# Build a RAG knowledge chatbot with OpenAI and Google Drive: n8n workflow

> Started by someone opening a chat with it, and works across Google Drive, Supabase and Supabase vectors.

Source: https://millionrounds.com/workflow/build-a-rag-knowledge-chatbot-with-openai-and-google-drive (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [Data collection](https://millionrounds.com/workflows?category=Data+collection#library)

# Build a RAG knowledge chatbot with OpenAI and Google Drive

Started by someone opening a chat with it, and works across Google Drive, Supabase and Supabase vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 17 steps, 1 loop or waits: a working afternoon to understand properly.

Medium Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fbuild-a-rag-knowledge-chatbot-with-openai-and-google-drive)

**17 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**1** Loop or waits

**3** Services it connects to

## The flow

Starts it 3 Model 9 Logic 2 Service 3

The steps in the order they run, from what starts it.

- 1 **File Updated** Google Drive trigger *Starts it*
- 2 **Recursive Character Text Splitter** Text Splitter Recursive Character Text Splitter *Model*
- 3 **File Created** Google Drive trigger *Starts it*
- 4 **Embeddings OpenAI** Embeddings Open Ai *Model*
- 5 **Postgres Chat Memory** Memory Postgres Chat *Model*
- 6 **OpenAI Chat Model** Lm Chat Open Ai *Model*
- 7 **When chat message received** Chat trigger *Starts it*
- 8 **Embeddings OpenAI1** Embeddings Open Ai *Model*
- 9 **Set File ID** Set *Logic*
- 10 **Default Data Loader** Document Default Data Loader *Model*
- 11 **Supabase Vector Store** Vector Store Supabase *Model*
- 12 **RAG AI Agent** Agent *Model*
- 13 **RAG Vector store** Vector Store Supabase *Model*
- 14 **Loop Over Items** Split In Batches *Logic*
- 15 **Delete a row** Supabase *Service*
- 16 **Download the file** Google Drive *Service*
- 17 **Extract from File** Extract From File *Service*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 17, 1 loop or waits
- **Size:** Medium: a working afternoon
- **Connects to:** Google Drive, Supabase, Supabase vectors
- **Model:** OpenAI
- **Retrieval:** Answers are drawn from documents you supply, not from the model alone

## Questions

What does it need to run?

An n8n account, plus your own Google Drive, Supabase and Supabase vectors accounts, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on someone opening a chat with it.

Does it use AI?

Yes. It calls OpenAI. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Plan an afternoon. 17 steps is enough that you should read each one before running it.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

**Before you run it**

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 17 steps before you point it at anything that matters.

## Run it in n8n

- **Create your n8n account** Free to start. n8n is where the workflow runs. [Create an n8n account ↗](https://millionrounds.com/n8n)
- **Download the workflow** Free with a Millionrounds account. [Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fbuild-a-rag-knowledge-chatbot-with-openai-and-google-drive)
- **Paste it into n8n** Open a new workflow in n8n and paste. Then connect your own accounts.

Stored with the author's traces removed: canvas notes, instance identifiers, saved credential names and any live document they had left behind.

## Connects to

Each opens the other workflows that use it.

[Google Drive](https://millionrounds.com/workflows?service=Google+Drive#library)[Supabase](https://millionrounds.com/workflows?service=Supabase#library)[Supabase vectors](https://millionrounds.com/workflows?service=Supabase+vectors#library)

## Workflows like it

Also data collection, with the same services where possible, closest in size first.

- [**AI study assistant with RAG and Drive** 17 steps · calls a model · Google Drive, Postgres](https://millionrounds.com/workflow/ai-study-assistant-with-rag-and-drive)
- [**Build your own RAG-ready vector database in seconds – no SQL, no** 17 steps · calls a model · Google Drive, Supabase](https://millionrounds.com/workflow/build-your-own-rag-ready-vector-database-in-seconds-no-sql-n)
- [**Index n8n workflows and enable semantic AI search with OpenAI** 17 steps · calls a model · Supabase, Supabase vectors](https://millionrounds.com/workflow/index-n8n-workflows-and-enable-semantic-ai-search-with-opena)
- [**Bulk WhatsApp & Gmail messenger with Google Sheets** 17 steps · Gmail, Google Drive](https://millionrounds.com/workflow/bulk-whatsapp-and-gmail-messenger-with-google-sheets)
- [**Generate and validate bulk QR codes with Google Sheets** 17 steps · Google Drive, Google Sheets](https://millionrounds.com/workflow/generate-and-validate-bulk-qr-codes-with-google-sheets)
- [**Two-way property repair management system with Google Sheets** 17 steps · Gmail, Google Drive](https://millionrounds.com/workflow/two-way-property-repair-management-system-with-google-sheets)