs: Better Together August 2024 Product Update Pinecone Helps Deep Talk Deliver World-Class AI Assistants with Lower Engineering Overhead | Pinecone Assembled Delivers Better, Faster AI- Driven Support with Pinecone | Pinecone Llama 3.1 Agent using LangGraph and Ollama Build knowledgeable AI with Pinecone serverless, now generally available on Microsoft Azure Pinecone serverless is now generally available on Google Cloud, adding knowledge to AI assistants and other applications Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI Bridging Dense and Sparse Maximum Inner Product Search | Pinecone Refine Retrieval Quality with Pinecone Rerank Introducing reranking to Pinecone Inference to simplify building accurate AI July 2024 Product Update Connect to Pinecone within your platform to enable a seamless AI development experience Introducing Pinecone API Versioning RAG Brag with Inkeep Co-Founder Nick Gomez LangGraph and Research Agents Introducing Pinecone Inference to streamline your AI workflow Build Privacy-aware AI software using Pinecone
Use the Pinecone Plugin for Claude Code to develop AI Applications Faster
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!