Building RAG Chatbot for Movie Recommendations with Qdrant & OpenAI

n8n AI Content Creation Product

Contributed by Sabrina Ramonov 🍄. From the community directory at agents.sabrina.dev, republished with permission and full credit.

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 no's" 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.

- 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 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 (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 to your GitHub.

Tools used: Qdrant, OpenAI

Download the workflow template (JSON)

Original tutorial

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