AI Agents

Convert Documents into Study Notes Using Qdrant

42 nodes 224 147 Automatic trigger
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Workflow Description

Automates the transformation of uploaded documents into structured study notes. Text content is split into digestible segments, converted to embeddings, stored in Qdrant for semantic retrieval, and reformatted into clear notes using the Mistral language model.

How it works

  1. 1.Detect uploaded documents and load their content locally
  2. 2.Split long texts into manageable study-sized chunks
  3. 3.Convert chunks into numerical embeddings and store them in Qdrant
  4. 4.Retrieve relevant passages using semantic search
  5. 5.Reformat retrieved content into coherent study notes via Mistral
  6. 6.Export organized notes to text files

Use cases

  • Transform books and research papers into concise, comprehensible study notes
  • Automatically process large batches of educational documents at scale
  • Build a searchable digital library of study notes from document collections

Requirements

  • Local document upload capability enabled
  • Active Mistral Cloud API connection for language model processing
  • Qdrant vector database instance configured and accessible

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.

Apps Used

Local File Trigger Document Default Data Loader Text Splitter Embeddings Lm Chat Mistral Cloud Split In Batches Output Parser Vector Store LLM Chain Read Write File

Details

Trigger Automatic trigger
Nodes 42
Apps 10
Views 224
Downloads 147

How to Use

  1. 1.Click "Download Template"
  2. 2.Open your n8n dashboard
  3. 3.Go to Workflows > Import from File
  4. 4.Select downloaded file and configure credentials

Nodes Used (42)

/

Local File Trigger

Local File Trigger

#1

Default Data Loader

Document Default Data Loader

#2

Recursive Character Text Splitter

Text Splitter Recursive Character Text Splitter

#3

Embeddings Mistral Cloud

Embeddings Mistral Cloud

#4

Mistral Cloud Chat Model

Lm Chat Mistral Cloud

#5

Mistral Cloud Chat Model1

Lm Chat Mistral Cloud

#6

Prep Incoming Doc

Set

#7

Settings

Set

#8

Merge

Merge

#9

Get Doc Types

Set

#10

Split Out Doc Types

Split Out

#11

For Each Doc Type...

Split In Batches

#12

Item List Output Parser

Output Parser Item List

#13

Vector Store Retriever

Retriever Vector Store

#14

Embeddings Mistral Cloud1

Embeddings Mistral Cloud

#15

Qdrant Vector Store1

Vector Store Qdrant

#16

Mistral Cloud Chat Model2

Lm Chat Mistral Cloud

#17

Split Out

Split Out

#18

Aggregate

Aggregate

#19

Mistral Cloud Chat Model3

Lm Chat Mistral Cloud

#20

Discover

Chain Retrieval Qa

#21

2secs

Wait

#22

Get Generated Documents

Set

#23

Generate

LLM Chain

#24

Prep For AI

Set

#25

To Binary

Convert To File

#26

Export to Folder

Read Write File

#27

Get FileType

Switch

#28

Import File

Read Write File

#29

Extract from PDF

Extract From File

#30

Extract from DOCX

Extract From File

#31

Extract from TEXT

Extract From File

#32

Summarization Chain

Chain Summarization

#33

Sticky Note

Sticky Note

#34

Sticky Note1

Sticky Note

#35

Sticky Note2

Sticky Note

#36

Sticky Note3

Sticky Note

#37

Sticky Note4

Sticky Note

#38

Qdrant Vector Store

Vector Store Qdrant

#39

Sticky Note5

Sticky Note

#40

Interview

LLM Chain

#41

Sticky Note6

Sticky Note

#42

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