# Breakdown documents into study notes with Qdrant: n8n workflow

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

Source: https://millionrounds.com/workflow/breakdown-documents-into-study-notes-with-qdrant (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [Documents and files](https://millionrounds.com/workflows?category=Documents+and+files#library)

# Breakdown documents into study notes with Qdrant

Started by an event in a connected app, and works across Qdrant. It puts Mistral behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 35 steps, 1 branch, 2 loops or waits: large, and worth reading before you trust it with anything live.

Large Calls a model: Mistral Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fbreakdown-documents-into-study-notes-with-qdrant)

**35 steps** Large: read it before trusting it with anything live

**1** Branch: places the flow splits

**2** Loops or waits

**1** Service it connects to

## The flow

Starts it 1 Model 16 Logic 12 Service 6

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

- 1 **Local File Trigger** Local File trigger *Starts it*
- 2 **Recursive Character Text Splitter** Text Splitter Recursive Character Text Splitter *Model*
- 3 **Embeddings Mistral Cloud** Embeddings Mistral Cloud *Model*
- 4 **Mistral Cloud Chat Model** Lm Chat Mistral Cloud *Model*
- 5 **Mistral Cloud Chat Model1** Lm Chat Mistral Cloud *Model*
- 6 **Item List Output Parser** Output Parser Item List *Model*
- 7 **Embeddings Mistral Cloud1** Embeddings Mistral Cloud *Model*
- 8 **Mistral Cloud Chat Model2** Lm Chat Mistral Cloud *Model*
- 9 **Mistral Cloud Chat Model3** Lm Chat Mistral Cloud *Model*
- 10 **Settings** Set *Logic*
- 11 **Default Data Loader** Document Default Data Loader *Model*
- 12 **Qdrant Vector Store** Vector Store Qdrant *Model*
- 13 **Interview** Chain Llm *Model*
- 14 **Summarization Chain** Chain Summarization *Model*
- 15 **Qdrant Vector Store1** Vector Store Qdrant *Model*
- 16 **Discover** Chain Retrieval Qa *Model*
- 17 **Generate** Chain Llm *Model*
- 18 **Import File** Read Write File *Service*
- 19 **Merge** Merge *Logic*
- 20 **Split Out** Split Out *Logic*
- 21 **Vector Store Retriever** Retriever Vector Store *Model*
- 22 **Aggregate** Aggregate *Logic*
- 23 **2secs** Wait *Logic*
- 24 **Get FileType** Switch *Logic*
- 25 **Prep For AI** Set *Logic*
- 26 **For Each Doc Type...** Split In Batches *Logic*
- 27 **Extract from PDF** Extract From File *Service*
- 28 **Extract from DOCX** Extract From File *Service*
- 29 **Extract from TEXT** Extract From File *Service*
- 30 **Get Doc Types** Set *Logic*
- 31 **Get Generated Documents** Set *Logic*
- 32 **Prep Incoming Doc** Set *Logic*
- 33 **Split Out Doc Types** Split Out *Logic*
- 34 **To Binary** Convert To File *Service*
- 35 **Export to Folder** Read Write File *Service*

## How it's built

- **Starts on:** An event in a connected app
- **Steps:** 35, with 1 branch, 2 loops or waits
- **Size:** Large: read it before trusting it with anything live
- **Connects to:** Qdrant
- **Model:** Mistral
- **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 Qdrant account, and a key for Mistral. 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 Mistral. 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?

Take your time. At 35 steps it is large: read it before trusting it with anything live.

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 35 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%2Fbreakdown-documents-into-study-notes-with-qdrant)
- **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.

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

## Workflows like it

Also documents and files, with the same services where possible, closest in size first.

- [**My solution for the "Agentic Arena Community Contest" with RAG** 34 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/my-solution-for-the-agentic-arena-community-contest-with-rag)
- [**Build a PDF Document RAG System with Mistral and Qdrant** 29 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/build-a-pdf-document-rag-system-with-mistral-and-qdrant)
- [**Document RAG & chat agent: Google Drive to Qdrant with Mistral** 25 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/document-rag-and-chat-agent-google-drive-to-qdrant-with-mist)
- [**Build a financial documents assistant with Qdrant and Mistral** 23 steps · calls a model · Qdrant](https://millionrounds.com/workflow/build-a-financial-documents-assistant-with-qdrant-and-mistra)
- [**Process documents & build semantic search with OpenAI** 23 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/process-documents-and-build-semantic-search-with-openai)
- [**Filter by keyword within given documents with OpenAI** 18 steps · calls a model · Qdrant](https://millionrounds.com/workflow/filter-by-keyword-within-given-documents-with-openai)