# Vectorize Medical Procedures for Semantic Search with TUSS: n8n workflow

> Started by an event in a connected app. It calls services over plain HTTP rather than through connectors.

Source: https://millionrounds.com/workflow/vectorize-medical-procedures-for-semantic-search-with-tuss (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)

# Vectorize Medical Procedures for Semantic Search with TUSS

Started by an event in a connected app. It calls services over plain HTTP rather than through connectors. It puts Gemini behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 8 steps, 1 loop or waits: small enough to read in one sitting and change without fear.

Small Calls a model: Gemini Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fvectorize-medical-procedures-for-semantic-search-with-tuss)

**8 steps** Small: readable in one sitting

**0** Branches: places the flow splits

**1** Loop or waits

**0** Services by connector: it calls them over plain HTTP

## The flow

Starts it 1 Model 4 Logic 2 Service 1

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

- 1 **Token Splitter** Text Splitter Token Splitter *Model*
- 2 **Embeddings Google Gemini** Embeddings Google Gemini *Model*
- 3 **VECTORIZE TUSS TABLE** Manual trigger *Starts it*
- 4 **Data Loader** Document Default Data Loader *Model*
- 5 **Postgres PGVector Store2** Vector Store PGVector *Model*
- 6 **ORACLE DATABASE CONNECTION** Oracle Database with Parameterization *Service*
- 7 **FOR - MEDICAL PROCEDURES** Split In Batches *Logic*
- 8 **COLLECTION OF MEDICAL PROCEDURES** Code *Logic*

## How it's built

- **Starts on:** An event in a connected app
- **Steps:** 8, 1 loop or waits
- **Size:** Small: readable in one sitting
- **Connects to:** Nothing by connector; it calls services over plain HTTP
- **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, 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?

Not long. At 8 steps it can be read in one sitting and changed without fear.

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 8 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%2Fvectorize-medical-procedures-for-semantic-search-with-tuss)
- **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.

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