Build a Customer Assistant AI with Multi-Domain RAG, n8n & Pinecone
Pinecone's Developer Relations team show you how to build a live n8n workflow that routes rental property guest queries to the right knowledge base automatically.
Jenna Pederson, Roie Schwaber-Cohen
無料アカウントを作成する
練習とトラックであなたの進捗を記録しましょう。
Googleで登録続行すると、次に同意したものとみなされます: 利用規約、 プライバシーポリシー 、およびデータが米国内に保管されることに同意したものとみなされます。関連
チュートリアル
Building Intelligent Applications with Pinecone Canopy: A Beginner's Guide
Explore using Canopy as an open-source Retrieval Augmented Generation (RAG) framework and context built on top of the Pinecone vector database.
チュートリアル
n8n: A Guide With Practical Examples
Learn how to use n8n to build AI agents that automate email processing and create a retrieval-augmented generation (RAG) agent for document question answering.
チュートリアル
Agentic RAG: Step-by-Step Tutorial With Demo Project
Learn how to build an Agentic RAG pipeline from scratch, integrating local data sources and web scraping to generate context-aware responses to user queries.



Session Resources
Confidently wrong is worse than just wrong. When your AI searches one giant knowledge base for everything, it finds something—and serves it up like it's correct. That's not a model problem. That's a knowledge architecture problem.
In this hands-on session, Jenna Pederson and Roie Schwaber-Cohen from Pinecone's Developer Relations team, show you how to build a live n8n workflow that routes rental property guest queries to the right knowledge base automatically. It's a concrete demo of a pattern that applies anywhere you have different users or steps in a workflow that need different context.
Key Takeaways