Google Drive to Supabase contextual vector database sync for RAG applications

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Important notice

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

1. Workflow Overview

Workflow Summary This automation keeps your Supabase vector database synchronized with documents stored in Google Drive, while also making the data contextual and vector based for better retrieval....

Best for

Engineering automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.memorybufferwindow, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.googledrive, n8n-nodes-base.supabase, n8n-nodes-base.switch, n8n-nodes-base.crypto

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: Google Drive to Supabase contextual vector database sync for RAG applications
Workflow name: Google Drive to Supabase contextual vector database sync for RAG applications

Workflow Summary

This automation keeps your Supabase vector database synchronized with documents stored in Google Drive, while also making the data contextual and vector based for better retrieval.

When a file is added or modified, the workflow extracts its text, splits it into smaller chunks, and enriches each chunk with contextual metadata (such as summaries and document details). It then generates embeddings using OpenAI and stores both the vector data and metadata in Supabase. If a file changes, the old records are replaced with updated, contextualized content.

The result is a continuously updated and context-aware vector database, enabling highly accurate hybrid search and retrieval.

To setup

1. Connect Google Drive

• 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).

2. Configure Supabase

• Please refer to the Setting Up Supabase Sticky Note.

3. Connect OpenAI (or your embedding model)

• Add your OpenAI API key in n8n credentials.

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 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 2 - When chat message received

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

Block 3 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 4 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 5 - Google Drive

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

Block 6 - Search Record Manager

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

Block 7 - Create Row in Record Manager

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

Block 8 - Switch

Type / Role: n8n-nodes-base.switch - switch
Config choices: Version 3.2

Block 9 - Delete Previous Vectors

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

Block 10 - Generate Hash

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

Block 11 - Update Record Manager

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

Block 12 - Aggregate

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

Block 13 - Basic LLM Chain

Type / Role: @n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices: Version 1.7

Block 14 - Structured Output Parser

Type / Role: @n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices: Version 1.3

Block 15 - OpenAI Chat Model1

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 16 - Extract from File

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

Block 17 - Set Text

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 18 - Loop Over Items1

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

Block 19 - Search Record Manager1

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

Block 20 - If1

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 21 - Delete Previous Vectors1

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

Block 22 - Aggregate1

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

Block 23 - Delete Record from Record Manager1

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

Block 24 - Google Drive2

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

Showing the first 24 of 76 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Google Drive to Supabase contextual vector database sync for RAG applications
Complexity	advanced
Nodes	76
Categories	Engineering, AI RAG
Author	Michael Taleb
Published	03 Sept 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/8200/8200.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 Engineering workflows AI RAG workflows

Frequently asked questions

What does Google Drive to Supabase contextual vector database sync for RAG applications 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 Engineering, AI RAG use case.

Michael Taleb

3 workflows

Nodes

@n8n/n8n-nodes-langchain.agent @n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.memorybufferwindow @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.googledrive n8n-nodes-base.supabase n8n-nodes-base.switch n8n-nodes-base.crypto

Complexity

advanced

Published 03 Sept 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/8200/8200.json

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Google Drive to Supabase contextual vector database sync for RAG applications

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Workflow Summary

This automation keeps your Supabase vector database synchronized with documents stored in Google Drive, while also making the data contextual and vector based for better retrieval.

The result is a continuously updated and context-aware vector database, enabling highly accurate hybrid search and retrieval.

To setup

1. Connect Google Drive

2. Configure Supabase

3. Connect OpenAI (or your embedding model)

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - AI Agent

Block 2 - When chat message received

Block 3 - Simple Memory

Block 4 - OpenAI Chat Model

Block 5 - Google Drive

Block 6 - Search Record Manager

Block 7 - Create Row in Record Manager

Block 8 - Switch

Block 9 - Delete Previous Vectors

Block 10 - Generate Hash

Block 11 - Update Record Manager

Block 12 - Aggregate

Block 13 - Basic LLM Chain

Block 14 - Structured Output Parser

Block 15 - OpenAI Chat Model1

Block 16 - Extract from File

Block 17 - Set Text

Block 18 - Loop Over Items1

Block 19 - Search Record Manager1

Block 20 - If1

Block 21 - Delete Previous Vectors1

Block 22 - Aggregate1

Block 23 - Delete Record from Record Manager1

Block 24 - Google Drive2

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