# Build a cost-efficient Lookio RAG chatbot with OpenAI: n8n workflow

> Started by someone opening a chat with it. It calls services over plain HTTP rather than through connectors.

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

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

# Build a cost-efficient Lookio RAG chatbot with OpenAI

Started by someone opening a chat with it. It calls services over plain HTTP rather than through connectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 13 steps: a working afternoon to understand properly.

Medium Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fbuild-a-cost-efficient-lookio-rag-chatbot-with-openai)

**13 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**0** Loops or waits

**0** Services by connector: it calls them over plain HTTP

## The flow

Starts it 1 Model 11 Service 1

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

- 1 **When chat message received** Chat trigger *Starts it*
- 2 **Very small model** Lm Chat Open Ai *Model*
- 3 **Mini model** Lm Chat Open Ai *Model*
- 4 **Large model** Lm Chat Open Ai *Model*
- 5 **Simple Memory** Memory Buffer Window *Model*
- 6 **Find past messages** Memory Manager *Model*
- 7 **Simple response** Chain Llm *Model*
- 8 **Intent router** Text Classifier *Model*
- 9 **Prepare retrieval query** Chain Llm *Model*
- 10 **Write the final response** Chain Llm *Model*
- 11 **Store messages** Memory Manager *Model*
- 12 **Respond to Chat** Chat *Model*
- 13 **RAG via Lookio** Http Request *Service*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 13
- **Size:** Medium: a working afternoon
- **Connects to:** Nothing by connector; it calls services over plain HTTP
- **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, 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. 13 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 13 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-cost-efficient-lookio-rag-chatbot-with-openai)
- **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.

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Also ai assistants, with the same services where possible, closest in size first.

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