# Create AI-ready vector datasets for LLMs with Bright Data: n8n workflow

> Started by an event in a connected app, and works across Pinecone. It puts Gemini behind a retrieval step, so answers are drawn from documents you supply...

Source: https://millionrounds.com/workflow/create-ai-ready-vector-datasets-for-llms-with-bright-data (Millionrounds; every figure on the page links to its filing or article)

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

# Create AI-ready vector datasets for LLMs with Bright Data

Started by an event in a connected app, and works across Pinecone. It puts Gemini 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: Gemini Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fcreate-ai-ready-vector-datasets-for-llms-with-bright-data)

**16 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 11 Logic 1 Service 3

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

- 1 **When clicking ‘Test workflow’** Manual trigger *Starts it*
- 2 **Embeddings Google Gemini** Embeddings Google Gemini *Model*
- 3 **Recursive Character Text Splitter** Text Splitter Recursive Character Text Splitter *Model*
- 4 **Google Gemini Chat Model1** Lm Chat Google Gemini *Model*
- 5 **Google Gemini Chat Model2** Lm Chat Google Gemini *Model*
- 6 **Google Gemini Chat Model** Lm Chat Google Gemini *Model*
- 7 **Structured Output Parser** Output Parser Structured *Model*
- 8 **Set Fields - URL and Webhook URL** Set *Logic*
- 9 **Pinecone Vector Store** Vector Store Pinecone *Model*
- 10 **Default Data Loader** Document Default Data Loader *Model*
- 11 **AI Agent** Agent *Model*
- 12 **Structured JSON Data Formatter** Chain Llm *Model*
- 13 **Information Extractor with Data Formatter** Information Extractor *Model*
- 14 **Make a web request** Http Request *Service*
- 15 **Webhook for structured data** Http Request *Service*
- 16 **Webhook for structured AI agent response** Http Request *Service*

## How it's built

- **Starts on:** An event in a connected app
- **Steps:** 16
- **Size:** Medium: a working afternoon
- **Connects to:** Pinecone
- **Model:** Gemini
- **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 Pinecone account, and a key for Gemini. 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 Gemini. 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%2Fcreate-ai-ready-vector-datasets-for-llms-with-bright-data)
- **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.

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

## Workflows like it

Also data collection, with the same services where possible, closest in size first.

- [**Scrape and ingest web pages into a Pinecone RAG stack** 15 steps · calls a model · Pinecone](https://millionrounds.com/workflow/scrape-and-ingest-web-pages-into-a-pinecone-rag-stack)
- [**Synchronize MySQL database schemas to Pinecone with OpenAI** 17 steps · calls a model · MySQL, Pinecone](https://millionrounds.com/workflow/synchronize-mysql-database-schemas-to-pinecone-with-openai)
- [**Guide students with RAG and OpenAI** 14 steps · calls a model · Google Sheets, Pinecone](https://millionrounds.com/workflow/guide-students-with-rag-and-openai)
- [**Export WordPress to Pinecone vector store with OpenAI** 19 steps · calls a model · Pinecone](https://millionrounds.com/workflow/export-wordpress-to-pinecone-vector-store-with-openai)
- [**AI-powered Asana task prioritization with OpenAI and Pinecone** 10 steps · calls a model · Asana, Pinecone](https://millionrounds.com/workflow/ai-powered-asana-task-prioritization-with-openai-and-pinecon)
- [**Send personalized SaaS welcome emails with Stripe and Pinecone** 10 steps · calls a model · Gmail, Google Sheets](https://millionrounds.com/workflow/send-personalized-saas-welcome-emails-with-stripe-and-pineco)