# Detect multi-modal plagiarism with OpenAI and Whisper: n8n workflow

> Started by another system calling it, and works across in-memory vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you...

Source: https://millionrounds.com/workflow/detect-multi-modal-plagiarism-with-openai-and-whisper (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [AI assistants](https://millionrounds.com/workflows?category=AI+assistants#library)

# Detect multi-modal plagiarism with OpenAI and Whisper

Started by another system calling it, and works across in-memory vectors. It puts OpenAI behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 25 steps: large, and worth reading before you trust it with anything live.

Large Calls a model: OpenAI Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fdetect-multi-modal-plagiarism-with-openai-and-whisper)

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

**0** Branches: places the flow splits

**0** Loops or waits

**1** Service it connects to

## The flow

Starts it 1 Model 18 Logic 5 Service 1

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

- 1 **OpenAI Embeddings** Embeddings Open Ai *Model*
- 2 **Document Loader** Document Default Data Loader *Model*
- 3 **OpenAI GPT Model** Lm Chat Open Ai *Model*
- 4 **Text Similarity Output Parser** Output Parser Structured *Model*
- 5 **Code Analysis Output Parser** Output Parser Structured *Model*
- 6 **Multi-Modal Output Parser** Output Parser Structured *Model*
- 7 **Audio Analysis Output Parser** Output Parser Structured *Model*
- 8 **Reasoning Output Parser** Output Parser Structured *Model*
- 9 **Webhook** Webhook *Starts it*
- 10 **Store in Submission Repository** Vector Store In Memory *Model*
- 11 **Retrieval Vector Store** Vector Store In Memory *Model*
- 12 **Text Similarity Agent** Agent *Model*
- 13 **Code Analysis Agent** Agent *Model*
- 14 **Multi-Modal Agent** Agent *Model*
- 15 **Audio Analysis Agent** Agent *Model*
- 16 **Reasoning & Aggregation Agent** Agent *Model*
- 17 **Extract PDF/DOCX Text** Extract From File *Service*
- 18 **Transcribe Audio (Whisper)** Open Ai *Model*
- 19 **OCR & Image Analysis** Open Ai *Model*
- 20 **Vector Store Retriever Tool** Retriever Vector Store *Model*
- 21 **Merge Agent Outputs** Merge *Logic*
- 22 **Format Final Report** Set *Logic*
- 23 **Combine All Extracted Data** Merge *Logic*
- 24 **Aggregate Agent Results** Aggregate *Logic*
- 25 **Normalize Submission Data** Set *Logic*

## How it's built

- **Starts on:** Another system calling it
- **Steps:** 25
- **Size:** Large: read it before trusting it with anything live
- **Connects to:** in-memory vectors
- **Model:** OpenAI
- **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 in-memory vectors account, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on another system calling it.

Does it use AI?

Yes. It calls OpenAI. 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 25 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 25 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%2Fdetect-multi-modal-plagiarism-with-openai-and-whisper)
- **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.

[in-memory vectors](https://millionrounds.com/workflows?service=in-memory+vectors#library)

## Workflows like it

Also ai assistants, with the same services where possible, closest in size first.

- [**Analyze and modernize university curricula with OpenAI** 24 steps · calls a model · in-memory vectors, n8n data table](https://millionrounds.com/workflow/analyze-and-modernize-university-curricula-with-openai)
- [**Segment players and predict churn with OpenAI** 23 steps · calls a model · in-memory vectors, n8n data table](https://millionrounds.com/workflow/segment-players-and-predict-churn-with-openai)
- [**Create a knowledge base chatbot with Google Drive and OpenAI** 20 steps · calls a model · Google Drive, in-memory vectors](https://millionrounds.com/workflow/create-a-knowledge-base-chatbot-with-google-drive-and-openai)
- [**Auto workflow positioning with OpenAI and in-memory vectors** 31 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/auto-workflow-positioning-with-openai-and-in-memory-vectors)
- [**Run Bunker Guard NPC chat and RAG lore checks with Gemini** 19 steps · calls a model · Google Drive, in-memory vectors](https://millionrounds.com/workflow/run-bunker-guard-npc-chat-and-rag-lore-checks-with-gemini)
- [**Gmail smart auto-responder with OpenAI and Google Drive** 17 steps · calls a model · Gmail, Google Drive](https://millionrounds.com/workflow/gmail-smart-auto-responder-with-openai-and-google-drive)