n8n intermediate course Module 2: Embeddings & Vector DBs in n8n (Supabase + Gemini)

Stop keyword search—build semantic search. In this module, we turn text into embeddings and store them in a vector database so your AI Agent can answer with meaning, not just matching words. 🚀

What you’ll learn:
- Embeddings 101: how vectors capture meaning
- Supabase + pgvector setup (copy‑paste SQL included)
- n8n pipeline: Google Drive ➜ Gemini embeddings ➜ Supabase
- Query via an AI Agent using semantic search/RAG

Perfect for automation builders who want fast, accurate answers from their knowledge base. Works with OpenAI or Gemini embedding models—set vector size to your model’s dimensions.

If this helped, drop a like, comment your questions, and subscribe for Module 3! 🔔

📥Resources Download: https://buymeacoffee.com/lakwatseros/e/455000

▶️ Related Videos/Playlist:
- https://www.youtube.com/playlist?list=PLdCUjRNpeV7_HBKyJ5KhvG_H9kYCsNklz
- https://youtu.be/L38B05ILhZ0

Links:
📚 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

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#n8n #Supabase #pgvector #VectorDatabase #Embeddings #RAG #SemanticSearch #AIWorkflow #GeminiAI #Automation