Скачать или смотреть This One Fix Made Our RAG Agents 10x Better (n8n)

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Chapters:
0:00 - Overview
0:11 - Chunking
1:17 - Correctly Configuring the Splitter
2:34 - Configuring in Advanced RAG Pipelines
3:10 - Handling Google Docs
3:43 - Converting PDFs to Markdown
4:02 - Processing HTML
4:14 - Troubleshooting Markdown
4:48 - Agent in Action

In this video, I'll show you a tiny fix for your RAG agents in n8n that can make them significantly more effective. This simple change might only take 10 seconds, but it's a detail that many advanced users might have missed.

We start by revisiting how traditional RAG pipelines chunk data—typically by characters with some overlap—and how this can result in out-of-context or poorly structured segments. You’ll learn why splitting documents by markdown headings can offer much more coherent chunks in your vector database. A quick walkthrough demonstrates how to ensure the markdown splitter is actually enabled in the recursive character splitter, highlighting the misleading UI default that can trip up even advanced users.

The video then shows how to correctly convert content into markdown before embedding it, covering sources like HTTP responses, Google Docs, PDFs, and HTML. We explore nodes that make this possible, like HTML-to-Markdown, code-based conversion for Docs, and OCR tools like Mistral for PDFs. Special attention is given to preserving markdown structure throughout the pipeline to prevent malformed chunks.

We wrap up with a real-world RAG agent example. The agent accurately retrieves context-rich results thanks to proper markdown chunking—showing the value of this approach.