# Answer customer questions and book jobs with OpenAI RAG: n8n workflow

> Started by someone opening a chat with it, and works across Gmail, Google Calendar, Google Sheets and in-memory vectors.

Source: https://millionrounds.com/workflow/answer-customer-questions-and-book-jobs-with-openai-rag-2 (Millionrounds; every figure on the page links to its filing or article)

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

# Answer customer questions and book jobs with OpenAI RAG

Started by someone opening a chat with it, and works across Gmail, Google Calendar, Google Sheets and in-memory vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 16 steps: a working afternoon to understand properly.

Medium Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fanswer-customer-questions-and-book-jobs-with-openai-rag-2)

**16 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**0** Loops or waits

**4** Services it connects to

## The flow

Starts it 2 Model 10 Logic 1 Service 3

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

- 1 **Run once to build knowledge index** Manual trigger *Starts it*
- 2 **Split knowledge into chunks** Text Splitter Recursive Character Text Splitter *Model*
- 3 **OpenAI embeddings for indexing** Embeddings Open Ai *Model*
- 4 **Receive customer chat message** Chat trigger *Starts it*
- 5 **OpenAI chat model (gpt-4o-mini)** Lm Chat Open Ai *Model*
- 6 **Conversation memory** Memory Buffer Window *Model*
- 7 **OpenAI embeddings for retrieval** Embeddings Open Ai *Model*
- 8 **business_knowledge** Tool Vector Store *Model*
- 9 **check_availability** Google Calendar Tool *Service*
- 10 **create_booking** Google Sheets Tool *Service*
- 11 **escalate_to_human** Gmail Tool *Service*
- 12 **Edit your services, prices and policies** Set *Logic*
- 13 **Load knowledge as documents** Document Default Data Loader *Model*
- 14 **Build knowledge index** Vector Store In Memory *Model*
- 15 **AI receptionist agent** Agent *Model*
- 16 **Search business knowledge** Vector Store In Memory *Model*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 16
- **Size:** Medium: a working afternoon
- **Connects to:** Gmail, Google Calendar, Google Sheets, in-memory 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 Gmail, Google Calendar, Google Sheets and in-memory 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. 16 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 16 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%2Fanswer-customer-questions-and-book-jobs-with-openai-rag-2)
- **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.

[Gmail](https://millionrounds.com/workflows?service=Gmail#library)[Google Calendar](https://millionrounds.com/workflows?service=Google+Calendar#library)[Google Sheets](https://millionrounds.com/workflows?service=Google+Sheets#library)[in-memory vectors](https://millionrounds.com/workflows?service=in-memory+vectors#library)

## Workflows like it

Also customer support, with the same services where possible, closest in size first.

- [**Answer customer questions and book jobs with OpenAI RAG** 16 steps · calls a model · Gmail, Google Calendar](https://millionrounds.com/workflow/answer-customer-questions-and-book-jobs-with-openai-rag)
- [**Answer support FAQs with Gemini and Supabase** 16 steps · calls a model · Google Sheets, Supabase vectors](https://millionrounds.com/workflow/answer-support-faqs-with-gemini-and-supabase)
- [**Automate IT support: Convert emails to Jira tickets** 16 steps · calls a model · Gmail, Jira](https://millionrounds.com/workflow/automate-it-support-convert-emails-to-jira-tickets)
- [**Automate WhatsApp customer support with FAQ** 16 steps · calls a model · Google Calendar, Google Sheets](https://millionrounds.com/workflow/automate-whatsapp-customer-support-with-faq)
- [**Automate product training & customer support with WhatsApp** 16 steps · calls a model · Google Sheets, WhatsApp](https://millionrounds.com/workflow/automate-product-training-and-customer-support-with-whatsapp)
- [**Automated customer support with OpenAI** 16 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/automated-customer-support-with-openai)