AI Agents

Automated workflow

42 nodes 196 121 Automatic trigger
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Workflow Description

An intelligent workflow that monitors local files automatically, extracts documents, splits and vectorizes their content using Mistral embeddings, stores vectors in a Qdrant database, and enables an AI agent to answer queries accurately with retrieval-augmented generation.

How it works

  1. 1.Monitor local file system and trigger workflow when new documents arrive
  2. 2.Extract and parse document content, then split text into meaningful chunks
  3. 3.Convert text chunks into semantic embeddings using Mistral Cloud
  4. 4.Store embeddings in Qdrant vector database for fast similarity search
  5. 5.Process user queries through retrieval-augmented QA chain powered by LLM

Use cases

  • Build intelligent document search systems for large knowledge bases
  • Create automated Q&A systems that reference company documentation
  • Streamline document ingestion and organization for enterprise workflows

Requirements

  • Active Mistral Cloud API account for embeddings and language model access
  • Operational Qdrant vector database instance with network connectivity
  • Read and write permissions for the monitored local file directory

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 196
Downloads 121

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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