OpenAI Embeddings → Supabase

Hi! I’m Amanda, a creator of intelligent automations using n8n and Make. I’ve been building AI-powered workflows for over 2 years, always focused on usability and innovation. This one here is very spe

• Create a Google Drive folder to watch. • Connect your Google Drive account in n8n and authorize access. • Point the Google Drive Trigger node to this folder (new/modified files trigger the flow).

Chat with docs - 5minAI New version. Uses httpRequest, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Event-driven trigger; 62 nodes.

I prepared a detailed guide that illustrates the entire process of building an AI agent using Supabase and Google Drive within N8N workflows.

YouTube Agent. Uses supabase, agent, lmChatAnthropic, outputParserStructured. Webhook trigger; 56 nodes.

Wordpress Ai Chatbot To Enhance User Experience With Supabase And Openai. Uses manualTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter. Event-driven trigger; 53 nodes.

RAG & GenAI App With WordPress Content. Uses manualTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter. Event-driven trigger; 53 nodes.

RAG & GenAI App With WordPress Content. Uses manualTrigger, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter. Event-driven trigger; 53 nodes.

OIL Rag. Uses lmChatOpenAi, embeddingsOpenAi, agent, telegramTrigger. Event-driven trigger; 53 nodes.

Turn your docs into an AI-powered internal or public-facing assistant. This chatbot workflow uses RAG (Retrieval-Augmented Generation) with Supabase vector search to answer employee or customer questi

Telegram dummy_client. Uses telegramTrigger, agent, lmChatOpenAi, telegram. Event-driven trigger; 48 nodes.

This n8n workflow automates the process of ingesting files from Google Drive into a Supabase database, preparing them for a knowledge base system. It supports text-based files (PDF, DOCX, TXT, etc.) a

Ultimate Agentic RAG AI Agent Template. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, stickyNote. Event-driven trigger; 42 nodes.

This template crawls a website from its sitemap, deduplicates URLs in Supabase, scrapes pages with Crawl4AI, cleans and validates the text, then stores content + metadata in a Supabase vector store us

📺 Full walkthrough video: https://youtu.be/r5kN_la0O7I

🛠️ How It Works: System Architecture Workflow ini bekerja melalui empat lapisan proses utama yang terintegrasi secara otomatis: Input Processing & Routing Telegram Trigger: Menangkap setiap pesan masu

This RAG workflow allows you to build a smart chat assistant that can answer user questions based on any collection of documents you provide. It automatically imports and processes files from Google D

Indoor Farming Agent. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Webhook trigger; 36 nodes.

This workflow creates a conversational assistant that can answer questions based on your Google Drive documents. It automatically processes various file types and uses Retrieval-Augmented Generation (

Upsert Huge Documents In A Vector Store With Supabase And Notion. Uses embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa. Chat trigger; 34 nodes.

RAG on living data. Uses embeddingsOpenAi, textSplitterTokenSplitter, splitInBatches, chainRetrievalQa. Chat trigger; 34 nodes.

This workflow adds the capability to build a RAG on living data. In this case Notion is used as a Knowledge Base. Whenever a page is updated, the embeddings get upserted in a Supabase Vector Store.

Ai Agent To Chat With Files In Supabase Storage. Uses httpRequest, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, extractFromFile. Event-driven trigger; 33 nodes.

Ai Agent To Chat With Files In Supabase Storage. Uses httpRequest, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, extractFromFile. Event-driven trigger; 33 nodes.