# Hr policy retrieval with Slack and RAG: n8n workflow template

> Started by an event in a connected app, and works across S3, Slack and in-memory vectors. It puts OpenAI behind a retrieval step, so answers are drawn from...

Source: https://millionrounds.com/workflow/hr-policy-retrieval-with-slack-and-rag (Millionrounds; every figure on the page links to its filing or article)

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

# Hr policy retrieval with Slack and RAG

Started by an event in a connected app, and works across S3, Slack 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%2Fhr-policy-retrieval-with-slack-and-rag)

**16 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**0** Loops or waits

**3** Services it connects to

## The flow

Starts it 2 Model 10 Logic 2 Service 2

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

- 1 **Slack Trigger** Slack trigger *Starts it*
- 2 **Simple Memory** Memory Buffer Window *Model*
- 3 **Knowledge Base Embeddings** Embeddings Open Ai *Model*
- 4 **Conversation Model** Lm Chat Open Ai *Model*
- 5 **Knowledge Base Response Chat Model** Lm Chat Open Ai *Model*
- 6 **Manual Trigger** Manual trigger *Starts it*
- 7 **Knowledge Base Embeddings Generator** Embeddings Open Ai *Model*
- 8 **Document Loader** Document Default Data Loader *Model*
- 9 **Chatbot** Agent *Model*
- 10 **Knowledge Base** Vector Store In Memory *Model*
- 11 **Knowledge Base Search** Tool Vector Store *Model*
- 12 **S3 Document Downloader** Aws S3 *Service*
- 13 **Knowledge Base storage** Vector Store In Memory *Model*
- 14 **Response** Set *Logic*
- 15 **Try Again** Set *Logic*
- 16 **Send a message** Slack *Service*

## How it's built

- **Starts on:** An event in a connected app
- **Steps:** 16
- **Size:** Medium: a working afternoon
- **Connects to:** S3, Slack, 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 S3, Slack 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 an event in a connected app.

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%2Fhr-policy-retrieval-with-slack-and-rag)
- **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.

[S3](https://millionrounds.com/workflows?service=S3#library)[Slack](https://millionrounds.com/workflows?service=Slack#library)[in-memory vectors](https://millionrounds.com/workflows?service=in-memory+vectors#library)

## Workflows like it

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

- [**Analyze high-priority tenders from Airtable** 16 steps · calls a model · Airtable, Gmail](https://millionrounds.com/workflow/analyze-high-priority-tenders-from-airtable)
- [**Archive and index legal PDFs from Gmail with Gemini and Sheets** 16 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/archive-and-index-legal-pdfs-from-gmail-with-gemini-and-shee)
- [**Automate AI-powered employee onboarding emails and Slack updates** 16 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/automate-ai-powered-employee-onboarding-emails-and-slack-upd)
- [**Automate client onboarding with OpenAI and Google Drive** 16 steps · calls a model · ClickUp, Gmail](https://millionrounds.com/workflow/automate-client-onboarding-with-openai-and-google-drive)
- [**Automate employee onboarding with Groq and Gmail** 16 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/automate-employee-onboarding-with-groq-and-gmail)
- [**Benchmark procurement prices and flag overpricing** 16 steps · calls a model · Google Sheets, RSS](https://millionrounds.com/workflow/benchmark-procurement-prices-and-flag-overpricing)