Building a RAG Chatbot for Movie Recommendations with Qdrant and OpenAI - n8n Workflow | Neura Market
Building a RAG Chatbot for Movie Recommendations with Qdrant and OpenAI
Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your big nos to the chatbot, for example: A movie about wizards but not Harry Potter, and get top-3 recommendations.
## How it works
- [A video with the full design process](https://www.youtube.com/watch?v=O5m8M7rqQQ)
- Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI;
- Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat;
- Create a workflow which calls [Qdrant's Recommendation API](https://qdrant.tech/articles/new-recommendation-api/) to retrieve top-3 recommendations of movies based on your positive and negative examples.
## Set Up Steps
- You'll need to create a free tier [Qdrant Cluster](https://cloud.qdrant.io/) (Qdrant can also be used locally; it's open-sourced) and set up API credentials.
- You'll need OpenAI credentials.
- You'll need GitHub credentials & to upload the [IMDB Kaggle dataset](https://www.kaggle.com/datasets/omarhanyy/imdb-top-1000) to your GitHub.
Platform
n8n
Category
AI
Price
Free
Creator
Jenny
set
merge
github
splitOut
aggregate
stickyNote
httpRequest
manualTrigger
agent
extractFromFile
How to import this workflow into n8n
1Purchase or download the workflow to get the n8n workflow JSON file.
2In your n8n instance, open Workflows and choose "Import from File" (or paste the JSON with Ctrl+V on the canvas).
3Open each node marked with a credential warning and connect your own accounts and API keys.
4Run the workflow once manually to verify the data flow, then toggle it to Active.