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

$14.99

Creator

Jenny

• set
• merge
• github
• splitOut
• aggregate
• stickyNote
• httpRequest
• manualTrigger
• agent
• extractFromFile