# Internal Document RAG Chatbot with OpenAI and Supabase vectors: n8n workflow

> Started by someone opening a chat with it, and works across Supabase vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents...

Source: https://millionrounds.com/workflow/internal-document-rag-chatbot-with-openai-and-supabase-vecto (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [Documents and files](https://millionrounds.com/workflows?category=Documents+and+files#library)

# Internal Document RAG Chatbot with OpenAI and Supabase vectors

Started by someone opening a chat with it, and works across Supabase vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 14 steps: a working afternoon to understand properly.

Medium Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Finternal-document-rag-chatbot-with-openai-and-supabase-vecto)

**14 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**0** Loops or waits

**1** Service it connects to

## The flow

Starts it 3 Model 9 Logic 2

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

- 1 **Webhook - Ingest Internal Docs** Webhook *Starts it*
- 2 **Embeddings OpenAI - Ingest** Embeddings Open Ai *Model*
- 3 **Recursive Character Text Splitter** Text Splitter Recursive Character Text Splitter *Model*
- 4 **Chat Trigger - Internal RAG Chat** Chat trigger *Starts it*
- 5 **OpenAI Chat Model - Answer** Lm Chat Open Ai *Model*
- 6 **Embeddings OpenAI - Retrieve** Embeddings Open Ai *Model*
- 7 **Code - Normalize and Validate Docs** Code *Logic*
- 8 **Supabase Vector Store - Insert Documents** Vector Store Supabase *Model*
- 9 **Default Data Loader - JSON Content** Document Default Data Loader *Model*
- 10 **Question and Answer Chain - RAG** Chain Retrieval Qa *Model*
- 11 **Supabase Vector Store - Retrieve** Vector Store Supabase *Model*
- 12 **Respond - Ingest Done** Respond To Webhook *Starts it*
- 13 **Set - Chat Output** Set *Logic*
- 14 **Vector Store Retriever** Retriever Vector Store *Model*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 14
- **Size:** Medium: a working afternoon
- **Connects to:** 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 Supabase vectors account, 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. 14 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 14 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%2Finternal-document-rag-chatbot-with-openai-and-supabase-vecto)
- **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.

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

## Workflows like it

Also documents and files, with the same services where possible, closest in size first.

- [**Document-based AI chatbot with RAG and OpenAI** 13 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/document-based-ai-chatbot-with-rag-and-openai)
- [**Chat with Telegram and Supabase** 12 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/chat-with-telegram-and-supabase)
- [**Multi-format document processing for RAG chatbot** 16 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/multi-format-document-processing-for-rag-chatbot)
- [**AI process document uploader with OpenAI and Supabase** 11 steps · calls a model · Supabase vectors](https://millionrounds.com/workflow/ai-process-document-uploader-with-openai-and-supabase)
- [**Build a RAG document chatbot with Supabase and OpenRouter** 10 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/build-a-rag-document-chatbot-with-supabase-and-openrouter)
- [**PDF to SQL Server with OpenAI and Google Drive** 10 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/pdf-to-sql-server-with-openai-and-google-drive)