Data & Databases

Transform queries into visual insights with OpenAI

19 nodes 346 155 Automatic trigger
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Workflow Description

An intelligent automation that converts text-based queries into visual representations through OpenAI, maintains conversation history, and classifies requests to deliver precise, shareable insights with complete context tracking.

How it works

  1. 1.Receive query via chat trigger and store in conversation memory buffer
  2. 2.Analyze and classify query type using information extraction and text classification
  3. 3.Process through OpenAI agent to generate appropriate visual representation
  4. 4.Document results and conversation history for future reference

Use cases

  • Convert complex data questions into actionable charts and visual reports
  • Automate report generation and share structured insights across your team

Requirements

  • Valid OpenAI API key with model access
  • Optional X/Twitter account for publishing visualized results

Service Value

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

Apps Used

OpenAI Twitter/X Chat Trigger AI Agent Memory If

Details

Trigger Automatic trigger
Nodes 19
Apps 6
Views 346
Downloads 155

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 (19)

/

OpenAI Chat Model

OpenAI

#1

Execute Workflow

Execute Workflow

#2

Execute "Generate a chart" tool

Execute Workflow Trigger

#3

OpenAI - Generate Chart definition with Structured Output

HTTP Request

#4

Set response

Set

#5

When chat message received

Chat Trigger

#6

Set Text output

Set

#7

Set Text + Chart output

Set

#8

AI Agent

Agent

#9

Window Buffer Memory

Memory Buffer Window

#10

Sticky Note1

Sticky Note

#11

Sticky Note

Sticky Note

#12

Sticky Note2

Sticky Note

#13

OpenAI Chat Model Classifier

OpenAI

#14

Sticky Note3

Sticky Note

#15

Text Classifier - Chart required?

If

#16

Sticky Note4

Sticky Note

#17

User question + Agent initial response

Set

#18

Information Extractor - User question

Information Extractor

#19