The One RAG Method for Incredibly Accurate Responses (n8n)

Описание: 👉 Upgrade your n8n AI Agents with our Advanced RAG workflows https://www.theaiautomators.com/?utm_...

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Chapters:
0:00 - Overview
1:38 - Demo
2:39 - Dynamic Filter Creation
3:15 - Dynamic JSON Schema
3:42 - Hybrid Search & Reranking
4:52 - Supabase Metadata Ingestion
8:55 - Supabase Metadata Filtering
12:12 - Supabase Edge Function
15:36 - Pinecone Metadata Ingestion
19:17 - Pinecone Metadata Filtering

Discover how to take your RAG agents to the next level by implementing dynamic metadata filtering for vastly improved retrieval accuracy. In this video, we walk through how to build intelligent agents that enrich and filter your vector data on the fly using both Supabase and Pinecone, with advanced workflows that go far beyond the default n8n nodes.

You'll learn how to automatically enrich documents during the ingestion phase with metadata like document date, department, and category. See how this metadata is used in real-time to construct highly targeted filters based on user queries—ensuring only the most relevant data is retrieved.

We explore how to set up metadata tables in Supabase, auto-generate filters from user questions using JSON schema and structured output parsers, and enable hybrid search with dense and sparse embeddings. The video also demonstrates using Cohere for re-ranking search results and how to implement these same filtering techniques in Pinecone, including Unix timestamp handling for date filtering.

Whether you're working with company policies, product categories, or time-sensitive documents, this video equips you with the tools to significantly increase your RAG agent’s precision and reliability.

If you're serious about building scalable, high-performing RAG pipelines with advanced filtering and hybrid search, this video is packed with everything you need.