#46 Max: Make Your n8n RAG Agents 10x Smarter with Reranking & Metadata

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Tired of your n8n RAG agent confidently giving you wrong or irrelevant answers? 😫 The problem is that basic vector search isn't smart enough. We're revealing two pro techniques that will make your AI agents 10x smarter.

We’ll talk about:

• A deep dive into two powerful techniques—Reranking and Metadata Filtering—to fix failing RAG agents in n8n.
• How Vector Reranking acts as a quality control manager, ensuring your AI gets the most contextually relevant information, not just mathematically similar results.
• A step-by-step guide to implementing Cohere's powerful reranker model directly within n8n's Supabase Vector Store node.
• The power of Metadata Filtering for "surgical precision," and how to use AI to automatically structure and tag your documents for better retrieval.
• The complete data preparation workflow: from processing a PDF to creating a structured, metadata-rich knowledge base in Supabase.

Keywords: n8n, RAG, Retrieval-Augmented Generation, AI Agents, Reranking, Cohere, Metadata, Supabase, Vector Database, OpenAI, PDF Parsing, AI Data Processing, n8n tutorial

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