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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 Use the Pinecone Plugin for Claude Code to develop AI Applications Faster 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 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
Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge
Lea Wang-Tomic, Jenna Pederson · 2026-01-28 · via Pinecone

Special offer for n8n users: Build with the Assistant node before May 1, 2026 to get a discount when upgrading to Pinecone's Standard plan. Add the Pinecone Assistant node and connect to Pinecone in n8n to claim this offer.

Introducing the Pinecone Assistant node in n8n

Connect Pinecone Assistant to any n8n integration and start building knowledgeable AI workflows that understand your data.

Pinecone Assistant n8n node. Turn any data source into knowledgable AI workflows.

What is Pinecone Assistant?

Pinecone Assistant delivers trusted knowledge for RAG and agentic workflows. Focus on building AI applications instead of piecing together pipelines and managing infrastructure.

Upload files (PDFs, docx, text, JSON, Markdown) and start querying immediately. The entire pipeline is handled automatically: chunking, embedding, vector search, query planning, and reranking.

The official Pinecone Assistant n8n node brings Assistant's end-to-end RAG capabilities directly into n8n workflows, letting you connect any data source to AI-backed automation.

What the node does:

  • List and select assistants dynamically
  • Manage files: upload with metadata, update content and metadata, and delete
  • Retrieve context snippets with advanced metadata filtering

Drag, drop, and connect

Just add the Assistant node to your workflow and connect it to any data source (e.g., Google Drive docs, Slack messages, webhooks). Assistant automatically handles chunking, embedding, and reranking for you.

Transform data into trusted knowledge

Turn any n8n data source into queryable knowledge that delivers accurate, grounded context for AI applications.

Example Workflows

→ Chat with your Google Drive documents

Connect your Google Drive and chat with your documents in natural language using this workflow.

→ Auto-answer Slack questions

Monitor Slack channels and automatically respond with relevant documentation from your Dropbox knowledge base using this workflow.

→ Extract insights from LinkedIn comments

Use Apify and Pinecone Assistant to analyze and extract insights from LinkedIn post comments using this workflow.

Pinecone Assistant node vs. Pinecone Vector Store node

The Pinecone Vector Store node is available already in n8n, giving you direct access to Pinecone's vector database. The Pinecone Vector Store node is ideal when you need full control over your RAG pipeline—choosing your own embedding models, customizing chunking strategies, and building custom workflows.

The new Pinecone Assistant node offers a different approach: a fully managed RAG service that handles the entire pipeline for you. Assistant automatically handles query planning and semantic search to retrieve the most relevant results. Instead of configuring embeddings, chunking parameters, and reranking logic, you get production-ready RAG with a single node.

Key differences:

  • End-to-end vs. DIY: Single node handles entire pipeline with production-ready accuracy vs. separate nodes for each step
  • One key vs. many: One Pinecone API key vs. multiple API keys (vector database + embedding + reranker)
  • Automatic file management: Direct upload (PDF, DOCX, TXT, JSON, MD) and intelligent processing vs. manual vector operations

Use Pinecone Assistant node to: Build AI workflows with trusted, production-ready knowledge retrieval. Minimal setup, zero configuration, maximum accuracy.

Use Pinecone Vector Store node for: Full control over embedding models, chunking strategies, search approach (ie. semantic, hybrid), and custom pipelines when you need granular customization.

Get Started

Sign up for Pinecone

Note: n8n users can access an exclusive Standard plan trial with bonus credits through March 31st. See details below.

Build Your First Workflow

Follow our complete quickstart guide to:

  • Install the Pinecone Assistant node in n8n
  • Get your API keys
  • Create your first assistant
  • Import a ready-to-use workflow template
  • Start chatting with your documents

Limited-Time Offer for n8n Users

Sign up for Pinecone's Standard plan trial before May 1st and receive:

  • Standard free trial (3 weeks, $300 credits) to explore all features
  • $40/month in credits for 6 months (starting after your trial ends)
  • Waived Assistant hourly fees

Offer details: Start using the n8n Assistant node and claim this promotion before May 1, 2026. Upgrade to a paid Standard plan before July 1st, 2026 to activate your 6-month benefits.