AutomationFlows › Recipes › Supabase → Supabase Vector Store

Supabase → Supabase Vector Store

When you need Supabase and Supabase Vector Store talking to each other, here are the 79 n8n workflows in the catalog that already do it. Each is integration-tagged and privacy-stripped — copy the JSON and import.

Workflows that pair Supabase with Supabase Vector Store

AI & RAG
Bread-meat-delivery

Bread-Meat-Delivery. Uses lmChatOpenAi, agent, httpRequest, redis. Webhook trigger; 91 nodes.

OpenAI Chat, Agent, HTTP Request +14
AI & RAG
My Workflow 14 (supabase)

My workflow 14. Uses supabase, redis, openAi, googleCalendarTool. Webhook trigger; 91 nodes.

Supabase, Redis, OpenAI +13
AI & RAG
AI Personal Assistant with Gpt-4o, RAG & Voice for Whatsapp Using 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

OpenAI Chat, Redis, OpenAI +11
AI & RAG
Google Drive to Supabase Contextual Vector Database Sync for RAG Applications

• 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).

Agent, Chat Trigger, Memory Buffer Window +14
AI & RAG
Search Worflow Docker Complete

Search Worflow Docker Complete. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 71 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +14
AI & RAG
Generate Incident Response Reports with Supabase, Gemini and Claude

This workflow ingests incident and playbook content from GitHub into Supabase (including pgvector embeddings with Google Gemini) and, on a webhook trigger, enriches a test incident with historical mat

Document Default Data Loader, Google Gemini Embeddings, HTTP Request +9
AI & RAG
Crawl4 AI

crawl4 ai. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 65 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +12
AI & RAG
Search Worflow Docker

Search Worflow Docker. Uses documentDefaultDataLoader, textSplitterCharacterTextSplitter, vectorStoreSupabase, embeddingsOllama. Scheduled trigger; 65 nodes.

Document Default Data Loader, Text Splitter Character Text Splitter, Supabase Vector Store +12
AI & RAG
Chat with Docs - 5minai New Version

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

HTTP Request, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +10
AI & RAG
AI Agent to Chat with Files in Supabase Storage and Google Drive

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

HTTP Request, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +10
AI & RAG
Youtube Agent

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

Supabase, Agent, Anthropic Chat +10
AI & RAG
🤖 Create a Documentation Expert Bot with Rag, Gemini, and Supabase

This template is a complete, hands-on tutorial for building a RAG (Retrieval-Augmented Generation) pipeline. In simple terms, you'll teach an AI to become an expert on a specific topic—in this case, t

Memory Buffer Window, Supabase Vector Store, Document Default Data Loader +8
AI & RAG
My Workflow 2529

My workflow 2529. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, googleDrive. Event-driven trigger; 54 nodes.

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +11
AI & RAG
Base_to_copy

05. Base_To_Copy. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, googleDrive. Event-driven trigger; 54 nodes.

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +11
AI & RAG
Wordpress AI Chatbot to Enhance User Experience with Supabase and Openai

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

OpenAI Embeddings, Document Default Data Loader, Text Splitter Token Splitter +9
AI & RAG
RAG & Genai App with Wordpress Content

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

OpenAI Embeddings, Document Default Data Loader, Text Splitter Token Splitter +9
AI & RAG
RAG & Genai App with Wordpress Content (embeddings Open Ai)

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

OpenAI Embeddings, Document Default Data Loader, Text Splitter Token Splitter +9
AI & RAG
Oil RAG

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

OpenAI Chat, OpenAI Embeddings, Agent +12
AI & RAG
Agente_rag

Agente_RAG. Uses supabase, embeddingsOpenAi, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Chat trigger; 50 nodes.

Supabase, OpenAI Embeddings, Document Default Data Loader +9
AI & RAG
Company Knowledge Base Agent (rag)

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

Supabase Vector Store, Document Default Data Loader, Text Splitter Recursive Character Text Splitter +11
AI & RAG
Interactive Knowledge Base Chat with Supabase RAG Using AI 📚💬

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

Google Drive Trigger, Postgres, Supabase +11
AI & RAG
Create a Self-updating RAG Chatbot with Google Drive, Gemini, and Supabase

This template creates a comprehensive, production-ready Retrieval-Augmented Generation (RAG) system. It builds a sophisticated AI agent that can answer questions based on documents stored in a specifi

Reranker Cohere, Supabase Vector Store, Agent +10
AI & RAG
Yt Processing Updated

YT Processing UPDATED. Uses googleSheets, youTube, httpRequest, googleDrive. Event-driven trigger; 44 nodes.

Google Sheets, YouTube, HTTP Request +11
AI & RAG
Ultimate Agentic RAG AI Agent Template

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

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +10

See more Supabase workflows · Supabase Vector Store workflows

FAQ

How do I trigger a Supabase Vector Store action from Supabase?

Most workflows in this list use either a Supabase webhook trigger (real-time) or a polling trigger (every N minutes). From there, downstream Supabase Vector Store nodes handle the action. Open any workflow's detail page to see the exact node graph.

Do I need both a Supabase and a Supabase Vector Store account?

Yes — n8n connects to each integration via your own credentials. AutomationFlows strips credential IDs before publishing, so you'll add your own.

Are these Supabase → Supabase Vector Store workflows free?

Yes — every workflow on AutomationFlows is free to browse and copy. Pro adds a multi-signal QualityScore on every workflow plus bulk JSON download.