# Building RAG chatbot for movie recommendations with Qdrant: n8n workflow

> Started by someone opening a chat with it, and works across GitHub and Qdrant. It puts OpenAI behind a retrieval step, so answers are drawn from documents...

Source: https://millionrounds.com/workflow/building-rag-chatbot-for-movie-recommendations-with-qdrant (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)

# Building RAG chatbot for movie recommendations with Qdrant

Started by someone opening a chat with it, and works across GitHub and Qdrant. 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%2Fbuilding-rag-chatbot-for-movie-recommendations-with-qdrant)

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

**0** Branches: places the flow splits

**0** Loops or waits

**2** Services it connects to

## The flow

Starts it 3 Model 8 Logic 8 Service 6

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

- 1 **When clicking ‘Test workflow’** Manual trigger *Starts it*
- 2 **Embeddings OpenAI** Embeddings Open Ai *Model*
- 3 **Token Splitter** Text Splitter Token Splitter *Model*
- 4 **When chat message received** Chat trigger *Starts it*
- 5 **OpenAI Chat Model** Lm Chat Open Ai *Model*
- 6 **Call n8n Workflow Tool** Tool Workflow *Model*
- 7 **Window Buffer Memory** Memory Buffer Window *Model*
- 8 **Execute Workflow Trigger** Execute Workflow trigger *Starts it*
- 9 **GitHub** Github *Service*
- 10 **Qdrant Vector Store** Vector Store Qdrant *Model*
- 11 **Default Data Loader** Document Default Data Loader *Model*
- 12 **AI Agent** Agent *Model*
- 13 **Embedding Recommendation Request with Open AI** Http Request *Service*
- 14 **Embedding Anti-Recommendation Request with Open AI** Http Request *Service*
- 15 **Extract from File** Extract From File *Service*
- 16 **Extracting Embedding** Set *Logic*
- 17 **Extracting Embedding1** Set *Logic*
- 18 **Merge** Merge *Logic*
- 19 **Calling Qdrant Recommendation API** Http Request *Service*
- 20 **Retrieving Recommended Movies Meta Data** Http Request *Service*
- 21 **Split Out1** Split Out *Logic*
- 22 **Split Out** Split Out *Logic*
- 23 **Merge1** Merge *Logic*
- 24 **Selecting Fields Relevant for Agent** Set *Logic*
- 25 **Aggregate** Aggregate *Logic*

## How it's built

- **Starts on:** Someone opening a chat with it
- **Steps:** 25
- **Size:** Large: read it before trusting it with anything live
- **Connects to:** GitHub, Qdrant
- **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 GitHub and Qdrant accounts, and a key for OpenAI. You connect your own accounts after importing it; none are included.

What starts it?

It starts on someone opening a chat with 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%2Fbuilding-rag-chatbot-for-movie-recommendations-with-qdrant)
- **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.

[GitHub](https://millionrounds.com/workflows?service=GitHub#library)[Qdrant](https://millionrounds.com/workflows?service=Qdrant#library)

## Workflows like it

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

- [**Build a self-updating RAG system with OpenAI and Qdrant** 25 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/build-a-self-updating-rag-system-with-openai-and-qdrant)
- [**Building RAG chatbot for movie recommendations with Qdrant** 25 steps · calls a model · GitHub, Qdrant](https://millionrounds.com/workflow/building-rag-chatbot-for-movie-recommendations-with-qdrant-2)
- [**AI-powered system for Creating & Executing your own Claude Skills** 24 steps · calls a model · GitHub](https://millionrounds.com/workflow/ai-powered-system-for-creating-and-executing-your-own-claude)
- [**Build a RAG system with Qdrant and Gemini** 24 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/build-a-rag-system-with-qdrant-and-gemini)
- [**Build and update RAG system with Google Drive and Qdrant** 24 steps · calls a model · Google Drive, Qdrant](https://millionrounds.com/workflow/build-and-update-rag-system-with-google-drive-and-qdrant)
- [**Recipe recommendations with Qdrant and Mistral** 24 steps · calls a model · Qdrant](https://millionrounds.com/workflow/recipe-recommendations-with-qdrant-and-mistral)