AI Agents

Build an intelligent agent with memory and document analysis

38 nodes 289 141 Automatic trigger
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Workflow Description

Comprehensive automation that combines document processing, text embeddings, and vector storage to create a smart agent with context awareness, interacting across multiple platforms including Twitter and chat interfaces.

How it works

  1. 1.Load documents and split them into processed text chunks
  2. 2.Convert texts to numerical embeddings and store in vector database
  3. 3.Run intelligent agent managing memory and responding to queries
  4. 4.Distribute responses across Twitter and chat interface

Use cases

  • Smart customer support agent that understands your company knowledge base
  • Distributed document analysis system answering user questions across multiple channels

Requirements

  • OpenAI API key for language model functionality
  • Qdrant account or local server for vector storage
  • Twitter/X credentials for automated posting

Service Value

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

Apps Used

Embeddings Document Default Data Loader Text Splitter Twitter/X Split In Batches Vector Store AI Agent Memory Chat Trigger OpenAI Tool

Details

Trigger Automatic trigger
Nodes 38
Apps 11
Views 289
Downloads 141

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 (38)

/

When clicking ‘Test workflow’

Manual Trigger

#1

Embeddings Mistral Cloud

Embeddings Mistral Cloud

#2

Default Data Loader

Document Default Data Loader

#3

Recursive Character Text Splitter

Text Splitter Recursive Character Text Splitter

#4

Get Tax Code Zip File

HTTP Request

#5

Extract Zip Files

Compression

#6

Files as Items

Split Out

#7

Extract PDF Contents

Extract From File

#8

Extract From Chapter

Set

#9

Map To Sections

Set

#10

Execute Workflow Trigger

Execute Workflow Trigger

#11

Get Mistral Embeddings

HTTP Request

#12

Content Chunking @ 50k Chars

Set

#13

Split Out Chunks

Split Out

#14

For Each Section...

Split In Batches

#15

Sections To List

Split Out

#16

Only Valid Sections

Filter

#17

Use Qdrant Search API1

HTTP Request

#18

Use Qdrant Scroll API

HTTP Request

#19

Get Search Response

Set

#20

Sticky Note

Sticky Note

#21

Sticky Note1

Sticky Note

#22

Sticky Note2

Sticky Note

#23

Qdrant Vector Store

Vector Store Qdrant

#24

Sticky Note3

Sticky Note

#25

Sticky Note4

Sticky Note

#26

AI Agent

Agent

#27

Window Buffer Memory

Memory Buffer Window

#28

When chat message received

Chat Trigger

#29

Window Buffer Memory1

Memory Buffer Window

#30

OpenAI Chat Model

OpenAI

#31

1sec

Wait

#32

Ask Tool

Tool Workflow

#33

Search Tool

Tool Workflow

#34

Switch

Switch

#35

Get Ask Response

Set

#36

Sticky Note5

Sticky Note

#37

Sticky Note6

Sticky Note

#38

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