AI for Marketing Ops

RAG Agents Workflow

n8n just leveled up RAG agents with reranking and metadata. No-code AI agent that enhances vector-based document retrieval with Cohere-powered re-ranker and metadata filtering.

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Workflow Overview

This system demonstrates a no-code AI agent that enhances vector-based document retrieval with a Cohere-powered re-ranker and metadata filtering. It uses a Supabase vector database to store chunked data and dynamically applies metadata filters for targeted queries.

Pricing & Implementation

  • • Platform Cost: n8n ($20/month for cloud plan)
  • • Setup Time: 2-3 hours with tutorial
  • • ROI Timeframe: Typically 4-6 weeks

Use our ROI calculator for your specific use case.

Required Tools & Integrations

  • n8n (cloud or self-hosted)
  • Supabase vector database ($25/month)
  • Cohere API for re-ranking ($20/month)
  • Document processing APIs
  • Metadata filtering system

Total monthly cost: ~$65-85 depending on query volume.

Steps

  1. Set up Supabase vector database for document storage
  2. Implement document chunking and metadata extraction
  3. Configure Cohere re-ranker for improved relevance
  4. Set up dynamic metadata filtering for targeted queries
  5. Implement accurate rule-based content extraction
  6. Optimize query performance and result ranking

Business Benefits & ROI

  • Accuracy: Enhanced document retrieval with intelligent re-ranking
  • Precision: Metadata filtering for highly targeted results
  • Performance: Optimized vector search with Supabase
  • Scalability: Handle large document repositories efficiently
  • Rule-based: Accurate extraction of structured content

Video Tutorial Available

Developed by Nate Herk | AI Automation. Free template available via Skool community with complete setup guide.

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Ready to Deploy

Perfect for knowledge management systems, document search applications, and AI-powered content discovery platforms.

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