# Build a RAG demo with EcoHash and BGE: n8n workflow template

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

Source: https://millionrounds.com/workflow/build-a-rag-demo-with-ecohash-and-bge (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 RAG demo with EcoHash and BGE

Started by an event in a connected app, and works across in-memory vectors. It puts a language model behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 10 steps: a working afternoon to understand properly.

Medium Calls a model Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fbuild-a-rag-demo-with-ecohash-and-bge)

**10 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**0** Loops or waits

**1** Service it connects to

## The flow

Starts it 1 Model 6 Logic 2 Service 1

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

- 1 **Manual Trigger Execution** Manual trigger *Starts it*
- 2 **Load Default Data** Document Default Data Loader *Model*
- 3 **Generate EcoHash Embeddings** Embeddings Eco Hash *Model*
- 4 **Run EcoHash Chat Model** Lm Chat Eco Hash *Model*
- 5 **Reorder with EcoHash Reranker** Eco Hash Reranker *Service*
- 6 **Generate Document Set** Code *Logic*
- 7 **Insert into Vector Store** Vector Store In Memory *Model*
- 8 **Retrieve from Vector Store** Vector Store In Memory *Model*
- 9 **EcoHash Analysis Agent** Agent *Model*
- 10 **Consolidate Items to Single** Code *Logic*

## How it's built

- **Starts on:** An event in a connected app
- **Steps:** 10
- **Size:** Medium: a working afternoon
- **Connects to:** in-memory vectors
- **Model:** A language model, family not declared
- **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 a language model. 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 a language model. 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. 10 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 10 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-rag-demo-with-ecohash-and-bge)
- **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 ai assistants, with the same services where possible, closest in size first.

- [**Save Costs In RAG Workflows with Tool** 10 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/save-costs-in-rag-workflows-with-tool)
- [**Simple RAG chatbot with Gemini and Google Drive** 11 steps · calls a model · Google Drive, in-memory vectors](https://millionrounds.com/workflow/simple-rag-chatbot-with-gemini-and-google-drive)
- [**Basic RAG chat with Groq and in-memory vectors** 12 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/basic-rag-chat-with-groq-and-in-memory-vectors)
- [**Build an in-memory RAG knowledge base chat with OpenAI** 12 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/build-an-in-memory-rag-knowledge-base-chat-with-openai)
- [**IPL cricket rules Q&A chat bot with RAG and Gemini** 12 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/ipl-cricket-rules-q-and-a-chat-bot-with-rag-and-gemini)
- [**RAG Starter Template with Form and OpenAI** 8 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/rag-starter-template-with-form-and-openai)