# Evaluate RAG response accuracy with OpenAI:: n8n workflow

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

Source: https://millionrounds.com/workflow/evaluate-rag-response-accuracy-with-openai (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)

# Evaluate RAG response accuracy with OpenAI:

Started by someone opening a chat with it, and works across in-memory vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 21 steps: large, and worth reading before you trust it with anything live.

Large Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fevaluate-rag-response-accuracy-with-openai)

**21 steps** Large: read it before trusting it with anything live

**0** Branches: places the flow splits

**0** Loops or waits

**1** Service it connects to

## The flow

Starts it 3 Model 11 Logic 3 Service 4

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

- 1 **When clicking ‘Execute workflow’** Manual trigger *Starts it*
- 2 **Embeddings OpenAI** Embeddings Open Ai *Model*
- 3 **Recursive Character Text Splitter** Text Splitter Recursive Character Text Splitter *Model*
- 4 **OpenAI Chat Model** Lm Chat Open Ai *Model*
- 5 **Embeddings OpenAI1** Embeddings Open Ai *Model*
- 6 **When chat message received** Chat trigger *Starts it*
- 7 **When fetching a dataset row** Evaluation trigger *Starts it*
- 8 **OpenAI Chat Model1** Lm Chat Open Ai *Model*
- 9 **Structured Output Parser** Output Parser Structured *Model*
- 10 **Get Datasheet** Http Request *Service*
- 11 **Simple Vector Store** Vector Store In Memory *Model*
- 12 **Default Data Loader** Document Default Data Loader *Model*
- 13 **AI Agent** Agent *Model*
- 14 **Simple Vector Store1** Vector Store In Memory *Model*
- 15 **Remap Input** Set *Logic*
- 16 **Document Grounding** Chain Llm *Model*
- 17 **Evaluation** Evaluation *Service*
- 18 **Set Outputs** Evaluation *Service*
- 19 **Get Documents** Set *Logic*
- 20 **No Operation, do nothing** No Op *Logic*
- 21 **Set Metrics** Evaluation *Service*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 21
- **Size:** Large: read it before trusting it with anything live
- **Connects to:** 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 in-memory 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?

Take your time. At 21 steps it is large: read it before trusting it with anything live.

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 21 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%2Fevaluate-rag-response-accuracy-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.

## Connects to

Each opens the other workflows that use it.

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

## Workflows like it

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

- [**Evaluation metric example: RAG document relevance with OpenAI** 19 steps · calls a model · Google Sheets, in-memory vectors](https://millionrounds.com/workflow/evaluation-metric-example-rag-document-relevance-with-openai)
- [**Answer product queries with WhatsApp and OpenAI** 18 steps · calls a model · WhatsApp, in-memory vectors](https://millionrounds.com/workflow/answer-product-queries-with-whatsapp-and-openai)
- [**Generate business requirement documents with Google Workspace** 28 steps · calls a model · Google Drive, Google Sheets](https://millionrounds.com/workflow/generate-business-requirement-documents-with-google-workspac)
- [**Build a retrieval-based chatbot with Telegram and OpenAI** 13 steps · calls a model · Google Drive, Telegram](https://millionrounds.com/workflow/build-a-retrieval-based-chatbot-with-telegram-and-openai)
- [**AI-powered company documents Q&A assistant with Google Drive** 12 steps · calls a model · Google Drive, in-memory vectors](https://millionrounds.com/workflow/ai-powered-company-documents-q-and-a-assistant-with-google-d)
- [**Chat with PDF and CSV** 11 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/chat-with-pdf-and-csv)