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When building RAG workflows in n8n, you have two primary options for working with Pinecone: the Pinecone Assistant node and the Pinecone Vector Store node.
An n8n workflow using multiple, specialized knowledge bases because sometimes you need knowledge isolation between clients, locations, or even products in your RAG workflows.
The new n8n community node for Pinecone Assistant handles the entire RAG pipeline in a single node so you don't have to think about file storage, chunking your data, creating embeddings, query planning, vector search, and reranking.
I've been working to build out resources for people to learn more about retrieval-augmented generation (RAG) — and to learn more about it myself. This post covers what RAG is, what I found and suggestions for how to tackle the resources.
In this blog post, I'll show you how to avoid the IDE-docs-console-app shuffle by adding the Pinecone MCP server to your IDE. You'll be able to search the official Pinecone docs and create and manage indexes without flipping back and forth between apps.