Create RAG vector database from Google Drive documents using Gemini & Supabase

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

How it works This workflow automates the process of converting Google Drive documents into searchable vector embeddings for AI powered applications: • Takes a Google Drive folder URL as input • Ini...

Best for

Document Extraction automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.embeddingsgooglegemini, @n8n/n8n-nodes-langchain.documentdefaultdataloader, n8n-nodes-base.postgres, n8n-nodes-base.code, n8n-nodes-base.executeworkflowtrigger, n8n-nodes-base.stickynote, n8n-nodes-base.splitinbatches, n8n-nodes-base.googledrive

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Mantaka Mahir.

Original n8n.io source

1.1 Workflow description

Title: Create RAG vector database from Google Drive documents using Gemini & Supabase
Workflow name: Create RAG vector database from Google Drive documents using Gemini & Supabase

How it works

This workflow automates the process of converting Google Drive documents into searchable vector embeddings for AI-powered applications:

• Takes a Google Drive folder URL as input • Initializes a Supabase vector database with pgvector extension • Fetches all files from the specified Drive folder • Downloads and converts each file to plain text • Generates 768-dimensional embeddings using Google Gemini • Stores documents with embeddings in Supabase for semantic search

Built for the Study Agent workflow to power document-based Q&A, but also works perfectly for any RAG system, AI chatbot, knowledge base, or semantic search application that needs to query document collections.

Set up steps

Prerequisites: • Google Drive OAuth2 credentials • Supabase account with Postgres connection details • Google Gemini API key (free tier available)

Setup time: ~10 minutes

Steps:

Add your Google Drive OAuth2 credentials to the Google Drive nodes
Configure Supabase Postgres credentials in the SQL node
Add Supabase API credentials to the Vector Store node
Add Google Gemini API key to the Embeddings node
Update the input with your Drive folder URL
Execute the workflow

Note: The SQL query will drop any existing "documents" table, so backup data if needed. Detailed node-by-node instructions are in the sticky notes within the workflow.

Works with: Study Agent (main use case), custom AI agents, chatbots, documentation search, customer support bots, or any RAG application.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Embeddings Google Gemini4

Type / Role: @n8n/n8n-nodes-langchain.embeddingsGoogleGemini - embeddingsGoogleGemini
Config choices: Version 1

Block 2 - Default Data Loader2

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1.1

Block 3 - Execute a SQL query

Type / Role: n8n-nodes-base.postgres - postgres
Config choices: Version 2.6

Block 4 - Code in JavaScript

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 5 - When Executed by Another Workflow

Type / Role: n8n-nodes-base.executeWorkflowTrigger - executeWorkflowTrigger
Config choices: Version 1.1

Block 6 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 7 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 8 - Sticky Note2

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 9 - Sticky Note3

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 10 - Loop Over Items

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 11 - Search files and folders

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 12 - Insert into Supabase Vectorstore

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreSupabase - vectorStoreSupabase
Config choices: Version 1

Block 13 - Download File

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 14 - Sticky Note4

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 15 - Sticky Note5

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 16 - Sticky Note6

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

3. Summary Table

Workflow	Create RAG vector database from Google Drive documents using Gemini & Supabase
Complexity	advanced
Nodes	16
Categories	Document Extraction, AI RAG
Author	Mantaka Mahir
Published	09 Nov 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/10651/10651.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Document Extraction workflows AI RAG workflows

Frequently asked questions

What does Create RAG vector database from Google Drive documents using Gemini & Supabase do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI RAG use case.

Mantaka Mahir

3 workflows

Nodes

@n8n/n8n-nodes-langchain.embeddingsgooglegemini @n8n/n8n-nodes-langchain.documentdefaultdataloader n8n-nodes-base.postgres n8n-nodes-base.code n8n-nodes-base.executeworkflowtrigger n8n-nodes-base.stickynote n8n-nodes-base.splitinbatches n8n-nodes-base.googledrive

Complexity

advanced

Published 09 Nov 2025

Likes 0

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Install path: /data/workflows/10651/10651.json

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Create RAG vector database from Google Drive documents using Gemini & Supabase

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

How it works

Set up steps

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Embeddings Google Gemini4

Block 2 - Default Data Loader2

Block 3 - Execute a SQL query

Block 4 - Code in JavaScript

Block 5 - When Executed by Another Workflow

Block 6 - Sticky Note1

Block 7 - Sticky Note

Block 8 - Sticky Note2

Block 9 - Sticky Note3

Block 10 - Loop Over Items

Block 11 - Search files and folders

Block 12 - Insert into Supabase Vectorstore

Block 13 - Download File

Block 14 - Sticky Note4

Block 15 - Sticky Note5

Block 16 - Sticky Note6

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions