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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.
Last year, I set a goal for myself to build more demos in Python as a way to learn the language. I focused mostly on building, but not really on building efficiently. I didn't take the time to learn the tooling ecosystem beyond what was installed by default