# Optimize unstructured data for RAG with Blockify IdeaBlocks: n8n workflow

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

Source: https://millionrounds.com/workflow/optimize-unstructured-data-for-rag-with-blockify-ideablocks (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)

# Optimize unstructured data for RAG with Blockify IdeaBlocks

Started by someone opening a chat with it, and works across Google Drive 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. 15 steps, 1 loop or waits: a working afternoon to understand properly.

Medium Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Foptimize-unstructured-data-for-rag-with-blockify-ideablocks)

**15 steps** Medium: a working afternoon

**0** Branches: places the flow splits

**1** Loop or waits

**2** Services it connects to

## The flow

Starts it 2 Model 7 Logic 3 Service 3

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

- 1 **When clicking ‘Execute workflow’** Manual trigger *Starts it*
- 2 **RAG Chatbot** Chat trigger *Starts it*
- 3 **Embed Individual IdeaBlocks (Already Separated)** Text Splitter Character Text Splitter *Model*
- 4 **Embeddings OpenAI** Embeddings Open Ai *Model*
- 5 **OpenAI Chat Model** Lm Chat Open Ai *Model*
- 6 **Download.TXT File for Ingest** Google Drive *Service*
- 7 **AI Agent** Agent *Model*
- 8 **Default Data Loader** Document Default Data Loader *Model*
- 9 **Simple IdeaBlock Vector Store** Vector Store In Memory *Model*
- 10 **Query Data Tool** Vector Store In Memory *Model*
- 11 **Extract Text from.TXT File** Extract From File *Service*
- 12 **Loop Over Chunks** Split In Batches *Logic*
- 13 **Chunk Text** Code *Logic*
- 14 **Blockify Ingest API** Http Request *Service*
- 15 **Extract IdeaBlocks from API Response** Set *Logic*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 15, 1 loop or waits
- **Size:** Medium: a working afternoon
- **Connects to:** Google Drive, 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 Google Drive 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. 15 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 15 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%2Foptimize-unstructured-data-for-rag-with-blockify-ideablocks)
- **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.

[Google Drive](https://millionrounds.com/workflows?service=Google+Drive#library)[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.

- [**Answer voice queries from a webhook over Google Drive docs** 15 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/answer-voice-queries-from-a-webhook-over-google-drive-docs)
- [**Build a chatbot trained on your internal company data in 20mins** 15 steps · calls a model · Google Drive, Pinecone](https://millionrounds.com/workflow/build-a-chatbot-trained-on-your-internal-company-data-in-20m)
- [**Create a company policy chatbot with RAG and Pinecone** 15 steps · calls a model · Google Drive, Pinecone](https://millionrounds.com/workflow/create-a-company-policy-chatbot-with-rag-and-pinecone)
- [**Email daily job matches from Harman careers with OpenAI** 15 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/email-daily-job-matches-from-harman-careers-with-openai)
- [**HR knowledge chatbot with Google Drive and Gemini** 15 steps · calls a model · Google Drive, Pinecone](https://millionrounds.com/workflow/hr-knowledge-chatbot-with-google-drive-and-gemini)
- [**Optimize Amazon ads with OpenAI** 15 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/optimize-amazon-ads-with-openai)