AutomationFlows › Recipes › Textsplittercharactertextsplitter → Supabase Vector Store

Textsplittercharactertextsplitter → Supabase Vector Store

When you need Textsplittercharactertextsplitter and Supabase Vector Store talking to each other, here are the 106 n8n workflows in the catalog that already do it. Each is integration-tagged and privacy-stripped — copy the JSON and import.

Workflows that pair Textsplittercharactertextsplitter with Supabase Vector Store

AI & RAG
My Workflow (output Parser Structured)

My Workflow. Uses outputParserStructured, httpRequest, lmChatGoogleGemini, chainLlm. Scheduled trigger; 82 nodes.

Output Parser Structured, HTTP Request, Google Gemini Chat +15
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
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
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
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
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
AI & RAG
N8n RAG AI Agent

n8n RAG AI Agent. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, googleDrive. Event-driven trigger; 41 nodes.

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +10
AI & RAG
From Sitemap Crawling to Vector Storage: Creating an Efficient Workflow for RAG

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

HTTP Request, XML, Document Default Data Loader +5
AI & RAG
Build a Product Catalog Chatbot with Mistral Ai, Google Drive & Supabase RAG

This workflow builds a dual-system that connects automated document ingestion with a live product catalog chatbot powered by Mistral AI and Supabase.

Google Drive, Document Default Data Loader, Text Splitter Character Text Splitter +6
AI & RAG
Index Google Drive Files Into a Supabase Vector Store with Openai Embeddings

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

Document Default Data Loader, OpenAI Embeddings, Google Drive +6
AI & RAG
Page Management

Page Management. Uses documentDefaultDataLoader, embeddingsOpenAi, googleDrive, googleDriveTrigger. Event-driven trigger; 37 nodes.

Document Default Data Loader, OpenAI Embeddings, Google Drive +12
AI & RAG
Btl AI Page Management

BTL AI Page Management. Uses documentDefaultDataLoader, embeddingsOpenAi, googleDrive, googleDriveTrigger. Event-driven trigger; 37 nodes.

Document Default Data Loader, OpenAI Embeddings, Google Drive +12
AI & RAG
Indoor Farming Agent

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

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +16
AI & RAG
Ai-powered RAG Document Processing & Chatbot with Google Drive, Supabase, Openai

This workflow is perfect for: Businesses and teams who need an automated solution to organize, analyze, and retrieve insights from their internal documents. Researchers who want to quickly analyze and

Document Default Data Loader, Google Drive Trigger, Text Splitter Character Text Splitter +10
AI & RAG
Google Drive to Pinecone Vector Storage Workflow

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 (

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +9
AI & RAG
Chat with Documents via Rag: Google Drive to Gpt-5 with Supabase Vector Database

This workflow is your all-in-one pipeline to turn any document into a powerful searchable knowledge base using RAG (Retrieval-Augmented Generation). From the moment a file lands in your Google Drive,

Google Drive, Document Default Data Loader, OpenAI Embeddings +16
AI & RAG
Crawl4ai-rag

crawl4Ai-rag. Uses httpRequest, xml, documentDefaultDataLoader, textSplitterCharacterTextSplitter. Event-driven trigger; 30 nodes.

HTTP Request, XML, Document Default Data Loader +10
AI & RAG
Supabase RAG AI Agent Pdfs & More

Supabase RAG AI Agent PDFs & More. Uses lmChatOpenAi, documentDefaultDataLoader, embeddingsOpenAi, toolVectorStore. Event-driven trigger; 29 nodes.

OpenAI Chat, Document Default Data Loader, OpenAI Embeddings +9

See more Textsplittercharactertextsplitter workflows · Supabase Vector Store workflows

FAQ

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

Most workflows in this list use either a Textsplittercharactertextsplitter 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 Textsplittercharactertextsplitter 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 Textsplittercharactertextsplitter → 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.