Stop guessing your RAG results—rank them right. In Module 3 of the n8n intermediate course, we enrich your vector database with metadata and add Cohere reranking so the most relevant chunks rise to the top. 🔎
You’ll learn to:
- Add metadata for precise filtering (year=2025, category=Services, type=Business Forms)
- Query with metadata in n8n and pass dynamic filters from an AI node ⚙️
- Rerank results with Cohere so similarity ≠ relevance anymore 🧠
What we cover:
1) Why metadata matters: embeddings capture meaning, metadata gives structure for clean filters.
2) Adding metadata: Default Data Loader → Options → Add Option → Metadata → Add property (Name, Value). Need to scrape site content into your vector store? Try firecrawl to automate it. 🚀
3) Querying with metadata: In Vector Store, turn on Include Metadata → set Metadata Filter (Name, Value). No AI input for Value? Use an AI node before Vector Store to decide the filter and pass it forward.
4) Why rerankers: they reorder candidates so the best answer wins (e.g., “pricing table” beats “discount policies”).
5) Adding a reranker: Enable Rerank Results, increase Limit, add the Cohere node, enter API key, choose model, set Top N to send to your AI.
Key takeaways:
- Metadata makes retrieval targeted and reliable.
- Rerankers boost accuracy by reordering similarity matches.
- Together, they supercharge your n8n RAG workflows. ✅
If this helped, like/save and drop your questions below. Share with a builder working on RAG in n8n! 🙌
📥 Download the workflow here: https://buymeacoffee.com/lakwatseros/e/461695
📚 Free n8n course: In the community Guides section
🤝 Join the community: https://www.facebook.com/share/g/1FHSvHmJzt/
🗓️ Book a 1:1 session: https://forms.gle/SZPLGfntX58qmnoJ9
🤖 Try n8n (affiliate): https://n8n.partnerlinks.io/mcfxblk63tm3
🔥 Try firecrawl: https://firecrawl.link/lakwatseros
🌐 All tools & links: https://www.lakwatseros.com/home
#n8n #RAG #VectorDatabase #Metadata #Rerankers #Cohere #SemanticSearch #AIWorkflows #Automation #Embeddings