📥 Transform Google Drive documents into vector embeddings

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Important notice

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

1. Workflow Overview

Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n. ️ What It Does This workflow monitors a Google Drive f...

Best for

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

Tools used

@n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter, @n8n/n8n-nodes-langchain.vectorstorepgvector, n8n-nodes-base.manualtrigger, n8n-nodes-base.splitinbatches, n8n-nodes-base.googledrive, n8n-nodes-base.scheduletrigger, n8n-nodes-base.stickynote

Source and attribution

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

Original n8n.io source

1.1 Workflow description

Title: 📥 Transform Google Drive documents into vector embeddings
Workflow name: 📥 Transform Google Drive documents into vector embeddings

Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n.

⚙️ What It Does

This workflow monitors a Google Drive folder for new files, supports multiple file types (PDF, TXT, JSON), and processes them into vector embeddings using OpenAI’s text-embedding-3-small model. These embeddings are stored in a Postgres database using the PGVector extension, making them query-ready for semantic search or RAG-based AI agents.

After successful processing, files are moved to a separate “vectorized” folder to avoid duplication.

💡 Use Cases

Powering Retrieval-Augmented Generation (RAG) AI agents
Semantic search across private documents
AI assistant knowledge ingestion
Automated document pipelines for indexing or classification

🧠 Workflow Highlights

Trigger Options: Manual or Scheduled (3 AM daily by default)
Supported File Types: PDF, TXT, JSON
Embedding Stack: LangChain Text Splitter, OpenAI Embeddings, PGVector
Deduplication: Files are moved after processing
License: CC BY-SA 4.0
Author: AlexK1919

🛠 What You’ll Need

Google Drive OAuth2 credentials (connected to Search Folder, Download File, and Move File nodes)
OpenAI API Key (used in the Embeddings OpenAI node)
Postgres + PGVector database (connected in the Postgres PGVector Store node)

🔧 Step-by-Step Setup Instructions

Create Google OAuth2 credentials in n8n and connect them to all Google Drive nodes.
Set your source folder ID in the Search Folder node — this is where incoming files are placed.
Set your processed folder ID in the Move File node — files will be moved here after vectorization.
Ensure you have a PGVector-enabled Postgres instance and input the table name and collection in the Postgres PGVector Store node.
Add your OpenAI credentials to the Embeddings OpenAI node and select text-embedding-3-small.
Optional: Activate the Schedule Trigger node to run daily or configure your own schedule.
Run manually by triggering When clicking ‘Test workflow’ for on-demand ingestion.

🧩 Customization Tips

Want to support more file types or enhance the pipeline?

Add new extractors: Use Extract from File with other formats like DOCX, Markdown, or HTML.
Refine logic by file type: The Switch node routes files to the correct extraction method based on MIME type (application/pdf, text/plain, application/json).
Pre-process with OCR: Add an OCR step before extraction to handle scanned PDFs or images.
Add filters: Enhance the Search Folder or Switch node logic to skip specific files or folders.

📄 License

This workflow is available under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to use, adapt, and share this workflow for non-commercial purposes under the terms of this license.

Full license details: https://creativecommons.org/licenses/by-nc-sa/4.0/

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 - Default Data Loader

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

Block 2 - Recursive Character Text Splitter

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

Block 3 - Postgres PGVector Store

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

Block 4 - When clicking ‘Test workflow’

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

Block 5 - Loop Over Items

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

Block 6 - Move File

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

Block 7 - Download File

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

Block 8 - Search Folder

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

Block 9 - Schedule Trigger

Type / Role: n8n-nodes-base.scheduleTrigger - scheduleTrigger
Config choices: Version 1.2

Block 10 - Sticky Note

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

Block 11 - Switch

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

Block 12 - Extract from PDF

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

Block 13 - Extract from Text

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

Block 14 - Extract from JSON

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

Block 15 - Embeddings OpenAI

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

3. Summary Table

Workflow	📥 Transform Google Drive documents into vector embeddings
Complexity	advanced
Nodes	15
Categories	Document Extraction, AI RAG
Author	Alex Kim
Published	21 Apr 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/3647/3647.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 📥 Transform Google Drive documents into vector embeddings 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.

Alex Kim

15 workflows

Nodes

@n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplitterrecursivecharactertextsplitter @n8n/n8n-nodes-langchain.vectorstorepgvector n8n-nodes-base.manualtrigger n8n-nodes-base.splitinbatches n8n-nodes-base.googledrive n8n-nodes-base.scheduletrigger n8n-nodes-base.stickynote

Complexity

advanced

Published 21 Apr 2025

Likes 0

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Install path: /data/workflows/3647/3647.json

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📥 Transform Google Drive documents into vector embeddings

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

⚙️ What It Does

💡 Use Cases

🧠 Workflow Highlights

🛠 What You’ll Need

🔧 Step-by-Step Setup Instructions

🧩 Customization Tips

📄 License

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Default Data Loader

Block 2 - Recursive Character Text Splitter

Block 3 - Postgres PGVector Store

Block 4 - When clicking ‘Test workflow’

Block 5 - Loop Over Items

Block 6 - Move File

Block 7 - Download File

Block 8 - Search Folder

Block 9 - Schedule Trigger

Block 10 - Sticky Note

Block 11 - Switch

Block 12 - Extract from PDF

Block 13 - Extract from Text

Block 14 - Extract from JSON

Block 15 - Embeddings OpenAI

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